{ "exported_at": "2026-11-15 17:39:30", "count": 1232, "records": [ { "id": 1, "title": "An expert system for material handling equipment selection", "title_ar": "", "title_en": "An expert system for material handling equipment selection", "author": "Eisa Abdullah Eisa S. Al-Meshaiei", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Warwick", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "expert-systems", "research_subdomain": "ai-industry", "categories": "expert-systems; ai-applications; ai-industry", "keywords": "", "abstract": "Manufacturing Systems are subject to increasingly frequent changes in demand in terms of number and type of products they produce. It is impractical to continually reconfigure the facilities, but it is possible to modify the material handling arrangements so that the selected equipment is the most appropriate for the current requirements. The number of decisions that need to be made coupled with the rate at which decisions must be taken adds significant difficulty to the problem of equipment selection. Furthermore there are relatively few experts who have the necessary range of knowledge coupled with the ability to use this knowledge to select the most appropriate material handling solution in any situation. Access to such experts is therefore greatly restricted and decisions are more commonly made by less experienced people, who depend on equipment vendors for information, often resulting in poor equipment selection. This research first examines the significance of appropriate material handling equipment choice in dynamic environments. The objective is to construct a computer based expert system utilising knowledge from the best available sources in addition to a systematic procedure for selection of material handling equipment. A new system has been produced, based on the Flex language, which elicits from the inexperienced user details of the handling requirements in order to build an equipment specification. It then selects from among 11 handling solution groups and provides the user with information supporting the selection. Original features of the system are the way in which the knowledge is grouped, the ability of the procedure to deal with quantifiable and non-quantifiable equipment and selection factors, selection of decision analysis method and the validation of the final choice to establish confidence in the results. The system has been tested using real industrial data and has been found in 81% of cases to produce results which are acceptable to the experts who provided the information.", "source_url": "https:\/\/openalex.org\/W1498062261", "pdf_url": "http:\/\/wrap.warwick.ac.uk\/36429\/1\/WRAP_THESIS_Al-Meshaiei_1999.pdf", "doi": "10.82444\/warw.33601843", "handle": "", "repository_name": "Warwick Research Archive Portal (University of Warwick)", "source_record_id": "W1498062261", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 3, "title": "Computer aided neuro fuzzy based expert Knowledge system to design concrete mix", "title_ar": "", "title_en": "Computer aided neuro fuzzy based expert Knowledge system to design concrete mix", "author": "Rajeev S. Kale", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "India", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "expert-systems", "research_subdomain": "", "categories": "expert-systems", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2791234029", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Shodhganga", "source_record_id": "W2791234029", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 4, "title": "Framework for the development of data-driven mamdani-type fuzzy decision support systems based on fuzzy set theory using clusters and pivot tables", "title_ar": "", "title_en": "Framework for the development of data-driven mamdani-type fuzzy decision support systems based on fuzzy set theory using clusters and pivot tables", "author": "Yamid Fabian Hernández Julio", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad del Norte", "university_source": "repository_host", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "medical-ai; expert-systems", "keywords": "", "abstract": "The objectives of this thesis were to design, implement and validate a framework for the development of data-driven Mamdani-type fuzzy Decision support systems using clusters and pivot tables. For the first objective we designed an architecture of layers with their components for their respective implementation. The second objective was to develop an efficient implementation of each of the designed layers with their respective components in the MATLAB platform. The third objective was to provide an efficient validation of the complete framework in real-world classification problems for validating if it could extract significant features and the knowledge base and rule base for the development of Data-driven Mamdani-type Decision support systems. For validating the proposed methodology, we applied our algorithms on four public datasets including Wisconsin, and Coimbra breast cancer, wart treatment, and compared them with other related works obtained from the literature. According to the obtained results it could be demonstrated that the knowledge database and the knowledge rule base could be extracted transparently. The bene its of these expert systems are multifold: assisting physicians in selecting the best treatment method, saving time for patients, reducing the treatment cost, and improving the quality of treatment.", "source_url": "https:\/\/openalex.org\/W7201288618", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace Main Institutional Repository University of the North (Universidad del Norte)", "source_record_id": "W7201288618", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 6, "title": "Temporal logic-based fuzzy decision support system for diagnosis of rheumatic fever and rheumatic heart disease", "title_ar": "", "title_en": "Temporal logic-based fuzzy decision support system for diagnosis of rheumatic fever and rheumatic heart disease", "author": "Sanjib Raj Pandey", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Greenwich", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "medical-ai; expert-systems", "keywords": "", "abstract": "This is a collaboration project between the Nepal Heart Foundation (NHF) and the University of Greenwich (UoG), United Kingdom (UK). Our mutual aim, agreed at the outset, has been to analyse, design and develop a cost effective Clinical Decision Support System (CDSS) for diagnosis and recognition of Acute Rheumatic Fever (ARF) and Rheumatic Heart Disease (RHD) at an early stage by developing\/adopting UK’s and NHF’s treatment practices and procedures that would be appropriate for the Nepalese environment and lifestyle. The Application we developed was designed for use by community health workers and doctors in the rural areas of Nepal where laboratory facilities, expert services and technology are often deficient. The research undertaken investigated three problems that previously had not been addressed in the Artificial Intelligence (AI) community. These are: 1) ARF in Nepal has created a lot of confusion in diagnosis and treatment, due to the lack of standard procedures; 2) the adoption of foreign guidelines is often not effective and does not suit the Nepali environment and lifestyle and 3) the value of combining (our proposed) Hybrid Approach (Knowledge-based System (KBS), Temporal Theory (TT) and Fuzzy Logic (FL)) to design and develop an application to diagnose ARF cases at an early stage in English and Nepali. This research presents, validates and evaluates a proposed Hybrid Approach to diagnose ARF at three different stages: 1) Detected; 2) Suspected and 3) Not-detected and also to identify the severity level of detected ARF in the forms of Severe Case, Moderate Case or Mild Case. The Hybrid Approach is a combination of the KBS\/Boolean Rule Model, Temporal Model and Fuzzy Model. The KBS\/Boolean Rule Model has four components for design and implementation of KBS. These are: identifying the ARF stage in a case; Rule Pattern Matching; New Rule Formation and Rule Selection Mechanism. The Temporal Model has four components namely: Descriptive Explanation of ARF symptoms; Temporal Lookup-Table\/Rules and Temporal Reasoning which produce a Temporal Template for demonstrating the relationship between the signs and ARF. The Fuzzification, Fuzzy Inferences and Defuzzification components are applied to design and implement a Fuzzy Model. The research undertaken divided the overall ARF diagnosis problem, in effect its requirements, into several sub-problems and each model of the Hybrid Approach addressed particular sub-problems for example, Identify the stage of the ARF component of the KBS\/Boolean Rule Model used to solve the question of identifying the stage of ARF based on the symptoms presented. Each problem was therefore handled using a particular model’s components. This significantly helped to improve maintainability, reliability and the overall quality of our final ARF Diagnosis Application. The developed ARF Diagnosis Application was experimentally tested and evaluated by NHF’s experts and users through applying NHF’s data sets consisting of 676 real patients’ records. The ARF Diagnosis Application was found to match 99% of the cases derived from NHF’s datasets. The overall ARF diagnostics performance and accuracy was 99.36%. Therefore, the experiments and evaluations of our ARF Diagnosis Application proved that it was logically and technically feasible to employ the Hybrid Approach for developing a new and practical ARF Diagnosis Application. The Application was ultimately developed and succeeded in embracing NHF’s requirements and guidelines thereby matching the Nepalese setting and making it suitable for use in Nepal having fully by met the exigencies of the Nepalese environment and lifestyle. Application of a new ARF diagnosis system (ours) proved that the Hybrid Approach, applied methods of diagnosis of ARF, medication and treatment plan, including help and supporting information which were identified and defined, were shown to be appropriate to support effectively community health workers and doctors who actively care for ARF and RHD cases in rural Nepal.", "source_url": "https:\/\/openalex.org\/W2775104210", "pdf_url": "http:\/\/gala.gre.ac.uk\/id\/eprint\/18088\/1\/Sanjib%20Raj%20Pandey%202016.pdf", "doi": "", "handle": "", "repository_name": "Greenwich Academic Literature Archive (University of Greenwich)", "source_record_id": "W2775104210", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 7, "title": "SISTEM PAKAR DIAGNOSA AWAL KANKER KULIT MELANOMA MALIGNA BERBASIS WEB MENGGUNAKAN METODE FUZZY RULE BASED CLASSIFICATION SYSTEMS", "title_ar": "", "title_en": "", "author": "Machrita Fitri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitas Pendidikan Indonesia", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2016, "language": "id", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; medical-ai; expert-systems; predictive-analytics", "keywords": "", "abstract": "Kulit adalah organ paling terluas dan terluar bagi tubuh manusia. Dan karena letaknya yang paling terluar, maka kulit akan menjadi organ pertama yang rentan terserang bakteri atau virus. Namun sering kali penyakit kulit dianggap remeh oleh sebagian orang, dikarenakan ketidaktahuan mereka akan penyakit kulit dan bahayanya. Untuk itu dengan permasalahan yang melatarbelakangi tersebut, penulis akan merancang sebuah sistem pakar yang akan mengadopsi pengetahuan seorang pakar\/ahli, dalam kasus ini adalah tentang penyakit kanker kulit melanoma maligna yang akan diimplementasikan berbasis web. Aplikasi sistem pakar ini akan dibangun menggunakan metode Fuzzy Rule-based Classification Systems (FRBCS) untuk menentukan pengguna mengidap penyakit kanker kulit melanoma maligna atau tidak. FRBCS adalah suatu metode yang berbasis fuzzy rule, dimana FRBCS terdiri dari 2 tahapan yaitu konstruksi atau pembangunan model dan tahap prediksi. Dalam tahap kontruksi model dibutuhkan knowledge \/ pengetahuan yang terdiri dari rule based dan database. Yang mana pada tahap ini semua data yang bersangkutan dengan penyakit kanker kulit melanoma maligna didapatkan dari seorang pakar\/ahli. Dan tahap prediksi adalah tahap pengambilan keputusan yang terdiri dari fuzzyfication, inference, dan classification. Sedangkan untuk implementasi sistem akan menggunakan bahasa pemrograman PHP dan MySQL untuk pembangunan databasenya. Untuk validasi sistem yang telah dibuat, beberapa eksperimen dilakukan dengan menggunakan data yang diperoleh dari seorang pakar\/dokter sebuah Rumah Sakit Islam di Yogyakarta. Dari eksperimen yang dilakukan dengan melakukan pengujian terhadap 30 data testing didapat akurasi sebesar 86.67% untuk data uji coba tersebut.;---The skin is the largest organ of the human body and the outer. And because it is the most outer skin will be the first organ susceptible to bacteria or viruses. But too often the skin disease underestimated by most people, because of their ignorance of skin diseases and dangers. For that the underlying problem, the author will devise an expert system that will adopt the knowledge of an expert \/ specialist, in this case is about the skin cancer malignant melanoma which will be implemented based on web. This expert system application will be built using the method of Fuzzy Rule-based Classification Systems (FRBCS) to determine the user suffering from malignant melanoma skin cancer or not. FRBCS is a method based on fuzzy rule, where FRBCS consists of two phases, namely the construction or development stage models and predictions. In the construction phase of the model required knowledge consists of rule-based and database. Which at this stage all of the pertinent data with malignant melanoma skin cancer obtained from an expert \/ specialist. And the prediction stage is the stage of decision-making that consists of fuzzyfication, inference, and classification. As for the implementation of the system will use the programming language PHP and MySQL for database development. For the validation of the system have been made, several experiments were conducted using data obtained from a specialist \/ doctor an Islamic Hospital in Yogyakarta. From the experiments carried out with a test of 30 testing data is obtained an accuracy of 86.67% for the test data.", "source_url": "https:\/\/openalex.org\/W2794677018", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Repository at Universitas Pendidikan Indonesia (Universitas Pendidikan Indonesia)", "source_record_id": "W2794677018", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 8, "title": "Fuzzy model rozhodování investora do fotovoltaických technologií v předprojekční fázi", "title_ar": "", "title_en": "Fuzzy model rozhodování investora do fotovoltaických technologií v předprojekční fázi", "author": "Michal Pavlíček", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Brno University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "computer-vision; expert-systems", "keywords": "", "abstract": "Dissertation deals with fuzzy knowledge base supporting investor’s investment decision into the photovoltaic technologies during pre-design phase, when the engineering solution is not known yet. Probably, majority of investors have particular image about the cost and risk coming from investment into the photovoltaic technologies. However, this image is limited to the restricted knowledge areas of each investor. During the period, when the investor is planning investment into the costly photovoltaic technologies at complex, vague and heavily qualified information about the conditions and risks of investment in the specific region, where the environment is constantly inconsistent and multidimensional, it is possible to solve this complex situation with fuzzy logic. This dissertation is focusing on creation of fuzzy knowledge base with selected installed projects in Europe since 2008 and its use with expert system. Furthermore, is covering the definition and description of the variables, which are included in the investor decision making process. The complete designed architecture of the fuzzy knowledge base is tuned and five projects with different size are tested. The fuzzy knowledge base consists of overall 24 variables and 187 statements. The fuzzy knowledge base is tested with 5 projects.", "source_url": "https:\/\/openalex.org\/W2304027425", "pdf_url": "https:\/\/dspace.vut.cz\/bitstreams\/6ac89afb-b256-4d26-ae45-9ad176f90977\/download", "doi": "", "handle": "", "repository_name": "Brno University of Technology Digital Library (Brno University of Technology)", "source_record_id": "W2304027425", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 9, "title": "PEMBANGUNAN SISTEM PAKAR UNTUK PREDIKSI DIAGNOSA PENYAKIT GANGGUAN TIDUR DENGAN METODE FUZZY RULE-BASED CLASIFICATION SYSTEMS", "title_ar": "", "title_en": "", "author": "Mila Pradini Okvitasari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitas Pendidikan Indonesia", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2016, "language": "id", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; medical-ai; expert-systems; predictive-analytics; transformers", "keywords": "", "abstract": "Gangguan tidur merupakan kelainan yang dapat menyebabkan masalah pada pola tidur seseorang, baik karena tidak bisa tertidur, sering terbangun pada malam hari atau ketidakmampuan untuk kembali tidur setelah terbangun. Hal ini dapat menyebabkan terjadinya kecelakaan, serta penurunan produktivitas pada seseorang. Pada saat ini salah satu cara untuk menyelesaikan permasalahan tersebut dapat dilakukan dengan cara konsultasi terhadap dokter. Namun hal ini menjadi masalah jika saat berkonsultasi harus disertakan dengan tatap muka. Yang mana semakin hari seseorang akan semakin sibuk dengan urusannya dan akhirnya menganggap sepele hal ini. Hal tersebut yang melatarbelakangi dilakukannya penelitian ini yang bertujuan untuk membuat sistem diagnosa penyakit gangguan tidur dengan metode Fuzzy Rule-Based Classifications Systems (FRBCS). FRBCS adalah suatu metode yang berbasis fuzzy rule, dimana FRBCS terdiri dua tahapan yaitu konstruksi model dan prediksi. Didalam konstruksi model dibutuhkan knowledge yang terdiri dari rule based dan database. Knowledge tersebut didapatkan dari seorang pakar, yang mana pakar tersebut mendefiniskan hubungan antara gejala dengan jenis gangguan tidur. Sedangkan prediksi adalah proses untuk mengambil keputusan dengan tahapan yaitu fuzzyfication, inference, dan classification. Adapun implementasi platform dalam penelitian ini yaitu dengan menggabungkan PHP dengan bahasa pemrograman R menggunakan package R-shinny. Untuk validasi sistem yang telah dibuat, beberapa eksperimen dilakukan dengan menggunakan data yang diperoleh dari suatu Rumah Sakit Jiwa di Jawa Barat. Dari eksperimen yang dilakukan dengan melakukan pengujian terhadap 33 data testing didapat akurasi sebesar 84.85% dengan hasil rata-rata 0.0133 detik untuk prosesnya. ;--- Sleep disorder is a disorder that can cause problems in a person's sleep patterns, either because they can not fall asleep, frequent waking during the night or the inability to return to sleep after waking. This can cause accidents, and decreased productivity in person. At this time one of the ways to solve these problems can be done by consulting the doctor. But this becomes a problem if the time of consultation should be included with face to face. Which is getting day someone will be busy with his business and finally to disregard it. This is the background of this research which aims to make the system a diagnosis of sleep disorders with Fuzzy Rule-Based Classifications Systems (FRBCS). FRBCS is a fuzzy rule-based method, which consists of two phases, namely FRBCS construction models and predictions. In the construction of the model required knowledge that consists of rule-based and database. The knowledge obtained from an expert to define the relationship between the type of sleep disorder symptoms. While the prediction is the process for taking decisions with a step that is fuzzyfication, inference, and classification. The implementation of this research platform which combine PHP programming language R using R-shiny package. For validation system that has been built, several experiments were conducted using data obtained from a Psychiatric Hospital in West Java. From the experiments carried out with a test of 33 testing data is obtained an accuracy of 84.85% with an average yield of 0.0133 seconds to process.", "source_url": "https:\/\/openalex.org\/W2901676950", "pdf_url": "http:\/\/repository.upi.edu\/26509\/2\/S_KOM_1200730_Abstract.pdf", "doi": "", "handle": "", "repository_name": "Repository at Universitas Pendidikan Indonesia (Universitas Pendidikan Indonesia)", "source_record_id": "W2901676950", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 10, "title": "Fuzzy Rule-Based Systems: Design, Analysis, and Applications", "title_ar": "", "title_en": "Fuzzy Rule-Based Systems: Design, Analysis, and Applications", "author": "Jeremy Kerr-Wilson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Alberta", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; expert-systems; data-mining; predictive-analytics; xai", "keywords": "", "abstract": "The extraction of knowledge from data is a relatively recent computational pursuit which has been the focus of significant research attention and has an extensive field of potential applications. With the advent of widespread data collection describing a variety of systems spanning many fields of expertise, the extraction of useful knowledge from data, for prediction or insight, has extraordinary potential and significant value. This realization has led to the development of machine learning, a collection of computational processes and algorithms through which sophisticated computer models can be constructed on the basis of data. Human centric systems, and more specifically certain forms of fuzzy systems, are a notable subset of machine learning which focus on knowledge extraction using multi-valued logic and set theory. Fuzzy systems are particularly well-suited to human-centric modelling as fuzzy sets describe real world systems, as perceived by humans, much more accurately than binary models. Fuzzy rule-based models are a form of fuzzy model which is particularly well-suited to human centric tasks due to the high degree of readability and interpretability conveyed to an expert reader. This, combined with their strong predictive ability, makes fuzzy rule-based systems an excellent candidate for those computational modelling pursuits where predictive accuracy may not be the singular requirement of a model. The objective of this dissertation is to design, analyze, and develop novel applications, methodologies, and algorithms for use with fuzzy rule-based systems, seeking to further their utility in predictive and human-centric modelling. In this dissertation, fuzzy rule-based systems are applied to different problem types, combined with existing computational data-structures and architectures, extracted from data in novel manners and formats, and analyzed to assess certain aspects of rule quality. Acknowledging the critical role of human centricity in computational modelling, we develop a set of fuzzy rule stability criteria which aim to quantify aspects of fuzzy rule quality while capturing critical non-numerical aspects of rule quality such as repeatability, consistency, and generalizability. We examine the generation of fuzzy rule-based models using hierarchical clustering and extract granular fuzzy models from data, forming information granules in the consequent parts of the fuzzy rules. We make use of fuzzy rule-based systems as the component models (weak learners) of a boosted ensemble, exploring their predictive power and adaptability in this environment. Finally, a novel fuzzy rule architecture is proposed using a hierarchical structure, alongside a generation procedure for extracting this hierarchical structure form data, with the aim of improving predictive performance and increasing the interpretability of the system.Each of these topics are justified with extensive experimental studies, using real-world data sets available from public repositories, demonstrating the feasibility or superiority of the proposed methods as compared to existing methodologies.", "source_url": "https:\/\/openalex.org\/W3163827966", "pdf_url": "", "doi": "10.7939\/r3-rmnz-bn16", "handle": "", "repository_name": "ERA: Education and Research Archive (University of Alberta)", "source_record_id": "W3163827966", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 11, "title": "Sistem Pendukung Keputusan Pemilihan Varietas Tanaman Hortikultura Dengan Metode Fuzzy", "title_ar": "", "title_en": "Sistem Pendukung Keputusan Pemilihan Varietas Tanaman Hortikultura Dengan Metode Fuzzy", "author": "Cahya Affrillah Prasetyo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "expert-systems", "research_subdomain": "ai-agriculture", "categories": "expert-systems; ai-applications; ai-agriculture", "keywords": "wedding, decision support system, AHP", "abstract": "Selection of plants on suitable land is very important, therefore it requires experience and knowledge that is qualified so that plants can grow well, especially for horticultural crops, this horticultural plant selection system makes calculations based on fuzzy methods which can make it easier for new or old farmers. The results of this fuzzy calculation have a match with the results of experts as much as 12 out of 15 experiments, this makes the system accuracy of 86.7%. Key words : wedding, decision support system, AHP", "source_url": "https:\/\/openalex.org\/W3139849534", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3139849534", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 12, "title": "KOMBINASI SISTEM PAKAR METODE FUZZY INFERENCESYSTEM DAN CERTAINTY FACTOR UNTUK DIAGNOSAPENYAKIT GIGI", "title_ar": "", "title_en": "KOMBINASI SISTEM PAKAR METODE FUZZY INFERENCESYSTEM DAN CERTAINTY FACTOR UNTUK DIAGNOSAPENYAKIT GIGI", "author": "Ima Sintia ; Rusdi Efendi ; Mastura Diana Marieska", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; medical-ai; expert-systems; predictive-analytics", "keywords": "", "abstract": "ABSTRACT Dental disease is one of the many health problems that people complain about. Dental disease rank first in the list of 10 disease that are often complained by people in Indonesia. Based on these problem, we need a system that can provide the information needed to increase patient knowledge in knowing the illness based on the symptoms. in this research we use the fuzzy inference system and certainty factor to build an expert system to diagnose dental disease. The output generated from this system is compared to expert prediction. We use 30 data of dental disease patient to evaluate system. The system that we build has accuracy rate of 90,00%.", "source_url": "https:\/\/openalex.org\/W3135005205", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3135005205", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 13, "title": "Bidding decision template using a fuzzy system based on parameters", "title_ar": "", "title_en": "Bidding decision template using a fuzzy system based on parameters", "author": "Jadilson Astorino Marçola", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "responsible-ai; expert-systems; xai", "keywords": "", "abstract": "Taking into account the decision-making process of bidding, which there’re several strategic factors, complex and subjective, this document has as goal provide a new decision model, using a fuzzy system based on parameters, supporting the manager on binary decision-making of participating or not in a bidding. Beginning from a literature review about bidding, decision-making of bidding and fuzzy system, it approaches the new bidding decision-making, comparing to each others there are, as well as it application, here represented by a case study in a capital goods factory, with a strong national market performance. The obstruction to get information, the unstructured formulation of the decision-making process, the presence of limited resources, the increasing demands for product qualities and the possibility to help managers on process decision-making, motivated the document development. Taking into account that, during the whole information gathering process, it was verified the very qualitative variables presence, imprecise and the decision-making process based on human knowledge, whose behavior is influenced by perception, judgment and emotions, chose to develop a fuzzy model so, allowing the development of methods and techniques for representation and processing inaccurate information, abundant in the real world, and also difficult to process by conventional logic. The knowledge construction of the knowledge base of fuzzy systems, formed by database and by parameters was developed by the researcher, with specialists support. The expected benefit with the use of the new model is to get reduction of the manager's response time, more assertive decisions and saving of human and financial resources during the process of preparing proposals. With a historical database of a capital goods factory was possible to build up several bidding decision scenarios. Besides the model, a parameters fuzzy system was developed, whose results are in accordance with the decisions expected by those experts. The model, compared to the other models according to the literature review, was more robust, more interpretable and less dependent on the specialists.", "source_url": "https:\/\/openalex.org\/W7120586530", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120586530", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 14, "title": "KNOWLEDGE BASED EXPERT SYSTEM FOR CRANE SELECTION", "title_ar": "", "title_en": "KNOWLEDGE BASED EXPERT SYSTEM FOR CRANE SELECTION", "author": "MAHMOUD ELTOURKY", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "King Fahd University of Petroleum and Minerals", "university_source": "repository_host", "college": "", "department": "", "country": "Saudi Arabia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "expert-systems", "research_subdomain": "", "categories": "expert-systems", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7167385648", "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/141637\/1\/00.%20Final%20Master%20Thesis.pdf", "doi": "", "handle": "", "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)", "source_record_id": "W7167385648", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 15, "title": "An Expert System Integrated with a Bridge Information Management System (BrIMS), Cost Estimating, Deterioration Forecasting, and Linear Scheduling at the Conceptual Design Stage", "title_ar": "", "title_en": "An Expert System Integrated with a Bridge Information Management System (BrIMS), Cost Estimating, Deterioration Forecasting, and Linear Scheduling at the Conceptual Design Stage", "author": "Nizar Markiz", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Ottawa", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; expert-systems; predictive-analytics", "keywords": "", "abstract": "Major bridge stakeholders such as federal and provincial transportation agencies are in dire need for objective knowledge-based systems that assist decision-makers in the selection of bridge type. Besides that, estimating bridge construction costs at the conceptual design stage is an increasing necessity for accurate budgeting and effective allocation of funding. Whilst multiple bridge management systems have already been developed; they still possess major drawbacks pertaining to interoperability and integration with complex time and cost optimization-related problem solving. In another perspective, infrastructure restoration has been backlogged with multifaceted factors that have captured the attention of municipal and federal authorities. Several successful integrations of bridge information management systems (BrIMS) with decision support systems and computer-aided engineering design solutions have significantly leveraged downstream processes of bridge maintenance operations and inspired many researchers. The subjective nature of evaluating bridge conditions and deteriorations is the main factor that influences bridge maintenance, repair, and replacement decisions. In order to overcome this shortcoming, the objectives of this study are intended to demonstrate the viability of integrating a decision support system with a stochastic gamma deterioration model utilizing a probabilistic fuzzy logic strategic approach at the conceptual design stage. In summary, this study presents a systematic multi-objective knowledge-based approach for selecting bridge type, forecasting elemental deteriorations, linear scheduling, and estimating construction costs at the conceptual design stage. The proposed methodology comprises a framework to deploy a system that automatically generates conceptual cost estimates by integrating objective functions with bridge information modeling (BrIM) through an external data interchange protocol in synchrony with interoperability standards. Deployment of the developed system shall minimize the degree of subjectivity involved while decision makings pertaining to bridge projects and assists designers and cost engineers obtain results in an integrated quantitative, qualitative, and systematic manner. The successful deployment of the expert system signifies a technological achievement of novelty pertaining to the integration of bridge information modeling (BrIM) concept with probabilistic fuzzy logic strategic approaches at the conceptual design stage of bridges.", "source_url": "https:\/\/openalex.org\/W2784626353", "pdf_url": "", "doi": "10.20381\/ruor-21467", "handle": "", "repository_name": "uO Research (University of Ottawa)", "source_record_id": "W2784626353", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 17, "title": "Fuzzy Logic in Clinical Decision Support Systems", "title_ar": "", "title_en": "Fuzzy Logic in Clinical Decision Support Systems", "author": "Meng-Lung Chung", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Utrecht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "medical-ai; expert-systems", "keywords": "", "abstract": "The diagnostic process is an important activity that is aimed at explaining and treating a patient's health problem. However, this process is subject to diagnostic errors that jeopardize patient safety and healthcare quality. In response to this, diagnostic clinical decision support systems (CDSS) have been developed to aid physicians in clinical reasoning and decision making. Diagnostic systems that have been successfully implemented employ fuzzy logic techniques. The aim of this thesis is to examine how fuzzy reasoning can be used in these systems. This has led to the following research question: How can fuzzy logic be incorporated in diagnostic CDSS? In order to answer this question, literature on the operation of CDSS and the fundamentals of fuzzy logic has been reviewed. This literature has been used to create an example of a CDSS that uses fuzzy reasoning for the diagnosis of pneumonia (lung inflammation). Finally, the utility of using fuzzy logic in healthcare has been evaluated by discussing the difference between fuzzy and classical reasoning in CDSS. The CDSS for pneumonia diagnosis has demonstrated that fuzzy logic can be incorporated in CDSS by employing a fuzzy expert system that executes the reasoning process. Furthermore, it has been found that fuzzy-based systems are characterised by robust outputs -- which means that a change in input values will not cause a drastic change in the output. This feature is desirable in healthcare, as it should be prevented that minor alterations in symptoms, patient data or test results cause a major alteration in the diagnosis proposed by the CDSS. In addition, fuzzy CDSS are capable of handling contradictions. This property enables systems to alter their decisions as they obtain new information. Further research could examine different techniques to implement a fuzzy CDSS. Additionally, the CDSS for pneumonia diagnosis could be expanded by entering more symptoms and patient data into the system and by extending the rules in the knowledge base. The accuracy of the CDSS could then be evaluated by comparing the results with existing medical data about pneumonia diagnosis in patients.", "source_url": "https:\/\/openalex.org\/W3121625290", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Utrecht University Repository (Utrecht University)", "source_record_id": "W3121625290", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 18, "title": "Control of an autonomous tracked vehicle and arm using a rule-base reduction based fuzzy expert system", "title_ar": "", "title_en": "Control of an autonomous tracked vehicle and arm using a rule-base reduction based fuzzy expert system", "author": "Reid Farchmin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Illinois at Urbana-Champaign", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "expert-systems", "research_subdomain": "", "categories": "expert-systems", "keywords": "", "abstract": "One of the major deterrents in designing control systems for vehicles with an implement is the inability and difficulty of incorporating human tendencies into the design process of the arm and vehicle controller.In this thesis, the vehicle and implement model, control design, and performance of the control system were investigated.This specific application addressed the design of a 3-actuator electro-hydraulic implement on a tracked electric vehicle.The vehicle and arm models were implemented in the Matlab-Simulink work environment to allow for dynamic and kinematic modeling of the system at near-real time.This allowed for quick feedback on the effectiveness of the controller.A fuzzy expert controller was created for both the base vehicle and the implement which would be placed on top of the tracked vehicle.The fuzzy controllers for the vehicle and the implement were separate at the rule base level but were tied to one another based on key checkpoints and procedures.This ensured system modularity through allowing the user to easily and quickly swap out vehicles or implements based on the task at hand.The fuzzy rule base was then reduced with a hierarchical rule base reduction using the fuzzy relations control strategy (FRCS) as in [1], [2], to lower the number of duplicate or repetitive rules.This ensured the system reacted quicker to a given situation without affecting the performance of the controller.The proof of concept for a reduced rule-base fuzzy expert controller on a tracked vehicle with a bucket implement was proven to work fluidly and effectively.", "source_url": "https:\/\/openalex.org\/W7220797397", "pdf_url": "https:\/\/www.ideals.illinois.edu\/items\/124675\/bitstreams\/409758\/data.pdf", "doi": "", "handle": "", "repository_name": "Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)", "source_record_id": "W7220797397", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 19, "title": "Fuzzy logic control of MPPT controller for PV systems", "title_ar": "", "title_en": "Fuzzy logic control of MPPT controller for PV systems", "author": "Mahmud Sasi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Memorial University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "expert-systems", "research_subdomain": "ai-smart-cities", "categories": "expert-systems; ai-applications; ai-smart-cities", "keywords": "", "abstract": "This thesis presents a comparison between two methods to optimize the energy extraction in a photovoltaic (PV) power system. The maximum power of a PV module varies due to changing temperature, solar radiation, and load. To maximize efficiency, PV systems use a maximum power point tracker (MPPT) to constantly extract the highest power that can be produced by a solar panel and then deliver it to the load. The general structure of an MPPT system contains a DC-DC converter (an electronic device that converts a source of direct current DC from one voltage level to another) and a controller. The MPPT finds and maintains operations at the maximum power point using a tracking algorithm during variations in weather conditions. Many different algorithms of MPPT have been proposed and discussed in the literature, but most of these methods have disadvantages in terms of efficiency, accuracy, and flexibility. Because of the nonlinear behavior of PV module current-voltage characteristics and the nonlinearity of DC-DC converters due to switching, conventional controllers are unable to provide the best response, especially when dealing with wide parameter variations and line transients. The goal of this work is to design and implement a maximum power point tracker that uses a fuzzy logic control algorithm. Fuzzy logic naturally provides a superior controller for this type of nonlinear application. This method also benefits from the artificial intelligence approach for overcoming the complexity in modeling nonlinear systems. In order to succeed in this work, an MPPT system consisting of a PV module, a DC-DC converter, batteries, and a fuzzy logic controller is designed and simulated in Simulink. Analyses of buck, boost, and buck-boost converter characteristics are carried out to find the most suitable topology for the PV system used. An integrated model of the PV module with the identified converter and batteries is simulated in MATLAB to derive the expert knowledge needed to formulate and tune the fuzzy logic controller. The simulation results show that the fuzzy logic controller is able to obtain the desired outcomes and is ready to be applied to the hardware system. This entire research work aims to compare two types of controller-based MPPT techniques. Both MPPTs are based on the same topology of DC-DC converter and are applied with the same PV system specifications. That is, one of the MPPTs was kept at its original specifications and the other one was modified by changing the internal PIC 16F684 controller with an external Arduino Uno controller. Based on a MATLAB fuzzy logic design, the Arduino code was programmed and uploaded into an Arduino board by using Arduino software (IDE). The proposed method illustrates that the performance of MPPT is improved in terms of oscillations about the maximum power point, speed, and sensitivity to parameter variation. The results indicate that a significant amount of extra power can be extracted from a photovoltaic module by using a fuzzy logic-based maximum power point tracker in comparison with a PIC 16F684 controller-based maximum power tracker. Moreover, this gives improved efficiency for the operation of a PV power system, since batteries can be sufficiently charged and used during periods of low solar radiation.", "source_url": "https:\/\/openalex.org\/W2802625816", "pdf_url": "http:\/\/research.library.mun.ca\/12843\/1\/thesis.pdf", "doi": "", "handle": "", "repository_name": "Memorial University Research Repository (Memorial University)", "source_record_id": "W2802625816", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 20, "title": "Developing a Computational Framework for a Construction Scheduling Decision Support Web Based Expert System", "title_ar": "", "title_en": "Developing a Computational Framework for a Construction Scheduling Decision Support Web Based Expert System", "author": "Feroz Ahmed", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Southern Mississippi", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "expert-systems", "research_subdomain": "ai-education", "categories": "expert-systems; ai-applications; ai-education", "keywords": "", "abstract": "Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project is performed in a real time environment with real time events which are subject to change at any time. The development of a construction schedule requires knowledge of the construction process that takes place to complete a project. Most of this knowledge is acquired through years of work\/practical experiences. Currently, working professionals and\/or students develop construction schedules without the assistance of a decision support system (that provides work\/practical experiences captured in previous jobs or by other people). Therefore, a scheduling decision support expert system will help in decision-making by expediting and automating the situation analysis to discover the best possible solution. However, the algorithm\/framework needed to develop such a decision support expert system does not exist so far. Thus, the focus of my research is to develop a computational framework for a web-based expert system that helps the decision-making process during the preparation of a construction schedule. My research to develop a new computational framework for construction scheduling follows an action research methodology. The main foundation components for my research are scheduling techniques (such as: Job Shop Problem), path-finding techniques (such as: travelling salesman problem), and rule-based languages (such as JESS). My computational framework is developed by combining these theories. The main contribution of my dissertation to computational science is the new scheduling framework, which consists of a combination of scheduling algorithms that is tested with construction scenarios. This framework could be useful in more areas where automatic job and\/or task scheduling is necessary.", "source_url": "https:\/\/openalex.org\/W2994971918", "pdf_url": "https:\/\/aquila.usm.edu\/cgi\/viewcontent.cgi?article=2787&context=dissertations", "doi": "", "handle": "", "repository_name": "Aquila Digital Community (University of Southern Mississippi)", "source_record_id": "W2994971918", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 21, "title": "An improved method using fuzzy system based on hybrid boahs for phishing attack detection", "title_ar": "", "title_en": "An improved method using fuzzy system based on hybrid boahs for phishing attack detection", "author": "Noor Syahirah Nordin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universiti Malaysia Pahang", "university_source": "repository_host", "college": "", "department": "", "country": "Malaysia", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; expert-systems; ai-applications; predictive-analytics; optimization; ai-education; ai-cybersecurity", "keywords": "", "abstract": "A fuzzy system is a rule-based system that uses human expert's knowledge which holds the truth or false values to make a particular decision. However, it is difficult to generate fuzzy parameters manually to classify data when it comes to a very complex problem. Therefore, metaheuristic algorithm as the optimization method is needed to solve this issue. A small experiment was being performed to choose the best metaheuristic algorithm where seven metaheuristic algorithms were analyzed in terms of their performance measurement that including accuracy, recall, precision, and f-measure. The algorithms involved were Genetic Algorithm, Differential Evolution Algorithm, Particle Swarm Optimization, Butterfly Optimization Algorithm, Teaching-Learning-Based Optimization Algorithm, Harmony Search Algorithm and Gravitational Search Algorithm. The proposed method of this study is to cater the problems occur in fuzzy systems by using optimization method. Moreover, Butterfly Optimization Algorithm and Harmony Search Algorithm were combined as optimization method led to a new method named BOAHS. The proposed algorithm has utilized the advantages of both algorithms to balance the exploration and exploitation search process. The experiment was executed by using k-fold cross validation techniques for predicting the classification algorithm performance. Thereby, two datasets; Website Phishing Dataset and Phishing Websites Dataset were used to test the performance of the proposed method. As a result, the average accuracy value for both datasets are 98.69% and 98.80% respectively. The proposed method has proven to outperform the other methods including the standard BOA and HS algorithm.", "source_url": "https:\/\/openalex.org\/W7205250197", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UMP Institutional Repository (Universiti Malaysia Pahang)", "source_record_id": "W7205250197", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 22, "title": "FAME-X : failure mechanism identification expert system", "title_ar": "", "title_en": "FAME-X : failure mechanism identification expert system", "author": "Dimitrios Karampelas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "ai-industry", "categories": "medical-ai; expert-systems; ai-applications; ai-industry", "keywords": "", "abstract": "Although maintenance tries to prevent them, failures regularly occur in practice. Prevention prerequisites insight on the “how” a failure occurs. Failure mechanisms (FM) describe how components fail. However, operators or technicians as the first people to deal with a failure, normally lack the knowledge to access FM that act on a material level. Current failure reporting is typically done by entering a failure type\/failure mode\/cause manually or selecting from a drop-down menu. However, this process has been reported as problematic by several industries. Thus, this study proposes the use of an Expert System (ES) as an app in mobile devices to assist operators in assessing the appropriate FM. The basic idea behind the ES is simply that failure analysis expertise is transferred from a human to a computer in the form of heuristics. This knowledge is then stored in the computer and users call upon the computer for specific advice as needed. The computer asks questions regarding failure diagnostic characteristics, makes inferences and arrives at a specific conclusion. Then, like a human consultant, it gives advices on the most probable FM and explains, if necessary, the logic behind the advice. The ES is based on an extensive study of failure diagnostic characteristics and their contribution to FM. In contrast to failure modes, the proposed FM approach enhances the decision making upon proper remedial actions. The ES can help standardize failure mechanism identification for a given set of characteristics, unify failure terminology, and codify and document the failure investigation procedure.", "source_url": "https:\/\/openalex.org\/W2800358430", "pdf_url": "https:\/\/ris.utwente.nl\/ws\/files\/26745858\/thesis_D_Karampelas.pdf", "doi": "10.3990\/1.9789036545174", "handle": "", "repository_name": "", "source_record_id": "W2800358430", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 23, "title": "Fuzzy expert based real time monitoring system for patients with chronic heart failure through IOT", "title_ar": "", "title_en": "Fuzzy expert based real time monitoring system for patients with chronic heart failure through IOT", "author": "Muriuki, Isaack Mwenda", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Strathmore University", "university_source": "repository_host", "college": "", "department": "", "country": "Kenya", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "medical-ai; expert-systems", "keywords": "", "abstract": "Data from the World Health Organization has placed CHF as the number one global killer.It remains the only cardiovascular disease with an increasing hospitalization burden and a continuous drain on health care budgets.Heart failure is a complex clinical syndrome of symptoms that suggest the heart is unable to pump blood efficiently as it should.Heart failure signs and symptoms", "source_url": "https:\/\/openalex.org\/W2950212453", "pdf_url": "", "doi": "", "handle": "", "repository_name": "SU+ Digital Repository (Strathmore University)", "source_record_id": "W2950212453", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 24, "title": "SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR BREAST CANCER DETECTION", "title_ar": "", "title_en": "", "author": "Luis Fernando Sánchez Fuentes ; Santiago Enrique Monsalve Durán", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Industrial de Santander", "university_source": "repository_host", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2025, "language": "es", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "deep-learning; medical-ai; cnn", "keywords": "", "abstract": "Esta investigación analiza cómo el cambio de dominio afecta a los clasificadores de mamografías basados en CNN y propone estrategias de mitigación. Se estableció una línea de base con arquitecturas End2End preentrenadas de repositorios públicos. Entre las arquitecturas, se seleccionó el modelo con mejor rendimiento y se adaptó mediante el aprendizaje por transferencia y el ajuste fino a un conjunto de datos privado. Posteriormente, se adoptó un enfoque radiómico a nivel de paciente, integrando imágenes con cuatro vistas, descriptores manuales y se construyó un clasificador de refuerzo de gradiente. Se realizó una evaluación inicial de los modelos utilizando el conjunto de datos privado, y se informaron los indicadores AUC, sensibilidad y especificidad bajo un umbral optimizado con G-mean. Cuando se evaluó fuera del dominio sin adaptación, se identificaron caídas sustanciales en el rendimiento del modelo. Se entrenaron dos configuraciones de modelo utilizando aprendizaje por transferencia seguido de ajuste fino. Los resultados de ambas configuraciones mejoraron sustancialmente el rendimiento con respecto a la línea de base. La configuración 1 mejoró el AUC y la especificidad; la configuración 2 mejoró el AUC y la sensibilidad. El análisis radiómico mejoró simultáneamente la sensibilidad y la especificidad en comparación con la línea de base, con una ligera disminución del AUC. En conclusión, la adaptación del modelo al cambio de dominio es eficaz para reducir las pérdidas métricas, mientras que la radiómica es una alternativa en casos con restricciones y condiciones específicas.", "source_url": "https:\/\/openalex.org\/W7133382241", "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/f607ba41-8d89-4205-a804-816bc01ec2f7\/download", "doi": "", "handle": "", "repository_name": "Universidad Industrial de Santander", "source_record_id": "W7133382241", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 25, "title": "Deep Learning-Based Breast Cancer Detection with IoMT", "title_ar": "", "title_en": "Deep Learning-Based Breast Cancer Detection with IoMT", "author": "Bo Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Auckland", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; generative-ai; medical-ai; cnn; transformers; gan; medical-imaging", "keywords": "", "abstract": "Breast cancer remains a leading cause of cancer-related mortality among women worldwide, which demands innovations in early detection and diagnostic efficiency. Existing approaches often neglect peri-tumoral contextual features, rely on limited or unrealistic synthetic data, and impose latency bottlenecks in distributed healthcare settings. We propose a reserved-space strategy for Region of Interest (ROI) extraction that achieves 97.5% True Positive Rate (TPR) while preserving contextual features, thereby capturing diagnostically critical tumour margins that rigid bounding boxes overlook. To address data scarcity, we develop an enhanced Generative Adversarial Network (GAN) model incorporating Efficient Multi-scale Attention (EMA) modules and Hinge loss, which generates high-fidelity (15.2 Fréchet Inception Distance (FID)) ROI images that maintain anatomical plausibility in lesions and microcalcification diversity, resolving the over smoothing and pathological irrelevance of conventional GAN architectures. This GAN-augmented dataset improves classification AUC by 10% compared to traditional augmentation methods. Through systematic comparison, we demonstrate that the Swin Transformer outperforms the Convolutional Neural Network (CNN) and standard Vision Transformers (ViT) models in mammography classification with 86.8% Area Under the Curve (AUC). Finally, we design a distributed edge-fog-cloud Internet of Medical Things (IoMT) framework that implements our workflow to mitigate computational bottlenecks in clinical deployment.", "source_url": "https:\/\/openalex.org\/W7135345431", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ResearchSpace (University of Auckland)", "source_record_id": "W7135345431", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 26, "title": "Driven segmentation of synthetic microwave images for breast cancer detection", "title_ar": "", "title_en": "Driven segmentation of synthetic microwave images for breast cancer detection", "author": "Bahar, Özlem", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Istanbul Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, continuing to be the leading cause of cancer-related mortality among women. Current global health statistics reveal that millions of new breast cancer cases are diagnosed annually, with a disproportionate concentration in low- and middle-income countries (LMICs). In these regions, disparities in healthcare infrastructure, socioeconomic challenges, and limited access to advanced diagnostic technologies substantially hinder early detection efforts. Early diagnosis of breast cancer is of paramount importance, as timely intervention significantly improves patient survival rates, reduces the need for aggressive treatments, mitigates treatment-related side effects, and ultimately decreases the economic burden on healthcare systems. Despite technological advancements, conventional breast imaging modalities such as mammography, ultrasound, and magnetic resonance imaging (MRI) still face notable limitations globally in terms of accessibility, cost, and diagnostic efficacy. Mammography, regarded as the clinical gold standard for breast cancer screening, involves ionizing radiation and poses potential health risks with frequent use. Moreover, its diagnostic sensitivity is considerably reduced in women with dense breast tissue, leading to false-negative results and delayed treatment. Radiation-free ultrasound imaging requires highly skilled operators, resulting in variability in diagnostic accuracy and limiting its widespread application. Nuclear imaging techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) provide valuable dynamic and functional imaging data; however, their high costs, radiation exposure, and limited availability restrict their broader clinical use. MRI offers superior soft tissue contrast and spatial resolution but is associated with high operational costs, infrastructure demands, and limited accessibility, especially in LMICs. Collectively, these challenges underscore the urgent need for safe, cost-effective, scalable, and reliable novel imaging technologies for early tumor detection. Microwave Imaging (MWI) has recently emerged as a promising alternative modality that enables non-ionizing electromagnetic wave-based examination of the internal structural and functional properties of breast tissue. The fundamental principle of MWI relies on the dielectric property differences—specifically dielectric permittivity (∈_r) and electrical conductivity (σ)—between malignant tumors and normal breast tissue. This contrast provides functional imaging information complementary to the anatomical data obtained from conventional methods. Microwave imaging systems primarily consist of transmitting and receiving antennas, with the collected data computationally reconstructed into images. Additionally, MWI systems are inherently more affordable, portable, and easier to operate, making them highly suitable for resource-constrained clinical environments. Nevertheless, the clinical translation of MWI has been impeded by intrinsic limitations such as relatively low spatial resolution, sensitivity to noise and artifacts, and challenges in accurately localizing tumors within reconstructed images. In response to these challenges, the present thesis proposes a comprehensive deep learning framework aimed at enhancing the diagnostic performance and tumor localization accuracy of microwave breast imaging. Within this scope, synthetic breast tissue models were developed using a high-fidelity simulation platform based on the Reverse Time Migration (RTM) algorithm, which realistically models the propagation and scattering of electromagnetic waves in heterogeneous and homogeneous biological tissues. The RTM algorithm processes source and receiver wavefields bidirectionally and establishes correlations between them to generate an image. It consists of two steps involving the backpropagation of complex conjugate data into the imaging domain and computing the cross-correlation norm of the forward and backward propagated fields. Synthetic breast models were generated employing microwave difference imaging based on the total electric field, necessitating knowledge of the healthy breast state during imaging. The total electric field data required for microwave imaging were synthetically produced via numerical solutions of electromagnetic scattering problems. This study investigates deep learning-based segmentation of low-resolution microwave images generated from total electric field data. The dataset comprises 1000 synthetic transverse breast images, each containing a single tumor, initially created at a high resolution of 400×400 pixels.", "source_url": "https:\/\/openalex.org\/W7112466934", "pdf_url": "https:\/\/polen.itu.edu.tr\/bitstreams\/45f1e73e-aeb2-438f-96e9-0efeb0835bfb\/download", "doi": "", "handle": "", "repository_name": "Istanbul Technical University Academic Open Archive (Istanbul Technical University)", "source_record_id": "W7112466934", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 27, "title": "Breast cancer prediction from mammography images using deep learning techniques", "title_ar": "", "title_en": "", "author": "Şeyma Doğru", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2025, "language": "tr", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging", "keywords": "", "abstract": "Teknolojik ilerlemeler, birçok sektörde olduğu gibi sağlık alanında da akıllı makinelerin karar verme süreçlerine dahil edilmesine olanak tanımıştır. İnsanlardan daha hızlı analitik düşünebilme ve ilişkileri saptama yetenekleri sayesinde bu makineler, uzmanlara doğru kararların alınmasında önemli ölçüde destek sağlamaktadır. Özellikle kanserin erken teşhisinin hayati öneme sahip olması, bu alanda yapılan yapay zeka çalışmalarını hızlandırmıştır. Kanserin erken tanısı, hastaların sağkalım oranlarının artırılması, organ kaybının önlenmesi ve radyoterapi, kemoterapi gibi tedavilerin en az düzeyde uygulanması bakımından kritik önem taşımaktadır. Memede oluşan küçük lezyonların tespiti genellikle mümkün olmamaktadır. Bu da hastalığın ilerlemesine ve ileri safhalarda fark edilmesine sebep olmaktadır. Kontrastlı Spektral Mamografi (CESM), bu tespit zorluğunu gidermek için geliştirilmiş yeni bir görüntüleme tekniğidir. Meme hücrelerine enjekte edilen kontrast madde ile lezyonları görünür kılan Kontrastlı Spektral Mamografi, dijital mamografide gözden kaçabilen lezyonları yakalamaya yardımcı olur. Doktorların daha güvenilir tanı koymasına ve hastalığa erken fark edilmesine olanak tanır. Bu tez çalışmasında, meme kanserinin teşhis sürecine odaklanılarak Kontrastlı Spektral Mamografi (CESM) görüntüleri kullanılmıştır. Çalışmada iki farklı veri seti üzerinde çeşitli derin öğrenme modelleri eğitilerek analizler gerçekleştirilmiştir. Kullanılan bu veri setleri CDD-CESM veri seti ve CESM@UCBM veri setleridir. CDD-CESM veri setinde toplamda 2006 görüntü bulunmaktadır. Bu görüntülerin 1003 tanesi düşük enerjili görüntü, 1003 tanesi ise bileşik görüntüdür. CESM@UCBM veri setinde ise 1138 adet görüntü bulunmaktadır. Bu görüntülerden 569 tanesi düşük enerjili, 569 tanesi ise bileşik görüntüdür. Bu çalışma kapsamında yapılan eğitimlerde veri setlerine 20 farklı açıdan yaklaşılmıştır. Veri seti 3 sınıflı (Normal, İyi Huylu, Kötü Huylu) ve 2 sınıflı (İyi Huylu, Kötü Huylu) olarak, ayrı ayrı ve birlikte olacak şekilde eğitilmiştir. Aynı zamanda bu ele alım şekillerine veri artırma yöntemleri uygulanarak veriler tekrar eğitilmiştir. Bu kapsamda, ResNet50, DenseNet121, EfficientNetB0, VGG16 ve VGG19 mimarileri uygulanmış ve beşli çapraz doğrulama sonucunda DenseNet121 modeliyle ,46 kesinlik (precision), ,35 duyarlılık (recall) ve ,81 F1-Skor değerleri elde edilmiştir. Bu sonuçlar, önerilen yöntemin meme kanseri tanısında etkin bir destek aracı olabileceğini göstermektedir.", "source_url": "https:\/\/openalex.org\/W7216756002", "pdf_url": "https:\/\/acikerisim.btu.edu.tr\/bitstreams\/18957f3e-ea89-423c-a8a0-261f29685e20\/download", "doi": "", "handle": "", "repository_name": "Bursa Technical University Institutional Repository", "source_record_id": "W7216756002", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 28, "title": "Neural network optimization of Speckle-based phase-contrast X-ray imaging for mammography applications", "title_ar": "", "title_en": "Neural network optimization of Speckle-based phase-contrast X-ray imaging for mammography applications", "author": "Sofía Naranjo Barros", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "X-ray Phase-Contrast Imaging (PCI) has revolutionized imaging in biomedical and materials science by enhancing contrast for soft tissues and subtle structural details that are challenging to detect with conventional absorption-based methods. Speckle-Based Imaging (SBI), a subset of PCI, leverages speckle patterns created by random diffusers to retrieve phase information, offering improved soft tissue visualization without relying on highly coherent X-ray sources. This makes SBI particularly promising for mammography, where early detection of breast cancer is often limited by the low contrast between healthy and pathological tissues in traditional imaging techniques. Despite its advantages, SBI's reliance on multiple exposures for phase retrieval can increase radiation dose, posing a safety concern. This study seeks to optimize SBI for mammographic applications by integrating convolutional neural networks (CNNs) to enable single-exposure phase retrieval, significantly reducing radiation dose while maintaining high image quality. By adapting the CNN-based StrainNet-f model, the research explores the feasibility of applying CNNs in SBI for mammography. The optimized technique was validated computationally and experimentally by comparing retrieved images with theoretical benchmarks. The results provide a proof of principle for the application of CNNs in SBI, laying the groundwork for further research into developing safer and more efficient imaging techniques for breast cancer diagnostics.", "source_url": "https:\/\/openalex.org\/W7214878742", "pdf_url": "", "doi": "", "handle": "", "repository_name": "instname:Universidad de los Andes", "source_record_id": "W7214878742", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 29, "title": "Convolutional neural networks for mammograph analysis", "title_ar": "", "title_en": "Convolutional neural networks for mammograph analysis", "author": "A. Pereira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "The main theme of this study is the application of advanced artificial intelligence techniques, especially Convolutional Neural Networks (CNN), for early detection of breast cancer. With the increasing importance of automated medical image analysis, this study aims to evaluate the effectiveness of CNN in the analysis of mammograms, focusing particularly on segmentation and anomaly detection. To achieve this objective, images from renowned public databases such as the Digital Database for Screening Mammography (DDSM) and INbreast were analyzed. The study also involves the preprocessing of these images to ensure the quality of the input data and the implementation of different CNN architectures, comparing their performance in terms of accuracy and reliability. The methodology adopted included a comprehensive literature review and the implementation of data augmentation and preprocessing techniques to optimize CNN performance. The data were collected from public databases and renowned scientific journals, ensuring the relevance and timeliness of the information. The analysis of the results showed that CNNs can outperform traditional methods, offering greater accuracy in the segmentation of tumors and other anomalies, promoting faster and more reliable diagnoses. The results show that CNN EfficientNet and InceptionResNetV2 achieved greater accuracy, while CNN MobileNet and MultiResUNet achieved satisfactory accuracy and with lower processing consumption than the other implemented networks.", "source_url": "https:\/\/openalex.org\/W7120406163", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120406163", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 30, "title": "Computer aided detection for breast lesion in ultrasound and mammography", "title_ar": "", "title_en": "Computer aided detection for breast lesion in ultrasound and mammography", "author": "Richa Agarwal", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM). Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis. Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging. In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used. The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screening", "source_url": "https:\/\/openalex.org\/W3154566879", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W3154566879", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 31, "title": "Transformer-based Transfer Learning for Breast Cancer Detection via Digital Breast Tomosynthesis", "title_ar": "", "title_en": "Transformer-based Transfer Learning for Breast Cancer Detection via Digital Breast Tomosynthesis", "author": "Mikea Dimech", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; transfer-learning; cnn; transformers; medical-imaging", "keywords": "", "abstract": "Breast cancer has emerged as the most prevalent form of cancer worldwide and the leading cause of mortality from any cancer in women, emphasising the critical need for its early detection. Traditional 2D mammography has long been the cornerstone of breast cancer screening, given its proven efficacy in early prognosis and reduction of mortality. However, the limitations of this modality have precipitated the development and adoption of more advanced imaging techniques, such as Digital Breast Tomosynthesis (DBT), which yields a 3D view of the breast. The application of deep learning to classify these scans is paramount to increasing the efficiency and efficacy of screening. However, this task presents arduous challenges such as high computational demands, scarcity of diverse data, and generalisation across varying demographics and imaging equipment. Transfer learning has proven to be effective in overcoming these challenges by enabling models to leverage pre-existing knowledge and adapt to new tasks. However, its application to DBT has been confined to Convolutional Neural Network (CNN) architectures. Meanwhile, the Transformer paradigm, which has revolutionised various deep learning domains and shown promise in traditional mammography, has yet to be explored in the context of DBT via transfer learning, highlighting a notable research gap. This study thus investigates the performance of a pre-trained Transformer model, fine-tuned on a public DBT dataset, comparing it with a fine-tuned state-of-the-art CNN. While the Transformer outperforms the convolutional architecture, the results reveal severe challenges posed by the imbalanced dataset, with models developing biases towards the majority class. Data augmentation, while beneficial, could not fully address the issue, and the need for more diverse datasets became clear. Moreover, the results underscore the importance of preserving the high resolution of DBT images, with fine-grained details, such as subtle changes in breast tissue captured at higher resolutions, proving to be pivotal for accurate diagnosis. Finally, a comparative analysis between CNNs and transformer-based models revealed that Transformers excel in recognising broader patterns across the entire image, which aids to mitigate overfitting. By using pre-trained transformers and fine-tuning them on this dataset, their effectiveness was highlighted, suggesting they can match or even surpass CNNs in breast cancer detection via DBT.", "source_url": "https:\/\/openalex.org\/W4385850049", "pdf_url": "", "doi": "10.5281\/zenodo.8243430", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W4385850049", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 33, "title": "Diagnosis of breast cancer from images 2d mammography using shape descriptors 3d", "title_ar": "", "title_en": "Diagnosis of breast cancer from images 2d mammography using shape descriptors 3d", "author": "Johnatan Carvalho Souza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; computer-vision; medical-ai; medical-imaging", "keywords": "", "abstract": "Breast cancer is the second major cause of death by cancer in the female population and the fifth leading cause of death from cancer overall. However, it is known that breast cancer has a better prognosis and higher chances of cure if diagnosed at early stages. Therefore, an early detection is extremely important and the more information is available to the expert, the greater the chances of a correct diagnosis. This scenario justifies the need for the development of computational techniques to support the early detection of breast cancer. Therefore, the purpose of this work is to present a method for breast cancer classification from mammography images using shape analysis and pattern recognition techniques. For that, it is investigated the use of the descriptors Relief Index, Average Slope, Section Area, Section Convolution, D1dist, D2dist and D3dist. These shape descriptors are not traditional in the context of medical images analysis. They are so called \\dental\" shape descriptors, which have been used in dental ecology as ecometrics, characteristics of organisms that reflect a species’ ecology, to analyze dental shape of mammals and reconstruct past environments. Several experiments with combinations of descriptors are performed, producing several feature vectors. Then, these vectors are submitted to the support vector machine classifier. The proposed method revealed promising results. The best result, on average, was 92.58% accuracy, 92.80% sensitivity and 92.28% specificity", "source_url": "https:\/\/openalex.org\/W7120339843", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120339843", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 34, "title": "Mammogram and Tomosynthesis Classification Using Convolutional Neural Networks", "title_ar": "", "title_en": "Mammogram and Tomosynthesis Classification Using Convolutional Neural Networks", "author": "Xiaofei Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Kentucky", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; cnn; image-classification; medical-imaging", "keywords": "", "abstract": "Mammography is the most widely used method of screening for breast cancer. Traditional mammography produces two-dimensional X-ray images, while advanced tomosynthesis mammography produces reconstructed three-dimensional images. Due to high variability in tumor size and shape, and the low signal-to-noise ratio inherent to mammography, manual classification yields a significant number of false positives, thereby contributing to an unnecessarily large number of biopsies performed to reduce the risk of misdiagnosis. Achieving high diagnostic accuracy requires expertise acquired over many years of experience as a radiologist. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. The convolutional process involves simplifying an image containing millions of pixels to a set of small feature maps, thereby reducing the input dimension while retaining the features that distinguish different classes of images. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, high-quality original mammograms and tomosynthesis were obtained with approval from an institutional review board. Different classifiers based on convolutional neural networks were built to classify the 2-D mammograms and 3-D tomosynthesis, and each classifier was evaluated based on its performance relative to truth values generated by a board of expert radiologists. The results show that CNNs have great potential for automatic breast cancer detection using mammograms and tomosynthesis.", "source_url": "https:\/\/openalex.org\/W2747822775", "pdf_url": "https:\/\/uknowledge.uky.edu\/cgi\/viewcontent.cgi?article=1063&context=cs_etds", "doi": "10.13023\/etd.2017.364", "handle": "", "repository_name": "UKnowledge (University of Kentucky)", "source_record_id": "W2747822775", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 35, "title": "Mammography Classification and Nodule Detection using Deep Neural Networks", "title_ar": "", "title_en": "Mammography Classification and Nodule Detection using Deep Neural Networks", "author": "Fabian Leander Sinzinger", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; image-classification; medical-imaging", "keywords": "", "abstract": "Mammographic screenings are the most common modality for an early detection of breast cancer, but a robust annotation of the depicted breast tissue presents an ongoing challenge, even for well-experienced radiologists. Computer-aided diagnosis systems can support the human classification. Modern systems therefore often rely on deep-learning based methods. This thesis investigates the fully automatic on-image classification of mammograms into one of the classes benign, malignant (cancerous) or normal. In this context, we compare two different design paradigms, one straightforward end-to-end model with a more complex decomposition hierarchy. While the end-to-end model consists mainly of the deep-learning based classifier, the decomposition pipeline incorporates multiple stages i.e. a region of interest detection (realized as a fully convolutional architecture) followed by a classification stage. Contrary to initial expectations, the end-to-end classifier turned out to obtain a superior performance in terms of accuracy (end-to-end: 76.57 %, decomposition: 65.66 %, computed as mean over all three classes in a one vs. all evaluation) and an improved area under receiver operating characteristic-score. All discussed parametric models were trained from scratch without using pre-trained network weights. Therefore we discuss the choice of hyper-parameters, initialization, and choice of a feasible cost function. For a successful feature extraction, in the region of interest detection stage, the negative dice coefficient proved itself to be a more robust cost function than the also investigated sensitivity-specificity loss.", "source_url": "https:\/\/openalex.org\/W2977003717", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2977003717", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 36, "title": "Computer-aided Detection of Breast Cancer in Digital Tomosynthesis Imaging Using Deep and Multiple Instance Learning", "title_ar": "", "title_en": "Computer-aided Detection of Breast Cancer in Digital Tomosynthesis Imaging Using Deep and Multiple Instance Learning", "author": "Mina Yousefi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Concordia University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; generative-ai; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "Breast cancer is the most common cancer among women in the world. Nevertheless, early detection of breast cancer improves the chance of successful treatment. Digital breast tomosynthesis (DBT) as a new tomographic technique was developed to minimize the limitations of conventional digital mammography screening. A DBT is a quasi-three-dimensional image that is reconstructed from a small number of two-dimensional (2D) low-dose X-ray images. The 2D X-ray images are acquired over a limited angular around the breast. Our research aims to introduce computer-aided detection (CAD) frameworks to detect early signs of breast cancer in DBTs. In this thesis, we propose three CAD frameworks for detection of breast cancer in DBTs. The first CAD framework is based on hand-crafted feature extraction. Concerning early signs of breast cancer: mass, micro-calcifications, and bilateral asymmetry between left and right breast, the system includes three separate channels to detect each sign. Next two CAD frameworks automatically learn complex patterns of 2D slices using the deep convolutional neural network and the deep cardinality-restricted Boltzmann machines. Finally, the CAD frameworks employ a multiple-instance learning approach with randomized trees algorithm to classify DBT images based on extracted information from 2D slices. The frameworks operate on 2D slices which are generated from DBT volumes. These frameworks are developed and evaluated using 5,040 2D image slices obtained from 87 DBT volumes. We demonstrate the validation and usefulness of the proposed CAD frameworks within empirical experiments for detecting breast cancer in DBTs.", "source_url": "https:\/\/openalex.org\/W2904137965", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Spectrum Research Repository (Concordia University)", "source_record_id": "W2904137965", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 37, "title": "Automatic quality control methods for mammography and image quality of digital breast tomosynthesis systems", "title_ar": "", "title_en": "Automatic quality control methods for mammography and image quality of digital breast tomosynthesis systems", "author": "Veli-Matti Sundell", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; generative-ai; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "Breast cancer is the most common cancer among women worldwide, and it is often detected using mammography. Mammography is a two-dimensional imaging modality dedicated to breast imaging, which utilizes x-rays for image formation. To overcome limitations of mammography, digital breast tomosynthesis has also been developed. Mammography has a high image quality requirement, which necessitates systematic quality control. The aims of this dissertation were to develop automatic quality control methods for mammography and determine technical and subjective image quality of five digital breast tomosynthesis systems. Daily image quality control in HUS Diagnostic Center is performed using the American College of Radiology mammography accreditation phantom which contains fibers, microcalcification groups and masses. Phantom images are usually evaluated visually. To overcome limitations of visual evaluation, the phantom scoring task was automatized using wavelet transform and convolutional neural networks. To study technical image quality of five digital breast tomosynthesis systems, several image quality parameters were measured using phantoms. The subjective image quality of these digital breast tomosynthesis systems was studied using radiologists’ reviews of phantom images containing filament, microcalcification group and low-contrast detail targets. Both automated image quality analysis software resulted in similar target detection levels with visual evaluations made by the reviewers. The convolutional neural network-based scoring algorithm was slightly more accurate than the wavelet transform-based algorithm. In technical image quality evaluations of digital breast tomosynthesis systems notable differences between systems were observed in artefact spread in the z-direction, in-plane resolution, and noise power spectra of reconstructed planes. Notable in-plane resolution anisotropy was observed between x- and y-directions for most of the systems. The geometric accuracy was within 0.5% for every system. In subjective image quality reviews at similar dose levels, only one system differed significantly from all other systems in microcalcification scores. However, when using site-specific protocols, significant differences were found between the systems for all target scores. Moreover, a strong correlation was found between the reviewer scores and radiation doses in acquisitions. In conclusion, the developed automatic mammography quality control phantom scoring software provides an efficient method for monitoring the function and performance of digital mammography systems. This benefits especially a multi-unit center with systems from several vendors and different generations. The studied digital breast tomosynthesis systems have different technical and image reconstruction solutions, which lead to notable differences in technical image quality. The subjective image quality between digital breast tomosynthesis systems was found to be mostly related to dose level.", "source_url": "https:\/\/openalex.org\/W7199077371", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Työväentutkimus Vuosikirja", "source_record_id": "W7199077371", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 38, "title": "Automatic dense tissue segmentation based on fully convolutional network for mammography images *", "title_ar": "", "title_en": "", "author": "Carlos S. Benitez", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Industrial de Santander", "university_source": "repository_host", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2020, "language": "es", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "La densidad porcentual de seno (PD) es uno de los factores de riesgo más importantes asociados con el desarrollo del cáncer de seno. Por lo tanto, la estimación precisa de la PD es una tarea importante para la evaluación del riesgo de cáncer de seno basada en el análisis mamográfico. Para evitar variabilidad entre lectores se opta por el desarrollo de algoritmos de estimación de densidad automáticos. Sin embargo, la segmentación automática del tejido fibroglandular (FGT) es una tarea difícil ya que las características morfológicas tanto sutiles como complejas se proyectan en una Mamografía Digital de Campo Completo (FFDM). En este trabajo presentamos un algoritmo híbrido basado en una red completamente convolucional y un algoritmo de agrupamiento basado en intensidad para la estimación de tejido denso. Para fines de validación, utilizamos un conjunto de datos que se usan como referencia y consta de 582 mamografías con tejido denso segmentado manualmente por un radiólogo experto. Como resultado, la segmentación de tejido denso usando la selección de clúster demuestra una mejoría de 8% en la mediana del coeficiente de similitud de Dice (DSC) respecto a la segmentación dada por la red neuronal. las estimaciones de PD obtenidas con el método propuesto no arrojan diferencias estadísticamente significativas con respecto a las estimaciones de PD del radiólogo. Además, el método propuesto arroja unas medianas de DSC y error de PD de 0.795 y 0.077, respectivamente. Al compararse con un algoritmo clínicamente validado del estado del arte el algoritmo propuesto alcanzó un mayor rendimiento.", "source_url": "https:\/\/openalex.org\/W7133439234", "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/109eda54-9207-4cb8-a84d-26bb40951424\/download", "doi": "", "handle": "", "repository_name": "Universidad Industrial de Santander", "source_record_id": "W7133439234", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 39, "title": "Breast Cancer Detection fromHistopathological Images using DeepLearning and Transfer Learning", "title_ar": "", "title_en": "Breast Cancer Detection fromHistopathological Images using DeepLearning and Transfer Learning", "author": "Mansi Chowkkar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging", "keywords": "", "abstract": "Breast Cancer is the most common cancer in women and it's harming women's mental and physical health. Due to complexities present in Breast Cancer images, image processing technique is required in the detection of cancer. Early detection of Breast cancer required new deep learning and transfer learning techniques. In this paper, histopathological images are used as a dataset from Kaggle. Images are processed using histogram normalization techniques. This research project implements the Convolutional Neural Network(CNN) model based on deep learning and DenseNet-121 based on transfer-learning. Transfer learning uses the Imagenet pre-trained model for training. Hyper-parameter tuning is done for increasing accuracy and precision value. Research achieved 90.9 % test accuracy using the CNN model and 88.03 % accuracy by the transfer learning model.", "source_url": "https:\/\/openalex.org\/W3041001745", "pdf_url": "https:\/\/norma.ncirl.ie\/4282\/2\/mansichowkkarconfigurationmanual.pdf", "doi": "", "handle": "", "repository_name": "NORMA", "source_record_id": "W3041001745", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 40, "title": "Development of model observers for quantitative assessment of mammography image quality", "title_ar": "", "title_en": "Development of model observers for quantitative assessment of mammography image quality", "author": "Kretz, Tobias", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging", "keywords": "", "abstract": "Assurance of image quality has become a basic need in our society as images play a crucial role in this era of social media and digitization. Applications range from surveillance to medical imaging. Image quality is defined as the degree of clarity of its elements. Images undergo a chain of processes before being perceived by the viewer, starting from acquisition to digitization, compression, pre-processing and final presentation. Image quality expresses the extent of distortion of the original image information by various degradation processes. In mammography, image quality specifically defines the perceptibility of clinically relevant structures, like abnormal masses and suspicious micro-calcifications, that allows one to reliably detect breast cancer at an early stage. The image quality in mammography can be assessed by analyzing recordings of technical phantoms that contain artificial lesions with various diameters and contrast levels. This way, a contrast-detail information can be determined, which expresses the ability of an observer to detect the individual lesions with prescribed accuracy. Automatic procedures are favored for objectively determining this contrast-detail information. In this thesis, several data analysis methods for assessing image quality in mammography are developed, tested and compared to the current state-of-the-art procedure which is recommended by a European organization. The first contribution of this thesis is the implementation of a virtual mammography for the simulation of realistic images of a technical phantom. Simulated images of a known virtual specimen allow different image quality assessment procedures to be assessed and compared. In addition, virtual mammography makes it possible to set up large data sets at low cost that are required for the application of data-intensive techniques, such as deep learning. Automatic procedures for image quality assessment in mammography usually comprise expert pre-processing and the utilization of mathematical model observers. Our second contribution is the development of a method that utilizes a recently developed parametric model observer. This way, the contrast-detail information is based on established measures of image quality. Furthermore, an uncertainty of the prediction can be estimated. Compared to the current practice, our proposed approach reduces the workload for quality assurance measurements. Even though our method needs fewer images, it still requires expert pre-processing. Deep learning is able to avoid such expert pre-processing and to derive representative features automatically from the images. Our third contribution is the exploration of the applicability of deep learning methods for image quality assessment in mammography. Specifically, a deep learning observer is developed that estimates the contrast-detail information directly from a single image. No cumbersome pre-processing of the images is required. Our last contribution is the explanation of predictions of the developed deep learning observer as well as the estimation of the uncertainty of its prediction. The former gives interesting insights into the learned strategy, whereas the latter expresses the confidence about our proposed model.", "source_url": "https:\/\/openalex.org\/W3092046275", "pdf_url": "", "doi": "10.14279\/depositonce-10552", "handle": "", "repository_name": "DepositOnce", "source_record_id": "W3092046275", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 41, "title": "Methodology for the integration of different deep learning-based models for the detection of lesions in screening mammography.", "title_ar": "", "title_en": "", "author": "Erika Yesenia Suárez Bonilla ; David Esteban Ortega Figueroa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Industrial de Santander", "university_source": "repository_host", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2023, "language": "es", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; medical-ai; predictive-analytics; medical-imaging", "keywords": "", "abstract": "Los modelos de aprendizaje profundo han demostrado su potencial para la detección del cáncer de mama en mamografías de cribado. Existen muchas arquitecturas de aprendizaje y métodos de entrenamiento que realizar tareas de detección, clasificación y\/o localización. Sin embargo, la implementación y validación de estos plantea serias dudas sobre la reproducibilidad de los resultados. Este trabajo, se intereza en la integración de diferentes modelos para la detección de cáncer de mama. Para ello, construimos un conjunto de datos independiente, para analizar la reproducibilidad de los modelos, y realizar diferentes experimentos para su integración. Este trabajo incluye tres etapas. Primero, se realiza una evaluación comparativa de 5 modelos de cribado para la detección. Segundo, se realiza un análisis de saliencia para estudiar la relación entre las capacidades de detección y localización de lesiones de estos. Y por último, se prueban métodos de integración. Los resultados sugieren que el desempeño en saliencia no es un predictor preciso del rendimiento de un algoritmo en la detección de cáncer de mama. Por esto, la integración se basó únicamente en el procesamiento de las predicciones individuales de cada modelo. Así, el mejor de los resultados se obtuvo con regresión logística mediante validación cruzada, con un rendimiento de AUC = 0.841 (IC 95\\%: 0.759-0.923) a nivel de mama.", "source_url": "https:\/\/openalex.org\/W7133378455", "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/ce709672-5480-4297-a125-2f95f4bbfcc3\/download", "doi": "", "handle": "", "repository_name": "Universidad Industrial de Santander", "source_record_id": "W7133378455", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 42, "title": "Generating Audio Using Recurrent Neural Networks", "title_ar": "", "title_en": "Generating Audio Using Recurrent Neural Networks", "author": "Andrew Pfalz", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; generative-ai; predictive-analytics; rnn; speech-recognition", "keywords": "", "abstract": "Long Short Term Memory cells are a type of recurrent neural network that perform well when predicting sequence data. This works presents four approaches to modeling audio data. The models are trained to predict either raw audio samples or magnitude spectrum windows based on prior input audio. In a process called sampling, the models can then be employed to generate new audio using what they learned about the data they were trained on. Four methods for sampling are presented. The first has the model predict a vector for each vector in the input. The second has the model predict one magnitude spectrum window for each magnitude spectrum window in the input. The third has the model predict a single audio sample for each input vector. The fourth has the model predict a single sample for the entire input matrix. The end result is a method by which novel audio can be generated. This method differs from other synthesis techniques in that the content of the generated audio is not easily foreseeable. The various approaches to sampling afford different levels of control over the output. The biggest drawback to creating audio using this method is that the process is often too slow to run in real time. Finally two applications are presented that allow the user to easily create and launch jobs. Four musical compositions are presented that use the sounds created using predictions from the models. The process for generating the source materials for each piece and the manipulations that were made to them after they were generated are also described.", "source_url": "https:\/\/openalex.org\/W2915977493", "pdf_url": "https:\/\/repository.lsu.edu\/cgi\/viewcontent.cgi?article=5621&context=gradschool_dissertations", "doi": "10.31390\/gradschool_dissertations.4601", "handle": "", "repository_name": "Louisiana State University", "source_record_id": "W2915977493", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 43, "title": "NORMALISASI DOKUMEN TEKS MENGANDUNG KATA MIXED-CODE UNTUK ANALISIS SENTIMEN DENGAN METODE RECURRENT NEURAL NETWORK LONG SHORT-TERM MEMORY (RNN-LSTM)", "title_ar": "", "title_en": "", "author": "ANJAS WIRANTA TARIGAN ; Erlanshari Aan ; Susanto Agus", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bengkulu", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "id", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "deep-learning; rnn", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7126142997", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Bengkulu Scholar Repository (University of Bengkulu)", "source_record_id": "W7126142997", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 44, "title": "Stock market forecasting using recurrent neural network", "title_ar": "", "title_en": "Stock market forecasting using recurrent neural network", "author": "Qiyuan Gao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Missouri", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-industry", "keywords": "", "abstract": "In this research, we study the problem of stock market forecasting using Recurrent Neural Network(RNN) with Long Short-Term Memory (LSTM). The purpose of this research is to examine the feasibility and performance of LSTM in stock market forecasting. We optimize the LSTM model by testing different configurations, i.e., the number of neurons in hidden layers and number of samples in sequence. Instead of using daily stock price data, we collect hourly stock data from the IQFEED database in order to train our model with relatively low noise samples. Nevertheless, based on the prediction results of LSTM model, we build up a stock database with six U.S market stocks from five different industries. The average test accuracy of these six stocks is 54.83%, where the highest accuracy is at 59.5% while the lowest is at 49.75%. We then develop a trade simulator to evaluate the performance of our model by investing the portfolio within a period of 400 hours, the total profit gained by the model is $413,233.33 with $6,000,000 initial investment.", "source_url": "https:\/\/openalex.org\/W2566465989", "pdf_url": "", "doi": "", "handle": "", "repository_name": "MOspace Institutional Repository (University of Missouri)", "source_record_id": "W2566465989", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 45, "title": "IMPLEMENTASI MODEL CONVOLUTIONAL RECURRENT NEURAL NETWORK (CRNN) DENGAN BIDIRECTIONAL LONG SHORT-TERM MEMORY (BI-LSTM) UNTUK PENGENALAN EMOSI PADA SUARA", "title_ar": "", "title_en": "", "author": "ADHIRAJASA M. RADITYA", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Lampung University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "id", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "deep-learning; nlp; cnn; rnn; sentiment-analysis; speech-recognition", "keywords": "Speech Emotion Recognition, CRNN, BiLSTM, Log-Mel Spectrogram, Data Augmentation.", "abstract": "Pengenalan emosi pada suara merupakan tugas kompleks yang krusial untuk interaksi manusia-mesin. Penelitian ini mengimplementasikan dan mengevaluasi model Convolutional Recurrent Neural Network (CRNN) yang menggabungkan keunggulan Convolutional Neural Network (CNN) untuk ekstraksi fitur spasial dengan Bidirectional Long Short-Term Memory (BiLSTM) untuk analisis dependensi temporal. Model ini dilatih dan diuji menggunakan dataset CREMA-D, yang mencakup enam kelas emosi: marah, jijik, takut, bahagia, netral, dan sedih. Fitur diekstraksi dari sinyal audio menggunakan log-Mel spectrogram untuk merepresentasikan karakteristik suara yang relevan dengan persepsi manusia. Untuk meningkatkan variasi data dan generalisasi model, diterapkan teknik augmentasi data berupa pitch shifting dan time stretching. Hasil evaluasi menunjukkan bahwa model CRNN yang didukung dengan augmentasi data berhasil mencapai akurasi validasi sebesar 68.23%, sebuah peningkatan dari 66.08% yang dicapai oleh model tanpa augmentasi. Model ini menunjukkan performa yang menjanjikan, terutama dalam mengklasifikasikan emosi marah dan netral. Penelitian ini menyimpulkan bahwa arsitektur CRNN dengan BiLSTM efektif untuk tugas pengenalan emosi suara, dan augmentasi data berperan penting dalam meningkatkan kinerjanya. Kata Kunci: Pengenalan Emosi Suara, CRNN, BiLSTM, Log-Mel Spectrogram, Augmentasi Data. Speech emotion recognition is a complex task crucial for human-machine interaction. This study implements and evaluates a Convolutional Recurrent Neural Network (CRNN) model that combines the advantages of a Convolutional Neural Network (CNN) for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) for temporal dependency analysis. The model is trained and tested using the CREMA-D dataset, which includes six emotion classes: anger, disgust, fear, happiness, neutral, and sadness. Features are extracted from the audio signals using log-Mel spectrograms to represent sound characteristics relevant to human perception. To enhance data variation and model generalization, data augmentation techniques such as pitch shifting and time stretching were implemented. The evaluation results show that the CRNN model with data augmentation achieved a validation accuracy of 68.23%, an increase from the 66.08% achieved by the model without augmentation. The model shows promising performance, especially in classifying the anger and neutral emotions. This study concludes that the CRNN architecture with BiLSTM is effective for the task of speech emotion recognition, and that data augmentation plays a crucial role in enhancing its performance. Keywords: Speech Emotion Recognition, CRNN, BiLSTM, Log-Mel Spectrogram, Data Augmentation.", "source_url": "https:\/\/openalex.org\/W7126365563", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digilib Repository Unila (Lampung University)", "source_record_id": "W7126365563", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 46, "title": "LSTM recurrent neural network for identification of a thermal system", "title_ar": "", "title_en": "LSTM recurrent neural network for identification of a thermal system", "author": "Guilherme Nomura Mateus Leite", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Federal University of Technology – Paraná", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "deep-learning; ai-applications; rnn; ai-industry", "keywords": "", "abstract": "The focus of this graduation thesis is the application of a Long Short-Term Memory (LSTM) recurrent neural network (RNN) in the context of identifying and modeling a complex thermal system consisting of two heaters and two sensors. The proposed approach is based on the remarkable capability of LSTM cells within an RNN to capture and model long-term dependencies in sequential data, thus enabling precise characterization of the thermal system’s behavior. The effectiveness of this methodology is evaluated through the analysis of results obtained from detailed simulations and experimental tests conducted on the thermal system in question. Based on these results, it becomes evident that the application of LSTM recurrent neural networks in thermal system identification holds great potential for advancing the field of control and automation. This technique emerges as a current and effective tool for solving complex problems, offering new possibilities in the analysis and modeling of thermal systems and, by extension, in various practical applications in the field of electrical engineering and industrial automation.", "source_url": "https:\/\/openalex.org\/W7155453337", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/35172\/1\/redelstmsistematermico.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)", "source_record_id": "W7155453337", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 48, "title": "Generating rhyming poetry using LSTM recurrent neural networks", "title_ar": "", "title_en": "Generating rhyming poetry using LSTM recurrent neural networks", "author": "Cole Peterson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Victoria", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "deep-learning; rnn", "keywords": "", "abstract": "Current approaches to generating rhyming English poetry with a neural network involve constraining output to enforce the condition of rhyme. We investigate whether this approach is necessary, or if recurrent neural networks can learn rhyme patterns on their own. We compile a new dataset of amateur poetry which allows rhyme to be learned without external constraints because of the dataset’s size and high frequency of rhymes. We then evaluate models trained on the new dataset using a novel framework that automatically measures the system’s knowledge of poetic form and generalizability. We find that our trained model is able to generalize the pattern of rhyme, generate rhymes unseen in the training data, and also that the learned word embeddings for rhyming sets of words are linearly separable. Our model generates a couplet which rhymes 68.15% of the time; this is the first time that a recurrent neural network has been shown to generate rhyming poetry a high percentage of the time. Additionally, we show that crowd-source workers can only distinguish between our generated couplets and couplets from our dataset 63.3% of the time, indicating that our model generates poetry with coherency, semantic meaning, and fluency comparable to couplets written by humans.", "source_url": "https:\/\/openalex.org\/W3010625193", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UVic’s Research and Learning Repository (University of Victoria)", "source_record_id": "W3010625193", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 50, "title": "Harbour Porpoise Click Train Classification with LSTM Recurrent Neural Networks", "title_ar": "", "title_en": "Harbour Porpoise Click Train Classification with LSTM Recurrent Neural Networks", "author": "Filip Ärlemalm", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; rnn; speech-recognition", "keywords": "", "abstract": "The harbour porpoise is a toothed whale whose presence is threatened in Scandinavia. Onestep towards preserving the species in critical areas is to study and observe the harbourporpoise population growth or decline in these areas. Today this is done by using underwateraudio recorders, so called hydrophones, and manual analyzing tools. This report describes amethod that modernizes the process of harbour porpoise detection with machine learning. Thedetection method is based on data collected by the hydrophone AQUAclick 100. The data isprocessed and classified automatically with a stacked long short-term memory recurrent neuralnetwork designed specifically for this purpose.", "source_url": "https:\/\/openalex.org\/W3031199631", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3031199631", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 51, "title": "Long Short-term Memory (LSTM) recurrent neural networks for urban hydrological modelling", "title_ar": "", "title_en": "Long Short-term Memory (LSTM) recurrent neural networks for urban hydrological modelling", "author": "Vetle Øyri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "deep-learning; ai-applications; rnn; ai-smart-cities", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3152642817", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W3152642817", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 52, "title": "Predicting U.S. business cycles with recurrent neural networks : An extensive multivariate time-series analysis for comparing LSTM and GRU networks", "title_ar": "", "title_en": "Predicting U.S. business cycles with recurrent neural networks : An extensive multivariate time-series analysis for comparing LSTM and GRU networks", "author": "Markus M. Mäkelä", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Turku", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "This study examines how 22 different long short-term memory (LSTM) and gated recurrent unit (GRU) network architectures suit predicting U.S. business cycles. The networks create 91-day forecasts for the dependent variable by using multivariate time-series data comprising 26 leading indicators’ values for the previous 400 days. The proposed models are evaluated by using a train-test split, where the proposed models are trained with data from 1980 to 2005, and the out-of-sample set consists of data between 2005 and 2015. The performance is evaluated by using mean squared error (MSE) and mean absolute error (MAE), and early warning signs are also considered beneficial. The training algorithm consists of typical deep learning methods. MSE and L1 regularization are used for determining the cost, and minibatches of 32 examples are applied together with Nesterov accelerated momentum (NAG) learning algorithm. Early stopping is introduced to halt the training process when strong signs of overfitting are detected. Each proposed recurrent neural network (RNN) architecture is trained three times, and these three networks’ averaged predictions are examined when comparing the architectures. Performance-wise, a few LSTM networks stand out from the other proposed networks. Although the performance results favor the proposed LSTM networks slightly over their GRU equivalents, the difference is not substantial and, in turn, the proposed GRU networks offer less deviation in MSE and MAE between each architecture. However, these steadier performance results do not generate less volatile forecasts. Instead, the best performing networks and architectures differentiate by offering less volatile predictions that also vary less from the real values. Most of the models generate a considerable amount of early warning signs before the 2007 recession, which indicates their suitability for detecting turning points in business cycles. Moreover, a wide range of the proposed LSTM and GRU network architectures learn the general pattern, also the smaller architectures comprising only one hidden layer and less than 500 optimizable parameters. This suggests that these methods offer noteworthy solutions for business cycle forecasting and, more widely, supports applying nonlinear machine learning methods with multivariate data for macroeconomic forecasting tasks where prevalent methods have been found unable to deliver adequate accuracy.", "source_url": "https:\/\/openalex.org\/W3093480170", "pdf_url": "https:\/\/www.utupub.fi\/bitstreams\/0a464deb-b9ae-47f7-a531-c78d105d607e\/download", "doi": "", "handle": "", "repository_name": "UTUPub (University of Turku)", "source_record_id": "W3093480170", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 53, "title": "Evaluating word-vector generation techniques for use in semantic role labeling with recurrent LSTM neural networks", "title_ar": "", "title_en": "Evaluating word-vector generation techniques for use in semantic role labeling with recurrent LSTM neural networks", "author": "Daniel Toom", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; nlp; self-semi-supervised; rnn", "keywords": "", "abstract": "This is an investigation of how the performance on a natural language task, semantic role labeling (SRL), is affected by how the input text is encoded. Four methods for encoding words as dense vectors of floating-point numbers are evaluated: Noisecontrastive estimation (NCE), Continuous bag-of-words (CBOW), Skip-grams and Global vectors for word representation (GloVe). Vectors are generated using a corpus of Wikipedia articles as the training set, with different values for various parameters, such as vector length, context length and training methods. In order to evaluate the generated vectors, they are then used as the input into a recurrent bi-directional neural network using LSTM neurons. This network is trained using the standard CoNLL-2005 shared task data set, and evaluated with the associated test sets. The results show that there is no large or consistent advantage to using word encodings from any of the tried methods or any of the tried parameter settings over any of the other methods or parameter settings. The SRL system that was used thus seems to be fairly robust in regard to the choice of input vectors. All methods generate vectors that outperform random vectors, however, indicating that pretraining vectors has a positive effect. The labeling accuracy is close to but slightly worse than previously published state-of-the-art performance results from the use of a similar network on the SRL task.", "source_url": "https:\/\/openalex.org\/W2745353936", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2745353936", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 54, "title": "Prediction of Blood Glucose Levels for Diabetic Patients from LSTM-Type Recurrent Neural Networks", "title_ar": "", "title_en": "", "author": "Ismael Vega Rojas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Industrial de Santander", "university_source": "repository_host", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2023, "language": "es", "research_domain": "medical-ai", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; rnn; ai-industry", "keywords": "", "abstract": "En este trabajo de grado se presenta un modelo de estimación de glucosa para pacientes con diabetes mellitus tipo 1, donde se usa el simulador T1DMS (Visentin et al., 2018) con el que se obtiene un conjunto de mediciones de insulina y glucosa para entrenar el modelo de predicción. Para realizar este entrenamiento del modelo se utilizan series de tiempo, tomando como entrada valores pasados de glucosa en sangre e insulina inyectada y generando a la salida instantes de tiempo futuro de glucosa en sangre. Las secuencias de entrada constan de un conjunto de 15 muestras (timesteps) medidas cada minuto, mientras que la secuencia de salida, 210 muestras de glucosa futuras predichas mediante el modelo de estimación que corresponde a la curva completa de un episodio de glucosa después de una ingesta de glucosa por parte del paciente. Con estas secuencias de entradas y salidas se entrenan los modelos de Recurrent Neural Network (RNN) tipo LSTM, para 10 pacientes cada uno con un modelo personalizado, usando solo los episodios o curvas de glucosa de cada paciente en cada modelo. Y otro modelo generalizado, es decir usando los episodios o curvas de glucosa de todos los pacientes en un solo modelo. Para estimar el rendimiento del modelo, se usan las métricas de error MAE y RMSE, se evalúa el ajuste del modelo, se define la arquitectura final utilizando el número de capas y celdas que permitan una mejor precisión, al igual que el optimizador que mejor funcione, como los inicializadores adecuados, y las funciones de activación en cada capa del modelo, para que se obtengan las predicciones más exactas. Donde se toma como base lo aprendido en el curso electivo de Deep learning, dictado en el 2022-2, en la Universidad Industrial de Santander.", "source_url": "https:\/\/openalex.org\/W7133451770", "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/ead67cab-a089-4477-bdda-d989ffd8f2e2\/download", "doi": "", "handle": "", "repository_name": "Universidad Industrial de Santander", "source_record_id": "W7133451770", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 55, "title": "Probabilistic Residential Load Forecasting based on LSTM Recurrent Neural Networks: Implementation and Assessment", "title_ar": "", "title_en": "", "author": "Guillermo Moraleda Conejo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Comillas Pontifical University", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2017, "language": "es", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "Este trabajo pretende abordar dos objetivos. El primero sería realizar un estudio de los resultados obtenidos para la predicción del consumo eléctrico a corto plazo en las viviendas con un método de RNN LSTM en comparación con los resultados previamente obtenidos mediante Redes Neuronales “superficiales”. El segundo objetivo sería realizar un estudio de los perfiles de demanda de las viviendas para analizar la idoneidad de un método u otro según los perfiles de consumo de cada vivienda.", "source_url": "https:\/\/openalex.org\/W7170005206", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Comillas Repository (Comillas Pontifical University)", "source_record_id": "W7170005206", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 56, "title": "Intra-day photovoltaic generation forecast using LSTM recurrent neural networks and historical energy data", "title_ar": "", "title_en": "", "author": "Bruno Rapchaell Magalhaes da Cunha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "pt", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-smart-cities", "keywords": "", "abstract": "Este trabalho apresenta uma metodologia que utiliza dados históricos de energia para obter a previsão de geração de sistemas fotovoltaicos no horizonte de 1h, utilizando o conceito de janela temporal (time steps) e uma rede recorrente do tipo LSTM (Long Short Term Memory). Os dados históricos meteorológicos e de geração de energia utilizados foram captados no período de 1 ano. Uma análise comparativa utilizando o modelo LSTM para previsão de geração com diferentes variáveis de entrada e diferentes quantidades de time steps (tamanho da janela temporal) também foi conduzida. O modelo proposto (LSTM 1) que obtém a previsão da geração solar fotovoltaica a partir de uma série temporal de dados de energia possui um resultado bem próximo do melhor resultado obtido (LSTM4) que considera quatro variáveis meteorológicas (temperatura, umidade, irradiação e índice de chuva) como entrada do modelo de previsão. Considerando as métricas de erro RMSE (erro médio quadrático) e NRMSE (erro médio quadrático normalizado), os valores obtidos foram 0.398 e 0.110 para o modelo com 4 variáveis e 0.419 e 0.116 para o modelo proposto, respectivamente. Trata-se de uma abordagem mais simples devido ao fato de não necessitar de uma estação meteorológica para captar dados meteorológicos e também por diminuir a complexidade computacional do modelo LSTM utilizando uma única variável.", "source_url": "https:\/\/openalex.org\/W7120600188", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120600188", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 57, "title": "Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells", "title_ar": "", "title_en": "Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells", "author": "Jako Rostami ; Fredrik Hansson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period.", "source_url": "https:\/\/openalex.org\/W2989662800", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2989662800", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 58, "title": "Predicting Irregularities in Arrival Times for Toronto Transit Buses with LSTM Recurrent Neural Networks Using Vehicle Locations and Weather Data", "title_ar": "", "title_en": "Predicting Irregularities in Arrival Times for Toronto Transit Buses with LSTM Recurrent Neural Networks Using Vehicle Locations and Weather Data", "author": "Anshuman Kush", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Trent University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "ANN, LSTM, Machine Learning", "abstract": "Public transportation systems play important role in the quality of life of citizens in any metropolitan city. However, public transportation authorities face criticisms from commuters due to irregularities in bus arrival times. For example, transit bus users often complain when they miss the bus because it arrived too early or too late at the bus stop. Due to these irregularities, commuters may miss important appointments, wait for too long at the bus stop, or arrive late for work. This thesis seeks to predict the occurrence of irregularities in bus arrival times by developing machine learning models that use GPS locations of transit buses provided by the Toronto Transit Commission (TTC) and hourly weather data. We found that in nearly 37% of the time, buses either arrive early or late by more than 5 minutes, suggesting room for improvement in the current strategies employed by transit authorities. We compared the performance of three machine learning models, for which our Long Short-Term Memory (LSTM) [13] model outperformed all other models in terms of accuracy. The error rate for LSTM model was the lowest among Artificial Neural Network (ANN) and support vector regression (SVR). The improved accuracy achieved by LSTM is due to its ability to adjust and update the weights of neurons while maintaining long-term dependencies when encountering new stream of data. Author Keywords: ANN, LSTM, Machine Learning", "source_url": "https:\/\/openalex.org\/W7207339572", "pdf_url": "https:\/\/trentu.scholaris.ca\/bitstreams\/9ca192dc-5548-476b-9d72-f695657df587\/download", "doi": "", "handle": "", "repository_name": "Digital Collections (Trent University)", "source_record_id": "W7207339572", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 59, "title": "Dynamic Analysis of Recurrent Neural Networks", "title_ar": "", "title_en": "Dynamic Analysis of Recurrent Neural Networks", "author": "Han Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Texas A&M University Libraries", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; rnn; speech-recognition", "keywords": "", "abstract": "With the advancement in deep learning research, neural networks have become one of the most powerful tools for artificial intelligence tasks. More specifically, recurrent neural networks (RNNs) have achieved state-of-the-art in tasks such as hand-writing recognition and speech recognition. Despite the success of recurrent neural networks, how and why do neural nets work is still not sufficiently investigated. My work on the dynamical analysis of recurrent neural networks can help understand how the input features are extracted in the recurrent layer, how the RNNs make decisions, and how the chaotic dynamics of RNNs affects its behaviors. Firstly, I investigated the dynamics of recurrent neural networks as autonomous dynamical system in the experiment of a two-joint limb controlling task and compared the empirical result and the theoretical analysis. Secondly, I investigated the dynamics of non-autonomous recurrent neural networks on two benchmark tasks: sequential MNIST recognition task and DNA splice junction classification task. How the hidden states of long-short term memory (LSTM) and gated recurrent unit (GRU) cells learn new features and how the input sequence is extracted are demonstrated with experiments. Finally, based on the understanding of the external and internal dynamics of recurrent units, I proposed several algorithms for recurrent neural network compression. The algorithms demonstrate reasonable performance in compression ratio and are able to sustain the performance of the original models.", "source_url": "https:\/\/openalex.org\/W3120491280", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OakTrust (Texas A&M University Libraries)", "source_record_id": "W3120491280", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 60, "title": "Forex forecasting with Support vector regression and Long short-term memory recurrent neural network", "title_ar": "", "title_en": "Forex forecasting with Support vector regression and Long short-term memory recurrent neural network", "author": "Michal Bodický", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "In the last years, the field of machine learning boomed. That led to its numerous forecasting applications on prices of Foreign exchange market. Re- searchers there mostly compare neural networks to linear model baselines. The contribution of this thesis consists of a comprehensive performance com- parison between two promising machine learning methods, Support vector regression (SVR) and Long short-term memory recurrent neural network (LSTM RNN), in the forecasting of six highly traded currency pairs on one minute univariate time series data. First, it analyses methods' performances in the forecasting of one step ahead price while varying input dimensions of these methods. Next, it examines how methods perform in longer forecasts, that enabled by using a recurrent version of SVR. In the first analysis, LSTM RNN outperforms SVR in most of the cases several times. Performance of SVR is robust to varying input while LSTM RNN's performance fluctuates across dimensions. In the second analysis, LSTM RNN beats SVR mostly by order of magnitude. With increasing forecasting horizon, SVR's performance gets worse and LSTM RNN's performance remains stable. 1", "source_url": "https:\/\/openalex.org\/W7135617448", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135617448", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 61, "title": "Study in financial time series using recurrent neural networks", "title_ar": "", "title_en": "Study in financial time series using recurrent neural networks", "author": "Daniel Kazuyuki Fugii Matsumoto", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "Understanding the behavior of financial time series is very importance to forecast future values and make efficient decisions. This area is often the target of studies and new proposals in the attempt of its modeling, since it is very volatile in response to the dynamism of the market. This volatility is defined by the high number of variables and different sources of information, a low signal-to-noise ratio, and its predictions fail to generalize the problem. In addition to existing relations between unknown and random exogenous variables, capable of strong influence on the current structure of the data. However, just as incorrect information can cause great misfortune, more accurate information can generate proportional profits, thus ensuring that this problem is the subject of several studies throughout history. For this purpose a predictive model was designed using Recurrent Neural Networks (LSTM), fed from its historical dataset (time series) to forecast the oppening value trend of variation for an asset (stock). Thus a variety of experiments were performed and their results analyzed against the widely used metrics, like autoregressive models (ARIMA) to define a basis for comparisons and validate the model capacity. The results obtained were satisfactory, obtaining an acuracy of up to 74:50% by predicting the sign of the next opening value, inferring whether or not a given stock will rise in the future.", "source_url": "https:\/\/openalex.org\/W7120325349", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120325349", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 62, "title": "Efficient online training algorithms for recurrent neural networks", "title_ar": "", "title_en": "Efficient online training algorithms for recurrent neural networks", "author": "N. Mert Vural", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Bilkent University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to learn nonlinear temporal dependencies. As an RNN model, Long-Short-Term-Memory Networks (LSTMs) are commonly preferred in prac-tice, since these networks are capable of learning long-term dependencies while avoiding the exploding gradient problem. On the other hand, the performance improvement of LSTMs usually comes with the price of their large parameter size, which makes their training significantly demanding in terms of computational and data requirements. In this thesis, we address the computational challenges of LSTM training. We introduce two training algorithms, designed for obtaining the online regression performance of LSTMs with less computational requirements than the state-of-the-art. The introduced algorithms are truly online, i.e., they do not assume any underlying data generating process and future information, except that the dataset is bounded. We discuss theoretical guarantees of the introduced algo-rithms, along with their asymptotic convergence behavior. Finally, we demon-strate their performance through extensive numerical studies on real and synthetic datasets, and show that they achieve the regression performance of LSTMs with significantly shorter training times.", "source_url": "https:\/\/openalex.org\/W3131340183", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Bilkent University Institutional Repository (Bilkent University)", "source_record_id": "W3131340183", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 63, "title": "LSTM Neural Network Models for Market Movement Prediction", "title_ar": "", "title_en": "LSTM Neural Network Models for Market Movement Prediction", "author": "Edwin Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "Interpreting time varying phenomena is a key challenge in the capital markets. Time series analysis using autoregressive methods has been carried out over the last couple of decades, often with reassuring results. However, such methods sometimes fail to explain trends and cyclical fluctuations, which may be characterized by long-range dependencies or even dependencies between the input features. The purpose of this thesis is to investigate whether recurrent neural networks with LSTM-cells can be used to capture these dependencies, and ultimately be used as a complement for index trading decisions. Experiments are made on different setups of the S&P-500 stock index, and two distinct models are built, each one being an improvement of the previous model. The first model is a multivariate regression model, and the second model is a multivariate binary classifier. The output of each model is used to reason about the future behavior of the index. The experiment shows for the configuration provided that LSTM RNNs are unsuitable for predicting exact values of daily returns, but gives satisfactory results when used to predict the direction of the movement.", "source_url": "https:\/\/openalex.org\/W3047998486", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3047998486", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 64, "title": "Toxic comment classification using convolutional and recurrent neural networks", "title_ar": "", "title_en": "Toxic comment classification using convolutional and recurrent neural networks", "author": "Victor Martin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "cnn", "categories": "deep-learning; computer-vision; nlp; cnn; rnn", "keywords": "", "abstract": "This thesis aims to provide a reasonable solution for categorizing automatically sentences into types of toxicity using different types of neural networks. There are six types of categories: Toxic, severe toxic, obscene, threat, insult and identity hate. Three different implementations have been studied to accomplish the objective: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) and convolutional neural networks. The thesis is not thought to aim on improving the performance of every individual model but on the comparison between them in terms of natural language processing adequacy. In addition, one differential aspect about this project is the research of LSTM neurons activations and thus the relationship of the words with the final sentence classificatory decision. In conclusion, the three models performed almost equally and the extraction of LSTM activations provided a very accurate and visual understanding of the decisions taken by the network.", "source_url": "https:\/\/openalex.org\/W2943271338", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)", "source_record_id": "W2943271338", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 65, "title": "Recurrent neural networks for time series prediction", "title_ar": "", "title_en": "Recurrent neural networks for time series prediction", "author": "Bernardo Perez Orozco", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Oxford", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; rnn; ai-smart-cities", "keywords": "", "abstract": "Attempting to predict the future long precedes the time where we could first quantify much of our present. But nowadays, performing long-term and reliable time series forecasting remains as relevant as ever across a vast number of application domains, including astronomy, healthcare, meteorology, physiology, energy systems, econometrics, finance and sociology, amongst many others. Practitioners act upon these forecasts, and it is thus mandatory that such forecasts are accompanied by well-calibrated uncertainty assessments so as to enable a better-informed decision-making process. This thesis proposes a novel time series forecasting framework, namely a scalable and general-purpose recurrent neural network approach which provides probabilistic predictions. Our method recasts time series forecasting as a symbolic sequence learning task by introducing a discrete encoding scheme over measurements. Such symbol sequence tasks have been most successfully dealt with by Long Short-Term Memory (LSTM) models and extensions, such as the sequence-to-sequence model, which form the core of our methodology. This presentation is accompanied by a large-scale experiment where we show the effectiveness of our method over 45 different datasets in comparison with state-of-the-art baselines, such as Gaussian Process Regression (GPR). Crucially, our framework also offers a number of extensions that address a broad range of use cases beyond univariate time series prediction. For example, we show how predictive densities over the occurrence of critical events within a predictive horizon can be inferred from our forecasts. Whereas this requires access to labelled data, we provide a complementary method for recognising and labelling prototypical time series segments on the basis of the feature embedding learnt by our neural networks. Such motifs can subsequently be used to build predictive densities and characterise time series. Lastly, we extend our framework to allow for multivariate forecasting, enabling efficient inference of joint predictive densities over multiple correlated time series. Finally, time series data scarcity constrains the choice of a forecasting model, especially as over-parameterised models such as recurrent neural networks cannot generalise from limited data. In this thesis, we propose a novel approach to infer accurate and reliable predictive distributions from scarce time series data. We achieve this through a recurrent neural network approach that infers a joint feature representation over the space of quantised time series by means of a compilation of auxiliary datasets. Cross-task knowledge transfer is readily enabled by assuming an ordinal regression approach, as quantised time series can then be characterised in terms of their ordinal bin sequences.", "source_url": "https:\/\/openalex.org\/W7116933523", "pdf_url": "https:\/\/ora.ox.ac.uk\/objects\/uuid:fb471520-8541-4abb-95a0-7649809cac87\/files\/mce9a2419de2282d545b0853644f60f4c", "doi": "10.5287\/ora-eoxk5gbqv", "handle": "", "repository_name": "Oxford University Research Archive (ORA) (University of Oxford)", "source_record_id": "W7116933523", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 66, "title": "Deep Learning for Medical Image Segmentation", "title_ar": "", "title_en": "Deep Learning for Medical Image Segmentation", "author": "Binyan Hu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; self-semi-supervised; image-segmentation; medical-imaging; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Medical image segmentation (MIS) plays a fundamental role in a wide range of medical image analysis applications. Over the past decade, MIS has been significantly advanced by deep learning (DL). However, the effectiveness of DL-based methods typically depends on large-scale, precisely annotated datasets, which are often unavailable in the medical domain due to the high cost and expertise required for medical data acquisition and annotation. When trained on limited data, deep neural networks (DNNs) are prone to over-fitting, resulting in poor generalisation performance when deployed on previously unseen data. In MIS, this generalisation gap is exacerbated by two key challenges: 1) data scarcity: due to the limited quantity of annotated images available for training, over-fitting the training data causes poor generalisation performance on images unseen during training; 2) expert-specific annotation: variability in annotations across experts arises from the inherent complexity of medical images and differences in expert knowledge. Consequently, a model trained on certain experts' annotations may under-perform when deployed in clinical contexts with new images and new experts. This thesis aims to address these domain-specific limitations, with an overarching goal of generating enhanced supervision signals to improve the generalisation capability of DNNs for MIS across both images and user experts under constrained data settings. Toward achieving this goal, our contributions are threefold.First, we tackle the expert-specific annotation problem by leveraging the supervision from multiple experts via multi-expert annotation, aiming to achieve adaptive MIS that generalises to new experts' annotations. Through extensive empirical analysis, we investigate when and how multi-expert annotation improves a DNN model's generalisation capability to new experts' annotations and what DNN training method can best achieve such generalisation. Our findings provide detailed evidence for the efficacy of multi-expert annotation and practical insights into developing generalisable models that can efficiently adapt to new experts in the presence of expert-specific annotations.Second, we intend to mitigate the impact of data scarcity and improve the model's generalisation to new images by leveraging self-supervision.We develop an adaptive training method, which generates diverse self-supervised tasks from the existing annotated dataset, dynamically selects the most beneficial combination of tasks, and adaptively transfers the knowledge learnt from them to enrich the segmentation model's knowledge.Compared to existing approaches, our method integrates more diverse self-supervised tasks and offers superior effectiveness and efficiency in exploiting their supervision signals to benefit segmentation performance.The third study moves beyond the single-site setting and generates enhanced supervision via multi-site collaboration, where the goal is to improve generalisation performance for participating sites while navigating strict privacy constraints and potential security threats arising from non-transparent local processes. We propose a new federated learning method, tailored for the multi-site MIS scenario, to enable privacy-preserving defence against poisoning attacks by sustaining robust performance and localising the compromised site. Compared to previous approaches, our method provides utility-based defence and diagnosis, does not depend on public datasets, and works for the setting with few participating sites. These features enable more practical and effective federated learning with enhanced privacy preservation and security for multi-site MIS.Extensive experiments across diverse datasets and clinical settings demonstrate the effectiveness of the proposed methods in improving DNN model generalisation for MIS tasks. Collectively, this thesis contributes practical methodologies to address domain-specific challenges in MIS and advances the practical deployment of DNNs in real-world clinical environments.", "source_url": "https:\/\/openalex.org\/W7161281791", "pdf_url": "", "doi": "10.25916\/sut.32150593", "handle": "", "repository_name": "Swinburne figshare (Swinburne University of Technology)", "source_record_id": "W7161281791", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 67, "title": "Deep-Learning-Based 3D Medical Image Segmentation and Registration", "title_ar": "", "title_en": "Deep-Learning-Based 3D Medical Image Segmentation and Registration", "author": "Hao Xu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; few-zero-shot; self-semi-supervised; transformers; multimodal; image-segmentation; medical-imaging", "keywords": "", "abstract": "Understanding the importance of image segmentation and registration in clinical diagnosis and treatment is critical for advancing precision medicine. Medical image analysis involves complex data processing tasks, particularly in scenarios with limited annotated data and multi-modality feature inconsistencies. Recent deep learning-based methods have provided breakthroughs in image segmentation and registration, especially in modalities like MRI and CT. However, challenges such as one-shot learning, incremental learning, and efficient deformable registration still persist. This thesis introduces a series of innovative deep learning frameworks, focusing on three one-shot segmentation settings and three Segment Anything Model (SAM) -assisted registration methods, to address the issues of annotation scarcity and feature misalignment in medical imaging. For segmentation, we develop three methods for one-shot, class-incremental, and novel-class tract segmentation. These incorporate uncertainty estimation, knowledge distillation, and voxel-level contrastive learning to enhance performance and generalization in one-shot settings. For registration, we propose three SAM-guided frameworks that leverage segmentation masks and anatomical prompts, as well as attention mechanisms, prototype learning, and contour-aware losses. These approaches achieve state-of-the-art accuracy in unsupervised deformable registration. Overall, our methods yield substantial improvements in segmentation and registration accuracy across diverse datasets and demonstrate strong potential for clinical application.", "source_url": "https:\/\/openalex.org\/W7126877936", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Sydney eScholarship Repository (The University of Sydney)", "source_record_id": "W7126877936", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 68, "title": "Efficient and Generalised Deep Learning Models for Medical Image Segmentation", "title_ar": "", "title_en": "Efficient and Generalised Deep Learning Models for Medical Image Segmentation", "author": "Zhendi Gong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Nottingham", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; few-zero-shot; self-semi-supervised; cnn; transformers; image-segmentation; medical-imaging; ai-industry", "keywords": "", "abstract": "Medical image segmentation is a critical task in computer-assisted diagnosis and therapy planning. Accurate and efficient fully automated segmentation methods are essential for accelerating decision-making processes. Compared with natural image segmentation methods, current deep learning–based segmentation models for medical imaging face challenges related to data scarcity, inconsistent annotations, and high computational cost. Consequently, this thesis aims to develop efficiency and generalised medical image segmentation models, which are fundamental to the clinical adoption and industrial deployment of this technology in healthcare settings. This thesis systematically addresses these challenges through a series of progressively advanced deep learning models. The foundational work, CTranS, forms a baseline medical image segmentation model that combines a convolutional neural network (CNN) based encoder with a Transformer-based decoder. This initial contribution leverages the strengths of CNNs for local feature extraction and Transformers for global contextual understanding, achieving state-of-the-art (SOTA) performance. Subsequently, to enhance the generalisability, MO-CTranS is introduced, which is capable of learning from a mixture of multiple partially labelled datasets. MO-CTranS employs a CNN encoder and Transformer decoder with task-specific tokens to handle label inconsistencies and class imbalances, tackling the challenges of training a single unified model from fragmented and heterogeneously annotated medical data. This innovation moves beyond single-task, fully-annotated model training towards a more scalable and clinically realistic approach. Despite the superior performance of CTranS and MO-CTranS, the computational efficiency of the Transformer blocks hinders their clinical application. Thus, CRFTrans is proposed to enhance the model efficiency. As a novel recursive Vision Transformer layer designed to replace the commonly used cascaded Transformer layers, this approach represents a fundamental shift from simply stacking layers to a recursive refinement process, demonstrating that comparable performance can be achieved with significantly reduced computational complexity, thus facilitating deployment in resource-constrained clinical environments. However, these methods still depend on extensive manual annotation, which remains challenging and time-consuming in clinical practice. Thus, to further enhance both efficiency and generality, a novel semi-supervised medical image segmentation framework, referred to as SSL-MedSAM2, has been developed. SSL-MedSAM2 integrates a training-free few-shot segmentation branch for pseudo labelling and a fully supervised learning branch for label refinement. This design allows the model to leverage a small fraction of labelled images together with a large unlabelled set, dramatically decreasing the annotation effort. Together, these contributions result in deep learning models that are significantly more data-efficient and broadly applicable than conventional approaches. The overall impact of this work is to facilitate the reliable and scalable deployment of segmentation models in real-world clinical settings by bridging the gap among accuracy, efficiency, and generalisability.", "source_url": "https:\/\/openalex.org\/W7140609462", "pdf_url": "https:\/\/repository.nottingham.ac.uk\/bitstreams\/1a58d983-6aaa-4bbb-a40b-688e6d3af90c\/download", "doi": "10.17639\/7969", "handle": "", "repository_name": "Nottingham ePrints (University of Nottingham)", "source_record_id": "W7140609462", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 70, "title": "GENERALIZING MEDICAL IMAGE SEGMENTATION TASK WITH EFFICIENT DEEP LEARNING MODELS", "title_ar": "", "title_en": "GENERALIZING MEDICAL IMAGE SEGMENTATION TASK WITH EFFICIENT DEEP LEARNING MODELS", "author": "Abel A. Reyes-Angulo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Michigan Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; transformers; image-segmentation; medical-imaging", "keywords": "", "abstract": "Medical Image Segmentation is a critical task in the field of medical imaging, playing a crucial role in diagnostics, treatment planning, and disease monitoring. The emergence of Deep Learning (DL) has ushered in a new era in Artificial Intelligence (AI), propelling remarkable advancements in key domains like language translation, object recognition, and recommendation systems. This evolution has been accompanied by continuous enhancements in computational efficiency and improvements in predictive accuracy. The introduction of sophisticated algorithms, such as convolutional neural networks (CNNs) and transformers, exemplifies these advancements. DL algorithms have demonstrated exceptional efficacy in medical image segmentation tasks, showcasing the potential for AI-driven early diagnostics. However, the deployment of AI systems in clinical environments is often hindered by the substantial computational demands and complexity of cutting-edge DL models. In this research proposal, we explore various methodologies to enrich the visual feature representation for medical images. We focus on integrating global context-oriented techniques, such as attention mechanisms, into the development of parameter-efficient deep learning models. Our goal is to create a generalized, end-to-end medical image segmentation framework that can accurately and efficiently segment medical images across different modalities and conditions. By leveraging advanced deep learning techniques and optimizing model architectures, we aim to enhance the performance and generalization capabilities of medical image segmentation models, ultimately contributing to improved clinical outcomes", "source_url": "https:\/\/openalex.org\/W4410497124", "pdf_url": "", "doi": "10.37099\/mtu.dc.etdr\/1941", "handle": "", "repository_name": "Digital Commons - Michigan Tech (Michigan Technological University)", "source_record_id": "W4410497124", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 71, "title": "Brain lesion segmentation in medical images using classical machine learning and deep learning", "title_ar": "", "title_en": "Brain lesion segmentation in medical images using classical machine learning and deep learning", "author": "M.N. Karakolev", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7225767627", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Theses database, Tilburg University", "source_record_id": "W7225767627", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 72, "title": "Study on diagnostic-assisted deep learning models in medical image segmentation and translation", "title_ar": "", "title_en": "Study on diagnostic-assisted deep learning models in medical image segmentation and translation", "author": "Tao Hu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7146384150", "pdf_url": "https:\/\/nagoya.repo.nii.ac.jp\/record\/2013022\/files\/k15505_review.pdf", "doi": "", "handle": "", "repository_name": "Nagoya University Repository (NAGOYA Repository)", "source_record_id": "W7146384150", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 73, "title": "Active deep learning for medical imaging segmentation", "title_ar": "", "title_en": "Active deep learning for medical imaging segmentation", "author": "Marc Górriz Blanch", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; image-classification; image-segmentation; medical-imaging", "keywords": "", "abstract": "This thesis proposes a novel active learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our approach tries to apply in segmentation existing active learning techniques, which is becoming an important topic today because of the many problems caused by the lack of large amounts of data. We explore different strategies to study the image information and introduce a previously used cost-effective active learning method based on the selection of high confidence predictions to assign automatically pseudo-labels with the aim of reducing the manual annotations. First, we made a simple application for handwritten digit classification to get started to the methodology and then we test the system with a medical image database for the treatment of melanoma skin cancer. Finally, we compared the traditional training methods with our active learning proposals, specifying the conditions and parameters required for it to be optimal.", "source_url": "https:\/\/openalex.org\/W2768586247", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)", "source_record_id": "W2768586247", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 74, "title": "Medical image segmentation using deep learning", "title_ar": "", "title_en": "", "author": "Ruya Kaymak", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Fırat University", "university_source": "repository_host", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2019, "language": "tr", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "Derin öğrenme, Konvolüsyonel Sinir Ağı, Tam Konvolüsyonel Ağ, Tıbbi görüntü bölütleme.", "abstract": "Bu tezde, ilk olarak son yıllarda makine öğrenmesi alanında çok fazla ilgi duyulan derin öğrenme yaklaşımı hakkında detaylı bilgilere yer verilmiştir. Daha sonra, en tehlikeli cilt kanseri türü olan melanomun tespitine yardımcı olabilmek adına otomatik bir cilt lezyonu bölütleme uygulaması gerçekleştirilmiştir. Bu tıbbi görüntü bölütleme uygulaması, derin öğrenme tabanlı olan Konvolüsyonel Sinir Ağı (KSA) mimarilerinde değişiklik yapılarak elde edilen Tam Konvolüsyonel Ağ (TKA) mimarileriyle gerçekleştirilmiştir. Uygulama için gerçekleştirilen deneysel çalışmalarda veri seti olarak ISIC 2017 kullanılmış olup FCN-AlexNet, FCN-8s, FCN-16s ve FCN-32s isimli dört farklı TKA mimarisinden yararlanılmıştır. Bu mimariler ve veri seti dikkate alındığında, bu çalışma bilimsel yazında ilk kez gerçekleştirilmiştir. Deneysel çalışmalar için öncelikle TKA'lar ayrı ayrı eğitilmiş ve eğitilen bu ağ modellerinin geçerlilik veri seti üzerindeki doğrulukları ve Dice katsayıları karşılaştırılmıştır. Ayrıca, lezyon bölütleme çıkarımları görselleştirilerek kullanılan TKA mimarilerinin lezyonları ne kadar keskinlikte bölütleyebildiği gözlemlenmiştir. Elde edilen deneysel sonuçlar, cilt lezyonu bölütleme uygulamalarında TKA'ların elverişli olduğunu göstermiştir. Bununla beraber, deneysel sonuçların bilimsel yazına ve tıbbi görüntü bölütleme ile ilgili çalışmalar yapan araştırmacılara katkı sağlayacağı düşünülmektedir. Anahtar Kelimeler: Derin öğrenme, Konvolüsyonel Sinir Ağı, Tam Konvolüsyonel Ağ, Tıbbi görüntü bölütleme.", "source_url": "https:\/\/openalex.org\/W7217362636", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace - FIRAT (Fırat University)", "source_record_id": "W7217362636", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 75, "title": "DEEP LEARNING FOR VOLUMETRIC MEDICAL IMAGE SEGMENTATION", "title_ar": "", "title_en": "DEEP LEARNING FOR VOLUMETRIC MEDICAL IMAGE SEGMENTATION", "author": "Zhuotun Zhu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3201052870", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3201052870", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 78, "title": "Privacy-preserving and data-efficient deep learning for medical image segmentation", "title_ar": "", "title_en": "Privacy-preserving and data-efficient deep learning for medical image segmentation", "author": "Zhou ZHENG", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; responsible-ai; image-segmentation; medical-imaging; fairness", "keywords": "", "abstract": "Medical image segmentation that aims to delineate regions of interest (ROIs) has been a long-standing problem in medical image analysis.It plays an essential role in computer-aided diagnosis (CAD) and computer-aided surgery (CAS).The development of Deep learning (DL) techniques has significantly advanced these areas by providing accurate and automatic segmentation.Despite such remarkable progress and achievements, DL in medical image segmentation still faces several challenges, typically privacy preservation and data efficiency.On one aspect, DL typically requires numerous samples for training.However, sharing medical images among hospitals or medical institutes is usually impeded due to privacy regulations, making it hard to collect large-scale datasets.Moreover, the performance of DL models greatly depends on fully and densely annotated labels, but obtaining such ideal annotations is a bottleneck, particularly in the medical field.By contrast, medical image datasets usually come with imperfect annotations in practice.These limitations emphasize the importance of developing privacy-preserving and data-efficient DL techniques for medical image segmentation with highly distributed datasets accompanying imperfect annotations.Driven by these perspectives, this thesis reevaluates the application of DL in medical image segmentation, particularly the 3-dimensional (3D) abdominal multi-organ segmentation from computed tomography (CT) images and the surgical instrument segi LIST OF FIGURES 4.5 Ablation study on different methods adopted by local clients for handling various imperfect annotations.IL: ideal annotation type.SL: scarce annotation type.NL: noisy annotation type.", "source_url": "https:\/\/openalex.org\/W7145109004", "pdf_url": "https:\/\/nagoya.repo.nii.ac.jp\/record\/2011569\/files\/k15072_thesis.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7145109004", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 80, "title": "Deep Learning for Medical Image Segmentation using Prior Knowledge and Topology", "title_ar": "", "title_en": "Deep Learning for Medical Image Segmentation using Prior Knowledge and Topology", "author": "Chen Zhao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Michigan Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; gnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Image segmentation refers to the division of a digital image into distinct segments or groups of pixels\/voxels. However, most of the existing deep learning approaches lack the utilization of prior knowledge, such as shape information, which could improve segmentation accuracy. In addition, conventional image segmentation frequently falls short in preserving intricate spatial details, motivating the innovation of strategies for multi-scaled feature integration. Furthermore, traditional image segmentation methods primarily concentrate on pixel-level or region-level analysis. However, given the inherent morphological similarities among various image objects, the significance of topology information surpasses that of pixel-level data in the realm of medical image semantic segmentation, and the incorporation of topology information for image segmentation is important. The first aim of this dissertation is to incorporate shape priors into medical image segmentation. A shape-prior-V-Net (SP-V-Net) is proposed, which contains a shape transformation module to refine the segmentation results according to the shape prior. SP-V-Net has been applied to lung segmentation and proximal femur segmentation. The second aim aims to improve image segmentation by leveraging hierarchical features. Two approaches are proposed: the feature pyramid U-Net++ (FP-U-Net++), which dynamically aggregates the feature pyramid in the decoder of U-Net ++, and the multi-input multi-scale U-Net (MIMS U-Net), which integrates the features in the encoder of the U-Net. The third aim explores topology-based image semantic segmentation using graph neural networks. Three graph-matching networks have been developed, including association graph-based, edge attention graph matching, and hyper-association graph matching networks. The proposed graph-matching networks convert the graph-matching problems into a vertex classification problem using an association graph, where the positive vertex indicates the nodes from two individual graphs are matched. These models were applied to coronary artery semantic labeling on invasive coronary angiograms. Moreover, this study presents a pioneering approach for topology-based image semantic labeling using graph matching. The successful completion of these aims contributes technically accurate and clinically applicable algorithms and techniques for medical image segmentation. The outcomes of this dissertation provide valuable tools for the medical imaging and computer vision communities, advancing the field and improving patient care through accurate and efficient medical image segmentation.", "source_url": "https:\/\/openalex.org\/W4390970363", "pdf_url": "https:\/\/digitalcommons.mtu.edu\/cgi\/viewcontent.cgi?article=2771&context=etdr", "doi": "10.37099\/mtu.dc.etdr\/1691", "handle": "", "repository_name": "Digital Commons - Michigan Tech (Michigan Technological University)", "source_record_id": "W4390970363", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 82, "title": "Research on Medical Image Segmentation Based on Deep Learning Methods", "title_ar": "", "title_en": "Research on Medical Image Segmentation Based on Deep Learning Methods", "author": "Yuxiang Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; medical-ai; cnn; transformers; image-segmentation; medical-imaging", "keywords": "", "abstract": "Medical imaging involves the technique and process of generating visual representations of a patient's body for clinical analysis and medical intervention.Healthcare professionals heavily depend on medical images for accurate diagnosis and treatment.In clinical practice, segmentation is typically performed manually.However, when processing a vast number of medical images, the quality of segmentation can vary based on the expertise of the medical professional.This variability underscores the need for a more consistent layers.MDSU-Net uses DSC layer to reduce model complexity without degrading model performance, which is suitable for different segmentation tasks.MDSU-Net registers a Dice score of 0.7055 on ATLAS, 0.9760 on CHAOS, and 0.8883 on NERVE, respectively.Notably, these scores outperform the state-of-the-art (SoTA) benchmarks.Additionally, in terms of 3D medical image segmentation, we propose Dual Encoder U-Net (DEU-Net), which uses Transformer and CNN respectively to extract 3D medical image features in the encoder.Transformer is a pre-trained model in BTCV, which improves its ability to capture contextual features of medical images and increases the learning speed.Besides, we introduce CBAM with each convolutional layer in the encoder part to enhance CNN's feature extraction capabilities for 3D medical images.To fuse the two kinds of features, we proposed a Dual Feature Fusion Module (DFFM) to fuse the features extracted from the Transformer and CNN in the encoder, making full use of the feature extraction capabilities of the two extractors for datasets of different sizes.We compare DEU-Net with other SoTA methods on the BraTS 2020 and BraTS 2021 datasets, and the results show that the performance of DEU-Net has improved in 3D medical image segmentation task.", "source_url": "https:\/\/openalex.org\/W7145787761", "pdf_url": "https:\/\/tokushima-u.repo.nii.ac.jp\/record\/2012049\/files\/k3814_fulltext.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7145787761", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 83, "title": "Volumetric medical image segmentation with deep learning pipelines", "title_ar": "", "title_en": "Volumetric medical image segmentation with deep learning pipelines", "author": "Imad Eddine Toubal", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Missouri", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Automated semantic segmentation in the domain of medical imaging can enable a faster, more reliable, and more affordable clinical workflow. Fully convolutional networks (FCNs) have been heavily used in this area due to the level of success that they have achieved. In this work, we first leverage recent architectural innovations to make an initial segmentation: (i) spatial and channel-wise squeeze and excitation mechanism; (ii) a 3D U-Net++ network and deep supervision. Second, we use classical methods for refining the initial segmentation: (i) spatial normalization and (ii) local 3D refinement network applied to patches. Finally, we put our methods together in a novel segmentation pipeline. We train and evaluate our models and pipelines on a dataset of a 120 abdominal magnetic resonance - volumetric - images (MRIs). The goal is to segment five different organs of interest (ORI): liver, kidneys, stomach, duodenum, and large bowel. Our experiments show that we can generate full resolution segmentation of comparable quality to the state-of-the-art methods without adding computational cost.", "source_url": "https:\/\/openalex.org\/W4321613625", "pdf_url": "https:\/\/mospace.umsystem.edu\/xmlui\/bitstream\/10355\/94345\/1\/ToubalImadEddineResearch.pdf", "doi": "10.32469\/10355\/94345", "handle": "", "repository_name": "MOspace Institutional Repository (University of Missouri)", "source_record_id": "W4321613625", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 84, "title": "Impact of Rater Style on Deep Learning Segmentation in Medical Imaging", "title_ar": "", "title_en": "", "author": "Olivier Vincent", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "École Polytechnique de Montréal", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2021, "language": "fr", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; responsible-ai; image-segmentation; medical-imaging; fairness", "keywords": "", "abstract": "La sclerose en plaques est la maladie auto-immune la plus courante du systeme nerveux central. Elle se caracterise par la presence de lesions dans le cerveau et la moelle epiniere, visibles en imagerie par resonance magnetique (IRM). Cependant, pour extraire des infor-mations utiles des images, il est necessaire de segmenter les lesions sur ces images, ce qui est un processus long et couteux lorsqu’il est realise manuellement par un expert tel qu’un radiologue. L’objectif de ce projet est d’utiliser des methodes innovantes d’apprentissage profond pour ameliorer la segmentation des images medicales. Premierement, pour aborder la generalisation a travers di˙erents contrastes dans la segmen-tation de la sclerose en plaques de la moelle epiniere, nous mettons en œuvre la modula-tion lineaire par caracteristique (FiLM) pour tirer parti de la grande variete de parametres d’acquisitions IRM dans le modele de segmentation, en apprenant les caracteristiques de chaque contraste. Fait interessant, un U-Net bien optimise a atteint les memes performances que notre FiLMed-Unet sur un ensemble de donnees a contrastes multiples (0,72 de score Dice), ce qui suggere qu’il existe un goulot d’etranglement dans cette tâche, qui n’est pas la generalisation a travers di˙erents contrastes. Ce goulot d’etranglement provient probable-ment de la variabilite interexperts, qui est estimee a 0,61 de score Dice dans notre ensemble de donnees. Deuxiemement, afin de s’attaquer a ce goulot d’etranglement, nous quantifions le style des experts qui annotent les donnees sous forme de biais et de consistance. Cela nous permet ensuite d’explorer l’impact des styles d’annotations sur les modeles d’apprentissage profond. Deux ensembles de donnees publics multievaluateurs et multicentriques sont utilises, un de lesions de sclerose en plaques cerebrales et un de segmentation de la matiere grise de la moelle epiniere. Sur les deux ensembles de donnees, les resultats montrent une correlation (R2 = 0, 60 et 0, 93) entre le biais de l’expert et l’incertitude du modele d’apprentissage profond. L’impact de la fusion d’annotations des experts sur cette relation est egalement etudie, et nous montrons que les consensus multicentriques sont plus eycaces que les con-sensus monocentriques pour reduire l’incertitude, car le style de l’expert n’est pas specifique a l’individu, mais principalement au centre.----------ABSTRACT Multiple sclerosis is the most common autoimmune disease of the central nervous system. It is characterized by the presence of lesions in the brain and spinal cord, which are visible in magnetic resonance imaging (MRI). However, to extract useful information from the images, it is necessary to segment the lesions on these images, which is a long and expensive process when performed manually by an expert such as a radiologist. The goal of this project is to use innovative deep learning methods to improve segmentation of medical images. First, to tackle generalization across imaging contrasts in spinal cord multiple sclerosis seg-mentation we implement Feature-wise Linear Modulation (FiLM) to leverage physics knowl-edge within the segmentation model and learn the characteristics of each contrast. Inter-estingly, a well-optimized U-Net reached the same performance as our FiLMed-Unet on a multi-contrast dataset (0.72 of Dice score), which suggests that there is a bottleneck in spinal MS lesion segmentation di˙erent from the generalization across varying contrasts. This bot-tleneck likely stems from inter-rater variability, which is estimated at 0.61 of Dice score in our dataset. Second, as a follow-up we quantify rater style in the form of bias and consistency and explore the impacts on deep learning models. Two multi-rater and multi-center public datasets are used, consisting of brain multiple sclerosis lesion and spinal cord grey matter segmentation. On both datasets, results show a correlation (R2 = 0.60 and 0.93) between rater bias and deep learning uncertainty. The impact of label fusion between raters’ annotations on this relationship is also explored, and we show that multi-center consensuses are more e˙ective than single-center consensuses to reduce uncertainty, since rater style is not individual-specific but mostly center-specific.", "source_url": "https:\/\/openalex.org\/W3211467155", "pdf_url": "https:\/\/publications.polymtl.ca\/6283\/1\/2021_OlivierVincent.pdf", "doi": "", "handle": "", "repository_name": "PolyPublie (École Polytechnique de Montréal)", "source_record_id": "W3211467155", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 88, "title": "Semi-Supervised Deep Learning using Consistency-Based Methods for Segmentation of Medical Images", "title_ar": "", "title_en": "Semi-Supervised Deep Learning using Consistency-Based Methods for Segmentation of Medical Images", "author": "Axel Rönnberg", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; self-semi-supervised; predictive-analytics; image-segmentation; medical-imaging; ai-education", "keywords": "", "abstract": "In radiation therapy, a form of cancer treatment, accurately locating the anatomical structures is required in order to limit the impact on healthy cells. The automatic task of delineating these structures and organs is called segmentation, where each pixel in an image is classified and assigned a label. Recently, deep neural networks have proven to be efficient at automatic medical segmentation. However, deep learning requires large amounts of training data. This is a restricting feature, especially in the medical field due to factors such as patient confidentiality. Nonetheless, the main challenge is not the image data itself but the lack of high-quality annotations. It is thus interesting to investigate methods for semi-supervised learning, where only a subset of the images re- quires annotations. This raises the question if these methods can be acceptable for organ segmentation, and if they will result in an increased performance in comparison to supervised models. A category of semi-supervised methods applies the strategy of encouraging consistency between predictions. Consistency Training and Mean Teacher are two methods in which the network weights are updated in order to minimize the impact of input perturbations such as data augmentations. In addition, the Mean Teacher method trains two models, a Teacher and a Student. The Teacher is updated as an average of consecutive Student models, using Temporal Ensembling. To resolve the question whether semi-supervised learning could be beneficial, the two mentioned techniques are investigated. They are used in training deep neural networks with an U-net architecture to segment the bladder and anorectum in 3D CT images. The results showed signs of promise for Consistency Training and Mean Teacher, with nearly all model configurations having improved segmentations. Results also showed that the methods caused a reduction in performance variance, primarily by limiting poor delineations. With these results in hand, the use of semi-supervised learning should definitely be considered. However, since the segmentation improvement was not repeated in all experiment configurations, more research needs to be done.", "source_url": "https:\/\/openalex.org\/W3111652972", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3111652972", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 89, "title": "Medical image segmentation based on deep feature learning and multistage classification", "title_ar": "", "title_en": "Medical image segmentation based on deep feature learning and multistage classification", "author": "Xiaohong Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "a divide-and-conquer funnel-structured classification framework.", "source_url": "https:\/\/openalex.org\/W3033608360", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/137579\/2\/Amended%20thesis%20wxh.pdf", "doi": "10.32657\/10356\/137579", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W3033608360", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 90, "title": "Detection-aided medical image segmentation using deep learning", "title_ar": "", "title_en": "Detection-aided medical image segmentation using deep learning", "author": "Míriam Bellver Bueno", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; object-detection; image-segmentation; medical-imaging", "keywords": "", "abstract": "A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and also to assess the response to the according treatments. In this thesis we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans, as well as other anatomical structures and organs of the human body. We have used Convolutional Neural Networks (CNNs), that have proven good results in a variety of tasks, including medical imaging. The network to segment the lesions consists of a cascaded architecture, which first focuses on the liver region in order to segment the lesion. Moreover, we train a detector to localize the lesions and just keep those pixels from the output of the segmentation network where a lesion is detected. The segmentation architecture is based on DRIU (Maninis, 2016), a Fully Convolutional Network (FCN) with side outputs that work at feature maps of different resolutions, to finally benefit from the multi-scale information learned by different stages of the network. Our pipeline is 2.5D, as the input of the network is a stack of consecutive slices of the CT scans. We also study different methods to benefit from the liver segmentation in order to delineate the lesion. The main focus of this work is to use the detector to localize the lesions, as we demonstrate that it helps to remove false positives triggered by the segmentation network. The benefits of using a detector on top of the segmentation is that the detector acquires a more global insight of the healthiness of a liver tissue compared to the segmentation network, whose final output is pixel-wise and is not forced to take a global decision over a whole liver patch. We show experiments with the LiTS dataset for the lesion and liver segmentation. In order to prove the generality of the segmentation network, we also segment several anatomical structures from the Visceral dataset.", "source_url": "https:\/\/openalex.org\/W2760887678", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W2760887678", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 91, "title": "New and Advanced Explainable Artificial Intelligence Approaches for Managing Project Risks", "title_ar": "", "title_en": "New and Advanced Explainable Artificial Intelligence Approaches for Managing Project Risks", "author": "Bodrunnessa Badhon", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "generative-ai", "research_subdomain": "xai", "categories": "machine-learning; generative-ai; responsible-ai; expert-systems; ai-applications; predictive-analytics; xai; fairness; ai-smart-cities", "keywords": "", "abstract": "Project Risk Management (PRM) is a critical component of modern project governance, ensuring that uncertainties are systematically identified, assessed, and mitigated to minimize their impact on project outcomes. However, existing PRM practices remain largely subjective and qualitative, relying on expert judgment and static checklists that fail to capture dynamic interdependencies and evolving risk interactions. Recent advances in Artificial Intelligence (AI), particularly Machine Learning (ML) have improved predictive accuracy in PRM, but the opacity of many high-performing models has hindered adoption in safety-critical and managerial contexts where transparency, traceability, and stakeholder trust are essential. This thesis addresses these challenges by developing a suite of Explainable Artificial Intelligence (XAI) frameworks to support trustworthy project risk analysis. XAI has emerged as a promising approach to enhance transparency and interpretability in AI-based risk analysis; however, existing methods still exhibit several critical limitations that motivate this research. Current XAI techniques struggle to capture interrelations and cascading effects among risk factors, often producing fragmented explanations that overlook the structural dependencies underlying overall project vulnerability. In PRM, this limitation is particularly critical as risks are inherently interdependent, and disturbances in one aspect can propagate across multiple dimensions of project performance. Without incorporating such relationships into XAI models, the underlying causes of risk cannot be effectively identified, leading to explanations that lack depth and meaningful insight. Moreover, many post-hoc XAI models exhibit inconsistent or unstable explanations due to random neighborhood sampling or sensitivity to input variation, leading to unreliable outputs that are difficult to reproduce. In addition, existing XAI approaches remain largely static, offering one-way descriptions that overlook the temporal evolution and causal propagation of risks. Finally, they lack mechanisms for interactive engagement, preventing decision-makers from dynamically querying model behavior or exploring alternative “what-if” scenarios through conversational interfaces. Collectively, these limitations underscore the need for an integrated XAI-driven framework that unifies interpretability, scalability, causality, and interactivity. To address these shortcomings, this thesis defines four interrelated research objectives. Each objective focuses on a specific methodological limitation, namely interpretability, scalability, causality, and interactivity, while collectively forming a cohesive, progressive framework for explainable, trustworthy project risk management. The research commences by addressing the lack of interpretability and structured reasoning in existing risk models through the Interpretable Risk Assessment Framework based on Belief Rule-Based systems (IRAF-BRB). Traditional AI models for risk prediction often sacrifice interpretability for performance, making their outputs difficult for decision-makers to trust. IRAF-BRB resolves this issue by integrating expert knowledge with data-driven learning under explicit interpretability constraints. It ensures that model parameters remain meaningful, consistent with domain expertise, and explainable in linguistic terms. By optimizing belief rules using a modified Differential Evolution strategy while preserving human-interpretable reasoning, IRAF-BRB achieves both transparency and predictive reliability. This framework establishes the foundation for interpretable AI in PRM by showing that high accuracy can coexist with auditable, expert-aligned rule-based reasoning. While the IRAF-BRB ensures interpretability, it remains limited in scalability and adaptability to high-dimensional data. To overcome these limitations, the second framework, the Multi-Module Explainable Artificial Intelligence for Project Risk Management (MMXAI-PRM), extends explainability to complex, data-rich environments. MMXAI-PRM integrates generative modeling and post-hoc explanation to handle nonlinear relationships and class imbalance that traditional interpretable models cannot address. By combining synthetic data generation and local explanation, it enables consistent, stable interpretive reasoning even when training data are limited or heterogeneous. The framework provides richer explanatory insight by identifying key risk interactions and quantifying their relative influence, thereby enabling more informed prioritization and mitigation strategies. This contribution advances scalability and generalizability in explainable risk modeling, bridging the gap between interpretable logic-based systems and high-capacity learning models. Despite these improvements, earlier frameworks remain limited to static state-based representations that do not explicitly model the temporal evolution or causal propagation of risks.", "source_url": "https:\/\/openalex.org\/W7153988587", "pdf_url": "", "doi": "10.26190\/unsworks\/32194", "handle": "", "repository_name": "UNSWorks (UNSW Sydney)", "source_record_id": "W7153988587", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 92, "title": "Explainable Artificial Intelligence in the Image Domain and its Applications to the Medical Field", "title_ar": "", "title_en": "Explainable Artificial Intelligence in the Image Domain and its Applications to the Medical Field", "author": "Mirtha Lucas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "DePaul University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; responsible-ai; self-semi-supervised; predictive-analytics; cnn; gan; medical-imaging; xai; fairness", "keywords": "", "abstract": "This dissertation investigates the development of Explainable Artificial Intelligence (XAI) methods for deep learning models in the image domain, with a particular focus on medical imaging applications. Although neural networks achieve high predictive performance, their lack of interpretability limits their adoption in critical domains such as healthcare, where transparency and trust are essential. This work addresses this challenge by proposing novel approaches that improve the interpretability and reliability of model predictions. A primary contribution is the introduction of Riemann–Stieltjes Integrated Grad-CAM (RSI Grad-CAM), a gradient-based attribution method that generates more relevant and spatially localized saliency maps. The method is evaluated using quantitative metrics and statistical analysis, demonstrating improved performance over established techniques such as Grad-CAM and Integrated Grad-CAM. A secondary contribution is the analysis of the interpretability of intermediate layers in convolutional neural networks, demonstrating that clinically meaningful features, such as spiculation in lung nodules, can be identified within specific layers of the model. A third contribution is the development of a human-centered framework based on a Variational Autoencoder-Auxiliary Classifier Wasserstein Generative Adversarial Network (VAE-AC-WGAN), which generates contrastive explanations aligned with clinical reasoning. Experimental results demonstrate that the proposed method produces higher-quality reconstructions and more informative anomaly maps than baseline approaches (e.g., medXGAN), as reflected in improved structural similarity (SSIM) and classifier-based evaluation metrics (AUROC). Furthermore, human evaluation indicates that the generated explanations better capture clinically relevant features, such as spiculation, supporting their interpretability in medical imaging contexts. Overall, this work advances XAI by enhancing model transparency and supporting the trustworthy integration of AI systems into medical decision-making.", "source_url": "https:\/\/openalex.org\/W7172068913", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Commons-DePaul (DePaul University)", "source_record_id": "W7172068913", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 93, "title": "ARTIFICIAL INTELLIGENCE AND THE DUTY TO GIVE REASONS: Beyond the Black Box and the Limits of Explainability", "title_ar": "", "title_en": "ARTIFICIAL INTELLIGENCE AND THE DUTY TO GIVE REASONS: Beyond the Black Box and the Limits of Explainability", "author": "Lucena, Rafael Nogueira de", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "xai", "categories": "deep-learning; responsible-ai; xai; fairness", "keywords": "", "abstract": "The growing integration of Artificial Intelligence (AI) into public administration challenges a fundamental pillar of the rule of law: the duty to give reasons for decisions. The opacity of advanced models, such as Deep Learning (\"black boxes\"), creates a direct conflict with the need for transparency, justification, and oversight of administrative acts. In response, \"Explainable Artificial Intelligence\" (XAI) has been proposed as the solution to align technology with legal guarantees. This dissertation investigates whether XAI, in its current conception, is an adequate and sufficient tool to fulfill the duty to give reasons required by Administrative Law. Through a methodology that combines a systematic literature review, a conceptual analysis of the technology, and a legal analysis of legal reasoning, the study argues that XAI is fundamentally incompatible with normative requirements. The research demonstrates that a technical \"explanation,\" focused on post-hoc computational processes, differs structurally from a legal \"justification,\" which demands an ex ante rationale based on facts and legal norms. The conclusion is that XAI is an insufficient solution, as it fails to address the core issues of the legality and fairness of a decision. Instead of attempting to explain opaque systems, this work highlights the need to adopt inherently interpretable models for high-stakes decisions and to develop an algorithmic governance framework that subordinates technology to the principles of law, ensuring that automated decisions remain transparent, contestable, and legitimate.", "source_url": "https:\/\/openalex.org\/W7115034453", "pdf_url": "https:\/\/run.unl.pt\/bitstreams\/6f9c6509-594b-47b6-a73d-1700c32ea3ac\/download", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7115034453", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 95, "title": "Insurance Fraud Detection Using Supervised Machine Learning and Explainable Artificial Intelligence", "title_ar": "", "title_en": "Insurance Fraud Detection Using Supervised Machine Learning and Explainable Artificial Intelligence", "author": "Paavo Laine", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Turku", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; ai-applications; predictive-analytics; xai; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "Insurance fraud is a significant problem that causes financial losses for insurance companies and higher prices for honest customers. Machine learning has been widely used in insurance fraud detection systems. However, fraudulent claims often represent only a small percentage of the data, and standard machine learning models struggle with this extreme data imbalance. Additionally, the most advanced models are often black boxes, meaning their predictions are not interpretable to outside observers. This inability to explain decisions is problematic given the highly regulated nature of the insurance industry. This thesis aims to develop an insurance fraud detection system by utilizing machine learning models and tools from explainable artificial intelligence (XAI) research. A publicly available, labeled dataset of vehicle insurance fraud is used to train and evaluate the models. Based on the literature, four commonly used machine learning models are selected, trained, and evaluated. A logistic regression model and a decision tree are used as transparent baseline models, and they are compared with the more advanced black-box ensemble models: random forest and eXtreme Gradient Boosting (XGBoost). The class imbalance problem is addressed with cost-sensitive learning. To make the black-box models interpretable, this thesis uses Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The goal is not only to effectively detect insurance fraud, but also to provide explanations for why specific claims are suspicious. The trained models were evaluated using various metrics that account for the imbalanced nature of the data. XGBoost was found to be the highest-performing model, while random forest also outperformed logistic regression and decision tree. Both SHAP and LIME were successfully applied to the XGBoost model to generate explanations for the predictions. SHAP was found to be a more robust and reliable method compared to LIME.", "source_url": "https:\/\/openalex.org\/W7220817285", "pdf_url": "https:\/\/www.utupub.fi\/bitstreams\/84c64979-f048-47ea-990c-3f572f326d5a\/download", "doi": "", "handle": "", "repository_name": "UTUPub (University of Turku)", "source_record_id": "W7220817285", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 96, "title": "Federated Learning of Explainable Artificial Intelligence models: real world applications and theoretical advances", "title_ar": "", "title_en": "Federated Learning of Explainable Artificial Intelligence models: real world applications and theoretical advances", "author": "F. Ruffini", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Pisa", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; predictive-analytics; xai; federated-learning; fairness", "keywords": "", "abstract": "This thesis aims to provide a personal contribution to the research area of Trustworthy Artificial Intelligence (AI), focusing on some aspects still largely unexplored. The objective is to develop systems that achieve high predictive accuracy while creating trust between stakeholders and the AI system. Since trustworthiness is a multidimensional topic, this work focuses on two of its key requirements: transparency and data privacy. In this context, the thesis explore the use of Fed-XAI models (explainable AI models trained through Federated Learning), to simultaneously ensure data privacy and provide explanations. At the time of writing, most existing works address these issues separately. Furthermore, this work provides a comparison between the explanations generated by post-hoc methods applied to opaque models and those derived from interpretable by design models. Such systematic comparison remains largely unexplored in the current literature, predominantly focusing on post-hoc methods alone. The thesis is structured as a three-act play. The first two acts examine the application of different Fed-XAI models to real-world datasets, initially focusing on interpretable-by-design approaches and subsequently comparing them with opaque models. These analyses highlight the limitations of existing strategies and motivate the methodological framework introduced in the third act, which represents the main theoretical contribution of this thesis.", "source_url": "https:\/\/openalex.org\/W7140268698", "pdf_url": "https:\/\/etd.adm.unipi.it\/theses\/available\/etd-01272026-184626\/unrestricted\/PhDAIThesis_RuffiniXVIII_pdfA.pdf", "doi": "", "handle": "", "repository_name": "Electronic Theses and Dissertations Repository (University of Pisa)", "source_record_id": "W7140268698", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 97, "title": "Explainable artificial intelligence for analysing mosquito flight behaviour", "title_ar": "", "title_en": "Explainable artificial intelligence for analysing mosquito flight behaviour", "author": "Yasser M. Qureshi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Warwick", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "machine-learning; computer-vision; medical-ai; responsible-ai; predictive-analytics; optimization; image-segmentation; xai", "keywords": "", "abstract": "Mosquitoes are among the deadliest organisms on Earth, transmitting diseases that threaten global health. Malaria alone kills over half a million people annually, mostly young children. While insecticide-treated bed nets and indoor residual spraying have reduced transmission, these measures are increasingly compromised by insecticide-resistant mosquito populations and their behavioural adaptations. Despite well-documented genetic resistance, behavioural resistance remains poorly understood but critical to intervention efficacy. This thesis helps to address this gap by developing a machine learning and explainable AI (XAI) framework to analyse mosquito flight behaviours, focusing on identifying traits linked to resistance. The framework integrates preprocessing, trajectory segmentation, feature extraction, classification, and SHapley Additive exPlanations (SHAP) to provide interpretable insights. New techniques addressed tracking challenges like occlusions and spatial distortions, ensuring robustness. Three experimental conditions were analysed: (1) mating swarm data to classify males from non-males, (2) behavioural differences between insecticide-susceptible (IS) and insecticide-resistant (IR) mosquitoes near untreated nets, and (3) analogous trials involving permethrin-treated nets. The primary contributions of this thesis include the identification of behavioural signatures associated with insecticide resistance. IR mosquitoes exhibited reduced flight speeds, more direct paths, and lower fractal dimensions than IS mosquitoes, suggesting goal-directed flight. Exposure to permethrin-treated nets triggered erratic flight patterns in both IR and IS mosquitoes, with SHAP analysis identifying key features predictive of irritancy. Comparative analyses using this framework evaluated the performance of different tracking systems, confirming that 2D telecentric imaging matches 3D stereoscopic tracking performance, with singlecamera setups offering similar approximations at long ranges. Additionally, synthetic trajectories enhanced model interpretability by probing decision boundaries. The framework’s versatility and interpretability provide insights that could inform the design of interventions targeting mosquito vulnerabilities, with potential applications to other complex behavioural systems where understanding subtle patterns is essential.", "source_url": "https:\/\/openalex.org\/W7128281259", "pdf_url": "", "doi": "10.82444\/warw.33910837", "handle": "", "repository_name": "Warwick Research Archive Portal (University of Warwick)", "source_record_id": "W7128281259", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 98, "title": "Human-in-the-loop and Storytelling-based Explainable Frameworks for the Next Generation of Explainable Ensemble Methods and Artificial Intelligence Techniques", "title_ar": "", "title_en": "Human-in-the-loop and Storytelling-based Explainable Frameworks for the Next Generation of Explainable Ensemble Methods and Artificial Intelligence Techniques", "author": "Akshat Dubey", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; predictive-analytics; llm; medical-imaging; xai; fairness", "keywords": "", "abstract": "Artificial intelligence (AI) is being adopted in diverse fields, including medical diagnosis and scientific research, to assist with decision-making thanks to its ability to learn underlying patterns from large amounts of data. However, these advances present a challenge. Modern AI systems, particularly deep learning (DL) models and large ensembles, are often difficult for audiences, such as domain experts, to understand due to their inherent black-box nature. This lack of transparency can reduce trust, impede adoption in critical fields, and cause problems with new regulations, such as the European Union’s (EU) AI Act, which impose strict transparency, accountability, and human oversight requirements. This thesis makes the following primary contributions: First, the thesis introduces a five-layer nested model for AI design and validation (Regulation, Domain, Data, Model, Prediction). This model systematically translates regulatory requirements into specific design and validation methodologies, surfacing threats to validity and compliance at each stage. Second, the thesis develops human-centered explainability frameworks, including Storytelling explainable AI (XAI) for medical imaging, which integrates multi-task distillation with visual and textual narratives. Third, the thesis introduces UbiQTree & UbiQVision, frameworks for uncertainty quantification in explainable AI (XAI). UbiQTree is instantiated for ensemble tree models, and UbiQVision is instantiated for deep vision models. They decompose shapley additive explanations (SHAP) based attributions into aleatoric, epistemic, and entangled components using Dirichlet processes and Dempster–Shafer theory (DST). This transformation shifts the focus from point-estimate explanations to distributions of explanations, allowing practitioners to calibrate explanations and facilitate meaningful human intervention. Finally, the thesis demonstrates domain-centric explainability through PepTriX, a framework for explainable peptide analysis. PepTriX integrates protein language models (PLM), predicted 3D structures, graph attention networks (GAT), and cross-modal co-attention to yield mechanistically interpretable representations. Together, these contributions reflect the thesis title, \"Human-in-the-Loop and Storytelling-Based Explainable Frameworks for the Next Generation of Explainable Ensemble Methods and Artificial Intelligence Techniques\". The work argues that explainability should be a core design principle throughout the AI lifecycle, not just an afterthought. Treating explanations as a means of communication and storytelling — i.e., narratives emphasizing the human reasoning process — brings AI systems closer to becoming not only good at achieving enhanced performance metrics, but also accountable, understandable, and better suited to users’ needs. This thesis is based on peer-reviewed publications and submissions covering AI regulations, human-centered explainability, uncertainty quantification, and domain-specific applications. The thesis allows for interpretive\/interpretative human-in-the-loop (HITL) interaction, enabling domain users to inspect and calibrate model behavior as necessary. Together, these works demonstrate that developing trustworthy AI in critical fields requires a comprehensive approach encompassing regulatory compliance, human involvement, technical methodologies, and domain-specific relevance.", "source_url": "https:\/\/openalex.org\/W7172247662", "pdf_url": "", "doi": "10.17169\/refubium-53170", "handle": "", "repository_name": "", "source_record_id": "W7172247662", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 99, "title": "Privacy Risks of Explainable Artificial Intelligence (XAI) in Healthcare : An Empirical Study Using Machine Learning Models and Post-hoc Explainability Methods", "title_ar": "", "title_en": "Privacy Risks of Explainable Artificial Intelligence (XAI) in Healthcare : An Empirical Study Using Machine Learning Models and Post-hoc Explainability Methods", "author": "Olli Saaristo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Turku", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; xai; fairness; ai-cybersecurity", "keywords": "", "abstract": "This thesis examines the cybersecurity and privacy implications of Explainable Artificial Intelligence (XAI) methods in selected Machine Learning (ML) models. The study focuses on whether post-hoc explainability methods can expose sensitive information from healthcare datasets when applied to machine learning models. The study was conducted using a modified synthetic healthcare dataset. The dataset consisted of 1,000 synthetic patient records with intentionally modified values to simulate privacy-related anomalies. Three machine learning models were implemented using the Scikit-learn library in JupyterLab: Decision Trees (DTs), Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN). The explainability methods applied in the study were Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), and Explain Like I’m Five (ELI5). The results indicate that XAI methods improve the transparency and interpretability of ML models but may also increase the exposure of sensitive information by highlighting influential patient features and unusual data patterns. Decision Trees worked as white-box models and were the most directly interpretable models, whereas SVMs and KNN worked as black-box models and required additional XAI methods for interpretability. However, the applied privacy-preserving techniques may influence model behavior and should be carefully considered when interpreting the results. Increased privacy protection may be associated with reduced model accuracy and a potential increase in bias. The study concludes that explainability and privacy must be carefully balanced when using AI systems in healthcare environments. European Union regulations and global legislation emphasize that patient information must be handled with appropriate privacy and security measures.", "source_url": "https:\/\/openalex.org\/W7220813494", "pdf_url": "https:\/\/www.utupub.fi\/bitstreams\/fd3678ab-e714-44eb-b959-a604fa1c9a22\/download", "doi": "", "handle": "", "repository_name": "UTUPub (University of Turku)", "source_record_id": "W7220813494", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 100, "title": "Understanding and the Black Box Problem in Artificial Intelligence", "title_ar": "", "title_en": "Understanding and the Black Box Problem in Artificial Intelligence", "author": "Barnaby Crook", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bayreuth", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; ai-applications; xai; fairness; ai-industry", "keywords": "", "abstract": "Artificial intelligence (AI) systems developed with automated machine learning procedures are used throughout industry, science, and society. However, the opacity of these systems threatens to undermine the satisfaction of critical desiderata including safety, trust, and fairness. This worry is known as the black box problem (BBP). In this dissertation, I develop a novel conceptual framework for analysing the BBP and, through five interrelated papers, address three key research questions about its nature and our prospects for solving it. First, I consider the central epistemic construct of understanding, arguing that an abilities-based view is best-suited to the requirements of BBP analysis. Conceptualising understanding as abilities, I claim, appropriately balances theoretical justification with practical utility and can help coordinate interdisciplinary efforts in explainable AI (XAI). Second, I address the potential of XAI, a research field which attempts to render AI systems comprehensible to human stakeholders. I focus on mechanistic interpretability, an approach which employs functional decomposition to characterise the internal structure of AI systems. Mechanistic interpretability can deliver detailed explanations, but at significant costs in time and labour. Additionally, I present a framework for evaluating explainability approaches, using it to show how performance-explainability trade-offs can be effectively analysed. Third, I address the solubility of the BBP in particular contexts. In neuroscience, I argue against critics that artificial neural network models, in conjunction with XAI, can enhance our understanding of the brain. In psychiatry, I argue that even if the opacity of AI systems is overcome, data-driven approaches are unlikely to deliver transformative improvements to patient outcomes. Overall, this dissertation demonstrates how an abilities-based view of understanding and a considered assessment of XAI’s potential can be combined to yield new insights into the BBP and inform its resolution across diverse contexts.", "source_url": "https:\/\/openalex.org\/W7143534649", "pdf_url": "https:\/\/epub.uni-bayreuth.de\/id\/eprint\/8992\/1\/Crook_Barnaby_Understanding.pdf", "doi": "10.15495\/epub_ubt_00008992", "handle": "", "repository_name": "EPub Bayreuth (University of Bayreuth)", "source_record_id": "W7143534649", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 101, "title": "Explainable artificial intelligence for integrity assessment of civil engineering infrastructure: Insights from various engineering applications", "title_ar": "", "title_en": "Explainable artificial intelligence for integrity assessment of civil engineering infrastructure: Insights from various engineering applications", "author": "Masoud Haghbin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Granada", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "xai", "categories": "machine-learning; computer-vision; responsible-ai; predictive-analytics; xai; fairness", "keywords": "", "abstract": "The incorporation of Artificial Intelligence (AI) into civil engineering, especially structural engineering, has the potential to improve decision-making by making it more precise and data driven. However, relying on AI to make decisions without understanding its reasoning poses a serious risk: It undermines the essential concept of human judgment. This concern has led to global regulatory measures, including the European Union AI Act, which highlights the importance of explainability and human oversight in creating trustworthy AI. In response to these needs, eXplainable AI (XAI) has attracted significant attention from researchers over the past five years. It serves as a technical tool to shed light on the internal computing processes of AI systems, which are often seen as black boxes, addressing safety and ethical implications. Most research into the application of XAI in civil engineering is conducted under controlled and standardized conditions. However, real-world engineering problems often occur in complex and changing environments. These problems often span multiple domains, making their modeling challenging despite their importance. In addition, the presence of scarce and poor quality data and real-world behaviors that are difficult to replicate in laboratory experiments (e.g., in the presence of damage) add to these complications. These inherent issues challenge the fundamental assumptions of many AI models, which are usually trained and validated in controlled or synthetic environments. Consequently, both the predictive accuracy and the reliability of model explanations can deteriorate when these models are extended to real-world situations, a risk that is often underestimated or overlooked. In 2026, the European Union’s AI Act will come into force for critical infrastructures, such as the vast majority of civil engineering infrastructure, making explainability and human oversight a legal requirement. There is a significant technical gap, as well as pressing safety, ethical, and regulatory concerns, due to the lack of assessments of the robustness of XAI models in real-world applications. This thesis shifts the focus from presenting novel AI models that excel in standardized benchmark problems to understanding how cutting-edge but existing AI and XAI methods perform under the irregular and data-poor conditions typically found in real-world civil engineering systems. This research highlights the need for realistic domain-aware deployment strategies that prioritize robustness, interpretability, and practical insights over purely theoretical advances. Rather than relying on idealized datasets or conditions favorable to AI, such as clean, balanced, and well-annotated data, which do not accurately represent realworld engineering conditions, this thesis focuses on investigating, understanding, and improving the behavior of the AI model in complex environments using real data from various case studies. To achieve these objectives, the thesis begins by investigating a complex type of rail surface degradation problem known as corrugation. This phenomenon is affected by a combination of dynamic and static factors, including vehicle-track interaction, material properties, and environmental conditions. However, measuring or integrating these contributing variables presents significant challenges as they are often difficult to assess directly, expensive, or even unfeasible to acquire in real-world situations. In order to ensure a realistic and application-oriented approach, a minimal set of input parameters (acceleration and velocity), which are readily available and can be easily measured from the moving vehicle, have been selected to predict the rail corrugation profile based on time series data of these input parameters. To align with practical constraints in the field, multiple damaged zones were considered in order to capture the variability and complexity of actual conditions of the rail infrastructure. Despite the common practice of removing outliers as an initial step in data preprocessing, the research presented in this thesis intentionally refrains from doing so in this particular scenario. Retaining outliers is crucial for accurately representing the unpredictable nature of real-world engineering data, where noise, anomalies, and irregular patterns are inherent in reality. Excluding them could lead to an overly simplified understanding of the problem and diminish the relevance of AI models in practical applications. The XAI method, Grad-CAM, is applied to enhance transparency and increase human oversight in the prediction of rail corrugation under the aforementioned realistic conditions. This approach allows for visual interpretation of the model decision-making process, helping to bridge the gap between black-box predictions and domain-relevant understanding.", "source_url": "https:\/\/openalex.org\/W7154665432", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Institutional Repository of the University of Granada (University of Granada)", "source_record_id": "W7154665432", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 102, "title": "Bridging the Gap: From Artificial Intelligence and Optimization Theory to Action", "title_ar": "", "title_en": "Bridging the Gap: From Artificial Intelligence and Optimization Theory to Action", "author": "Periklis Petridis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Massachusetts Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; generative-ai; medical-ai; responsible-ai; ai-applications; predictive-analytics; xai; ai-industry; ai-smart-cities", "keywords": "", "abstract": "Despite significant theoretical advances in Operations Research (OR) and Artificial Intelligence (AI), a persistent gap remains between these developments and their practical implementation in real-world settings. Despite significant progress in these fields, many OR and ML approaches struggle to scale to realistic problem sizes, lack robustness to uncertainty, or fail to address implementation constraints faced by practitioners in industry. Through four distinct works conducted in collaboration with industry partners, this research demonstrates how methodological advancements can bridge this theory-practice divide while maintaining rigorous theoretical foundations and guarantees. In the first part, we focus on optimization methodologies that scale traditional OR approaches to handle real-world problem sizes and uncertainty. In Chapter 2, we develop a stochastic Benders decomposition scheme for large-scale network design problems, a class of problems ubiquitous in logistics, transportation, and energy sectors. By incorporating sampling techniques within the decomposition framework, we achieve deterministic optimality guarantees while reducing computational costs, enabling solutions for networks with 700 nodes—an order of magnitude larger than previously tractable instances—while achieving optimality gaps of 5-7% compared to 16-27% for traditional deterministic approaches. In Chapter 3, we present a holistic framework for industrial decarbonization, developed with a major phosphate producer planning to quadruple energy consumption while transitioning to renewable sources. Our robust optimization approach combines strategic capacity expansion planning over a 25-year horizon with adaptive operational models, providing 95% reliability guarantees while balancing solar and wind integration with battery storage to meet a projected 12 TWh annual demand. In the second part, we shift our focus to developing AI systems that address the unique challenges of medical data abstraction and clinical decision support. In Chapter 4, we address the challenge of automating clinical data abstraction from electronic health records, collaborating with the Society of Thoracic Surgeons to populate their Adult Cardiac Surgery Database. Our AI pipeline combines 31 models per target variable with a two-tiered quality control system, achieving over 99% accuracy while automatically extracting 43-50% of registry variables, demonstrating how AI can dramatically reduce manual abstraction burden while maintaining clinical standards. In Chapter 5, we extend this healthcare AI focus by developing xHAIM (Explainable Holistic AI in Medicine), which addresses the limitations of current clinical AI systems in handling extensive patient records, providing interpretability, and incorporating medical knowledge. Through semantic similarity techniques and generative AI, xHAIM improves predictive performance while generating clinically grounded explanations that enhance trust and adoption by healthcare practitioners.", "source_url": "https:\/\/openalex.org\/W7123375454", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace@MIT (Massachusetts Institute of Technology)", "source_record_id": "W7123375454", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 104, "title": "Applications of artificial intelligence for explainability and uncertainty quantification for performance-based design and damage classification in civil engineering", "title_ar": "", "title_en": "Applications of artificial intelligence for explainability and uncertainty quantification for performance-based design and damage classification in civil engineering", "author": "Shabbir, Khurram", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; predictive-analytics; transformers; xai; fairness; ai-smart-cities", "keywords": "", "abstract": "Structural resilience and safety are critical concerns in modern infrastructure, particularly in the face of increasing seismic risks and aging buildings. While advanced artificial intelligence (AI) techniques offer promising solutions for structural design, damage assessment, and retrofitting, the \"black-box\" nature of many AI models limits their trustworthiness and adoption in safety-critical fields. This research integrates explainable AI (XAI) techniques to bridge the gap between complex machine learning models and engineering practice, developing innovative frameworks for enhanced interpretability in civil engineering applications. A primary contribution is Seismo-XAI, a web-based tool that delivers transparent, interpretable insights into building performance under seismic loads, empowering engineers to optimize designs and retrofits. The study also introduces a novel hybrid framework combining microstructure-informed finite element modelling with computer vision to advance damage classification in concrete, synergizing vision transformers' computational efficiency with physics-based finite element analysis. To address data limitations, a synthetic dataset of concrete microstructures was generated using stochastic Monte Carlo augmentation. For uncertainty quantification in seismic assessment, this research employs artificial neural networks with the quality-driven lower upper bound estimation method to establish prediction intervals for long-term ground motion effects. This distribution-free machine learning approach provides both local and global levels probabilistic damage assessment, enhancing reliability in post-earthquake evaluations and early warning systems. Validation through benchmark structures and real-world case studies demonstrates the frameworks’ effectiveness in promoting reliable design practices for seismic resilience and damage assessment. The findings highlight the potential of XAI to transform structural engineering by providing transparent, interpretable, and actionable insights, fostering trust in AI-driven solutions. This research lays the foundation for future advancements in and development of AI applications for structural health monitoring and disaster management for urban infrastructure.", "source_url": "https:\/\/openalex.org\/W7115275889", "pdf_url": "https:\/\/aaltodoc.aalto.fi\/bitstreams\/22ee9f47-ed26-415f-8ef3-c22a3082d694\/download", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W7115275889", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 105, "title": "GENERATIVE LARGE LANGUAGE MODELS FOR TRANSPARENT ARTIFICIAL INTELLIGENCE IN CLINICAL RESEARCH: ENHANCING INTERPRETABILITY THROUGH APPRAISAL AND EXPLANATION", "title_ar": "", "title_en": "GENERATIVE LARGE LANGUAGE MODELS FOR TRANSPARENT ARTIFICIAL INTELLIGENCE IN CLINICAL RESEARCH: ENHANCING INTERPRETABILITY THROUGH APPRAISAL AND EXPLANATION", "author": "Fangwen Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "McMaster University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; generative-ai; medical-ai; responsible-ai; transfer-learning; predictive-analytics; transformers; llm; xai", "keywords": "", "abstract": "Background The rapid growth of medical literature necessitates effective, transparent automation tools for classification. Generative large language models (LLMs), including the Generative Pre-trained Transformer (GPT), have the potential to provide transparent classification and explain other black box models. Objective This sandwich thesis evaluates the performance of GPT in 1) classifying biomedical literature compared with a fine-tuned BioLinkBERT model, and 2) explaining the decision of encoder-only models with feature attributions compared to traditional eXplainable AI (XAI) frameworks like SHapley Additive exPlanations (SHAP) and integrated gradients (IG). Methods Randomly sampled, manually annotated clinical research articles from the Health Information Research Unit (HIRU) were used along with a top-performing BioLinkBERT classifier. In Chapter 2, GPT-4o and GPT-o3-mini were used either alone or with BioLinkBERT’s predictions in the prompt to classify article methodological rigour based on HIRU’s criteria. Either the title and abstract or the full text was provided to GPT. Performance was compared to the BioLinkBERT model and assessed primarily using Matthew’s correlation coefficient (MCC). In Chapter 3, GPT-4o was used to generate feature attributions for the BioLinkBERT model through masking perturbations and was compared to SHAP and IG using a modified area under the perturbation curve (AOPC) metric which gives a measure of performance. Results GPT-4o alone, using full text (MCC 0.429), achieved comparable classification performance to BioLinkBERT (MCC 0.466). Performance was worse with other models and inputs. As a perturbation explainer, GPT-4o’s (AOPC 0.029) performance was poor and significantly underperformed compared to SHAP (AOPC 0.222) and IG (AOPC 0.225). The identified important tokens by GPT did not align with the manual appraisal criteria. Conclusion GPT has potential in appraising biomedical literature, even without explicit training. GPT’s transparency through textual explanations improves interpretability. GPT’s poor performance in generating faithful feature attributions warrants future research. The inherent variability and stochasticity of GPT outputs necessitate careful prompting and reproducibility measures.", "source_url": "https:\/\/openalex.org\/W7214197727", "pdf_url": "", "doi": "10.71548\/13232", "handle": "", "repository_name": "MacSphere (McMaster University)", "source_record_id": "W7214197727", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 106, "title": "PERBANDINGAN KINERJA CNN DALAM KLASIFIKASI TUMOR OTAK DENGAN PENDEKATAN EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI)", "title_ar": "", "title_en": "PERBANDINGAN KINERJA CNN DALAM KLASIFIKASI TUMOR OTAK DENGAN PENDEKATAN EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI)", "author": "NIM.: 23206051017 Amin Darussalam", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Sunan Kalijaga State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; predictive-analytics; cnn; image-classification; medical-imaging; xai", "keywords": "", "abstract": "Brain tumors are life-threatening conditions that require accurate and timely diagnosis. In clinical practice, diagnosis is commonly performed through Magnetic Resonance Imaging (MRI) analysis by medical experts, which is time-consuming and subject to variability. Recent advances in Convolutional Neural Networks (CNN) have shown promising performance in medical image classification; however, their black-box nature limits clinical trust and interpretability. This study proposes a comparative analysis of CNN-based models integrated with Explainable Artificial Intelligence (XAI) to improve both classification performance and model transparency. A transfer learning approach is employed using two architectures, ResNet50 and MobileNetV2, on a multi-class brain tumor MRI dataset. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize discriminative regions influencing model predictions. Experimental results demonstrate that ResNet50 achieves an Accuracy of 93,7%, outperforming MobileNetV2 with 92.93%. Furthermore, Grad-CAM visualizations consistently highlight tumor-relevant regions, indicating that the models learn meaningful spatial features for classification. These findings confirm that integrating CNN with XAI not only provides high classification Accuracy but also improves model interpretability, thereby increasing its potential applicability in supporting reliable and transparent medical diagnosis.", "source_url": "https:\/\/openalex.org\/W7214133979", "pdf_url": "https:\/\/digilib.uin-suka.ac.id\/id\/eprint\/78375\/1\/23206051017_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf", "doi": "", "handle": "", "repository_name": "Digital Library UIN Sunan Kalijaga (Sunan Kalijaga State Islamic University)", "source_record_id": "W7214133979", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 109, "title": "Explainable Artificial Intelligence across Domains: Refinement of SHAP and Practical Applications", "title_ar": "", "title_en": "Explainable Artificial Intelligence across Domains: Refinement of SHAP and Practical Applications", "author": "Veera Raghava Reddy Kovvuri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Swansea University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; xai", "keywords": "", "abstract": "Explainable Artificial Intelligence (XAI) has become a crucial area within AI, emphasizing the transparency and interpretability of complex models. In this context, this research meticulously examines diverse datasets from medical, financial, and socio-economic domains, applying existing XAI techniques to enhance understanding and clarity of the results. This work makes a notable contribution to XAI by introducing the Controllable fActor Feature Attribution (CAFA) approach, a novel method that categorizes dataset features into ‘controllable’ and ‘uncontrollable’ groups. This categorization enables a more nuanced and actionable analysis of feature importance. Furthermore, the research proposes an extension to CAFA, the Uncertainty-based Controllable fActor Feature Attribution (UCAFA) method, which incorporates a Variational Autoencoder (VAE) to ensure that perturbations remain within the expected data distribution, thereby enhancing the reliability of feature attributions. The effectiveness and versatility of CAFA are showcased through its application in two distinct domains: Medical and socio-economic. In the medical domain, a case study is conducted on the efficacy of COVID-19 non-pharmaceutical control measures, providing valuable insights into the impact and effectiveness of different strategies employed to control the pandemic. Additionally, UCAFA is applied to the medical domain, demonstrating its ability to improve the reliability of feature attributions by considering uncertainty. The socio-economic domain is investigated by applying CAFA to several datasets, yielding insights into income prediction, credit risk assessment, and recidivism prediction. In the financial domain, the analysis focuses on global equity funds using established XAI methodologies, particularly the integration of the XGBoost model with Shapley values. This analysis provides critical insights into fund performance and diversification strategies across G10 countries. This thesis highlights the potential of CAFA and UCAFA as promising directions in the domain of XAI, setting the stage for advanced research and applications.", "source_url": "https:\/\/openalex.org\/W4400780014", "pdf_url": "", "doi": "10.23889\/suthesis.67149", "handle": "", "repository_name": "Cronfa (Swansea University)", "source_record_id": "W4400780014", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 110, "title": "Interpretable representation learning for visual intelligence", "title_ar": "", "title_en": "Interpretable representation learning for visual intelligence", "author": "Bolei Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Massachusetts Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; robotics; responsible-ai; predictive-analytics; object-detection; video-analysis; xai", "keywords": "", "abstract": "Recent progress of deep neural networks in computer vision and machine learning has enabled transformative applications across robotics, healthcare, and security. However, despite the superior performance of the deep neural networks, it remains challenging to understand their inner workings and explain their output predictions. This thesis investigates several novel approaches for opening up the \"black box\" of neural networks used in visual recognition tasks and understanding their inner working mechanism. I first show that objects and other meaningful concepts emerge as a consequence of recognizing scenes. A network dissection approach is further introduced to automatically identify the internal units as the emergent concept detectors and quantify their interpretability. Then I describe an approach that can efficiently explain the output prediction for any given image. It sheds light on the decision-making process of the networks and why the predictions succeed or fail. Finally, I show some ongoing efforts toward learning efficient and interpretable deep representations for video event understanding and some future directions.", "source_url": "https:\/\/openalex.org\/W2911266282", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace@MIT (Massachusetts Institute of Technology)", "source_record_id": "W2911266282", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 111, "title": "Explainable artificial intelligence (XAI) for healthcare decision-making", "title_ar": "", "title_en": "Explainable artificial intelligence (XAI) for healthcare decision-making", "author": "Zhiwei Zeng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; self-semi-supervised; xai; fairness; ai-cybersecurity", "keywords": "", "abstract": "With the increasing ubiquity of Artificial Intelligence (AI) in daily life and into more critical applications, explainability has been brought under the spotlight due to ethical, social, and legal concerns. Explainability is particularly important in application domains involving high-stakes decision-making, such as healthcare, medicine, law, and defence, as simply having high performance may not satisfy all the desiderata of stakeholders. Stakeholders in these domains often require greater insights into the decision-making process, better support for tracing decision trails and identifying errors, as well as greater trustworthiness from an AI system. An eXplainable AI (XAI) is one that has the ability to explain the outputs or functioning of a model in a clear and understandable manner to a human. In the explanation process of XAI, explainability approaches provide explanatory information to human stakeholders in order to facilitate their understanding of the outputs and functioning of an AI model. The renewed understanding then affects the extent to which the desiderata are satisfied. Over the past decade, a plethora of explainability approaches have been proposed, ranging from ante-hoc methods that seek to build explainability features into a model itself to post-hoc methods that use techniques to improve the explainability of an already developed model. Despite the rapid growth of the XAI field, three key open problems emerge from a review of the existing XAI methods. Firstly, post-hoc methods have been increasingly used for high-stakes decisions, but most of them provide explanatory information that is not faithful or not insightful to what the original model computes. Secondly, most existing methods can only provide associational explanations and lack the structure and reasoning ability to provide contrastive and counterfactual explanations. Thirdly, most existing methods do not provide explanations to suit different levels of understanding. Research problem 1 - Post-hoc explainability methods have quickly gained popularity as they are mostly model-agnostic and highly extensible. However, without elucidating or understanding the model’s inner workings, explanations generated by post-hoc methods may not faithfully represent the underlying relationships of the model and often do not provide enough information to facilitate the understanding of how the output is obtained. In this thesis, I propose an ante-hoc explainability methodology to support the making and explaining of high-stakes decisions in healthcare. It is an argumentative methodology that can infuse explainability into the model and generate explanations that are faithful and insightful to the inner workings and internal representations of the model. It follows a “debate-like” process that shares many similarities with the way humans deliberate and make decisions, thereby giving it unique advantages in generating human-understandable explanations. I instantiate the argumentative methodology in three explainable decision-making approaches for the modelling and reasoning of contexts, qualitative preferences as well as both attack and support relationships. Research problem 2 - Although contrastive and counterfactual explanations have attracted more research interest recently, existing XAI methods mostly generate associational explana- tions. Most existing methods are good at answering “Why X? ” but poor at answering questions that require higher-level inference, such as “Why not-X? ”. This can be attributed to the fact that most of them lack the necessary structures and mechanisms to perform high-level logical reasoning. Building on the structures and reasoning ability of the three proposed explain- able decision-making approaches, I construct different forms of contrastive and counterfactual explanations that can answer questions such as “Why X instead of Y? ” and “Why not-X? ”. Research problem 3 - Different levels of understanding are often required in most practical scenarios. However, most existing XAI methods do not provide explanations that can cater to the different levels of understanding. In this thesis, I propose and study formal properties of explanatory information in order to generate such explanations. I propose notions and computational constructs for generating explanations that can provide different amounts of justification or have different dialectical strengths, as well as focused explanations that only include selective information in the decision-making process. As a case study, I implement a proposed explainable decision-making approach for the diagnostics and prognostics of Alzheimer’s Disease (AD). Empirical evaluation with real-world AD datasets and human evaluations were conducted to assess the performance and explainability of the approach.", "source_url": "https:\/\/openalex.org\/W4224077403", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/155849\/4\/Thesis_final_Zhiwei%20Zeng_signed%20with%20watermark.pdf", "doi": "10.32657\/10356\/155849", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4224077403", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 112, "title": "Explainable artificial intelligence for patient stratification and drug repositioning", "title_ar": "", "title_en": "Explainable artificial intelligence for patient stratification and drug repositioning", "author": "Zainab Al-Taie", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Missouri", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "medical-ai; responsible-ai; xai", "keywords": "", "abstract": "Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. There is a crucial need to develop data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Exploratory mining mimicking the trial recruitment process as well as network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The method represents a human-in-the-loop framework, where medical experts use data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. To examine the validity of our method, we implemented our method on individual cancer types and on pan-cancer data to consider the inter- and intra-heterogeneity within a cancer type and among cancer types. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups showed that most of these drugs are FDA-approved drugs for cancer, and others are non-cancer related, but have the potential to be repurposed for cancer. We have discovered novel cancer-related mechanisms that these drugs can target in different cancer types to reduce cancer treatment costs and improve patient survival. Further wet lab experiments to validate these findings are required prior to initiating clinical trials using these repurposed therapies.", "source_url": "https:\/\/openalex.org\/W4312064622", "pdf_url": "https:\/\/mospace.umsystem.edu\/xmlui\/bitstream\/10355\/93219\/1\/AlTaieZainabResearch.pdf", "doi": "10.32469\/10355\/93219", "handle": "", "repository_name": "MOspace Institutional Repository (University of Missouri)", "source_record_id": "W4312064622", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 113, "title": "Uncertainty-aware and Explainable Artificial Intelligence for Identification of Human Errors in Nuclear Power Plants", "title_ar": "", "title_en": "Uncertainty-aware and Explainable Artificial Intelligence for Identification of Human Errors in Nuclear Power Plants", "author": "Bhavya Reddy Kotla", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "responsible-ai; xai", "keywords": "", "abstract": "Nuclear Power Plants (NPPs) can face challenges in maintaining standard operations due to a range of issues, including human mistakes, mechanical breakdowns, electrical problems, measurement errors, and external influences. Swift and precise detection of these issues is crucial for stabilizing the NPPs. Identifying such operational anomalies is complex due to the numerous potential scenarios. Additionally, operators need to promptly discern the nature of an incident by tracking various indicators, a process that can be mentally taxing and increase the likelihood of human errors. Inaccurate identification of problems leads to inappropriate corrective actions, adversely affecting the safety and efficiency of NPPs. In this study, we leverage ensemble and uncertainty-aware models to identify such errors, and thereby increase the chances of mitigating them, using the data collected from a physical testbed. Furthermore, the goal is to identify both certain and reliable models. For this, the two main aspects of focus are, EXplainable Artificial intelligence (XAI) and Uncertainty Quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study, we leverage measures like entropy and mutual information along with SHAP (SHapley Additive explanations) and LIME (Local Interpretable Model-Agnostic Explanations) to gain insights into the features contributing to the identification. Our results show that uncertainty-aware models combined with XAI tools can explain the AI-prescribed decisions, with the potential of better explaining errors for the operators.", "source_url": "https:\/\/openalex.org\/W4400578241", "pdf_url": "", "doi": "10.31979\/etd.v2w8-38zh", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4400578241", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 114, "title": "Explainable artificial intelligence approaches for fault diagnosis in rotating machinery", "title_ar": "", "title_en": "", "author": "Lucas Costa Brito", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "pt", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "computer-vision; medical-ai; responsible-ai; ai-applications; xai; ai-industry", "keywords": "", "abstract": "Devido ao crescente interesse pelo aumento da produtividade e redução de custos no ambiente industrial, novas técnicas de monitoramento de máquinas rotativas estão surgindo.A Inteligência Artificial (IA) é uma das abordagens que tem sido proposta para analisar os dados coletados (por exemplo, sinais de vibração) fornecendo um diagnóstico da condição de operação do ativo.Atualmente, vários modelos baseados em aprendizado de máquina e aprendizado profundo têm alcançado excelentes resultados no diagnóstico de falhas.No entanto, para aumentar ainda mais a adoção e difusão de tais tecnologias, usuários e especialistas humanos devem receber explicações dos modelos.Outra questão está relacionada, na maioria dos casos, com a indisponibilidade de dados históricos rotulados que inviabilizam o uso de modelos supervisionados.Para superar esses problemas, esta tese propõe novas metodologias para diagnóstico de falhas em máquinas rotativas baseadas em inteligência artificial explicável e análise de vibração.A possibilidade de reduzir o número de parâmetros monitorados é apresentada.Uma nova estrutura é proposta para identificação automática de bandas de frequência relevantes em sinais de vibração, chamada de Fator de Relevância de Banda (BRF).Além disso, uma identificação não supervisionada da falha em máquinas rotativas através da análise de vibração e classificação não supervisionada do tipo de falha, com base na análise da relevância dos parâmetros é apresentada.Por fim, é desenvolvida uma nova abordagem de classificação baseado na transferência de aprendizado em um conjunto de dados sintético, sem a necessidade de ter sinais de condições reais de falha, denominada Detecção de Falhas usando Inteligência Artificial Explicável (FaultD-XAI).A eficácia das abordagens propostas é mostrada em diferentes conjuntos de dados contendo falhas mecânicas em máquinas rotativas.", "source_url": "https:\/\/openalex.org\/W4308971906", "pdf_url": "https:\/\/repositorio.ufu.br\/bitstream\/123456789\/36369\/4\/ExplainableArtificialIntelligence.pdf", "doi": "10.14393\/ufu.te.2022.557", "handle": "", "repository_name": "", "source_record_id": "W4308971906", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 115, "title": "Effective Explainable Artificial Intelligence using Visual Explanations in Images", "title_ar": "", "title_en": "Effective Explainable Artificial Intelligence using Visual Explanations in Images", "author": "Rami Ibrahim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; predictive-analytics; cnn; xai", "keywords": "", "abstract": "Improved interpretations for CNN predictions.", "source_url": "https:\/\/openalex.org\/W4311681011", "pdf_url": "http:\/\/repository.library.carleton.ca\/downloads\/pc289k188", "doi": "10.22215\/etd\/2022-15159", "handle": "", "repository_name": "Carleton University's Institutional Repository (MacOdrum Library, Carleton University)", "source_record_id": "W4311681011", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 116, "title": "DOCUMENT CLASSIFICATION USING MACHINE LEARNING", "title_ar": "", "title_en": "DOCUMENT CLASSIFICATION USING MACHINE LEARNING", "author": "Ankit Basarkar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "machine-learning", "research_subdomain": "", "categories": "machine-learning", "keywords": "", "abstract": "To perform document classification algorithmically, documents need to be represented such that it is understandable to the machine learning classifier. The report discusses the different types of feature vectors through which document can be represented and later classified. The project aims at comparing the Binary, Count and TfIdf feature vectors and their impact on document classification. To test how well each of the three mentioned feature vectors perform, we used the 20-newsgroup dataset and converted the documents to all the three feature vectors. For each feature vector representation, we trained the Naïve Bayes classifier and then tested the generated classifier on test documents. In our results, we found that TfIdf performed 4% better than Count vectorizer and 6% better than Binary vectorizer if stop words are removed. If stop words are not removed, then TfIdf performed 6% better than Binary vectorizer and 11% better than Count vectorizer. Also, Count vectorizer performs better than Binary vectorizer, if stop words are removed by 2% but lags behind by 5% if stop words are not removed. Thus, we can conclude that TfIdf should be the preferred vectorizer for document representation and classification.", "source_url": "https:\/\/openalex.org\/W4232107035", "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=1531&context=etd_projects", "doi": "10.31979\/etd.6jmu-9xdt", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4232107035", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 117, "title": "Interpretable Measures for Arabic Readability", "title_ar": "", "title_en": "Interpretable Measures for Arabic Readability", "author": "Rabih, Nour", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Arab Emirates", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; responsible-ai; expert-systems; ai-applications; predictive-analytics; transformers; arabic-nlp; xai; ai-education", "keywords": "", "abstract": "Readability assessment is a crucial task in natural language processing (NLP), with wide reaching implications in education, accessibility, and language learning. While prior research has primarily focused on predicting readability levels, this work shifts the focus toward understanding why a text is assigned a particular level. By identifying key linguistic features that drive readability, the goal is to enhance interpretability in Arabic text complexity assessment. Arabic poses unique challenges for automatic readability evaluation due to its morphological richness, variation across dialects, and limited availability of annotated corpora. The complex structure of Arabic words, and extensive derivational morphology add layers of difficulty in determining textual complexity. These challenges necessitate a more comprehensive approach to readability evaluation beyond traditional classification. To address these limitations, this work leverages the Balanced Arabic Readability Evaluation Corpus (BAREC)—a large-scale, manually annotated dataset of 68,182 sentences across 19 readability levels, from kindergarten to postgraduate comprehension. BAREC is designed to ensure genre diversity, topical variety, and balanced target audiences, making it a strong foundation for Arabic readability modeling. This study systematically engineers and evaluates a comprehensive set of linguistic features from BAREC’s annotation guidelines, including morphology, syntax, vocabulary, syllables, and content and vocabulary-based measures. Through extensive experimentation, it investigates the effectiveness of these features in explaining and predicting readability levels across three task configurations: foundational levels (1–11), advanced levels (12–19), and the full 19-level scale. In this study, we explore a variety of modeling approaches, including traditional machine learning classifiers, a fully interpretable rule-based model, and an AraBERT model based on transformers. Furthermore, a hybrid model is proposed that integrates linguistic features with contextual embeddings to improve both performance and transparency. The results demonstrate that linguistic features significantly enhance interpretability, especially in low-resource settings, and that feature-driven models can provide human readable justifications aligned with expert annotation practices. This research not only advances Arabic readability prediction but also contributes interpretable insights into text complexity, paving the way for more accessible and pedagogically meaningful NLP tools in Arabic education.", "source_url": "https:\/\/openalex.org\/W7106299741", "pdf_url": "", "doi": "10.82419\/276", "handle": "", "repository_name": "MBZUAI iRep", "source_record_id": "W7106299741", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 118, "title": "Machine Learning of Antonyms in English and Arabic Corpora", "title_ar": "", "title_en": "Machine Learning of Antonyms in English and Arabic Corpora", "author": "Luluh Aldhubayi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "machine-learning; computer-vision; nlp; predictive-analytics; arabic-nlp", "keywords": "", "abstract": "Identifying lexical semantic relations in the text has been a long-standing dream of artificial intelligence and the target of many researchers' attention over the past years. This thesis addresses the problem of identifying antonymy relations, such as(hot\/cold) in an automatic method. This work presents three key points in capturing antonymy word pairs: extracting word pairs examples from a textual corpus, representing antonymy in a pair vector space model, and using a machine learning classifier to predict the antonymy relation. Researchers have found that discriminating antonymy from synonymy is a non-trivial task. Both relations show similar semantic distributions as they are found in similar contexts. This issue affects many similarity-based applications by displaying opposite words instead of synonyms. Moreover, both traditional and modern vector space models such as Bag-of-Words and Word Embeddings models show poor discrimination between antonymy and synonymy words. Therefore, this work proposed antonymy pair vector representation based on symmetric classified patterns extracted from a corpus. Besides, we are motivated by extracting novel antonymy and opposites relations between word pairs. This research aims to capture and predict antonymy pairs generated by a textual corpus to make computers able to understand and capture opposition relation in the text. Our research proposes the Antonymy classifier which combines two approaches: the pattern-based approach and a machine learning classifier. We use the pattern-based approach to extract word pairs and patterns. We also propose using distant supervision learning to label the extracted pairs automatically. Distant supervision uses an external knowledge base (the Open Multilingual WordNet) to generate positive and negative antonymy instances. It also extracts every sentence from a corpus which shows both canonical antonymy pairs such as(national\/international)and non-canonical antonymy or opposites pairs such as (internal\/international) that might provide statistical evidence for an antonymy relation. In addition, this work presents a pattern classifier model which automatically extracts and classifies antonymy patterns by computing the average co-occurrence association between positive(antonymy) and negative (non-antonymy)instances in the training set. A part of these patterns such as (between X and Y, both X and Y, from X to Y ) were found in related linguistic studies on manual patterns extraction and analysis. We also found some novel textual patterns that are highly associated with antonymy pairs such as (however X or Y, what is X and what is Y) and more. This work also shows experiments in extracting and predicting antonymy on the English BNC and SkELL corpora and the Arabic ArTenTen corpus. The overall outcomes showed a positive prediction improvement in distinguishing antonym pairs compared to previous attempts. Also, we presented new antonymy pairs that are not found in the English and Arabic WordNet. The antonymy classifier model uses a machine learning algorithm to extract and classify novel adjectival and noun antonymous pairs such as (verbal\/visual), (input\/output), (life\/death) and(material\/spiritual). Therefore, the work presented in this research is a promising method for better extraction and classification of antonymy pairs and patterns in a corpus.", "source_url": "https:\/\/openalex.org\/W2972103852", "pdf_url": "http:\/\/etheses.whiterose.ac.uk\/23975\/1\/L_ALDHUBAYI_PHD_2019.pdf", "doi": "", "handle": "", "repository_name": "White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)", "source_record_id": "W2972103852", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 119, "title": "Arabic dialect texts classification", "title_ar": "", "title_en": "Arabic dialect texts classification", "author": "Areej O. Alshutayri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "machine-learning; nlp; arabic-nlp", "keywords": "", "abstract": "This study investigates how to classify Arabic dialects in text by extracting features which show the differences between dialects. There has been a lack of research about classification of Arabic dialect texts, in comparison to English and some other languages, due to the lack of Arabic dialect text corpora in comparison with what is available for dialects of English and some other languages. What is more, there is an increasing use of Arabic dialects in social media, so this text is now considered quite appropriate as a medium of communication and as a source of a corpus. We collected tweets from Twitter, comments from Facebook and online newspapers from five groups of Arabic dialects: Gulf, Iraqi, Egyptian, Levantine, and North African. The research sought to: 1) create a dataset of Arabic dialect texts to use in training and testing the system of classification, 2) find appropriate features to classify Arabic dialects: lexical (word and multi-word-unit) and grammatical variation across dialects, 3) build a more sophisticated filter to extract features from Arabic-character written dialect text files. In this thesis, the first part describes the research motivation to show the reason for choosing the Arabic dialects as a research topic. The second part presents some background information about the Arabic language and its dialects, and the literature review shows previous research about this subject. The research methodology part shows the initial experiment to classify Arabic dialects. The results of this experiment showed the need to create an Arabic dialect text corpus, by exploring Twitter and online newspaper. The corpus used to train the ensemble classifier and to improve the accuracy of classification the corpus was extended by collecting tweets from Twitter based on the spatial coordinate points and comments from Facebook posts. The corpus was annotated with dialect labels and used in automatic dialect classification experiments. The last part of this thesis presents the results of classification, conclusions and future work.", "source_url": "https:\/\/openalex.org\/W2947159833", "pdf_url": "http:\/\/etheses.whiterose.ac.uk\/23600\/1\/Areej%20Alshutayri%20Thesis.pdf", "doi": "", "handle": "", "repository_name": "White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)", "source_record_id": "W2947159833", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 120, "title": "Enhanced root extraction and document classification algorithm for Arabic text", "title_ar": "", "title_en": "Enhanced root extraction and document classification algorithm for Arabic text", "author": "Amal Alsaad", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Brunel University London", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "machine-learning; deep-learning; nlp; arabic-nlp", "keywords": "", "abstract": "Many text extraction and classification systems have been developed for English and other international languages; most of the languages are based on Roman letters.However, Arabic language is one of the difficult languages which have special rules and morphology.Not many systems have been developed for Arabic text categorization.Arabic language is one of the Semitic languages with morphology that is more complicated than English.Due to its complex morphology, there is a need for pre-processing routines to extract the roots of the words then classify them according to the group of acts or meaning.In this thesis, a system has been developed and tested for text classification.The system is based on two stages, the first is to extract the roots from text and the second is to classify the text according to predefined categories.The linguistic root extraction stage is composed of two main phases.The first phase is to handle removal of affixes including prefixes, suffixes and infixes.Prefixes and suffixes are removed depending on the length of the word, while checking its morphological pattern after each deduction to remove infixes.In the second phase, the root extraction algorithm is formulated to handle weak, defined, eliminated-long-vowel and two-letter geminated words, as there is a substantial great amount of irregular Arabic words in texts.Once the roots are extracted, they are checked against a predefined list of 3800 triliteral and 900 quad literal roots.Series of experiments has been conducted to improve and test the performance of the proposed algorithm.The obtained results revealed that the developed algorithm has better accuracy than the existing stemming algorithm.The second stage is the document classification stage.In this stage two nonparametric classifiers are tested, namely Artificial Neural Networks (ANN) and Support Vector Machine (SVM).The system is trained on 6 categories: culture, economy, international, local, religion and sports.The system is trained on 80% of the available data.From each category, the 10 top frequent terms are selected as features.Testing the classification algorithms has been done on the remaining 20% of the documents.The results of ANN and SVM are compared to the standard method used for text classification, the terms frequency-based method.Results show that ANN and SVM have better accuracy (80-90%) compared to the standard method (60-70%).The proposed method proves the ability to categorize the Arabic text documents into the appropriate categories with a high precision rate.", "source_url": "https:\/\/openalex.org\/W2556582302", "pdf_url": "http:\/\/bura.brunel.ac.uk\/bitstream\/2438\/13510\/1\/FulltextThesis.pdf", "doi": "", "handle": "", "repository_name": "Brunel University Research Archive (BURA) (Brunel University London)", "source_record_id": "W2556582302", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 121, "title": "Sentiment analysis of dialectical Arabic social media content using a hybrid linguistic-machine learning approach", "title_ar": "", "title_en": "Sentiment analysis of dialectical Arabic social media content using a hybrid linguistic-machine learning approach", "author": "GN Alwakid", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis; arabic-nlp", "keywords": "", "abstract": "Despite the enormous increase in the number of Arabic posts on social networks, the sentiment analysis research into extracting opinions from these posts lags behind that for the English language. This is largely attributed to the challenges in processing the morphologically complex Arabic natural language and the scarcity of Arabic NLP tools and resources. This complex task is further exacerbated when analysing dialectal Arabic that do not abide by the formal grammatical structure. Based on the semantic modelling of the target domain’s knowledge and multi-factor lexicon-based sentiment analysis, the intent of this research is to use a hybrid approach, integrating linguistic and machine learning methods for sentiment analysis classification of dialectal Arabic. First, a dataset of dialectal Arabic tweets was collected focusing on the unemployment domain, which is annotated manually. The tweets cover different dialectal Arabic in Saudi Arabia for which a comprehensive Arabic sentiment lexicon was constructed. This approach to sentiment analysis also integrated a novel light stemming mechanism towards improved Saudi dialectal Arabic stemming. Subsequently, a novel multi-factor lexicon-based sentiment analysis algorithm was developed for domain-specific social media posts written in dialectal Arabic. The algorithm considers several factors (emoji, intensifiers, negations, supplications) to improve the accuracy of the classifications. Applying this model to a central problem of sentiment analysis in dialectical Arabic, these operational techniques were deployed in order to assess analytical performance across social media channels which are vulnerable to semantic and colloquial variations. Finally, this study presented a new hybrid approach to sentiment analysis where domain knowledge is utilised in two methods to combine computational linguistics and machine learning; the first method integrates the problem domain semantic knowledgebase in the machine learning training features set, while the second uses the outcome of the lexicon-based sentiment classification in the training of the machine learning methods. By integrating these techniques into a single, hybridised solution, a greater degree of accuracy and consistency was achieved than applying each approach independently, confirming a pragmatic solution to sentiment classification in dialectical Arabic text.", "source_url": "https:\/\/openalex.org\/W3158386543", "pdf_url": "http:\/\/irep.ntu.ac.uk\/id\/eprint\/42474\/1\/Ghadah%20Alwakid%202021.pdf", "doi": "", "handle": "", "repository_name": "Nottingham Trent University's Institutional Repository (Nottingham Trent Repository)", "source_record_id": "W3158386543", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 122, "title": "Automatic text classification using bag of words and bag of concepts based representations", "title_ar": "", "title_en": "Automatic text classification using bag of words and bag of concepts based representations", "author": "Alaa Y. Alahmadi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dublin Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "machine-learning; nlp; data-mining; arabic-nlp", "keywords": "", "abstract": "Automatic Text Classification (ATC) is one of the most important tasks in data mining for organizing information and knowledge discovery. The goal of ATC is to alleviate the need of manually organizing large collections of text documents, which is done by assigning one or more predefined categories to a given textual document via applying appropriate natural language processing techniques. Overall, the classification process involves three components: text pre-processing, text representation and the classifier which is built using one of the Machine Learning (ML) algorithms. In general, all existing text representations are based on the Bag-of-Words (BOW) and Bag-of-Concepts (BOC) models and their variations. The BOW representation model ignores the semantic connections between words by breaking terms into their constituent words, and synonymous words are considered as independent words with no semantic association. The BOW limitations are addressed by using concepts as features in BOC model to represent text in ATC systems. The aim of this work is to investigate and assess the effect of communally available text representation models on the performance of ATC system, in term of the accuracy of the classification and the efficiency of implementation. To achieve that, both BOW and BOC representation models are used with the ATC system and Wikipedia as a knowledge base is utilized to provide concepts. In addition, different strategies that use both words and concepts to build combined models are reviewed and compared to BOW and BOC representation models. Moreover, two languages are used to evaluate these representation models in their ATC system, which are English and Arabic. For Arabic ATC system, different variations of BOW representation models are compared which is a result of different methods that used in text pre-processing component. Furthermore, WordNet as KBs is used to provide concepts to represent Arabic text in the ATC system. This is then followed by attempts to enrich text representation by combining the features of both BOW and BOC models, in order to further enhance the performance of the ATC. Our investigation has resulted in the development of two new strategies, namely Adding Unmapped Concepts (AUC) and Using Concepts for Terms which do not appear in the Document (CTD). Both developed strategies improve ATC systems’ performance in comparison with BOW and BOC representation models. They also bring text classification to a qualitatively new level of performance when compared to other strategies. In addition, CTD developed strategy reduced the time and memory required compared to other strategies used to enrich text representation in ATC systems. The results of our experiments show that text representation is a key element affecting the performance of both English and Arabic ATC systems, and the developed strategies show improvement in both languages in ATC systems. Furthermore, using Wikipedia concepts to build BOC model for Arabic ATC shows more efficiency for representing text than BOW model which does not line with what has been stated in English ATC. The reason behind that is the complex nature of the Arabic language which contains rich morphology and a large degree of the inflections and derivations. In addition, Arabic suffers from poor a morphological tool which makes Wikipedia concepts better features to represent text.", "source_url": "https:\/\/openalex.org\/W2531375441", "pdf_url": "", "doi": "10.34961\/3639", "handle": "", "repository_name": "Arrow@dit (Dublin Institute of Technology)", "source_record_id": "W2531375441", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 123, "title": "Text mining based on analogical proportions: Application to Arabic text summarization andclassification", "title_ar": "", "title_en": "Text mining based on analogical proportions: Application to Arabic text summarization andclassification", "author": "Chouigui, Amina", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "machine-learning; nlp; ai-applications; data-mining; predictive-analytics; arabic-nlp; ai-education", "keywords": "", "abstract": "A huge number of textual documents regularly enrich the World Wide Web. This big amount of data, produced by various sources such as news websites, is among the main sources of documents on the Internet and Web 2.0. However, the extraction of useful information from these unstructured data becomes a hard task requiring a complex step of data processing and management. Given the huge amount of textual data in the Web, there is a serious need for text mining applications such as text summarization and classification.This thesis studies the ability of Analogical proportions as a promising tool for text summarization and classification. Analogical proportions (AP) are statements of the form “a is to b as c is to d” usually denoted a : b :: c : d and expressing that “a differs from b as c differs from d”, as well as “b differs from a as d differs from c”. Analogical inference is based on the assumption that if four items a, b, c, d are making a valid analogical proportion and if d is unknown, this enables us to predict the value of d on the basis of the values of the triplet (a, b, c). Based on the above principle, analogical proportions have recently proven their efficiency to classify “structured” datasets.Our main interest in this work is to validate their efficiency to deal with “unstructured” datasets. We first propose in this thesis a new free structured Arabic dataset in a standard XML TREC format, named ANT corpus (https:\/\/antcorpus.github.io\/). This latter is collected using RSS feeds from various news websites. It is used for many text-mining tasks, such as Arabic text summarization and classification. The ANT collection contains documents from five various news websites that are classified into six categories. This resource is available online in different versions. We exploit the ANT corpus v2.1 to treat two different problems: i) extractive text summarization and ii) text classification.The goal of automatic text summarization is to represent the main text by an extracted set of sentences. Various types of summarizers that are either extractive, abstractive or hybrid algorithms are available in the literature, such as TextRank, LexRank, LSA and Luhn. In this current work, we focus on unsupervised single- document extractive summarization.For this purpose, we first investigate the ability of analogical proportions to represent the relationship between documents and their corresponding summaries. Then, we suggest two various procedures to quantify the relevance\/irrelevance of a given term (keyword), issued from the original text, to build its summary. In the first model, the analogical proportion, which represents this relationship, considers only the existence\/non- existence of the keyword in the main text or summary in a binary way without any interest in the keyword frequency of the input text. In the second model, the keyword frequency is quantified in the analogical proportion. Then, we develop two algorithms respectively called AATS1 and AATS2 that implement respectively the first and second models. To assess the efficiency of the suggested algorithms, we exploit two datasets: ANT corpus v2.1 and EASC (https:\/\/sourceforge.net\/projects\/easc-corpus\/). Inboth collections, the input document and its reference summary are available. To evaluate the proposed summarizers, we compute the ROUGE and BLEU metrics that compare the extracted summaries, from each category to the referencesummaries. The achieved results show the interest of analogical proportions for text summarization if compared to language-independent summarizers or Arabic summarizers, using large (ANT corpus) and small (EASC) corpora. The proposed summarizers outperform the language-independent summarizers in terms of BLEU-1 and ROUGE-1 scores for the \"Education\" category from the EASC dataset. On the other hand, our AATS algorithms perform better than the three Arabic single document extractive summarizers (the score-based summarizer (2021), ASDKGA (2018) and LCEAS(2015)), tested on the EASC dataset, in terms of ROUGE-1 score. In particular, the AATS algorithms significantly outperform three among four language- independent summarizers (LexRank, TextRank, Luhn and LSA) in case of BLEU-1 for the ANT corpus, and they are not significantly outperformed by any other summarizer in the case of theEASC corpus.We also use the ANT corpus v2.1 for automatic text classification. The process of classifying or categorizing texts consists to assign a predefined class label to an unlabeled text based on its content. The classification model efficiency is sensitive to the high dimensionality of data that can affect the classifier accuracy. Many fields of application can benefit from text classification achievements, in particular recommendation systems, search engines and NLP tools.Great research effort has been devoted to studying the classification problems of English texts.", "source_url": "https:\/\/openalex.org\/W7106863718", "pdf_url": "https:\/\/hal.science\/tel-05312294v1\/document", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W7106863718", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 124, "title": "Aspect-based Sentiment Analysis for Arabic Reviews Using Deep Learning", "title_ar": "", "title_en": "Aspect-based Sentiment Analysis for Arabic Reviews Using Deep Learning", "author": "Nada Almani", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Surrey", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; computer-vision; nlp; responsible-ai; transfer-learning; sentiment-analysis; arabic-nlp; xai", "keywords": "", "abstract": "Sentiment analysis is a branch of machine learning that concerns about finding and classifying the polarity for given text. Because of the availability of huge amount of opinionated data that need to be analyzed and interpreted, a lot of recent machine learning research is focused on sentiment analysis applications. it gained a lot of interest due to the. Many sentiment analysis systems are modeled by using different machine learning techniques, but recently, deep learning, by using Artificial Neural Network (ANN) architecture, has showed significant improvements with high tendency to reveal the underlying semantic meaning in the input text. However, the output of these models could not be explained and the efficiency could not be analyzed because ANN models are considered as a black box and the success of these models comes at the cost of interpretability. The main objective of the presented work is developing Arabic sentiment analysis system that understands semantics in input reviews without using any linguistic resources. The first proposed model is Deep Attention-based Review Level Sentiment Analysis model (DARLSA) that use binary classifier to detect reviews’ polarities. Different scenarios and architectures were examined to test the ability of the proposed model to extract salient words out of the input. The results proved the ability of the proposed model to understand a given review by highlighting the most informative words to the class label. The model detected Arabic natural language linguistic features, such as intensification and negation styles, efficiently. Also, the effect of applying transfer learning technique on the problem of Arabic sentiment analysis is experimented on review level model. The second proposed architecture is Deep Attention-based Aspect Level Sentiment Analysis model (DAALSA) for classifying reviews polarity with respect to an aspect into three classes, positive, negative and neutral. Different models were proposed to test the effect of using different attention scoring functions on the classification performance. The results distinguished one model with superior performance compared to other proposed models. To obtain intuitive explanation of the trained models, both models are enhanced with visualization option. The final review representation is a distributed dense vector generated after passing through multi-layers neural network. Heatmap representation is used to visualize the final review representation. In addition, the attention layer’s scoring vector is visualized as well.", "source_url": "https:\/\/openalex.org\/W2996475580", "pdf_url": "", "doi": "10.15126\/thesis.00852989", "handle": "", "repository_name": "Surrey Research Insight Open Access (The University of Surrey)", "source_record_id": "W2996475580", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 125, "title": "Machine Learning Approaches for Arabic Dialect Identification using Monodialectal Language Models", "title_ar": "Machine Learning Approaches for Arabic Dialect Identification using Monodialectal Language Models", "title_en": "", "author": "Reem AlYami", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "King Fahd University of Petroleum and Minerals", "university_source": "repository_host", "college": "", "department": "", "country": "Saudi Arabia", "governorate": "", "publication_year": 2023, "language": "ar", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; llm; arabic-nlp", "keywords": "", "abstract": "‫الرسالة‬ ‫ملخص‬ ‫اليامي‬ ‫ريـــم‬‫الاسم:‬ ‫اللغة‬ ‫ٔحادية‬ ‫ا‬ ‫سياقية‬ ‫نماذج‬", "source_url": "https:\/\/openalex.org\/W7167170043", "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/142308\/1\/Reem_AlYami_Master_Thesis_2022_Signed.pdf", "doi": "", "handle": "", "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)", "source_record_id": "W7167170043", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 127, "title": "The enhancement of Arabic information retrieval using Arabic text summarization", "title_ar": "", "title_en": "The enhancement of Arabic information retrieval using Arabic text summarization", "author": "Ahmad Ababneh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Huddersfield", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; nlp; data-mining; speech-recognition; arabic-nlp", "keywords": "", "abstract": "The massive upload of text on the internet makes the text overhead one of the important challenges faces the Information Retrieval (IR) system. The purpose of this research is to maintain reasonable relevancy and increase the efficiency of the information retrieval system by creating a short and informative inverted index and by supporting the user query with a set of semantically related terms extracted automatically. To achieve this purpose, two new models for text mining are developed and implemented, the first one called Multi-Layer Similarity (MLS) model that uses the Latent Semantic Analysis (LSA) in the efficient framework. And the second is called the Noun Based Distinctive Verbs (NBDV) model that investigates the semantic meanings of the nouns by identifying the set of distinctive verbs that describe them. The Arabic Language has been chosen as the language of the case study, because one of the primary objectives of this research is to measure the effect of the MLS model and NBDV model on the relevancy of the Arabic IR (AIR) systems that use the Vector Space model, and to measure the accuracy of applying the MLS model on the recall and precision of the Arabic language text extraction systems. The initiating of this research requires holding a deep reading about what has been achieved in the field of Arabic information retrieval. In this regard, a quantitative relevancy survey to measure the enhancements achieved has been established. The survey reviewed the impact of statistical and morphological analysis of Arabic text on improving the AIR relevancy. The survey measured the contributions of Stemming, Indexing, Query Expansion, Automatic Text Summarization, Text Translation, Part of Speech Tagging, and Named Entity Recognition in enhancing the relevancy of AIR. Our survey emphasized the quantitative relevancy measurements provided in the surveyed publications. The survey showed that the researchers achieved significant achievements, especially in building accurate stemmers, with precision rates that convergent to 97%, and in measuring the impact of different indexing strategies. Query expansion and Text Translation showed a positive relevancy effect. However, other tasks such as Named Entity Recognition and Automatic Text Summarization still need more research to realize their impact on Arabic IR. The use of LSA in text mining demands large space and time requirements. In the first part of this research, a new text extraction model has been proposed, designed, implemented, and evaluated. The new method sets a framework on how to efficiently employ the statistical semantic analysis in the automatic text extraction. The method hires the centrality feature that estimates the similarity of the sentence with respect to every sentence found in the text. The new model omits the segments of text that have significant verbatim, statistical, and semantic resemblance with previously processed texts. The identification of text resemblance is based on a new multi-layer process that estimates the text-similarity at three statistical layers. It employes the Jaccard coefficient similarity and the Vector Space Model (VSM) in the first and second layers respectively and uses the Latent Semantic Analysis in the third layer. Due to high time complexity, the Multi-Layer model restricts the use of the LSA layer for the text segments that the Jaccard and VSM layers failed to estimate their similarities. ROUGE tool is used in the evaluation, and because ROUGE does not consider the extract’s size, it has been supplemented with a new evaluation strategy based on the ratio of sentences intersections between the automatic and the reference extracts and the condensation rate. The MLS model has been compared with the classical LSA that uses the traditional definition of the singular value decomposition and with the traditional Jaccard and VSM text extractions. The results of our comparison showed that the run of the LSA procedure in the MLS-based extraction reduced by 52%, and the original matrix dimensions dwindled by 65%. Also, the new method achieved remarkable accuracy results. We found that combining the centrality feature with the proposed multi-layer framework yields a significant solution regarding the efficiency and precision in the field of automatic text extraction. The automatic synonym extractor built in this research is based on statistical approaches. The traditional statistical approach in synonyms extraction is time-consuming, especially in real applications such as query expansion and text mining. It is necessary to develop a new model to improve the efficiency and accuracy during the extraction. The research presents the NBDV model in synonym extraction that replaces the traditional tf.idf weighting scheme with a new weighting scheme called the Orbit Weighing Scheme (OWS). The OWS weights the verbs based on their singularity to a group of nouns.", "source_url": "https:\/\/openalex.org\/W3009531587", "pdf_url": "http:\/\/eprints.hud.ac.uk\/id\/eprint\/35169\/1\/FINAL%20THESIS%20-%20ABABNEH.pdf", "doi": "", "handle": "", "repository_name": "University of Huddersfield Repository (University of Huddersfield)", "source_record_id": "W3009531587", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 128, "title": "Classification of medical documents and Opinions using machine learning", "title_ar": "", "title_en": "Classification of medical documents and Opinions using machine learning", "author": "Fatiha Youbi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; nlp; medical-ai; data-mining; sentiment-analysis", "keywords": "", "abstract": "In recent years, there has been increasing interest in the use of text mining (TM) and machine learning in healthcare. Document classi cation has been a common application, with many studies focusing on classifying medical reports from unstructured text data. However, there is also a need to utilize TM and machine learning for sentiment analysis of medical textual data in social networks and medical forums. In this thesis, the focus was on two TM applications in the medical domain: classifying autopsy reports to detect the manner of death in Wilaya of Tlemcen and analyzing patient and public opinions on healthcare and the COVID-19 pandemic using machine learning techniques. The experiments conducted in both studies showed that automated models for opinion analysis are task-speci c and that feature extraction and deep learning classi er architecture play important roles in the success of these models. The ndings could be useful for improving strategies related to drugs monitoring and COVID-19 surveillance. Future directions include exploring other types of deep learning techniques, using clinical documents for sentiment analysis, and analyzing Algerian health status based on machine learning and deep learning classi ers.", "source_url": "https:\/\/openalex.org\/W7135334549", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/c81c9cdb-90b6-4887-a5e6-fb6fcf10f7ad\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135334549", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 129, "title": "Supervised Learning for Multi-Domain Text Classification", "title_ar": "", "title_en": "Supervised Learning for Multi-Domain Text Classification", "author": "Siva Charan Reddy Gangireddy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; predictive-analytics; sentiment-analysis", "keywords": "", "abstract": "Digital information available on the Internet is increasing day by day. As a result of this, the demand for tools that help people in finding and analyzing all these resources are also growing in number. Text Classification, in particular, has been very useful in managing the information. Text Classification is the process of assigning natural language text to one or more categories based on the content. It has many important applications in the real world. For example, finding the sentiment of the reviews, posted by people on restaurants, movies and other such things are all applications of Text classification. In this project, focus has been laid on Sentiment Analysis, which identifies the opinions expressed in a piece of text. It involves categorizing opinions in text into categories like 'positive' or 'negative'. Existing works in Sentiment Analysis focused on determining the polarity (Positive or negative) of a sentence. This comes under binary classification, which means classifying the given set of elements into two groups. The purpose of this research is to address a different approach for Sentiment Analysis called Multi Class Sentiment Classification. In this approach the sentences are classified under multiple sentiment classes like positive, negative, neutral and so on. Classifiers are built on the Predictive Model, that consists of multiple phases. Analysis of different sets of features on the data set, like stemmers, n-grams, tf-idf and so on, will be considered for classification of the data. Different classification models like Bayesian Classifier, Random Forest and SGD classifier are taken into consideration for classifying the data and their results are compared. Frameworks like Weka, Apache Mahout and Scikit are used for building the classifiers.", "source_url": "https:\/\/openalex.org\/W4231646185", "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=1483&context=etd_projects", "doi": "10.31979\/etd.yvh3-uzqb", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4231646185", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 130, "title": "Handwritten Digit Recognition and Classification Using Machine Learning", "title_ar": "", "title_en": "Handwritten Digit Recognition and Classification Using Machine Learning", "author": "Ke Zhao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Technological University Dublin", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; computer-vision; image-classification", "keywords": "", "abstract": "In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy.", "source_url": "https:\/\/openalex.org\/W2921368729", "pdf_url": "https:\/\/arrow.dit.ie\/cgi\/viewcontent.cgi?article=1190&context=scschcomdis", "doi": "", "handle": "", "repository_name": "Arrow - TU Dublin (Technological University Dublin)", "source_record_id": "W2921368729", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 131, "title": "Ontology learning from the Arabic text of the Qur’an : concepts identification and hierarchical relationships extraction", "title_ar": "", "title_en": "Ontology learning from the Arabic text of the Qur’an : concepts identification and hierarchical relationships extraction", "author": "SM Alrehaili", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "machine-learning; nlp; ai-applications; arabic-nlp; ai-education", "keywords": "", "abstract": "Recent developments in ontology learning have highlighted the growing role ontologies play in linguistic and computational research areas such as language teaching and natural language processing. The ever-growing availability of annotations for the Qur’an text has made the acquisition of the ontological knowledge promising. However, the availability of resources and tools for Arabic ontology is not comparable with other languages. Manual ontology development is labour-intensive, time-consuming and it requires knowledge and skills of domain experts. This thesis aims to develop new methods for Ontology learning from the Arabic text of the Qur’an, including concepts identification and hierarchical relationships extraction. The thesis presents a methodology for reducing human intervention in building ontology from Classical Arabic Language of the Qur’an text. The set of concepts, which is a crucial step in ontology learning, was generated based on a set of patterns made of lexical and inflectional information. The concepts were identified based on adapted weighting schema that exploit a combination of knowledge to learn the relevance degree of a term. Statistical, domain-specific knowledge and internal information of Multi-Word Terms (MWTs) were combined to learn the relevance of generated terms. This methodology which represents the major contribution of the thesis was experimentally investigated using different terms generation methods. As a result, we provided the Arabic Qur’anic Terms (AQT) as a training resource for machine learning based term extraction. This thesis also introduces a new approach for hierarchical relations extraction from Arabic text of the Qur’an. A set of hierarchical relations occurring between identified concepts are extracted based on hybrid methods including head-modifier, set of markers for copula construct in Arabic text, referents. We also compared a number of ontology alignment methods for matching ontological bilingual Qur’anic resources. In addition, a multi-dimensional resource named Arabic Qur’anic Database (AQD) about the Qur’an is made for Arabic computational researchers, allowing regular expression query search over the included annotations. The search tool was successfully applied to find instances for a given complex rule made of different combined resources.", "source_url": "https:\/\/openalex.org\/W2806278576", "pdf_url": "http:\/\/etheses.whiterose.ac.uk\/20309\/1\/Alrehaili_SM_Computing_PhD2017.pdf", "doi": "", "handle": "", "repository_name": "White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)", "source_record_id": "W2806278576", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 132, "title": "Detection of anti-social behaviour in online communication in Arabic", "title_ar": "", "title_en": "Detection of anti-social behaviour in online communication in Arabic", "author": "Azalden Alakrot", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Limerick", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "machine-learning; nlp; medical-ai; ai-applications; arabic-nlp; ai-education", "keywords": "", "abstract": "Anti-social behaviour on social media cannot be easily ignored as it affects a large and growing percentage of the world’s population. It often has a nega tive effect on people’s lives; incidents of online abuse that may seem insignif icant can have a cumulative impact on mental health. An increasing num ber of incidents of suicide and violence have been reportedly provoked by anti-social behaviour on social media. Most of the existing machine-learning approaches for detection of offensive language are specifically tailored for online communication in English. Solutions targeting Arabic language are rare, while, as we also demonstrate in this thesis, offensive language is wide spread in Arabic social media as well. Our hypothesis has been that Arabic may require a specific approach different from the solutions for English due to the specific linguistic characteristics of Arabic text and the unique to Ara bic mixture of dialects frequently observed within the same conversation on social media. The objective of this thesis is to contribute to the work on the automatic pre vention of anti-social behaviour in online written communication in Ara bic by introducing a large dataset of YouTube comments and proposing a text-mining pipe-line for training a binary classifier. The main challenge to automatic detection of offensive language is the absence of appropriate training datasets. Thus, as part of this work we undertook to collect data from Arabic social media (Arabic YouTube channels) and construct a labelled dataset. Then we utilised this dataset to experiment with a variety of text pre processing techniques, feature-selection methods, and classification machine learning algorithms in order to recommend a process for automatic detection of offensive language in online written communication in Arabic. Our results are encouraging; they suggest Support Vector Machines classifier can be ef fectively deployed for the detection of offensive language in online written communication in Arabic. We believe that the proposed text-mining process will open the door for further research in this direction and will eventually result in effective automatic prevention of incidents of verbal abuse on Ara bic social media.", "source_url": "https:\/\/openalex.org\/W3139753181", "pdf_url": "", "doi": "10.34961\/3378", "handle": "", "repository_name": "University of Limerick Institutional Repository (University of Limerick)", "source_record_id": "W3139753181", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 133, "title": "Object classification using spectral imaging and machine learning.", "title_ar": "", "title_en": "", "author": "Carlos Eduardo López Joya", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Industrial de Santander", "university_source": "repository_host", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2020, "language": "es", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; cnn", "keywords": "", "abstract": "Una de las grandes aplicaciones de las imágenes espectrales es la clasificación de materiales, dentro de los que podemos encontrar clasificación de alimentos, cultivos y patologías en humanos. Tradicionalmente las imágenes espectrales se pueden clasificar empleando información del ángulo del vector de información espectral de cada píxel o firma espectral, específicamente empleando la métrica del mapeador de ángulo espectral (SAM) que se utiliza para comparar una firma espectral del material a clasificar conocido y el de un píxel espacial no clasificado en la imagen. Por otro lado, recientemente se han desarrollado una serie de algoritmos para la clasificación, entre ellos se encuentran los basados en redes neuronales convolucionales (CNN), las cuales han mostrado un rendimiento sobresaliente para la clasificación de imágenes espectrales de manera automática. Por lo tanto, en este proyecto se presenta un clasificador de objetos empleando imágenes espectrales adquiridas en el laboratorio de óptica del grupo HDSP, por medio de un algoritmo basado en aprendizaje de máquina con CNN, además, se propone realizar un proceso de segmentación de imágenes para extraer el área de interés dentro de la escena. Dentro de los diversos algoritmos que se presentan en este proyecto se pueden agrupar en dos bloques uno que emplea directamente la firma espectral (SpectralNet1) y otro que incluye también información espacial (CarlosNet1 y CarlosNet2). Dentro de los modelos propuestos de aprendizaje de máquina el modelo que obtiene los mejores resultados para llevar a cabo la tarea de clasificación es CarlosNet1 obteniendo un 97.3% de precisión en el conjunto de datos de validación.", "source_url": "https:\/\/openalex.org\/W7133427189", "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/ea2ba16b-2f80-47c3-9b74-1c51878b6db4\/download", "doi": "", "handle": "", "repository_name": "Universidad Industrial de Santander", "source_record_id": "W7133427189", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 134, "title": "A Support System for Diacritic-aware Classical Arabic Language Processing: Integration of Speech, Text, and Vision Modalities", "title_ar": "", "title_en": "A Support System for Diacritic-aware Classical Arabic Language Processing: Integration of Speech, Text, and Vision Modalities", "author": "Adel AbdelSabour Ahmad Aly", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Washington", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; multimodal; image-segmentation; llm; machine-translation; speech-recognition; arabic-nlp; ai-education", "keywords": "", "abstract": "Artificial intelligence (AI) impresses us daily, outperforming humans in complex games and tasks. Yet, AI and Large Language Models (LLMs) stumble to grasp a language that is thousands of years old. It's Arabic, where subtle diacritical marks can completely alter a word's meaning. Top language models like ChatGPT and Google's Gemini face challenges with Arabic's unique features, potentially leading to critical misunderstandings. The main obstacle is adapting successful NLP systems from other languages to Arabic without understanding its distinct nuances. This dissertation presents a support system—a Multimodal Integration System—for diacritic-aware Classical Arabic language processing. The system integrates speech, text, and vision modalities to address the unique challenges of Arabic's rich linguistic features, such as diacritics and linguistic styles. Arabic has multiple correct linguistic styles, each preserving the same text but with different diacritics. These variations reflect regional dialects, adding meaning, and altering grammar and rhetoric. The Holy Quran, with its 20 linguistic styles based on a single core text, serves as our ideal dataset. We developed innovative databases and models that push the boundaries of Arabic language processing. Our scalable databases store texts in 7 different Arabic linguistic styles. QR-Vision excels at recognizing precise diacritics in images. QRDiaRec adds diacritics to un-diacritical text in various styles. QRSR and DASAM specialize in speech processing and alignment for Arabic diacritical text and voice. SemSim stands out as a dual-space similarity explorer, analyzing numeric and semantic data. Our methodology involves advanced techniques in data modeling, data quality validation, deep learning, computer vision, signal processing, and interactive data visualization.Our findings demonstrate improvements in addressing key challenges in Arabic Natural Language Processing (NLP). For text processing, we created an Automated Diacritization Deep Learning model. The model supports multiple Arabic diacritical styles, a unique feature in the field. Our best-performing model achieved a 94.2% accuracy rate in adding correct diacritics to Arabic text. In image processing, we built a specialized Optical Character Recognition (OCR) model for diacritic-aware Arabic text. Our OCR model reached an accuracy rate of 91.67% showing an improvement over the existing models. For speech processing, we developed two key systems. The first system, operating at the sentence level, combines our novel FuzTPI algorithm with machine learning models. This hybrid approach achieved up to 96% accuracy in audio segmentation and text-audio classification. Our second system focuses on word-level segmentation and alignment for Arabic diacritic-based speech. It achieved R² values of 0.959 for word start times and 0.957 for end times. These results show an improvement over existing Arabic speech recognition technologies. The dissertation is structured around these three modalities, with each section detailing the challenges, methodologies, and results achieved in processing Arabic with diacritics. They have far-reaching implications for applications such as machine translation, information retrieval, speech recognition, natural language understanding, educational technology, and the preservation of linguistic heritage. By addressing the unique challenges of Arabic diacritics across multiple modalities, this research paves the way for more nuanced and culturally sensitive AI applications in Arabic-speaking contexts.", "source_url": "https:\/\/openalex.org\/W7130276073", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ResearchWorks at the University of Washington (University of Washington)", "source_record_id": "W7130276073", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 136, "title": "Developing Efficient Machine Learning Algorithms for Anomaly Detection in Network Traffic for Cybersecurity", "title_ar": "", "title_en": "Developing Efficient Machine Learning Algorithms for Anomaly Detection in Network Traffic for Cybersecurity", "author": "Ganeshkumar Prabakaran", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "National College of Ireland", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; expert-systems; ai-applications; data-mining; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "In this study, efficient machine learning algorithms to detect network traffic anomalies are developed to enhance the current cybersecurity systems. The conventional rule-based intrusion detection is not able to detect the changing and unknown attacks, making it necessary to adopt adaptive and data-driven solutions. In this study, the systematic preprocessing with the NSLKDD benchmark dataset, exploratory analysis and implementation of the supervised and unsupervised models, Decision Tree, Random Forest and K-Means clustering are applied. Accuracy, precision, recall, F1-score, ROC-AUC and purity measures are used to evaluate model performance. It has been found that supervised models vastly outperform the unsupervised methods and that the results of the random forest are nearly perfect in detecting and having exceptional discriminatory ability. K-Means showed mediocre clustering as well as poor ROC. All in all, the results reveal that optimised supervised learning, in this case, Random Forest, is a very capable and computationally efficient approach to anomaly detection in networks and enhancing real-time systems of intrusion detection.", "source_url": "https:\/\/openalex.org\/W7211507184", "pdf_url": "https:\/\/norma.ncirl.ie\/9817\/1\/ganeshkumarprabakaran.pdf", "doi": "", "handle": "", "repository_name": "TRAP@NCI (National College of Ireland)", "source_record_id": "W7211507184", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 137, "title": "Machine Learning-Based Network Intrusion Detection for Industrial Control", "title_ar": "", "title_en": "Machine Learning-Based Network Intrusion Detection for Industrial Control", "author": "Dehlaghi Ghadim, Alireza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Mälardalen University College", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; ai-applications; self-semi-supervised; federated-learning; ai-cybersecurity; ai-industry; ai-smart-cities", "keywords": "", "abstract": "The growing connectivity of Industrial Control Systems (ICS) has increased their exposure to cyber threats, posing serious risks to both critical infrastructure (e.g., power plants, water systems) and industrial operations. Although Machine Learning (ML)-based Intrusion Detection Systems (IDS) show potential to detect complex and novel attacks, their practical application in ICS environments remains challenging. This thesis investigates the feasibility, design considerations, and barriers to applying ML-based IDS in ICSs. Key challenges include limited labeled data, high sensitivity to false alarms, distributed architectures, and constrained hardware. Through empirical evaluations and prototype implementations, we show how tailored ML and system design strategies can address these issues. Our first goal is to enable experimentation with cybersecurity and intrusion detection in industrial systems. The thesis introduces ICSSIM, a novel framework for creating flexible, scalable, and cost-effective ICS testbeds. It also presents ICSFlowGenerator, a tool for analyzing network traffic and computing customized network flow parameters. Using this tool, the thesis presents ICS-Flow, a new dataset developed to train and evaluate anomaly detection models, which serves as a realistic benchmark for assessing ML-based intrusion detection in ICS networks. In addition, the thesis tackles several technical deployment barriers. To support distributed ICS architectures and minimize the reliance on central servers, federated learning is explored as a decentralized training strategy. It also investigates semi-supervised learning techniques for detecting anomalies using only normal traffic. To reduce the burden of false alarms, we integrate a decision support system with the IDS to filter alerts and suggest mitigation actions. Furthermore, the thesis emphasizes the importance of temporal traffic patterns in identifying attack types and evaluates the efficiency and resource demands of various ML models on ICS-representative hardware. Collectively, these contributions advance the practical application of ML-based intrusion detection in ICSs.", "source_url": "https:\/\/openalex.org\/W7112960186", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DiVA (Mälardalen University College)", "source_record_id": "W7112960186", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 138, "title": "Adaptive Machine Learning for Real-Time Intrusion Detection in Networks", "title_ar": "", "title_en": "Adaptive Machine Learning for Real-Time Intrusion Detection in Networks", "author": "Anju Sobha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "National College of Ireland", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; deep-learning; ai-applications; quantum-ml; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "With the complexity of the cyber threats, the issue of developing more intelligent and flexible systems that can detect the known and unknown intrusions in real-time has become a burning matter. The paper suggests a hybrid threat detection scheme that combines the different data sampling techniques and imbalanced data treatment with the classical machine learning (ML) models in such a way that IDS performance becomes more efficient and precise to make a generic space optimised and self-learning cyber security module. Classic ML models like a Random Forest, support Vegetable Machines (SVM), and Neural Network have been extensively applied in the intrusion detection because they have the capacity to learn a pattern based on the past network traffic data. Nevertheless, they are constrained in scaling to high-dimensional data, identifying high-dimensional data or in recognizing zero-day attacks. This work implements test cybersecurity data, such as NSL-KDD etc., to gain an insight into the performance comparison of classical and quantum-enhanced models. Normalization and dimensionality reduction techniques of data preprocessing are used to provide the best possible features representation of classical and quantum classifiers. The findings reveal that classical models are performing well with the traditional forms of attacks, to identify the slightest anomalies and attack vectors complicated in nature with better accuracy and reduced false-positive rates in predetermined cases.", "source_url": "https:\/\/openalex.org\/W7165311969", "pdf_url": "https:\/\/norma.ncirl.ie\/9379\/1\/anjusobha.pdf", "doi": "", "handle": "", "repository_name": "TRAP@NCI (National College of Ireland)", "source_record_id": "W7165311969", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 139, "title": "Quantum machine learning for enhanced intrusion detection", "title_ar": "", "title_en": "Quantum machine learning for enhanced intrusion detection", "author": "Ameel Valjee", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cape Town", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; ai-applications; quantum-ml; cnn; ai-cybersecurity", "keywords": "", "abstract": "A vital component in ensuring network security is an Intrusion Detection System (IDS), and classical Machine Learning (ML) models often struggle to keep pace with sophisticated cyber threats. Quantum Computing, a probabilistic computational class utilising the principles of quantum mechanics, has shown its potential to address some of these challenges through quantum parallelism and entanglement. In this work, we explore the use of Quantum Machine Learning (QML), specifically Quantum Convolutional Neural Network (QCNN) and Quantum Support Vector Machine (QSVM), to determine the benefits of using quantum kernels over classical kernels. We investigate multiple ansatz designs, and entanglement patterns, that generate sufficient representations of vectors in the Hilbert space to distinguish normal from malicious activity. The results have shown improvements in both models compared to their classical counterparts, with QSVM being the best performing when circuit block entanglement is used.", "source_url": "https:\/\/openalex.org\/W7167298308", "pdf_url": "https:\/\/open.uct.ac.za\/bitstreams\/c0725b4b-aee0-43e7-ae32-403ac5fdd427\/download", "doi": "", "handle": "", "repository_name": "Open University of Cape Town (University of Cape Town)", "source_record_id": "W7167298308", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 141, "title": "Intrusion detection systems using machine learning", "title_ar": "", "title_en": "Intrusion detection systems using machine learning", "author": "Στυλιανός Ε. Ρήγας", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Thessaly", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "machine-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7223490461", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/89973\/4\/35543.pdf", "doi": "", "handle": "", "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)", "source_record_id": "W7223490461", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 142, "title": "Cyberattacks intrusion detection using machine learning and artificial intelligence", "title_ar": "", "title_en": "Cyberattacks intrusion detection using machine learning and artificial intelligence", "author": "Ευαγγελία Ν. Κορνηλίου", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Thessaly", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "machine-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7123374029", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/87493\/4\/32923.pdf", "doi": "", "handle": "", "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)", "source_record_id": "W7123374029", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 143, "title": "Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms", "title_ar": "", "title_en": "Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms", "author": "Nevrus Kaja", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Michigan", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; robotics; responsible-ai; expert-systems; ai-applications; fairness; ai-cybersecurity", "keywords": "", "abstract": "Automotive technology has continued to advance in many aspects. As an outcome of such advancements, autonomous vehicles are closer to commercialization and have brought to life a complex automotive technology ecosystem [1]. Like every other technology, these developments bring benefits but also introduce a variety of risks. One of these risks in the automotive space is cybersecurity threats. In the case of cars, these security challenges can produce devastating results and tremendous costs, including loss of life. Therefore, conducting a clear analysis, assessment and detection of threats solves some of the cybersecurity challenges in the automotive ecosystem. This dissertation does just that, by building a three-step framework to analyze, assess,and detect threats using machine learning algorithms. First, it does an analysis of the connected vehicle threats while leveraging the STRIDE framework [2]. Second, it presents an innovative, Fuzzy based threat assessment model (FTAM). FTAM leverages threat characterizations from established threat assessment models while focusing on improving its assessment capabilities by using Fuzzy logic. Through this methodology, FTAM can improve the efficiency and accuracy of the threat assessment process by using Fuzzy logic to determine the “degree” of the threat over other existing methods. This differs from the current threat assessment models which use subjective assessment processes based on table look-ups or scoring. Thirdly, this dissertation proposes an intrusion detection system (IDS) to detect malicious threats while taking in consideration results from the previous assessment stage. This IDS uses the dataset provided from Wyoming Connected Vehicle Deployment program [3] and consists of a two-stage intrusion detection system based on supervised and unsupervised machine learning algorithms. The first stage uses unsupervised learning to detect whether there is an attack present and the second stage classifies these attacks in a supervised learning fashion. The second stage also addresses data bias and eliminates the number of false positives. The simulation of this approach results in an IDS able to detect and classify attacks at a 99.965% accuracy and lowers the false positives rate to 0%.", "source_url": "https:\/\/openalex.org\/W2950711294", "pdf_url": "https:\/\/deepblue.lib.umich.edu\/bitstreams\/379b5e3d-3843-4c2a-abfa-84d77c90685e\/download", "doi": "", "handle": "", "repository_name": "Deep Blue (University of Michigan)", "source_record_id": "W2950711294", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 144, "title": "A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest", "title_ar": "", "title_en": "A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest", "author": "Ilemona S Atawodi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Southern Mississippi", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "The evolving area of cybersecurity presents a dynamic battlefield for cyber criminals and security experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date. A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this originally presented problems of low accuracy, but the growth in the area of machine learning over the last decade has led to vast improvements in machine learning algorithms and their requirements. This research applies k nearest neighbours with 10-fold cross validation and random forest machine learning algorithms to a network-based intrusion detection system in order to improve the accuracy of the intrusion detection system. This project focused on specific feature selection improve the increase the detection accuracy using the K-fold cross validation algorithm on the random forest algorithm on approximately 126,000 samples of the NSL-KDD dataset.", "source_url": "https:\/\/openalex.org\/W2945719300", "pdf_url": "", "doi": "10.13140\/rg.2.2.35116.41609", "handle": "", "repository_name": "Aquila Digital Community (University of Southern Mississippi)", "source_record_id": "W2945719300", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 145, "title": "Deep Reinforcement Learning for Intrusion Detection and Response in Software-Defined Networks", "title_ar": "", "title_en": "Deep Reinforcement Learning for Intrusion Detection and Response in Software-Defined Networks", "author": "Trung Phan Van", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Chemnitz University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; reinforcement-learning; ai-applications; transfer-learning; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "This dissertation presents a unified set of learning-based solutions for improving cybersecurity in Software-Defined Networking (SDN) environments. The main objectives are to provide fine-grained traffic flow information for accurate attack detection, jointly optimize feature set and machine learning model selection for intrusion detection, develop adaptive intrusion response mechanisms that minimize disruption to legitimate traffic, and address the shortage of labeled training data for machine learning-based security applications in SDN-based networks. To achieve these objectives, the dissertation makes four main contributions: DeepMatch: A fine-grained traffic flow measurement framework based on a Partially Observable Markov Decision Process (POMDP) and a Deep Dueling Neural Network-based control algorithm. DeepMatch dynamically adjusts flow rule matching strategies to maximize traffic flow granularity while proactively preventing flow-table overflow in switches in the SDN data plane. \/\/ DeepGuard: A machine learning-based intrusion detection framework that integrates DeepMatch to preserve important runtime traffic flow features. DeepGuard formulates the joint selection of feature sets and machine learning models as a learning-based optimization problem and applies Q-Learning to identify effective configurations for accurate and efficient attack detection. \/\/ DeepAir: An adaptive intrusion response framework that models response-policy selection as a Markov Decision Process (MDP). By leveraging a Double Deep Q-Network (DDQN), DeepAir learns effective response policies that mitigate malicious traffic in real time while reducing the negative impact on benign traffic forwarding and limiting policy deployment overhead. \/\/ DeepTransfer: A selective transfer learning framework that addresses the lack of labeled SDN attack detection data. DeepTransfer applies DDQN to guide the transfer of attack detection knowledge from legacy network environments to SDN-based target domains, thereby improving detection performance while reducing the risk of negative transfer across heterogeneous network domains. \/\/ Extensive experimental evaluations validate the effectiveness of the proposed frameworks under different security scenarios, particularly Denial-of-Service and Distributed Denial-of-Service (DoS\/DDoS) attacks. The results demonstrate that the proposed solutions improve traffic flow visibility, feature set-model selection, intrusion detection accuracy, response adaptability, QoS preservation for benign traffic, and learning stability compared with existing approaches. Collectively, the contributions of this dissertation advance intelligent and adaptive cybersecurity mechanisms for SDN infrastructures.", "source_url": "https:\/\/openalex.org\/W7214800756", "pdf_url": "https:\/\/monarch.qucosa.de\/api\/qucosa%3A107046\/attachment\/ATT-0\/", "doi": "10.60687\/2026-0177", "handle": "", "repository_name": "Qucosa - Monarch (Chemnitz University of Technology)", "source_record_id": "W7214800756", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 146, "title": "Evaluating Self-Supervised Learning for Network Intrusion Detection", "title_ar": "", "title_en": "Evaluating Self-Supervised Learning for Network Intrusion Detection", "author": "Bryan A. Marin Samano", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; ai-applications; transfer-learning; self-semi-supervised; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "This study examines whether self-supervised learning (SSL) improves anomaly detection performance in network intrusion detection systems when labeled data is limited. Although machine learning—based systems have advanced intrusion detection, their effectiveness depends heavily on large, up-to-date labeled datasets, which are costly to obtain and quickly become outdated. In contrast, real-world networks continuously generate vast amounts of unlabeled traffic. The central objective of this project is to evaluate whether representations learned from unlabeled network data can enhance classification performance under constrained labeling conditions. To investigate this question, I implemented a two-stage SSL pipeline using a synthetic dataset constructed to approximate the statistical properties of the CIC-IDS2017 benchmark. A baseline neural network trained exclusively on labeled data was compared to a model pretrained on unlabeled traffic using an autoencoder and subsequently fine-tuned for binary intrusion detection. The experimental design intentionally limited labeled training samples while providing abundant unlabeled data for representation learning. Preliminary results indicate that the SSL-pretrained model achieved higher test accuracy than the supervised baseline under constrained labeling conditions. Initial experiments suggest that, in this configuration, self-supervised pretraining did outperform fully supervised learning. This study highlights important implementation and design considerations for applying SSL in cybersecurity contexts and provides a reproducible framework for further experimentation using real-world datasets and refined pretraining strategies.", "source_url": "https:\/\/openalex.org\/W7171335856", "pdf_url": "", "doi": "", "handle": "", "repository_name": "SUNY Digital Repository Support (State University of New York System)", "source_record_id": "W7171335856", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 147, "title": "Class Imbalance and Time-To-Detection in the Performance Analysis of Machine Learning-Based Intrusion Detection Systems", "title_ar": "", "title_en": "Class Imbalance and Time-To-Detection in the Performance Analysis of Machine Learning-Based Intrusion Detection Systems", "author": "Shabanfar, Mohammad Pasha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Concordia University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; responsible-ai; ai-applications; object-detection; fairness; ai-cybersecurity; ai-industry; ai-smart-cities", "keywords": "", "abstract": "The increasing reliance on Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems has raised critical concerns regarding their vulnerability to cyberattacks. While machine learning (ML) methods have emerged as effective tools for detecting such intrusions, their real-world applicability is challenged by two major issues: the imbalance in cybersecurity datasets and the limited focus on the time required to detect attacks—referred to as Time-To-Detection (TTD). To address these two issues and suggest better practices for ML-based IDS researchers, this thesis examines the gaps in the literature and, through two respective case studies, aims to suggest practices toward a more precise and practical performance assessment of ML-based intrusion detection systems (IDS). First, the thesis examines the class imbalance problem prevalent in popular Information Technology (IT) and Operational Technology (OT) cybersecurity datasets, where normal traffic often vastly outnumbers attack instances. This imbalance leads to biased model performance and inflated accuracy scores, which can over- or under-assess a model’s ability to identify the minority classes correctly. Through a case study with several ML models on a realistic dataset, we demonstrate how imbalanced classes should be considered in the performance evaluation, and how imbalance learning techniques like resampling should be properly utilized for robust performance of ML-based IDS. Second, this thesis examines TTD, a crucial but often understressed performance indicator that measures how promptly an ML-based detection system identifies the onset of an attack. In addition to traditional metrics that focus solely on classification performance in ML communities, this thesis proposed a TTD model based on real-world responsiveness in OT systems by defining vari ous stages of the detection process for ML researchers to quantify and measure temporal overheads accordingly. We also demonstrate how the proposed TTD model can be applied to OT datasets through a case study, thereby suggesting it as a best practice for a more comprehensive evaluation of ML-based intrusion detectors in practical OT use cases. Through the two studies above, the thesis offers a more comprehensive and practical approach to evaluating ML-based IDS. It demonstrates how thoughtful consideration and integration of class imbalance and detection timeliness in the development and assessment of ML-based IDS representation is essential to deploy trustworthy and efficient cybersecurity solutions in critical infrastructure systems.", "source_url": "https:\/\/openalex.org\/W7111482866", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Spectrum Research Repository (Concordia University)", "source_record_id": "W7111482866", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 148, "title": "Enhancing IoT Network Security Through Intrusion Detection Using Machine Learning", "title_ar": "", "title_en": "Enhancing IoT Network Security Through Intrusion Detection Using Machine Learning", "author": "Mughogho, Elizabeth", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "machine-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; ai-applications; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "With the increasing growth of Internet of Things (IoT) devices, network infrastructures have become more vulnerable to cyber-attacks. Traditional network security measures often fall short in detecting sophisticated intrusion patterns in real-time, highlighting the need for intelligent and integrated detection systems. This study proposes a machine learning-based approach to enhance IoT network security by leveraging advanced classification models. The process involves data preprocessing, normalisation, and feature selection using mutual information to identify the most impactful attributes. We evaluated several supervised learning algorithms specifically, Decision Trees, Random Forests, and XGBoost using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. To enhance detection performance further, we implemented a soft voting ensemble classifier that combines the strengths of the individual models. The study also focuses on binary classification by distinguishing benign from malicious traffic, simplifying the real-time detection tasks. The ensemble model demonstrates superior accuracy, robustness, and generalisation, making it a viable solution for modern IoT intrusion detection systems. All experiments and evaluations are conducted using the CIC IoT 2023 dataset, a comprehensive and up-to-date benchmark for IoT security research.", "source_url": "https:\/\/openalex.org\/W7112491508", "pdf_url": "https:\/\/norma.ncirl.ie\/8948\/2\/elizabethmughoghoconfigurationmanual.pdf", "doi": "", "handle": "", "repository_name": "NORMA", "source_record_id": "W7112491508", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 149, "title": "Improved Machine Learning-Based Intrusion Detection Systems for Secure Network Virtualization", "title_ar": "", "title_en": "Improved Machine Learning-Based Intrusion Detection Systems for Secure Network Virtualization", "author": "Albin Biju", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; rnn; ai-cybersecurity", "keywords": "", "abstract": "The adoption of network virtualization and cloud computing has introduced new complexities and attack vectors, which demands more intelligent and scalable security solutions. In such cases, traditional Intrusion Detection Systems (IDS) often have struggle to provide effective protection. This research addresses this challenge by designing and developing an improved machine learning-based IDS with security for network virtualization in mind. The project is motivated by the need for security to be integrated into the system that not only attains high accuracy in detection but also provides good operational management and scalability under a cloud environment. Following through a quantitative experimental study the research laid the foundation on the CIC-IDS2017 dataset for training and evaluation on a battery of machine learning models that include Random Forest, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. After a series of solid preprocessing, feature engineering, and hyperparameter tuning procedures, the Tuned Random Forest model emerged as a winner, attaining an accuracy of 98.47% and a strong weighted F1-score of 0.98 as per this research study. A core contribution of this work is an implementation of an end-to-end real-time IDS system that incorporates this highly accurate model and integrates it into one of the AWS cloud architectures. The system uses AWS Lambda for scalable, serverless inference; Amazon CloudWatch for rich monitoring with custom metrics and automated alarms; and Amazon Security Lake for standardized long-term event logging in the OCSF-open cybersecurity schema framework format. Real-time visualization and administrative control of the complete system are done through a web dashboard based on Flask. The evaluation establishes the efficacy of the ML model and the successful integration of the cloud components, establishing a scalable, manageable, and highly visible security solution apposite for modern virtualized networks.", "source_url": "https:\/\/openalex.org\/W7140068874", "pdf_url": "https:\/\/norma.ncirl.ie\/9201\/2\/albinbijuconfigurationmanual.pdf", "doi": "", "handle": "", "repository_name": "NORMA", "source_record_id": "W7140068874", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 150, "title": "Design and Evaluation of a Supervised Machine Learning-Based Intrusion Detection System.", "title_ar": "", "title_en": "Design and Evaluation of a Supervised Machine Learning-Based Intrusion Detection System.", "author": "Tarquin Qazi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; ai-applications; data-mining; predictive-analytics; ai-education; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "This project involves designing and evaluating a supervised machine-learning-based intrusion detection system (IDS) using the CIC-IDS2017 dataset.The project is structured around the cross-industry standard process for data mining (CRISP-DM) framework.The dataset comprises over 2.8 million labelled instances and was cleaned and reduced from 78 to 63 features via redundancy analysis.These data were used to train and tune three supervised classifiers: Logistic Regression algorithm as a linear baseline, a Decision Tree algorithm and a Random Forest algorithm.Hyperparameter optimisation was conducted using RandomizedSearchCV with 3-fold cross-validation, optimising for the F1-macro scoring metric.While the random forest performed similarly on majority classes, the final decision tree model generalised significantly better on rare attacks such as Heartbleed and Infiltration.On the test set, the decision tree achieved a macro-F1 score of 0.96 and an accuracy score of 99.88%, with a minimal train\/test gap of 0.0008.To evaluate its real-world viability, the model was deployed via a live capture test using Nmap, Wireshark and CICFlowMeter, alongside a cross-dataset evaluation on CIC-IDS2018.The final results highlighted the inherent challenges of cross-dataset generalisation: while the model maintained high precision, it exhibited reduced recall on unseen data.", "source_url": "https:\/\/openalex.org\/W7214345887", "pdf_url": "https:\/\/arc.cct.ie\/cgi\/viewcontent.cgi?article=1163&context=ict", "doi": "10.63227\/652.299.144", "handle": "", "repository_name": "", "source_record_id": "W7214345887", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 151, "title": "Investigation of Machine Learning Algorithms for Intrusion Detection System in Cybersecurity", "title_ar": "", "title_en": "Investigation of Machine Learning Algorithms for Intrusion Detection System in Cybersecurity", "author": "Mohmmed Alrowaily", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of South Florida", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; ai-applications; fairness; ai-cybersecurity", "keywords": "", "abstract": "The proliferation in usage and complexity of modern communication and network systems, a large number of trustworthy online Services and systems have been deployed. Even so, cybersecurity threats are still growing. An Intrusion Detection System (IDS) play a vital role in ensuring the security of communication networks, and it is taken into account as the subsequent security gate after the firewall. The IDS informs the system or network administrator in order to take specific actions to evade the suspicious activities. Three significant contributions are made during the course of this research to illustrate the feasibility of these IDS approaches. In the first contribution, we investigate the effectiveness of using conventional machine learning techniques based intrusion detection systems. The second contribution proposes an ensemble learning algorithm for cybersecurity threat detection. The third contribution proposes a hybrid feature selection approach for improving network attack detection. All presented algorithms were evaluated on the recent public CICIDS2017 dataset, which consist of benign and the most cutting-edge common attacks, and compared with other approaches. This research considers several machine learning classifiers, and feature selection techniques in order to study their classification performance under attack over different metrics. The empirical results of the three implemented systems conclude that the chosen minimized features provide promising performance to develop IDS that is effective and efficient for network intrusion detection. Moreover, these models not only improves the classification accuracy but also reduces the false alarm rate in the classification of IDS attacks.", "source_url": "https:\/\/openalex.org\/W3216292950", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Commons - University of South Florida (University of South Florida)", "source_record_id": "W3216292950", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 153, "title": "Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems", "title_ar": "", "title_en": "Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems", "author": "Zheni Svetoslavova Stefanova", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of South Florida", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; ai-applications; predictive-analytics; fairness; ai-cybersecurity", "keywords": "", "abstract": "Given the continuing advancement of networking applications and our increased dependence upon software-based systems, there is a pressing need to develop improved security techniques for defending modern information technology (IT) systems from malicious cyber-attacks. Indeed, anyone can be impacted by such activities, including individuals, corporations, and governments. Furthermore, the sustained expansion of the network user base and its associated set of applications is also introducing additional vulnerabilities which can lead to criminal breaches and loss of critical data. As a result, the broader cybersecurity problem area has emerged as a significant concern, with many solution strategies being proposed for both intrusion detection and prevention. Now in general, the cybersecurity dilemma can be treated as a conflict-resolution setup entailing a security system and minimum of two decision agents with competing goals (e.g., the attacker and the defender). Namely, on the one hand, the defender is focused on guaranteeing that the system operates at or above an adequate (specified) level. Conversely, the attacker is focused on trying to interrupt or corrupt the system’s operation. In light of the above, this dissertation introduces novel methodologies to build appropriate strategies for system administrators (defenders). In particular, detailed mathematical models of security systems are developed to analyze overall performance and predict the likely behavior of the key decision makers influencing the protection structure. The initial objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks at a very early stage, i.e., in order to minimize potentially critical consequences and damage to system privacy and stability. Furthermore, another key objective is also to develop effective intrusion prevention (response) mechanisms. Along these lines, a machine learning based solution framework is developed consisting of two modules. Specifically, the first module prepares the system for analysis and detects whether or not there is a cyber-attack. Meanwhile, the second module analyzes the type of the breach and formulates an adequate response. Namely, a decision agent is used in the latter module to investigate the environment and make appropriate decisions in the case of uncertainty. This agent starts by conducting its analysis in a completely unknown milieu but continually learns to adjust its decision making based upon the provided feedback. The overall system is designed to operate in an automated manner without any intervention from administrators or other cybersecurity personnel. Human input is essentially only required to modify some key model (system) parameters and settings. Overall, the framework developed in this dissertation provides a solid foundation from which to develop improved threat detection and protection mechanisms for static setups, with further extensibility for handling streaming data.", "source_url": "https:\/\/openalex.org\/W2912471702", "pdf_url": "https:\/\/digitalcommons.usf.edu\/context\/etd\/article\/8564\/viewcontent\/etd__7367.pdf", "doi": "", "handle": "", "repository_name": "Digital Commons - University of South Florida (University of South Florida)", "source_record_id": "W2912471702", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 154, "title": "Machine learning in intrusion detection : topics from scientific literature", "title_ar": "", "title_en": "", "author": "Elina Peronius", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Jyväskylä", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "fi", "research_domain": "machine-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "Koneoppimisen ominaisuudet ovat tehneet monista sen menetelmistä käytettyjä hyökkäysten havaitsemisessa. Nykyinen kirjallisuus, joka käsittelee koneoppimista hyökkäysten havaitsemissa, on vailla hyvää yleiskatsausta koko aihealueen kirjallisuuteen. Olemassa olevan datamäärän vuoksi perinteisten metodien käyttö data analyysissä olisi työlästä ja tehotonta. Tämä tutkimus lähestyy haastetta käyttämällä automaattista tekstianalyysimenetelmää nimeltä dynaaminen aihemallinnus. Dynaaminen aihemallinnus kykenee tunnistamaan aiheiden kehittymisen ajan myötä, mikä tekee siitä hyvän mallinnusvaihtoehdon käytettäväksi dokumentteihin, jotka kuvaava kehittyvää sisältöä. Dynaamisella aihemallinnuksella löydettiin 21 aihetta, joista 15 oli tulkittavia. Tulkittavat aiheet nimettiin, tosin nimeämisessä heijastuu vain yhden henkilön mielipide. Tämän tutkimuksen tärkeimmät tuotokset ovat nykyisen kirjallisuuden kartoitus. Käytetyt koneoppimisen metodit ovat hyvin tutkittu alue, joka tekee niiden kontekstien, joissa näitä menetelmiä käytetään tunnistamisesta mielenkiintoisemman osan löydöksistä. Useita puutteita tunnistettiin datan keräyksessä, datan prosessoinnissa, mallin evaluoinnissa ja aiheiden tulkinnassa. Tämän vuoksi tulosten validiteetti pitää joissain määrin kyseenalaistaa. Valitun tekstianalyysimenetelmän ominaisuuksien vuoksi tuloksista puuttuu rikkaus, joka yleensä liitetään perinteisiin tutkimusmenetelmiin. Tämän vuoksi lisätutkimuksien aiheiksi ehdotetaan aiheita, jotka pyrkivät korjaamaan tämän puutoksen. Tälle aihealueelle löydettyjen aiheiden tulevaisuuden kehittyminen ja uusien aiheiden ilmaantumisen tunnistaminen olisivat myös hyödyllisiä.", "source_url": "https:\/\/openalex.org\/W3036668371", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Jyväskylä University Digital Archive (University of Jyväskylä)", "source_record_id": "W3036668371", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 155, "title": "Implementation of Machine Learning techniques and Artificial Intelligence for the intrusion detection in communications networks", "title_ar": "", "title_en": "", "author": "Pablo Monteagudo-Mesas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2019, "language": "es", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-cybersecurity", "keywords": "", "abstract": "El desarrollo de softwares maliciosos ha aumentado. Este proyecto, además de hacer una descripción de las características principales de la ciberseguridad, así como de las principales amenazas para los sistemas de seguridad, también constituye una revisión de las diferentes técnicas de machine learning para la detección de intrusos, que se aplicarán en las etapas de pre procesamiento y procesamiento de datos. Se presenta una introducción al machine learning en problemas separables lineales y no lineales, así como una explicación concisa del modelo Logistic Regression. Para la detección de anomalías a partir de técnicas de machine learning se emplea un conjunto de datos, KDD Cup ’99. Se describe ese conjunto de datos y se implementan las principales técnicas de pre procesamiento de datos e ingeniería de características: selección de funciones, eliminación de outliers, codificación (one-hot encoder), etc. Posteriormente se procede a una división de este conjunto de datos en dos subconjuntos de entrenamiento y de prueba, y se realiza un análisis exploratorio de estos datos. Se implementan seis modelos diferentes para predecir los diferentes tipos de ataques que pueden surgir en cualquier registro de conexión. Para ello, se ajusta cada modelo al conjunto de datos de entrenamiento para hacer las predicciones sobre datos que no han sido entrenados. Se han comparado entre sí los diferentes modelos implementados en función a los resultados obtenidos de las diferentes métricas de evaluación implementadas, ya que no todos los modelos funcionan de manera eficiente para el mismo problema, y se selecciona un modelo como ganador según la naturaleza del problema descrito. Para la disciplina profesional, este proyecto integra dos temas de interés, ciberseguridad e inteligencia artificial, dos áreas que en la actualidad y en el futuro tendrán un gran impacto en la sociedad. Abstract: The development of malicious software has increased. In this project in addition to do a description of the main features of cybersecurity, as well as the main threats to security systems, also constitutes a review of the different machine learning techniques for the intrusion detection to be applied in data preprocessing and processing stages. It is presented an introduction to Machine learning in linear and non-linear separable problems, as well as a concise explanation of the Logistic Regression model. For anomaly detection from machine learning techniques a data set is employed, KDD Cup ’99. That data set is described and the main techniques of data processing and features engineering are implemented: selection of functions, remove of outliers, coding (one-hot encoder), etc. Subsequently, this data set is divided into two subsets of training and testing, and an exploratory analysis of these data is carried out. Six different models are implemented and evaluated to predict the different types of attacks that may arise in any connection record. To do this, each model is fitted to the training data set to make predictions about data that have not been trained. The different models implemented have been compared among themselves based on the results obtained from the different evaluation metrics implemented, since not all the models work efficiently for the same problem, and a model is selected such as winner according to the nature of the problem described. For professional discipline, this project integrates two topics of interest, cybersecurity and artificial intelligence, two areas that currently and in the future will have a great impact on society.", "source_url": "https:\/\/openalex.org\/W3045311108", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Hispana", "source_record_id": "W3045311108", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 156, "title": "Deep reinforcement learning in cybersecurity", "title_ar": "", "title_en": "Deep reinforcement learning in cybersecurity", "author": "Izquierdo García-Faria, Tomás", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; reinforcement-learning; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "There is a lot of expectation on how Artificial Intelligence (AI) is going to have an impact on Cybersecurity. From new sophisticated attacks to new ways of defending a system from cybercriminals. A lot of techniques are being studied by universities and security organizations in order to implement intelligent systems to defend systems, as well as researching how can AI give solution to many problems not yet solved. However this sector has some limitations for AI to have a real impact. AI is a discipline which, in its machine learning part, needs data in order to give good results and big companies are not willing to admit there attacks or do not even know they are under attack. In particular network intrusion is one of the most common ways attackers enter a system and exploit its threats. With the adoption of IoT systems in our society more and more, companies, cities, and citizens, are going to fill their surroundings with devices that communicate to the exterior. Under these emerging threats every system and network they are connected to have to be secure and should be able to detect intrusion. This thesis will try to present a conjunction of different techniques, presented by their authors as the Rainbow architecture, used in reinforcement learning in order to tackle this problem. It will give solution to systems such as intrusion detection systems (IDS) and can be extended to intrusion prevention systems (IPS) or web application firewalls (WAF).", "source_url": "https:\/\/openalex.org\/W7111963675", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W7111963675", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 157, "title": "Intrusion detection system using machine learning techniques", "title_ar": "", "title_en": "", "author": "Sidi Mohamed Mortada Korti ; Mehdi Noureddine Mediani", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2022, "language": "fr", "research_domain": "machine-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "Avec l’important d´eveloppement des technologies de l’information et de la communication et particuli`erement Internet, le monde a connu durant la derni`ere d´ecennie un mouvement d’innovations sans pr´ec´edent. Malheureusement ces avanc´ees furent accompagn´ees par la progression des cybers attaques, la protection de ces r´eseaux de communication apparait comme le prochain d´efi des futures d´ecennies. En tant que premier rempart, les syst`emes de d´etection d’intrusion ont fait l’objet de nombreuses recherches et jouent un rˆole crucial dans la s´ecurit´e des r´eseaux. Ce m´emoire pr´esente les travaux men´es dans le cadre du domaine de la d´etection d’anomalies dans les r´eseaux avec le but de d´evelopper un mod`ele capable de d´etecter et de classifier un large ´eventail d’attaques tout en s’adaptant `a un sc´enario de menace en constante ´evolution. L’approche propos´ee a ´et´e test´ee sur la base de donn´ees publique CIC-IDS2017. La base de donn´ees sera d’abord pr´etrait´ee et normalis´ee, puis appliqu´ee `a divers algorithmes d’apprentissage automatique de classification pour cr´eer des mod`eles et comparer leurs performances `a l’aide de diff´erentes mesures d’´evaluation telles que (Accuracy, Precision, Recall, F1-score, etc.). Les r´esultats exp´erimentaux ont montr´e que les performances des algorithmes d’apprentissage automatique utilis´es ont atteint un score de pr´ecision relativement ´elev´e : Random Forest 97.02%, Decision Tree 96.74%, K-Nearest Neighbors 96.24%, MLP 87.57% et SVM 81.12%.", "source_url": "https:\/\/openalex.org\/W7135293651", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/0ee94ab7-b81a-49a0-8fc5-641d4148e74b\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135293651", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 158, "title": "Applied Machine Learning for Cybersecurity in Spam Filtering and Malware Detection", "title_ar": "", "title_en": "Applied Machine Learning for Cybersecurity in Spam Filtering and Malware Detection", "author": "Mark Sokolov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "East Carolina University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; computer-vision; ai-applications; predictive-analytics; ai-cybersecurity", "keywords": "", "abstract": "Machine learning is one of the fastest-growing fields and its application to cybersecurity is increasing. In order to protect people from malicious attacks, several machine learning algorithms have been used to predict the malicious attacks. This research emphasizes two vulnerable areas of cybersecurity that could be easily exploited. First, we show that spam filtering is a well known problem that has been addressed by many authors, yet it still has vulnerabilities. Second, with the increase of malware threats in our world, a lot of companies use AutoAI to help protect their systems. Nonetheless, AutoAI is not perfect, and data scientists can still design better models. In this thesis I show that although there are efficient mechanisms to prevent malicious attacks, there are still vulnerabilities that could be easily exploited. In the visual spoofing experiment, we show that using a classifier trained on data using Latin alphabet, to classify a message with a combination of Latin and Cyrillic letters leads to much lower classification accuracy. In Malware prediction experiment, our model has been able to predict malware attacks on Microsoft computers and got higher accuracy than any well known Auto AI.", "source_url": "https:\/\/openalex.org\/W3119405474", "pdf_url": "https:\/\/thescholarship.ecu.edu\/bitstreams\/d4f123e1-2fc0-4f47-82f1-195a4c5cdbd5\/download", "doi": "", "handle": "", "repository_name": "The Scholarship East Carolina University's Institutional Repository (East Carolina University)", "source_record_id": "W3119405474", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 160, "title": "Embedding Network Information for Machine Learning-based Intrusion Detection", "title_ar": "", "title_en": "Embedding Network Information for Machine Learning-based Intrusion Detection", "author": "Jonathan Daniel DeFreeuw", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-cybersecurity", "keywords": "", "abstract": "As computer networks grow and demonstrate more complicated and intricate behaviors, traditional intrusion detections systems have fallen behind in their ability to protect network resources. Machine learning has stepped to the forefront of intrusion detection research due to its potential to predict future behaviors. However, training these systems requires network data such as NetFlow that contains information regarding relationships between hosts, but requires human understanding to extract. Additionally, standard methods of encoding this categorical data struggles to capture similarities between points. To counteract this, we evaluate a method of embedding IP addresses and transport-layer ports into a continuous space, called IP2Vec. We demonstrate this embedding on two separate datasets, CTU'13 and UGR'16, and combine the UGR'16 embedding with several machine learning methods. We compare the models with and without the embedding to evaluate the benefits of including network behavior into an intrusion detection system. We show that the addition of embeddings improve the F1-scores for all models in the multiclassification problem given in the UGR'16 data.", "source_url": "https:\/\/openalex.org\/W3040722536", "pdf_url": "https:\/\/vtechworks.lib.vt.edu\/bitstreams\/0ed788d2-ba89-4286-9d72-7d4e14a05684\/download", "doi": "", "handle": "", "repository_name": "VTechWorks (Virginia Tech)", "source_record_id": "W3040722536", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 163, "title": "Research on Several Problems of Quantum Machine Learning", "title_ar": "", "title_en": "Research on Several Problems of Quantum Machine Learning", "author": "Cong Lei", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; quantum-ml; predictive-analytics; gnn", "keywords": "", "abstract": "Quantum machine learning methods, including quantum kernels and variational quantum algorithms (VQAs), are poised to demonstrate quantum advantages in machine learning, chemistry, condensed matter physics, and materials science. However, these methods face critical challenges hindering practical quantum advantates: quantum kernels require meticulous design to outperform classical alternatives, while VQA training is obstructed by barren plateaus (BPs) in the optimization landscape. To address these issues, this thesis proposes two novel frameworks to guide quantum kernel design and tackle quantum neural network (QNN) training challenges in VQAs. Specifically, to resolve quantum kernel design challenges, we propose a data-driven method for automatic quantum kernel design, leveraging classical machine learning to learn from input data and identify optimal kernel structures. Experiments show this method finds suitable quantum kernels that avoid vanishing similarity, a common flaw in traditional designs. To improve VQA trainability, we propose an encoder-decoder parameter prediction framework, where a graph neural network (GNN) acts as the encoder to process information based on the QNN’s directed acyclic graph (DAG). Experiments confirm the parameters from this framework significantly enhance VQAs’ robustness, convergence speed, and trainability. In conclusion, we propose two schemes, QuKerNet and QuGHN, for automatic quantum kernel design and enhanced VQA trainability. QuKerNet considers optimal circuit layouts and variational parameters, and is more resource-efficient for contemporary quantum hardware by accounting for qubit topology and count. QuGHN is versatile, applicable to circuits with varying qubits, ansatzes, and tasks; its parameters enable faster convergence and more stable VQA solutions. These advancements enhance quantum machine learning practicality on near-term machines and its utility in large-scale quantum circuit simulations.", "source_url": "https:\/\/openalex.org\/W7134018758", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Sydney eScholarship Repository (The University of Sydney)", "source_record_id": "W7134018758", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 165, "title": "Towards Real-World Quantum Machine Learning", "title_ar": "", "title_en": "Towards Real-World Quantum Machine Learning", "author": "Utkarsh Singh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "quantum-ml", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; quantum-ml; predictive-analytics; ai-cybersecurity", "keywords": "", "abstract": "Quantum machine learning (QML) promises new representational and computational capabilities, yet practical deployment on near-term hardware is hampered by resource overheads, depth constraints, and fragile trainability. This thesis advances resource-aware QML by proposing architectures and kernels that retain expressivity while sharply reducing qubit counts, circuit depth, and entangling-gate budgets. The work is presented in an article-based format with three core contributions. First, I introduce a Coherent Feed-Forward Quantum Neural Network (CFF-QNN) that preserves quantum coherence across all layers, mirrors the flexibility of classical feed-forward networks (adjustable hidden layers and nodes), and decouples qubit requirements from input feature dimension. Compared to prevailing QNN baselines, the CFF-QNN reduces both depth and CNOT count by over 50% while achieving strong performance on standard benchmarks (e.g., 91% accuracy on Wisconsin breast cancer and 85% on credit-card fraud). This contribution is accompanied by an international patent filing (WO2025050205A1). Second, I develop a resource-efficient quantum kernel that enables high-dimensional embeddings with substantially fewer qubits and entanglers, achieving linear scaling of entangling gates in the number of qubits. Empirically, the kernel delivers competitive or superior performance to widely used classical kernels and to popular quantum feature maps (e.g., on the Parkinson's disease dataset), with noisy simulations and small-scale runs on superconducting hardware indicating suitability for near-term devices. This contribution is covered by a companion patent filing (WO2025073041A1). Third, I propose a quantum reservoir computing (QRC) scheme that reuses a fixed quantum feature-map circuit as the reservoir while injecting temporal memory via an explicit feedback loop. The register is recycled across time, so quantum resources remain constant, no quantum parameters are trained, and only a lightweight classical readout is fitted. Experiments on chaotic time series (e.g., Mackey-Glass) show competitive predictive accuracy with strict resource efficiency, and illuminate how feedback delay and entangling structure affect memory and error. Collectively, these results chart a path toward practical QML: coherent architectures and kernels that are trainable, scalable in input dimension, frugal in quantum resources, and viable on noisy intermediate-scale devices—while providing design guidelines for future, larger-scale implementations.", "source_url": "https:\/\/openalex.org\/W7154617415", "pdf_url": "https:\/\/ruor.uottawa.ca\/bitstreams\/f83095ea-3ec4-4fde-815e-213defcc8056\/download", "doi": "10.20381\/ruor-31858", "handle": "", "repository_name": "", "source_record_id": "W7154617415", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 167, "title": "Quantum machine learning in personalized (precision) medicine", "title_ar": "", "title_en": "", "author": "Λεωνίδας Θ. Τσιούγκος", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Thessaly", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2026, "language": "el", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml", "keywords": "", "abstract": "Περίληψη Η εξατομικευμένη ιατρική επιδιώκει την προσαρμογή της πρόληψης, της διάγνωσης και της θεραπείας στα ιδιαίτερα χαρακτηριστικά κάθε ασθενούς, αξιοποιώντας δεδομένα από πολλαπλές πηγές, όπως το γονιδίωμα, οι εικόνες ιατρικής απεικόνισης, τα ηλεκτρονικά μητρώα υγείας και οι βιοδείκτες.Ο τεράστιος όγκος και η πολυπλοκότητα αυτών των δεδομένων θέτουν σοβαρές υπολογιστικές προκλήσεις για τις κλασικές μεθόδους μηχανικής μάθησης.Η κβαντική μηχανική μάθηση (Quantum Machine Learning, QML) συνδυάζει τις αρχές της κβαντικής υπολογιστικής με τη μηχανική μάθηση και προτείνεται ως μια πολλά υποσχόμενη προσέγγιση για την αντιμετώπιση αυτών των προκλήσεων.Στην παρούσα πτυχιακή εργασία διερευνάται η τρέχουσα κατάσταση, οι εφαρμογές και οι προκλήσεις της κβαντικής μηχανικής μάθησης στο πεδίο της εξατομικευμένης ιατρικής.Ακολουθώντας μια δομημένη μεθοδολογία ανασκόπησης της βιβλιογραφίας, εξετάζονται τα βασικά κβαντικά μοντέλα μηχανικής μάθησης (κβαντικά νευρωνικά δίκτυα, μεταβολικοί κβαντικοί ταξινομητές, κβαντικές μηχανές διανυσμάτων υποστήριξης, κβαντικά συνελικτικά δίκτυα και υβριδικά μοντέλα), οι τεχνικές κωδικοποίησης κλασικών δεδομένων σε κβαντικές καταστάσεις, καθώς και οι κλινικές περιοχές εφαρμογής, με έμφαση στην ογκολογία ακριβείας, την ανάλυση πολυ-ωμικών δεδομένων, την ιατρική απεικόνιση, την υποστήριξη κλινικών αποφάσεων και την ανακάλυψη και τον σχεδιασμό φαρμάκων.Τα ευρήματα υποδεικνύουν ότι, αν και τα υβριδικά κβαντικά-κλασικά μοντέλα έχουν επιδείξει ανταγωνιστικά ή, σε ορισμένες περιπτώσεις, ελαφρώς βελτιωμένα αποτελέσματα σε σχέση με τα κλασικά αντίστοιχά τους, δεν υπάρχει ακόμη σαφής και συστηματική απόδειξη πρακτικού κβαντικού πλεονεκτήματος στην εξατομικευμένη ιατρική.Οι κυριότερες προκλήσεις σχετίζονται με τους περιορισμούς του θορυβώδους ενδιάμεσης κλίμακας κβαντικού υλικού (NISQ), την κωδικοποίηση δεδομένων υψηλής διάστασης, το φαινόμενο των «άγονων οροπεδίων» κατά την εκπαίδευση, τη μείωση διαστατικότητας, την ερμηνευσιμότητα και τη διαθεσιμότητα κατάλληλων συνόλων δεδομένων.Η εργασία καταλήγει ότι η κβαντική μηχανική μάθηση βρίσκεται σε πρώιμο αλλά ταχέως", "source_url": "https:\/\/openalex.org\/W7223547892", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/89943\/4\/35516.pdf", "doi": "", "handle": "", "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)", "source_record_id": "W7223547892", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 168, "title": "Cloud-Based Quantum Machine Learning for Weather Forecasting", "title_ar": "", "title_en": "Cloud-Based Quantum Machine Learning for Weather Forecasting", "author": "Abhishek Nachankar ; Amartya Choubey ; Amit Gond", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml; predictive-analytics", "keywords": "", "abstract": "Quantum Machine Learning (QML):An emerging research area that integrates principles of quantum computing with machine learning algorithms to enhance computational efficiency, scalability, and learning capability for complex data-driven problems. Cloud-Based Quantum Computing:A computing paradigm that provides remote access to quantum processors and simulators through cloud platforms, enabling scalable, cost-effective, and accessible quantum experimentation without the need for dedicated quantum hardware. Hybrid Quantum–Classical Models:Machine learning architectures that combine classical data preprocessing and optimization techniques with quantum circuits to leverage the strengths of both classical and quantum computation. Weather Forecasting:The process of predicting atmospheric conditions such as temperature, humidity, pressure, and wind speed using data-driven models and computational techniques. Variational Quantum Circuits (VQC):Parameterized quantum circuits optimized using classical algorithms, widely used in quantum machine learning for classification and regression tasks on Noisy Intermediate-Scale Quantum (NISQ) devices. NISQ Devices:Current-generation quantum computers characterized by limited qubit counts and noise, which influence the performance and accuracy of present-day quantum algorithms.", "source_url": "https:\/\/openalex.org\/W7127654488", "pdf_url": "", "doi": "10.5281\/zenodo.18482284", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7127654488", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 169, "title": "Quantum machine learning for drug development", "title_ar": "", "title_en": "", "author": "Πανάγος, Κωνσταντίνος Ε. ; Πανάγος, Κωνσταντίνος Ε.", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Thessaly", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2025, "language": "el", "research_domain": "medical-ai", "research_subdomain": "quantum-ml", "categories": "machine-learning; medical-ai; quantum-ml", "keywords": "", "abstract": "ΠΕΡΙΛΗΨΗΗ παρούσα διπλωματική εργασία εξετάζει το ρόλο της κβαντικής μηχανικής μάθησης στη βελτίωση της φαρμακευτικής έρευνας.Στόχος της είναι η διερεύνηση καινοτόμων τεχνολογιών, με το συνδυασμό της μηχανικής μάθησης και της κβαντικής υπολογιστικής, που μπορούν να βελτιώσουν τη διαδικασία ανάπτυξης νέων φαρμάκων, μειώνοντας το χρόνο και το κόστος.Πραγματοποιείται σύγκριση μεταξύ κλασικών και κβαντικών προσεγγίσεων σε ζητήματα επεξεργασίας δεδομένων, πρόβλεψης και ανάλυσης.Εξετάζονται οι βασικές αρχές της μηχανικής μάθησης και της κβαντικής υπολογιστικής, καθώς και η εφαρμογή τους σε προβλήματα μεγάλης υπολογιστικής πολυπλοκότητας, όπως η ανάλυση μοριακών αλληλεπιδράσεων.Παρουσιάζονται τόσο θεωρητικές όσο και πρακτικές προσεγγίσεις, οι οποίες αναδεικνύουν τη δυνατότητα των τεχνολογιών αυτών να βελτιώσουν την αποδοτικότητα.Τα αποτελέσματα της έρευνας καταδεικνύουν τη δυναμική των τεχνολογιών που εξετάζονται όσον αφορά τη διαχείριση μεγάλων δεδομένων και τη μοντελοποίηση σύνθετων συστημάτων στην παραγωγική διαδικασία φαρμάκων.Η εργασία παρέχει ένα γενικό πλαίσιο για τη μελλοντική αξιοποίηση αυτών των τεχνολογιών στη φαρμακευτική έρευνα και καλλιεργεί την ανάγκη για περαιτέρω μελέτη και εξέλιξη στον κλάδο αυτό.", "source_url": "https:\/\/openalex.org\/W7112023997", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/85520\/4\/30881.pdf", "doi": "", "handle": "", "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)", "source_record_id": "W7112023997", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 170, "title": "Quantum Machine Learning for Identifying Fast Radio Bursts", "title_ar": "", "title_en": "Quantum Machine Learning for Identifying Fast Radio Bursts", "author": "Agastya Kapur", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; quantum-ml; cnn", "keywords": "", "abstract": "Since their discovery in 2007, fast radio bursts (FRBs) have become a major focus of radio astronomy research because they enable researchers to probe the intergalactic medium beyond the Milky Way. Finding more FRBs will enable better understanding of the local and distant universe and the physics that drives these violent ultra-luminous events. Even though various classical machine learning algorithms have been used to search for FRBs, here I show, for the first time, that quantum machine learning algorithms can be used for detecting FRBs in large datasets. Quantum machine learning algorithms are still in their infancy, but show potential to outperform classical machine learning methods. I investigate how effective quantum machine learning, specifically the quantum convolutional neural network (QCNN), can be for identifying FRBs in radio astronomy datasets. The QCNNs developed for this work use 8, 10 and 12 qubits. These models can be run on existing quantum hardware; however, I run classical simulations to test the quantum models in a noise-free environment. Using a new FRB training dataset created for this work, QCNNs were trained, and their performance was evaluated by comparing them to classical machine learning models: the random forest and convolutional neural network. I demonstrate that the best quantum model outperforms the classical models tested. It was observed that performance improved with increasing numbers of qubits. The best QCNN model developed for this work was also applied to real data from the CSIRO Parkes radio telescope Murriyang and was successfully used to find a radio burst from a real astronomical source. This work demonstrates that QCNNs can detect FRBs in real astronomy datasets.", "source_url": "https:\/\/openalex.org\/W7147047024", "pdf_url": "", "doi": "10.25949\/31825186", "handle": "", "repository_name": "Macquarie University", "source_record_id": "W7147047024", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 171, "title": "Quantum machine learning: current state and challenges", "title_ar": "", "title_en": "Quantum machine learning: current state and challenges", "author": "Ζαχαρούλη, Θεανώ Χ. ; Ζαχαρούλη, Θεανώ Χ.", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Thessaly", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml", "keywords": "", "abstract": "Over the past decade, the field of Classical Machine Learning has experienced rapid growth, primarily driven by the widespread adoption of the internet and the increasing interconnectivity of digital devices.This expansion has led to the generation of vast volumes of data requiring effective analysis and processing.Machine Learning, as a subfield of Artificial Intelligence, leverages tools mainly from Statistics to identify patterns and extract meaningful insights from data without the need for explicit programming.At the same time, Quantum Computing introduces a fundamentally different paradigm for information processing, utilizing principles of Quantum Mechanics, such as superposition and entanglement.The convergence of these two scientific domains has given rise to the emerging field of Quantum Machine Learning.The present thesis aims to investigate whether the integration of Machine Learning and Quantum Computing can offer tangible advancements in the efficiency and performance of Artificial Intelligence systems.Based on current research findings, this study explores the potential, advantages, and limitations of this interdisciplinary approach, as well as its prospects for development in the coming years.", "source_url": "https:\/\/openalex.org\/W7112324068", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/86415\/4\/31843.pdf", "doi": "10.26253\/heal.uth.42770", "handle": "", "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)", "source_record_id": "W7112324068", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 172, "title": "Quantum Machine Learning", "title_ar": "", "title_en": "Quantum Machine Learning", "author": "Cristian Romero García", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad del Pais Vasco", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml; data-mining; predictive-analytics", "keywords": "", "abstract": "[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow for an exponential speed-up in the algorithms. This field is currently one of the main research areas in companies like Google and Microsoft, due to the revolution that they could provide in data management.", "source_url": "https:\/\/openalex.org\/W7061493127", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Communities in ADDI (Universidad del Pais Vasco)", "source_record_id": "W7061493127", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 173, "title": "Quantum machine learning", "title_ar": "", "title_en": "", "author": "Jan Svoboda", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Czech Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2024, "language": "cs", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml", "keywords": "", "abstract": "Přestože kvantové počítání stále není používáno v reálných aplikacích, jeho studium je nezbytné pro pochopení konceptů kvantového počítání a rozšíření znalostí o jeho možnostech. Tato práce porovnává kvantové strojové učení s kernelovými metodami v důsledku jejich přirozené podobnosti. Cílem práce je vytvořit obecný postup pro vytvoření kvantového kernelu, který bude těžko simulovatelný na klasických počítačích. Zmíněné postupy jsou posléze testovány na reálných datech s poněkud neuspokojivými výsledky.", "source_url": "https:\/\/openalex.org\/W7197628744", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Cvut DSpace (Czech Technical University)", "source_record_id": "W7197628744", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 174, "title": "Quantum machine learning", "title_ar": "", "title_en": "Quantum machine learning", "author": "Riu Vicente, Jordi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; reinforcement-learning; quantum-ml; optimization", "keywords": "", "abstract": "We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm when applied to the MaxCut problem. We explore Q-learning based techniques both for continuous and discrete action environments with regular and irregular graphs.", "source_url": "https:\/\/openalex.org\/W7113567326", "pdf_url": "https:\/\/upcommons.upc.edu\/bitstreams\/7e0aa1a9-6da4-4f83-8c44-076cb8797aa7\/download", "doi": "", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W7113567326", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 175, "title": "Quantum machine learning for supervised pattern recognition.", "title_ar": "", "title_en": "Quantum machine learning for supervised pattern recognition.", "author": "Maria Schuld", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of KwaZulu-Natal", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "quantum-ml", "categories": "machine-learning; deep-learning; computer-vision; quantum-ml; data-mining; predictive-analytics", "keywords": "", "abstract": "Humans are experts at recognising patterns in past experience and applying them to new tasks. For example, after seeing pictures of a face we can usually tell if another image contains the same person or not. Machine learning is a research discipline at the intersection of computer science, statistics and mathematics that investigates how pattern recognition can be performed by machines and for large amounts of data. Since a few years machine learning has come into the focus of quantum computing in which information processing based on the laws of quantum theory is explored. Although large scale quantum computers are still in the first stages of development, their theoretical description is well-understood and can be used to formulate `quantum software' or `quantum algorithms' for pattern recognition. Researchers can therefore analyse the impact quantum computers may have on intelligent data mining. This approach is part of the emerging research discipline of quantum machine learning that harvests synergies between quantum computing and machine learning. The research objective of this thesis is to understand how we can solve a slightly more specific problem called supervised pattern recognition based on the language that has been developed for universal quantum computers. The contribution it makes is twofold: First, it presents a methodology that understands quantum machine learning as the combination of data encoding into quantum systems and quantum optimisation. Second, it proposes several quantum algorithms for supervised pattern recognition. These include algorithms for convex and non-convex optimisation, implementations of distance-based methods through quantum interference, and the preparation of quantum states from which solutions can be derived via sampling. Amongst the machine learning methods considered are least-squares linear regression, gradient descent and Newton's method, k-nearest neighbour, neural networks as well as ensemble methods. Together with the growing body of literature, this thesis demonstrates that quantum computing offers a number of interesting tools for machine learning applications, and has the potential to create new models of how to learn from data.", "source_url": "https:\/\/openalex.org\/W2898293445", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ResearchSpace (University of KwaZulu-Natal)", "source_record_id": "W2898293445", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 177, "title": "Quantum machine learning in chemical space", "title_ar": "", "title_en": "Quantum machine learning in chemical space", "author": "Felix A. Faber", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Basel", "university_source": "repository_host", "college": "", "department": "", "country": "Switzerland", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; ai-applications; quantum-ml; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "This thesis focus on the overlap of first principle quantum methods and machine learning in computational chemistry and materials science, commonly referred to as Quantum Machine Learning (QML). Assessing and benchmarking the performance of existing machine learning models on various classes of compounds and chemical properties is a substantial part of this thesis. These results are used to understand better which machine learning models are best suited for a given combination of properties and compounds. For example, thirteen electronic ground state properties of $\\\\sim$131k organic molecules, calculated at hybrid-DFT level of theory, were used to gauge the predictive accuracy of combinations of representations and regressors. The out-of-sample prediction errors of the models on the hybrid-DFT quality data are on par with, or close to, the CCSD(T) error to experimental values, indicating that reference data need to go beyond hybrid-DFT if QML predictions are to surpass chemical accuracies. Another area of focus is on developing new and accurate QML models. A new representation of atoms in its chemical environment is introduced, by rethinking the way structural and chemical compound information is encoded into training data. The representation interpolates elemental properties across both atoms and compounds, making it well suited for datasets with high compositional and structural degrees of freedom. Numerical results evidence that, compared to current benchmarks, this representation yield superior predictive power in combination with kernel ridge regression on a diverse set of systems, including diverse organic molecules, non-covalently bonded protein side-chains, water clusters, and crystalline solids. Furthermore, the role of response operators when learning response properties of the energy is discussed, leading to a formalism for learning response properties of the energy by applying the corresponding response operator directly to the quantum machine learning model. Using this formalism leads to train QML models results in lower out-of-sample errors than learning the corresponding properties directly. The formalism can also be used to reproduce accurate normal modes and IR-spectra in molecules. Finally, the applicability of QML models is explored. A machine learning model which encodes the elemental identities of the atoms placed in each site, to exhaustively screen the formation energy of $\\\\sim$2 milion Elpasolite crystals. The resulting model's accuracy improves systematically with additional training data, reaching an accuracy of 0.1 eV\/atom when trained on 10k crystals. Out of the $\\\\sim$2 million crystals, we identify 90 unique structures which span the convex hull of stability, among which NFAl$_2$Ca$_6$, with uncommon stoichiometry and a negative atomic oxidation state for Al.", "source_url": "https:\/\/openalex.org\/W3001664965", "pdf_url": "https:\/\/edoc.unibas.ch\/72296\/1\/Thesis_Faber.pdf", "doi": "10.5451\/unibas-007149354", "handle": "", "repository_name": "edoc (University of Basel)", "source_record_id": "W3001664965", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 178, "title": "Exploring Information for Quantum Machine Learning Models", "title_ar": "", "title_en": "Exploring Information for Quantum Machine Learning Models", "author": "Michael Telahun", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Louisville", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; deep-learning; quantum-ml", "keywords": "", "abstract": "Quantum computing performs calculations by using physical phenomena and quantum mechanics principles to solve problems. This form of computation theoretically has been shown to provide speed ups to some problems of modern-day processing. With much anticipation the utilization of quantum phenomena in the field of Machine Learning has become apparent. The work here develops models from two software frameworks: TensorFlow Quantum (TFQ) and PennyLane for machine learning purposes. Both developed models utilize an information encoding technique amplitude encoding for preparation of states in a quantum learning model. This thesis explores both the capacity for amplitude encoding to provide enriched state preparation in learning methods and a deep analysis of data properties that provide insights into training data using a Variational Quantum Classifier (VQC). The advent of these new methods begs the question of how to best use these tools, we aim to give some overview explanation for the applicable state of quantum machine learning given actual device constraints. The results show there is a clear advantage for using amplitude encoding over other methods as we show using a hybrid quantum-classical neural network in TFQ. Additionally, there are several steps of preprocessing that can lead to more feature rich data when utilizing a VQC, in essence the no free lunch theorem holds true for quantum learning methods as it does in classical techniques. Information albeit encoded in a quantum form does not change the steps of preparing data but involves new ways to comprehend and appreciate these novel methods.", "source_url": "https:\/\/openalex.org\/W3145589299", "pdf_url": "https:\/\/ir.library.louisville.edu\/cgi\/viewcontent.cgi?article=4808&context=etd", "doi": "10.18297\/etd\/3433", "handle": "", "repository_name": "ThinkIR: The University of Louisville's Institutional Repository (University of Louisville)", "source_record_id": "W3145589299", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 179, "title": "With a quantum machine learning application", "title_ar": "", "title_en": "", "author": "Carolina Allende Amen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "es", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml", "keywords": "", "abstract": "El desarrollo de tesis presentado se centra en el uso de un circuito variacional cuántico para lograr la descomposición unitaria de un algoritmo cuántico. El estudio utiliza técnicas de optimización clásicas y explota las propiedades cuánticas como el entrelazamiento y su relación con la correlación. Este circuito variacional tiene la capacidad de traducir diversos algoritmos cuánticos. Además, se incluye un caso de estudio en el que se aplica esta descomposición para crear un clasificador utilizando un conjunto de datos conocido. En este caso, los datos clásicos se codifican en estados cuánticos.", "source_url": "https:\/\/openalex.org\/W7125353988", "pdf_url": "https:\/\/rad.ort.edu.uy\/bitstreams\/0f6383b4-03e6-4496-86bb-f5aa335a8b40\/download", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7125353988", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 180, "title": "Advances in Quantum Machine Learning", "title_ar": "", "title_en": "Advances in Quantum Machine Learning", "author": "A. S. Wilson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bristol", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7139548657", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Bristol Research (University of Bristol)", "source_record_id": "W7139548657", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 181, "title": "SPARSITY AND WEAK SUPERVISION IN QUANTUM MACHINE LEARNING", "title_ar": "", "title_en": "SPARSITY AND WEAK SUPERVISION IN QUANTUM MACHINE LEARNING", "author": "Seyran Saeedi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Virginia Commonwealth University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "self-semi-supervised", "categories": "machine-learning; computer-vision; self-semi-supervised; quantum-ml; predictive-analytics", "keywords": "", "abstract": "Quantum computing is an interdisciplinary field at the intersection of computer science, mathematics, and physics that studies information processing tasks on a quantum computer. A quantum computer is a device whose operations are governed by the laws of quantum mechanics. As building quantum computers is nearing the era of commercialization and quantum supremacy, it is essential to think of potential applications that we might benefit from. Among many applications of quantum computation, one of the emerging fields is quantum machine learning. We focus on predictive models for binary classification and variants of Support Vector Machines that we expect to be especially important when training data becomes so large that a quantum algorithm with a guaranteed speedup becomes useful. We present a quantum machine learning algorithm for training Sparse Support Vector Machine for problems with large datasets that require a sparse solution. We also present the first quantum semi-supervised algorithm, where we still have a large dataset, but only a small fraction is provided with labels. While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors to obtain the corresponding labels. Here, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines. The algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss while maintaining the same quantum speedup as the Quantum LS-SVM.", "source_url": "https:\/\/openalex.org\/W3112171833", "pdf_url": "", "doi": "10.25772\/037r-wn88", "handle": "", "repository_name": "VCU Scholars Compass (Virginia Commonwealth University)", "source_record_id": "W3112171833", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 183, "title": "Quantum machine learning in drug discovery", "title_ar": "", "title_en": "", "author": "Maria Avramouli", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "el", "research_domain": "medical-ai", "research_subdomain": "quantum-ml", "categories": "machine-learning; medical-ai; quantum-ml", "keywords": "", "abstract": "Τα φάρμακα μέσω της αλληλεπίδρασης τους κυρίως με συγκεκριμένες πρωτεΐνες-στόχους, ρυθμίζουν βιοχημικά μονοπάτια και φυσιολογικές λειτουργίες με στόχο την πρόληψη και τη θεραπεία των διαφόρων ασθενειών. Το αυξανόμενο κόστος και οι μακροχρόνιοι περίοδοι ανάπτυξης των νέων φαρμάκων αποτελούν σημαντική πρόκληση για τη φαρμακοβιομηχανία. Σύμφωνα με πρόσφατα δεδομένα, το 92.5% των υποψηφίων φαρμάκων αποτυγχάνει. Αυτό οδηγεί σε σημαντική απώλεια πόρων και χρόνου με το μέσο συνολικό κόστος ανάπτυξης ενός φαρμάκου να ανέρχεται στα 879.3 εκατομμύρια δολάρια και τον μέσο χρόνο ανάπτυξης του στα 15 χρόνια. Το κόστος της ανάπτυξης νέων φαρμάκων αναμένεται να συνεχίσει να αυξάνεται στο μέλλον, γεγονός που κάνει επιτακτική την ανάγκη εύρεσης νέων προσεγγίσεων που θα μειώσουν το κόστος και τον χρόνο ανάπτυξης. Τα τελευταία χρόνια, η μηχανική μάθηση έχει συνεισφέρει σημαντικά προς αυτή την κατεύθυνση. Επιπλέον, η μεγάλη αύξηση των διαθέσιμων βιολογικών δεδομένων έχει βοηθήσει τις μεθόδους βαθιάς μάθησης να επιδείξουν ανώτερες επιδόσεις σε σύγκριση με τη συμβατική μηχανική μάθηση και άλλες παραδοσιακές υπολογιστικές μεθόδους. Ωστόσο, οι μέθοδοι αυτές είναι πολύπλοκες και χρονοβόρες, γεγονός που αποτελεί σημαντικό εμπόδιο στην πρακτική εφαρμογή τους. Στο πλαίσιο αυτό, η κβαντική υπολογιστική και η κβαντική μηχανική μάθηση προσφέρουν θεωρητικά υποσχόμενες λύσεις για την αποτελεσματική αντιμετώπιση αυτών των προκλήσεων. Η παρούσα διατριβή πραγματεύεται τη κλασική μηχανική μάθηση και την κβαντική υπολογιστική, καθώς και τον τρόπο με τον οποίο αυτά τα πεδία μπορούν να συνδυαστούν αποτελεσματικά, στη δημιουργία ενός νέου ερευνητικού πεδίου αυτό της κβαντικής μηχανικής μάθησης. Επιπλέον, στη συγκεκριμένη εργασία παρουσιάζονται αναλυτικά οι εφαρμογές της κβαντικής μηχανικής μάθησης στο πεδίο της ανακάλυψης φαρμάκων. Η πρόβλεψη της συγγένειας πρόσδεσης φαρμάκων σε πρωτεΐνες-στόχους, είναι ένα από τα πιο σημαντικά θέματα στην ανακάλυψη φαρμάκων. Παρουσιάζουμε τους κλασικούς αλγόριθμους μηχανικής μάθησης που προβλέπουν τη συγγένεια πρόσδεσης και συζητάμε διάφορα θέματα όπως η αναπαράσταση των δεδομένων. Προτείνουμε υβριδικά κβαντο-κλασικά νευρωνικά δίκτυα για την πρόβλεψη της συγγένειας πρόσδεσης πρωτεΐνης-στόχου και πιθανού φαρμάκου καθώς και ένα πλαίσιο υλοποίησης και αξιολόγησης κλασικών και υβριδικών κβαντο-κλασικών νευρωνικών δικτύων. Αρχικά, σχεδιάζουμε ένα μεταβλητό κβαντικό αλγόριθμο για την πρόβλεψη τιμών και ένα κλασικό συνελικτικό νευρωνικό δίκτυο μιας διάστασης για την παράλληλη εξαγωγή χαρακτηριστικών των πρωτεϊνών και των φαρμάκων. Διατηρώντας το κλασικό συνελικτικό νευρωνικό δίκτυο σταθερό, υλοποιούμε διάφορα κλασικά και υβριδικά κβαντο-κλασικά μοντέλα. Στην κλασική προσέγγιση, υλοποιούνται τρία κλασικά τμήματα πρόβλεψης διαφορετικών πολυπλοκοτήτων χρησιμοποιώντας τρία πλήρως συνδεδεμένα στρώματα. Ενώ στην υβριδική προσέγγιση, υλοποιούνται πέντε κβαντικά τμήματα πρόβλεψης, συνδυάζοντας τον μεταβλητό κβαντικό αλγόριθμο πρόβλεψης, με διαφορετικές κβαντικές κωδικοποιήσεις, και με κβαντικά κυκλώματα διαφορετικών qubits. Τα πειράματα εκτελέστηκαν σε προσομοιωτή και τα ευρήματά μας δείχνουν σαφώς ότι τα υβριδικά κβαντο-κλασικά μοντέλα με χαμηλότερη πολυπλοκότητα αποδίδουν βελτιωμένη ακρίβεια και γενίκευση, επιδεικνύοντας το κβαντικό πλεονέκτημα. Επιπλέον, τα υβριδικά μοντέλα επιταχύνουν τη διαδικασία εκπαίδευσης όσον αφορά τις επαναλήψεις και επιτυγχάνουν ταχύτερη σταθεροποίηση. Τα ευρήματα υποδεικνύουν ότι η κβαντική μηχανική μάθηση αποτελεί μια υποσχόμενη κατεύθυνση. Τέλος, η παρούσα διατριβή παρέχει σημαντικές γνώσεις και μεθοδολογίες που μπορούν να βελτιώσουν την εφαρμογή και την αξιολόγηση των τεχνικών κβαντικής μηχανικής μάθησης, ενισχύοντας την πρακτική εφαρμογή τους σε κβαντικές θορυβώδες- ενδιάμεσης κλίμακας συσκευές.", "source_url": "https:\/\/openalex.org\/W4407379535", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/85482\/4\/30830.pdf", "doi": "10.12681\/eadd\/58304", "handle": "", "repository_name": "", "source_record_id": "W4407379535", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 184, "title": "On the uniform expressivity of graph neural networks", "title_ar": "", "title_en": "On the uniform expressivity of graph neural networks", "author": "Eran Rosenbluth", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "The main subject of this work are certain parameterized algorithms, known as Graph Neural Networks, which operate on graphs. Specifically, we analyze what functions on graphs these algorithms can compute, or in other words, we analyze their expressivity.", "source_url": "https:\/\/openalex.org\/W7130370241", "pdf_url": "", "doi": "10.18154\/rwth-2025-11092", "handle": "", "repository_name": "RWTH Publications (RWTH Aachen)", "source_record_id": "W7130370241", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 185, "title": "On Counting Substructures with Graph Neural Networks", "title_ar": "", "title_en": "On Counting Substructures with Graph Neural Networks", "author": "Tahmasebi, Behrooz", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Massachusetts Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "To achieve a graph representation, most Graph Neural Networks (GNNs) follow two steps: first, each graph is decomposed into a number of subgraphs (which we call the recursion step), and then the collection of subgraphs is encoded by several iterative pooling steps. While recently proposed higher-order networks show a remarkable increase in the expressive power through a single recursion on larger neighborhoods followed by iterative pooling, the power of deeper recursion in GNNs without any iterative pooling is still not fully understood. To make it concrete, we consider a pure recursion-based GNN which we call Recursive Neighborhood Pooling GNN (RNPGNN). The expressive power of an RNP-GNN and its computational cost quantifies the power of (pure) recursion for a graph representation network. We quantify the power by means of counting substructures, which is one main limitation of the Message Passing graph Neural Networks (MPNNs), and show how RNP-GNN can exploit the sparsity of the underlying graph to achieve low-cost powerful representations. We also compare the recent lower bounds on the time complexity and show how recursion-based networks are near optimal.", "source_url": "https:\/\/openalex.org\/W7113167203", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace@MIT (Massachusetts Institute of Technology)", "source_record_id": "W7113167203", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 186, "title": "Spiking graph neural networks: A survey", "title_ar": "", "title_en": "Spiking graph neural networks: A survey", "author": "Sathvik Murli", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rutgers University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "deep-learning; ai-applications; cnn; gnn; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "Spiking Neural Networks (SNNs) offer a biologically grounded alternative to conventional Artificial Neural Networks by replacing continuous activations with discrete, asynchronous spike events, while Graph Neural Networks (GNNs) provide a principled way to learn over non-Euclidean relational data. This paper surveys the emerging family of Spiking Graph Neural Networks (SGNNs), which unify these paradigms under a common modular template of a graph convolution backbone, a spike encoding stage, one or more Leaky Integrate-and-Fire (LIF) neuron layers, and a classification readout. Ten representative architectures are compared along four axes: the neuron model, the graph convolution mechanism, the treatment of time (static versus dynamic graphs), and the underlying representation space (Euclidean versus Riemannian). We further review training algorithms, showing that the non-differentiable spikefunction has driven three complementary solutions: surrogate-gradient Backpropagation Through Time, implicit or manifold-based differentiation with O(1) memory, and biologically-local Spike-Timing Dependent Plasticity. To demonstrate practical utility, we also present a case study applying an GNN inspired by SiGNN to malicious DNS-over-HTTPS traffic detection, where per-timestamp graphs are processed by graph convolutional layers followed by LIF-based spiking layers. The results indicate that SGNNs remain resilient to the strong class imbalance of the benchmark, supporting their viability for cybersecurity classification. Taken together, the surveyed work suggests that spiking graph models can inherit the energy benefits of neuromorphic hardware without sacrificing the expressive power of modern graph learning.", "source_url": "https:\/\/openalex.org\/W7165632984", "pdf_url": "", "doi": "10.7282\/t3-1wzw-gs74", "handle": "", "repository_name": "Rutgers University Community Repository (Rutgers University)", "source_record_id": "W7165632984", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 187, "title": "Recommender Systems Enhanced by Graph Neural Networks", "title_ar": "", "title_en": "Recommender Systems Enhanced by Graph Neural Networks", "author": "Filip Stupka", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "Graph neural networks, with their ability to capture relations well, are a modern and attractive field of research. The thesis analyses existing recommendation systems that exploit graph neural networks, namely SR-GNN and TGN, to capture complex patterns in user behaviour. Because the long-term user's action history is often unavailable, the work focuses on models that base their recommendations on the current session. The work compares the models against competitive k-nearest-neighbour baselines and outlines the strengths and weaknesses of both approaches. Part of the work is a review of the theoretical background of the analysed models and the related concepts from both the recommendation systems and graph neural networks fields.", "source_url": "https:\/\/openalex.org\/W7171276206", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7171276206", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 188, "title": "Causal Inference Explanations for Graph Neural Networks", "title_ar": "", "title_en": "Causal Inference Explanations for Graph Neural Networks", "author": "Sahil Satish Kumar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; predictive-analytics; gnn; xai; fairness", "keywords": "", "abstract": "The increasing popularity of Artificial Intelligence, notably Deep Learning, has prompted its application across diverse real-world scenarios. Graph Neural Networks, a type of Deep Learning model developed for graph-structured data, have gained significant popu- larity owing to their versatility and efficacy. Despite their high accuracy and performance, contemporary Deep Learning models often function as black box systems, lacking trans- parency and trustworthiness crucial for deployment in critical decision-making domains. In response, Explainable Artificial Intelligence has emerged, aiming to enhance the trustworthiness of black box models by devising explanation methods that clarify their inner workings. However, prevalent explanation techniques predominantly leverage correlation and association rather than employing causality, a significant aspect of human comprehension. In this thesis, we propose a novel explanation method using causal inference developed specifically for Graph Neural Networks. Our approach seeks to illuminate the decision- making process of Graph Neural Networks, thereby augmenting their transparency and trustworthiness. To show the generality of our approach we do a benchmark evaluation of our method against state-of-the-art explanation methods on a variety of datasets, tasks and Graph Neural Networks architectures. Furthermore, we apply and evaluate our explanation method in two real-world use cases: medical referral recommendation and space object conjunction prediction. In both cases, we demonstrate the applicability of our method and discuss its limitations.", "source_url": "https:\/\/openalex.org\/W7197425670", "pdf_url": "https:\/\/run.unl.pt\/bitstreams\/811956c8-4867-4ac0-a47e-557471695537\/download", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7197425670", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 189, "title": "The expressivity and explainability of graph neural networks", "title_ar": "", "title_en": "The expressivity and explainability of graph neural networks", "author": "Matthew Morris", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Oxford", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; responsible-ai; predictive-analytics; cnn; gnn; xai", "keywords": "", "abstract": "Information stored as unary and binary facts is often referred to as a knowledge graph (KG). KGs are used in many applications, and different methods for learning on them have been proposed. Among these approaches, graph neural networks (GNNs) are effective and widely-used, due to their strong structural inductive biases. Message Passing Neural Networks (MPNNs) are a powerful variety of GNNs, of which Graph Convolutional Networks (GCNs) are a common instantiation. However, there are many open questions pertaining to the expressivity of GNNs: i.e., which functions GNNs are able to represent. A prominent subset of methods for analysing GNN expressivity is to compare them to formal logic. Furthermore, GNNs are black boxes that do not readily provide explanations for their predictions. Explainability and interpretability are crucial for models that operate in high-stakes domains. Thus, various methods seek to provide explanations for GNN predictions, including explanations based on logical rules. However, these rules are often not faithful to the predictions of the model, and there is increasing interest in logical explanations that faithfully characterize the predictions of GNNs. Such explanations have been shown to be attainable by restricting the weights of GCN models to be non-negative: these GCNs are called “monotonic”. In this thesis, I consider the expressivity and explainability of different MPNNs. First, I define a new class of graph functions that allow for symmetry-breaking and propose extensions to MPNNs to learn such functions. Next, I consider monotonic GCNs used with scoring functions for predicting binary facts, providing an upperbound on their logical expressivity and recovering sound rules for predictions. I also relax the constraint of non-negative weights for GCNs with sum aggregation, showing that, without adaptions to their training regime, they provably learn no sound rules in practice. Finally, I also consider monotonic GCNs with mean aggregation, proving the limited class of simple monotonic rules that can be sound for them and providing a rule language for sound explanations. My experiments validate my theoretical results, showing that my novel MPNNs can learn symmetrybreaking graph functions, that monotonic GCNs still perform well on benchmark datasets, and that sound rules for explanations can be recovered in practice.", "source_url": "https:\/\/openalex.org\/W7166129915", "pdf_url": "", "doi": "10.5287\/ora-j0xrqajjx", "handle": "", "repository_name": "Oxford University Research Archive (ORA) (University of Oxford)", "source_record_id": "W7166129915", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 190, "title": "Graph Neural Networks for Writer Identification and Retrieval", "title_ar": "", "title_en": "", "author": "Oliver Pilizar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2026, "language": "de", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "Schreiberidentifizierung (Writer Identification) zielt darauf ab, ein handschriftliches Dokument seinem korrekten Autor zuzuordnen, während Schreibersuche (Writer Retrieval) darauf abzielt, Dokumente desselben Autors aus einer Datenbank abzurufen. Während aktuelle State-of-the-Art-Ansätze auf Dokumentenebene hohe Leistung erzielen, führt die Verwendung von statischen Aggregationsmethoden wie Mittelwert- oder Summenbildung, die Wort- oder Zeilenfeatures zu globalen Repräsentationen von Dokumenten zusammenfassen und dabei feingranulare Details nicht erfassen, zu einem Leistungsabfall auf Wort- und Zeilenebene. In dieser Arbeit wird eine gelernte Aggregationsfunktion auf Basis von Graph Neural Networks vorgestellt, wobei jeder Graph ein Dokument oder eine Zeile als Ganzes repräsentiert. Innerhalb jedes Graphen kodieren die einzelnen Knoten aggregierte Repräsentationen lokaler Handschriftmerkmale, während die Graphstruktur deren räumliche Beziehungen modelliert. Es werden Experimente durchgeführt, um den Einfluss verschiedener Designentscheidungen zu untersuchen, einschließlich der Anzahl und Art der Merkmale, der Strategien zur Graphkonstruktion sowie der Architekturen der Graph Neural Networks. Die Experimente werden hinsichtlich der Identifikationsgenauigkeit, der Hard-Top-k-Metrik und der Mean Average Precision evaluiert. Die Ergebnisse heben das Potenzial von Graph Neural Networks als gelernte Aggregationsfunktion im Kontext der Schreiberidentifizierung und -suche hervor. Beispielsweise erzielt die beste Konfiguration im Rahmen der Schreibersuche auf Dokumentenebene eine mittlere durchschnittliche Präzision (mean Average Precision) von 79,36% und 66,58% auf Zeilenebene im CVL-Datensatz mit durchschnittlich 250 Featurepunkten pro Zeile. Dies zeigt eine starke Leistung auf Dokumentenebene und kommt insbesondere der Aufgabe auf Zeilenebene zugute, bei der nur eine begrenzte Menge an Handschriftinformationen zur Verfügung steht.", "source_url": "https:\/\/openalex.org\/W7162668370", "pdf_url": "https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/228324\/1\/Pilizar%20Oliver%20-%202026%20-%20Graph%20Neural%20Networks%20for%20Writer%20Identification%20and...pdf", "doi": "10.34726\/hss.2026.125394", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W7162668370", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 191, "title": "Learning latent graph structures for graph neural networks", "title_ar": "", "title_en": "Learning latent graph structures for graph neural networks", "author": "Haicang Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; self-semi-supervised; data-mining; transformers; gnn", "keywords": "", "abstract": "Graphs are powerful tools for modeling complex relationships between entities, making them essential in various domains, such as social networks, citation networks, molecular graphs, and road networks. In recent years, Graph Neural Networks (GNNs) have gained significant attention for learning on graphs. However, existing GNNs are very limited in effectively learning various latent graph structures, which are not immediately apparent but very important for graph learning, such as graph clusters. Consequently, there is a pressing need to design novel approaches that enable GNNs to capture diverse latent structures in graph learning. To this end, this dissertation aims to design novel methodologies that can integrate latent structures into graph neural networks to bolster their learning performance. In particular, the focus is on three types of latent graph structures: graph clusters, implicit sparsity and latent structure from the Third Law of Geography. With our investigation on these structures and method designs, we can obtain more insights on both graph structures and graph learning, and further improve the performance of graph neural networks and their real-world applications. First, we highlight the importance of graph clusters in graph learning, and identify the benefits of incorporating graph clusters into graph attention which lacks cluster information. To incorporate graph clusters into graph attention, we proposed Differentiable Clustering for Graph Attention (DCAT), which enhances graph attention by learning cluster-aware attention scores. In DCAT, graph clusters are learned through a differentiable objective based on a relaxed form of modularity maximization. Thus the objective can be optimized together with the semi-supervised loss in an end-to-end manner. Our analysis indicates that DCAT can allocate higher attention scores to nodes within the same cluster, allowing them to have a higher influence on node representation learning. Second, we investigate the latent sparsity phenomenon in real-world graphs, where a large portion of edges connect irrelevant nodes, potentially degrading GNN performance. To address this issue, we propose a novel graph selective attention mechanism,which employs various forms of learnable node-node dissimilarity to learn the latent sparsity. With selective attention, each node can effectively filter out irrelevant edges and selectively attend to its most irrelevant neighbors. We further build the graph selective attention network (SAT) based on it, and conduct theoretical analysis on its expressiveness. Third, we investigate a latent structure in road networks according to the Third Law of Geography, which has not been explored in previous literature. This law suggests a latent structure which goes beyond the spatial relationships of geospatial entities according to their geographic configurations. To learn from the latent structure, we propose a novel graph contrastive learning method, with geographic configuration aware graph augmentation and spectral negative sampling. Our proposed approaches have been evaluated against a number of state-of-the-art baselines across various real-world tasks. The results demonstrate our methods can perform robustly, showcasing learning latent graph structures is indeed an effective way to advance graph neural networks. The methodologies proposed in this thesis inaugurate new directions that can further strengthen graph representation learning for various real-world tasks.", "source_url": "https:\/\/openalex.org\/W4410047771", "pdf_url": "", "doi": "10.32657\/10356\/184288", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4410047771", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 192, "title": "On the Adversarial Robustness of Graph Neural Networks", "title_ar": "", "title_en": "On the Adversarial Robustness of Graph Neural Networks", "author": "Sofiane Ennadir", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dalarna University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; cnn; gnn; ai-cybersecurity", "keywords": "", "abstract": "Graph Neural Networks (GNNs) have emerged as the standard paradigm for machine learning on graph-structured data, demonstrating remarkable success in diverse applications such as molecular design, anomaly detection within networks, and recommendation systems. However, despite their effectiveness in learning meaningful representations for nodes and graphs, GNNs remain vulnerable to adversarial attacks. These attacks, which are small strategically crafted perturbations to the input graph, can result in unreliable predictions. This specific vulnerability raises serious concerns regarding the deployment of GNNs in safety-critical domains like finance and healthcare, where ensuring robustness is crucial. Consequently, understanding and enhancing the adversarial robustness of GNNs has become a critical research focus, involving both the design of potent attack strategies and the development of resilient defense mechanisms. Many existing defense methods rely on pre-processing techniques or modifications to the message-passing framework to mitigate attacks, often by discarding or re-weighting parts of the input graph. Although these defenses have shown great results, they are frequently based on heuristic reasoning and lack strong theoretical guarantees. Specifically, given the input graphs' rich topological aspect, a deeper understanding of their vulnerabilities and internal behaviors is essential, especially regarding how an attack can propagate through the network. Moreover, current defense methodologies are typically evaluated only against the state-of-the-art attacks available at the evaluation time; in the absence of theoretical guarantees, these defenses remain susceptible to more advanced or previously unseen attack strategies. This gap underscores the need for mechanisms that not only exhibit robust empirical performance but also provide certifiable robustness for long-term effectiveness. Furthermore, most current approaches entail high computational overhead, limiting their practical feasibility in real-world applications. In this thesis, we address key challenges in GNN adversarial robustness, focusing on the aforementioned drawbacks. First, we introduce defense mechanisms that are both empirically effective and grounded in solid theoretical analysis, thereby offering provable robustness against evolving attacks. Second, we investigate how to reconcile strong defense performance with computational efficiency, which is an essential requirement in multiple domains such as applications in the mobile and online platforms. Achieving this balance is critical for broadening the deployment of robust GNNs in practical settings. Finally, we explore often overlooked factors related to the training dynamics, such as weight initialization and the number of training epochs, that can substantially influence a model’s underlying robustness, illustrating how effective parameter selection can bolster resilience with very limited costs. The contributions of this thesis are organized around four core pillars. In the first, we propose an adaptation of Graph Convolutional Networks (GCNs) using orthogonal weight matrices, showing both theoretically and empirically that this design can significantly enhance model robustness. In the second contribution, we present a simple yet powerful technique for injecting noise into hidden representations during training, which substantially improves robustness with minimal additional computational cost, consequently offering a more lightweight alternative to many existing, high-complexity defense methods. The third work examines the neglected interplay between training dynamics (e.g., number of epochs, initialization strategies) and model vulnerability, demonstrating how careful tuning of these parameters can enhance a model's underlying robustness. Finally, we propose a novel adversarial attack approach that generates adversarial graphs from scratch via a learnable generator, rather than merely perturbing existing graphs, thereby introducing new perspectives on attack methodologies. Through these contributions, the current thesis aims to provide theoretical insights and tools that could help advance the current understanding of adversarial attacks in the context of GNNs. These contributions and insights can advance the development of robust GNNs, paving the way for safer and more reliable graph-based machine learning systems.", "source_url": "https:\/\/openalex.org\/W7148218355", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Diva portal (Dalarna University Library)", "source_record_id": "W7148218355", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 193, "title": "Graph Neural Networks", "title_ar": "", "title_en": "Graph Neural Networks", "author": "Fernando Gama", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Pennsylvania", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; robotics; ai-applications; optimization; gnn; ai-smart-cities", "keywords": "", "abstract": "The theme of this dissertation is machine learning on graph data. Graphs are generic models of signal structure that play a crucial role in tackling problems in a diverse array of fields, including smart grids, sensor networks, and robot swarms. Thus, developing machine learning models that can successfully learn from graph data is a promising area of research with high potential impact. This dissertation focuses particularly on the topic of graph neural networks (GNNs) as the main machine learning model for successfully addressing problems involving graph data. GNNs are nonlinear representation maps that exploit the underlying graph structure to improve learning and achieve better performance. One of the key properties of GNNs is that they are local and distributed mathematical models, making them particularly relevant for problems involving physical networks. The overarching objective of this dissertation is to characterize the representation space of GNNs. This entails several research directions. First, we define a mathematical framework that provides the general tools and lays the groundwork for the analysis and design of concrete GNN models. Second, we derive fundamental properties and theoretical insights that serve as a foundation for understanding the success observed when employing GNNs in practical problems involving graph data. Third, we explore new application domains that are naturally suited for the use of GNNs based on the properties that these exhibit. We leverage graph signal processing (GSP) and its key concepts of graph filtering and graph frequency domain to provide a general mathematical framework for characterizing GNNs. We derive the properties of permutation equivariance and stability to perturbations of the graph support and use these to explain the improved performance of GNNs over linear graph filers. We also show how these two properties help explain the scalability and transferability of GNNs. We explore the use of GNNs in learning decentralized controllers and showcase their success in the problem of flocking.", "source_url": "https:\/\/openalex.org\/W3098598627", "pdf_url": "", "doi": "10.17023\/h7qz-6762", "handle": "", "repository_name": "ScholarlyCommons (University of Pennsylvania)", "source_record_id": "W3098598627", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 194, "title": "Explaining Graph Neural Networks", "title_ar": "", "title_en": "Explaining Graph Neural Networks", "author": "Tobias Christian Nauen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Leibniz Universität Hannover", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; predictive-analytics; gnn; xai", "keywords": "", "abstract": "Graph Neural Networks are an up-and-coming class of neural networks that operate on graphs and can therefore deal with connected, highly complex data. As explaining neural networks becomes more and more important, we investigate different ways to explain graph neural networks and contrast gradient-based explanations with the interpretability by design approach KEdge. We extend KEdge, to work with probability distributions different from HardKuma. Our goal is to test the performance of each method to judge which one works best under given circum- stances. For this, we extend the notion of fidelity from hard attribution weights to soft attribution weights and use the resulting metric to evaluate the explanations generated by KEdge, as well as by the gradient-based techniques. We also compare the predictive performance of models that use KEdge with different distributions. Our experiments are run on the Cora, SightSeer, Pubmed, and MUTAG datasets. We find that KEdge outperforms the gradient based attribution techniques on graph classification problems and that it should be used with the HardNormal, HardKuma, or HardLaplace distributions, depending on if the top priority is model performance or attribution quality. To compare different metrics of judging attributions in the text domain, we visualize attribution weights generated by different models and find, that metrics which compare model attributions to human explanations lead to bad attribution weights.", "source_url": "https:\/\/openalex.org\/W3217520584", "pdf_url": "", "doi": "10.15488\/11525", "handle": "", "repository_name": "Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover)", "source_record_id": "W3217520584", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 195, "title": "Graph Neural Networks and its applications", "title_ar": "", "title_en": "Graph Neural Networks and its applications", "author": "Pau Rodríguez Esmerats", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "This project will explore some of the most prominent Graph Neural Network variants and apply them to two tasks: approximation of the community detection Girvan-Newman algorithm and compiled code snippet classification.", "source_url": "https:\/\/openalex.org\/W2986402582", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W2986402582", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 196, "title": "Graph Neural Networks: Techniques and Applications", "title_ar": "", "title_en": "Graph Neural Networks: Techniques and Applications", "author": "Zhiqian Chen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; cnn; gnn; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "Effective information analysis generally boils down to the geometry of the data represented by a graph. Typical applications include social networks, transportation networks, the spread of epidemic disease, brain's neuronal networks, gene data on biological regulatory networks, telecommunication networks, knowledge graph, which are lying on the non-Euclidean graph domain. To describe the geometric structures, graph matrices such as adjacency matrix or graph Laplacian can be employed to reveal latent patterns. This thesis focuses on the theoretical analysis of graph neural networks and the development of methods for specific applications using graph representation. Four methods are proposed, including rational neural networks for jump graph signal estimation, RemezNet for robust attribute prediction in the graph, ICNet for integrated circuit security, and CNF-Net for dynamic circuit deobfuscation. For the first method, a recent important state-of-art method is the graph convolutional networks (GCN) nicely integrate local vertex features and graph topology in the spectral domain. However, current studies suffer from drawbacks: graph CNNs rely on Chebyshev polynomial approximation which results in oscillatory approximation at jump discontinuities since Chebyshev polynomials require degree $Omega$(poly(1\/$epsilon$)) to approximate a jump signal such as $|x|$. To reduce complexity, RatioanlNet is proposed to integrate rational function and neural networks for graph node level embeddings. For the second method, we propose a method for function approximation which suffers from several drawbacks: non-robustness and infeasibility issue; neural networks are incapable of extracting analytical representation; there is no study reported to integrate the superiorities of neural network and Remez. This work proposes a novel neural network model to address the above issues. Specifically, our method utilizes the characterizations of Remez to design objective functions. To avoid the infeasibility issue and deal with the non-robustness, a set of constraints are imposed inspired by the equioscillation theorem of best rational approximation. The third method proposes an approach for circuit security. Circuit obfuscation is a recently proposed defense mechanism to protect digital integrated circuits (ICs) from reverse engineering. Estimating the deobfuscation runtime is a challenging task due to the complexity and heterogeneity of graph-structured circuit, and the unknown and sophisticated mechanisms of the attackers for deobfuscation. To address the above-mentioned challenges, this work proposes the first graph-based approach that predicts the deobfuscation runtime based on graph neural networks. The fourth method proposes a representation for dynamic size of circuit graph. By analyzing SAT attack method, a conjunctive normal form (CNF) bipartite graph is utilized to characterize the complexity of this SAT problem. To overcome the difficulty in capturing the dynamic size of the CNF graph, an energy-based kernel is proposed to aggregate dynamic features.", "source_url": "https:\/\/openalex.org\/W3080427735", "pdf_url": "", "doi": "", "handle": "", "repository_name": "VTechWorks (Virginia Tech)", "source_record_id": "W3080427735", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 197, "title": "Towards robust and efficient graph neural networks", "title_ar": "", "title_en": "Towards robust and efficient graph neural networks", "author": "Haoyang Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "HKUST Electronic Theses Towards robust and efficient graph neural networks by Haoyang Li thesis 2023 1 online resource (xiv, 119 pages) : illustrations (some color) Graph Neural Networks (GNNs) have achieved great success in various graph tasks, such as node", "source_url": "https:\/\/openalex.org\/W4386964662", "pdf_url": "", "doi": "10.14711\/thesis-991013223045503412", "handle": "", "repository_name": "", "source_record_id": "W4386964662", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 198, "title": "Graph neural network for situation recognition", "title_ar": "", "title_en": "Graph neural network for situation recognition", "author": "Mohammed Suhail", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of British Columbia", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; transformers; gnn", "keywords": "", "abstract": "Understanding images beyond salient actions involves reasoning about scene con-text, objects, and the roles they play in the captured event. Situation recognition has recently been introduced as the task of jointly reasoning about the verbs (actions) and a set of semantic-role and entity (noun) pairs in the form of action frames. Labeling an image with an action frame requires an assignment of values (nouns) to the roles based on the observed image content. Among the inherent challenges are the rich conditional structured dependencies between the output role assignments and the overall semantic sparsity. In this work, we propose a novel mixture-kernel attention graph neural network (GNN) architecture designed to address these challenges. Our GNN enables dynamic graph structure during training and inference, through the use of a graph attention mechanism, and context-aware interactions between role pairs. It also alleviates semantic sparsity by representing graph kernels using a convex combination of learned basis. We illustrate the efficacy of our model and design choices by conducting experiments on imSitu benchmark dataset, with accuracy improvements of up to 10% over state-of-the-art", "source_url": "https:\/\/openalex.org\/W3003036658", "pdf_url": "", "doi": "10.14288\/1.0384601", "handle": "", "repository_name": "cIRcle (University of British Columbia)", "source_record_id": "W3003036658", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 199, "title": "Towards panoramic explainability of graph neural networks", "title_ar": "", "title_en": "Towards panoramic explainability of graph neural networks", "author": "Ge Lv", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; responsible-ai; gnn; xai", "keywords": "", "abstract": "HKUST Electronic Theses Towards panoramic explainability of graph neural networks by Ge Lv thesis 2023 1 online resource (xiii, 103 pages) : illustrations (some color) Graph Neural Networks (GNNs) have demonstrated outstanding effectiveness on both heterogeneous and homogeneous graphs. However,", "source_url": "https:\/\/openalex.org\/W4405880892", "pdf_url": "", "doi": "10.14711\/thesis-991013246559103412", "handle": "", "repository_name": "", "source_record_id": "W4405880892", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 200, "title": "Graph Neural Networks for Malware Classification", "title_ar": "", "title_en": "Graph Neural Networks for Malware Classification", "author": "Vrinda Malhotra", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; expert-systems; ai-applications; cnn; gnn; image-segmentation; ai-cybersecurity", "keywords": "", "abstract": "Malware is a growing threat to the digital world. The first step to managing this threat is malware detection and classification. While traditional techniques rely on static or dynamic analysis of malware, the generation of these features requires expert knowledge. Function call graphs (FCGs) consist of program functions as their nodes and their interprocedural calls as their edges, providing a wealth of knowledge that can be utilized to classify malware without feature extraction that requires experts. This project treats malware classification as a graph classification problem, setting node features using the Local Degree Profile (LDP) model and using different graph neural networks (GNN) to generate embeddings for each graph which can then be classified. We particularly highlight Jumping Knowledge-based Graph Isomorphism Network (JK-GIN), Jumping Knowledge-based GraphSAGE (JK-GrahSAGE), UnetGraph, and Deep Graph Convolutional Network (DGCNN) since they performed the best and had similar runtimes. These models performed better than existing state-of-the-art approaches in terms of F1 scores. They also address the over-smoothing problem that is rampant with other GNN models.", "source_url": "https:\/\/openalex.org\/W4380786281", "pdf_url": "", "doi": "10.31979\/etd.znr4-vz7n", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4380786281", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 201, "title": "Scaling graph neural networks to larger graphs", "title_ar": "", "title_en": "Scaling graph neural networks to larger graphs", "author": "Zhang, William", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Harvard University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; data-mining; predictive-analytics; transformers; gnn", "keywords": "", "abstract": "Graph-structured data appears abundantly in both the social and natural sciences. How- ever, the common supervised-learning techniques in machine learning are not readily ap- plicable to graph data because they require their inputs to be structured as feature vectors. The current paradigm of learning on graph data is using graph neural networks (GNN). Computation on GNN’s follow the pattern of message-passing of vectors between neigh- boring nodes and message-aggregation. This thesis first motivates and then provides an overview of GNNs. It then discusses the connections between GNNs and Transformers, another recent machine learning model that has taken over the state-of-the-art in many ap- plications. We exploit this connection to develop a procedure that trades off model perfor- mance for training time and execution speed. By clustering nodes together, we can obtain approximations of the representations of each node, and we can leverage this approxima- tion to train and predict with the models much faster. We establish that our method. Addi- tionally, we open source our code for reproducibility of the experiment.", "source_url": "https:\/\/openalex.org\/W7112491723", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)", "source_record_id": "W7112491723", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 202, "title": "A benchmark for graph neural networks for computer network modeling", "title_ar": "", "title_en": "A benchmark for graph neural networks for computer network modeling", "author": "Sergi Carol Bosch", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; predictive-analytics; gnn", "keywords": "", "abstract": "Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay).This thesis introduces the benchmark for computer network modeling using the RouteNet Graph Neural Network as well as a routing creation algorithm.", "source_url": "https:\/\/openalex.org\/W2979944539", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W2979944539", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 203, "title": "Neural architecture search for heterogeneous graph neural networks", "title_ar": "", "title_en": "Neural architecture search for heterogeneous graph neural networks", "author": "Yuhui Ding", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "991013021482403412 HKUST Electronic Theses Neural architecture search for heterogeneous graph neural networks by Yuhui Ding thesis 2021 1 online resource (ix, 53 pages) : color illustrations In this thesis, we propose a novel framework to automatically utilize task-dependent semantic information", "source_url": "https:\/\/openalex.org\/W4298077232", "pdf_url": "", "doi": "10.14711\/thesis-991013021482403412", "handle": "", "repository_name": "", "source_record_id": "W4298077232", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 204, "title": "Malware Classification using Graph Neural Networks", "title_ar": "", "title_en": "Malware Classification using Graph Neural Networks", "author": "Manasa Mananjaya", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "gnn", "categories": "deep-learning; nlp; ai-applications; gnn; ai-cybersecurity", "keywords": "", "abstract": "Word embeddings are widely recognized as important in natural language pro- cessing for capturing semantic relationships between words. In this study, we conduct experiments to explore the effectiveness of word embedding techniques in classifying malware. Specifically, we evaluate the performance of Graph Neural Network (GNN) applied to knowledge graphs constructed from opcode sequences of malware files. In the first set of experiments, Graph Convolution Network (GCN) is applied to knowledge graphs built with different word embedding techniques such as Bag-of-words, TF-IDF, and Word2Vec. Our results indicate that Word2Vec produces the most effective word embeddings, serving as a baseline for comparison with three GNN models- Graph Convolution network, Graph Attention network (GAT), and GraphSAGE network (GraphSAGE). For the next set of experiments, we generate vector embeddings of various lengths using Word2Vec and construct knowledge graphs with these embed- dings as node features. Through performance comparison of the GNN models, we show that larger vector embeddings improve the models’ performance in classifying the malware files into their respective families. Our experiments demonstrate that word embedding techniques can enhance feature engineering in malware analysis.", "source_url": "https:\/\/openalex.org\/W4380791910", "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=2268&context=etd_projects", "doi": "10.31979\/etd.68ya-hj74", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4380791910", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 205, "title": "Graph Neural Networks Exploration", "title_ar": "", "title_en": "", "author": "Barbara Bobeničová", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Czech Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2023, "language": "cs", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "deep-learning; cnn; gnn", "keywords": "", "abstract": "Táto práca sa venuje rozboru metód grafových neurónových sietí pre klasifikáciu vrcholov a grafov. Skúma súčasné knižnice na prácu s grafovými neurónovými sieťami ako StellarGraph, PyTorch Geometric a DGL. Na vybraných datasetoch z Open Graph Benchmark sú otestované a porovnané grafové algoritmy Graph Convolutional Networks, GraphSAGE a Graph Attention Networks. Dosiahnuté výsledky sú porovnané so state of the art výsledkami.", "source_url": "https:\/\/openalex.org\/W7198205004", "pdf_url": "https:\/\/dspace.cvut.cz\/bitstreams\/ce60c1c9-36d6-4791-b523-549f548180cc\/download", "doi": "", "handle": "", "repository_name": "Cvut DSpace (Czech Technical University)", "source_record_id": "W7198205004", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 206, "title": "Unsupervised Graph Neural Networks", "title_ar": "", "title_en": "Unsupervised Graph Neural Networks", "author": "William Leeney", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bristol", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7135714388", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Bristol Research (University of Bristol)", "source_record_id": "W7135714388", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 207, "title": "Network modeling using graph neural networks", "title_ar": "", "title_en": "Network modeling using graph neural networks", "author": "Miquel Ferriol-Galmés", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; ai-applications; gnn; ai-smart-cities", "keywords": "", "abstract": "(English) Network modeling is central to the field of computer networks. Models are useful in researching new protocols and mechanisms, allowing administrators to estimate their performance before their actual deployment in production networks. Network models also help to find optimal network configurations, without the need to test them in production networks. Arguably, the most prevalent way to build these network models is through the use of discrete event simulation (DES) methodologies which provide excellent accuracy. State-of-the-art network simulators include a wide range of network, transport, and routing protocols, and are able to simulate realistic scenarios. However, this comes at a very high computational cost that depends linearly on the number of packets being simulated. As a result, they are impractical in scenarios with realistic traffic volumes or large topologies. In addition, and because they are computationally expensive, they do not work well in real-time scenarios. Another network modeling alternative is Queuing Theory (QT) where networks are represented as inter-connected queues that are evaluated analytically. While QT solves the main limitation of DES, it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In this context, Machine Learning (ML) has recently emerged as a practical solution to achieve data-driven models that can learn complex traffic models while being extremely accurate and fast. More specifically, Graph Neural Networks (GNNs) have emerged as an excellent tool for modeling graph-structured data showing outstanding accuracy when applied to computer networks. However, some challenges still persist: 1. Queues and Scheduling Policies: Modeling queues, scheduling policies, and Quality-of-Service (QoS) mappings within GNN architectures poses another challenge, as these elements are fundamental to network behavior. 2. Traffic Models: Accurately modeling realistic traffic patterns, which exhibit strong autocorrelation and heavy tails, remains a challenge for GNN-based solutions. 3. Training and Generalization: ML models, including GNNs, require representative training data that covers diverse network operational scenarios. Creating such datasets from real production networks is unfeasible, necessitating controlled testbeds. The challenge lies in designing GNNs capable of accurate estimation in unseen networks, encompassing different topologies, traffic, and configurations. 4. Generalization to Larger Networks: Real-world networks are often significantly larger than testbeds. Scaling GNNs to handle networks with hundreds or thousands of nodes is a pressing challenge, one that requires leveraging domain-specific network knowledge and novel architectural approaches. This dissertation represents a step forward in harnessing Graph Neural Networks (GNN models) for network modeling, by proposing a new GNN-based architecture with a focus on addressing these critical challenges while being fast and accurate. (Català) El modelatge de xarxes és fonamental en el camp de les xarxes informàtiques. Els models són útils per investigar nous protocols i mecanismes, permetent als administradors estimar el seu rendiment abans del seu desplegament real a les xarxes de producció. Els models de xarxa també ajuden a trobar configuracions de xarxa òptimes, sense necessitat de provar-les a les xarxes de producció. Sens dubte, la manera més freqüent de construir aquests models de xarxa és mitjançant l'ús de metodologies de simulació d'esdeveniments discrets (DES) que proporcionen una precisió excel·lent. Els simuladors de xarxa d'última generació inclouen una àmplia gamma de protocols de xarxa, transport i encaminament, i són capaços de simular escenaris realistes. Tanmateix, això comporta un cost computacional molt elevat que depèn linealment del nombre de paquets que s'estimulen. Com a resultat, són poc pràctics en escenaris amb volums de trànsit realistes o topologies grans. A més, i com que són computacionalment costosos, no funcionen bé en escenaris en temps real. Una altra alternativa de modelització de xarxes és la teoria de la cua (QT) on les xarxes es representen com a cues interconnectades que s'avaluen analíticament. Tot i que QT soluciona la principal limitació del DES, imposa supòsits forts sobre el procés d'arribada de paquets, que normalment no es compleixen a les xarxes reals. En aquest context, l'aprenentatge automàtic (ML) ha sorgit recentment com una solució pràctica per aconseguir models basats en dades que poden aprendre models de trànsit complexos alhora que són extremadament precisos i ràpids. Més concretament, les xarxes neuronals de gràfics (GNN) han sorgit com una excel·lent eina per modelar dades estructurades en gràfics que mostren una precisió excepcional quan s'apliquen a xarxes d'ordinadors. Aquesta tesi pretén ser un pas endavant en l'aplicació de les xarxes neuronals de gràfics per al modelatge de xarxes.", "source_url": "https:\/\/openalex.org\/W3096859638", "pdf_url": "https:\/\/upcommons.upc.edu\/bitstream\/2117\/422057\/1\/TMFG1de1.pdf", "doi": "10.5821\/dissertation-2117-422057", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W3096859638", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 208, "title": "Towards Fair and Explainable Machine Learning Modeling in Mental Health", "title_ar": "", "title_en": "Towards Fair and Explainable Machine Learning Modeling in Mental Health", "author": "Gizem Soğancıoğlu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Utrecht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; xai; fairness", "keywords": "", "abstract": "As machine learning continues to evolve, its integration into mental health practices promises to provide more effective, efficient, and accessible care. However, addressing ethical considerations remains important to harnessing the full potential of machine learning in mental health. In this thesis, we focus on two challenges of responsible machine learning; fairness and explainability within the mental health domain. Our work involves developing and validating concept-based models to improve explainability in mental health applications, as well as identifying, evaluating, and mitigating bias in AI models within this field. This thesis is organized around five sub-research questions designed to address the main research question: `How can we develop algorithmic fairness and explainability methods to address the specific needs of the mental health domain?' We begin by exploring a range of concept-bottleneck models, demonstrating their potential in affective computing (Chapter 2) and mental health applications (Chapter 3). These findings motivate future research to integrate higher-level, clinically validated concepts into complex models, enabling clinicians to justify and correct model predictions. Despite promising results, we identify gender bias in these models. To address this, we further investigate existing gender biases in training datasets, pre-trained word embeddings, and machine learning models in the mental health domain (Chapter 4). We demonstrate how even accurate prevalence rate differences in training data can negatively impact downstream models and recommend a counterfactual data augmentation approach as a bias mitigation strategy. Next, we seek to bridge the gap between clinicians and mathematical fairness objectives by conducting interviews with clinicians, showing how clinical insights can inform the selection of fairness measures for real-world applications (Chapter 5). Based on their input, we introduce a gain-based model selection method that prioritizes the performance of individual groups over parity-based fairness measures. Finally, we present new bias mitigation techniques, ProxyMute and ProxyROAR, which automatically remove sensitive information (e.g., gender-related features) from the feature space using explainability methods (Chapter 6). We show that these approaches provide a viable alternative when dictionary-based methods are not suitable. Collectively, these studies make a significant contribution by highlighting both the potential and the challenges of applying fairness and explainability methods in the mental health domain, while also proposing novel approaches to address the limitations of existing methods to ensure the responsible use of AI models in this field. All the codes of experiments are made publicly available to speed up the research in this field.", "source_url": "https:\/\/openalex.org\/W4409017205", "pdf_url": "", "doi": "10.33540\/2843", "handle": "", "repository_name": "Utrecht University Repository (Utrecht University)", "source_record_id": "W4409017205", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 210, "title": "Bias Mitigation in Machine Learning Models for Fair Decision-Making", "title_ar": "", "title_en": "Bias Mitigation in Machine Learning Models for Fair Decision-Making", "author": "Suresh Kumar Thuraka", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "National College of Ireland", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; predictive-analytics; fairness", "keywords": "", "abstract": "The thesis research topic is AI Fairness (bias mitigation in machine learning models). We use Adult Census Income to compare the merits of reweighting, adversarial debiasing, and prejudice removal methods through experiments. Although the baseline models had high accuracies (84.1 percent), they had gender bias (males were 2.7 times more likely to be given favorable predictions). We achieved a greater reduction of up to 93.7 percent in statistical parity difference with an accuracy loss of only 0.7 percent using our optimized GerryFair implementation, in contrast to the other methods. Nevertheless, intersectional analysis demonstrated continued unfairness, especially to black females (4 times lower predictive rates than White males). The analysis has shown that the existing fairness methods must respond to implementation issues and intersectional dynamics more effectively to bring the much-desired equity to-real-life systems.", "source_url": "https:\/\/openalex.org\/W7204020044", "pdf_url": "https:\/\/norma.ncirl.ie\/9563\/2\/sureshkumarthurakaconfigurationmanual.pdf", "doi": "", "handle": "", "repository_name": "TRAP@NCI (National College of Ireland)", "source_record_id": "W7204020044", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 211, "title": "Adaptive and Responsible Machine Learning: From Incremental Learning to Unlearning and Safe Generation", "title_ar": "", "title_en": "Adaptive and Responsible Machine Learning: From Incremental Learning to Unlearning and Safe Generation", "author": "Sayedmoslem Shokrolahi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Queen's University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; responsible-ai; ai-applications; multimodal; image-segmentation; diffusion; xai; fairness; ai-cybersecurity", "keywords": "", "abstract": "Modern machine learning (ML) systems are increasingly required to operate in dynamic environments, where models must adapt to new information, remove obsolete or sensitive knowledge, and satisfy fairness and safety requirements. This thesis addresses these challenges by developing mechanisms for controlling what a model learns, retains, and forgets. The first part studies incremental learning (IL) in data-free and class-incremental settings. To mitigate catastrophic forgetting and inter-task confusion, an embedding-based regularization strategy is proposed that separates current and previously learned class representations using stored class centroids. In addition, a generative replay mechanism reusing a previously trained model synthesizes representative samples of past tasks. A knowledge distillation formulation operating on embedding clusters is further developed to preserve intra-class and inter-class structures, enabling scalable IL without storing historical data. The second part investigates machine unlearning in discriminative models under strict data-free constraints. A new unlearning framework, termed MaxDiv, is proposed to remove the influence of designated training data by generating erasing samples that reverse the feature distribution of the data to be forgotten. The proposed method combines erasure-oriented objectives with representation-preserving losses to achieve effective forgetting while maintaining performance on remaining data. Experiments demonstrate improved forgetting guarantees, resistance to model inversion attacks, and superior preservation of retained knowledge compared to existing unlearning approaches. The third part extends machine unlearning to generative models, where the goal is often to control undesirable behaviors such as bias, unsafe content, and ethical misalignment. Following the responsible AI paradigm, this thesis studies responsible image generation in text-to-image (T2I) diffusion models (DMs) as a representation-level intervention problem. We first focus on U-Net-based DMs, where precise concept vectors are learned in the bottleneck layer to generate responsible images that preserve foreground and background content. We then demonstrate that multi-modal attention heads in ViT-based DMs exhibit interpretable and near-linear properties analogous to the bottleneck layer in U-Net-based architectures. Building on this observation, we introduce an architecture-agnostic framework for learning interpretable concept vectors that enable controllable, disentangled, and human-interpretable manipulation of internal representations across U-Net- and ViT-based diffusion backbones. Overall, this thesis provides a unified framework for adaptive and responsible ML.", "source_url": "https:\/\/openalex.org\/W7207902332", "pdf_url": "", "doi": "", "handle": "", "repository_name": "QSpace (Queen's University Library)", "source_record_id": "W7207902332", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 212, "title": "Developing and Evaluating Fairness-Aware Machine Learning Methods; Ethical Responsibility and Accountability for Algorithmic Bias in Hiring", "title_ar": "", "title_en": "Developing and Evaluating Fairness-Aware Machine Learning Methods; Ethical Responsibility and Accountability for Algorithmic Bias in Hiring", "author": "Tim Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "machine-learning; computer-vision; responsible-ai; predictive-analytics; face-recognition; video-analysis; fairness", "keywords": "", "abstract": "Introduction Machine learning systems have increasingly taken control of making high-stake decisions about people’s lives. This can include who advances in a hiring pipeline, who is released before trial, or whose face a security camera flag. The reason behind this mass adoption is the efficiency and apparent objectivity that machine learning systems bring. What gets left out is that this objectivity that is spoken of by organizations is often a fiction. When training a model using historical data, it is often shaped by the discrimination apparent within this data. So instead of correcting them, it encodes and automates these patterns. Amazon’s recruiting algorithm penalized resumes containing the word “women’s” as it had learned from a male-dominated workforce. HireVue’s video interviewing system was responsible for generating disparate outcomes across demographic groups and even faced EEOC charges in 2025. These are the inevitable outcomes that come from machine learning systems optimizing for accuracy with no questions raised about fairness. My thesis addresses this problem with two directions: A technical project proposing a framework for evaluating fairness-aware machine learning methods, and an STS project that uses Actor-Network Theory to analyze how ethical responsibility for discriminatory hiring algorithms is distributed and evaded. Technical Project The technical portion of my thesis proposes a systematic framework for comparing bias mitigation techniques across three pipeline stages, which are preprocessing, in-processing, and postprocessing. It includes the use of three datasets: The Adult Income dataset, a synthetic pretrial risk assessment dataset that will be constructed from public criminal justice statistics, and facial recognition benchmarks. The synthetic dataset is critical to the project as it allows control over what known bias patterns are explicitly implemented into the data and therefore the framework can test whether each method removed them, which is impossible with proprietary tools like COMPAS. The evaluation will use Python, scikit-learn, and IBM’s AI Fairness 360 toolkit to measure not only the predictive performance but also fairness metrics which will include demographic parity, equalized odds, and equalized opportunity. The expected result from the project is a practical and empirically grounded understanding of the tradeoffs that come from each type of method and in which domains each of them work best. STS Research Paper In my STS research, I applied Actor-Network Theory to discover and trace how responsibility for biased hiring algorithms is formed and deflected. ANT treats both human and non-human actors symmetrically, so executives, data scientists, training datasets, and legal frameworks are all actants that actively shape the outcomes; therefore blame cannot rest at any single node. Through case studies of Amazon’s recruiting tool, HireVue’s video system, and the 2024 Mobley v. Workday lawsuit, I have mapped how accountability spreads when no one actor has full control of the system. The employers will blame vendors, vendors claim they only built the tool, and regulators find civil rights laws ill-suited to algorithmic decision-making. Drawing from the works of O’Neil, Noble, and Eubanks, my research argues this diffusion is a structural feature of how these networks are formed and recommends three specific interventions: mandatory algorithmic impact assessments, enforceable third-party bias audits with the inclusion of public disclosure, and vendor liability reforms. Conclusion When put together, these two projects illustrate a core tension as the technical work demonstrates that fairness interventions are feasible, while the STS research shows that this technical feasibility will not matter if there are no accountability structures that require their use. The technical framework strengthens the argument that developers who create the algorithms and employers who use them have an obligation to act. The STS analysis details why these tools remain underused, as organizations have very little reason to adopt these fairness-aware methods when the alternative carries no real legal or reputational risk. Hiring is a primary domain where this often occurs in and automating discrimination at scale behind the façade of objectivity causes harm that is harder to detect and contest than its human equivalent. Addressing this issue requires both the technical capability of building fairer systems and the institutional will to require that organizations use them.", "source_url": "https:\/\/openalex.org\/W7159615210", "pdf_url": "", "doi": "10.18130\/svmp-4c59", "handle": "", "repository_name": "Libra", "source_record_id": "W7159615210", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 213, "title": "Trustworthy AI Under Distribution Shifts: Enhancing Generalization and Fairness in Deep Learning", "title_ar": "", "title_en": "Trustworthy AI Under Distribution Shifts: Enhancing Generalization and Fairness in Deep Learning", "author": "Gezheng Xu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Western University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "fairness", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; responsible-ai; transfer-learning; predictive-analytics; optimization; fairness", "keywords": "", "abstract": "Recent advances in machine learning and deep learning have achieved remarkable success across a wide range of applications, yet they largely rely on data-driven paradigms that assume the training and test data share similar distributions. In real-world scenarios, however, this assumption often fails. Distribution shifts between training and deployment environments or among population subgroups can significantly compromise the performance and reliability of AI systems. Moreover, privacy constraints frequently prevent direct access to source data, and ethical concerns surrounding fairness raise additional challenges for the trustworthy use of AI in sensitive domains such as healthcare and finance. These factors together highlight the urgent need for a deeper and more principled understanding of trustworthy AI under distribution shifts. This thesis examines the influence of distribution shifts along two complementary dimensions of trustworthy AI: generalizability and fairness. On one hand, we focus on the noisy and unreliable signals caused by distribution shift, which place learning in a weak-supervision regime and thereby limit AI systems’ generalizability. On the other hand, we focus on the distribution shifts that arise from structural data imbalance or scarcity rooted in historical and societal factors, which give rise to socially unfavorable model behaviors and, in turn, exacerbate fairness issues in the decision-making process. From the generalizability perspective, the thesis studies the domain adaptation problem under data privacy constraints, formulated as the Source-Free Domain Adaptation (SFDA) problem. SFDA is examined in both classification and regression tasks, with a focus on pseudo-label noise mitigation and uncertainty modeling, respectively. For the classification problem, a noise-robust loss–based noise-and-variance control method (NVC-LLN) is proposed to mitigate the unbounded label noise inherent in SFDA. For the regression task, a histogram-based uncertainty reconstruction framework, MERCI, is proposed to refine continuous supervision signals and align feature presentations under distribution shift. For the fairness problem, this thesis focuses on the multiple-subgroup situation, ranging from a single sensitive attribute with multiple subgroups to multiple sensitive attributes with intersectional bias, covering both unfairness discovery and mitigation. For subgroup discovery, a Bias-Guided Generative Network (BGGN) is proposed to identify underrepresented subgroups instead of relying on traditional search methods. For mitigation, a generalization error bound is derived under the PAC-Bayes framework, motivating a probabilistic-predictor-based bilevel optimization algorithm, FAMS, which achieves both fairness and accuracy across multiple subgroups under limited-data conditions. Collectively, these studies advance the development of responsible, adaptive, and human-centric AI systems. These systems are capable of performing reliably across diverse and dynamic environments.", "source_url": "https:\/\/openalex.org\/W7205536362", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Scholarship@Western (Western University)", "source_record_id": "W7205536362", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 214, "title": "Engineering Fairness in Machine Learning Systems", "title_ar": "", "title_en": "", "author": "Moses Openja", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "École Polytechnique de Montréal", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2026, "language": "fr", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; medical-ai; responsible-ai; self-semi-supervised; llm; fairness", "keywords": "", "abstract": "s systèmes d’apprentissage automatique (AA), y compris les modèles classiques, les réseaux de neurones profonds (DNN) et les grands modèles de langage (LLMs), sont de plus en plus déployés dans des domaines à enjeux élevés tels que la santé, le recrutement, la fi-nance et les politiques publiques, où des décisions injustes ou biaisées peuvent avoir des conséquences sociétales majeures. Bien que des progrès substantiels aient été réalisés en matière d’apprentissage équitable, la plupart des approches existantes considèrent l’équité comme une contrainte statique lors de l’entraînement et supposent des distributions de don-nées fixes, des attributs sensibles prédéfinis et une possibilité de réentraîner sans restriction. En pratique, toutefois, les systèmes d’apprentissage automatique évoluent en permanence : le biais peut être introduit lors du prétraitement des données et de la sélection des carac-téristiques, émerger au cours de l’entraînement à travers l’apprentissage des représentations, persister dans des modèles préentraînés, ou être amplifié après le déploiement en raison de changements de distribution. Il est donc essentiel de garantir que l’équité n’est pas compro-mise tout au long du développement et de l’évolution des systèmes d’AA—non seulement pour produire des systèmes logiciels de qualité, mais aussi pour la conformité réglementaire et l’audit. Cette thèse envisage l’équité comme une propriété de qualité au niveau logi-ciel des systèmes d’AA, qui doit être continuellement testée, diagnostiquée et corrigée tout au long du cycle de vie du système. S’inspirant de l’ingénierie logicielle et de l’assurance qualité, la thèse adopte une démarche proactive en matière d’équité, mettant l’accent sur la détection précoce et la correction à faible coût des composants générant des biais, tout en développant également des mécanismes de correction post-entraînement et post-déploiement spécifiques aux contextes où le réentraînement n’est pas faisable. Aux stades des données et de l’entraînement, la thèse propose une méthode systématique pour identifier les carac-téristiques du modèle susceptibles de produire des biais, même sans connaître à l’avance les attributs sensibles. Grâce à une analyse cause-à-effet, cette approche permet aux experts du domaine de raisonner sur les sources de biais et démontre empiriquement que corriger ou supprimer les caractéristiques moins importantes mais génératrices de biais peut améliorer significativement l’équité sans détériorer la performance prédictive. Pour les DNN entraînés, la thèse introduit FairFLRep, un cadre de localisation et de répa-ration des défauts sensible à l’équité qui considère les comportements injustes comme des défectuosités localisées au sein des représentations apprises. FairFLRep identifie les poids de neurones contribuant de manière disproportionnée aux disparités entre sous-groupes et applique des mises à jour ciblées et minimales des paramètres, inspirées par les techniques de correction logicielle issues de l’ingénierie logicielle traditionnelle. En outre, la thèse étudie l’équité dans les modèles transformateurs préentraînés au travers de deux études complé-mentaires. La première examine la manière dont les biais sociaux sont représentés en interne dans les transformateurs, à partir d’un ensemble de données de relations biaisées — des triplets codant des stéréotypes selon divers types de biais — et de techniques d’attribution neuronale. L’étude montre que la connaissance biaisée est souvent concentrée dans de petits sous-ensembles de neurones tout en étant aussi redondamment encodée à travers le réseau. Ces résultats établissent le biais comme un phénomène à la fois architectural et représen-tatif, plutôt qu’un simple artéfact lié aux données, et en évaluent l’impact sur des tâches d’ingénierie logicielle en aval. Sur la base de ces observations, la seconde étude propose un cadre de réparation rigoureux pour les transformateurs préentraînés. Elle introduit une lo-calisation neuronale contrastive et optimale selon Pareto afin d’identifier les neurones de biais de manière concentrée et fiable, puis applique une optimisation axée sur la justesse à l’aide de la méthode d’optimisation par essaim particulaire (PSO) pour les corriger. Les résultats démontrent que la réparation ciblée au niveau des neurones est efficace pour atténuer les biais et constitue une alternative légère et pratique au réentraînement global. Enfin, la thèse aborde la réparation de l’équité sous changement de distribution, un phénomène fréquent dans les déploiements réels, même lorsque les facteurs sources du décalage de données ou de biais sont inconnus ou impossibles à mesurer. Pour dépasser les limites des approches de réparation statiques comme FairFLRep, la thèse propose DShift-FRep, un cadre adaptatif intégrant un alignement des données, un finetuning léger à point de départ chaud, et une réparation ciblée de neurones via PSO.", "source_url": "https:\/\/openalex.org\/W7220748911", "pdf_url": "https:\/\/publications.polymtl.ca\/76114\/1\/2026_MosesOpenja.pdf", "doi": "", "handle": "", "repository_name": "PolyPublie (École Polytechnique de Montréal)", "source_record_id": "W7220748911", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 215, "title": "Bias and Fairness in AI Decision Making for Hiring Processes", "title_ar": "", "title_en": "Bias and Fairness in AI Decision Making for Hiring Processes", "author": "Muhammad Edwin Dwi Putra", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; predictive-analytics; xai; fairness", "keywords": "", "abstract": "Artificial intelligence algorithms have been found to perpetuate societal biases that have been embedded in the system over decades due to historical discrimination. In the recruitment sector, this often manifested in biased hiring decisions, where the AI-driven hiring tools inherited patterns that led to unfair outcomes. The purpose of this research was to detect and mitigate such bias by providing transparent explanations for an AI-driven decision, which supports fairness in recruitment processes. A machine learning model was developed to assess candidate suitability based on multiple recruitment-related features. The SHAP (SHapley Additive exPlanations) framework was applied to interpret the model's predictions and to identify the influence of individual features, with particular attention to gender as a sensitive attribute. The purpose of using the XAI (explainable AI) technique was to identify and examine potential bias in order to mitigate it. Examination and analysis of sensitive variables behind an outcome, such as gender, revealed a measurable influence on model predictions. These findings helped identify potential biases in the recruitment model and informed modifications aimed to reduce their impact. Incorporating XAI-based transparency into recruitment algorithms provided a systematic and actionable approach to bias detection. This approach enhanced the fairness of the AI recruitment tools and fostered greater trust in their adoption.", "source_url": "https:\/\/openalex.org\/W7116823264", "pdf_url": "https:\/\/ltu.diva-portal.org\/smash\/get\/diva2:2007317\/FULLTEXT01", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7116823264", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 216, "title": "A Comparative Benchmark of Fairness Metrics in Machine Learning", "title_ar": "", "title_en": "A Comparative Benchmark of Fairness Metrics in Machine Learning", "author": "Md Asif Shahariar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; predictive-analytics; xai; fairness", "keywords": "", "abstract": "Machine learning is no longer limited to low-risk tasks, but it is also being used in critical decision-making systems. Consequently, machine learning has a direct effect on individuals' lives in domains such as recidivism prediction (COMPAS) and loan approval. Thus, errors or biases generated by such algorithms may have severe consequences in the real world. Therefore, our expectations of ML are not limited to accuracy. In addition to making correct predictions, we expect the models to behave in an unbiased manner toward individuals and groups. Many mathematical definitions of fairness have been proposed by researchers, but there have been few large, systematic comparisons of fairness metrics applied to standard machine learning models. The main task of this thesis is to examine in detail how the balance or trade-off between accuracy and fairness occurs in different models. The study used two socially sensitive datasets, COMPAS recidivism and Adult Income. These two datasets were used to train and test nine machine learning models. The models used in this study include nine widely used binary classifiers, ranging from simple and interpretable algorithms (e.g., Logistic Regression and Support Vector Machine) to high-performance ensemble methods (e.g., Random Forest, XGBoost, and LightGBM). Each model was carefully evaluated using Demographic Parity Difference (DPD) and Equalized Odds Difference (EOD) which are two important group fairness criteria, and traditional performance metrics including Accuracy and F1-score. The outcome of the experiment clearly shows a trade-off between accuracy and fairness despite the fact that the nature of this relationship varies greatly among the model architectures. Although ensemble algorithms including XGBoost and LightGBM achieved the highest predictive accuracy, they also had the largest fairness gaps. On the other hand, less complex models like Decision Trees and Multi-layer Perceptrons tended to be more fair and less accurate. One of the key findings in the hyperparameter sensitivity analysis is the fact that fairness is not fixed. It varied with hyperparameters for models like Logistic Regression and SVM, but was less responsive for others. Furthermore, statistical significance testing highlighted that while performance advantages were often robust, differences in fairness rankings were frequently statistically insignificant, cautioning against over-reliance on minor metric variations. This thesis develops a robust empirical foundation for fairness assessment in machine learning through broad benchmarking with both sensitivity analysis and statistical testing. This study serves as a methodological guide and a source of empirical evidence for practitioners and researchers, providing a framework for evaluating fairness based on evidence-based insights. These results indicate that fairness is not universal or transferable across models or datasets, it must be evaluated contextually, with awareness of model behavior and tuning sensitivity.", "source_url": "https:\/\/openalex.org\/W7132936585", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/232398\/4\/ShahariarMdAsif.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7132936585", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 217, "title": "Towards Fair AI Systems: Programmer Practices and Human Machine Interaction", "title_ar": "", "title_en": "Towards Fair AI Systems: Programmer Practices and Human Machine Interaction", "author": "Seyedmohammadreza Shahsahebi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; fairness", "keywords": "", "abstract": "This thesis studies fairness in AI-assisted decision systems as a behavioral and organizational problem that unfolds across the development, deployment, and learning stages of algorithmic systems. Rather than treating bias as a purely technical property of machine learning models, the thesis examines how fairness outcomes are shaped by the choices of algorithm developers, the cognition of human decision makers, and the structure of repeated interaction between humans and machines. Chapter 2 examines the development stage through two behavioral experiments in which participants build and select machine learning models under different organizational conditions. The results show that stringent accuracy targets increase the likelihood of selecting the least fair model, while fairness norm messaging and accountability requirements shift participants toward fairer submissions. Chapter 3 studies the deployment stage through an analytical model in which a rationally inattentive Decision Maker observes a potentially biased Machine signal, forms a Bayesian belief, and decides how much costly additional information to acquire before taking a final action. The analysis shows that awareness of group-specific Machine error rates can improve both fairness and accuracy relative to unawareness, although awareness does not eliminate disparities under all parameter configurations. Chapter 4 extends this framework by introducing an experience-based learning phase in which the DM learns perceived group-specific error rates from repeated feedback before entering the steady decision phase. The results show that asymmetric sensitivity to Machine errors, group representation in the learning environment, and model-card initialization jointly shape the speed and direction of learned beliefs, which then determine whether the human-Machine system mitigates, preserves, or reverses the Machine’s original bias. Across the three essays, the thesis shows that fair AI systems require governance mechanisms that shape developer behavior, disclosure practices that support informed human oversight, and learning environments that calibrate users’ beliefs about algorithmic errors.", "source_url": "https:\/\/openalex.org\/W7204485062", "pdf_url": "https:\/\/ucalgary.scholaris.ca\/bitstreams\/80772d94-2b0b-45a1-a92f-edc9dd8311fe\/download", "doi": "10.11575\/prism\/51798", "handle": "", "repository_name": "", "source_record_id": "W7204485062", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 218, "title": "Explainable AI: Enhancing Machine Learning Model Interpretability with Generative AI", "title_ar": "", "title_en": "Explainable AI: Enhancing Machine Learning Model Interpretability with Generative AI", "author": "Castelhano, Inês Dinis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; medical-ai; responsible-ai; ai-applications; llm; xai; fairness; ai-cybersecurity", "keywords": "", "abstract": "In today’s data-driven world, machine learning (ML) powers critical decisions across sectors such as healthcare and finance, but the opacity of complex “black-box” models undermines trust, accountability, and adoption. This thesis tackles the urgent challenge of explainability by exploring how Large Language Models (LLMs) can transform post-hoc explanations into clear, actionable insights for diverse stakeholders. Moving beyond traditional techniques like SHAP and Counterfactuals, which often overwhelm with complexity, this research introduces a dynamic framework that integrates LLMs as narrative explainers. The methodology combines robust ML pipelines with post-hoc interpreters, enhanced through prompt engineering, to generate explanations in both technical and business-friendly formats. Experiments on real-world datasets, including emergency healthcare and bank fraud detection, benchmarked leading LLMs such as GPT-4o, Claude 3, LLaMA 3, and DeepSeek. Results show that GPT-4o consistently delivers the most accurate, fluent, and stakeholderaligned explanations, while local open-weight models offer competitive, privacy-preserving alternatives. The evaluation, comprising linguistic heuristics, semantic similarity metrics, and human judgment, demonstrated significant gains in clarity, completeness, and trustworthiness over conventional explainers. Crucially, counterfactual-based narratives proved highly intuitive for decision-making, while SHAP-based explanations achieved greater technical depth. By reframing LLMs as interpretable mediators rather than mere translators, this study provides empirical evidence that generative AI can close the gap between ML performance and human understanding. The contributions extend beyond academic insight, offering practical guidelines for deploying Explainable AI in high-stakes domains where transparency is not optional but essential for fairness, accountability, and trust.", "source_url": "https:\/\/openalex.org\/W7112581040", "pdf_url": "https:\/\/run.unl.pt\/bitstreams\/3e76fd1f-bd42-49bf-922d-b79fcffa9a17\/download", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7112581040", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 219, "title": "Artificial Intelligence in Public Employment Services: Machine Learning, Job Match Quality, and Fairness in Labour Market Matching", "title_ar": "", "title_en": "Artificial Intelligence in Public Employment Services: Machine Learning, Job Match Quality, and Fairness in Labour Market Matching", "author": "Sabrina Mühlbauer", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Regensburg", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; predictive-analytics; fairness", "keywords": "", "abstract": "This dissertation examines the use of artificial intelligence and machine learning in public employment services. Using comprehensive administrative labour market data, it investigates three key dimensions of algorithm-based occupational recommendations: predictive accuracy, job match quality, and fairness. The results demonstrate that machine learning methods outperform conventional statistical approaches in predicting occupational transitions. Furthermore, a matching index is developed that incorporates expected employment stability and wages alongside predicted employment probabilities. Finally, the dissertation examines potential biases and inequalities in algorithmic recommendations from an intersectional perspective. The findings highlight that the responsible use of artificial intelligence in public employment services requires considering not only predictive performance but also the quality and fairness of occupational recommendations.", "source_url": "https:\/\/openalex.org\/W7225830611", "pdf_url": "https:\/\/epub.uni-regensburg.de\/81058\/1\/dissertation3.pdf", "doi": "10.5283\/epub.81058", "handle": "", "repository_name": "University of Regensburg Publication Server (University of Regensburg)", "source_record_id": "W7225830611", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 220, "title": "RESPONSIBLE RISK ASSESSMENT OF MACHINE LEARNING TOOLS FOR PROMOTION BOARDS", "title_ar": "", "title_en": "RESPONSIBLE RISK ASSESSMENT OF MACHINE LEARNING TOOLS FOR PROMOTION BOARDS", "author": "Michael R. Burke", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Naval Postgraduate School", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; robotics; responsible-ai; predictive-analytics; fairness", "keywords": "", "abstract": "This thesis evaluates an artificial intelligence (AI) model's ability to support U.S. Marine Corps promotion boards, investigating how data drift and feedback loops impact its performance and reliability over time. Using the DataRobot automated machine learning (AutoML) platform, a model was trained on fiscal year (FY) 2023 Major promotion board data and subsequently tested against FY24 and FY25 data to measure performance degradation and detect statistical drift in key predictors. The model demonstrated strong but brittle performance, maintaining high ranking accuracy across years while relying heavily on administrative features like promotion zone, which exhibited significant data drift. The research concludes that without robust governance, the model's dependence on procedural proxies risks amplifying biases and capturing administrative timing rather than a holistic view of merit, potentially undermining the promotion system's integrity. It is recommended that the Marine Corps use the AI tool for pre-screening to improve efficiency, while implementing annual retraining, continuous drift monitoring, and explicit fairness thresholds to ensure long-term effectiveness and trust.", "source_url": "https:\/\/openalex.org\/W7134863984", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)", "source_record_id": "W7134863984", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 221, "title": "FairPath: A Human-in-the-Loop Framework for Adversarial Fairness Testing in Machine Learning Pipelines", "title_ar": "", "title_en": "FairPath: A Human-in-the-Loop Framework for Adversarial Fairness Testing in Machine Learning Pipelines", "author": "Matheus de Morais Leça", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Calgary", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; fairness", "keywords": "", "abstract": "This thesis investigates how fairness is addressed within machine learning pipelines and proposes a framework for adversarial fairness testing through a human-in-the-loop approach. Although several fairness metrics and mitigation techniques have been proposed, existing approaches often present technical barriers that limit collaboration between machine learning practitioners and non-technical stakeholders, hindering the identification and mitigation of fairness issues before deployment. To address this gap, this research introduces a framework integrating fairness assessment, bias mitigation, explanatory feedback, and decision support mechanisms to enable accessible and structured fairness evaluation. A tool named FairPath was developed and evaluated through quantitative benchmarking and qualitative heuristic evaluation against comparable approaches. This thesis addresses five research questions: RQ1: How is bias identified and measured in the software fairness literature? RQ2: What data improvement approaches are proposed to enhance fairness? RQ3: What effects do data improvement approaches have on bias mitigation and representativeness enhancement? RQ4: What ethical considerations are examined in research on data improvement approaches? RQ5: Can a fairness tool enable accessible bias mitigation while preserving model utility? By addressing these questions, this research aims to bridge fairness assessment and stakeholder collaboration, reducing barriers to effective bias identification and mitigation while supporting the development of transparent, accountable, and socially responsible machine learning systems.", "source_url": "https:\/\/openalex.org\/W7212152316", "pdf_url": "https:\/\/ucalgary.scholaris.ca\/bitstreams\/26cedc4f-1a2c-494a-9087-abab61e79183\/download", "doi": "10.11575\/prism\/51959", "handle": "", "repository_name": "Libraries and Cultural Resources (University of Calgary)", "source_record_id": "W7212152316", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 222, "title": "Towards Trustworthy Machine Learning: Fairness Under Dynamics and Counterfactuals", "title_ar": "", "title_en": "Towards Trustworthy Machine Learning: Fairness Under Dynamics and Counterfactuals", "author": "Ozgur Guldogan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; responsible-ai; few-zero-shot; predictive-analytics; llm; fairness", "keywords": "", "abstract": "Machine learning systems increasingly make decisions with long-term consequences for individuals and society. Designing trustworthy machine learning systems therefore requires going beyond static accuracy and one-shot fairness criteria. Decisions may alter future populations through feedback, individuals may improve and reapply, uncertainty may call for set-valued predictions rather than point estimates, and large-scale deployment may be constrained by the efficiency of modern generative models. This thesis explores these challenges through contributions spanning fairness under dynamics, counterfactual fairness under predictive uncertainty, and efficient inference serving for large language models. We first study fairness in dynamic decision-making settings such as lending, hiring, and admissions, where rejected individuals may invest effort to improve and reapply. We introduce Equal Improvability, a fairness notion requiring that rejected individuals from different groups have comparable opportunities to become accepted after bounded effort. We develop optimization-based methods for learning classifiers that satisfy this criterion and show that they can promote more equitable long-term population dynamics. Next, we consider sequential selection by multiple decision-makers from a shared applicant pool. We propose fair-greedy policies that balance utility with long-term representation targets, characterize convergence under positive reinforcement dynamics, and show that policy design must account for more complex feedback mechanisms to avoid unintended negative reinforcement. We then address fairness under counterfactual reasoning and predictive uncertainty. Counterfactually Fair Conformal Prediction constructs prediction sets that are both statistically valid and counterfactually fair. By aggregating conformity scores across counterfactual interventions on protected attributes, the method produces prediction sets that remain invariant under counterfactual changes to the protected attribute while preserving finite-sample marginal coverage guarantees. Finally, we study computational efficiency in generative machine learning systems. Multi-Bin Batching is an inference-serving policy for large language models that groups requests by predicted generation lengths to reduce inefficiency from heterogeneous runtimes. Queueing-theoretic analysis characterizes the throughput benefits of length-aware batching and shows that the effective service rate approaches the idealized static-batching capacity as the number of bins increases. Experiments on simulated and real inference workloads demonstrate substantial throughput improvements over static batching. Together, these contributions provide fairness notions and algorithms that remain meaningful under feedback, uncertainty, and counterfactual interventions, while also developing principled serving mechanisms that support scalable deployment of generative models.", "source_url": "https:\/\/openalex.org\/W7213476610", "pdf_url": "https:\/\/escholarship.org\/content\/qt0r1882jr\/qt0r1882jr.pdf", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W7213476610", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 223, "title": "Enhancing Fairness in Supervised Machine Learning", "title_ar": "", "title_en": "Enhancing Fairness in Supervised Machine Learning", "author": "Bita Omidi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Ottawa", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "fairness", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; predictive-analytics; fairness", "keywords": "", "abstract": "The increasing influence of machine learning algorithms and artificial intelligence on the high-impact domains of decision-making has led to an increasing concern for the ethical and legal challenges posed by sensitive data-driven systems. Machine learning can identify the statistical patterns in the historically collected big data generated by a huge number of instances that might be affected by human and structural biases. ML algorithms have the potential to amplify these inequities. Lately, there have been several attempts to reduce bias in artificial intelligence in order to maintain fairness in machine learning projects. These methods fall under three categories of pre-processing, in-processing, and post-processing techniques. There are at least 21 notations of fairness in the recent literature, which not only provide different measurement methods of fairness but also lead to completely different concepts. It is worth mentioning that, it is impossible to satisfy all of the definitions of fairness at the same time and some of them are incompatible with each other. As a result, it is important to choose a fairness definition that need to be satisfied according to the context that we are working on. The current study investigates some of the most common definitions and metrics for fairness introduced by researchers to compare three of the proposed de-biasing techniques regarding their effects on the performance and fairness measures through empirical experiments on four different datasets. The de-biasing methods include the “Reweighing Algorithm”, “Adversarial De-biasing Method”, the “Reject Option Classification Method” performed on the classification tasks of “Survival of patients with heart failure”(Heart Failure Dataset), “Prediction of hospital readmission among diabetes patients” (Diabetes Dataset), “Credit classification of bank account holders” (German Credit Dataset), and “The COVID19 related anxiety level classification of Canadians” (CAMH Dataset). Findings show that the adversarial de-biasing in-processing method can be the best technique for mitigating bias working with the deep learning classifiers when we are capable of changing the classification process. This method has not led to a considerable reduction of accuracy except for the CAMH dataset. The “Reject Option Classification” which is a post-processing method, causes the most deterioration of prediction accuracy in all datasets. On the other hand, this method has the best performance in alleviating the bias generated through the classification process. The “Reweighing Algorithm” as a pre-processing technique does not cause a considerable reduction in the accuracy and is capable of reducing bias in classification tasks, although its performance is not as strong as the Reject Option classifier.", "source_url": "https:\/\/openalex.org\/W3196886932", "pdf_url": "https:\/\/ruor.uottawa.ca\/bitstreams\/a217da9e-f2fa-421a-b062-ba76c25339ab\/download", "doi": "10.20381\/ruor-26258", "handle": "", "repository_name": "uO Research (University of Ottawa)", "source_record_id": "W3196886932", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 224, "title": "Algorithmic fairness and bias mitigation in clinical machine learning for equitable patient outcomes", "title_ar": "", "title_en": "Algorithmic fairness and bias mitigation in clinical machine learning for equitable patient outcomes", "author": "Jenny Yang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Oxford", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "fairness", "categories": "machine-learning; medical-ai; responsible-ai; fairness", "keywords": "", "abstract": "In recent years, the integration of machine learning algorithms into clinical settings has shown immense potential for improving healthcare outcomes. However, concerns regarding fairness and equity in machine learning models have garnered increasing attention, particularly in healthcare where biased algorithms can perpetuate existing disparities. This thesis investigates the role of fairness-aware algorithms in addressing these issues within clinical machine learning applications. Through case studies and empirical analyses, this research explores how biases manifest and impact model performance across diverse patient populations, highlighting the challenges and opportunities in promoting fairness within clinical machine learning. Subsequently, drawing on datasets from multiple healthcare institutions, we propose and assess the effectiveness of fairness-aware techniques in advancing equitable healthcare outcomes. Ultimately, this thesis contributes to the ongoing dialogue on fairness in machine learning, providing insights and recommendations for the development of ethically sound and socially responsible machine learning algorithms in healthcare.", "source_url": "https:\/\/openalex.org\/W7125526399", "pdf_url": "https:\/\/ora.ox.ac.uk\/objects\/uuid:5161ad38-3b84-4420-92da-9e3b9bd0fb62\/files\/ddn39x239v", "doi": "10.5287\/ora-d5gqgqd4v", "handle": "", "repository_name": "Oxford University Research Archive (ORA) (University of Oxford)", "source_record_id": "W7125526399", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 226, "title": "Assessing the Fairness of AI Recruitment systems", "title_ar": "", "title_en": "Assessing the Fairness of AI Recruitment systems", "author": "Akhil Krishnakumar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "responsible-ai; expert-systems; fairness", "keywords": "", "abstract": "Businesses have leveraged Artificial Intelligence (AI) into many of their operational activities such as marketing, sales, and finance for its speed and cost-effectiveness. Lately, AI has also found applications in organizational recruitment processes. Unlike the conventional rule-based systems, present-day AI systems learn from data patterns—supported by the growing volumes of (big) data and increasing computing capacity—and make decisions independently without any human interventions. Thus, the perception that AI is fact-oriented and unbiased has led to this change in organizational recruitment practices. Though recent studies have shown that AI decisions could be unfair, scientific research on the fairness of AI recruitment systems is limited. This research fills this gap by designing a conceptual model to assist top-level HR managers in assessing the fairness of AI recruitment tools while drawing from information systems and responsible innovation literature. Guided by Design Science Research (DSR), the development of the model entailed three cycles of research, i.e., relevance cycle (which focused on design environment), rigor cycle (which focused on the existing knowledge base), and design cycle (which focused on development and evaluation). The design environment was explored by reviewing the literature on fairness in recruitment and algorithmic biases. Understanding both the recruitment fairness and potential causes of unfairness in AI helped to define the goal of the conceptual model. The design cycle was informed by the design principles for responsible AI, namely Accountability, Responsibility, and Transparency (ART), and General Data Protection Regulation (GDPR). The model presents seven dimensions which translate the principles to design requirements to assess the fairness of AI recruitment system. They are: (1)Justification; (2)Explanation; (3)Anticipation; (4)Reflexiveness; (5)Inclusion; (6)Responsiveness; and (7)Auditablity. The model also ties these concepts with specific criteria of conventional recruitment fairness such as consistency, interpersonal fairness, job-relatedness, and statistical parity. Finally, the completeness of the model was evaluated by discussing its alignment with other frameworks that had similar objective and utility of the model was validated by collecting feedback from the intended users. This thesis project makes several scientific and practical contributions. The research discusses the potential risks of using AI in the context of HR recruitment systems thereby contributes to the limited literature available in this respect. By using the DSR methodology for building the assessment model, this research serves as a case for DSR methodology in designing a non-IS artifact. Furthermore, the thesis has unified scattered studies in recruitment justice to provide a comprehensive overview of the characteristics of a fair recruitment system. Building on the theoretical contributions, the study has developed an assessment model to assist top-level HR managers in assessing the fairness of an AI recruitment tool. Employing this assessment tool can have positive effects on a business organization and society by eradicating the unfairness or bias that AI recruitment tools can bring into the organization. It would also raise awareness regarding the risks of AI. Given that the GDPR (article 35) mandate organizations to take responsibility in assessing the impact while introducing automated processing in new contexts or purposes, the assessment model designed in this study supports these regulations.", "source_url": "https:\/\/openalex.org\/W2910105146", "pdf_url": "https:\/\/repository.tudelft.nl\/file\/File_cf61d8bb-ea31-4e80-8850-24363402c87e", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2910105146", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 227, "title": "\"Ethics, Governance, and Bias Mitigation in AI-Enabled loT Applications: A Focus on Transparency and Fairness", "title_ar": "", "title_en": "\"Ethics, Governance, and Bias Mitigation in AI-Enabled loT Applications: A Focus on Transparency and Fairness", "author": "Nadine Y. Fares", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "York University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "responsible-ai; ai-applications; xai; fairness; ai-industry; ai-smart-cities", "keywords": "", "abstract": "In the development of smart cities, the integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies promises to address urban challenges yet raises significant security and ethical concerns. This study emphasizes the need for robust governance frameworks to ensure the ethical deployment of these technologies, focusing on the roles of data privacy, security, and bias mitigation. It highlights the importance of Explainable AI (XAI) techniques, like LIME and SHAP, for enhancing transparency and accountability in AI decision-making processes. Through a detailed examination of existing governance frameworks and the introduction of a bias detection tool, this work aims to embed ethical considerations into the fabric of AI-driven systems in smart cities. The discussion extends to exploring common domains and AI implementations across different industries, offering insights into comprehending and ensuring the responsible use of AI and IoT technology. This lays the groundwork for future research in this developing area.", "source_url": "https:\/\/openalex.org\/W7171271463", "pdf_url": "https:\/\/yorkspace.library.yorku.ca\/bitstreams\/21897da7-867b-41da-9673-7e89552b2069\/download", "doi": "", "handle": "", "repository_name": "York University Digital Library (York University)", "source_record_id": "W7171271463", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 229, "title": "Evaluating Bias in Machine Learning-Enabled Radiology Image Classification", "title_ar": "", "title_en": "Evaluating Bias in Machine Learning-Enabled Radiology Image Classification", "author": "Dina Atia", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Massachusetts Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; computer-vision; medical-ai; responsible-ai; image-classification; medical-imaging; fairness", "keywords": "", "abstract": "As machine learning grows more prevalent in the medical field, it is important to ensure that fairness is considered as a central criterion in the evaluation of algorithms and models. Building upon previous work, we study a set of machine learning models used to detect spinal fractures, comparing their performance across various age, sex, and geographic groups. This serves not only as an audit of this particular set of models but also contributes to the development of a meaningful standard for fairness in the space of Machine Learning for Healthcare. We analyze the 10 highest-performing models from a competition hosted by the Radiological Society of North America in 2022. In this competition, teams competed to design and train machine learning models to detect and locate cervical spine fractures, a severe injury with high mortality rates, with high accuracy. We split the data into subgroups across the categories of sex, age, and continent, then compare them across seven performance metrics. We find the models to be fair overall, with similar performance across the given metrics. Additionally, we perform an intersectional analysis, where we compare the same metrics, but instead split the data based on intersections of the above attributes, and again find fair overall performance. Taking a holistic look at the results, the models appear to be fair under a variety of comparative metrics. However, future work is needed to determine whether or not the models we studied would in fact be fair for a more representative population.", "source_url": "https:\/\/openalex.org\/W7130669443", "pdf_url": "https:\/\/dspace.mit.edu\/bitstream\/1721.1\/151662\/1\/atia-dinaatia-meng-eecs-2023-thesis.pdf", "doi": "", "handle": "", "repository_name": "DSpace@MIT (Massachusetts Institute of Technology)", "source_record_id": "W7130669443", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 231, "title": "Machine Learning for Cooperative Logistics: Multi-Agent Reinforcement Learning to Negotiate Fair Outcomes", "title_ar": "", "title_en": "Machine Learning for Cooperative Logistics: Multi-Agent Reinforcement Learning to Negotiate Fair Outcomes", "author": "Stephen Mak", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cambridge", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; reinforcement-learning; transformers", "keywords": "", "abstract": "Effectively managing businesses is perhaps the ultimate testbed for AI. It requires creativity. It requires reasoning. It requires intelligence. Logistics forms the backbone of our economy. From the food we eat, to the computers we work on, to the planes we fly on – logistics has played a central role. Yet, profit margins for logistics providers are thin, and customers have ever-increasing expectations. Worse, humanity is not on track to decarbonise road freight transportation. One effective method to alleviate this pressure is Cooperative Logistics. Here, companies cooperate by pooling their resources together (such as trucks, drivers, and\/or warehouses) to improve their individual performance (such as profitability and carbon emissions). But if companies cooperate, how much should everyone be fairly paid? This is known as fair gain sharing. Prior methods do not scale because they require evaluating O(2n) NP-hard vehicle routing problems (VRPs) for n companies – limiting practical cooperation to ~20 companies – forgoing significant economies of scale. Scaling existing methods to 1,000 companies would therefore require solving 1 × 10^301 NP-hard VRPs. We propose MAGS (Multi-Agent Gain Sharing) to enable fair gain sharing at scale. MAGS effectively handles over 1,000 agents using a principled, game-theoretic approach. This is achieved by training deep multi-agent reinforcement learning agents to negotiate fair gain shares. This is challenging due to the n-player, mixed-motive environment with agents learning concurrently. Crucially, we eliminate reliance on the characteristic function. We additionally leverage transformers to design a reward model for coalition structure generation. We show that in a real-world setting with 1,000 carriers, we can potentially reduce carbon emissions by 22.1%1, whilst improving average profitability from £66.71 per day to £259.26 per day. This is particularly important as global freight activity is expected to double by 2050. Furthermore, this thesis lays the principled foundations for Agentic AI and Human-AI natural language negotiation in future work. This thesis therefore contributes to both the artificial intelligence (AI) and supply chain management (SCM) literature.", "source_url": "https:\/\/openalex.org\/W7147016666", "pdf_url": "https:\/\/www.repository.cam.ac.uk\/bitstreams\/898c5761-d491-476e-90be-19e4e014764a\/download", "doi": "10.17863\/cam.128509", "handle": "", "repository_name": "Apollo (University of Cambridge)", "source_record_id": "W7147016666", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 244, "title": "Steganographic Capacity of Selected Machine Learning and Deep Learning Models", "title_ar": "", "title_en": "Steganographic Capacity of Selected Machine Learning and Deep Learning Models", "author": "Lei Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; deep-learning; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic based attack, information would be hidden in a learning model, which might then be used to gain unauthorized access to a computer, or for other malicious purposes. In this research, we determine the steganographic capacity of various classic machine learning and deep learning models. Specifically, we determine the number of low-order bits of the trained parameters of a given model that can be altered without significantly affecting the performance of the model. We find that the steganographic capacity of learning models is surprisingly high, and that there tends to be a clear threshold after which model performance rapidly degrades.", "source_url": "https:\/\/openalex.org\/W4380786307", "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=2271&context=etd_projects", "doi": "10.31979\/etd.q6u5-n9x8", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4380786307", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 246, "title": "Machine Learning for Credit Scoring", "title_ar": "", "title_en": "Machine Learning for Credit Scoring", "author": "Elena Myazina", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "machine learning, credit scoring, logistic regression, neural networks, random forest", "abstract": "Title: Machine Learning for Credit Scoring Author: Elena Myazina Department \/ Institute: Department of Theoretical Computer Science and Mathematical Logic Supervisor of the master thesis: Mgr. Martin Pilát, Ph.D, Department of Theoretical Computer Science and Mathematical Logic Abstract: Credit scoring is a technique used by banks to evaluate their clients who ask for different types of loan. Its goal is to predict, whether a given client will pay their loan or not. Traditionally, mathematical models based on logistic regression are used for this task. In this thesis, we approach the problem of credit scoring from a machine learning point of view. We investigate several machine learning methods (including neural networks, random forests, support vector machines and other), and evaluate their performance for the credit scoring task on three publicly available datasets.. Keywords: machine learning, credit scoring,logistic regression, neural networks, random forest", "source_url": "https:\/\/openalex.org\/W7135805946", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135805946", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 252, "title": "Benchmarking machine learning models at the edge", "title_ar": "", "title_en": "Benchmarking machine learning models at the edge", "author": "Md. Masud Haider", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "machine-learning; computer-vision", "keywords": "", "abstract": "Recently the use of resource-constrained edge devices to perform machine learning tasks has gained substantial research attention. The need to perform such tasks arises from the vast amounts of sensors, and IoT and smart devices that generate large swaths of data at an enormous rate. To process such data in a real-time manner, cloud computing has proven to be suboptimal. As an alternative, edge devices paired with ML models can execute demanding tasks in diverse environments. This study explored a range of machine learning models coupled with several low and high-end edge devices to test their suitability for deployment in an edge environment. Unlike previous works, this experiment takes into account the initial heavy lifting that machine learning models and devices go through before performing the actual task of classification or detection. To systematically benchmark and gather results, this study divides the ML pipeline into initial and execution phases while thoroughly measuring each phase’s time and resource usage. This type of rigorous performance metric analysis enables decision-making about which machine learning model and devices have better suitability for latency and speed. Moreover, to add a device in the suit of existing ones and replicate the benchmark workflow, this study has automated the benchmark with the Ansible automation tool. The thesis research presents its findings from different devices. The division of phases allows for a clear understanding of the bottlenecks experienced by the devices and their capabilities. Moreover, an underlying crucial detail such as the warmup was revealed by scrutinizing the performances of the devices from the very beginning. The thesis concludes with performance highlights and limitations of the benchmark, identifying possible future research directions that can be developed further.", "source_url": "https:\/\/openalex.org\/W7133090547", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/161905\/2\/HaiderMd.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133090547", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 255, "title": "Ensemble Deep Learning Architectures for Brain Tumor Classification and Segmentation in MRI Images", "title_ar": "", "title_en": "Ensemble Deep Learning Architectures for Brain Tumor Classification and Segmentation in MRI Images", "author": "Mallie, Dagmawi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Turku", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Brain tumors (BTs) are the abnormal growth of brain cells. Depending on aggressiveness, location, and impact on brain function, they are classified as either benign or malignant, originating within the brain or metastasizing from other areas of the body. Traditionally, BTs are detected through medical imaging, combined with clinical assessment and expert interpretation. The emergence of machine learning (ML) and advanced artificial intelligence (AI) techniques, particularly deep learning (DL), has changed the reliance that traditional procedures have on radiologists in early tumor detection. Diagnosing BTs involves various imaging techniques and tests to confirm the presence, type, and characteristics of the tumor. Magnetic Resonance Imaging (MRI) is a non-invasive technique that uses magnetic fields and radio waves to paint detailed images of the brain’s soft tissues, which is essential for identifying the tumor’s nature. MRI is more sensitive than computed tomography (CT); thus, it is more efficient in detecting smaller tumors. This thesis investigates the use of ensemble techniques based on deep convolutional neural network (DCNN) architectures for accurate BT classification and segmen- tation. For classification, the ensemble combines ResNet-50, DenseNet-169, and EfficientNet-vS, resulting in a performance improvement of over 0.2% by achieving an accuracy of 99.437% . For tumor segmentation, a similar ensemble strategy is employed using U-Net, U-Net++, and Attention U-Net. This approach also yields consistent, strong results across all evaluation metrics, with improvements of around 1%. All data used in this thesis were sourced from Kaggle, a widely used public platform for researchers. For the classification task, approximately 20,000 MRI scans were collected from three Kaggle datasets. For the segmentation task, a total of 4,293 MRI scans were obtained from the same platform. The results demonstrate that even general-purpose, publicly available DCNN models can detect, classify, and segment BTs with high accuracy, underscoring their potential for real-world clinical applications.", "source_url": "https:\/\/openalex.org\/W7112128316", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UTUPub (University of Turku)", "source_record_id": "W7112128316", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 256, "title": "Comparative Performance Analysis of Deep Learning Models for Brain Tumor Classification from MRI Images", "title_ar": "", "title_en": "Comparative Performance Analysis of Deep Learning Models for Brain Tumor Classification from MRI Images", "author": "Hassan Khan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; transformers; medical-imaging", "keywords": "", "abstract": "Brain tumors are among the most serious categories of neurological disease, with accurate classification from magnetic resonance imaging (MRI) playing a critical role in treatment planning and patient outcomes.Manual interpretation of MRI scans by radiologists is timeconsuming, subject to inter-observer variability, and constrained by the availability of specialist expertise, particularly in resource-limited settings.These factors have driven growing interest in automated deep learning solutions for brain tumor MRI classification.While convolutional neural networks (CNNs) have demonstrated strong performance on this task through transfer learning, the emergence of Vision Transformers and hybrid CNN-Transformer architectures has created a diverse model landscape whose relative merits remain poorly understood.Existing comparative studies benchmark either multiple CNN variants in isolation or transformer-based models separately, without unified head-tohead evaluation across all three architectural families under identical experimental conditions.Furthermore, deployment-relevant efficiency metrics -inference latency, model size, and GPU memory -are rarely reported alongside accuracy, limiting practical model selection guidance for clinical settings with varying hardware constraints.This thesis addresses these gaps by conducting a systematic benchmark of nine deep learning architectures -four CNN-based (VGG16, ResNet50, DenseNet121, EfficientNet-B0), three Transformer-based (ViT-B\/16, Swin Transformer, DeiT-B), and two Hybrid\/Lightweight (MobileNetV2, MaxViT-Tiny) -evaluated on the Brain Tumor MRI Dataset comprising 7,200 T1-weighted MRI images across four classes (glioma, meningioma, pituitary tumor, no tumor).All models were trained under identical conditions using ImageNet transfer learning and assessed on both classification performance (accuracy, precision, recall, F1-score, AUC-ROC) and computational efficiency (parameter count, model size, inference latency, peak GPU memory).The Swin Transformer achieves the highest test accuracy (98.52%,F1 = 0.9852), demonstrating that hierarchical transformer architectures can outperform CNNs when appropriately designed for the task.MobileNetV2 leads all models on efficiency metrics with 2.2M parameters and 7.55 ms inference latency.Pareto analysis identifies three deployment-optimal models: Swin-T for high-resource GPU environments, DenseNet121 (98.15%, 7.0M parameters) for standard workstations, and MobileNetV2 (97.04%) for edge devices.Efficiency varies by a factor of 61× in parameter count and 25× in GPU memory across the nine models, while accuracy spans only ~2 percentage points, underscoring that architectural design matters far more than raw model size.This study provides the first unified benchmark comparing CNN, Transformer, and Hybrid architectures for brain tumor MRI classification under identical conditions with both accuracy and efficiency metrics reported, yielding evidence-based model selection guidelines for clinical deployment across diverse computational environments.", "source_url": "https:\/\/openalex.org\/W7215551487", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/57ebe372-c179-491f-bae8-4ad920908485\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215551487", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 257, "title": "Tumor Classification İn Brain Mri İmages Using An Optimized Hybrid Deep Learning Model", "title_ar": "", "title_en": "", "author": "Veysel Tileği ; Danışman: Doç. Dr. Ali Yıldız", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2025, "language": "tr", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "Son yıllarda derin öğrenme tabanlı yöntemler, tıbbi görüntü analizinde manyetik rezonans (MR) görüntülerinden beyin tümörlerinin otomatik tespiti ve sınıflandırılmasında dikkat çekici başarılar göstermiştir. Ancak MR görüntülerinin manuel olarak yorumlanması, zaman alıcı, uzmanlara bağımlı ve öznel bir süreç olduğundan, otomatik karar destek sistemlerine duyulan gereksinim giderek artmaktadır. Bu tez çalışmasında, literatürdeki önde gelen Konvolüsyonel sinir ağı (Convolutional Neural Networks – CNN) mimarilerinin EfficientNet, ResNet ve DenseNet yapısal üstünlükleri birleştirilerek NeuroTriNet adı verilen yeni bir hibrit CNN modeli önerilmiştir. Önerilen mimari, EfficientNet'in parametre verimliliğini, ResNet'in gradyan kararlılığını ve DenseNet'in yoğun öznitelik paylaşımını tek bir modelde bütünleştirmektedir. Modelin hiperparametreleri, Optuna tabanlı Hiperparametre Optimizasyonu (HPO) yöntemi ile Tree-Structured Parzen Estimator (TPE) örnekleme stratejisi ve MedianPruner erken durdurma algoritması kullanılarak optimize edilmiştir. Eğitim sürecinde, sınıf dengesizliklerini azaltmak amacıyla Focal Loss fonksiyonu tercih edilmiştir. Deneysel tez çalışmaları, birleştirilmiş beyin MR veri kümesi üzerinde yürütülmüştür. Veri kümesi, açık kaynak olarak sunulan IEEE ve Kaggle kaynaklarından elde edilen glioma, meningioma ve sağlıklı beyin sınıflarından oluşmaktadır. Eğitim verileri için çeşitli veri artırma teknikleri uygulanmış, görüntüler 256×256x3 boyutunda normalize edilmiştir. Performans değerlendirmesi doğruluk, makro F1-skoru ve ROC-AUC metrikleri üzerinden gerçekleştirilmiştir. Önerilen NeuroTriNet modeli, doğrulama kümesi üzerinde ,95 doğruluk ve ,95 makro F1-skoru elde ederek yüksek ve dengeli bir sınıflandırma başarımı sergilemiştir. Bağımsız test kümesi sonuçları incelendiğinde ise modelin ,81 doğruluk ve ,71 makro F1-skoru ile güçlü bir genelleme yeteneğine sahip olduğu görülmüştür. Bu tez çalışmasında, beyin tümörü sınıflandırması alanında optimize edilmiş, açıklanabilir ve hesaplama açısından verimli bir derin öğrenme modeli sunmaktadır. Önerilen NeuroTriNet, medikal görüntü analizinde hibrit CNN mimarilerinin etkinliğini ortaya koyarak, klinik karar destek sistemlerinin geliştirilmesine katkı sağlamayı hedeflemektedir.", "source_url": "https:\/\/openalex.org\/W7165072612", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Mersin Üniversitesi Açık Erişim Sistemi\/Mersin University Open Access System", "source_record_id": "W7165072612", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 258, "title": "Brain Tumor Detection and Classification from MRI Images", "title_ar": "", "title_en": "Brain Tumor Detection and Classification from MRI Images", "author": "Anjaneya Teja Sarma Kalvakolanu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "California State Polytechnic University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; image-segmentation; medical-imaging", "keywords": "", "abstract": "A brain tumor is detected and classified by biopsy that is conducted after the brain surgery. Advancement in technology and machine learning techniques could help radiologists in the diagnosis of tumors without any invasive measures. We utilized a deep learning-based approach to detect and classify the tumor into Meningioma, Glioma, Pituitary tumors. We used registration and segmentation-based skull stripping mechanism to remove the skull from the MRI images and the grab cut method to verify whether the skull stripped MRI masks retained the features of the tumor for accurate classification. In this research, we proposed a transfer learning based approach in conjunction with discriminative learning rates to perform the classification of brain tumors. The data set used is a 3064 T MRI images dataset that contains T1 flair MRI images. We achieved a classification accuracy of 98.83%, 96.26%, and 95.18% for training, validation, and test sets and an F1 score of 0.96 on the T1 Flair MRI dataset.", "source_url": "https:\/\/openalex.org\/W3158119991", "pdf_url": "https:\/\/digitalcommons.calpoly.edu\/cgi\/viewcontent.cgi?article=3799&context=theses", "doi": "10.15368\/theses.2021.16", "handle": "", "repository_name": "DigitalCommons - CalPoly (California State Polytechnic University)", "source_record_id": "W3158119991", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 259, "title": "Advancements in Automated Brain Tumor Detection Using Deep Learning on MRI Imagery", "title_ar": "", "title_en": "Advancements in Automated Brain Tumor Detection Using Deep Learning on MRI Imagery", "author": "Md. Sohan Biswas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging", "keywords": "Brain tumor, Convolutional Neural Networks, Deep Learning, Transfer Learning", "abstract": "The goal of this thesis is to solve the significant problem of inter-observer variability-induced diagnostic delays by presenting a sophisticated deep learning system for automated brain tumor diagnosis utilizing MRI imagery. Pituitary adenoma, extra-axial meningioma, infiltrative glioma, and no-tumor controls are the four classes of brain tumors that are represented in the 7,143 T1 weighted MRI images taken from Kaggle Brain Tumor Dataset and various hospitals in Bangladesh, including Ibn Sina Medical College, Dhaka Medical College, and Comilla Medical College. Using three pretrained convolutional neural network (CNN) architectures; MobileNetV2, EfficientNetB0, and VGG16 optimized through selective layer unfreezing and on-the-fly data augmentation, including random brightness and contrast adjustments to mitigate scanner artifacts, the study applies transfer learning to improve diagnostic accuracy. With a test accuracy of 98.5%, a macro F1-score of 0.98, and an area under the curve (AUC) ≥0.99, MobileNetV2 outperformed the other models after being trained on 5,832 photos. Particularly noteworthy was the MobileNetV2 model's real-time inference ability (0.97) and quick processing times, especially in low-resource settings where prompt diagnosis can have a major impact on survival outcomes. The study highlights the clinical viability of applying AI-assisted diagnostics in various contexts, enhancing access to superior radiological evaluations and possibly cutting down on diagnostic delays in practical applications. Keywords: Brain tumor, Convolutional Neural Networks, Deep Learning, Transfer Learning, MRI Classification Source Code:GitHub: Advancements in Automated Brain Tumor Detection Using Deep Learning on MRI Imagery Author: Md. Sohan BiswasORCID: 0009-0000-5888-373X", "source_url": "https:\/\/openalex.org\/W7201959393", "pdf_url": "", "doi": "10.5281\/zenodo.21845324", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7201959393", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 260, "title": "Artificial Intelligence for Medical Diagnosis Using Machine Learning for Brain Tumor MRI Classification : магистерская диссертация", "title_ar": "", "title_en": "Artificial Intelligence for Medical Diagnosis Using Machine Learning for Brain Tumor MRI Classification : магистерская диссертация", "author": "М. Ш. С. Мако ; M. S. S. Mako", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Ural Federal University", "university_source": "repository_host", "college": "", "department": "", "country": "Russia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; cnn; medical-imaging; xai", "keywords": "", "abstract": "The integration of Artificial Intelligence (AI) into the domain of medical diagnostics has ushered in a new era of precision, speed, and reliability, particularly in the analysis of medical imaging. Among the most promising applications is the use of Machine Learning (ML) techniques for the classification and diagnosis of brain tumors from Magnetic Resonance Imaging (MRI) scans. Brain tumors, whether malignant or benign, pose significant health threats and require early, accurate detection to improve prognosis and treatment outcomes. Traditional methods for diagnosing brain tumors are often time-consuming, subject to inter-observer variability, and reliant on the expertise of radiologists. This thesis explores the use of AI-driven machine learning models to automate and enhance the diagnostic process by classifying brain tumor types from MRI images. This research presents a comprehensive study on the implementation of various machine learning algorithms—including Convolutional Neural Networks (CNNs), Support Vector Machines (SVM), and ensemble models—for the classification of different types of brain tumors, such as gliomas, meningiomas, and pituitary tumors. The dataset employed comprises a substantial number of pre-processed and labeled MRI images sourced from publicly available medical imaging repositories. The study begins with a detailed examination of MRI imaging modalities, the clinical characteristics of common brain tumors, and the importance of accurate classification. A robust preprocessing pipeline was designed to handle data augmentation, normalization, and artifact removal, which are critical for enhancing image quality and improving model generalization. Feature extraction techniques, both handcrafted and automated via deep learning, were analyzed to assess their effectiveness in capturing the unique spatial and textural patterns of different tumor types. Subsequently, multiple supervised learning algorithms were trained and tested using cross-validation to compare their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve. CNN-based architectures, particularly those leveraging transfer learning (e.g., ResNet, VGGNet, and EfficientNet), demonstrated superior performance compared to traditional classifiers, achieving classification accuracies exceeding 90% in test scenarios. In addition, model interpretability techniques such as Grad-CAM and SHAP were applied to visualize and explain the decision-making process of the neural networks, addressing one of the key challenges in deploying AI systems in clinical environments: trust and transparency.", "source_url": "https:\/\/openalex.org\/W7117637577", "pdf_url": "https:\/\/elar.urfu.ru\/bitstream\/10995\/147597\/1\/m_th_m.s.s.mako_2025.pdf", "doi": "", "handle": "", "repository_name": "Electronic scientific archive of UrFU (Ural Federal University)", "source_record_id": "W7117637577", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 261, "title": "Brain Tumor Classification using Deep Learning- Poster", "title_ar": "", "title_en": "Brain Tumor Classification using Deep Learning- Poster", "author": "Rayen Bentemessek", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; cnn; medical-imaging; xai", "keywords": "", "abstract": "The project presents deep learning solutions to classify brain tumors through MRI images. Two Convolutional Neural Network (CNN) models were developed, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were implemented following CRISP-DM methodology from data understanding to deployment, and they were evaluated using different metrics such as accuracy, precision, recall and F1-score. Key Highlights: •The custom CNN model achieved higher accuracy but failed to locate tumors. •ResNet50 provided a good performance while balancing explainability through Grad-CAM. •Model was deployed through Gradio to demonstrate a real-world use of the solution.", "source_url": "https:\/\/openalex.org\/W4417120498", "pdf_url": "", "doi": "10.63227\/652.199.43", "handle": "", "repository_name": "", "source_record_id": "W4417120498", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 262, "title": "Image analysis and machine learning in patients with brain tumors", "title_ar": "", "title_en": "Image analysis and machine learning in patients with brain tumors", "author": "Alice Neimantaite", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Gothenburg University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; multimodal; medical-imaging", "keywords": "", "abstract": "Diffuse lower-grade gliomas (dLGG) are slowly growing primary brain tumors. Patients with dLGG represent a heterogeneous group with various symptoms, diverse progression of the disease as well as survival length. Collecting multidimensional patient data gives the opportunity for application of advanced analysis methods. These methods have shown a great promise with the ability to recognize complex patterns in the data and the potential to improve patient treatment and knowledge on the disease. However, the methods should be applied with consideration to the clinical context. This thesis aims to apply advanced analysis methods for the patients with dLGG in a clinically meaningful manner. Depending on the research question, patients with suspected or histomolecularly confirmed dLGG diagnosis from Sahlgrenska University hospital and external sites were included in the studies of the thesis. The analysed data included multi-modal MRI, clinical data, neuropsychological data and health-related quality of life (HRQoL) assessments. Radiomics was applied for survival prediction (Study I), deep learning for tumor classification (Study II), correlation analysis for investigation of symptom interconnection (Study III) and lesion mapping to find brain structures at risk in isocitrate dehydrogenase (IDH)- mutated gliomas (Study IV). The results showed that radiomics can identify clinically relevant information and a model combining radiomics features with clinical variables improved survival prediction (Study I). Deep learning showed similar performance as the clinical model in classification of IDH-mutation status in non-enhancing gliomas (Study II). Correlation networks revealed that self-reported and observer-assessed variables on mental fatigue and cognitive functioning were not connected (Study III). Lesion mapping quantified brain structures at risk in IDH-mutated gliomas, which were often related to advanced brain functions (Study IV). Advanced analysis methods have aided in the understanding of dLGG in several perspectives from symptoms to prognostic factors for patient survival. The machine learning model showed better performance than clinical models in survival prediction, but not in clinically relevant tumor classification. Our findings strengthen the notion of the importance to apply these methods in a relevant context to gain insight where the methods are clinically useful.", "source_url": "https:\/\/openalex.org\/W7217647342", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Gothenburg University Publications Electronic Archive (Gothenburg University)", "source_record_id": "W7217647342", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 263, "title": "Hybrid Approach for the Classification and Detection of Brain Tumor", "title_ar": "", "title_en": "Hybrid Approach for the Classification and Detection of Brain Tumor", "author": "Venkateshwar Rao K ; Sunny Sowmith Uppala ; Srikar Jakkena", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; predictive-analytics; cnn; object-detection; image-segmentation; medical-imaging; xai", "keywords": "", "abstract": "Brain tumors represent one of the most severeneurological disorders and require early detection for effectivetreatment. MRI imaging is widely used because it providesdetailed visualization of soft tissues and tumor structures withoutradiation exposure. Despite its advantages, interpreting MRIscans manually is complex and prone to inter-observer variability.Recent advancements in Artificial Intelligence (AI) and DeepLearning have enabled automated medical image analysis withhigh accuracy. Convolutional Neural Networks (CNNs) havedemonstrated strong performance in feature extraction and classification tasks. However, standalone CNN models often requirelarge datasets and may suffer from overfitting when applied tolimited medical datasets. In recent years, Artificial Intelligence(AI) and Deep Learning have significantly transformed medicalimage analysis. Convolutional Neural Networks (CNNs) havedemonstrated remarkable success in various computer visiontasks, including object detection, segmentation, and classification.In medical imaging, CNN-based architectures have been appliedto detect tumors, segment lesions, and classify disease typeswith high accuracy. Despite these advancements, standalone deeplearning models often face challenges such as overfitting, highcomputational complexity, and limited interpretability. Hybridmodels that combine deep learning feature extraction with classical machine learning classifiers have shown promising resultsin improving generalization performance. Additionally, segmentation of tumor regions before classification can significantlyimprove prediction accuracy by eliminating irrelevant background information. This work proposes a comprehensive hybridarchitecture that integrates: • U-Net for tumor segmentation• MobileNetV2 for transfer learning-based feature extraction• SVM and Random Forest for classification • Grad-CAM forexplainability The proposed approach aims to balance accuracy,computational efficiency, and interpretability in a unified framework.", "source_url": "https:\/\/openalex.org\/W7154615453", "pdf_url": "", "doi": "10.5281\/zenodo.19601617", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7154615453", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 264, "title": "Brain Tumor Classification using Deep Learning Custom CNN vs. ResNet50.", "title_ar": "", "title_en": "Brain Tumor Classification using Deep Learning Custom CNN vs. ResNet50.", "author": "Rayen Bentemessek ; Muhammad Iqbal", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; predictive-analytics; cnn; medical-imaging; xai", "keywords": "", "abstract": "The project presents a deep learning solution to classify brain tumors through MRI images. Following the CRISP-DM framework, two Convolutional Neural Network (CNN) models were developed and evaluated, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were assessed using standard performance metrics such as accuracy, precision, recall and F1-score. Despite the higher test accuracy achieved by the custom CNN, further interpretability indicated inconsistent attention to the actual tumor regions also known as shortcut learning. On the other hand, ResNet50 showed more reliable and clinically relevant focus which supported its selection as the final model. The selected model was deployed using Gradio, to demonstrate a real-world application with real-time predictions and visual explanation to reduce the black box nature of AI. The results showcase that despite the good performance of custom and tailored CNN models on specific datasets, generalization and real-world relevance are equally important for reliable deployment.", "source_url": "https:\/\/openalex.org\/W4417156896", "pdf_url": "", "doi": "10.63227\/652.299.65", "handle": "", "repository_name": "", "source_record_id": "W4417156896", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 265, "title": "Human brain tumor classification using images of magnetic resonance and deep learning architectures", "title_ar": "", "title_en": "Human brain tumor classification using images of magnetic resonance and deep learning architectures", "author": "Pedro Lavarda Zorel", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Federal University of Technology – Paraná", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "As the population and life expectancy around the world increase, diseases like cancer are becoming increasingly common. Research conducted by the National Cancer Institute indicates that in 2022 alone, more than 625,000 cancer cases were registered in Brazil, 11,000 of which were Central Nervous System (CNS) cancer. Early detection plays a fundamental role in treating this disease, and the most common way to diagnose brain cancer is through magnetic resonance imaging (MRI); however, the analysis of these images requires trained professionals. As a way to contribute to diagnosis, recent studies explore the use of image processing and Machine Learning algorithms to assist in the detection of human brain tumors. Considering this context, this work presents the creation of models based on convolutional neural networks (CNNs) and pre-trained architectures, ResNet and EfficientNet, to perform the automatic classification of different types of human brain tumors. Different configurations and architectures were evaluated, and the model that showed the best performance in the early detection of brain cancer was EfficientNet-B3, with an accuracy of 98.56%. However, regarding the balance of performance and accuracy, ResNet-50V2 proved superior, using less computational power while achieving an accuracy of 97.88%. For training the models, image pre-processing techniques were used in order to improve the model’s results and contribute to its final accuracy.", "source_url": "https:\/\/openalex.org\/W7155671230", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/40220\/1\/classificacaotumorescerebraisaruitetura.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)", "source_record_id": "W7155671230", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 266, "title": "Brain Tumor Segmentation using Deep Learning: A Hybrid Model.", "title_ar": "", "title_en": "Brain Tumor Segmentation using Deep Learning: A Hybrid Model.", "author": "Gishene, Tigabu Yaya", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Helsinki", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "Accurate brain tumor segmentation from MRI scans is crucial for diagnosis, treatment planning, and prognosis. However, manual segmentation is labor-intensive, prone to variability, and susceptible to errors, emphasizing the need for automated solutions. This study proposes the development and evaluation of deep learning architectures for brain tumor segmentation using the BraTS 2020 dataset. We propose and compare two approaches: (1) a standalone 3D U-Net, which processes 3D MRI volumes, and (2) a hybrid 2D model that combines a pre-trained VGG19 encoder with a U-Net decoder for 2D slice-based segmentation. The hybrid model leverages VGG19’s powerful feature extraction capabilities while maintaining the structural advantages of U-Net. To optimize the hybrid model, we applied a data transformation strategy that transforms volumetric MRI data into 2D slices, aligning with the inherent architecture of VGG19. The segmentation models were trained and evaluated using three out of four MRI modalities from the BraTS 2020 dataset, T1Gd, T2, and FLAIR, which provide complementary information for accurate tumor structure identification. Performance evaluation showed that the 3D U-Net exhibits limitations in segmenting tumor subregions, achieving Dice Similarity Coefficients (DSC) of 0.7870 (Necrotic and Non-Enhancing Tumor Core, NCR\/NET), 0.8609 (Edema, ED), and 0.8058 (Enhancing Tumor, ET), with corresponding Intersection over Union (IoU) scores of 0.6489, 0.7558, and 0.6747, respectively. While the model demonstrates high specificity across all tumor classes, its lower sensitivity suggests the need for improvements to detect all instances of pathological tissue more effectively. On the other hand, the hybrid U-Net + VGG19 model demonstrates clearly superior segmentation results, outperforming many existing methods. It achieved DSC scores of 0.9078 (NCR\/NET), 0.9245 (ED), and 0.9175 (ET), consistently exceeding 0.91 across key tumor subregions. Additionally, the hybrid model achieves superior segmentation performance across the three tumor structures, whole tumor (WT), tumor core (TC), and enhancing tumor (ET), achieving DSC scores of 0.9620, 0.9550, and 0.9175, respectively. As observed from the performance evaluation results of the models, the hybrid U-Net with VGG19 provides a computationally efficient and highly accurate approach for brain tumor segmentation.", "source_url": "https:\/\/openalex.org\/W7115224021", "pdf_url": "https:\/\/www.doria.fi\/bitstream\/10024\/192488\/2\/gishene_tigabu_yaya.pdf", "doi": "", "handle": "", "repository_name": "Doria (University of Helsinki)", "source_record_id": "W7115224021", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 267, "title": "MRI Brain Tumor Segmentation and Classification with Deep Learning", "title_ar": "", "title_en": "MRI Brain Tumor Segmentation and Classification with Deep Learning", "author": "区文伟 ; Wen-Wei Chu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Taiwan", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "學位別：碩士 語文別：中文 指導教授：邱泓文 口試委員：陳俊璋 Chun-Chang Chen;徐建業 Chien-Yeh Hsu 電子論文連結：https:\/\/handle.ncl.edu.tw\/11296\/z99eks", "source_url": "https:\/\/openalex.org\/W7111894610", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Taipei Medical University Repository", "source_record_id": "W7111894610", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 268, "title": "Automatic segmentation and classification of brain tumors based on multisequence MRI images with deep learning methods", "title_ar": "", "title_en": "Automatic segmentation and classification of brain tumors based on multisequence MRI images with deep learning methods", "author": "Sérgio Rafael Mano Pereira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "Programa de Doutoramento em Informatica (MAP-i) das Universidades do Minho, de Aveiro e do Porto", "source_url": "https:\/\/openalex.org\/W3165025355", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W3165025355", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 269, "title": "Deep learning methods for MRI brain image analysis: 3D convolutional neural networks for Alzheimer's disease detection and brain tumor classification", "title_ar": "", "title_en": "Deep learning methods for MRI brain image analysis: 3D convolutional neural networks for Alzheimer's disease detection and brain tumor classification", "author": "Mahmood Nazari ; Karl Bäckström", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Chalmers University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "In this thesis, we investigate deep learning methods for medical image analysis, in particular brain disease detection and classification.We attempt to solve two problems, namely (1) Alzheimer's disease (AD) detection and (2) Brain tumor (BT) classification into high-grade and low-grade gliomas.Efficient and accurate diagnosis of AD is essential for initiating treatment at an early stage, increasing the chances of postponing the irreversible neurodegenerative effects of the disease.To this end, many studies have investigated image processing and machine learning methods for computer-aided-diagnosis (CAD) of AD.In the first part of our work, we continue along this line, and propose a simple 3D CNN architecture for feature extraction and classification of pre-processed T1-weighted MRI brain images for AD detection.The dataset used for training and evaluation consists of 340 subjects, and was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI).The resulting test performance on unseen data is a classification accuracy of 98.74%, with a sensitivity (AD detection rate) of 100% which is comparable with the current state of the art.In this thesis, we thoroughly investigate the effect of some parameters involved in the CNN training process, such as dropout probability and learning rate.We also show the impact of data pre-processing on the end performance, by comparing several different pre-processing pipelines.For the second part of our work, namely brain tumor classification, we employ a similar method using CNNs, but using a different architecture.The dataset was obtained from the MICCAI BRaTS competition 2017 and consists of 285 subjects.Our preliminary study on brain tumor classification has shown promising results with a classification accuracy of 85.96%, but further work is required for a more in-depth study of the topic.", "source_url": "https:\/\/openalex.org\/W2762624069", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Chalmers Publication Library (Chalmers University of Technology)", "source_record_id": "W2762624069", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 270, "title": "Hybrid Model - Statistical Features and Deep Neural Network for Brain Tumor Classification in MRI Images", "title_ar": "", "title_en": "Hybrid Model - Statistical Features and Deep Neural Network for Brain Tumor Classification in MRI Images", "author": "Mustafa Rashid Ismael", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Western Michigan University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; medical-imaging", "keywords": "", "abstract": "A brain tumor is the most common disease that affects the central nervous system (CNS), the brain, and spinal cord. It can be diagnosed using the safest and most reliable imaging modality, the Magnetic Resonance Imaging (MRI), by radiologists who may use the assistance of computer-aided diagnosis (CAD) tools. Automated diagnosis is sought because it is essential to overcome the drawbacks of the manual diagnosis, such as time and the stress of viewing MRI images for long hours, and the human error potential. Image analysis and machine learning algorithms are tools that can be used to build an intelligent CAD system capable of analyzing brain tumors and formulating a diagnosis on its own. Hence, it is essential to design a CAD system that is capable of extracting meaningful and precise information, and rendering an error-free diagnosis. Consequently, many researchers have proposed different methods to develop a CAD system to detect and classify abnormal growths in brain images. This dissertation presents a hybrid system for tumor classification from brain MRI images. The hybrid system is composed of a set of statistical-based features and deep neural networks. Segments of the MRI, from within the region of interest (ROI), are transformed into the two-dimensional Discrete Wavelet Transform and the two-dimensional Gabor filter methods. This allows the set of features to encompass all the directional information of the spatial domain tumor characteristics. A classifier system is developed using two types of neural network algorithms, Stacked Sparse Autoencoder (SSA) and Softmax Classifier. For the sparse autoencoder training, the sparsity regularization and L2-weight regularization are proposed. Sparsity regularization is used for its ability to control the firing of the neurons in the hidden layer, whereas L2-weight regularization is used for its ability to reduce the effect of overfitting. Two national brain tumor datasets were used to verify and validate the proposed system. The first dataset is a much larger dataset consisting of 3,064 slices of T1-weighted MRI with three kinds of tumors: Meningioma, Glioma, and Pituitary. The second dataset consists of 200 MRI slices with low-grade and high-grade Glioma tumors collected from the BRATS dataset. Implementation results using the first dataset achieved a total accuracy of 94.0%, and a specificity of 96.2%, 97.8%, and 97.3% for Meningioma, Glioma, and Pituitary tumors respectively. Using the second dataset, accuracy is at 98.8 %. Experimental results indicate not only that this system is effective, but also show that it outperforms the comparable methods.", "source_url": "https:\/\/openalex.org\/W2929671662", "pdf_url": "https:\/\/scholarworks.wmich.edu\/cgi\/viewcontent.cgi?article=4293&context=dissertations", "doi": "", "handle": "", "repository_name": "ScholarWorks - WMU (Western Michigan University)", "source_record_id": "W2929671662", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 271, "title": "Brain MRI Image segmentation using deep learning techniques", "title_ar": "", "title_en": "Brain MRI Image segmentation using deep learning techniques", "author": "Niladri Sekhar Bandyopadhyay", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; rnn; image-classification; image-segmentation; medical-imaging", "keywords": "", "abstract": "It is the aim of this paper to find out how brain MRI image classification accuracy can be improved through a new hybrid deep learning model that incorporates Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), U-Net, ResNet, and GoogleNet. Given the importance of early detection of brain tumors for effective treatment, the new model was trained on a large dataset of brain MRI images resulting in a high validation accuracy of 97 percent. Despite this high performance, there were 20 misclassified images out of 600 indicating areas that could be tweaked. The research shows that integrating several advanced deep learning techniques greatly enhances tumor detection precision; it also reveals hybrid models’ potential in medical image analysis.", "source_url": "https:\/\/openalex.org\/W7127238775", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORMA", "source_record_id": "W7127238775", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 272, "title": "A Deep Learning Web Application for MRI Brain Tumor Images Analysis", "title_ar": "", "title_en": "", "author": "Bouchra Goumidi ; Abir Fettah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2020, "language": "fr", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "La possibilité d’améliorer la détection des maladies et la planification des traitements s’accompagne d’algorithmes précis et entièrement automatiques de classification et de segmentation des tumeurs cérébrales à partir de l’imagerie volumétrique 3D par résonance magnétique (IRM). Le gliome est l’un des types les plus courants des tumeurs cérébrales primaires et est divisé en deux grades selon l’Organisation mondiale de la santé (OMS). L’interprétation humaine de la grande quantité de données d’images IRM est non seulement non reproductible mais dépend également de l’expertise, du temps et des efforts. Dans ce travail, nous essayons de résoudre ce problème en développant une application web pour classer les tumeurs cérébrales en deux classes : Gliome de haut grade (GHG) et Gliome de bas grade (GBG). La solution proposée comprend deux parties. Tout d’abord, l’application d’un modèle pré-entraîné basé sur un réseau de neurones convolutif utilisant VGG-16, ResNet-50 et Inception V3 pour transférer les connaissances acquises à quatre ensembles de données, un pour chaque modalité d’IRM (T1, T1ce ,T2 et FLAIR), afin de sélectionner la modalité qui présente des caractéristiques et des informations plus spécifiques, en passant par le prétraitement des images ainsi l’augmentation des données.Pour améliorer encore la classification des tumeurs, les coupes non tumorales ont été supprimées de la classe HGG de l’ensemble de données sélectionné, puis ont été utilisées séparément pour entraîner les trois modèles.Deuxièmement, nous présentons notre application web Med3csEG qui est l’une des systèmes d’aide à la décision en mentionnant toutes les étapes de la modélisation et de la réalisation de chaque fonctionnalité qui sont introduites dans la page d’accueil, les pages d’authentification et d’enregistrement et la page principale pour compléter la tâche de classification des tumeurs cérébrales. Les évaluations sur BraTS 2019, attestent que la modalité T1 présente les caractéristiques les plus discriminantes pour distinguer les gliomes LG et HG avec 0,9513, 0,907 et 0,9487 pour la précision, la sensibilité et la spécificité respectivement. Le modèle Inception V3 entraîné sur l’ensemble de données T1 surpasse les autres modèles avec 0,9975, 0,9894 et 1 pour la précision, la sensibilité et la spécificité respectivement. Les résultats expérimentaux montrent que l’utilisation du modèle Inception V3 avec la modalité IRM T1 peut permettre d’obtenir de bons résultats dans la classification des tumeurs cérébrales.", "source_url": "https:\/\/openalex.org\/W7135251127", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/0b6438f2-41df-4d08-a6b0-d459081a5f94\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135251127", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 274, "title": "Skull stripping MRI images of the brain using deep learning", "title_ar": "", "title_en": "Skull stripping MRI images of the brain using deep learning", "author": "Øystein Aas Eide", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "Skull stripping is the task of finding pixels or voxels that establishes where the brain is in amedical image. It is an important step for many medical applications that analysis the brain.Today manual skull stripping is still the best way to get good and accurate results. Manual skullstripping is a laborious process since it can take between 6 and 8 hours (Eskildsen et al., 2012).There exist many tools for performing skull stripping automatically. Some of these tools arevery good, but they can struggle on Magnetic Resonance Imaging (MRI) scans with differentmodalities (Kleesiek et al., 2016). In this decade deep learning has become very popular invarious fields including medical image analysis, which includes tasks such as medical imagesegmentation. According to Litjens et al. (2017) the amount of published papers on this subjecthave gone from close to 0 in 2012 to over 200 papers published in 2017. In this thesis threedifferent deep learning architectures will be used to do skull stripping. Also, an experiment onliver segmentation will be done to see if the methods will work on other medical imaging tasks.The deep learning architectures will be tested on three different data sets containing MRI scansof the brain. One of these data sets contains only patients with tumors. The architectures weretrained on different data sets to test how well they will perform when they are faced with MRIscans from different sources. Various experiments will be done to see how training and testingon different compositions of these data sets will make the architectures perform.Deep learning methods take a long time to train. In this thesis, the deep learning architectureswill be tested on different hardware configurations to see how long they take to train on thedifferent set ups. In total four different configurations will be tested with different CentralProcessing Units (CPUs) and different Graphics Processing Units (GPUs).", "source_url": "https:\/\/openalex.org\/W2901941464", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W2901941464", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 275, "title": "Deep learning for brain tumor segmentation and survival prediction.", "title_ar": "", "title_en": "Deep learning for brain tumor segmentation and survival prediction.", "author": "Tirivangani Magadza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of KwaZulu-Natal", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; multimodal; image-segmentation; medical-imaging", "keywords": "", "abstract": "A brain tumor is an abnormal growth of cells in the brain that multiplies uncontrolled. The death of people due to brain tumors has increased over the past few decades. Early diagnosis of brain tumors is essential in improving treatment possibilities and increasing the survival rate of patients. The life expectancy of patients with glioblastoma multiforme (GBM), the most malignant glioma, using the current standard of care is, on average, 14 months after diagnosis despite aggressive surgery, radiation, and chemotherapies. Despite considerable efforts in brain tumor segmentation research, patient diagnosis remains poor. Accurate segmentation of pathological regions may significantly impact treatment decisions, planning, and outcome monitoring. However, the large spatial and structural variability among brain tumors makes automatic segmentation a challenging problem, leaving brain tumor segmentation an open challenge that warrants further research endeavors. While several methods automatically segment brain tumors, deep learning methods are becoming widespread in medical imaging due to their resounding performance. However, the boost in performance comes at the cost of high computational complexity. Therefore, to improve the adoption rate of computer-assisted diagnosis in clinical setups, especially in developing countries, there is a need for more computational and memoryefficient models. In this research, using a few computational resources, we explore various techniques to develop deep learning models accurately for segmenting the different glioma sub-regions, namely the enhancing tumor, the tumor core, and the whole tumor. We quantitatively evaluate the performance of our proposed models against the state-of-the-art methods using magnetic resolution imaging (MRI) datasets provided by the Brain Tumor Segmentation (BraTS) Challenge. Lastly, we use segmentation labels produced by the segmentation task and MRI multimodal data to extract appropriate imaging\/radiomic features to train a deep learning model for overall patient survival prediction.", "source_url": "https:\/\/openalex.org\/W4399612389", "pdf_url": "https:\/\/researchspace.ukzn.ac.za\/bitstreams\/179a4807-2828-41c0-8d3f-771f55ce5b41\/download", "doi": "10.29086\/10413\/23075", "handle": "", "repository_name": "ResearchSpace (University of KwaZulu-Natal)", "source_record_id": "W4399612389", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 277, "title": "Supervised learning-based multimodal MRI brain image analysis", "title_ar": "", "title_en": "Supervised learning-based multimodal MRI brain image analysis", "author": "Mohammadreza Soltaninejad", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Lincoln", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; multimodal; image-segmentation; medical-imaging", "keywords": "", "abstract": "Medical imaging plays an important role in clinical procedures related to cancer, such as diagnosis, treatment selection, and therapy response evaluation. Magnetic resonance imaging (MRI) is one of the most popular acquisition modalities which is widely used in brain tumour analysis and can be acquired with different acquisition protocols, e.g. conventional and advanced. Automated segmentation of brain tumours in MR images is a difficult task due to their high variation in size, shape and appearance. Although many studies have been conducted, it still remains a challenging task and improving accuracy of tumour segmentation is an ongoing field. The aim of this thesis is to develop a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from multimodal MRI images.In this thesis, firstly, the whole brain tumour is segmented from fluid attenuated inversion recovery (FLAIR) MRI, which is commonly acquired in clinics. The segmentation is achieved using region-wise classification, in which regions are derived from superpixels. Several image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomised trees (ERT) classifies each superpixel into tumour and non-tumour. Secondly, the method is extended to 3D supervoxel based learning for segmentation and classification of tumour tissue subtypes in multimodal MRI brain images. Supervoxels are generated using the information across the multimodal MRI data set. This is then followed by a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The information from the advanced protocols of diffusion tensor imaging (DTI), i.e. isotropic (p) and anisotropic (q) components is also incorporated to the conventional MRI to improve segmentation accuracy. Thirdly, to further improve the segmentation of tumour tissue subtypes, the machine-learned features from fully convolutional neural network (FCN) are investigated and combined with hand-designed texton features to encode global information and local dependencies into feature representation. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI training dataset using the FCN. The machine-learned features, along with hand-designed texton features are then applied to random forests to classify each MRI image voxel into normal brain tissues and different parts of tumour.The methods are evaluated on two datasets: 1) clinical dataset, and 2) publicly available Multimodal Brain Tumour Image Segmentation Benchmark (BRATS) 2013 and 2017 dataset. The experimental results demonstrate the high detection and segmentation performance of theIIIsingle modal (FLAIR) method. The average detection sensitivity, balanced error rate (BER) and the Dice overlap measure for the segmented tumour against the ground truth for the clinical data are 89.48%, 6% and 0.91, respectively; whilst, for the BRATS dataset, the corresponding evaluation results are 88.09%, 6% and 0.88, respectively. The corresponding results for the tumour (including tumour core and oedema) in the case of multimodal MRI method are 86%, 7%, 0.84, for the clinical dataset and 96%, 2% and 0.89 for the BRATS 2013 dataset. The results of the FCN based method show that the application of the RF classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising segmentations. The Dice overlap measure for automatic brain tumor segmentation against ground truth for the BRATS 2013 dataset is 0.88, 0.80 and 0.73 for complete tumor, core and enhancing tumor, respectively, which is competitive to the state-of-the-art methods. The corresponding results for BRATS 2017 dataset are 0.86, 0.78 and 0.66 respectively.The methods demonstrate promising results in the segmentation of brain tumours. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. In the experiments, texton has demonstrated its advantages of providing significant information to distinguish various patterns in both 2D and 3D spaces. The segmentation accuracy has also been largely increased by fusing information from multimodal MRI images. Moreover, a unified framework is present which complementarily integrates hand-designed features with machine-learned features to produce more accurate segmentation. The hand-designed features from shallow network (with designable filters) encode the prior-knowledge and context while the machine-learned features from a deep network (with trainable filters) learn the intrinsic features.", "source_url": "https:\/\/openalex.org\/W2784389611", "pdf_url": "", "doi": "10.24385\/lincoln.24325888", "handle": "", "repository_name": "Lincoln Repository (University of Lincoln)", "source_record_id": "W2784389611", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 278, "title": "Automatic Arabic Check Processing Using Deep Learning Techniques", "title_ar": "", "title_en": "Automatic Arabic Check Processing Using Deep Learning Techniques", "author": "Suzan Elmajali", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "King Fahd University of Petroleum and Minerals", "university_source": "repository_host", "college": "", "department": "", "country": "Saudi Arabia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "deep-learning; nlp; arabic-nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7167189961", "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/143737\/1\/Suzan_Elmajali_Thesis.pdf", "doi": "", "handle": "", "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)", "source_record_id": "W7167189961", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 279, "title": "Gender bias in natural language processing", "title_ar": "", "title_en": "Gender bias in natural language processing", "author": "Christine Basta", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; machine-translation; speech-recognition; fairness", "keywords": "", "abstract": "(English) Gender bias is a dangerous form of social bias impacting an essential group of people. The effect of gender bias is propagated to our data, causing the accuracy of the predictions in models to be different depending on gender. In the deep learning era, our models are highly impacted by the training data transferring the negative biases in the data to the models. Natural Language Processing models encounter this amplification of bias in the data. Our thesis is devoted to studying the issue of gender bias in NLP applications from different points of view. To understand and manage the effect of bias amplification, evaluation and mitigation approaches have to be explored. The scientific society has exerted significant efforts in these two directions to enable proposing solutions to the problem. Our thesis is devoted to these two main directions; proposing evaluation schemes, whether as datasets or mechanisms, besides suggesting mitigation techniques. For evaluation, we proposed techniques for evaluating bias in contextualized embeddings and multilingual translation models. Besides, we presented benchmarks for evaluating bias for speech translation and multilingual machine translation models. For mitigation direction, we proposed different approaches in machine translation models by adding contextual text, contextual embeddings, or relaxing the architecture’s constraints. Our evaluation studies concluded that gender bias is encoded strongly in contextual embeddings representing professions and stereotypical nouns. We also unveiled that algorithms amplify the bias and that the system’s architecture impacts the behavior. For the evaluation purposes, we contributed to creating several benchmarks for the evaluation purpose; we introduced a benchmark that evaluates gender bias in speech translation systems. This research suggests that the current state of speech translation systems does not enable us to evaluate gender bias accurately because of the low quality of speech translation systems. Additionally, we proposed a toolkit for building multilingual balanced datasets for training and evaluating NMT models. These datasets are balanced within the gender occupation-wise. We found out that high-resource languages usually tend to predict more precise male translations. Our mitigation studies in NMT suggest that the nature of datasets and languages needs to be considered to apply the right approach. Mitigating bias can rely on adding contextual information. However, in other cases, we need to rethink the model and relax some influencing conditions to the bias that do not affect the general performance but reduce the effect of bias amplification. (Español) El prejuicio de género es una forma peligrosa de sesgo social que afecta a un grupo esencial de personas. El efecto del prejuicio de género se propaga a nuestros datos, lo que hace quela precisión de las predicciones en los modelos sea diferente según el género. En la era del aprendizaje profundo, nuestros modelos se ven afectados por los datos de entrenamiento que transfieren los prejuicios de los datos a los modelos. Los modelos de procesamiento del lenguaje natural pueden además amplificar este sesgo en los datos. Para comprender el efecto de la amplificación del prejuicio de género, se deben explorar enfoques de evaluación y mitigación. La sociedad científica ha visto la importancía de estas dos direcciones para posibilitar la propuesta de soluciones al problema. Nuestra tesis está dedicada a estas dos direcciones principales; proponiendo esquemas de evaluación, ya sea como conjuntos de datos y mecanismos de evaluación, además de sugerir técnicas de mitigación. Para la evaluación, propusimos técnicas para evaluar el prejuicio en representaciones vectoriales contextualizadas y modelos de traducción multilingüe. Además, presentamos puntos de referencia para evaluar el prejuicio de la traducción de voz y los modelos de traducción automática multilingüe. Para la dirección de mitigación, propusimos diferentes enfoques en los modelos de traducción automática agregando texto contextual, incrustaciones contextuales o relajando las restricciones de la arquitectura. Nuestros estudios de evaluación concluyeron que el prejuicio de género está fuertemente codificado en representaciones vectoriales contextuales que representan profesiones y sustantivos estereotipados. También revelamos que los algoritmos amplifican el sesgo y que la arquitectura del sistema afecta el comportamiento. Para efectos de evaluación, contribuimos a la creación de varios datos de referencia para fines de evaluación; presentamos un conjunto de datos que evalúa el sesgo de género en los sistemas de traducción de voz. Esta investigación sugiere que el estado actual de los sistemas de traducción del habla no nos permite evaluar con precisión el sesgo de género debido a la baja calidad de los sistemas de traducción del habla.", "source_url": "https:\/\/openalex.org\/W4387558641", "pdf_url": "https:\/\/upcommons.upc.edu\/bitstream\/2117\/379361\/1\/TCRSB1de1.pdf", "doi": "10.5821\/dissertation-2117-379361", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W4387558641", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 280, "title": "Classification of hate speech in social media using Machine Learning and Natural Language Processing", "title_ar": "", "title_en": "Classification of hate speech in social media using Machine Learning and Natural Language Processing", "author": "Sampath Reddy Kalwa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; predictive-analytics; rnn; speech-recognition", "keywords": "", "abstract": "This study aims to apply Natural Language Processing (NLP) and develop Machine Learning algorithms for detecting hate speech in social media. The “Hate Speech and Offensive Language dataset” is used, which comprises 24,783 manually annotated English tweets labelled as hate speech, offensive language, or neither. Random Forest Classifier, RNN, LSTM, and Bi-LSTM models are evaluated for classifying hate speech and recommending effective strategies for minimising offensive language on social media platforms. Model Comparison helps to understand the best-fitted model in the context of predicting and classifying hate speech in social media platforms. All the objectives of this research are achieved through the entire data analysis, from data pre-processing to the evaluation of different ML techniques. More Neural Network models and hyperparameter-tuned ML-models would enhance the model performance in classifying hate speech in the future.", "source_url": "https:\/\/openalex.org\/W7167554688", "pdf_url": "https:\/\/norma.ncirl.ie\/9471\/1\/sampathreddykalwa.pdf", "doi": "", "handle": "", "repository_name": "NORMA", "source_record_id": "W7167554688", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 281, "title": "A deep learning odyssey on structured natural language processing", "title_ar": "", "title_en": "A deep learning odyssey on structured natural language processing", "author": "Amulya Gupta", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; nlp; transformers", "keywords": "", "abstract": "Availability of immense amount of unstructured text has surged the demand for intelligent natural language processing (NLP) systems. Specifically, these systems are expected to represent, extract, transfer and utilize knowledge in order to be successful on language related problems. In this work, we have directed our efforts to explore various forms of knowledge, especially linguistic and latent structures, available for a NLP system. Additionally, we investigate the significance of aforementioned structures in various language relevant tasks ranging from highly researched semantic matching to less explored abstract text summarization, and many more. In chapter 2, we study the interaction between explicit linguistic structures and implicit structure revealed by attention mechanisms. This investigation on various semantic matching datasets, especially question-answering, display significant patterns of substitutability between the two types of structures (explicit and implicit), and may affect other modalities. As a result, we develop a multi-view progressive attention mechanism which is general enough to operate on various linguistic structures of text. Moving further, to represent and extract latent structure from a collection of documents, we propose a family of vector-quantization-based topic models (VQ-TMs) in Chapter 3. Specifically, VQ-TMs consider dense and global topic embeddings which are homomorphic to word embeddings. Moreover, the family exploits vector-quantization (VQ) technique to promote discreteness in the overall architecture. Lastly, the learned topics are successfully transferred to a different task of code-generation. VQ-TMs exploit VQ technique while learning topics representation. Although VQ method aids in learning discrete topic embeddings, yet, the method is built upon a geometric intuition. Moreover, VQ-TMs utilizes the learned topic representations as reference knowledge in a downstream task. Some natural questions arise. Is it possible to exploit probabilistic intuition to infuse discreteness while learning topics representation? Additionally, can the learned topics be leveraged to control sentence generation and augment the performance in aspect-aware item recommendation? In order to push the envelope further, we explore these ideas in Chapter 4.", "source_url": "https:\/\/openalex.org\/W4393304434", "pdf_url": "https:\/\/dr.lib.iastate.edu\/bitstreams\/aaac0de0-3b7a-4bdf-9328-aee7958dd5de\/download", "doi": "10.31274\/td-20240329-4", "handle": "", "repository_name": "", "source_record_id": "W4393304434", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 282, "title": "Deep Learning Methods for Natural Language Processing Considering Writers and Background World Knowledge", "title_ar": "", "title_en": "Deep Learning Methods for Natural Language Processing Considering Writers and Background World Knowledge", "author": "大輔 大葉", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "deep-learning; nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7207841427", "pdf_url": "https:\/\/repository.dl.itc.u-tokyo.ac.jp\/record\/2011970\/files\/A40698.pdf", "doi": "10.15083\/0002011970", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7207841427", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 283, "title": "Arabic Handwriting Recognition Using Deep Learning Neural Networks", "title_ar": "", "title_en": "Arabic Handwriting Recognition Using Deep Learning Neural Networks", "author": "Mohamed Eltay", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "King Fahd University of Petroleum and Minerals", "university_source": "repository_host", "college": "", "department": "", "country": "Saudi Arabia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "deep-learning; computer-vision; nlp; image-classification; arabic-nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7167164100", "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/141562\/1\/MS_Thesis_08-7-2020.pdf", "doi": "", "handle": "", "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)", "source_record_id": "W7167164100", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 284, "title": "Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music", "title_ar": "", "title_en": "Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music", "author": "Deepthi Peri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; predictive-analytics; rnn; speech-recognition", "keywords": "", "abstract": "In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. It encompasses the characteristics of a scale, a mode, and a tune, with none of them fully describing it, rendering the Raga a unique concept in ICM. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Identifying and categorizing the Raga is challenging due to its dynamism and complex structure as well as the polyphonic nature of ICM. Hence, Raga recognition—identify the constituent Raga in an audio file—has become an important problem in music informatics with several known prior approaches. Advancing the state of the art in Raga recognition paves the way to improving other Music Information Retrieval tasks in ICM, including transcribing notes automatically, recommending music, and organizing large databases. This thesis presents a novel melodic pattern-based approach to recognizing Ragas by representing this task as a document classification problem, solved by applying a deep learning technique. A digital audio excerpt is hierarchically processed and split into subsequences and gamaka sequences to mimic a textual document structure, so our model can learn the resulting tonal and temporal sequence patterns using a Recurrent Neural Network. Although training and testing on these smaller sequences, we predict the Raga for the entire audio excerpt, with the accuracy of 90.3% for the Carnatic Music Dataset and 95.6% for the Hindustani Music Dataset, thus outperforming prior approaches in Raga recognition.", "source_url": "https:\/\/openalex.org\/W3086226352", "pdf_url": "", "doi": "", "handle": "", "repository_name": "VTechWorks (Virginia Tech)", "source_record_id": "W3086226352", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 286, "title": "Machine Learning Models for Efficient and Robust Natural Language Processing", "title_ar": "", "title_en": "Machine Learning Models for Efficient and Robust Natural Language Processing", "author": "Emma Strubell", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Massachusetts Amherst", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; cnn; rnn; transformers", "keywords": "", "abstract": "Natural language processing (NLP) has come of age. For example, semantic role labeling (SRL), which automatically annotates sentences with a labeled graph representing who did what to whom, has in the past ten years seen nearly 40% reduction in error, bringing it to useful accuracy. As a result, a myriad of practitioners now want to deploy NLP systems on billions of documents across many domains. However, state-of-the-art NLP systems are typically not optimized for cross-domain robustness nor computational efficiency. In this dissertation I develop machine learning methods to facilitate fast and robust inference across many common NLP tasks. First, I describe paired learning and inference algorithms for dynamic feature selection which accelerate inference in linear classifiers, the heart of the fastest NLP models, by 5-10 times. I then present iterated dilated convolutional neural networks (ID-CNNs), a distinct combination of network structure, parameter sharing and training procedures that increase inference speed by 14-20 times with accuracy matching bidirectional LSTMs, the most accurate models for NLP sequence labeling. Finally, I describe linguistically-informed self-attention (LISA), a neural network model that combines multi-head self-attention with multi-task learning to facilitate improved generalization to new domains. We show that incorporating linguistic structure in this way leads to substantial improvements over the previous state-of-the-art (syntax-free) neural network models for SRL, especially when evaluating out-of-domain. I conclude with a brief discussion of potential future directions stemming from my thesis work.", "source_url": "https:\/\/openalex.org\/W2982543341", "pdf_url": "", "doi": "10.7275\/15200331", "handle": "", "repository_name": "ScholarWorks@UMassAmherst (University of Massachusetts Amherst)", "source_record_id": "W2982543341", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 288, "title": "Hand Gesture Segmentation and Sign Language Recognition Using Deep Learning", "title_ar": "", "title_en": "Hand Gesture Segmentation and Sign Language Recognition Using Deep Learning", "author": "M Faisal Nurnoby", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "King Fahd University of Petroleum and Minerals", "university_source": "repository_host", "college": "", "department": "", "country": "Saudi Arabia", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; image-segmentation", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7167381339", "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/141918\/1\/MS_THESIS_G201706690_M_FAISAL.pdf", "doi": "", "handle": "", "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)", "source_record_id": "W7167381339", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 289, "title": "Natural Language Generation with Reinforcement Learning", "title_ar": "", "title_en": "Natural Language Generation with Reinforcement Learning", "author": "Thomas Scialom", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "gan", "categories": "deep-learning; nlp; generative-ai; reinforcement-learning; responsible-ai; gan; fairness", "keywords": "", "abstract": "Natural Language Generation (NLG) is the subfield of Natural Language Processing, where the task is to produce natural language outputs. Despite the important progress fostered by the application of Deep Learning, generated texts are still inconsistent and contain factual inconsistencies. At the root cause, we argue in this thesis that deep learning models in NLG suffers from inherent flaws in algorithms, which limits their efficiency. At training time, the standard training strategy, Teacher Forcing, induces the so called exposure bias, a mismatch with inference time, where the errors accumulate. Moreover, NLG suffers from a second flaw: its the automatic evaluation does not reflect well human judgement. In this thesis, we explore how to improve both evaluation and training in NLG toward more reliable systems. In particular, we propose a Question Answering based metric. We show how this metric can be used as a reward in a Reinforcement Learning setup to improve NLG models. Toward this objective, we also explore learned rewards that are the discriminators, and introduce several new algorithms that benefit NLG during training and decoding times. In particular, we propose to combine Monte Carlo Tree Search with Generative Adversarial Networks, resulting in state-of-the-art models.", "source_url": "https:\/\/openalex.org\/W4392267362", "pdf_url": "https:\/\/hal.sorbonne-universite.fr\/tel-03951598v1\/document", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W4392267362", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 290, "title": "Association knowledge in natural language learning", "title_ar": "", "title_en": "Association knowledge in natural language learning", "author": "Pengfei Yu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Illinois at Urbana-Champaign", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "Association is an important feature of natural language originates from the human's cognitive ability to associate concepts.It is the key to unveiling the computational mechanism of natural language, which is closely related to the research of natural language processing (NLP).However, the current dominant learning paradigm, which is based on neural models that combine distributed representations with probabilistic modeling, demonstrates insufficient capabilities in modeling associations in natural language.To compensate for the deficiency, we mathematically formulate the concept of Association Knowledge as the joint distribution over the probabilities of instances and establish a general methodology to incorporate association knowledge into the training architecture of neural models.We delve into Association Knowledge through a series of case studies across various dimensions, including associations among types of knowledge, languages, instances and unstructured information.These case studies span both smaller neural models and large language models.Through our investigations, we demonstrate that explicitly integrating Association Knowledge into neural architectures markedly improves model performance and efficiency.This enhanced capability is evident in diverse scenarios, from improving event detection in lifelong learning settings and facilitating robust cross-lingual translations, to enhancing the detection of long-tail mentions and refining the updates in large language models.Collectively, our findings underscore the pivotal role of Association Knowledge in advancing the state of NLP by fostering more robust and knowledge-aware neural models.vi", "source_url": "https:\/\/openalex.org\/W7220808657", "pdf_url": "https:\/\/www.ideals.illinois.edu\/items\/134236\/bitstreams\/442112\/data.pdf", "doi": "", "handle": "", "repository_name": "Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)", "source_record_id": "W7220808657", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 291, "title": "Deep learning based dynamic turkish sign language recognition with leap motion", "title_ar": "", "title_en": "Deep learning based dynamic turkish sign language recognition with leap motion", "author": "Burçak Demircioğlu Kam", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Istanbul Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3132949820", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Istanbul Technical University Academic Open Archive (Istanbul Technical University)", "source_record_id": "W3132949820", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 292, "title": "Resources and Applications for Dialectal Arabic: the Case of Levantine", "title_ar": "", "title_en": "Resources and Applications for Dialectal Arabic: the Case of Levantine", "author": "Chatrine Qwaider ; Kathrein Abu Kwaik", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Gothenburg University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; transfer-learning; self-semi-supervised; transformers; llm; sentiment-analysis; arabic-nlp; ai-industry", "keywords": "", "abstract": "This is a thesis about the computational study of Dialectal Arabic (DA). In particular, the thesis studies DA, with a special emphasis on Levantine Arabic, and develops tools and resources for the computational study of Dialectal Arabic Natural Language Processing (DANLP). It investigates the creation of fine-grained resources that can be used for a variety of computational tasks, and a number of effective models that can deal with the complexity of fine-grained dialectal data. Dialect Identification (DI), as well as Sentiment Analysis (SA) are the Natural Language Processing (NLP) tasks investigated in this thesis. In the first part (Study 1 and Study 2), I study the DI task on both coarse-grained and fine-grained levels. For this reason, I build the first annotated Levantine (SHAMI) Dialect Corpus (SDC). Furthermore, I explore the ability of n-gram language models, Machine Learning (ML) algorithms and ensemble learning techniques to classify and detect 26 Arabic varieties. In the second part, I conduct a linguistic study to measure the lexical distance between MSA and DA, and between the dialects themselves. This is done to check whether transferring knowledge from one variety to another is possible. In the third part, studies 4,5 and 6, I explore Arabic Sentiment Analysis (SA). I investigate the idea of knowledge transfer between MSA and the dialects using SA as a case study. Furthermore, I implement various models such as the pre-trained language model BERT, Deep Learning (DL), ML and feature engineering approaches to detect the sentimental polarity of DA data. I introduce two valuable resources for this task, one focusing on Levantine sentiment (Shami-Senti), and the other for DA in general (ATSAD). I exploit different ways of annotation, e.g. human, lexicon-based and automatic distant supervision annotation. The last study is about choosing the best model for DI and SA. I exploit well-known models and approaches using various kinds of DA resources. The thesis contributes to the field of DANLP in a number of ways. The introduced valuable resources can be seen as a stepping stone for a deeper investigation and understanding of issues in DANLP. They are also reliable and can be used by researchers to address different NLP tasks. The cross-dialectal linguistic studies will open up prospects for researchers to fine-tune models and transfer knowledge among Arabic varieties. A big part of the contribution lies in designing DI and SA models. I implement several ML models that use feature engineering approaches and N-gram language models to identify the dialect or detect the sentiment. For DI, I design and implement an ensemble learning model that is able to handle fine-grained dialects. Additionally, I exploit the usage of DL models on different SA dialectal datasets and achieve competitive results. For both tasks, I exploit the recent pre-trained language models and perform a comparison to choose the best model. I also implement a semi-supervised approach for automatic labelling and annotating data with the help of self-training techniques to improve the performance of the dataset. These models will help researchers dive deeper into DANLP and create practical and industrial systems.", "source_url": "https:\/\/openalex.org\/W7216829323", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Gothenburg University Publications Electronic Archive (Gothenburg University)", "source_record_id": "W7216829323", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 294, "title": "Sentiment Analysis for the Low-Resourced Latinised Arabic \"Arabizi\"", "title_ar": "", "title_en": "Sentiment Analysis for the Low-Resourced Latinised Arabic \"Arabizi\"", "author": "Taha Tobaili", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The Open University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "deep-learning; nlp; ai-applications; sentiment-analysis; arabic-nlp; ai-industry", "keywords": "", "abstract": "The expansion of digital communication mediums from private mobile messaging into the public through social media presented an opportunity for the data science research and industry to mine the generated big data for artificial information extraction. A popular information extraction task is sentiment analysis, which aims at extracting polarity opinions, positive, negative, or neutral, from the written natural language. This science helped organisations better understand the public’s opinion towards events, news, public figures, and products. However, sentiment analysis has advanced for the English language ahead of Arabic. While sentiment analysis for Arabic is developing in the literature of Natural Language Processing (NLP), a popular variety of Arabic, Arabizi, has been overlooked for sentiment analysis advancements. Arabizi is an informal transcription of the spoken dialectal Arabic in Latin script used for social texting. It is known to be common among the Arab youth, yet it is overlooked in efforts on Arabic sentiment analysis for its linguistic complexities. As to Arabic, Arabizi is rich in inflectional morphology, but also codeswitched with English or French, and distinctively transcribed without adhering to a standard orthography. The rich morphology, inconsistent orthography, and codeswitching challenges are compounded together to have a multiplied effect on the lexical sparsity of the language, where each Arabizi word becomes eligible to be spelled in many ways, that, in addition to the mixing of other languages within the same textual context. The resulting high degree of lexical sparsity defies the very basics of sentiment analysis, classification of positive and negative words. Arabizi is even faced with a severe shortage of data resources that are required to set out any sentiment analysis approach. In this thesis, we tackle this gap by conducting research on sentiment analysis for Arabizi. We addressed the sparsity challenge by harvesting Arabizi data from multi-lingual social media text using deep learning to build Arabizi resources for sentiment analysis. We developed six new morphologically and orthographically rich Arabizi sentiment lexicons and set the baseline for Arabizi sentiment analysis on social media.", "source_url": "https:\/\/openalex.org\/W3127469975", "pdf_url": "", "doi": "10.21954\/ou.ro.00011f21", "handle": "", "repository_name": "Open Research Online (The Open University)", "source_record_id": "W3127469975", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 295, "title": "Traffic flow forecasting with deep learning", "title_ar": "", "title_en": "Traffic flow forecasting with deep learning", "author": "Per Øyvind Kanestrøm", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; rnn; object-detection; ai-smart-cities", "keywords": "", "abstract": "In recent years there has been a vast increase in available data with the ad- vancement of smart cities. In the domain of Intelligent Transportation Systems (ITS) this modernisation can positively effect transportation networks, thus cut- ting down travel time, increase efficacy, and reduce environmental impact from vehicles. Norwegian Public Roads Administration (NPRA) is currently deploying a new vehicle detector system named Datainn on all public roads in Norway. Datainn sends metadata on all detected vehicles in real time. This includes information about speed, gap between vehicles, weight, and classification of vehicle type. Many machine learning approaches has been researched in literature on how to forecast traffic flow information. One such approach is that of using Artificial Neural Networks (ANNs). In this research ANN based methods have been explored. This was done by first performing a state-of-the-art Structured Literature Review (SLR) on ANN methods in literature. From the review, Stacked Sparse Autoencoder (SSAE) model was compared with recent advances of Long Short-Term Memory (LSTM) and Deep Neural Network (DNN) on four different prediction horizons. The data foundation was the new Datainn system using traffic data from a highway around Norway s capitol, Oslo. Further, the model performance was assessed with extended feature vectors including more metadata from Datainn. The results found that the LSTM model always outperformed DNN and SSAE, although in general the performance characteristics was somewhat similar. Ex- tending the feature vector with more variables had a negative effect on DNN, while resulting in better performance for Recurrent Neural Network (RNN) on long-term (60 minute) forecasting horizons. For SSAE it had a slight positive effect, but not enough get better results than RNN or DNN.", "source_url": "https:\/\/openalex.org\/W2727387027", "pdf_url": "", "doi": "", "handle": "", "repository_name": "BIBSYS Brage (BIBSYS (Norway))", "source_record_id": "W2727387027", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 296, "title": "Modeling and Analysis of Data-Driven and Knowledge-Enhanced Traffic Prediction", "title_ar": "", "title_en": "Modeling and Analysis of Data-Driven and Knowledge-Enhanced Traffic Prediction", "author": "Lilan Peng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; cnn; gnn; ai-smart-cities", "keywords": "", "abstract": "The rapid growth of urban populations has put unprecedented pressure on transportation systems, causing widespread problems such as congestion, accidents, and environmental pollution. Accurate traffic prediction is therefore essential for building smart cities and intelligent transportation systems, as it enables evidence-based policymaking, risk assessment, and efficient traffic management. Early research on traffic prediction relied mainly on statistical models and machine learning techniques, which struggled to capture complex nonlinear spatio-temporal dependencies in large-scale networks. The advent of traffic big data has prompted a paradigm shift towards deep learning approaches. Recent advances in traffic prediction have increasingly integrated advanced temporal models with Graph Neural Networks to model complex temporal dynamics and localized spatial patterns, which yield incremental yet meaningful performance gains. However, these existing approaches still face four key limitations: (1) Traffic patterns exhibit cross-scale temporal evolution (ranging from minutes to months). (2) Spatial correlations are dynamic and multi-scale among different time scales. (3) Traffic data involves not only pairwise spatial relationships but also high-order functional dependencies among multiple regions and short or long-term temporal dependencies across different time periods. (4) Current data-driven models often lack the constraint of domain knowledge, which may lead the models to learn certain false correlations arising from data noise or randomness, thereby limiting the predictive performance. In response to these challenges, this dissertation investigates four core research questions: (1) How to learn cross-scale temporal dependencies? (2) How to capture multi-scale spatial dependencies? (3) How to model hierarchical spatio-temporal semantic associations? (4) How to integrate domain knowledge into data-driven traffic prediction models? Specifically, four traffic prediction models are developed: (1) Cross-Scale Temporal Relationship Representation Method. Dynamic temporal cross-scale representation model (DSTMR) adopts a hierarchical residual-like layer with gating mechanisms to capture multi-scale temporal dependencies. A dynamic graph generation module is designed to adaptively learn dynamic spatial correlations and generate a dynamic spatial graph structure. It further integrates a graph neural network to model spatial correlations, and is applied to bus station passenger flow forecasting. (2) Multi-Scale Spatial Relationship Representation Method. A multi-scale graph structure learning model (DMGL) is developed. This model optimizes graph structure learning strategies and constructs multi-scale graphs to capture spatial information at different time scales. Specifically, a dynamic multi-scale graph generation module is first designed to construct multi-scale dynamic graph structures through time decomposition and aggregation. Experimental results on real taxi regional passenger flow datasets demonstrate that DMGL achieves significant improvements in prediction performance compared to baseline methods. (3) Hierarchical Spatio-Temporal Semantic Associations Representation Method. A spatio-temporal mixed-order relationship representation model (MixSTRL) is designed. It introduces a spatial mixed-order representation that combines a graph neural network for pairwise spatial dependencies and hypergraph neural networks for high-order spatial relationships, integrated through a squeeze-and-excitation mechanism. Furthermore, it constructs a temporal mixed-order representation module that fuses a temporal convolutional network for short-term dynamics with temporal hypergraph neural networks for long-range periodic dependencies, employing a gated fusion strategy. Experimental evaluations on three taxi regional passenger flow datasets verify the effectiveness. (4) Knowledge-Enhanced and Hierarchical Spatial Pattern Learning Method (KA-HSPL). This method first designs a spatio-temporal embedding module that integrates knowledge-augmented spatial embedding and period-aware temporal embedding. Then, it develops a hierarchical spatial pattern learning module, which combines an adaptive graph convolutional network, a customized hypergraph convolutional network, and a spatio-temporal fusion module to model both pairwise and high-order spatial relationships. Finally, it employs a time-aware graph convolutional network to capture dynamic temporal correlations and devises a spatio-temporal joint graph convolutional network for integrated spatio-temporal dependency learning. The experiments on six highway section speed datasets demonstrate the model's effectiveness. (5) Intelligent Traffic Early Warning Demo System. To integrate and implement the designed four urban traffic prediction models, this dissertation develops an intelligent traffic early warning demo system.", "source_url": "https:\/\/openalex.org\/W7222948398", "pdf_url": "", "doi": "10.18445\/20261006-080745-0", "handle": "", "repository_name": "University Library Hagen (UB Hagen)", "source_record_id": "W7222948398", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 297, "title": "Prediction of Deep Learning Methods using Wavelet Transform", "title_ar": "", "title_en": "", "author": "Gökçe Nur Taşağıl Arslan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2025, "language": "tr", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; predictive-analytics; cnn; rnn", "keywords": "", "abstract": "Hava kirliliği, PM10 gibi partikül madde içeren yanma gazlarının salınması sonucu ortaya çıkmaktadır. Doğru ve güvenilir teknikler ile PM10 konsantrasyonlarının tahmini, sağlık risklerini düşürmek için göz önüne alınmalıdır. Ortam hava kalitesi ve halk sağlığı üzerindeki zararlı etkileri en aza indirmek için önleyici tedbirleri yürütmek üzere erken uyarı sistemlerinin potansiyelinin olması, PM10'u tahmin etme sayesinde mümkün hale gelmektedir. Bu çalışma, saatlik geçmiş hava kirletici konsantrasyonları ve meteorolojik verileri kullanarak, Ayrık Dalgacık Dönüşümü (DWT), Çok Katmanlı Algılayıcılar (MLP), Yinelemeli Sinir Ağları (RNN), Uzun Kısa Süreli Bellek (LSTM), Çift Yönlü LSTM (BiLSTM), Kapılı Yinelemeli Birim (GRU) ve Evrişimli Sinir Ağı (CNN) kullanarak 24 saat sonraki PM10 konsantrasyonunu tahmin etmek için bir yöntem önermektedir. İlk olarak, yüksek performanslı modelleri belirlemek için dalgacık tipleri kullanılır ve bu modeller arasında derin öğrenme tekniklerinde iyi performans gösterenler belirlenir. Modeller karşılaştırılırken RMSE, MAE ve R2 değerleri gibi performans parametreleri dikkate alınmıştır. En iyi model, en düşük RMSE (1,908), MAE (0,994), ve en yüksek R2 ()'ye sahip Dmey dalgacık dönüşümü kullanılan DWT-GRU-BiLSTM modeli olmuştur. Bu çalışmada kullanılan model, gelecekteki araştırmalarda bölgesel PM konsantrasyon seviyelerini doğru bir şekilde tahmin etmek için kullanılabilir. Sürdürülebilir akıllı çevre sistemleri kurmak ve önleyici eylemlerde bulunabilen kurum ve kuruluşların doğru zamanda harekete geçmesine yardımcı olmak için, uygulanan bu hibrit derin öğrenme algoritmalarının doğru tahminleme yeteneğinden yararlanılabilir. Hava kirliliğinin arttığı ve insan sağlığı için tehdit oluşturduğu mevcut ortamda, alınabilecek her türlü önleyici tedbir, tüm canlılar için yaşam kalitesini iyileştirecek, hava kirliliğinden kaynaklanan sağlık sorunlarını ve ölümleri azaltacak ve nihayetinde refah derecesini yükseltecektir. Ayrıca, bu bulgular İstanbul gibi yoğun nüfuslu, endüstriyel ve yoğun trafiğe sahip şehirlerin hava kalitesiyle ilgili ikna edici ampirik kanıtlar sunabilir.", "source_url": "https:\/\/openalex.org\/W7219471140", "pdf_url": "https:\/\/avesis.yildiz.edu.tr\/file?id=4447b2e1-fa87-4912-a948-b76c5deb0c71", "doi": "", "handle": "", "repository_name": "Yildiz Technical University - AVESIS", "source_record_id": "W7219471140", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 298, "title": "Large-Scale Traffic Flow Prediction Using Deep Learning in the Context of Smart Mobility", "title_ar": "", "title_en": "Large-Scale Traffic Flow Prediction Using Deep Learning in the Context of Smart Mobility", "author": "Arief Barkah Koesdwiady", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Waterloo", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "generative-ai", "research_subdomain": "gan", "categories": "machine-learning; deep-learning; generative-ai; ai-applications; predictive-analytics; gan; ai-smart-cities", "keywords": "", "abstract": "Designing and developing a new generation of cities around the world (termed as smart cities) is fast becoming one of the ultimate solutions to overcome cities' problems such as population growth, pollution, energy crisis, and pressure demand on existing transportation infrastructure. One of the major aspects of a smart city is smart mobility. Smart mobility aims at improving transportation systems in several aspects: city logistics, info-mobility, and people-mobility. The emergence of the Internet of Car (IoC) phenomenon alongside with the development of Intelligent Transportation Systems (ITSs) opens some opportunities in improving the tra c management systems and assisting the travelers and authorities in their decision-making process. However, this has given rise to the generation of huge amount of data originated from human-device and device-device interaction. This is an opportunity and a challenge, and smart mobility will not meet its full potential unless valuable insights are extracted from these big data. Although the smart city environment and IoC allow for the generation and exchange of large amounts of data, there have not been yet well de ned and mature approaches for mining this wealth of information to bene t the drivers and traffic authorities. The main reason is most likely related to fundamental challenges in dealing with big data of various types and uncertain frequency coming from diverse sources. Mainly, the issues of types of data and uncertainty analysis in the predictions are indicated as the most challenging areas of study that have not been tackled yet. Important issues such as the nature of the data, i.e., stationary or non-stationary, and the prediction tasks, i.e., short-term or long-term, should also be taken into consideration. Based on this observation, a data-driven traffic flow prediction framework within the context of big data environment is proposed in this thesis. The main goal of this framework is to enhance the quality of traffic flow predictions, which can be used to assist travelers and traffic authorities in the decision-making process (whether for travel or management purposes). The proposed framework is focused around four main aspects that tackle major data-driven traffic flow prediction problems: the fusion of hard data for traffic flow prediction; the fusion of soft data for traffic flow prediction; prediction of non-stationary traffic flow; and prediction of multi-step traffic flow. All these aspects are investigated and formulated as computational based tools\/algorithms\/approaches adequately tailored to the nature of the data at hand. The first tool tackles the inherent big data problems and deals with the uncertainty in the prediction. It relies on the ability of deep learning approaches in handling huge amounts of data generated by a large-scale and complex transportation system with limited prior knowledge. Furthermore, motivated by the close correlation between road traffic and weather conditions, a novel deep-learning-based approach that predicts traffic flow by fusing the traffic history and weather data is proposed. The second tool fuses the streams of data (hard data) and event-based data (soft data) using Dempster Shafer Evidence Theory (DSET). One of the main features of the DSET is its ability to capture uncertainties in probabilities. Subsequently, an extension of DSET, namely Dempsters conditional rules for updating belief, is used to fuse traffic prediction beliefs coming from streams of data and event-based data sources. The third tool consists of a method to detect non-stationarities in the traffic flow and an algorithm to perform online adaptations of the tra c prediction model. The proposed detection approach is developed by monitoring the evolution of the spectral contents of the traffic flow. Furthermore, the approach is specfi cally developed to work in conjunction with state-of-the-art machine learning methods such as Deep Neural Network (DNN). By combining the power of frequency domain features and the known generalization capability and scalability of DNN in handling real-world data, it is expected that high prediction performances can be achieved. The last tool is developed to improve multi-step traffic flow prediction in the recursive and multi-output settings. In the recursive setting, an algorithm that augments the information about the current time-step is proposed. This algorithm is called Conditional Data as Demonstrator (C-DaD) and is an extension of an algorithm called Data as Demonstrator (DaD). Furthermore, in the multi-output setting, a novel approach of generating new history-future pairs of data that are aggregated with the original training data using Conditional Generative Adversarial Network (C-GAN) is developed. To demonstrate the capabilities of the proposed approaches, a series of experiments using arti cial and real-world data are conducted.", "source_url": "https:\/\/openalex.org\/W2802252725", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UWSpace (University of Waterloo)", "source_record_id": "W2802252725", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 299, "title": "Deep Learning for Computer Vision in Smart Cities", "title_ar": "", "title_en": "Deep Learning for Computer Vision in Smart Cities", "author": "David García-Retuerta", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Salamanca", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; video-analysis; ai-industry; ai-smart-cities", "keywords": "", "abstract": "The Digital Age has caused a rapid shift from traditional industry to an economy mainly based upon information technology. According to recent studies, 74 zettabytes (ZB) of data have been generated, captured and replicated in the world in 2021, with video accounting for 82% of internet traffic. This figure has been amplified due to the coronavirus pandemic, and it is expected to keep increasing, reaching 149 ZB by 2024. Processing this impressive amount of information is one of the main scientific challenges of our time. Against this backdrop, Machine Learning (ML) and two related paradigms have emerged: big data and deep learning. These disciplines take advantage of mathematical optimization methods, bioinspiration and modern Graphics Processing Units (GPUs) to manage large datasets efficiently and effectively.", "source_url": "https:\/\/openalex.org\/W4380486366", "pdf_url": "https:\/\/gredos.usal.es\/bitstream\/10366\/150948\/1\/PDII_Garcia%20RetuertaD_SmartCities.pdf", "doi": "10.14201\/gredos.150948", "handle": "", "repository_name": "Gredos (University of Salamanca)", "source_record_id": "W4380486366", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 301, "title": "Short Term Traffic Flow Prediction Using Deep Learning Approach", "title_ar": "", "title_en": "Short Term Traffic Flow Prediction Using Deep Learning Approach", "author": "Sivabalaji Manoharan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "National College of Ireland", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "Intelligent transportation systems helps travellers reach their destination at an estimated time. Smart cities have deployed latest technologies to satisfy the needs of travellers through efficient navigation. Adverse weather conditions may lead to increase in traffic congestion as well as accidents. Rainfall, Snow and wet pavements are a major cause for the traffic flow during winter across all areas. Several methods have been researched at a deep level for predicting traffic flow and still an efficient method to satisfy real world traffic problems have not been identified. The accident and weather data are investigated in this paper to predict the flow of traffic in Leeds city. Three models were developed through deep learning neural network algorithm for predicting traffic flow. The predicted Deep neural network model is compared with the superior support vector machine model and the accuracy is higher for the Deep neural networks.", "source_url": "https:\/\/openalex.org\/W2593566730", "pdf_url": "http:\/\/trap.ncirl.ie\/2520\/1\/sivabalajimanoharan.pdf", "doi": "", "handle": "", "repository_name": "TRAP@NCI (National College of Ireland)", "source_record_id": "W2593566730", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 303, "title": "Deep learning-based traffic flow prediction and traffic management system for urban transportation networks", "title_ar": "", "title_en": "Deep learning-based traffic flow prediction and traffic management system for urban transportation networks", "author": "Han Zhao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; reinforcement-learning; ai-applications; predictive-analytics; cnn; ai-smart-cities", "keywords": "", "abstract": "Due to the growth of the population and improved living standards, a lot of urbanization policies are implemented by the governments in many countries.As a result, the total amount of vehicles on the streets is growing rapidly, and traffic congestion occurs frequently.The recurring and non-recurring congestion has resulted in high economic costs, environmental pollution and severe noise pollution.Hence, how to alleviate traffic congestion and delays have become an essential problem in this urbanized world.In this thesis, we proposed a deep learning-based traffic prediction and management system to tackle the traffic congestion problem.First, we developed a domain-adversarial-based graph network for short-term traffic flow prediction.Contrast experiments have been conducted on several opensource traffic datasets, which proved that our proposed method had made some improvements compared to other baselines.Secondly, we proposed an optimized vehicle route guidance and navigation system.The employed deep reinforcement learning method and graph convolutional network can extract several features from traffic data and topological data.The proposed route guidance system can assign the best route and re-route the vehicles in a complex urban transportation network with dynamic traffic conditions.Last but not least, we proposed a deep reinforcement learning-based traffic signal control strategy to reduce the travel delay time and queue length by adjusting phase splits.TD3 algorithm is employed to generate optimal phase splits for the next cycle of traffic signals.The performance of traffic signal control by this strategy is evaluated through several urban road networks with true traffic demand or manually generated demand via VISSIM.", "source_url": "https:\/\/openalex.org\/W4389209251", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/171885\/4\/zhaohan_final_thesis.pdf", "doi": "10.32657\/10356\/171885", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4389209251", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 304, "title": "Traffic Forecasting Applications Using Crowdsourced Traffic Reports and Deep Learning", "title_ar": "", "title_en": "Traffic Forecasting Applications Using Crowdsourced Traffic Reports and Deep Learning", "author": "Ali Alammari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; cnn; rnn; ai-smart-cities", "keywords": "", "abstract": "Intelligent transportation systems (ITS) are essential tools for traffic planning, analysis, and forecasting that can utilize the huge amount of traffic data available nowadays. In this work, we aggregated detailed traffic flow sensor data, Waze reports, OpenStreetMap (OSM) features, and weather data, from California Bay Area for 6 months. Using that data, we studied three novel ITS applications using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first experiment is an analysis of the relation between roadway shapes and accident occurrence, where results show that the speed limit and number of lanes are significant predictors for major accidents on highways. The second experiment presents a novel method for forecasting congestion severity using crowdsourced data only (Waze, OSM, and weather), without the need for traffic sensor data. The third experiment studies the improvement of traffic flow forecasting using accidents, number of lanes, weather, and time-related features, where results show significant performance improvements when the additional features where used.", "source_url": "https:\/\/openalex.org\/W4407267545", "pdf_url": "https:\/\/digital.library.unt.edu\/ark:\/67531\/metadc1703305\/m2\/1\/high_res_d\/ALAMMARI-DISSERTATION-2020.pdf", "doi": "10.12794\/metadc1703305", "handle": "", "repository_name": "University of North Texas Digital Library (University of North Texas)", "source_record_id": "W4407267545", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 305, "title": "Deep learning models in urban logistics for air quality prediction in the city of Bucaramanga", "title_ar": "", "title_en": "", "author": "Paula Andrea Abril Ortiz ; Édgar Leonardo Porras Ojeda", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Industrial de Santander", "university_source": "repository_host", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2019, "language": "es", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-smart-cities", "keywords": "", "abstract": "El aumento de la población de las últimas décadas y el flujo de personas de las zonas rurales a las grandes ciudades ha ocasionado un incremento del volumen de desplazamientos de pasajeros y mercancías, llevando a la sobresaturación de la circulación vehicular y al aumento de las necesidades de movilidad para satisfacer la competitividad comercial y la calidad de vida de sus habitantes, convirtiendo el tráfico en un factor relevante en la contaminación del aire y un importante elemento de riesgo medioambiental para la salud de grupos vulnerables como mujeres, niños y adultos mayores. Por ello, la gestión óptima de la movilidad urbana se enfoca un modelo capaz de cubrir tanto el aumento de la urbanización como su impacto en la calidad del aire, lo cual representa un reto para los gobiernos a nivel mundial en cuanto el diseño y ejecución de metodologías y estrategias orientadas la reducción de los niveles de contaminación del aire en pro de mejorar la salud cardiovascular y respiratoria de la población. Teniendo en cuenta lo anterior, este proyecto se orienta a la aplicación, valoración y comparación de modelos Deep Learning (DL) entre ellos el LSTM, analizando los datos meteorológicos y de material particulado de tres estaciones de monitoreo ambiental del Área -Metropolitana de Bucaramanga AMB, con el objetivo de obtener un modelo predictivo que apoye la planeación de estrategias orientadas a mejorar la calidad del aire.", "source_url": "https:\/\/openalex.org\/W7133456949", "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/d0df09f4-c674-4b89-a85e-1ecdb2d1ec18\/download", "doi": "", "handle": "", "repository_name": "Universidad Industrial de Santander", "source_record_id": "W7133456949", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 307, "title": "Encrypted Web Traffic Classification Using Deep Learning", "title_ar": "", "title_en": "Encrypted Web Traffic Classification Using Deep Learning", "author": "Iman Akbari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Waterloo", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "deep-learning; computer-vision; ai-applications; cnn; rnn; ai-smart-cities", "keywords": "", "abstract": "Traffic classification is essential in network management for operations ranging from capacity planning, performance monitoring, volumetry, and resource provisioning, to anomaly detection and security. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, e.g., as a de-facto in HTTP\/2 and QUIC protocols. In the current state of encrypted traffic classification using Deep Learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations exist that are commonly ignored. In this thesis, we design a novel feature engineering approach that generalizes well for encrypted web protocols, and develop a neural network architecture based on Stacked Long Short-Term Memory (LSTM) layers and Convolutional Neural Networks (CNN) that works very well with our feature design. We evaluate our approach on a real-world traffic dataset from a major ISP and Mobile Network Operator. We achieve an accuracy of 95% in service-level classification with less raw traffic and smaller number of parameters, out-performing a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted web protocols. We also evaluate our approach on a public QUIC dataset with finer and application-level granularity in labeling, achieving an overall accuracy of 99%.", "source_url": "https:\/\/openalex.org\/W3162766776", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UWSpace (University of Waterloo)", "source_record_id": "W3162766776", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 308, "title": "Deep learning with applications for spatiotemporal prediction", "title_ar": "", "title_en": "Deep learning with applications for spatiotemporal prediction", "author": "Jiachuan Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "HKUST Electronic Theses Deep learning with applications for spatiotemporal prediction by Jiachuan Wang thesis 2024 1 online resource (1 unnumbered page, xv, 115 pages) : illustrations (some color) Spatiotemporal prediction has garnered significant attention for many years. In recent years,", "source_url": "https:\/\/openalex.org\/W4405880469", "pdf_url": "", "doi": "10.14711\/thesis-991013270958103412", "handle": "", "repository_name": "", "source_record_id": "W4405880469", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 309, "title": "Security of Smart Cyber-Physical Grids: A Deep Learning Approach", "title_ar": "", "title_en": "Security of Smart Cyber-Physical Grids: A Deep Learning Approach", "author": "Jacob Sakhnini", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Guelph", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "ai-cybersecurity", "categories": "deep-learning; ai-applications; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "Cyber physical systems are widely used in critical infrastructure; among the most notable applications is the smart cyber-physical grid. The smart grid technologies are accompanied with various advantages including more efficient power generation and increased integration of green energy sources. As such, many cities around the world are investing in smart cyber-physical grid technologies. The use of this technology, however, comes with great risk to cyber threats. Furthermore, current state of the art defense methods lack in robustness, scalability,and computational efficiency. This thesis presents a deep learning based solution for attack detection in cyber-physical systems, particularly in the case of the smart cyber-physical grid.The research methods implemented in this thesis focus on improving robustness, scalability,and computational efficiency of intelligent attack detection algorithms by presenting heuristic methods for feature extraction and a novel deep learning approach that proved robust to varying attack sparsity and data imbalance.", "source_url": "https:\/\/openalex.org\/W3040743421", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Atrium (University of Guelph)", "source_record_id": "W3040743421", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 311, "title": "Modeling Vehicular Traffic Shock Wave with Machine Learning Approaches", "title_ar": "", "title_en": "Modeling Vehicular Traffic Shock Wave with Machine Learning Approaches", "author": "Ji‐Hyoung Kim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "The current trend of transforming old cities into smart cities has revealed many issues of the modern cities. One of the issues is the prevailing traffic jam on highways of the modern cities. The vehicular shock wave has been a problem on highways since it is one of the main causes of the traffic jam. The combination of heavy traffic and small traffic perturbations or unexpected driver actions are the main causes of shock waves. In order to alleviate road traffic caused by shock waves, it is crucial to have a system that predicts shock waves and informs them to the drivers. In this dissertation, we analyzed 6 months of freeway traffic data of Los Angeles, CA, provided by CalTrans PeMS (Performance Measurement System) and obtained the vehicular shock wave propagation speed of each freeway. Based on this information, we propose a machine learning approaches to predict shock waves. We utilize Hidden Markov Model (HMM) to predict if the shock wave will occur and propagate based on neighboring lanes' traffic information. Addtionally, HMM is used to estimate the probability of lane change from one lane to other lanes based on the occupancy of a lane. Baum-Welch algorithm is used to predict the parameters (occupancy and state).We also utilized Deep Learning (DL) in order to predict the shock wave occurrence and propagation. We compared Stacked AutoEncoder (SAE), Deep Belief Networks (DBN), and HMM for the accuracy of the prediction of shock wave occurrences and propagation. These approaches have been tested on the same PeMS data sets and achieved good accuracy. In the future, our models will be used to include modern collision prevention techniques (e.g., anti-shock wave strategies) to test their efficacy and help to reduce the number of potential accidents and save energy in the process. Also, our models can be used to improve traffic simulators to provide driving patterns that are close to real human's.", "source_url": "https:\/\/openalex.org\/W2561644561", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W2561644561", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 312, "title": "Strategic Design of Smart Bike-Sharing Systems for Smart Cities", "title_ar": "", "title_en": "Strategic Design of Smart Bike-Sharing Systems for Smart Cities", "author": "Huthaifa I. Ashqar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "Traffic congestion has become one of the major challenging problems of modern life in many urban areas. This growing problem leads to negative environmental impacts, wasted fuel, lost productivity, and increased travel time. In big cities, trains and buses bring riders to transit stations near shopping and employment centers, but riders then need another transportation mode to reach their final destination, which is known as the last mile problem. A smart bike-sharing system (BSS) can help address this problem and encourage more people to ride public transportation, thus relieving traffic congestion. At the strategic level, we start with proposing a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. In the transportation sector, researchers can use smartphones to track and obtain information of multi-mode trips. These data can be used to recognize the user's transportation mode, which can be then utilized in several different applications; such as planning new BSS instead of using costly surveys. Next, a new method is proposed to quantify the effect of several factors such as weather conditions on the prediction of bike counts at each station. The proposed approach is promising to quantify the effect of various features on BSSs in cases of large networks with big data. Third, these resulted significant features were used to develop state-of-the-art toolbox algorithms to operate BSSs efficiently at two levels: network and station. Finally, we proposed a quality-of-service (QoS) measurement, namely Optimal Occupancy, which considers the impact of inhomogeneity in a BSS. We used one of toolbox algorithms modeled earlier to estimate the proposed QoS. Results revealed that the Optimal Occupancy is beneficial and outperforms the traditionally-known QoS measurement.", "source_url": "https:\/\/openalex.org\/W3016951819", "pdf_url": "", "doi": "", "handle": "", "repository_name": "VTechWorks (Virginia Tech)", "source_record_id": "W3016951819", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 313, "title": "Scalable and accurate forecasting for smart cities", "title_ar": "", "title_en": "Scalable and accurate forecasting for smart cities", "author": "Pasan Karunaratne", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Melbourne", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; ai-applications; predictive-analytics; xai; ai-smart-cities", "keywords": "", "abstract": "Cities are getting bigger, better and smarter. The increased connectivity of people and devices and the availability of cheap sensors has led to a surge in public and government interest in smart city initiatives. This public interest, along with the recent increased interest in machine learning techniques has led to growing research focus into the mining and analysis of data in smart city settings. Much of the analysis in smart city settings is based on forecasting on time series data recorded by smart sensors for planning purposes. For example, utility companies can use electricity load forecasting on smart meter data for capacity planning, and prediction of pedestrian counts and passenger flow in public transportation systems can help in planning to reduce traffic congestion. Though forecasting in smart city settings yields such benefits, it also entails unique challenges, such as challenges related to multi-step prediction, challenges related to low quality training data due to sensors encountering vandalism, malfunction or communication failures, and challenges in maintaining predictive throughput in systems involving increasingly larger numbers of smart sensors. Improving accuracy is a primary goal in any forecasting task, which is especially challenging in multi-step prediction scenarios. We address this challenge by providing new methods to incorporate prior knowledge uniquely relevant to smart cities, such as the periodic behaviour of sensor time series data over the Monday-Friday working week. Specifically, we propose novel kernel function compositions which can incorporate such prior knowledge to kernel-based Bayesian forecasting techniques, with the goal of improving prediction accuracy and robustness to spurious data. We develop our kernel compositions for the state of the art Gaussian Process Regression technique. The new kernel compositions we develop enable prior knowledge relating to multiple periodic effects of the working week (e.g. daily, weekly, holiday effects) and their interactions to be incorporated in the same model. We also provide methods to mitigate the effects of convergence to local optima in the optimisation process over the hyperparameters used in the Gaussian Process models. We address the challenges relating to missing training data in smart city settings by making use of data of other related sensors (which may have more complete data) to mitigate the impact the low quality data has on prediction accuracy. To this end, we develop multi-task learning methods (which are able to learn joint representations from multiple sensors) to improve Gaussian Process Regression prediction accuracy with missing training data values. We also provide equivalent expressions to our multi-task learning methods as combinations of commonly used kernel functions in Gaussian Processes. This enables the straightforward implementation of these methods in popular machine learning toolkits. We address the scalability challenge of large volumes of sensor data in two steps. One, we focus on an interpretable label-based forecasting algorithm which allows for high-throughput predictions due to the minimal number of operations needed to be done in the forecasting stage. We perform numerous enhancements on this algorithm in order to improve its prediction accuracy, including filtering, windowing and ensembling methods as well as methods of incorporating exogenous variables. Our scalable forecasting methods are then developed using this enhanced base algorithm. We develop methods which enable the initial step of the algorithm to be performed using algorithms developed for stream processing, which not only allows for the algorithm to be parallelised across multiple machines, but also enables it to run on real-time data streams. We address the scalability challenges in scenarios with both a single fast stream and a large number of streams, especially with regard to synchronisation issues between multiple machines. We demonstrate the effectiveness of our methods on multiple real-world publicly available datasets to illustrate the potential generalisability of our techniques.", "source_url": "https:\/\/openalex.org\/W2897407191", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Minerva Access (University of Melbourne)", "source_record_id": "W2897407191", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 314, "title": "Deep Learning-Based Time Series Prediction Techniques", "title_ar": "", "title_en": "Deep Learning-Based Time Series Prediction Techniques", "author": "Faria Ferdowsy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7215320880", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/cd92228b-c11b-44ad-abc6-03c56b6166f5\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215320880", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 315, "title": "Enhancing the Education of Accessibility with Artificial Intelligence", "title_ar": "", "title_en": "Enhancing the Education of Accessibility with Artificial Intelligence", "author": "Fitsum Gizachew Deriba", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "generative-ai", "research_subdomain": "ai-education", "categories": "generative-ai; ai-applications; ai-education", "keywords": "", "abstract": "The global reliance on digital technologies in all aspects of life highlights the fundamental need for universal accessibility.This study addresses these need to effectively integrate accessible design education within Ethiopian Higher Education (EHE), recognizing the importance for digital inclusion and the limited pedagogical strategies for its effective integration to create universally accessible digital environments.It investigates the multifaceted role of Artificial Intelligence (AI) as a pedagogical tool to enhance students' design learning and application of accessible design principles.This dissertation includes four studies-one systematic and three empirical-that explored on the accessibility and inclusive design gaps in digital technologies and EHE computing curricula, focusing on three aspects: i) the role of Generative AI (GenAI) in accessibility and design education, ii) pedagogical efficacy of human and AI feedback modalities, and iii) the lasting impact of AI interventions in the design education domain.Using a mixed-methods sequential design, the research followed a structured process of data collection, preparation, exploration, and analysis.Participants were recruited from computing schools within EHE institutions.The qualitative analysis, employing thematic analysis, analyzed student experiences regarding GenAI's role in design, challenges associated with its use, and its potential future impact on inclusive design and accessibility.The", "source_url": "https:\/\/openalex.org\/W7215306484", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/1426332a-3a5e-484e-9459-8606455733c8\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215306484", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 316, "title": "Perceptions of Artificial Intelligence in Higher Education", "title_ar": "", "title_en": "Perceptions of Artificial Intelligence in Higher Education", "author": "Freeman, Kelly", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Seton Hall University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; responsible-ai; ai-applications; predictive-analytics; llm; fairness; ai-education; ai-industry", "keywords": "", "abstract": "Artificial Intelligence (AI), specifically Artificial Intelligence in Education (AIED), is a growing field of research and practice in higher education. AIED is increasingly permeating the student journey, from admissions through graduation, and is affiliated with tools such as early alert systems, adaptative tutoring, predictor systems, and language learning chatbots. These emergent technologies provide previously unforeseen opportunities to advance student success, such as widespread individualization, yet pose challenges related to technical literacy and ethical and safety concerns. After the initial excitement surrounding AI chatbots in 2022, catalyzed by the introduction of ChatGPT, the pace of technological innovation continues to accelerate, with AI becoming increasingly embedded across society. While existing research indicates a connection between AI perceptions and technical literacy, the current literature only scratches the surface of AIED perceptions. The higher education industry is at a critical juncture where it can either establish guidelines and standards for ethical AI use in education that align with pedagogical best practices or allow AI to outpace education, potentially diminishing its relevance to students preparing for the modern world. This mixed methods explanatory sequential design content analysis aims to understand how individuals that post about higher education perceived AIED from July 2022 to July 2024 using social media data from X, formerly Twitter, and Reddit. Using social media data allowed the researcher to gain insight into how AIED is perceived and discussed by individuals that post about higher education unprompted. This study also used Bronfenbrenner’s ecological model adapted to the higher education ecosystem, alongside Kelman’s 1985 and Dholakia et al.’s Social Influence and Social Consumer Influence theories, to place the perceptions of these individuals about AIED in context. Overall, the findings confirmed that despite widespread use of generative AI, AIED is still a new and emerging topic and industry wide AI literacy levels are still developing. In addition, the study found that individuals who discuss AI on social media showed concerns about the impact of AI on higher education and that x-risk related concerns were more prevalent on social media than in the literature. These findings highlighted the need for the higher education industry stakeholders to demonstrate ownership for AI Literacy and Responsible and Ethical AI use. As individuals question if higher education is worth the expense and will remain relevant, historical technological inertia must be overcome to protect the value of higher education as AI technology continues to advance. Ultimately as AI becomes more integral to everyday life, institutions need to demonstrate they can adapt to an AI-centric future.", "source_url": "https:\/\/openalex.org\/W7110542908", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Seton Hall University eRepository (Seton Hall University)", "source_record_id": "W7110542908", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 317, "title": "Programming education using artificial intelligence.", "title_ar": "", "title_en": "Programming education using artificial intelligence.", "author": "Sičiovas, Rokas,", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Repository of Kaunas University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Lithuania", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "ai-education", "categories": "nlp; ai-applications; ai-education", "keywords": "", "abstract": "This project presents a programming learning opportunity that uses artificial intelligence tools. Learning is a very important aspect in order to improve, develop a personality, and create a better future. The system being developed is very relevant these days, especially in the field of programming. Therefore, it is a relevant topic for people who want to learn to program. The literature analysis has shown that AI is entering education and is progressing very quickly. There are already places where AI tools are being tested or even fully participating in education. Therefore, this topic is very necessary and especially relevant these days. This system will provide an opportunity to write code in the Moodle system, receive automatic assessment, and most importantly, the AI tool will perform code analysis, and there will be an opportunity to communicate with the AI tool, on a question-answer basis, and the chatbot will help navigate between topics and tasks within the Moodle course itself. Such integration will help people have everything in one place and get feedback faster. The main goal of the work is to improve programming learning opportunities using artificial intelligence. The system was implemented using the PHP programming language. Two new Moodle plugins were created – a chatbot plugin and a programming task plugin. The chatbot contains information about the Moodle course, and the programming task plugin contains a compiler, automatic testing, AI tool code analysis, and communication with the AI tool, using the question-answer principle. A detailed user guide has been prepared, which allows you to familiarize yourself with the installed plugins and their operation. The system was tested by students and teachers; a total of 48 people registered for the Moodle course. 32 people participated in the survey. This makes up 67% of all registered people. The survey results are positive, and the testing was successful. However, in order to implement such a solution in full-fledged education, certain strict rules are needed.", "source_url": "https:\/\/openalex.org\/W7164623440", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTUePubl (Repository of Kaunas University of Technology)", "source_record_id": "W7164623440", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 318, "title": "Artificial Intelligence as a tool in education", "title_ar": "", "title_en": "", "author": "Amàlia Gregori Faus", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2025, "language": "ca", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "[CA] La Intel·ligència Artificial és una tecnologia existent de fa dècades en els camps de la computació i dels sistemes d'informació, entre d'altres, però és recentment quan s'ha estés el seu ús al dia a dia de totes les persones, incloent també dins el sector de l'educació. Aquest treball reflexiona i qüestiona la natura i propòsit de la Intel·ligència Artificial en l'educació, més enllà de l exalçament i la condemnació a la qual és sotmesa quasi a parts iguals. S inclou una revisió bibliogràfica sobre l ús de la Intel·ligència Artificial en l educació, concretant objectius, eines emprades i resultats obtesos amb la seua aplicació, que aporta una visió diversa i emblemàtica de les possibilitats dins d aquest domini. Aquesta revisió s amplia reflexionant sobre aquests usos i l impacte que té la seua aplicació a l aula i en la societat, fent èmfasi especialment en alguns aspectes rellevants, com són el procés d aprenentatge i l ús judiciós d' eines de les Tecnologies de la Informació i la Comunicació (TIC) a l aula. Per acabar, es realitza una nova proposta d ús de la Intel·ligència Artificial, coherent amb els raonaments prèviament exposats. En essència, es pretén aprofundir en el paper de la Intel·ligència Artificial en l educació i, en última instància, en la societat.", "source_url": "https:\/\/openalex.org\/W7206269745", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Hispana", "source_record_id": "W7206269745", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 319, "title": "Using Artificial Intelligence in Citizenship Education Classes", "title_ar": "", "title_en": "Using Artificial Intelligence in Citizenship Education Classes", "author": "Karolína BALOVÁ", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "fairness", "categories": "nlp; generative-ai; responsible-ai; ai-applications; fairness; ai-education", "keywords": "", "abstract": "This diploma thesis explores the possibilities of using artificial intelligence in education, with a particular emphasis on its application in citizenship education. The theoretical part provides a comprehensive overview of the types of artificial intelligence, its learning methods and its historical development. Particular attention is paid to generative artificial intelligence, its benefits, risks and specific applications for use in school practice. Attention is also paid to the ethical aspects of its integration into the educational environment. The thesis also analyses the connection of artificial intelligence with key competences according to the Framework Educational Programme for Primary Education and presents education for citizenship in its context. It also includes an overview of current initiatives and projects aimed at integrating artificial intelligence into education in general and those that focus specifically on citizenship education. The practical part of the thesis contains suggestions for lessons that link citizenship education with AI. These lessons include activities with chatbots and other AI applications, most of which have been practically tested to verify their effectiveness and benefits for teaching. It also shows how educators can use AI to simplify their work.", "source_url": "https:\/\/openalex.org\/W7128986388", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7128986388", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 320, "title": "The integration of artificial intelligence into education – challenges and opportunities", "title_ar": "", "title_en": "The integration of artificial intelligence into education – challenges and opportunities", "author": "Olga Seidykh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "responsible-ai; ai-applications; fairness; ai-education", "keywords": "", "abstract": "This paper examines the integration of artificial intelligence into the educational process as a key factor in the modernisation of the education system. It analyses the potential for using AI in adaptive learning, automated assessment, the creation of intelligent learning systems, and educational data analytics. It identifies the main challenges related to ethical and social aspects, as well as the prospects for technological development in shaping 21st-century skills. The paper emphasises the combination of innovation with pedagogical expertise. У роботі розглядається інтеграція штучного інтелекту в освітній процес як ключовий чинник модернізації системи навчання. Проаналізовано можливості використання ШІ для адаптивного навчання, автоматизованого оцінювання, створення інтелектуальних навчальних систем та аналітики освітніх даних. Визначено основні виклики, пов’язані з етичними та соціальними аспектами, а також перспективи розвитку технологій у формуванні навичок XXI століття. Робота акцентує на поєднанні інновацій з педагогічною майстерністю.", "source_url": "https:\/\/openalex.org\/W7200001666", "pdf_url": "https:\/\/dspace.nuft.edu.ua\/bitstreams\/6ef4406e-5279-48ed-a876-04eed9efaba1\/download", "doi": "", "handle": "", "repository_name": "Institutional Repository of National University of Food Technologies", "source_record_id": "W7200001666", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 321, "title": "Artificial intelligence in education: advantages and disadvantages", "title_ar": "", "title_en": "", "author": "O. Melnyk ; О. Д. Мельник", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Vinnytsia National Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2025, "language": "uk", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "Штучний інтелект (ШІ) активно змінює сферу освіти, пропонуючи як значні переваги, так і певні виклики. У цій статті розглядається роль ШІ в навчальному процесі, зокрема його здатність покращувати освітній досвід завдяки персоналізованим програмам, автоматичному оцінюванню та оптимізації вибору курсів. ШІ сприяє розвитку дистанційного навчання та зменшує адміністративне навантаження на викладачів, дозволяючи їм більше уваги приділяти викладанню та взаємодії зі студентами. Водночас існують занепокоєння щодо можливого зниження рівня соціалізації та критичного мислення студентів. Повна заборона використання ШІ в освіті є недоцільною, однак важливо забезпечити його етичне та відповідальне застосування. Основним викликом залишається пошук балансу між використанням можливостей ШІ та збереженням важливих людських аспектів навчального процесу.", "source_url": "https:\/\/openalex.org\/W7213690493", "pdf_url": "https:\/\/ir.lib.vntu.edu.ua\/bitstream\/123456789\/48876\/3\/23879.pdf", "doi": "", "handle": "", "repository_name": "Repository of Vinnytsia National Technical University (Vinnytsia National Technical University)", "source_record_id": "W7213690493", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 322, "title": "Prospects For Using Artificial Intelligence in Education", "title_ar": "", "title_en": "Prospects For Using Artificial Intelligence in Education", "author": "Mykhailo Hrama", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "responsible-ai; ai-applications; fairness; ai-education", "keywords": "", "abstract": "This article explores the fundamental shifts in the educational paradigm driven by the implementation of artificial intelligence. It focuses on the transition from standardized models to hyperpersonalized learning, the transformation of the teacher's role toward mentorship, and issues of global accessibility and inclusivity. Ethical challenges, such as data privacy and algorithmic bias, are examined as critical factors requiring a balanced approach for the successful integration of technology into the learning process. У цій статті досліджуються фундаментальні зрушення в освітній парадигмі, зумовлені впровадженням штучного інтелекту. Вона зосереджена на переході від стандартизованих моделей до гіперперсоналізованого навчання, трансформації ролі вчителя в бік наставництва, а також на питаннях глобальної доступності та інклюзивності. Етичні проблеми, такі як конфіденційність даних та алгоритмічна упередженість, розглядаються як критичні фактори, що вимагають збалансованого підходу для успішної інтеграції технологій у навчальний процес.", "source_url": "https:\/\/openalex.org\/W7199872861", "pdf_url": "https:\/\/dspace.nuft.edu.ua\/bitstreams\/28a9d1bd-8881-4945-b2b8-6dedd91ab534\/download", "doi": "", "handle": "", "repository_name": "Institutional Repository of National University of Food Technologies", "source_record_id": "W7199872861", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 323, "title": "Artificial intelligence, education, and complex thinking: paths for reconnection of knowledge", "title_ar": "", "title_en": "Artificial intelligence, education, and complex thinking: paths for reconnection of knowledge", "author": "Priscila Gonsales", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "fairness", "categories": "computer-vision; responsible-ai; ai-applications; fairness; ai-education; ai-cybersecurity", "keywords": "", "abstract": "The advances in artificial intelligence (AI) applications based on data processing (Big Data) have brought a new context to digital culture or cyberculture. Increasingly present in our daily lives, AI is a technology that makes use of statistical probability models that work from correlations and identification of patterns in data. In education, AI has often been pointed out as a source for improving teaching from a merely utilitarian and tool viewpoint, that is, solely for the customization of the content transmission and monitoring\/evaluation of apprehension of such content. However, AI has a multidimensional character, involving benefits and risks, as well as social, economic, legal, and environmental impacts that are practically unknown by educators and managers. This research highlights the need for a transdisciplinary vision for AI in education, as a field of knowledge and its various implications, based on Edgar Morin's complex thinking. A framework launched in 2020 by a group of researchers from the European Commission (BIDARRA et al., 2020) organizes in three aspects the interface between AI and education, highlighting the need to go beyond the use in content teaching: 1) learning with AI (study of AI applications aimed at teaching); 2) learning about AI (understanding the functioning of AI aimed at professional education for AI developers); 3) learning for AI (understanding the impacts of AI on society, ethical issues such as fake news, privacy, and security). Through the analysis of documents, publications and recent studies (2018-2021), the research addresses the urgency of institutional management policies, teacher training and educational governance that promote the reform of thinking for the reconnection of knowledge (MORIN, 2011b), considering principles of complexity such as dialogic, circularity, the \"ecology of action\" in order to promote an ecosystemic view of the current context of technology and, consequently, of a world in constant transformation", "source_url": "https:\/\/openalex.org\/W7120732715", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120732715", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 325, "title": "Ortaokul Öğrencilerine Yönelik Geliştirilen Yapay Zekâ Eğitim İçeriğinin Yapay Zekâ Okuryazarlığına Etkisi = Artificial İntelligence Education For Secondary Students The Effect Of İts Content On Artificial İntelligence Literacy", "title_ar": "", "title_en": "", "author": "Sakarya Üniversitesi, Eğitim Bilimleri Enstitüsü, Bilgisayar ve Öğretim Teknolojileri Eğitimi Anabilim Dalı, Bilgisayar ve Öğretim Teknolojileri Eğitimi Bilim Dalı ; Esra Soylu ; Doktor Öğretim Üyesi Hakkı Bağcı", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2023, "language": "tr", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.", "source_url": "https:\/\/openalex.org\/W7163603859", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Sakarya Üniversitesi Kurumsal Açık Akademik Arşivi", "source_record_id": "W7163603859", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 326, "title": "Students’ Perspectives on Artificial Intelligence in Education", "title_ar": "", "title_en": "Students’ Perspectives on Artificial Intelligence in Education", "author": "Thar Htin Shar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Waikato Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "This research aims to identify the perspectives of students on Artificial Intelligence (AI) in education. This research looks at the positive and negative impact of AI tracking systems and AI technologies. The expectations of AI in Education (AIED) are studied in this research. The data gathered from the online survey are analysed and divided into variables. Task- Technology Fit model is used to understand the relationship between the independent and dependent variables. These variables are used to define the outcome and results. By using the findings from the literature and analysis, the outcomes provide the results for hypotheses and research questions...", "source_url": "https:\/\/openalex.org\/W3016328903", "pdf_url": "http:\/\/researcharchive.wintec.ac.nz\/7115\/1\/Thar%20Shar%20Thesis%20with%20correction1.pdf", "doi": "", "handle": "", "repository_name": "Wintec Research Archive (Waikato Institute of Technology)", "source_record_id": "W3016328903", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 327, "title": "Artificial intelligence in higher education", "title_ar": "", "title_en": "", "author": "Lovro Šikić", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Zagreb Faculty of Humanities and SocialSciences", "university_source": "repository_host", "college": "", "department": "", "country": "Croatia", "governorate": "", "publication_year": 2021, "language": "sl", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "Ovaj rad nastoji prikazati način na koji je umjetna inteligencija implementirana u obrazovni sustav, te načine na koji umjetna inteligencija doprinosi poboljšanju kvalitete nastave. Obrađen je način na koji je umjetna inteligencija implementirana u obrazovni sustav, te na koje sve ona načine doprinosi poboljšanju kvalitete nastave. U radu je prikazano na koji način umjetna inteligencija utječe na studente i kako utječe na nastavnike. Anketa koja će biti provedena u sklopu ovog rada će provjeriti da li studenti smatraju da bi inteligentni sustavi mogli biti pomoć u sklopu njihovog obrazovanja. Na sveučilištima postoje inteligentni sustavi za poučavanje, no još uvijek nisu globalno rašireni, tj. ne predstavljaju globalni standard, ali analiza uloženih investicija i porast broja zaposlenih u području umjetne inteligencije, pokazuje kako će takvi, već prije spomenuti inteligentni sustavi, u obrazovanju postati standard. Kada je riječ o velikim poduzećima, oni su vodeći u svemu što se tiče razvoja umjetne inteligencije i njenog dijela znanog kao umjetna inteligencija u obrazovanju. Neosporna je činjenica, da velika poduzeća raspolažu skoro pa neiscrpnim količinama resursa (oprema, novac, stručnjaci...), te su u mogućnosti najviše napraviti za područje umjetne inteligencije.", "source_url": "https:\/\/openalex.org\/W7139179142", "pdf_url": "https:\/\/repozitorij.ffzg.unizg.hr\/islandora\/object\/ffzg:4149\/datastream\/PDF", "doi": "", "handle": "", "repository_name": "ODRAZ (University of Zagreb Faculty of Humanities and SocialSciences)", "source_record_id": "W7139179142", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 328, "title": "Meta-analysis study on artificial intelligence applications in education", "title_ar": "", "title_en": "", "author": "ESRA YAZAR", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Fırat University", "university_source": "repository_host", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2023, "language": "tr", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "Son yıllarda hızlı bir gelişim göstererek, hayatımızın her alanında karşımıza çıkan yapay zekâ kavramı, eğitim alanını da yoğun olarak etkilemektedir. Günümüzde eğitim ve teknoloji arasındaki ilişki ve karşılıklı bağımlılık, bu durumu kaçınılmaz kılmaktadır. Yapay zekâ teknolojileri; öğrenme öğretme ortamlarını, öğrenme zamanını ve motivasyonunu, öğrenme çıktılarının değerlendirilmesi gibi birçok açıdan eğitimi, dönüşüme uğratma potansiyeline sahiptir. Artık yabancı dil öğretiminden, matematik problemlerinin çözümüne kadar birçok alanda, eğitimde yapay zekâ uygulamalarından, farklı biçimlerde yararlanılmaktadır. Yapay zekâ, aynı zamanda hayat boyu öğrenmeye de destek vermektedir. Bu bağlamda, etkili eğitim-öğretim uygulamalarının geliştirilmesi ve işe koşulmasında, yapay zekânın rolü de gittikçe artmaktadır. Bu araştırmanın amacı, eğitimde yapay zekâ uygulamalarına ilişkin yapılan çalışmaların, meta-analiz yoluyla incelenerek, öğrenenlerin akademik başarıları ve tutumlarına etkisinin belirlenmesidir. 2000-2020 yılları arasını kapsayan, eğitimde yapay zekâ kullanımına ilişkin yapılan çalışmalara \"eğitimde yapay zekâ\", \"yapay zekâ\", \"eğitim\", \"artificial intelligence\", \"education\" anahtar kelimelerinden yararlanılarak, Google Akademik, Science Direct, ERIC, Yök Tez Merkezi, Proquest Digital Dissertations veri tabanlarından ulaşılmıştır. Bu kapsamda, yirmi beş araştırma akademik başarı; altı araştırma ise tutum değişkeni açısından çalışmaya dahil edilmiştir. Çalışmaya dahil edilen araştırmalar ortalama, standart sapma ve örneklem sayısı açısından incelenerek, etki büyüklük sonuçlarına ulaşılmıştır. Yapılan inceleme sonucunda, akademik başarıya dahil edilen çalışmalar arasında heterojenlik olduğu görülmüş, bu nedenle etki büyüklüğü hesaplamasında rastgele etkiler modeli kullanılmıştır. Rastgele etkiler modeline göre, etki büyüklüğü 0,666 olarak hesaplanmıştır. Bu sonuç, Cohen ve arkadaşlarının sınıflandırmasına göre, orta düzeyde etki göstermektedir. Meta-analize dahil edilen, tutuma ilişkin çalışmalar arasında, heterojenlik (Q= 0,337, p= 0.000) olduğu görüldüğünden etki büyüklüğü, rastgele etkiler modeline göre hesaplanmıştır. Etki büyüklüğü (Hedge's g=1,240), Cohen ve arkadaşlarının sınıflandırmasına göre güçlü düzeyde etki göstermiştir. Buna göre, eğitimde yapay zekâ uygulamalarının akademik başarı ve tutumları, olumlu açıdan etkilediği söylenebilir.", "source_url": "https:\/\/openalex.org\/W7218082699", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace - FIRAT (Fırat University)", "source_record_id": "W7218082699", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 329, "title": "Artificial intelligence in education: a paradigm shift", "title_ar": "", "title_en": "", "author": "Р. С. Луценко ; К. О. Озерова", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Vinnytsia National Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2024, "language": "uk", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "В публікаціїї розглядається інтеграція штучного інтелекту (ШІ) в освітній процес та її вплив на роль вчителя. Зазначається, що сучасний вчитель використовує ШІ як допоміжний інструмент для адаптації навчального матеріалу до індивідуальних потреб студентів. Основний акцент робиться на розвитку критичного мислення та творчості. Публікація описує виклики використання ШІ в освіті, зокрема збереження балансу між технологією та людською взаємодією, конфіденційністю та етикою використання студентів.", "source_url": "https:\/\/openalex.org\/W7213727468", "pdf_url": "https:\/\/ir.lib.vntu.edu.ua\/bitstream\/123456789\/43223\/1\/19927-70260-1-PB.pdf", "doi": "", "handle": "", "repository_name": "Repository of Vinnytsia National Technical University (Vinnytsia National Technical University)", "source_record_id": "W7213727468", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 330, "title": "Analysis of the use of artificial intelligence in education", "title_ar": "", "title_en": "", "author": "Алла Анатоліївна Шепель", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Zaporizhzhya National Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2024, "language": "uk", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "Шепель А. А. Аналіз використання штучного інтелекту в освіті: магістерська робота \/ А.А. Шепель – Запоріжжя: НУ «Запорізька політехніка», 2024. – 88 с.", "source_url": "https:\/\/openalex.org\/W7217702169", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Electronic Institutional Repository of Zaporizhzhya National Technical University (Zaporizhzhya National Technical University)", "source_record_id": "W7217702169", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 331, "title": "Critical thinking and artificial intelligence in Secondary Education", "title_ar": "", "title_en": "", "author": "Uncetabarrenechea Larrucea, Gonzalo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cantabria", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2023, "language": "es", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; ai-applications; llm; ai-education", "keywords": "", "abstract": "Este trabajo de fin de máster analiza la presencia y el valor del pensamiento crítico y la inteligencia artificial en el ámbito de la educación secundaria con el fin de obtener una visión integral de ambos. Se ha llevado a cabo una revisión de la literatura existente para presentar, definir y desarrollar términos fundamentales de cara a la comprensión en el plano educativo de ambos conceptos, subrayando la importancia de cultivar el pensamiento crítico entre el alumnado de secundaria y la creciente influencia de la inteligencia artificial. Se ha explorado en profundidad tanto el pensamiento crítico, desde la complejidad de su definición hasta su relevancia en la educación secundaria, como la inteligencia artificial y su aplicación en la enseñanza, con énfasis en la inteligencia artificial generativa y en la herramienta de reciente aparición ChatGPT, de cara a ponderar enfoques pedagógicos para entender ambos conceptos en conjunto. Además, se ha desarrollado en detalle una propuesta educativa innovadora basada en esta temática, que tiene como fin dotar al alumnado de secundaria de una base sólida para el desarrollo de competencias críticas para un escenario donde el pensamiento crítico y la ética en la interacción con la inteligencia artificial resultan habilidades cruciales.", "source_url": "https:\/\/openalex.org\/W7110522819", "pdf_url": "https:\/\/repositorio.unican.es\/xmlui\/bitstream\/10902\/35818\/1\/2023_UncetabarrenecheaLarruceaG.pdf", "doi": "", "handle": "", "repository_name": "UCrea (University of Cantabria)", "source_record_id": "W7110522819", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 334, "title": "Predictive Scheduling of Incoming Goods Using Machine Learning and Deep Learning Methods", "title_ar": "", "title_en": "", "author": "Johannes Simon Michael Rechenauer", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2026, "language": "de", "research_domain": "deep-learning", "research_subdomain": "data-mining", "categories": "machine-learning; deep-learning; ai-applications; data-mining; predictive-analytics; ai-education", "keywords": "", "abstract": "Die vorliegende Arbeit untersucht den Einsatz von Machine-Learning- und Deep-Learning-Verfahren zur prädiktiven Terminierung von Wareneingängen. Ausgangspunkt bilden betriebliche Herausforderungen, welche durch Lieferabweichungen entstehen und eine zuverlässige Materialversorgung erschweren. Die zentrale Zielsetzung besteht somit darin, Wareneingänge mithilfe datengetriebener Prognosemodelle so zu terminieren, dass Materialbedarfe gedeckt und gleichzeitig Lagerbestände möglichst gering gehalten werden. Methodisch orientiert sich diese Arbeit am CRISP-DM-Prozess, welcher als Rahmen dient, um die betriebliche Problemstellung systematisch in ein Data-Mining-Problem zu überführen. Dafür wird die Problemstellung zunächst konzeptionell in einem Unternehmen verortet. Gleichzeitig werden Grundlagen in den Bereichen logistische Planung, Data Mining, Deep Learning und Machine Learning gelegt. Darauf aufbauend wird ein Data-Mining-Projekt in einem realen Unternehmensumfeld ausgearbeitet. Dabei wird die Wareneingangsterminierung als betriebliche Problemstellung eingeordnet und als Klassifikationsproblem formuliert. Im Rahmen der Modellierungsphase werden daraufhin verschiedene Boosting-Modelle und Deep-Learning-Ansätze verglichen. Der Fokus liegt in diesem Zusammenhang nicht auf der isolierten Leistung einzelner Algorithmen, sondern auf der Leistungsfähigkeit der gesamten Modell-Pipeline. Diese umfasst die modellspezifische Datenvorverarbeitung, das Modelltraining, die Hyperparameter-Optimierung sowie die anschließende Evaluation. Dadurch werden die Verfahren unter möglichst praxisnahen Bedingungen hinsichtlich ihrer Vorhersageleistung und praktischen Anwendbarkeit miteinander verglichen. Die Ergebnisse zeigen, dass Boosting-Verfahren im untersuchten Anwendungsfall am besten abschneiden. Insbesondere die auf LightGBM basierende Pipeline erzielt die beste Kombination aus Vorhersageleistung und Effizienz, während die getesteten Deep-Learning-Modelle trotz längerer Trainingszeiten schwächere Ergebnisse liefern. Aufbauend auf den Ergebnissen des Modellvergleichs wird dargestellt, wie prognostizierte Verzögerungsklassen in konkrete Vorschläge zur Wareneingangsterminierung überführt und im Rahmen des CRISP-DM-Prozesses in einem Unternehmen implementiert werden können. Somit leistet die Arbeit einerseits einen Beitrag zur praxisnahen Ausarbeitung eines Data-Mining-Projekts und verdeutlicht andererseits, dass moderne Boosting-Verfahren insbesondere bei kleinen tabellarischen Datensätzen Deep-Learning-Architekturen überlegen sein können.", "source_url": "https:\/\/openalex.org\/W7223559876", "pdf_url": "https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/230957\/1\/Rechenauer%20Johannes%20Simon%20Michael%20-%202026%20-%20Praediktive%20Terminierung%20von...pdf", "doi": "10.34726\/hss.2026.144705", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W7223559876", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 335, "title": "Landslide Risk Assessment and Bridge Disaster Management Using Machine Learning and Deep Learning Models", "title_ar": "", "title_en": "Landslide Risk Assessment and Bridge Disaster Management Using Machine Learning and Deep Learning Models", "author": "Fudong Ren", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "machine-learning; deep-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7143658765", "pdf_url": "https:\/\/eprints.lib.hokudai.ac.jp\/repo\/huscap\/all\/95010\/Ren_Fudong.pdf", "doi": "10.14943\/doctoral.k16343", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7143658765", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 336, "title": "Text-to-point cloud image synthesis using deep learning", "title_ar": "", "title_en": "Text-to-point cloud image synthesis using deep learning", "author": "Βότσαρης, Εμμανουήλ Ε. ; Βότσαρης, Εμμανουήλ Ε.", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Thessaly", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "deep-learning; computer-vision; generative-ai", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7112122528", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/85698\/4\/31078.pdf", "doi": "", "handle": "", "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)", "source_record_id": "W7112122528", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 337, "title": "Incorporation of deep learning strategies to improve QOS in VXLAN-based networks", "title_ar": "", "title_en": "Incorporation of deep learning strategies to improve QOS in VXLAN-based networks", "author": "Kumarawadu KR Siriniwansa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; reinforcement-learning; responsible-ai; ai-applications; predictive-analytics; cnn; rnn; xai; ai-smart-cities", "keywords": "", "abstract": "This study examines how to improve Quality of Service (QoS) management in VXLAN-based overlay networks by integrating deep learning (DL) techniques. Due to issues like fluctuating workloads, complicated virtualized settings, and increased data traffic, traditional QoS mechanisms frequently fail to provide optimal performance metrics like packet loss, latency, and jitter in modern networks. Examining how DL models, such as CNNs, RNNs, autoencoders, and reinforcement learning, can forecast traffic patterns, identify abnormalities, and optimize resource allocation, the study reviews current QoS strategies in VXLAN. With a focus on situations like SDN, data centers, and NFV, it examines the possible advantages and intrinsic drawbacks of using DL for real-time QoS enhancement. The report also suggests future directions for real-world application while identifying present research limitations in areas like data availability, model explainability, and secure deployment. In order to improve efficiency, security, and user experience, work ultimately promotes intelligent, adaptive QoS frameworks driven by DL that can handle the complexity of large-scale, virtualized network infrastructures.", "source_url": "https:\/\/openalex.org\/W7132872346", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/231186\/2\/SiriniwansaKumarawaduKumuthuRandima.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7132872346", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 338, "title": "Deep Learning and Ideological Rhetoric", "title_ar": "", "title_en": "Deep Learning and Ideological Rhetoric", "author": "Brice D. L. Acree", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of North Carolina at Chapel Hill", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; cnn; speech-recognition", "keywords": "", "abstract": "Political conflict unfolds in language. To understand the quest for, and exercise of, power, we must understand political speech. For more than a decade, political methodologists have sought to develop methods for using language to study how politicians speak to the public, to the media, and to each other. This dissertation advances that line of research. Chapters 2 and 3 address `deep learning' methods for analyzing political texts. These computational models, built on artificial neural network architectures, seek to automatically learn complex patterns in data. Chapter 2 introduces basic deep learning models, and Chapter 3 introduces distributional word representations and convolutional neural network models. The final chapter points these methods toward a substantive problem. As a discipline, we have studied, and debated, the nature of political ideology. We have often found that, for citizens and elites alike, ideology is oriented around a single primary dimension: liberalism to conservatism. In Chapter 4, I offer a different view. Using the language from political ideologues and presidential candidates, I show that ideology can be much richer and varied than a single dimension will capture, but that political debate compresses ideological expression into a single dimension.", "source_url": "https:\/\/openalex.org\/W2966973115", "pdf_url": "", "doi": "10.17615\/mm0p-jk38", "handle": "", "repository_name": "Carolina Digital Repository (University of North Carolina at Chapel Hill)", "source_record_id": "W2966973115", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 339, "title": "DEEP LEARNING FOR DATA-DRIVEN DECISION-MAKING", "title_ar": "", "title_en": "DEEP LEARNING FOR DATA-DRIVEN DECISION-MAKING", "author": "Zhiqiang Wan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Rhode Island", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "ai-smart-cities", "categories": "deep-learning; medical-ai; reinforcement-learning; robotics; responsible-ai; ai-applications; ai-smart-cities", "keywords": "", "abstract": "As we are entering the big data era, data are becoming high-volume, complex, and heterogeneous. Nowadays, almost every field and sector of the modern society is being impacted by big data, ranging from energy system to health care, and from business to government. The excessive amount of data contains potential and highly useful values. Extracting values from these data and utilize it to support the decision-making process is of paramount importance to improve productivity in business and enhance the well-being of our society. However, data-driven decision-making also arises with many challenges, such as feature selection, data fusion, and real-time decision-making. To overcome these challenges, this dissertation will develop deep learning algorithms and frameworks for data-driven decision-making. This work is composed of three major parts: automatic feature extraction, heterogeneous data fusion, and deep reinforcement learning (DRL) based data-driven frameworks. This dissertation will first investigate the automatic feature extraction to avoid the burden of the “feature engineering” process. To make sure the feature extractor can be deployed to different scenarios, domain adaptive feature extraction will be studied. This dissertation will then examine the heterogeneous data fusion problem where feature refinement, normalization, transformation, and fusion will be investigated. With the automatic feature extraction and heterogeneous data fusion, this dissertation will develop data-driven decision-making frameworks for real-world applications, including robot-assisted pedestrian regulation and real-time electric vehicle (EV) charging management. Numerous experiments will be conducted to verify the effectiveness of the proposed frameworks.", "source_url": "https:\/\/openalex.org\/W3095226734", "pdf_url": "https:\/\/digitalcommons.uri.edu\/cgi\/viewcontent.cgi?article=2224&context=oa_diss", "doi": "10.23860\/wan-zhiqiang-2020", "handle": "", "repository_name": "Digital Commons - URI (University of Rhode Island)", "source_record_id": "W3095226734", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 342, "title": "Exploration of Deep Learning Models on Streamflow Simulations", "title_ar": "", "title_en": "Exploration of Deep Learning Models on Streamflow Simulations", "author": "Jinyang Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; rnn; image-segmentation; ai-agriculture", "keywords": "", "abstract": "Streamflow simulation and forecasting is an important approach for water resources management and flood mitigation, and both physically-based and data-driven models have been used to predict the streamflow at daily and hourly scale. Although physically-based models can help understand the underlying mechanisms of the hydrological process based on mathematical and physical equations, the performance of the models largely depends on the availability and quality of the spatial information. The computation cost also limits its application in the fast, reliable and accurate streamflow simulation and forecasting. In contrast, data-driven models, especially deep learning models, are capable of learning the nonlinear relationships between the inputs and outputs without explicitly referring to the physical process. In addition, deep learning techniques, powered by graphics processing units (GPUs) can allow researchers to train models 10 or 20 times faster, which is more promising for inputs-outputs simulation. Currently, the deep learning models rely heavily on prior streamflow information to make predictions, while the hydrological observations are limited in the real world applications. In this Thesis, the effectiveness of deep learning models on streamflow simulation without using the previous streamflow information was explored. In specific, the three deep learning models the ANN, RNN and LSTM models were used to simulate the streamflow at multiple lead time and different sites using the precipitation and soil moisture. In addition, the effectiveness of deep learning models on learning from a physically-based model to generate pixel-to-pixel streamflow mapping has also been tested.All three deep learning models can achieve reliable and accurate streamflow simulations at the lead time of 1-5 h at the Ninnekah site with Pearson correlation coefficient (r) over 0.9 for all three models. Based on the prediction of the peak and low flow on different sites, the LSTM model shows more consistent and reliable streamflow simulation results and can work better in different basin scales. For the U-net model, it can learn the patterns of ParFlow-CLM model well and achieves average r of 0.770 and 0.820 in the whole domain for validation and calibration, respectively. However, the models have limitations in predicting the peak due to the feature of the imbalanced data.", "source_url": "https:\/\/openalex.org\/W3188379214", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W3188379214", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 343, "title": "ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM", "title_ar": "", "title_en": "", "author": "Gian Maria Campedelli", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "it", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "Il terrorismo di stampo jihadista rappresenta una minaccia per la società e una sfida per gli scienziati interessati a comprenderne la complessità. Questa complessità richiede costantemente nuovi sviluppi in termini di ricerca sul terrorismo. Migliorare la conoscenza empirica rispetto a tale fenomeno può potenzialmente contribuire a sviluppare applicazioni concrete e, in ultima istanza, a prevenire danni all’uomo. In considerazione di tali aspetti, questa tesi presenta un nuovo quadro metodologico che integra scienza delle reti, modelli stocastici e apprendimento profondo per far luce sul terrorismo jihadista sia a livello esplicativo che predittivo. In particolare, questo lavoro compara e analizza le organizzazioni jihadiste più attive a livello mondiale (ovvero lo Stato Islamico, i Talebani, Al Qaeda, Boko Haram e Al Shabaab) per studiarne i pattern comportamentali e predirne le future azioni. Attraverso un impianto teorico che si poggia sulla concentrazione spaziale del crimine e sulle prospettive strategiche del comportamento terroristico, questa tesi persegue tre obiettivi collegati utilizzando altrettante tecniche ibride. In primo luogo, verrà esplorata la complessità operativa delle organizzazioni jihadiste attraverso l’analisi di matrici stocastiche di transizione e verrà presentato un nuovo coefficiente, denominato “Normalized Transition Similarity”, che misura la somiglianza fra paia di gruppi in termini di dinamiche operative. In secondo luogo, i processi stocastici di Hawkes aiuteranno a testare la presenza di meccanismi di dipendenza temporale all’interno delle più comuni sotto-sequenze strategiche di ciascun gruppo. Infine, il framework integrerà la meta-reti complesse e l’apprendimento profondo per classificare e prevedere i target a maggiore rischio di essere colpiti dalle organizzazioni jihadiste durante i loro futuri attacchi. Per quanto riguarda i risultati, le matrici stocastiche di transizione mostrano che i gruppi terroristici possiedono un ricco e complesso repertorio di combinazioni in termini di armi e obiettivi. Inoltre, i processi di Hawkes indicano la presenza di diffusa self-excitability nelle sequenze di eventi. Infine, i modelli predittivi che sfruttano la flessibilità delle serie temporali derivanti da grafi dinamici e le reti neurali Long Short-Term Memory forniscono risultati promettenti rispetto ai target più a rischio. Nel complesso, questo lavoro ambisce a dimostrare come connessioni astratte e nascoste fra eventi possano essere fondamentali nel rivelare le meccaniche del comportamento jihadista e come processi memory-like (ovvero molteplici comportamenti ricorrenti, interconnessi e non randomici) possano risultare estremamente utili nel comprendere le modalità attraverso cui tali organizzazioni operano.", "source_url": "https:\/\/openalex.org\/W3013089364", "pdf_url": "", "doi": "10.83049\/unicatt\/publicatt\/10280_70552", "handle": "", "repository_name": "", "source_record_id": "W3013089364", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 344, "title": "Deep Learning Approaches for Condition Monitoring and Prognostic Analysis", "title_ar": "", "title_en": "Deep Learning Approaches for Condition Monitoring and Prognostic Analysis", "author": "Zunya Shi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Michigan", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "Predictive analytics is a technique to make predictions about future unknown events by analyzing current and historical data. The market for predictive analysis is expected to reach $12.4 billion in 2022. Prognostic analysis is one dimension of predictive analytics in engineering discipline and focuses on predicting the time when a system or a component will no longer perform its intended function. A robust and accurate prognostic analysis makes for the increment of production reliability and safety as well as maintenance cost reduction, thus becoming one key task of condition-based maintenance (CBM). Given multiple sensors to simultaneously monitor the health condition during the age of Internet of Things (IoT) and Industry 4.0, traditional prognostic technologies have the limited ability of handling large-scale dataset embedded with complex structures. Therefore, this dissertation mainly focuses on developing high-performance Deep Learning (DL) technologies for condition monitoring and prognostic analysis under the data-rich environment. Besides the CBM, the proposed DL technologies can be also successfully extended and applied into other fields, like battery power estimation, Alzheimer’s Disease (AD) prediction, etc., which have been validated in the dissertation with real case studies. In addition, this dissertation also studied and answered research questions like how to evaluate the quality of features for prognostic analysis, how to select and extract high-level features from original dataset, how to capture the interactions between different features, and so forth. The answers for these questions play important roles in model development for prognostic analysis.", "source_url": "https:\/\/openalex.org\/W4287024228", "pdf_url": "", "doi": "10.7302\/2533", "handle": "", "repository_name": "Deep Blue (University of Michigan)", "source_record_id": "W4287024228", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 345, "title": "Modeling and Optimization of Active Distribution Network Operation Based on Deep Learning", "title_ar": "", "title_en": "Modeling and Optimization of Active Distribution Network Operation Based on Deep Learning", "author": "Wenlong Liao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalborg Universitet", "university_source": "repository_host", "college": "", "department": "", "country": "Denmark", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "During his doctoral studies, he aims to bridge the gap", "source_url": "https:\/\/openalex.org\/W4384202958", "pdf_url": "https:\/\/vbn.aau.dk\/files\/539422562\/PHD_WL.pdf", "doi": "10.54337\/aau539422562", "handle": "", "repository_name": "VBN Forskningsportal (Aalborg Universitet)", "source_record_id": "W4384202958", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 347, "title": "Deep learning-based damage detection on cherry leaves", "title_ar": "", "title_en": "", "author": "Hazel Bozcu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2024, "language": "tr", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "deep-learning; cnn; rnn", "keywords": "", "abstract": "Bu çalışmada tarımsal üretimde verim artışını sağlamak amacıyla kiraz yapraklarındaki hastalıkların tespiti için derin öğrenme yöntemlerinin kullanılması amaçlanmaktadır. Günümüzde yaprak hastalıkların tespiti uzman personel tarafından yapılabilmekte ancak uzun sürmektedir. Ayrıca, bu uzmanların sayısı yetersiz olabilir, bu da doğru tespitin zorluğuna işaret etmektedir. Bu nedenle, kiraz üretimini artırmak ve hastalıkları erken teşhis etmek için derin öğrenme temelli hastalık tespit uygulamalarının kullanımı bu çalışmanın ana amacıdır. Çalışmada iki farklı veri seti üzerinde kiraz yaprak hastalığı tespiti için farklı derin öğrenme modellerinin performansı incelenmiştir. Ortalama sonuçlara göre, MobileNet-V2 modeli .68 doğruluk ve .83 hassasiyet ile yüksek bir performans sergilemiş, Inception-V3 modeli .17 F1-Skor ile tatmin edici sonuçlar vermiştir. Önerilen CNN modeli .83 F1- Skor ile dikkat çekmiş, önerilen CNN + LSTM modeli .33 geri çağırma değeri ile tatmin edici sonuçlar sunmuş ve önerilen CNN + BiLSTM modeli .33 F1-Skor ile yüksek bir performans göstermiştir. En iyi sonuçlar kategorisinde, MobileNet-V2 modeli .03 doğruluk oranı ve .33 F1- Skor ile en iyi sonuçları vermiş, Inception-V3 modeli .00 doğruluk ve .33 hassasiyet ile tatmin edici bir performans sergilemiştir. Önerilen CNN modeli .66 F1-Skor ile iyi bir performans sergilemiş, önerilen CNN + LSTM modeli .67 doğruluk ve .67 F1-Skor ile en iyi sonuçları vermiş ve önerilen CNN + BiLSTM modeli .66 F1-Skor ile yüksek bir performans göstermiştir. Bu sonuçlar, kiraz yaprak hastalığı tespiti için derin öğrenme modellerinin umut veren sonuçlar elde ettiğini göstermektedir. Çalışma kapsamında en iyi sonuçlar MobileNet-V2 ve önerilen CNN + LSTM modelleri tarafından elde edilmiştir. Bu çalışmanın, çeşitli veri setleri kullanılarak güvenirliliği arttırılabilir, farklı derin öğrenme yöntemleri kullanılarak hastalık tespit başarım oranları yükseltilebilir ve hastalık tespit süreleri düşürülebilir.", "source_url": "https:\/\/openalex.org\/W7218050026", "pdf_url": "https:\/\/acikkaynak.bilecik.edu.tr\/bitstreams\/629b0f54-7406-4380-bfcc-442022f926d5\/download", "doi": "", "handle": "", "repository_name": "Bilecik Şeyh Edebali University Institutional Repository", "source_record_id": "W7218050026", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 348, "title": "Evaluation and validation of deep learning strategies for bioimage analyses", "title_ar": "", "title_en": "Evaluation and validation of deep learning strategies for bioimage analyses", "author": "Dennis Segebarth", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Würzburg University", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "fairness", "categories": "deep-learning; computer-vision; responsible-ai; fairness", "keywords": "", "abstract": "Significant advances in fluorescence imaging techniques enable life scientists today to gain insights into biological systems at an unprecedented scale. The interpretation of image features in such bioimage datasets and their subsequent quantitative analysis is referred to as bioimage analysis. A substantial proportion of bioimage analyses is still performed manually by a human expert - a tedious process that is long known to be subjective. Particularly in tasks that require the annotation of image features with a low signal-to-noise ratio, like in fluorescence images of tissue samples, the inter-rater agreement drops. However, like any other scientific analysis, also bioimage analysis has to meet the general quality criteria of quantitative research, which are objectivity, reliability, and validity. Thus, the automation of bioimage analysis with computer-aided approaches is highly desirable. Albeit conventional hard-coded algorithms are fully unbiased, a human user has to set its respective feature extraction parameters. Thus, also these approaches can be considered subjective. Recently, deep learning (DL) has enabled impressive advances in computer vision research. The predominant difference between DL and conventional algorithms is the capability of DL models to learn the respective task on base of an annotated training dataset, instead of following user-defined rules for feature extraction. This thesis hypothesized that DL can be used to increase the objectivity, reliability, and validity of bioimage analyses, thus going beyond mere automation. However, in absence of ground truth annotations, DL models have to be trained on manual and thus subjective annotations, which could cause the model to incorporate such a bias. Moreover, model training is stochastic and even training on the same data could result in models with divergent outputs. Consequently, both the training on subjective annotations and the model-to-model variability could impair the quality of DL-based bioimage analyses. This thesis systematically assessed the impacts of these two limitations experimentally by analyzing fluorescence signals of a protein called cFOS in mouse brain sections. Since the abundance of cFOS correlates with mouse behavior, behavioral analyses could be used for cross-validation of the bioimage analysis results. Furthermore, this thesis showed that pooling the input of multiple human experts during model training and integration of multiple trained models in a model ensemble can mitigate the impact of these limitations. In summary, the present study establishes guidelines for how DL can be used to increase the general quality of bioimage analyses.", "source_url": "https:\/\/openalex.org\/W3199015547", "pdf_url": "https:\/\/nbn-resolving.org\/urn:nbn:de:bvb:20-opus-243728", "doi": "10.25972\/opus-24372", "handle": "", "repository_name": "Online Publication Service of Würzburg University (Würzburg University)", "source_record_id": "W3199015547", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 349, "title": "Deep Learning and Reward Design for Reinforcement Learning", "title_ar": "", "title_en": "Deep Learning and Reward Design for Reinforcement Learning", "author": "Xiaoxiao Guo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Michigan", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "cnn", "categories": "deep-learning; computer-vision; nlp; reinforcement-learning; cnn", "keywords": "", "abstract": "One of the fundamental problems in Artificial Intelligence is sequential decision making in a flexible environment. Reinforcement Learning (RL) gives a set of tools for solving sequential decision problems. Although the theory of RL addresses a general class of learning problems with a constructive mathematical formulation, the challenges posed by the interaction of rich perception and delayed rewards in many domains remain a significant barrier to the widespread applicability of RL methods. The rich perception problem itself has two components: 1) the sensors at any time step do not capture all the information in the history of observations, leading to partial observability, and 2) the sensors provide very high-dimensional observations, such as images and natural languages, that introduce computational and sample-complexity challenges for the representation and generalization problems in policy selection. The delayed reward problem—that the effect of actions in terms of future rewards is delayed in time—makes it hard to determine how to credit action sequences for reward outcomes. This dissertation offers a set of contributions that adapt the hierarchical representation learning power of deep learning to address rich perception in vision and text domains, and develop new reward design algorithms to address delayed rewards. The first contribution is a new learning method for deep neural networks in vision-based real-time control. The learning method distills slow policies of the Monte Carlo Tree Search (MCTS) into fast convolutional neural networks, which outperforms the conventional Deep Q-Network. The second contribution is a new end-to-end reward design algorithm to mitigate the delayed rewards for the state-of-the-art MCTS method. The reward design algorithm converts visual perceptions into reward bonuses via deep neural networks, and optimizes the network weights to improve the performance of MCTS end-to-end via policy gradient. The third contribution is to extend existing policy gradient reward design method from single task to multiple tasks. Reward bonuses learned from old tasks are transferred to new tasks to facilitate learning. The final contribution is an application of deep reinforcement learning to another type of rich perception, ambiguous texts. A synthetic data set is proposed to evaluate the querying, reasoning and question-answering abilities of RL agents, and a deep memory network architecture is applied to solve these challenging problems to substantial degrees.", "source_url": "https:\/\/openalex.org\/W2751395930", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Deep Blue (University of Michigan)", "source_record_id": "W2751395930", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 350, "title": "Exploring deep learning techniques to tackle the sparsity problem in recommender systems", "title_ar": "", "title_en": "Exploring deep learning techniques to tackle the sparsity problem in recommender systems", "author": "Meshal Alfarhood", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Missouri", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; predictive-analytics; transformers", "keywords": "", "abstract": "With the inception of e-commerce in the early twenty-first century, people's lifestyles have drastically changed. People today tend to do many of their daily routines online, such as shopping, reading the news, and watching movies. Nevertheless, consumers often face diffculties while exploring related items such as new fashion trends because they are not aware of their existence due to the overwhelming amount of information available online. This phenomenon is widely known as \"information overload\". Therefore, recommender systems (RSs) are a critical solution for helping users make decisions when there are lots of choices. RSs have been integrated into and have become an essential part of every website due to their effectiveness in increasing customer interactions, attracting new customers, and growing business revenue. Machine learning, and deep learning (DL) in particular, have achieved a great success in resolving various computer science problems. Generally, DL-based approaches have enhanced performance remarkably compared with traditional approaches. Specifically, DL-based approaches have become the state-of-the-art techniques in RSs. Therefore, in this dissertation, three DL-based contributions are presented to address the natural data sparsity problem in RSs: (1) DeepHCF, a deep-hybrid, collaborative-filtering model that trains two deep models via joint training for rating prediction tasks; (2) CATA, a collaborative attentive autoencoder that integrates the attention mechanism to enhance the recommendation quality for ranking prediction tasks; and (3) CATA++, an extended version of CATA that employs a dual attentive autoencoder to leverage more of the item's content. All proposed models have gone through comprehensive experiments to evaluate their performance against state-of-the-art models using real-world datasets. Our experimental results show the superiority of our models over state-of-the-art models according to various evaluation metrics.", "source_url": "https:\/\/openalex.org\/W4389989985", "pdf_url": "https:\/\/mospace.umsystem.edu\/xmlui\/bitstream\/10355\/88863\/1\/AlfarhoodMeshal.pdf", "doi": "10.32469\/10355\/88863", "handle": "", "repository_name": "MOspace Institutional Repository (University of Missouri)", "source_record_id": "W4389989985", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 351, "title": "Community College Students' Deep Learning Approaches in OER Courses", "title_ar": "", "title_en": "Community College Students' Deep Learning Approaches in OER Courses", "author": "Kim Grewe", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Old Dominion University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "ai-education", "categories": "deep-learning; medical-ai; ai-applications; ai-education", "keywords": "", "abstract": "Open Educational Resources (OER) have the potential to bridge the gap for community college students not only because they are more affordable or provide access but also because they have the potential to make learning more meaningful for these same students. Although issues related to access and affordability have been extensively researched, less is known about the ways in which OER use may impact community college students’ deep approaches to learning. More qualitative research around OER efficacy from the student perspective is needed. The purpose of this study was to describe the ways students use OER and how students’ OER use may impact their deep approaches to learning. This study employed qualitative research methods, collecting data from focus groups composed of community college students. The major findings indicated that students use OER to relieve some of the financial stress associated with being a community college student. Students often go outside of the class environment to find OER to help them address learning preferences, diagnose and accommodate learning disabilities, remediate weak skills areas, and learn more about topics of interest. These students benefit from and sometimes participate in Open Pedagogical practices and demonstrate deep approaches to learning when they access openly licensed and freely shared OER and use OER to collaborate, peer validate, and publish work outside the class environment. Students also benefit from strong OER design. OER are often well-sequenced, using techniques like scaffolding and chunking to move learners through material at a manageable pace for optimal learning. OER are often interactive and make use of adaptive technologies to personalize instruction and engage learners. Presenting the students’ perspective through qualitative research is a critical component to better understanding the efficacy of OER for student learning in community college. Faculty should continue to explore OER use in their courses and should widen their traditional circles of collaboration to design their courses using OER. Community college administrators should begin to explore OER as part of their strategic plans for decreasing the financial burden of attending college for students and for increasing student learning and success at their colleges.", "source_url": "https:\/\/openalex.org\/W2963789226", "pdf_url": "", "doi": "10.25777\/w9a9-d072", "handle": "", "repository_name": "ODU Digital Commons (Old Dominion University)", "source_record_id": "W2963789226", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 352, "title": "Computer aided diagnosis system for breast cancer using deep learning.", "title_ar": "", "title_en": "Computer aided diagnosis system for breast cancer using deep learning.", "author": "Asma Baccouche", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Louisville", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; medical-imaging; ai-cybersecurity", "keywords": "", "abstract": "The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that", "source_url": "https:\/\/openalex.org\/W4306250372", "pdf_url": "https:\/\/ir.library.louisville.edu\/cgi\/viewcontent.cgi?article=5203&context=etd", "doi": "10.18297\/etd\/3931", "handle": "", "repository_name": "ThinkIR: The University of Louisville's Institutional Repository (University of Louisville)", "source_record_id": "W4306250372", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 353, "title": "Cyberthreat discovery in open source intelligence using deep learning techniques", "title_ar": "", "title_en": "", "author": "Eunice Picareta Branco", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2017, "language": "pt", "research_domain": "computer-vision", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "Face à necessidade crescente de se processar grandes quantidades de dados relativos a ameaças de segurança, fomos cativados pelo desafio da descoberta de ameaças cibernéticas em fontes abertas através do uso de técnicas de aprendizagem automática. Em termos de dados, isto significa que trabalhámos com informação recolhida de fontes abertas como o Twitter. O que distingue o nosso trabalho encontra-se no modo como escolhemos abordar este desafio. A nossa hipótese é a de que processar tais quantidades de dados através de métodos de aprendizagem automática representa uma vantagem significativa em termos de eficiência e adequação, pelo que recorremos a redes neuronais. Escolhemos esta abordagem uma vez que as abordagens de aprendizagem automática têm vindo a ganhar destaque merecido uma vez que asseguram uma maneira robusta de resolver um número de tarefas extremamente complexas no contexto de problemas de big data. Esta dissertação introduz conceitos e noções gerais em que o nosso trabalho se baseia, apresenta o trabalho relacionado consultado por forma a ser eventualmente útil em trabalhos futuros, apresenta também o trabalho que realizámos, os resultados obtidos, e elenca sugestões sobre linhas de progresso promissoras e trabalho futuro. Antes de discutir resultados, é necessário começar por introduzir conceitos centrais, o primeiro dos quais sendo o de aprendizagem automática. Aprendizagem automática (machine learning) pode ser definida como a área ou abordagem da inteligência artificial de forma a que o sistema tenha a aptidão de aprender e melhorar com a experiência. Isto significa que não é necessária programação explícita para resolver o problema de partida pois o sistema de aprendizagem procura por regularidades nos dados e adquire a capacidade de tomar melhores decisões com base nos dados de exemplo que recebe. Aprofundando esta abordagem, uma rede neuronal é um paradigma de processamento inspirado no modo como processos biológicos nervosos, como os que ocorrem no cérebro humano, processam informação. A chave deste paradigma é a conexão entre os elementos básicos do sistema. Este é composto por um grande número de elementos de processamento, os neurónios, organizados em rede que entregam as suas saídas uns aos outros para resolverem problemas específicos, cabendo notar que uma rede neuronal é tipicamente condicionada no seu desenho pelo problema que se pretende que resolva, ou seja, é configurada para uma única aplicação (e.g. reconhecimento de padrões, classificação de dados, etcetera). De entre as técnicas de aprendizagem automática, a aprendizagem profunda (deep learning) tem adquirido grande relevância e vários projectos têm procurado explorar as suas vantagens. Trata-se de uma subárea da aprendizagem automática, e em particular das redes neuronais, sendo que o que distingue esta abordagem consiste no facto de os dados de entrada passarem por várias camadas funcionais de neurónios, usualmente não lineares, até serem totalmente processados. No nosso projecto, a rede neuronal foi aplicada na resolução do problema que consiste na classificação de tweets em itens que se referem a uma ameaça de segurança, ou itens não relevantes a esse respeito. Com essa finalidade, foi implementada uma rede neuronal convolucional, que comparativamente necessita de pouca intervenção humana para ser posta a funcionar. A vantagem de se aliviar a necessidade de tal intervenção também se prende com o tipo da rede, que pode ser supervisionada ou não supervisionada. Em aprendizagem supervisionada, um conjunto de dados de treino injectado na rede é composto por pares de entrada\/saída, sendo que a entrada é tipicamente composta por um vector e a saída é o resultado pretendido para a entrada respetiva. A rede é treinada sobre todo o conjunto de dados para depois ser aplicada a novas situações ou dados de entrada desconhecidos. É assim necessário que o algoritmo de processamento generalize a partir dos dados de treino. No caso da aprendizagem não supervisada, os dados injectados na rede são apenas de entrada, o que obriga a rede a inferir funções que descrevem a possível estrutura subjacente aos dados, pois a sua classificação explícita não é fornecida à rede. Como os dados não estão associados à sua classificação, não é trivial avaliar a adequação do resultado obtido pela rede neste caso. Outro conceito importante é o de redes profundas (deep) vs. rasas (shallow). As redes neuronais são organizadas por camadas. Estas camadas são compostas por nós inter-conectados que contêm funções de activação, compreendendo a camada de entrada, as camadas escondidas, que pode englobar várias camadas para processamento de dados, e a camada de saída. O termo redes rasas é usado para descrever as redes que contêm apenas uma ou duas camadas escondidas, que são funcionalmente idênticas. No caso de redes profundas, estas tendem a ter mais camadas escondidas, com grupos de camadas com funcionalidades distintas.", "source_url": "https:\/\/openalex.org\/W2782794488", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W2782794488", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 354, "title": "Machine learning for offensive cyber operations", "title_ar": "", "title_en": "Machine learning for offensive cyber operations", "author": "Sommervoll, Åvald Åslaugson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "optimization", "categories": "machine-learning; reinforcement-learning; ai-applications; optimization; ai-education; ai-cybersecurity", "keywords": "", "abstract": "Cybersecurity with machine learning has received widespread attention in education, research, and innovation in both the private and public sectors. Unfortunately, while essential for strong cyber security, offensive cyber operations with machine learning have seen significantly less innovation, at least in open academic literature. This thesis's contribution to the field of offensive cyber operations with machine learning can naturally be divided into the following: 1. Algorithmic cryptanalysis with machine learning 2. SQL injection with machine learning The historical cipher Enigma's plugboard is shown to be susceptible to an attack powered by the machine learning technique Genetic Algorithms, being broken far faster than any earlier attack. Modern ciphers are naturally more robust than historical ciphers. The cryptographic algorithm ASCON is still secure, but the novel machine learning technique, The Phantom Gradient Attack, is shown to attack many of its subfunctions successfully. OWASP's top 10 had SQL injections as the number one web vulnerability in 2017; in 2021, it was number three. This thesis highlights the possibility of automated SQL injection exploitation and identification with reinforcement learning for accelerated penetration testing. The reinforcement learning agent can exploit all 5 SQL injection archetypes, distinguish between them, and determine whether or not the website is vulnerable.", "source_url": "https:\/\/openalex.org\/W3188417193", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W3188417193", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 355, "title": "Predicting refractive index structure parameter in the marine wave boundary layer using machine learning models: a thesis in Data Science", "title_ar": "", "title_en": "Predicting refractive index structure parameter in the marine wave boundary layer using machine learning models: a thesis in Data Science", "author": "Bhavana Chikkamuduvadi Renuka Gowda", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; medical-ai; ai-applications; predictive-analytics; ai-education", "keywords": "", "abstract": "The refractive index structure parameter, ??ₙ², quantifies optical turbulence and strongly influences the performance of electromagnetic and free-space optical systems operating within the marine wave boundary layer. Predicting ??ₙ² remains challenging due to nonlinear interactions among atmospheric variables and shows strong changes between daytime and nighttime, especially in coastal environments. This thesis examines these challenges using high-frequency meteorological data from ATI sonic anemometers and co-located scintillometer observations collected at the SMAST Pier in Buzzards Bay, Massachusetts, during the summer and fall of 2025. A random forest (RF) modeling framework is developed to evaluate not only predictive accuracy, but also the conditions under which machine-learning-based estimates of ??ₙ² agree or disagree with observations. Atmospheric data are strongly time-dependent, meaning that common random train-test splits can introduce information leakage and overestimate model skill. To avoid this problem, the analysis uses a blocked, leakage-free train-gap-test strategy based on decorrelation time scales. Low-frequency and seasonal background trends are removed using variance-preserving spectral filtering, and time-lagged features are included based on correlation analyses to better represent the physical memory of the system. Separate RF models developed for summer and fall are evaluated using within-season validation. Results show moderate overall predictive skill, with clear differences between daytime and nighttime performance. Nighttime predictions consistently explain more of the observed variability in log₁₀(??ₙ²) than daytime predictions, particularly during summer. This improved nighttime performance may be linked to more stable and less varying turbulence conditions after sunset. In contrast, daytime predictions show larger errors, likely due to stronger convection and rapidly changing surface-layer processes. Overall, this study shows that the reliability of machine-learning-based ??ₙ² predictions depend strongly on temporal structure, diurnal regime, and how autocorrelation is handled. The iv findings highlight the importance of using regime-aware validation and variance-based diagnostics rather than relying only on single accuracy metrics. This work provides practical insight into when ML-based turbulence predictions can be trusted in coastal marine environments and offers guidance for developing more robust and physically consistent forecasting approaches.", "source_url": "https:\/\/openalex.org\/W7143310057", "pdf_url": "", "doi": "10.62791\/20574", "handle": "", "repository_name": "", "source_record_id": "W7143310057", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 356, "title": "Machine-learning emulator for multispecies biological reference points in the Eastern Bering Sea Groundfish Complex: a thesis in Data Science", "title_ar": "", "title_en": "Machine-learning emulator for multispecies biological reference points in the Eastern Bering Sea Groundfish Complex: a thesis in Data Science", "author": "Rowan Raj Ignatius", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; ai-applications; predictive-analytics; fairness; ai-education", "keywords": "", "abstract": "Fisheries management increasingly relies on biological reference points, target fishing mortality and biomass levels, to set sustainable catch limits. For single-species stocks these reference points can often be computed from a fitted assessment model, but when a stock’s mortality is driven by multispecies predation rather, the reference points have no closed form and depend on the assumed status of every species in the system. Obtaining them requires repeatedly re-projecting a multispecies model forward, at every assessment year of simulations, and ignoring this predation-driven mortality process is known to bias both stock assessment outputs and the reference points used for management advice. This computational cost is one of the bottlenecks in evaluating harvest strategies against multispecies operating models, including one built here for the Eastern Bering Sea groundfish complex (walleye pollock, Pacific cod, arrow tooth flounder) using Rceattle, the R implementation of CEATTLE (Climate Enhanced, Age-based model with Temperature-specific Trophic Linkages and Energetics).Here we show that a gradient-boosted tree model, guided by age-structured natural-mortality and trained on terminal-year assessment output from 500 closed-loop management strategy evaluation simulations, can emulate these multispecies reference points, without re-running the dynamic projection, achieving cross-validated R2 of 0.80–0.95 across six targets. Feature importance analysis reveals that a single pollock-specific natural-mortality feature dominates prediction for four of the six targets, even for cod’s and arrow tooth flounder’s own reference points, a machine-learned signature of the predation coupling built into the operating model. This demonstrates that these emulator models can substitute for costly re-projection, offering a path to rapid screening of harvest-strategy across the simulation ensembles that multispecies management strategy evaluations increasingly require.", "source_url": "https:\/\/openalex.org\/W7212123147", "pdf_url": "", "doi": "10.62791\/20651", "handle": "", "repository_name": "", "source_record_id": "W7212123147", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 357, "title": "Predicting microbiologically influenced corrosion severity from electrochemical impedance spectroscopy using interpretable machine learning: a thesis in Data Science", "title_ar": "", "title_en": "Predicting microbiologically influenced corrosion severity from electrochemical impedance spectroscopy using interpretable machine learning: a thesis in Data Science", "author": "Raksha Mohan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "machine-learning; computer-vision; responsible-ai; predictive-analytics; face-recognition; xai", "keywords": "", "abstract": "Microbiologically influenced corrosion (MIC) accounts for an estimated 20–30% of global corrosion losses, yet reliable quantitative prediction of MIC severity remains unsolved. Electrochemical measurements combined with interpretable machine learning provide a promising framework for addressing this challenge. MIC depends on coupled microbial, biofilm, and interfacial electrochemical processes; charge-transfer resistance (Rct) and corrosion current density (icorr) serve as complementary proxies for corrosion severity, expressed as log₁₀(Rct) from electrochemical impedance spectroscopy (EIS) and log₁₀(icorr) from potentiodynamic polarization. However, physically interpretable predictive models for MIC remain limited. Here, we show that machine learning regressors can predict both log₁₀(Rct) and log₁₀(icorr) in MIC systems involving Pseudomonas and Vibrio across varying environmental conditions. A dataset of 116 EIS and 83 potentiodynamic polarization observations was compiled from ten peer-reviewed sources. Random Forest and Gradient Boosting Machine (GBM) regressors were compared using stratified five-fold crossvalidation, and Shapley additive explanations (SHAP) were used to interpret model behavior in physically meaningful terms. GBM outperformed Random Forest, achieving a higher cross-validated R². SHAP analysis identified double-layer capacitance admittance as the dominant predictor of corrosion severity in the EIS model, consistent with its role as a reporter of biofilm-induced interfacial disorder, while primer-derived organism-level descriptors contributed modest but interpretable signals consistent with known differences in metabolic versatility between the two organisms. This work establishes a reproducible and interpretable baseline for quantitative MIC prediction and, to our knowledge, represents one of the first applications of SHAP analysis to EIS-derived features in MIC while demonstrating the value of integrating organism-level descriptors with electrochemical features for corrosion modeling.", "source_url": "https:\/\/openalex.org\/W7167827826", "pdf_url": "", "doi": "10.62791\/20612", "handle": "", "repository_name": "", "source_record_id": "W7167827826", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 359, "title": "Variable-dependent predictability of coastal atmospheric parameters in the marine wave boundary layer using Physics-informed machine learning at sub-hourly resolution: a thesis in Data Science", "title_ar": "", "title_en": "Variable-dependent predictability of coastal atmospheric parameters in the marine wave boundary layer using Physics-informed machine learning at sub-hourly resolution: a thesis in Data Science", "author": "Rakesh Kumar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; ai-applications; predictive-analytics; xai; ai-education", "keywords": "", "abstract": "Advances in environmental sensing technologies have enabled the collection of high-frequency atmospheric observations, creating new opportunities for applying machine learning to short term environmental prediction. Despite considerable progress in data-driven atmospheric modeling, comparatively little attention has been given to how atmospheric predictability varies among variables governed by different physical processes, how strongly prediction depends on atmospheric memory, and how boundary-layer regime modifies the predictive information available from instantaneous observations. Understanding these relationships is essential for developing forecasting systems that are both accurate and physically interpretable. In this study, we systematically assess the short-term predictability of five key atmospheric variables—solar radiation, air temperature, wind speed, barometric pressure, and relative humidity—using approximately 2.2 years of high-frequency meteorological observations collected within the Marine Wave Boundary Layer (MWBL) at the School for Marine Science and Technology (SMAST), University of Massachusetts Dartmouth. To account for fundamentally different radiative forcing and boundary-layer dynamics, the observations were separated into daytime and nighttime regimes and analyzed independently. A unified physics informed machine learning framework incorporating atmospheric memory, temporal encoding, and wind-vector decomposition was developed and evaluated using a strict chronological train validation-test strategy to ensure realistic prediction conditions. The results show clear differences in predictability and optimal model complexity across the five atmospheric variables. Air temperature and barometric pressure exhibited strong temporal persistence and achieved high predictive performance using linear models, whereas solar radiation and wind speed benefited from nonlinear ensemble-learning approaches capable of capturing more complex atmospheric behavior. Relative humidity achieved strong predictive performance but exhibited greater sensitivity to rapid moisture variability and evolving boundary-layer conditions. Additional experiments conducted comparing the complete framework with a reduced framework, in which temporal-memory features were removed, demonstrated that atmospheric memory contributes to the predictability of all five atmospheric variables, although its importance varies considerably among them. Comparisons between daytime and nighttime conditions further showed that boundary-layer regime modifies the predictive information available from instantaneous atmospheric observations while preserving the overall relationship between atmospheric physics and model complexity. To evaluate practical applicability, model predictions were compared with independent observations collected from an ATMOS 41W all-in-one weather station deployed on the University of Massachusetts Dartmouth campus, providing an initial field-based validation of the proposed framework. The best-performing solar radiation prediction model was subsequently integrated into a prototype Stream lit-based real-time prediction dashboard, demonstrating a practical pathway from environmental data science research to operational coastal forecasting applications. Overall, this study demonstrates that atmospheric predictability is governed by the underlying physical processes of each atmospheric variable, that atmospheric memory provides the dominant source of predictive information, and that boundary-layer regime modifies predictive information without fundamentally altering the relationship between atmospheric physics and appropriate model complexity. These findings provide both scientific insight into coastal atmospheric predictability and a practical foundation for future environmental forecasting systems.", "source_url": "https:\/\/openalex.org\/W7212138321", "pdf_url": "", "doi": "10.62791\/20631", "handle": "", "repository_name": "", "source_record_id": "W7212138321", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 360, "title": "Machine Learning in Empirical Asset Pricing", "title_ar": "", "title_en": "Machine Learning in Empirical Asset Pricing", "author": "Jern Tat Chin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; predictive-analytics; xai", "keywords": "", "abstract": "This thesis consists of 5 chapters. Chapter 1 is the introduction of the thesis. Chapter 2, Chapter 3 and Chapter 4 are the main chapters of this thesis, which cover the U.S. bond market, international stock market and U.S. stock market respectively. Chapter 5 is the conclusion of the thesis. Chapter 1 presents the brief introduction to the 3 chapters covered in this thesis. Chapter 2 explores using technical indicators to forecast corporate bond returns with various machine learning (ML) models. We show that technical indicators yield statistically significant and economically meaningful results, consistently outperform ing bond characteristics. Although bond characteristics possess predictive power for bond returns, they do not provide incremental value beyond technical indicators across all bonds. Additionally, machine learning models do not offer substantial improve ments over the benchmark linear model. These results underscore the significance of technical indicators in the corporate bond market. Chapter 3 explores the use of various machine learning models to forecast stock returns in the international market. We calculate 107 technical variables and use them to train an array of machine learning models. Using data from 25 markets, we uncover machine learning’s superior performance over OLS (our benchmark model) in predicting future stock returns, in terms of both prediction accuracy and economic gains. We also show that machine learning generates superior performance among big, liquid firms and adds value during periods of downward market trend. The inter national heterogeinity in machine learning outperformance is associated with market features such as macroeconomic fundamentals, cultural traits, and the information environment. In Chapter 4, we apply genetic programming to systematically generate thousands of non-linear factor strategies and uncover various stock market anomalies that re main robust out-of-sample, even under multiple hypothesis testing. These non-linear strategies outperform raw anomalies by maintaining consistent profitability after controlling for arbitrage constraints (excluding microcaps), trading costs, recent data (post-2020), and risk adjustments; responding to Chen and Velikov (2023)’s critique of limited out-of-sample validity. Compared to Müller and Schmickler (2025)’s double sorted portfolio and single-sorted portfolio using pairwise interactions, our non-linear anomaly portfolio delivers superior performance while remaining transparent, inter pretable, and low-turnover. Moreover, we develop an interpretable non-linear four or five-factor model that consistently outperforms established benchmarks in both right-hand-side and left-hand-side asset pricing tests. Our non-linear factor models consist primarily of accounting-based anomalies, which enhances their viability net of trading costs, owing to the lower turnover and higher investment capacity associated with accounting variables. Chapter 5 summarizes the findings of the three chapters and concludes the thesis.", "source_url": "https:\/\/openalex.org\/W7131075723", "pdf_url": "https:\/\/openaccess.wgtn.ac.nz\/articles\/thesis\/Machine_Learning_in_Empirical_Asset_Pricing\/31396026\/1\/files\/62103639.pdf", "doi": "10.26686\/nav7-kenw", "handle": "", "repository_name": "Open Access Victoria University of Wellington | Te Herenga Waka (Figshare)", "source_record_id": "W7131075723", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 361, "title": "Comparative analysis of the efficacy of machine learning models in detecting cancer from medical imagery: a thesis in Computer Science", "title_ar": "", "title_en": "Comparative analysis of the efficacy of machine learning models in detecting cancer from medical imagery: a thesis in Computer Science", "author": "Kyle Andrew Furtado", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging", "keywords": "", "abstract": "According to a study performed by John Hopkins Medicine in 2023, misdiagnoses make up approximately 11.1% of total diagnoses in the United States, which result in anything from minor to fatal error for the patient. One of the main contributors to this unfortunate statistic is the heavy reliance on human-read medical scans. As a result, this study investigates the effectiveness of various Machine Learning (ML) models in classifying cancer from histopathological medical images. More specifically, Deep Learning (DL) Convolutional Neural Networks (CNNs) leveraging Transfer Learning (TL) are under analysis. For each experiment, one generic CNN is trained from scratch followed by four prominent pretrained models (i.e., DenseNet, VGG16, ResNet, and InceptionNet). The primary disease used for experimentation was colorectal cancer, while brief research was also done with lung cancer, leukemia, and breast cancer. Each model was trained across various levels of data augmentation and shuffles in data to provide a more verifiable set of findings when compared amongst each other. Results indicate optimal model selections for cancer detection under varying scenarios, such as training constraints (e.g., time and computational resources) along with dataset conditions (e.g., sizes and class balances). Overall, this research provides valuable insights on ideal CNNs for histopathological cancer detection that can be applied to developing future cancer detection software, reducing the heavy reliance on human-interpreted medical imagery diagnosis.", "source_url": "https:\/\/openalex.org\/W4409785731", "pdf_url": "", "doi": "10.62791\/20332", "handle": "", "repository_name": "", "source_record_id": "W4409785731", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 363, "title": "Machine Learning, Sociogeny, and the Substance of Race", "title_ar": "", "title_en": "Machine Learning, Sociogeny, and the Substance of Race", "author": "Ramon Amaro", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "machine-learning; medical-ai", "keywords": "", "abstract": "This thesis examines the relationship between machine learning, sociogeny, and race. Machine learning is understood here as an epistemic process that organises data into discrete spatio- temporal classifications. Following Kara Keeling, the thesis defines spatio-temporal classification as a coordinate that makes the racial imaginary possible, while itself eluding representation. Lastly, sociogeny, as defined by Frantz Fanon, is a ‘sociodiagnostic’ phenomenon that emerges at the ontological limit of the episteme, resulting in the discursive exclusion of the lived reality of racialised individuals. The thesis argues that an immediate parallel can be drawn between the scientific production of knowledge, as established truths, and perceptions of otherness. While historically science and technology have worked to support racial classification, contemporary machine leaning algorithms have been shown, even unwittingly, to replicate existing race relations. The thesis contends that racial classification in machine learning is symptomatic of a wider logics of pathology, as well as enumeration — which, if taken as a spatio-temporal coordinate, arrests self-actualisation. By aligning machine learning with Fanon’s notion of sociogeny, and the spatio-temporal coordinate, the thesis challenges views of a causal relationship between classification, racial position and psychic fragmentation. Instead, the thesis argues for an alternative view of sociogeny as a non-linear process, always-already in excess of racial perception under the conditions of duress. Ultimately, the thesis investigates machine learning as a methodological space to re-articulate the spatio-temporal coordinate and, in turn, affirmative black psychic generation.", "source_url": "https:\/\/openalex.org\/W2995141442", "pdf_url": "", "doi": "10.25602\/gold.00026850", "handle": "", "repository_name": "Goldsmiths Research Online", "source_record_id": "W2995141442", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 364, "title": "Artistic approaches to machine learning", "title_ar": "", "title_en": "Artistic approaches to machine learning", "author": "Drew Flaherty", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "", "categories": "machine-learning", "keywords": "", "abstract": "This research is about how Artificial Intelligence and Machine Learning may impact creative practice. The thesis looks at various implementations and models related to the subject from different cultural and technical viewpoints. The project also provides experimental creative outcomes from my personal practice along with a qualitative study into attitudes and perspectives from other creative practitioners.", "source_url": "https:\/\/openalex.org\/W3026944467", "pdf_url": "https:\/\/eprints.qut.edu.au\/200191\/1\/Drew_Flaherty_Thesis.pdf", "doi": "10.5204\/thesis.eprints.200191", "handle": "", "repository_name": "Queensland University of Technology", "source_record_id": "W3026944467", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 365, "title": "Application of machine learning in production scheduling", "title_ar": "", "title_en": "Application of machine learning in production scheduling", "author": "Georg Faustmann", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "For human experts, it is often too hard or too time-consuming to manually detect patterns in big data sets. Machine learning is applied in many areas to detect such patterns. Its applications are by no means limited to research, as machine learning also plays a big role in the industrial sector. This thesis applies machine learning to the following two topics. The first part of the thesis deals with product quality classification for automotive paint shops. The second part investigates automated parameter configuration for dispatching rules that are used in machine scheduling. We use a binary classification to predict the product quality of an automotive paint shop based on its scheduling data. We propose a set of features to characterize the production process. These features are used to classify whether or not the quality of the product is satisfactory. We can show that the best model we found performs better than a baseline model on an unseen data set. In the automated parameter configuration part of the thesis, we investigate machine learning methods based on multi-target regression to automatically configure dispatching rules for real-life planning scenarios where multiple objectives are considered. We propose a novel set of features to characterize instances of the parallel machine scheduling problem, and describe how supervised learning can be used to obtain optimized parameter configurations for given machine scheduling instances. Experimental results show that our approach can obtain high-quality solutions for real-life scheduling scenarios in short run times.", "source_url": "https:\/\/openalex.org\/W3183329507", "pdf_url": "https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/6541\/2\/Faustmann%20Georg%20-%202019%20-%20Application%20of%20machine%20learning%20in%20production...pdf", "doi": "10.34726\/hss.2019.68471", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W3183329507", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 366, "title": "Machine Learning for Automatic Machine-to-Machine (M2M) Communication in 5G", "title_ar": "", "title_en": "Machine Learning for Automatic Machine-to-Machine (M2M) Communication in 5G", "author": "Seifeddine Messaoud", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "ai-smart-cities", "categories": "machine-learning; computer-vision; medical-ai; ai-applications; ai-smart-cities", "keywords": "", "abstract": "Fifth Generation (\\gls{5G}) is envisioned by Telecom operators as the next mobile network generation which brings solutions to massive customers who are more and more demanding for both cost-efficient services as well as high satisfaction. Machine to machine (M2}) communication is an important component of the future 5G, which enables the ubiquitous connectivity between a myriad of machines without or with limited human intervention. Thus, the autonomous connection of devices facilitates the emergence of a wide range of intelligent M2M applications. These latter have exhibited a strong potential to improve human life in different fields such as eHealth, smart grids, smart home\/cities, intelligent transportation and surveillance, enabling partially the internet of things (IoT). By 2020, the number of connected devices is expected to reach 50 billions. To meet these huge demands, the network autonomic mechanisms, named as “Autonomic Networking”, are considered as good candidates to provide intelligent networking solutions for M2M communication in 5G networks. The objective of this thesis is to improve the self-organization and autonomic networking approaches with the integration of machine learning and data analytics for M2M communication. This approach based on machine learning and data analysis will be used to customize the \\gls{QoS} for users through the customization of Autonomic Networking.", "source_url": "https:\/\/openalex.org\/W4392279563", "pdf_url": "", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W4392279563", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 367, "title": "Application of Machine Learning in Peptide Design", "title_ar": "", "title_en": "Application of Machine Learning in Peptide Design", "author": "Sabino Sepčić", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; optimization", "keywords": "", "abstract": "The main purpose of this thesis is to explore the use of computational techniques such as machine learning and genetic algorithms in peptide design. P", "source_url": "https:\/\/openalex.org\/W3093245414", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3093245414", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 368, "title": "Applications of Machine Learning in Mental Healthcare", "title_ar": "", "title_en": "Applications of Machine Learning in Mental Healthcare", "author": "Elena Davcheva", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "machine-learning; medical-ai", "keywords": "", "abstract": "This thesis summarizes three studies in the area of machine learning applications within mental heathcare, specifically in the area of treatments and diagnostics. Mental healthcare today is challenging to provide worldwide because of a stark rise in demand for services. Traditional healthcare structures cannot keep up with the demand and information systems have the potential to fill in this gap. The thesis explores online mental health forums as a digital mental health platform and the possibility to automate treatments and diagnostics based on user-shared information.", "source_url": "https:\/\/openalex.org\/W3161669028", "pdf_url": "https:\/\/tuprints.ulb.tu-darmstadt.de\/17879\/1\/Applications%20of%20Machine%20Learning%20in%20Mental%20Healthcare_Davcheva.pdf", "doi": "10.26083\/tuprints-00017879", "handle": "", "repository_name": "Technischen Universität Darmstadt", "source_record_id": "W3161669028", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 369, "title": "Machine Learning for Materials Science", "title_ar": "", "title_en": "Machine Learning for Materials Science", "author": "Bertrand Rouet‐Leduc", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cambridge", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; ai-applications; quantum-ml; predictive-analytics; ai-industry", "keywords": "", "abstract": "Machine learning is a branch of artificial intelligence that uses data to automatically build inferences and models designed to generalise and make predictions. In this thesis, the use of machine learning in materials science is explored, for two different problems: the optimisation of gallium nitride optoelectronic devices, and the prediction of material failure in the setting of laboratory earthquakes. Light emitting diodes based on III-nitrides quantum wells have become ubiquitous as a light source, owing to their direct band-gap that covers UV, visible and infra-red light, and their very high quantum efficiency. This efficiency originates from most electronic transitions across the band-gap leading to the emission of a photon. At high currents however this efficiency sharply drops. In chapters 3 and 4 simulations are shown to provide an explanation for experimental results, shedding a new light on this drop of efficiency. Chapter 3 provides a simple and yet accurate model that explains the experimentally observed beneficial effect that silicon doping has on light emitting diodes. Chapter 4 provides a model for the experimentally observed detrimental effect that certain V-shaped defects have on light emitting diodes. These results pave the way for the association of simulations to detailed multi-microscopy. In the following chapters 5 to 7, it is shown that machine learning can leverage the use of device simulations, by replacing in a targeted and efficient way the very labour intensive tasks of making sure the numerical parameters of the simulations lead to convergence, and that the physical parameters reproduce experimental results. It is then shown that machine learning coupled with simulations can find optimal light emitting diodes structures, that have a greatly enhanced theoretical efficiency. These results demonstrate the power of machine learning for leveraging and automatising the exploration of device structures in simulations. Material failure is a very broad problem encountered in a variety of fields, ranging from engineering to Earth sciences. The phenomenon stems from complex and multi-scale physics, and failure experiments can provide a wealth of data that can be exploited by machine learning. In chapter 8 it is shown that by recording the acoustic waves emitted during the failure of a laboratory fault, an accurate predictive model can be built. The machine learning algorithm that is used retains the link with the physics of the experiment, and a new signal is thus discovered in the sound emitted by the fault. This new signal announces an upcoming laboratory earthquake, and is a signature of the stress state of the material. These results show that machine learning can help discover new signals in experiments where the amount of data is very large, and demonstrate a new method for the prediction of material failure.", "source_url": "https:\/\/openalex.org\/W2765454343", "pdf_url": "", "doi": "10.17863\/cam.13918", "handle": "", "repository_name": "Apollo (University of Cambridge)", "source_record_id": "W2765454343", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 371, "title": "Machine Learning for Metamorphic Testing", "title_ar": "", "title_en": "Machine Learning for Metamorphic Testing", "author": "Zheyu Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "Test oracle is a mechanism used to validate all the functionalities ofsoftware under test. However, the lack of test oracle makes the processof software testing difficult. Metamorphic testing is a state of artapproach for automated software testing without test oracles basedon metamorphic relations. Metamorphic relations are a set of propertiesbetween inputs and outputs that a software could have. However,it is usually difficult to identify metamorphic relations for unknownprograms. This thesis aims for automatic generation of metamorphicrelations by utilizing machine learning algorithms with the methodrandom walk kernel using input from control flow graphs. By applyingKanawala et al. [1] previous work in our targeted system environment,we encountered a series of difficulties, which we also describein this thesis. It is important to introduce an alternative solution forour test suite that is working. The performance of our model is evaluatedby different measures including area under the receiver operatingcharacteristic curve and mean squared error. The results show promisingapplications of automatically predicting metamorphic relations forunknown programs. The study was conducted on software system inthe telecommunicaiton domain.", "source_url": "https:\/\/openalex.org\/W2890328291", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2890328291", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 372, "title": "EVALUATION OF MACHINE LEARNING APPLICABILITY FOR USMC REENLISTMENT", "title_ar": "", "title_en": "EVALUATION OF MACHINE LEARNING APPLICABILITY FOR USMC REENLISTMENT", "author": "Terrazas, Gustavo A.", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Naval Postgraduate School", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "This research examines the applicability of machine learning algorithms to best predict the probability of reenlistment of enlisted first-term Marines. Given the availability of data today, machine learning can be a useful tool to make policy decisions that can impact the future Fleet Marine Force. This thesis uses demographic data, pre-boot-camp data, performance indicators, legal data, awards data, and selective reenlistment bonus indicators to identify factors that contribute to the prediction of reenlistment. This thesis applies data from the Total Force Data Warehouse (TFDW) and fits machine learning algorithms to assess their prediction accuracy. Measuring machine learning models by accuracy alone is not sufficient. An evaluation of top predictors is conducted to choose the best-preforming machine learning algorithm. Given the data used in this thesis, the machine learning algorithm that best predicts the probability of reenlistment is the C5 algorithm. Variables associated with deployment and performance are among the top ten predictors of importance.", "source_url": "https:\/\/openalex.org\/W7112536497", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)", "source_record_id": "W7112536497", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 373, "title": "Machine learning in neonatal intensive care", "title_ar": "", "title_en": "Machine learning in neonatal intensive care", "author": "Olli‐Pekka Rinta‐Koski", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "fairness", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; fairness", "keywords": "", "abstract": "This thesis deals with the applications of machine learning in the context of neonatal intensive care. The main focus is on prediction using multichannel time series data. The purpose of developing prediction methods is to create data-driven tools for helping care personnel to make more informed decisions. Preterm infants, born before 37 weeks of gestation, are subject to many developmental issues and health problems. Very Low Birth Weight infants, with a birth weight under 1500 g, are the most afflicted in this group. These infants require treatment in the neonatal intensive care unit before they are mature enough for hospital discharge. The neonatal intensive care unit is a data-intensive environment, where multi-channel physiological data is gathered from patients using a number of sensors to construct a comprehensive picture of the patients' vital signs. Neonatal intensive care requires combining information from multiple sources, often with severe and far-reaching consequences for the patient. Real-time data is available on multiple monitor screens in the ward, and is used by care personnel to help them make informed decisions. Analysis of trends and vast data sets, possibly spanning thousands of patients, requires the use of machine learning methods. In the research done for this thesis we have applied machine learning methods, in particular Gaussian process classification, to predict neonatal in-hospital mortality and morbidities. We have used time series data collected from Very Low Birth Weight infants treated in the neonatal intensive care unit of Helsinki University Hospital between 1999 and 2013. Our results show that machine learning models based on time series data alone have predictive power comparable with standard medical scores, and combining the two results in improved predictive ability. We have also studied the effect of observer bias on recording vital sign measurements in the neonatal intensive care unit, as well as conducted a retrospective cohort study on trends in the growth of Extremely Low Birth Weight (birth weight under 1000 g) infants during intensive care.", "source_url": "https:\/\/openalex.org\/W2894079146", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W2894079146", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 375, "title": "Leveraging Self-Attention in Transformer Models for Improved Retail Demand Forecasting", "title_ar": "", "title_en": "Leveraging Self-Attention in Transformer Models for Improved Retail Demand Forecasting", "author": "Rohith Sardena", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "National College of Ireland", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; predictive-analytics; rnn; transformers", "keywords": "", "abstract": "Inventory management and operation planning in retail sector involve accurate demand forecasting as a key element of the process. Linear models and tree-based models are typical examples of traditional forecasting models that may not work well in capturing complex, non-linear, and time-evolving demands that retail demand exhibits especially when it is affected by promotions, seasonal trends, and other external market factors. The study examines the possibility of deep learning models based on Transformers, which will use self-attention mechanisms, to improve the accuracy and scalability of retail demand forecasting systems. Model development was done using a detailed retail dataset with historical sales, inventory, pricing, discounts, promotions, weather conditions, and other contextual characteristics. A number of models were applied and tested in the study: Linear Regression, Decision Tree, Random Forest, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Median Absolute Error (MedianAE) were used to evaluate performance. The classical models were moderate in their performance, and Linear Regression and Random Forest injected MAEs of 7.47 and 7.55 respectively, and RMSEs of more than 8.6. Deep learning models, on the contrary, showed a dramatic increase. Transformer model performed the best and its MAE was 0.81, RMSE was 0.99, and MedianAE was 0.74, which are better than LSTM (MAE: 0.83) and GRU (MAE: 0.83). The results verify that self-attention mechanism of Transformer is capable of learning long-range dependencies and complex temporal interactions, giving significant improvement on the baseline models. This study shows the ability of Transformers to be an efficient and scalable method of real-time retail demand forecasting. Further development will consider multi-step forecasting, probabilistic additions and deployment in other retail areas.", "source_url": "https:\/\/openalex.org\/W7204618203", "pdf_url": "https:\/\/norma.ncirl.ie\/9664\/2\/rohithsardenaconfigurationmanual.pdf", "doi": "", "handle": "", "repository_name": "TRAP@NCI (National College of Ireland)", "source_record_id": "W7204618203", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 376, "title": "Understanding Human-Model Alignment in Visual Social Attention with Video Transformers", "title_ar": "", "title_en": "Understanding Human-Model Alignment in Visual Social Attention with Video Transformers", "author": "Sabrina M. Lopez", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; predictive-analytics; cnn; transformers; video-analysis; fairness", "keywords": "", "abstract": "Artificial Neural Networks (ANNs) are among the leading computational frameworks for approximating human visual processing across a range of visual tasks. Despite this, a notable gap remains between human and ANN performance in tasks that require social perception and cognition such as intent prediction and social interaction recognition. Among ANN architectures, vision transformers have recently emerged as particularly promising approximations of human visual processing as they exhibit a stronger shape bias and produce errors more consistent with those of human observers, and develop more globally uniform internal representations. This paper examines how transformers align with human perception when identifying social interactions in videos, in which two agents help, hinder, or have neutral interactions with each other in a 2D simulated environment. With the use of a dataset containing videos paired with human gaze recordings captured during the classification of helping and hindering social interactions, we assess the degree of human-model alignment through both classification accuracy and visual attention metrics across different models (i.e., TimeSformer, ViViT, V-JEPA 2), different pre-training datasets (e.g., Something Something V2, Kinetics 400), and different visualization methods (i.e., different GradCam methods and integrated gradients). Our findings suggest TimeSformer and ViViT models have strong potential as cognitive models of social interaction, with attention patterns — both static and dynamic — that align meaningfully with those of human participants. Meanwhile, despite their higher classification accuracy, V-JEPA 2 models achieve saliency alignment at or below that of convolutional neural network baselines. Notably, classification performance is not a reliable predictor of human-model attention alignment. Regarding pretraining datasets, larger and more diverse datasets tend to improve saliency alignment for classification-supervised models, though the optimal visualization method for extracting alignment varies by architecture.", "source_url": "https:\/\/openalex.org\/W7155351216", "pdf_url": "", "doi": "10.18130\/08r1-yn85", "handle": "", "repository_name": "Libra", "source_record_id": "W7155351216", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 377, "title": "Hybrid Deep Learning Model: LLM-Enhanced Linear Self-Attention Transformer-CNN for Stock Price Prediction", "title_ar": "", "title_en": "Hybrid Deep Learning Model: LLM-Enhanced Linear Self-Attention Transformer-CNN for Stock Price Prediction", "author": "Yilong Chen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Auckland University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; cnn; transformers; multimodal; llm", "keywords": "", "abstract": "The unpredictability and inherent complexities of financial markets pose persistent challenges in accurately forecasting stock prices. Recent innovations in deep learning methodologies, specifically the integration of technical analysis with linguistic processing through large language models (LLMs), have demonstrated significant potential in financial forecasting applications. Despite substantial advances in deep learning for stock price prediction, current approaches face critical limitations in effectively integrating multi-modal data while lacking sophisticated technical analysis capabilities, particularly when simultaneously modeling both temporal dependencies in financial time series and spatial features from stock chart visualizations across diverse market regimes and volatility conditions. This study proposes an innovative hybrid deep learning framework aimed at addressing these multi-modal integration challenges, technical analysis limitations, and cross-market adaptability issues through a structured three-phase methodology. In the initial phase, we leverage an LLM to conduct thorough technical analysis of historical financial data, transforming raw market information into rich textual representations. These representations encapsulate intricate financial patterns using well-established technical indicators such as Bollinger Bands (BBs). By interpreting these financial metrics, the LLM generates structured textual summaries that enhance the downstream predictive modeling process. The second phase involves dual processing pipelines. One pipeline utilizes an optimized attention-based Transformer to process the LLM-generated embeddings, capturing long-range market dependencies. Concurrently, the second pipeline deploys a Convolutional Neural Network (CNN) to analyze visual stock chart patterns, extracting spatially relevant features that complement the temporal insights derived from the Transformer model. In the concluding phase, the outputs from both pipelines—textual, sequential, and visual data—are aggregated to generate holistic stock price predictions. The architecture employs Transformers for extracting extended temporal relationships within market sequential information, whereas Convolutional Networks detect regional spatial configurations from graphical stock representations. Large language models enhance our dataset by generating sophisticated technical metrics, subsequently embedded into numerical vectors through FinBERT encoding techniques. The fusion of these multimodal representations enables a more robust and accurate forecasting model, capitalizing on the complementary strengths of each component to enhance predictive performance.", "source_url": "https:\/\/openalex.org\/W7131750035", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tuwhera (Auckland University of Technology)", "source_record_id": "W7131750035", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 378, "title": "Counter-Rotating Aperture Resolution: A Formal Stream-Processing Model for SHA-256 Recurrence, Root-Cover Coupling, and Transformer Language Attention", "title_ar": "", "title_en": "Counter-Rotating Aperture Resolution: A Formal Stream-Processing Model for SHA-256 Recurrence, Root-Cover Coupling, and Transformer Language Attention", "author": "Dean Kulik", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; predictive-analytics; transformers; llm", "keywords": "", "abstract": "Counter-Rotating Aperture Resolution: A Formal Stream-Processing Model for SHA-256 Recurrence, Root-Cover Coupling, and Transformer Language Attention Driven By Dean A. Kulik May 2026 Abstract This paper formalizes Counter-Rotating Aperture Resolution (CRAR): a model in which a fixed aperture and a moving data surface produce output through relative phase contact. The central claim is operational rather than metaphoric: a static output, or “still,” cannot identify the stream that generated it. A stream is an operator, recurrence, or transition law, and it is exposed only through controlled perturbation across the aperture. The theory is developed in three stages. First, we define stills, streams, apertures, and counter-rotating domains using a train-and-circle model. A processor may remain externally fixed while an oppositely moving surface passes beneath it; computation occurs at the contact point, not in absolute translation. Second, we instantiate the model in the SHA-256 message schedule. The lag set and seed width generate the aperture complement which organizes the direct-entry taxonomy. The first two expanded words have parent apertures whose unique intersection is . Thus is the Root-Cover Axis: the unique seed word that directly seeds both expansion roots and , has , has clean echo set , and saturates immediately at . The result is then correctly bounded: is the first triple-entry, length-sensitive face-selection hinge, while is the global coupling axis. Third, we translate the mathematics into transformer attention. Causal language models process symbolic streams by passing prompt tokens across fixed weights and a causal mask. Attention is not simple forward reading; it is counter-rotating aperture contact between a forward token stream and a backward-facing context field. We distinguish a forbidden seam, implemented by the causal mask for , from a balance seam, such as equal-score or orthogonality surfaces in . The SHA-to-transformer mapping is stated as a testable transport hypothesis, not as a completed proof. The result is a unified mathematical paper on why the word is the still, meaning is the stream crossing it, and controlled perturbation is the method that reveals the operator. 1. Scope and Thesis The working principle is: A still is a projected state. A stream is the transition law that moves states. If only the final state is observed, the underlying operator is generally non-identifiable. This matters for cryptographic schedules, language models, and any symbolic processor whose visible output hides the internal recurrence. In SHA-256, the final digest is a still. The message schedule is the stream. In language, a word token is a still. Meaning is the stream crossing the token through context, memory, and prediction. In both cases, the correct object is not the output value, but the preserved transition structure exposed by perturbation. This paper therefore studies the operator geometry rather than the final output. This paper does not claim a break of SHA-256. It does not claim that transformer attention has been fully proven equivalent to SHA-256 recurrence. It extracts the shared mathematics of aperture, counterflow, recurrence, masking, contamination, saturation, and projection. 2. Stills, Streams, and Operators Let denote a system state at discrete time . A still is a projection of a state: A stream is the transition law: or, more generally, The differential view is The still is residue. The stream is recurrence. 2.1 Static Non-Identifiability Lemma Lemma 2.1. Let be a many-to-one projection. Given only , the transition operator that generated is not uniquely identifiable. Proof. Since is many-to-one, there exist distinct states such that . There may exist distinct transition operators and and initial states satisfying Both generate the same projected still . Therefore alone cannot determine the stream operator. 2.2 Perturbation Principle A stream is exposed by controlled perturbation: Define a perturbation-response map for differentiable systems, or a Boolean contamination recurrence for discrete systems. The operator becomes visible through where motion can and cannot propagate. Thus: 3. The Train-Circle Model of Counter-Rotating Recursion Consider a train moving linearly: On the train is a circular recursive domain of radius with angular phase A point on the circle has global position The train moves forward in global time, but the circular domain returns to previous local phase states: Thus the system does not reverse external time; it returns to a previous domain state. 3.1 Radius as Phase Curvature The tangential velocity on the circle is At the center, , the point follows the train: Near the rim, phase dominates. Therefore: 3.2 Counter-Rotating Surface Now suppose an observer attempts to walk one way while the circular surface moves the opposite way. Let Then the observer has no external displacement: But the surface still passes beneath the contact point.", "source_url": "https:\/\/openalex.org\/W7162682989", "pdf_url": "", "doi": "10.5281\/zenodo.20426172", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7162682989", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 379, "title": "Hybrid CNN-Transformer Architectures for Image Denoising : Evaluating the quality-efficiency trade-off of integrating self attention into a CNN-based denoising model", "title_ar": "", "title_en": "", "author": "Filip Norgren", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "sv", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; cnn; transformers", "keywords": "", "abstract": "Bildbrusreducering är ett centralt problem inom datorseende där målet är att förbättra bilder som förämrats av brus som introduceras vid bildtagning, överföring eller lagring. Medan CNN-baserade brusreducerings modeller är effektiva har Transformer-baserade metoder visat ytterligare förbättringar, men ofta till en högre beräkningskostnad. Detta projekt undersöker avvägningen mellan kvalitet och effektivitet vid tillägg av self-attention, en mekanism som fångar beroenden på långa avstånd i en bild, i en CNN-baseradbrusreduceringsmodell. Mer specifikt utvärderas integrering av Swin Transformer lager, en Transformerdesign som använder lokala fönster, i en DnCNN-basmodell och om detta tillägg förbättrar brusreduceringskvaliteten, samt om eventuella kvalitetsvinster motiverar den extra beräkningskostnaden. Basmodellen DnCNN och de hybrida DnCNN-Swin varianterna tränades från grunden på bilder i gråskala från DIV2K-datasetet med syntetiskt additivt vitt Gaussiskt brus. Prestanda mättes med PSNR, SSIM och inferenstid. Resultaten visar att tillägget av self-attention gav mätbara men begränsade kvalitetsförbättringar, där den bästa hybridvarianten vinner 0.14 dB PSNR (28.68 vs. 28.54) och 0.0046 SSIM (0.8612 vs. 0.8566) gentemot basmodellen DnCNN. Däremot ökade beräkningskostnaden väsentligt när fler attention-lager lades till, där den bästa hybridvarianten krävde ungefär 23 gånger längre inferenstid än basmodellen", "source_url": "https:\/\/openalex.org\/W7167186457", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7167186457", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 380, "title": "A Comparative Forecasting Performance Analysis of Symbolic Representation Techniques for Time Series Using Transformer Models and Multi-Head Attention Mechanisms", "title_ar": "", "title_en": "A Comparative Forecasting Performance Analysis of Symbolic Representation Techniques for Time Series Using Transformer Models and Multi-Head Attention Mechanisms", "author": "Rami Alalwan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Klagenfurt", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; responsible-ai; ai-applications; predictive-analytics; rnn; transformers; xai; ai-industry", "keywords": "", "abstract": "The prediction of future values from historical time series data is widely applied infields such as industrial monitoring, finance, and decision support systems. Whilemost forecasting approaches operate directly on numerical time series, symbolicrepresentations have emerged as an alternative that can reduce dimensionality, fil-ter noise, and improve interpretability. However, their effectiveness for forecastingtasks particularly when combined with modern deep learning architectures—remainsinsufficiently explored. This thesis conducts a structured comparison between nu-merical and symbolic approaches to time series forecasting, employing deep learn-ing techniques with particular emphasis on Long Short-Term Memory (LSTM) mod-els and Transformer-based architectures. Numerical time series are transformedinto symbolic sequences using Symbolic Aggregate approXimation (SAX) and Com-pressed adaptive Brownian bridge-based symbolic aggregation (ABBA)-inspired rep-resentation, and both representations are evaluated under identical experimentalconditions. Experiments are conducted on datasets of increasing complexity, includ-ing simple synthetic signals, medium complexity averaged time series, and complexreal-world datasets. The results show that symbolic representations can outperformnumerical representations on simple datasets and achieve comparable performanceon medium complexity datasets, while no clear advantage is observed for highlycomplex and volatile time series. Symbolic inputs demonstrate robustness to noiseand strong pattern-learning capabilities, particularly for smooth or quasi-periodicsignals commonly found in industrial and IoT monitoring applications. However,for complex datasets, symbolic abstraction may lead to temporal misalignment andloss of fine-grained information. Additionally, the study highlights limitations ofthe Mean Absolute Percentage Error (MAPE) metric for complex and non-stationarydata, emphasizing the need for complementary evaluation measures. Overall, thiswork provides a structured evaluation of symbolic time series forecasting in the con-text of modern deep learning, clarifying when symbolic representations are benefi-cial and identifying their limitations as data complexity increases.", "source_url": "https:\/\/openalex.org\/W7139666984", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Klagenfurt", "source_record_id": "W7139666984", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 381, "title": "CONTRASTIVE ATTENTION TRANSFORMER FOR CLASSIFICATION", "title_ar": "", "title_en": "CONTRASTIVE ATTENTION TRANSFORMER FOR CLASSIFICATION", "author": "Yunjiao Bai", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rutgers University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; self-semi-supervised; predictive-analytics; transformers", "keywords": "", "abstract": "Most traditional classification methods process each data point in isolation, ignoring the structural information embedded in the relationships among data points. This dissertation develops the contrastive attention transformer, a novel classification architecture that attends a given point separately to each candidate class, producing one representation per class, and predicts the label by contrasting these representations through a shared classifier. We develop the framework for both binary and multiclass classification, proposing three loss functions under two complementary approaches. The comparative approach enforces the relative ordering of confidences across representations through the probability difference and proportional losses. The aggregation approach maximizes joint correctness through the conditional probability loss, which we show to coincide with a forecast aggregation model from decision analysis and to be equivalent to classification on the sum of the representations, motivating a flexible concatenation architecture. A key-value caching scheme further reduces the computational cost of attending to each class separately by 50 to 60 percent for large numbers of classes. We evaluate the framework on five binary classification tasks, four multiclass classification tasks, and a controlled CIFAR-100 experiment, spanning image, text, and tabular data. The best contrastive variant outperforms a feedforward network baseline and a standard transformer baseline with unrestricted attention on every binary and multiclass task. The CIFAR-100 experiment identifies the distinguishability of the classes, rather than the number of classes, as the primary driver of the contrastive method's advantage over unrestricted attention.", "source_url": "https:\/\/openalex.org\/W7220982118", "pdf_url": "", "doi": "10.7282\/t3-ger4-v617", "handle": "", "repository_name": "Rutgers University Community Repository (Rutgers University)", "source_record_id": "W7220982118", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 383, "title": "Transformers and other Attention-Based Algorithms for Biomedicine", "title_ar": "", "title_en": "Transformers and other Attention-Based Algorithms for Biomedicine", "author": "Marco Hernández Pérez", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Salamanca", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; self-semi-supervised; predictive-analytics; rnn; transformers; image-segmentation; medical-imaging", "keywords": "", "abstract": "[EN] This research explores the application of Transformer-based models and Hybrid Attention Mechanisms across three critical biomedical tasks: meningioma segmentation, epileptic seizure detection, and pathogenicity prediction of genomic variants. For meningioma segmentation, this research investigates refining skip connections in a U-Net architecture by incorporating Swin Transformers for Magnetic Resonance Imaging. The proposed SwinC U-Net model, evaluated on the Brain Tumor Segmentation Meningioma 2023 challenge dataset, achieved a Dice score of 0.8933±0.0016, precision of 0.9020±0.0041, and recall of 0.8939±0.0044, surpassing U-Net and Attention U-Net in precision and accuracy, while slightly underperforming in recall compared to U-Net. In epileptic seizure detection, this work addresses the challenge of real-time monitoring using microelectrode array data from rat brain slices with 4-aminopyridine-induced epileptic activity. Lightweight neural models were trained to detect seizures. The best performing model, GRU+Attention, achieved an event-level F1 score of 0.839, Jaccard index of 0.722, recall of 0.813, and precision of 0.867 on a held-out test set. The model was quantized to INT8, maintaining high event-level detection performance (F1 = 0.812, Jaccard = 0.709) and demonstrating real-time feasibility on embedded platforms like the Raspberry Pi 5 (latency: 2.61 ms) and Coral Dev Board (latency: 23.7 ms), with memory usage under 614 MB. For pathogenicity prediction, a Feature Tokenizer Transformer model was developed to classify genetic variants. The model used a set of inputs derived from next-generation sequencing data from the CLINVAR database, after being processed through the regular pipeline consisting of quality control, alignment, variant calling, filtering, indexing, and annotation. The semi-supervised approach allowed the model to extract insight from uncertain-labeled data. The model achieved a precision of 0.98, sensitivity of 0.92, specificity of 0.99, and F1 score of 0.95, outperforming ClinPred and REVEL in precision and specificity, but with slightly lower sensitivity than ClinPred. These studies demonstrate the versatility and efficacy of Attention-based models in handling diverse biomedical signal modalities. The results underscore the potential of Transformer architectures to advance biomedical data analysis, enhancing diagnostics and personalized medicine.", "source_url": "https:\/\/openalex.org\/W4414483146", "pdf_url": "", "doi": "10.14201\/gredos.167164", "handle": "", "repository_name": "Gredos (University of Salamanca)", "source_record_id": "W4414483146", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 384, "title": "Higher Order Transformers With Kronecker-Structured Attention", "title_ar": "", "title_en": "Higher Order Transformers With Kronecker-Structured Attention", "author": "Soroush Omranpour", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; transformers; image-classification; medical-imaging", "keywords": "", "abstract": "Modern datasets are increasingly high-dimensional and multiway, naturally represented as tensors with multiple indexed variables. Such data arise in numerous applications, including multivariate time series forecasting, 3D medical imaging, and climate modeling. While Transformers have demonstrated exceptional performance in sequence modeling and high-dimensional learning tasks, their direct application to multiway data remains computationally prohibitive due to the quadratic complexity of dot-product attention. Moreover, standard self-attention mechanisms do not inherently preserve the tensor structure, leading to inefficiencies in capturing cross-dimensional dependencies.In this work, we introduce the Higher-Order Transformer (HOT), a novel attention mechanism designed to process multiway data efficiently. HOT leverages Kronecker factorization to express multiway attention as either a sum of Kronecker products or mode-wise attention matrices, effectively capturing structured dependencies while significantly reducing computational and memory overhead. Unlike conventional self-attention, HOT maintains the original tensor structure, ensuring efficient representation of both dense and sparse interactions across dimensions. Through theoretical analysis, we show that HOT retains the expressiveness of full high-order attention while providing a mechanism to control complexity via the factorization rank.We evaluate HOT on challenging real-world datasets, including multivariate time series forecasting, 3D medical image classification, and stock market prediction. Experimental results demonstrate that HOT achieves competitive or superior performance compared to state-of-the-art Transformer-based baselines while requiring significantly fewer computational resources. Additionally, ablation studies highlight the impact of factorization rank, positional encoding strategies, and attention patterns, providing further insights into HOT’s efficiency and adaptability. Our findings underscore HOT's potential as a scalable and effective solution for high-dimensional multiway learning, paving the way for future extensions in autoregressive modeling, large-scale vision tasks, and cross-modal learning", "source_url": "https:\/\/openalex.org\/W7115035808", "pdf_url": "", "doi": "10.82308\/30876", "handle": "", "repository_name": "", "source_record_id": "W7115035808", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 385, "title": "Feedforward Neural Networks and the Transformer model", "title_ar": "", "title_en": "Feedforward Neural Networks and the Transformer model", "author": "Ebba Brunvall", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; transformers", "keywords": "", "abstract": "This thesis presents the mathematical framework underlying two central architectures in modern deep learning: the feedforward neural network and the transformer model. Beginning from first principles, the report introduces the perceptron model and builds toward the multi-layer perceptron by incorporating activation functions, layer structure, and matrix notation. The learning process is examined through parameter initialization, the cost function, the backpropagation algorithm, stochastic gradient descent, and regularization techniques including early stopping and dropout. Hyperparameter tuning via grid search is also discussed. The thesis then transitions to the transformer model, introduced in the 2017 paper \"Attention Is All You Need,\" covering the mathematical formulation of scaled dot-product self-attention, multi-head attention, tokenization and positional encoding. The encoder and decoder modules are derived in full matrix form, detailing their respective sublayers, residual connections and layer normalization. Finally, the optimization and regularization choices of the original transformer are discussed. The aim throughout is to bridge the gap between intuitive descriptions and mathematical exposition.", "source_url": "https:\/\/openalex.org\/W7205320669", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Högskolan Kristianstad Publikationer", "source_record_id": "W7205320669", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 386, "title": "Adapting the transformer attention mechanism for efficient and effective information retrieval", "title_ar": "", "title_en": "Adapting the transformer attention mechanism for efficient and effective information retrieval", "author": "Ferdinand Schlatt", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; transformers; llm", "keywords": "", "abstract": "Transformer-based language models have become the dominant architecture in natural language processing because they are effective across a wide range of tasks. However, an architecture that works for many tasks is not necessarily the best architecture for a specific task. In this dissertation, we investigate how we can adapt the attention mechanism inside a transformer layer to improve the effectiveness and efficiency of transformer-based language models for the specific task of informational retrieval. We introduce three attention adaptations. The first improves the efficiency of bi-encoders by pooling neighboring vectors inside the attention mechanism. The second improves the efficiency of cross-encoders by deactivating the attention between various sub-sequences, such as between query and document tokens. The third improves the effectiveness of cross-encoders by allowing multiple documents to attend to one another via an additional interaction token. Our analyses show that our attention adaptations lead to substantial effectiveness and efficiency improvements. In addition, we introduce two resources that simplify fine-tuning and evaluating bi-encoders and cross-encoders. The first is LightningIR, a framework that unifies the implementation of various model variants, and the second is Rank-DistiLLM, a set of high-quality distillation datasets for fine-tuning. We demonstrate that our resources can produce state-of-the-art bi-encoders and cross-encoders with minimal manual effort. We conclude that our attention adaptations demonstrate the potential of task-specific attention modifications, but the space of possible adaptations is not yet exhausted. We hope that our work inspires future research on task-specific adaptations for transformer-based models for information retrieval and other tasks.", "source_url": "https:\/\/openalex.org\/W7171513975", "pdf_url": "https:\/\/www.db-thueringen.de\/servlets\/MCRFileNodeServlet\/dbt_derivate_00072397\/Dissertation_FerdinandSchlatt_2026.pdf", "doi": "10.22032\/dbt.71291", "handle": "", "repository_name": "Common Library Network (Der Gemeinsame Bibliotheksverbund)", "source_record_id": "W7171513975", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 387, "title": "Explainable Transformers in Natural Language Processing", "title_ar": "", "title_en": "Explainable Transformers in Natural Language Processing", "author": "George Mihaila", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; nlp; medical-ai; responsible-ai; transformers; sentiment-analysis; speech-recognition; xai; fairness", "keywords": "", "abstract": "Transformer-based models have revolutionized natural language processing while becoming increasingly opaque in their decision-making processes. As these systems deploy in high-stakes domains such as healthcare, legal decision-making, and content moderation, their lack of transparency poses critical challenges, particularly where explainability is legally mandated or ethically required. This dissertation introduces ExpNet, a novel framework that learns to generate human-interpretable explanations from transformer attention mechanisms through supervised learning. Unlike existing approaches that apply fixed mathematical heuristics or rely on computationally expensive perturbation methods, ExpNet treats explanation generation as a learning problem. The framework extracts attention features and processes them through a lightweight neural network trained on human-annotated rationales, discovering optimal mappings from attention patterns to token importance scores. ExpNet demonstrates strong cross-task generalization capability through rigorous evaluation on diverse natural language processing tasks including sentiment analysis, grammatical acceptability judgment, and hate speech detection. When trained on tasks from certain domains and evaluated on entirely different tasks, ExpNet consistently outperforms thirteen baseline methods spanning model-agnostic, gradient-based, and attention-based approaches. This generalization achieved without task-specific retraining suggests that transformer attention encodes universal linguistic patterns, enabling the framework to explain models across a broad range of applications beyond its training domains. Quantitative results show ExpNet achieves substantially higher alignment with human rationales than all baselines across varied linguistic phenomena. The framework generates explanations orders of magnitude faster than perturbation-based methods, making it suitable for real-time deployment. This combination of superior explanation quality and computational efficiency addresses the critical gap between interpretability research and practical production requirements. The dissertation develops extensive analysis of explanation evaluation challenges in natural language processing. By learning from human reasoning, generalizing across diverse tasks, and maintaining computational efficiency, ExpNet provides a pathway toward transparent transformer-based systems suitable for high-stakes applications where explainability is imperative.", "source_url": "https:\/\/openalex.org\/W7166919207", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of North Texas Digital Library (University of North Texas)", "source_record_id": "W7166919207", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 388, "title": "Transformer Block Coupling and Self-Attention Flows for Token Evolution", "title_ar": "", "title_en": "Transformer Block Coupling and Self-Attention Flows for Token Evolution", "author": "Anton Pavlovych Sugolov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Toronto", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; transformers; llm", "keywords": "", "abstract": "Transformers and Large Language Models have achieved incredible performance in deep learning, especially in natural language processing, and a precise understanding of the internal mechanisms driving their success is essential. We first provide an empirical analysis of structural characteristics of transformer blocks, by analyzing the trajectories of LLM token embeddings as they traverse the model depth. We linearize the system along the trajectories through their Jacobian matrices and uncover transformer block coupling in a multitude of LLMs, characterized by the coupling of their top singular vectors across tokens and depth. Our findings reveal that coupling positively with model performance, and that this relationship is stronger than with other hyperparameters such as parameter count, model depth, and embedding dimension. Further, we investigate recent theoretical perspectives that cast transformers as iterative dynamical systems which modify embeddings. We provide an analysis of self-attention as a vector field that acts on Gaussian mixtures, building on recent works that construct interaction functionals on measures with similar properties to self-attention. Collectively, these insights offer a novel perspective on token interactions in transformers, opening new directions for studying their mechanisms as well as improving training and generalization.", "source_url": "https:\/\/openalex.org\/W7132879793", "pdf_url": "", "doi": "", "handle": "", "repository_name": "TSpace (University of Toronto)", "source_record_id": "W7132879793", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 389, "title": "Exploring Resource-Efficient Methods on Inhibitor Attention-Based Transformers", "title_ar": "", "title_en": "Exploring Resource-Efficient Methods on Inhibitor Attention-Based Transformers", "author": "Simon Engström", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Luleå University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; transformers", "keywords": "", "abstract": "In this thesis, quantization and knowledge distillation are applied to a transformer model with the inhibitor attention mechanism and the results are compared to that of a scaled dot-product attention (SDPA) model. The two main questions that are explored in this thesis are (1) whether an inhibitor model can be trained with knowledge distillation to match the performance of a SDPA model and (2) if inhibitor models are more robust than SDPA at low bit-width quantization. For the knowledge distillation, previous work trained an inhibitor model with DistilBERT as the teacher, yielding a GLUE score of 74.3. This work used a similar distillation method but replaced the teacher model with BERT, which resulted in a score of 75.0 on GLUE. This is not a significant increase, so using a bigger teacher model does not yield better results in this case. Quantization on the inhibitor model was also investigated and results of equivalent (number of parameters) inhibitor and dot-product based models were compared. 8-bit integer quantization showed little performance degradation (compared to floating-point models) for both quantization-aware training as well as post-training quantization for both model types. In the case of 4-bit quantization-aware training, the performance degradation was greater for the dot-product based model compared to the inhibitor model, where the inhibitor model scored 64.2 on GLUE while the dot-product model scored 59.3, down from 74.3 and 77.5 respectively. At this bit-width, quantization-aware training outperformed post-training quantization with the latter scoring 36.6 and 45.5 (GLUE score) for dot-product and inhibitor models respectively. With these results it can be concluded that inhibitor attention outperforms scaled dot-product attention at low bit-width quantization (specifically 4-bit in this case). It was found that inhibitor attention brought some challenges for integer-only calculations but one potential solution for computing inhibitor attention with integer-only arithmetic was found. It is not entirely efficient however, as many rescaling operations are required. It is therefore unclear how an efficient integer-only inhibitor model can be implemented using standard quantization schemes.", "source_url": "https:\/\/openalex.org\/W7133038095", "pdf_url": "https:\/\/ltu.diva-portal.org\/smash\/get\/diva2:2041996\/FULLTEXT01", "doi": "", "handle": "", "repository_name": "DiVA (Luleå University of Technology)", "source_record_id": "W7133038095", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 390, "title": "Towards the Renormalization of Transformer Language Models", "title_ar": "", "title_en": "Towards the Renormalization of Transformer Language Models", "author": "Jingcheng Niu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Toronto", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; transformers; llm", "keywords": "", "abstract": "Transformer language models (LMs), the cornerstone of the popular large language models (LLMs), have revolutionised the fields of artificial intelligence (AI) and natural language processing (NLP). However, we still understand relatively little about their computational mechanisms and the reasons for their effectiveness. The internal workings of these models remain enigmatic \"black boxes.\" In this thesis, I present a framework for interpreting LMs, which can be aptly described using the term \"renormalization.\" Inherent model components — such as layers, attention heads, MLPs, or neurons — should serve as the base units of abstraction for the renormalization process. This approach is preferable to using familiar linguistic or cognitive structures, as there is insufficient evidence to suggest that the internal mechanisms of LMs operate in a manner analogous to human linguistic or cognitive frameworks. Furthermore, our analysis of the Knowledge Neuron (KN) thesis suggests that neurons exhibit a remarkable degree of idiosyncrasy in the types of function and information they associate with. This finding underscores the necessity of conducting the coarse-graining process at the granularity of individual neurons. However, our examination also reveals critical limitations of the KN thesis, suggesting that simply decomposing the model without accounting for interactions between these units provides an incomplete explanation. These interactions can be captured through circuit discovery methods. To address this, we propose a novel approach that enables joint pruning of connection edges and weight parameters, facilitating efficient neuron-level granularity. As a result, our method surpasses previous approaches, achieving state-of-the-art results in LM interpretation. Finally, our findings reveal two key insights for applications: global representational techniques that involve modifications to the entire model should be prioritised, while conventional, pre-theoretical speculation from cognitive science and linguistics has to date not been a fruitful enterprise. To demonstrate this, we present a case study on the task of temporal relation extraction, where, by leveraging these insights, we propose two effective improvements that achieve state-of-the-art performance.", "source_url": "https:\/\/openalex.org\/W7133020277", "pdf_url": "https:\/\/utoronto.scholaris.ca\/bitstreams\/bf75fbd3-79b0-4850-ab50-e543a20674e3\/download", "doi": "", "handle": "", "repository_name": "TSpace (University of Toronto)", "source_record_id": "W7133020277", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 391, "title": "Long Text Understanding with Transformer-based Models", "title_ar": "", "title_en": "Long Text Understanding with Transformer-based Models", "author": "Yan Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; transformers", "keywords": "", "abstract": "Transformer-based models have achieved remarkable success across a wide range of natural language processing tasks. However, their performance degrades substantially when processing long inputs, including multi-turn dialogues, lengthy documents, and sequences beyond the training context window. These limitations arise not only from computational constraints but also from structural misalignment, where local predictions drift from global semantics, document coherence is fragmented, and positional extrapolation disrupts pretrained attention patterns. This thesis investigates long text understanding in Transformer-based models through the perspective of preserving structural alignment under constrained context regimes. Three complementary frameworks are proposed. First, for multi-turn dialogue understanding, a multi-level semantic distillation framework aligns intent, slot, and domain knowledge across utterance-, token-, and dialogue-level representations using heterogeneous teacher supervision to improve semantic consistency and robustness. Second, for long document understanding, a semantic-preserving chunk-level representation method compresses documents into meaningful chunks through unsupervised keyphrase extraction while retaining fine-grained token information. Third, for length extrapolation beyond the training context window, a training-free inference-time method stabilizes positional generalization by reusing pretrained positional intervals and locally interpolating attention logits. Experiments on multi-turn dialogue benchmarks, long document understanding tasks, and long-context evaluation suites demonstrate the effectiveness and generalizability of the proposed methods. Collectively, these contributions establish a unified framework for robust long text understanding by preserving semantic coherence, structural consistency, and positional stability under context constraints.", "source_url": "https:\/\/openalex.org\/W7213384544", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Sydney eScholarship Repository (The University of Sydney)", "source_record_id": "W7213384544", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 392, "title": "Two-Stream Transformer Architecture With Discrete Attention for Better Interpretrability and Separation of Model Concerns", "title_ar": "", "title_en": "Two-Stream Transformer Architecture With Discrete Attention for Better Interpretrability and Separation of Model Concerns", "author": "Jambay Kinley", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Harvard University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; nlp; responsible-ai; transformers; xai", "keywords": "", "abstract": "The transformer has become a central model for natural language processing tasks ranging from translation to classification to representation learning. Its success demonstrates the effectiveness of stacked attention as a replacement for recurrence for many tasks. Attention is broadly interpreted as selectively attending to different parts on an input. So, in theory, attention offers more insights into the model’s internal decisions; however, in practice, when stacked, it quickly becomes nearly as fully-connected, making it hard to disentangle final decision dependencies. In this work, we propose an alternative transformer architecture, discrete transformer, with the goal of improving model interpretability. We use discrete latent variable attention to ensure that decision steps only depend on a limited context. We separate out attention decisions from representation modeling by using a separate stream for each. Empirically, on both classification and translation tasks, this approach maintains similar levels of performance on several datasets as the standard transformer, while obtaining quantitatively better attention interpretability and separating out syntactic features in the learned representations. Finally, our two-stream formulation can be used to transfer knowledge in a multiview arithmetic evaluation task.", "source_url": "https:\/\/openalex.org\/W3082067696", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)", "source_record_id": "W3082067696", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 393, "title": "MAWT: Multi-Attention-Weight Transformers", "title_ar": "", "title_en": "MAWT: Multi-Attention-Weight Transformers", "author": "Enver Askin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; predictive-analytics; transformers; machine-translation", "keywords": "", "abstract": "Transformers are machine learning models designed to learn and predict sequential and structured data, which are crucial to tasks such as neural machine translation and semantic parsing.They have become state-of-the-art engines for both of these tasks and much research in natural language processing is devoted to increasing their performance by introducing modifications to their architectures.In light of this trend, this thesis introduces a new Transformer architecture called MAWT: Multi-Attention-Weight Transformers in an attempt to increase the accuracy and variety of the acceptable predictions of a Transformer.It attempts to achieve this by training multiple weights per each Transformer attention head, which then are used to test the accuracy of the engine.This creates a new architecture under which the system produces a candidate set of outputs (instead of a singly output), along with a method for selecting from the candidate set.My proposal rests on the assumption --motivated by statistical considerations --that having a candidate set increases the probability of finding an exact match within the set.Upon testing, I observed that my system outperforms the regular transformer on 5\/6 benchmark neural machine translation and semantic parsing datasets, where engine performance is measured by exact match accuracy.Exact match accuracy demands syntactic identity between the output and the target.In order to investigate how well my new architecture generalizes to measures of semantic equivalence that don't also demand syntactic identity, I also recorded the BLEU scores on these datasets.The BLEU score is a measure of performance based on n-grams rather than exact symbolic match (i.e., how many contiguous sequence of n-many strings from the predicted output match the desired output).The results I report on the BLEU scores are more iii mixed, raising important questions that I highlight about the role of syntax in measures of semantic equivalence.", "source_url": "https:\/\/openalex.org\/W4362577036", "pdf_url": "http:\/\/repository.library.carleton.ca\/downloads\/g732d990n", "doi": "10.22215\/etd\/2023-15442", "handle": "", "repository_name": "Carleton University's Institutional Repository (MacOdrum Library, Carleton University)", "source_record_id": "W4362577036", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 394, "title": "Sparse Attention Mechanisms in Vision Transformers: from Spatiotemporal Transformers to Instance-Dependent Attention Patterns", "title_ar": "", "title_en": "Sparse Attention Mechanisms in Vision Transformers: from Spatiotemporal Transformers to Instance-Dependent Attention Patterns", "author": "Brendan William Duke", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Toronto", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; cnn; transformers; image-segmentation; video-analysis; speech-recognition", "keywords": "", "abstract": "Deep learning (DL) algorithms have produced superior results for a variety of vision, language, and speech processing tasks, but have proven to be power and compute-hungry. In the past, hardware advancements have met the growing need for compute. Yet in recent years the end of Dennard scaling and Moore's law makes hardware advancements unlikely to continue at the same fast pace. Hence innovative algorithmic solutions are needed to meet DL algorithms' ever-growing demand for compute. In this dissertation we investigated methods using sparsity and conditional computation to meet this compute demand. The two main components of this contribution involve applying structure and dynamic prediction of sparsity patterns by machine learning algorithms. The hypothesis was that structured and dynamic sparse computation improves latency and memory footprint compared to existing approaches with equal algorithm effectiveness. To show this, we built on the state of the art in sparse computations. This dissertation fills in previously unexplored areas of applying structured sparsity to videos, and of predicting per-instance sparsity that is unstructured. The purpose was to investigate using structured and per-example sparsity to design performant and effective deep learning algorithms. Existing literature had investigated sparsity in computer vision, but only in the context of either convolutional neural networks (CNNs) or Transformers applied to image processing. Given the increased computational complexity, this raised the question of how sparsity fares when applied to video processing. We investigated this question in work presented at the CVPR 2021 conference called Sparse Spatiotemporal Transformers for Video Object Segmentation (SSTVOS). SSTVOS looks at the highly computationally demanding problem of applying Transformers to video by using sparse attention blocks. The sparse attention blocks in SSTVOS use a fixed sparsity pattern that takes advantage of a priori knowledge of which visual features to attend to. However, using fixed sparsity patterns limits us to problems where we have this a priori knowledge. Therefore we further executed a research direction to learn sparsity patterns that are performant (low latency and memory usage) and effective (superior predictive power) in a method called Sparsifiner. Sparsifiner predicts per-example sparsity patterns as a more flexible generalization of the fixed sparsity patterns in SSTVOS. With the expectation that a machine learning model learns to predict sparsity patterns superior to any fixed pattern designed by hand, the idea for Sparsifiner was born. Sparsifiner improves over existing sparsity methods in Vision Transformers~(ViTs) by predicting per-instance sparsity patterns with a computationally-efficient connectivity predictor module. The method serves as empirical evidence of the effectiveness of per-instance sparsity patterns compared to fixed sparsity patterns. Sparsifiner shows a Pareto-optimal trade-off between FLOPs and top-1 accuracy on ImageNet when compared to contemporary structured-sparse methods. Together, both SSTVOS and Sparsifiner constitute a basis for future work in the direction of structured and dynamic sparsity in deep learning.", "source_url": "https:\/\/openalex.org\/W7132914290", "pdf_url": "", "doi": "", "handle": "", "repository_name": "TSpace (University of Toronto)", "source_record_id": "W7132914290", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 395, "title": "Evaluation of the Transformer Model for Abstractive Text Summarization", "title_ar": "", "title_en": "Evaluation of the Transformer Model for Abstractive Text Summarization", "author": "Fredrik Jönsson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; nlp; rnn; transformers; machine-translation", "keywords": "", "abstract": "Being able to generate summaries automatically could speed up the spread and retention of information and potentially increase productivity in several fields. Using RNN-based encoder-decoder models with attention have been successful on a variety of language-related tasks such as automatic summarization but also in the field of machine translation. Lately, the Transformer model has been shown to outperform RNN-based models with attention in the relatedfield of machine translation. This study compares the Transformer model to a LSTM-based encoderdecoder model with attention on the task of abstractive summarization. Evaluation is done both automatically, using ROUGE score, as well as using human evaluators to estimate the grammar and readability of the generated summaries. The results show that the Transformer model produces better summaries both in terms of ROUGE score and when evaluated with human evaluators.", "source_url": "https:\/\/openalex.org\/W2991849516", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2991849516", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 396, "title": "Arbitrary-order Sampling and Hand Motion Modeling with Transformers", "title_ar": "", "title_en": "Arbitrary-order Sampling and Hand Motion Modeling with Transformers", "author": "Maxwell Clarke", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; responsible-ai; self-semi-supervised; transformers; fairness", "keywords": "", "abstract": "Transformer models have been quickly taking over many tasks in the field of deep learning, due to their versatility while maintaining high performance. This thesis provides an introduction to transformer models, presents experiments with new ways of sampling data with them, and then applies them to the domain of hand motion modeling. Firstly, a comprehensive introduction to transformer models is given, including the attention operation, masking, architecture variants, and different pre-training tasks. Secondly, an experiment is presented using a probabilistic transformer model on the MNIST dataset, which was trained so that it is capable of arbitrary-order sampling. The experiment compares different sampling orders, including some dynamic sampling order heuristics based on the entropy. The experiments find that such sampling orders introduce a statistical bias into the samples. Lastly, the problem domain of hand motion modeling is introduced, and transformer models are trained to generate hand-motion sequences, via self-supervised learning on a motion-capture dataset. Both deterministic and probabilistic models are trained. The deterministic models can generate realistic-looking hand motions, but cannot be directed to generate specific motions. The probabilistic model performs poorly.", "source_url": "https:\/\/openalex.org\/W4389570359", "pdf_url": "https:\/\/openaccess.wgtn.ac.nz\/articles\/thesis\/Arbitrary-order_Sampling_and_Hand_Motion_Modeling_with_Transformers\/24785205\/1\/files\/43587534.pdf", "doi": "10.26686\/wgtn.24785205", "handle": "", "repository_name": "Open Access Victoria University of Wellington | Te Herenga Waka (Figshare)", "source_record_id": "W4389570359", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 397, "title": "Improving end-to-end transformer model architecture in ASR", "title_ar": "", "title_en": "Improving end-to-end transformer model architecture in ASR", "author": "Yingzhu Zhao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; nlp; transformers; speech-recognition", "keywords": "", "abstract": "As a result of advancement in deep learning and neural network technology, end-to-end models have been introduced into automatic speech recognition (ASR) successfully and achieved superior performance compared to conventional hybrid systems. End-to-end models simplify the traditional GMM-HMM models by transcribing speech to text directly with fast computation speed and fast development time. Transformer model, the latest end-to-end model, has achieved a huge success not only in ASR but also in natural language processing and computer vision. In spite of its great performance, transformer model architecture can be further improved to better suit the characteristics of ASR. To be more specific, ASR performance is greatly affected by the speaker mismatch between training and test data, and speaker adaptation is a long-standing problem in ASR. Additionally, ASR has the characteristic to have monotonic alignment between text output and speech input. Different phonemes along an utterance may require different level of computation due to varying complexity and noise level. How to better deploy the transformer model for ASR remains a challenge. In this thesis, three novel architectural changes to the transformer model are proposed for the above ASR characteristics. For each of the proposed methods, detailed experimental evaluation is carried out to compare the performance against the transformer baseline with analysis. In the first study, to alleviate the performance drop in ASR due to speaker mismatch between training and test data, we present a unified framework for speaker adaptation, which consists of feature adaptation and model adaptation. A speaker-aware persistent memory model is presented to capture speaker knowledge through the persistent memory in order to generalize better to unseen test speakers, which belongs to the feature adaptation method. Furthermore, a model-based gradual pruning approach is deployed to free up partial model encoder parameters for target speaker adaptation without sacrificing the original model performance. Finally, instead of only adapting to general speakers or specific target speaker, we design a multi-speaker adaptation model capable of adapting to multiple speakers simultaneously, which is practical in ubiquitous environment with multiple users. Our proposed approach brings relative 2.74-6.52% word error rate (WER) reduction on general speaker adaptation. On target speaker adaptation, our method outperforms the baseline with up to 20.58% relative WER reduction, and surpasses the finetuning method by up to relative 2.54%. In the second study, we then address the text output speech input misalignment problem in transformer model which greatly affects the ASR accuracy during training and inference. An effective cross attention biasing technique in transformer is proposed that takes monotonic alignment between text output and speech input into consideration by making use of cross attention weights. Specifically, a Gaussian mask is applied on cross attention weights to limit the input speech context range locally given alignment information. A regularizer is further introduced for alignment regularization. Experiments on LibriSpeech dataset find that our proposed model can obtain improved output-input alignment for ASR, and yields 14.5%-25.0% relative WER reductions. In the final study, to resolve the problem of static number of layers in transformer model, we present universal speech transformer. It generalizes the speech transformer with dynamic numbers of encoder\/decoder layers, which can relieve the burden of tuning depth related hyperparameters. Universal transformer adds the depth and positional embeddings repeatedly for each layer, which dilutes the acoustic information carried by hidden representation, and it also performs a partial update of hidden vectors between layers, which is less efficient especially on the very deep models. For better use of universal transformer, we modify its processing framework by removing the depth embedding and only adding the positional embedding once at transformer encoder frontend. Furthermore, to update the hidden vectors efficiently, especially on the very deep models, we adopt a full update. Experiments on LibriSpeech, Switchboard and AISHELL-1 datasets show that our model outperforms a baseline by 3.88%-13.7%, and surpasses other model with less computation cost.", "source_url": "https:\/\/openalex.org\/W4367678477", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/166407\/2\/thesis_ZhaoYingzhu.pdf", "doi": "10.32657\/10356\/166407", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4367678477", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 398, "title": "Vision transformers with Inductive Bias introduced through self-attention regularization", "title_ar": "", "title_en": "Vision transformers with Inductive Bias introduced through self-attention regularization", "author": "MORMILLE LUIZ HENRIQUE BARBOSA", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; responsible-ai; cnn; transformers; fairness", "keywords": "", "abstract": "In recent years, the Transformer achieved remarkable results in computer vision related tasks, matching, or even surpassing those of convolutional neural networks (CNN). However, unlike CNNs, those vision transformers lack strong inductive biases and, to achieve state-of-the-art results, rely on large architectures and extensive pre-training on tens of millions of images. Introducing the appropriate inductive biases to vision transformers can lead to better convergence and generalization on settings with fewer training data. This work presents a novel way to introduce inductive biases to vision transformers: self-attention regularization. Two different methods of self-attention regularization were devised. Furthermore, this work proposes ARViT, a novel vision transformer architecture, where both self-attention regularization methods are deployed. The experimental results demonstrated that self-attention regularization leads to better convergence and generalization, especially on models pre-trained on mid-size datasets.", "source_url": "https:\/\/openalex.org\/W7145074315", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7145074315", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 401, "title": "Analisis Performa Zero-shot dan Few-shot Learning Model Generative AI Dibandingkan dengan Fine-Tuning Model Berbasis BERT dalam Klasifikasi Emosi Ulasan Produk E-commerce Berbahasa Indonesia=Performance Analysis of Zero-Shot and Few-Shot Learning in Generative AI Models Compared with Fine-Tuning BERT-Based Models for Emotion Classification of Indonesian E-Commerce Product Reviews", "title_ar": "", "title_en": "", "author": "Rasyad Bimasatya", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2026, "language": "id", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; generative-ai; transfer-learning; few-zero-shot; transformers", "keywords": "", "abstract": "Latar belakang. Pesatnya pertumbuhan e-commerce di Indonesia meningkatkan volume ulasan produk emosional yang sulit dianalisis secara manual. Meskipun model BERT hasil fine-tuning terbukti efektif, pendekatannya memerlukan banyak data berlabel dan sumber daya komputasi. Sebaliknya, Generative AI menawarkan fleksibilitas melalui zero-shot dan few-shot learning, namun performanya pada ulasan berbahasa Indonesia masih perlu dievaluasi. Tujuan. Penelitian ini bertujuan untuk menganalisis sejauh mana performa teknik zero-shot dan few-shot learning pada model Generative AI dalam klasifikasi emosi ulasan produk e-commerce berbahasa Indonesia, serta membandingkannya dengan model berbasis BERT hasil fine-tuning, baik dari sisi efektivitas maupun efisiensi. Metode. Penelitian menggunakan pendekatan kuantitatif dengan mengevaluasi performa model menggunakan metrik precision, recall, accuracy, F1 score, latency, dan throughput. Selain itu, Pearson’s correlation coefficient digunakan untuk menganalisis hubungan antara ukuran few-shot learning dan perubahan performa model Generative AI. Model terbaik selanjutnya diimplementasikan ke dalam sistem berbasis website. Hasil. IndoBERT hasil fine-tuning mencapai performa tertinggi dengan accuracy dan F1 score macro average sebesar 84%, serta efisiensi yang lebih baik dibandingkan model Generative AI. Penambahan contoh few-shot meningkatkan performa beberapa model, namun menurunkan efisiensi. Kesimpulan. Penelitian ini menunjukkan bahwa performa teknik zero-shot dan few-shot learning pada model Generative AI berada di bawah model berbasis BERT hasil fine-tuning dalam tugas klasifikasi emosi ulasan e-commerce berbahasa Indonesia. Model IndoBERT dengan pendekatan fine-tuning telah diimplementasikan dalam aplikasi berbasis Streamlit sebagai solusi praktis analisis emosi ulasan.", "source_url": "https:\/\/openalex.org\/W7217542476", "pdf_url": "https:\/\/repository.unhas.ac.id\/id\/eprint\/57569\/2\/H071221024-1-2.pdf", "doi": "", "handle": "", "repository_name": "Hasanuddin University Repository", "source_record_id": "W7217542476", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 402, "title": "Benchmarking ESG text classification in low resource settings : a comparative study of QLoRA fine-tuning, few-shot learning, and zero-shot ensemble", "title_ar": "", "title_en": "Benchmarking ESG text classification in low resource settings : a comparative study of QLoRA fine-tuning, few-shot learning, and zero-shot ensemble", "author": "P. Ghashghaei", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "transfer-learning", "categories": "machine-learning; nlp; transfer-learning; few-zero-shot", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7165257111", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Theses database, Tilburg University", "source_record_id": "W7165257111", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 403, "title": "Vision–Language Models for Zero- and Few-Shot Anomaly Detection in Chest X-Rays", "title_ar": "", "title_en": "Vision–Language Models for Zero- and Few-Shot Anomaly Detection in Chest X-Rays", "author": "Md Ashrafuzzaman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Jyväskylä", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; ai-applications; few-zero-shot; predictive-analytics; multimodal; llm; medical-imaging; ai-industry", "keywords": "Vision–Language Models, Anomaly Detection, Chest X-Ray, Zero-Shot Learning, Few-Shot Learning, Medical Imaging.", "abstract": "Deep Learning has achieved huge success in different fields lately, especially in the medical field; however, today’s traditional models are based on supervised learning and rely heavily on large amounts of expert-labeled data, which makes training time-consuming and a lot more expensive. This presents a significant challenge, especially for anomaly detection in medical images, where rare pathologies or unexpected findings are hard to find. For the workflow of Chest X-Ray (CXR) interpretation, the ability to find “out-of-distribution” anomalies without prior examples or a very limited number of ex-amples is critical for patient safety and efficiency in patient diagnosis. In this thesis I investigated the application of Vision-Language Models (VLMs) to per-form Zero-Shot and Few-Shot anomaly detection in Chest X-Rays. By using the joint em-bedding space of image and text representations (such as those found in CLIP or Bio-medCLIP), I propose a framework that identifies anomalies by measuring the semantic similarity between CXR images and the descriptive text prompts. I used different Prompt Engineering strategies ranging from simple labels to complex clinical descriptions to op-timize zero-shot performance. Subsequently, for few-shot adaptation, I used prototypical networks to compute class prototypes from the support embeddings without updating the model weights. Our result was inflated by patient-level data leakage, but after enforcing a strict patient-disjoint split, few-shot performance plateaued at 50-60% which indicates that prototypes captured patient identity rather than generalized pathology. In zero shot, BiomedCLIP achieved 55% balanced accuracy versus 41% for CLIP which demonstrates the benefits of medical pretraining while Qwen2-VL defaulted to predicting Normal, showing that just following instructions alone is not sufficient for medical anomaly detec-tion. This research shows that Vision-Language Models (VLMs) can be used for data-efficient and flexible medical AI systems. By reducing the reliance on large-scale, labeled datasets, these models can be more easily deployed for rare disease detection, and can assist radiol-ogists in further investigation and the identification of critical findings in resource con-strained environments. Keywords: Vision–Language Models, Anomaly Detection, Chest X-Ray, Zero-Shot Learning, Few-Shot Learning, Medical Imaging.", "source_url": "https:\/\/openalex.org\/W7198111354", "pdf_url": "https:\/\/jyx.jyu.fi\/bitstreams\/dce3f054-4d7e-4496-bafc-a9230d78c155\/download", "doi": "", "handle": "", "repository_name": "Jyväskylä University Digital Archive (University of Jyväskylä)", "source_record_id": "W7198111354", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 404, "title": "Attribute-based analysis of zero-shot learners", "title_ar": "", "title_en": "Attribute-based analysis of zero-shot learners", "author": "Vogel, Felix", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Goethe University Frankfurt", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; few-zero-shot; multimodal; llm", "keywords": "", "abstract": "Vision-language models trained on large, randomly collected data had a significant impact in many areas since they appeared. But although they show great performance in various fields, such as image-text-retrieval, their inner workings are still not fully understood. An interesting application of those models is the zero-shot scenario that is analyzed in this work. A new benchmark consisting of five experiment setups is proposed to evaluate how zero-shot learners handle class labels and attributes. The experiments are divided into two sections, the classification performance with class label containing the first three experiment setups and the attribute based classification performance containing the last two experimental setups. The suggested experiments rely on attribute-based datasets with attribute annotations per image. Since only a few datasets with image attribute annotations exist, this work proposes a method to generate image attribute annotations. For a dataset that contains attribute annotations for classes, image attribute annotations are created with the help of a vision-language (VL) model. To validate if current zero-shot learners are really capable of zero-shot, an occurrence analysis is suggested. The recently released LAION-400M data corpus and the publicly available OpenCLIP model trained on LAION-400M are leveraged and ImageNet, AWA2, and CUB are used as evaluation datasets. For each class of an evaluation dataset, the training samples corresponding to the evaluation class are counted and the correlation between the model performance and class occurrences in the training data is provided. Using the assigned image attributes for the AWA2 and the CUB dataset, the proposed benchmark is applied to three publicly available VL models (OpenCLIP, CLIP, FLAVA). With a different prompt generation technique, the five experiment setups are repeated for AWA2 which leads to similar results and indicates their robustness. The provided analysis shows that: • Most of the classes in popular zero-shot benchmarks are observed (a lot) during pretraining. • Zero-shot performance mainly comes out of models’ capability of recognizing class labels whenever they are present in the text, and a significantly lower performing capability of attribute-based zero-shot learning is only observed when class labels are not used. • The number of attributes used can have a significant effect on performance, and can easily cause a significant performance decrease.", "source_url": "https:\/\/openalex.org\/W7112135146", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt)", "source_record_id": "W7112135146", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 405, "title": "Zero- And Few-Shot Dark Kinase–Phosphosite Prediction Via Task-Aware Protein Embeddings", "title_ar": "", "title_en": "Zero- And Few-Shot Dark Kinase–Phosphosite Prediction Via Task-Aware Protein Embeddings", "author": "Zeynep Işık", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; medical-ai; transfer-learning; few-zero-shot; predictive-analytics; transformers; llm", "keywords": "", "abstract": "Accurately mapping kinases to their substrate phosphosites is fundamental fordecoding cellular signaling and understanding disease mechanisms. While highthroughputtechniques can identify the phosphosites, finding the kinase that catalyzesthe phosphorylation is challenging. Thus, over 95% of experimentally detectedhuman phosphosites lack kinase annotations. It is possible to formulatethe kinase-phosphosite association problem as a supervised multi-class classificationtask; however, a large portion of the human kinases are under-studied (darkkinases) and have few or no phosphosites associated with them, thus dark kinasesfall outside the reach of conventional supervised learning methods. In this thesis, weformulate kinase–phosphosite association as zero-shot and few-shot learning tasks:in the zero-shot setting, the model must predict associations for kinases never seenduring training; in the few-shot setting, it may leverage only a handful of labeledexamples.We employ transformer-based protein language models (pLMs) to embed both kinasedomains and phosphosite peptides, and we systematically explore domainadaptationstrategies—ranging from full fine-tuning and partial layer re-initializationto task-specific pre-training—under severe data constraints. Surprisingly, a denovo–trained ESM-1b model outperforms its fully fine-tuned pretrained counterpart,suggesting that general-purpose pLM embeddings may lack task-specific biochemicalcontext. Our best results are obtained by combining kinase- and phosphosite-aware pLMs with partial re-initialization of upper transformer layers. On the DARKINbenchmark, this approach delivers state-of-the-art performance in both zero-shotand few-shot kinase prediction, offering a promising direction for illuminating thedark phosphoproteome.", "source_url": "https:\/\/openalex.org\/W7124521015", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Sabanci University", "source_record_id": "W7124521015", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 406, "title": "LLMs for Structured Educational Data: A Zero and Few-Shot Approach to Student Performance Prediction", "title_ar": "", "title_en": "LLMs for Structured Educational Data: A Zero and Few-Shot Approach to Student Performance Prediction", "author": "Karandeep Singh Mann", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "National College of Ireland", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; responsible-ai; ai-applications; transfer-learning; few-zero-shot; predictive-analytics; llm; xai; ai-education", "keywords": "", "abstract": "Early and accurate prediction of student performance is crucial for enabling timely academic support to the at-risk students. The past work have used machine learning and deep learning approaches on educational datasets such as OULAD and achieved strong results for prediction of student outcomes. But they relied on feature engineering, required retraining and did not offered any interpretability. This study tries to check whether large language models (LLMs) can be used to overcome these limitations by using zero and few-shot prompting strategies on structured tabular datasets. Evaluation has been done on three open-source LLMs across binary, multiclass and per-class settings. The models reached up to accuracies of 89.6% and 75.9% on binary and multi-class predictions respectively, all without the need for fine-tuning. They even produced short natural language explanations to describe why a particular prediction was made. The findings show that LLMs can not only become an alternative to the traditional prediction models but can also guide educators to make informed decisions through their explanation based insights.", "source_url": "https:\/\/openalex.org\/W7163607681", "pdf_url": "https:\/\/norma.ncirl.ie\/9331\/2\/karandeepsinghmannconfigurationmanual.pdf", "doi": "", "handle": "", "repository_name": "TRAP@NCI (National College of Ireland)", "source_record_id": "W7163607681", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 408, "title": "Inductive and Transductive Learning with Vision-Language Models in Low-Shot Regimes", "title_ar": "", "title_en": "Inductive and Transductive Learning with Vision-Language Models in Low-Shot Regimes", "author": "Maxime Zanella", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Belgium", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; transfer-learning; few-zero-shot; predictive-analytics; multimodal; llm", "keywords": "", "abstract": "Vision-Language Models (VLMs) have revolutionized open-vocabulary recognition, yet adapting them to specialized domains under data-scarce conditions remains a significant challenge. This thesis investigates efficient strategies to bridge the gap between inductive fine-tuning and transductive inference, focusing on minimizing supervision and computational costs. We first address inductive few-shot learning with CLIP-LoRA, a parameter-efficient fine-tuning method. By injecting trainable low-rank matrices into the encoder, it outperforms existing approaches while eliminating dataset-specific hyperparameter tuning. For scenarios restricted to single-sample inference, we propose MTA (MeanShift for Test-time Augmentation). Formulated as a robust variation of the MeanShift algorithm, this training-free method refines predictions through iterative mode-seeking and automatic importance weighting of augmented views. Expanding into the transductive paradigm, we introduce TransCLIP. This framework explicitly models the underlying structure of unlabeled test batches by fitting a Gaussian Mixture Model (GMM), constrained by a text-driven Kullback-Leibler (KL) regularization term. This dual objective dynamically aligns visual statistics with textual priors, achieving substantial gains in both zero-shot and few-shot settings. Finally, to address realistic deployment challenges, we propose StatA (Statistical Anchor). We demonstrate that existing transductive methods fail under non-i.i.d. data streams and introduce a regularization technique based on the KL divergence with respect to initial text priors. Collectively, this work provides a suite of transparent, low-compute algorithms that, through the use of fixed hyperparameters, ensure operational simplicity and robust performance across challenging and diverse benchmarks.", "source_url": "https:\/\/openalex.org\/W7134152075", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B))", "source_record_id": "W7134152075", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 409, "title": "Zero\/Few-Shot Dark Kinase-Phosphosite Associatıon Predictıon With Biologically Grounded Data Augmentation", "title_ar": "", "title_en": "Zero\/Few-Shot Dark Kinase-Phosphosite Associatıon Predictıon With Biologically Grounded Data Augmentation", "author": "Mert Pekey", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; few-zero-shot; predictive-analytics", "keywords": "", "abstract": "Protein phosphorylation, a fundamental cellular process mediated by kinases, iscrucial for signaling, and its dysregulation is implicated in numerous human diseases.A significant challenge persists in identifying substrate phosphosites for the vastnumber of understudied ’dark’ kinases, for which conventional supervised machinelearning methods are ineffective due to data scarcity. To address this gap, thisthesis develops a zero- and few-shot learning framework and introduces biologicallygrounded data augmentation strategies, all evaluated on the DARKIN benchmark.We introduce two novel deep learning architectures: DARKIN-FT, a compatibilitybasedmodel that enhances performance through end-to-end fine-tuning of phosphositeencoder, and DARKIN-Interact, a binary classification model that directlycaptures kinase–substrate interactions via joint attention over sequence pairs. Thecentral contribution is a systematic investigation into biologically grounded dataaugmentation, evaluating three distinct strategies: (i) kinase-conditional phosphositegeneration via a fine-tuned ProGen2 model, (ii) weak supervision using predictionsfrom the Kinase Substrate Specificity Atlas (KSSA), and (iii) augmentationwith homologous sequences. Our results demonstrate that DARKIN-FT and DARKIN-Interact significantly outperformexisting baselines on the DARKIN benchmark. The investigation intodata augmentation yielded mixed results: while kinase conditional generation withProGen2 and weak labeling with KSSA degraded the performance, augmentationwith homologous sequences improved the Macro Average Precision of the DARKINInteractmodel. While the results are promising, challenges persist in disambiguatingkinases with high sequence similarity.Overall, this thesis establishes a framework for kinase–phosphosite interaction predictionin low-data regimes and provides valuable insights into the strengths andlimitations of data augmentation in the dark kinase-phosphosite association task.", "source_url": "https:\/\/openalex.org\/W7124522292", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Sabanci University", "source_record_id": "W7124522292", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 410, "title": "Learning from limited labeled data - Zero-Shot and Few-Shot Learning", "title_ar": "", "title_en": "Learning from limited labeled data - Zero-Shot and Few-Shot Learning", "author": "Yongqin Xian", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "few-zero-shot", "categories": "machine-learning; few-zero-shot", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3106090562", "pdf_url": "", "doi": "10.22028\/d291-32390", "handle": "", "repository_name": "MPG.PuRe (Max Planck Society)", "source_record_id": "W3106090562", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 411, "title": "Deep Zero- and Few-shot Learning in Computer Vision", "title_ar": "", "title_en": "Deep Zero- and Few-shot Learning in Computer Vision", "author": "Hongguang Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Australian National University", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; transfer-learning; few-zero-shot; cnn; object-detection; video-analysis; gan", "keywords": "", "abstract": "Most CNN models rely on the large-scale annotated training data, and the performance turns to be low when the size of training data is limited, therefore numbers of large-scale datasets are proposed for vision tasks. Though training on large-scale dataset can significantly improve the performance, creating such datasets for novel scenarios is costly. Moreover, relying on the number of samples is not consistent with human's learning abilities, as humans have the ability to understand novel concepts from limited examples. Transfer learning is a machine learning topic that addresses the above limitations in current deep models, and it studies how to make machines exploit the acquired knowledge to solve new problems, thus learning by generalizing from existing to new concepts, a trait akin to humans. Empirically, transfer learning for computer vision problems includes the following subtopics: i) zero-shot learning, ii) few-shot learning, and iii) domain adaptation. In this thesis, we study zero-shot and few-shot learning to demonstrate how to learn better from limited training samples. Zero-shot learning requires learning a mapping that associates feature vectors extracted from images with semantic annotations that describe objects and\/or scenes of interest. Our first work addresses the weakness of linear mapping in previous zero-shot learning models by learning via non-linear kernelized projections between features and attribute spaces to promote accuracy. We propose an easy learning objective inspired by the Linear Discriminant Analysis method with an incoherence mechanism. The dominant challenge in the generalized zero-shot setting is the unbalanced data distribution during training and testing, which makes it difficult for the classifier to distinguish if a testing sample is from a seen or unseen class. Inspired by GAN-based models, we propose the model selection mechanism, in which we leverage two sources of datapoints to train the model selection classifier to recognize which test datapoints come from seen and which from unseen classes. This way, generalized zero-shot learning can be decoupled into two disjoint classification tasks. Few-shot learning is a variant of knowledge transfer which is formulated as a meta-learning process. Firstly we investigate so-called second-order pooling and Power Normalizations in fine-grained classification tasks to study a proper way of using co-occurrence statistics for such tasks. Following above experiments and analysis, we propose the novel second-order similarity network, which measures image relations via co-occurrence relation descriptors, and significantly improves the performance of few-shot learning. Hallucinating auxiliary samples for training is also a popular and effective way to improve the accuracy of few-shot learning. To this end, we propose a saliency-guided hallucination strategy, which employs a saliency detector to segment foregrounds and backgrounds from sampled images forming an episode, then we mix every foreground with all available backgrounds in convolutional feature space to synthesize descriptors representing objects in various scenes. Furthermore, we note that previous few-shot learning methods fail to address the scale- and location-mismatch between support-query objects, thus leading to the performance loss. Inspired by the spatial-pyramid matching, we propose a novel spatial- and scale-matching network to explicitly match the support-query pairs over different scales and locations. Last but not least, we consider a more challenging scenario, namely few-shot action recognition, to study knowledge transfer in the video domain. Apart from the spatial information, robust aggregation over the temporal mode is fundamental in this task. To address this issue, we propose a novel action relation network with permutation-invariant attentions and auxiliary self-supervision tasks.", "source_url": "https:\/\/openalex.org\/W3112219073", "pdf_url": "", "doi": "10.25911\/5f5a020a5c26f", "handle": "", "repository_name": "ANU Open Research (Australian National University)", "source_record_id": "W3112219073", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 412, "title": "Zero-shot Learning: Recognition, Tagging, and Detection of Novel Concepts", "title_ar": "", "title_en": "Zero-shot Learning: Recognition, Tagging, and Detection of Novel Concepts", "author": "Shafin Rahman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Australian National University", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; few-zero-shot; predictive-analytics; cnn; object-detection; medical-imaging; fairness", "keywords": "", "abstract": "Recent advancements in deep neural networks have performed favourably well on the supervised object recognition task. Towards an ultimate automated visual recognition system, we identify three key shortcomings of the existing supervised learning approaches. First, the dependency on a significantly large volume of manually annotated examples (e.g., ImageNet dataset with ~10 million images) limits the scalability of deep networks. Second, once the model learning stage is complete, it is difficult to add new classes continually when new data becomes available. Lastly, such models lack the notion of human-like understanding, i.e., an object can be recognized by humans without having any visual examples and just by a semantic description of its distinctive characteristics. In this thesis, we investigate the zero-shot learning (ZSL) framework to address these limitations. ZSL aims to perform reasoning about previously unseen objects without observing even a single instance of them. Such a learning paradigm requires no visual examples of novel objects, no re-training to add new classes and does not rely solely on visual information. Considering the relationship among the semantic description of previously seen examples, ZSL incorporates human wisdom to the visual understanding developed by a machine. In this work, we specifically address three critical bottlenecks in ZSL research which give rise to three ZSL problem settings: (a) unified zero-shot recognition (ZSR), (b) zero-shot tagging (ZST), and (c) zero-shot detection (ZSD) of novel concepts. Established ZSR methods are not flexible enough to adapt to one\/few-shot learning (O\/FSL) scenario where one\/few labeled examples of unseen classes become available during the supervised learning stage. To provide a comprehensive and flexible solution, we present a novel 'unified' approach for ZSL and O\/FSL based on class adapting principal direction (CAPD) that computes class-specific discriminative information by relating the semantic description of categories. The primary objective is to learn a metric in the semantic embedding space that minimizes intra-class distances and maximizes inter-class distances. In the real-life scenario, instead of a single object per image, a scene may contain multiple seen and unseen concepts together. To adopt this, we present the Deep0Tag approach for zero-shot tagging (ZST) to assign multiple labels to an input. This method considers both global and local details to discover seen or unseen concepts from a given scene. We solve this problem by formulating a multiple instance learning (MIL) framework. Unlike traditional MIL solutions, our method runs end-to-end without using offline object proposal generation methods. While most of the ZSL methods provide answers to unseen categories in simple tasks, e.g., single or multi-label classification and retrieval, we also focus on predicting both multi-class category-label and precise location of each instance in a given image. To this end, we introduce a new challenge for ZSL called zero-shot detection (ZSD) that simultaneously recognizes and localizes multiple novel concepts. Similar to traditional object detection, we present zero-shot version of double (Faster R-CNN) and single (RetinaNet) stage end-to-end object detectors. For both of the cases, we design associated loss functions that consider visual-semantic relationships to train the network. In addition to inductive learning approaches, we also propose the first transductive learning method for ZSD to reduce the domain-shift and model-bias against unseen classes convincingly. Our transductive approach follows a self-learning mechanism that uses a novel hybrid pseudo-labeling technique. Finally, we recommend training and testing protocols to evaluate ZSD based on large-scale ILSVRC-2017 and MSCOCO-2014 datasets.", "source_url": "https:\/\/openalex.org\/W3036608216", "pdf_url": "", "doi": "10.25911\/5ee74e8179e71", "handle": "", "repository_name": "ANU Open Research (Australian National University)", "source_record_id": "W3036608216", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 413, "title": "Zero-shot Image Classification", "title_ar": "", "title_en": "Zero-shot Image Classification", "author": "Yang Long", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; computer-vision; generative-ai; few-zero-shot; image-classification", "keywords": "", "abstract": "Image classification is one of the essential tasks for the intelligent visual system. Conventional image classification techniques rely on a large number of labelled images for supervised learning, which requires expensive human annotations. Towards real intelligent systems, a more favourable way is to teach the machine how to make classification using prior knowledge like humans. For example, a palaeontologist could recognise an extinct species purely based on the textual descriptions. To this end, Zero-Shot Image Classification (ZIC) is proposed, which aims to make machines that can learn to classify unseen images like humans. The problem can be viewed from two different levels. Low-level technical issues are concerned by the general Zero-shot Learning (ZSL) problem which considers how to train a classifier on the unseen visual domain using prior knowledge. High-level issues incorporate how to design and organise visual knowledge representation to construct a systematic ontology that could be an ultimate knowledge base for machines to learn. This thesis aims to provide a thorough study of the ZIC problem, regarding models, challenges, possible applications, etc. Besides, each main chapter demonstrates an innovative contribution that is creatively made during my study. The first is to solve the problem of Visual-Semantic Ambiguity. Namely, the same semantic concepts (e.g. attributes) can refer to a huge variety of visual features, and vice versa. Conventional ZSL methods usually adopt a one-way embedding that maps such high-variance visual features into the semantic space, which may lead to degraded performance. As a solution, a dual-graph regularised embedding algorithm named Visual-Semantic Ambiguity Removal (VSAR) is proposed, which can capture the intrinsic local structure of both visual and semantic spaces. In the intermediate embedding space, the structural difference is reconciled to remove the ambiguity. The second contribution aims to circumvent costly visual data collection for conventional supervised classification using ZSL techniques. The key idea is to synthesise visual features from the semantic information, just like humans can imagine features of an unseen class from the semantic description of prior knowledge. Hereafter, new objects from unseen classes can be classified in a conventional supervised framework using the inferred visual features. To overcome the correlation problem, we propose an intermediate Orthogonal Semantic-Visual Embedding (OSVE) space to remove the correlated redundancy. The proposed method achieves promising performance on fine-grained datasets. In the third contribution, the graph constraint of VSAR is incorporated to synthesise improved visual features. The orthogonal embedding is reconsidered as an Information Diffusion problem. Through an orthogonal rotation, the synthesised visual features become more discriminative. On four benchmarks, the proposed method demonstrates the advantages of synthesised visual features, which significantly outperforms state-of-the-art results. Since most of ZSL approaches highly rely on expensive attributes, the fourth contribution of this thesis explores a more feasible but more effective Semantic Simile model to describe unseen classes. From a group of similes, e.g. an unknown animal has the same parts of a wolf, and the colour looks like a bobcat, implicit attributes are discovered by a graph-cut algorithm. Comprehensive experimental results suggest the simile-based implicit attributes can significantly boost the performance. To maximumly reduce the cost of building ontologies for ZIC, the final chapter introduces a novel scheme, using which ZIC can be achieved by only a few similes of each unseen class. No annotations of seen classes are needed. Such an approach finally sets ZIC attribute-free, which significantly improve the feasibility of ZIC. Unseen classes can be recognised using a conventional setting without expensive attribute ontology. It can be concluded that the methods introduced in this thesis provide fundamental components of a zero-shot image classification system. The thesis also points out four core directions for future ZIC research.", "source_url": "https:\/\/openalex.org\/W2770447235", "pdf_url": "", "doi": "", "handle": "", "repository_name": "White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)", "source_record_id": "W2770447235", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 414, "title": "Deepkinzero: zero-shot learning for predicting kinase phosphorylation sites", "title_ar": "", "title_en": "Deepkinzero: zero-shot learning for predicting kinase phosphorylation sites", "author": "Iman Deznabi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Bilkent University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; medical-ai; few-zero-shot; predictive-analytics; rnn", "keywords": "", "abstract": "Protein kinases are a large family of enzymes that catalyze the phosphorylation of other proteins. By acting as molecular switches for protein activity, the phosphorylation events regulate intracellular signal transduction, thereby assuming a central role in a broad range of cellular activities. On the other hand, aberrant kinase function is implicated in many diseases. Understanding the normal and malfunctioning signaling in the cell entails the identification of phosphorylation sites and the characterization of their interactions with kinases. Recent advances in mass spectrometry enable rapid identification of phosphosites at the proteome level. Alternatively, there are many computational models that predict phosphosites in a given input protein sequence. Once a phosphosite is identified, either experimentally or computationally, knowing which kinase would catalyze the phosphorylation on this particular site becomes the next question. Although a subset of available computational methods provides kinase-specific predictions for phosphorylation sites, due to the need for training data in such supervised methods, these tools can provide predictions only for kinases for which a substantial number of the phosphosites are already known. A particular problem that has not received any attention is the prediction of new sites for kinases with few or no a priori known sites. None of the current computational methods which rely on the classical supervised learning settings can predict additional sites for this kinases. We present DeepKinZero, the first zero-shot learning approach, that can predict phosphosites for kinases with no known phosphosite information. DeepKinZero takes a peptide sequence centered at the phosphorylation site and learns the embeddings of these phosphosite sequences via a bi-directional recurrent neural network, whereas kinase embeddings are based on protein sequence vector representations and the taxonomy of kinases based on their functional properties. Through a compatibility function that associates the representations of the site sequences and the kinases, DeepKinZero transfers knowledge from kinases with many known sites to those kinases with no known sites. Our computational experiments show that DeepKinZero achieves a 30-fold increase in accuracy compared to baseline models. DeepKinZero complements existing approaches by expanding the knowledge of kinases through mapping of the phosphorylation sites pertaining to understudied kinases with no prior information, which are increasingly investigated as novel drug targets.", "source_url": "https:\/\/openalex.org\/W2930422768", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Bilkent University Institutional Repository (Bilkent University)", "source_record_id": "W2930422768", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 415, "title": "Meta Approaches to Few-shot Image Classification", "title_ar": "", "title_en": "Meta Approaches to Few-shot Image Classification", "author": "Arkabandhu Chowdhury", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rice University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; transfer-learning; few-zero-shot; cnn; image-classification; fairness; ai-smart-cities", "keywords": "", "abstract": "Since the inception of deep Convolution Neural Network (CNN) architectures, we have seen a tremendous advancement in machine image classification. However, these methods require a large amount of data, sometimes in the order of millions, but often fail to generalize when the data set is small. And so, recently a new paradigm, called `Few-Shot Learning', has been developed to tackle this problem. Essentially, the goal of few-shot learning is to develop techniques that can rapidly generalize to new tasks containing very few samples-- in extreme cases one (called one-shot) or zero (called zero-shot)-- with labels. In this work, I will be particularly tackling few-shot learning in the application of image classification. The most common approach to it is known as Meta-learning or `learning to learn' where, rather than learning to solve a particular learning problem, the goal is to solve many learning problems in an attempt to learn how to learn to solve a particular type of problem. Another way to solve the problem is to re-purpose an existing learner for a new learning problem, known as Transfer learning. In my thesis, I propose two novel approaches, based on meta-learning and transfer learning, to tackle the few-shot (or one-shot) image classification. The first approach I propose is called meta-meta classification, where one uses a large set of learning problems to design an ensemble of learners, each of which has high bias and low variance and is skilled at solving a specific type of learning problems. The meta-meta classifier learns how to examine a given learning problem and combine the various learners to solve the problem. One type of image classification is the one-vs-all (OvA) classification problem, where only one image from the positive class is available for training along with images from a number of negative classes. I evaluate my approach on a one-shot, one-class-versus-all classification task and show that it is able to outperform traditional meta-learning as well as ensembling approaches. I evaluate my method using the popular 1,000 class Imagenet data (ILSVRC2012), the 200 class Caltech-UCSD Birds dataset, the 102 class FGVC-Aircraft dataset, and the 1,200 class Omniglot hand-written character dataset. I compare my results with a popular meta-learning algorithm, called model-agnostic meta learner (MAML), as well as an ensemble of multiple MAML models, and show my approach is able to outperform them in all the problems. The second approach we investigate uses the existing concept of transfer learning, where a simple Multi-Layer Perceptron (MLP) with a hidden layer is fine-tuned on top of pre-trained CNN backbones. Surprisingly, there have been very few works in the few-shot literature that have even examined the use of an MLP for fine-tuning pre-trained models (the assumption may be be that a hidden layer would provide too many parameters for few-shot learning). In order to avoid overfitting, we simply use an L2-regularizer. We argue that a diverse feature vector made of a variety of pre-trained libraries of models trained on a diverese dataset (such as, ILSVRC2012) is sufficiently capable of being re-purposed for small-data problems. We performed a series of experiments on both classification accuracy and feature behavior on multiple few-shot problems. We carefully picked the hyperparameters after validating on Caltech-UCSD Bird dataset and did our final evaluation on FGVC-Aircraft, FC100, Omniglot, Traffic Sign, FGCVx Fungi , QuickDraw, and VGG Flower datasets. Our experimental results showed significantly better performance compared to some baselines, such as, simple ensembling, standalone best model, as well as, some other competitive meta-learning techniques.", "source_url": "https:\/\/openalex.org\/W7203509209", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Rice Research Repository (Rice University)", "source_record_id": "W7203509209", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 416, "title": "Zero-shot learning to execute tasks with robots", "title_ar": "", "title_en": "Zero-shot learning to execute tasks with robots", "author": "Julian Alverio", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Massachusetts Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "few-zero-shot", "categories": "machine-learning; robotics; few-zero-shot", "keywords": "", "abstract": "Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020", "source_url": "https:\/\/openalex.org\/W3187004358", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace@MIT (Massachusetts Institute of Technology)", "source_record_id": "W3187004358", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 418, "title": "Low-shot Learning based on Pre-trained Model", "title_ar": "", "title_en": "Low-shot Learning based on Pre-trained Model", "author": "Ziqin Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Adelaide", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; transfer-learning; few-zero-shot; self-semi-supervised; cnn; transformers; multimodal; image-classification; image-segmentation; fairness", "keywords": "", "abstract": "In the field of computer vision, deep learning has demonstrated remarkable accomplishments. However, the dependence on extensive annotated datasets poses significant challenges, particularly when such data is scarce, costly, or ethically sensitive. This thesis addresses the crucial issue of low-shot learning by aiming to equip learning models with the capability to generalize effectively from limited examples. It explores various low-supervision methodologies, including self-supervised, unsupervised, semi-supervised, and active learning, with the goal of reducing the reliance on labeled data. One potential solution to this challenge is leveraging pre-trained models trained on large-scale datasets and transferring them to downstream tasks and unknown classes. Nevertheless, directly applying pre-trained models in a frozen state often fails to meet the requirements of specific tasks. Fine-tuning these models on base classes with limited training samples and then transferring them to novel domains presents obstacles, as it may disrupt the general knowledge encoded in the model, leading to overfitting on the base classes and suboptimal performance on unseen categories. The central focus of this research is to fully exploit the knowledge encoded in large pre-trained models and enhance their capacity to transfer to novel classes. This thesis introduces a systematic approach to balance generalization and task-specific adaptation, ensuring that fine-tuning preserves pre-trained knowledge while effectively adapting to new data. These models encapsulate extensive knowledge from diverse data sources, enabling swift adaptation to new tasks and classes with minimal additional training. The downstream tasks investigated in this doctoral work range from the low-shot problem for few-shot image classification to more intricate endeavors such as zero-shot and few-shot pixel-level semantic segmentation. The exploration encompasses various pre-trained model architectures, including convolutional and transformer-based models, spanning both pure vision and cross-modal vision-language paradigms. By harnessing the representational capabilities of state-of-the-art pre-trained models, this research aims to push the boundaries of what is achievable with limited data. The novel methodologies proposed strive to maximize performance, maintain generalization across domains, and improve inference efficiency, paving the way for broader adoption in low-data scenarios.", "source_url": "https:\/\/openalex.org\/W7216837125", "pdf_url": "https:\/\/digital.library.adelaide.edu.au\/server\/api\/core\/bitstreams\/c8d7f0de-908a-4f17-b259-cc6fb0be75e4\/content", "doi": "", "handle": "", "repository_name": "Adelaide Research & Scholarship (AR&S) (University of Adelaide)", "source_record_id": "W7216837125", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 419, "title": "On Zero-Shot Reinforcement Learning", "title_ar": "", "title_en": "On Zero-Shot Reinforcement Learning", "author": "Scott Jeen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "few-zero-shot", "categories": "machine-learning; reinforcement-learning; few-zero-shot", "keywords": "", "abstract": "Modern reinforcement learning (RL) systems capture deep truths about general, human problem-solving. In domains where new data can be simulated cheaply, these systems uncover sequential decision-making policies that far exceed the ability of any human. Society faces many problems whose solutions require this skill, but they are often in domains where new data cannot be cheaply simulated. In such scenarios, we can learn simulators from existing data, but these will only ever be approximately correct, and can be pathologically incorrect when queried outside of their training distribution. As a result, a misalignment between the environments in which we train our agents and the real-world in which we wish to deploy our agents is inevitable. Dealing with this misalignment is the primary concern of zero-shot reinforcement learning, a problem setting where the agent must generalise to a new task or domain with zero practice shots. Whilst impressive progress has been made on methods that perform zero-shot RL in idealised settings, new work is needed if these results are to be replicated in real-world settings. In this thesis, we argue that doing so requires us to navigate (at least) three constraints. First, the data quality constraint: real-world datasets are small and homogeneous. Second, the observability constraint: states, dynamics and rewards in the real-world are often only partially observed. And third, the data availability constraint: a priori access to data cannot always be assumed. This work proposes a suite of methods that perform zero-shot RL subject to these constraints. In a series of empirical studies we expose the failings of existing methods, and justify our techniques for remedying them. We believe these designs take us a step closer to RL methods that can be deployed to solve real-world problems.", "source_url": "https:\/\/openalex.org\/W4417060277", "pdf_url": "https:\/\/arxiv.org\/pdf\/2508.16496", "doi": "10.17863\/cam.121637", "handle": "", "repository_name": "arXiv (Cornell University)", "source_record_id": "W4417060277", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 420, "title": "Zero Shot Learning for Visual Object Recognition with Generative Models", "title_ar": "", "title_en": "Zero Shot Learning for Visual Object Recognition with Generative Models", "author": "Maunil R. Vyas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Arizona State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "few-zero-shot", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; ai-applications; few-zero-shot; ai-education", "keywords": "", "abstract": "abstract: Visual object recognition has achieved great success with advancements in deep learning technologies. Notably, the existing recognition models have gained human-level performance on many of the recognition tasks. However, these models are data hungry, and their performance is constrained by the amount of training data. Inspired by the human ability to recognize object categories based on textual descriptions of objects and previous visual knowledge, the research community has extensively pursued the area of zero-shot learning. In this area of research, machine vision models are trained to recognize object categories that are not observed during the training process. Zero-shot learning models leverage textual information to transfer visual knowledge from seen object categories in order to recognize unseen object categories. Generative models have recently gained popularity as they synthesize unseen visual features and convert zero-shot learning into a classical supervised learning problem. These generative models are trained using seen classes and are expected to implicitly transfer the knowledge from seen to unseen classes. However, their performance is stymied by overfitting towards seen classes, which leads to substandard performance in generalized zero-shot learning. To address this concern, this dissertation proposes a novel generative model that leverages the semantic relationship between seen and unseen categories and explicitly performs knowledge transfer from seen categories to unseen categories. Experiments were conducted on several benchmark datasets to demonstrate the efficacy of the proposed model for both zero-shot learning and generalized zero-shot learning. The dissertation also provides a unique Student-Teacher based generative model for zero-shot learning and concludes with future research directions in this area.", "source_url": "https:\/\/openalex.org\/W3033566688", "pdf_url": "https:\/\/d1rbsgppyrdqq4.cloudfront.net\/s3fs-public\/c7\/224428\/Vyas_asu_0010N_19723.pdf?versionId=9.mNojbapvXyDdAkYDVdACBOPX.DAQKJ&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ477LUGVF2%2F20260320%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20260320T113737Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=047adb58111dabff5588f6b4baa44d70b2fb05ab9cab0058f3017db023743c2c", "doi": "", "handle": "", "repository_name": "Arizona State University Library Digital Repository (Arizona State University)", "source_record_id": "W3033566688", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 421, "title": "Sensor-based human activity recognition via zero-shot learning", "title_ar": "", "title_en": "Sensor-based human activity recognition via zero-shot learning", "author": "Wei Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "few-zero-shot", "categories": "machine-learning; few-zero-shot", "keywords": "", "abstract": "Sensor-based activity recognition aims to recognize human activities from the sensor readings. In recent years, with the advance of the sensor technology and the development of the machine learning algorithms, it obtains much progress. Activity recognition is a classification problem. Thus, existing works on sensor-based activity recognition are generally based on supervised-classification algorithms, such as SVM, HMM, CRF, kNN, and decision tree. In these works, for each class to recognize, sufficient labeled training instances belonging to this class are needed. In practical applications, the process of obtaining labeled sensor readings is time consuming and costly. The activity classes having sufficient sensor readings are generally classes predefined during data collecting-and-labeling in the model learning phase. However, in many applications, the activities needed to recognize could contain not only the predefined classes, but also some previously unseen classes which do not have labeled training instances during model learning. In this thesis, we focus on sensor-based activity recognition including the unseen classes. Under this problem setting, the training instances belong to a set of activity classes (referred to as the seen classes), while the testing instances can come from the previously unseen activity classes (referred to as the unseen classes). For problems under this problem setting, as there are no labeled training instances belonging to the unseen classes, the zero-shot learning methods are used. We focus on three problems under this setting. Firstly, we study the problem of unseen activity recognition for one person. In this problem, the classes covered by the training and the testing instances are disjoint. We propose a method, referred to as Nonlinear Compatibility Based Method (NCBM), to address this problem. In our method, we adopt the binary attribute space as the semantic space, and use it to involve the semantic information. Then, we learn a compatibility function between the feature space and the semantic space. The recognition of the testing instances is achieved with the learned compatibility function and the unseen-class-prototypes in the semantic space. We conduct extensive experiments, and the experimental results show the effectiveness of our method. Secondly, we study the problem of activity recognition across smart homes with different binary sensors and label spaces. We propose a method, referred to as Binary Sensor Semantic and Time Information Method (BSST), to address this problem. In this problem, the deployed binary sensor networks and the activities needed to recognize in different smart homes are different. In our method, we utilize the characteristics of the binary sensors, the semantic information about the sensors and the activities, and the time information. Sensor readings in different smart homes are extracted into a common feature space, and each activity class has a defined prototype in the semantic space. Then, we learn a classification model in one smart home environment and use it to perform activity recognition in a new smart home environment. During model learning, no information about the new smart home environment is needed. The experimental results show the effectiveness of our method. Lastly, we study the problem of multi-resident activity recognition including unseen classes in smart homes. We propose a method, referred to as Multi-Resident with Unseen-Activity-Class Recognition Method (MRUA), to address this problem. In our method, the activity recognition for each resident is regarded as a learning task, and all tasks are learned jointly with multi-task learning techniques. The zero-shot learning techniques are also adopted. Thus, the unseen classes can be recognized. Through extensive experiments, we show the effectiveness of our method.", "source_url": "https:\/\/openalex.org\/W2982472831", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/104645\/1\/thesis_wangwei.pdf", "doi": "10.32657\/10356\/104645", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2982472831", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 422, "title": "Domain-Aware Continual Zero-Shot Learning", "title_ar": "", "title_en": "Domain-Aware Continual Zero-Shot Learning", "author": "Kai Yi ; Paul Janson ; Wenxuan Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "few-zero-shot", "categories": "machine-learning; computer-vision; few-zero-shot; predictive-analytics", "keywords": "", "abstract": "Modern visual systems have a wide range of potential applications in vision tasks for natural science research, such as aiding in species discovery, monitoring animals in the wild, and so on. However, real-world vision tasks may experience changes in environmental conditions, leading to shifts in how captured images are presented. To address this issue, we introduce Domain-Aware Continual Zero-Shot Learning (DACZSL), a task to recognize images of unseen categories in continuously changing domains. Accordingly, we propose a Domain-Invariant Network (DIN) to learn factorized features for shifting domains and improved textual representation for unseen classes. DIN continually learns a global shared network for domain-invariant and task-invariant features, and per-task private networks for task-specific features. Furthermore, we enhance the dual network with class-wise learnable prompts to improve class-level text representation, thereby improving zero-shot prediction of future unseen classes. To evaluate DACZSL, we introduce two benchmarks, DomainNet-CZSL and iWildCam-CZSL. Our results show that DIN significantly outperforms existing baselines by over 5% in harmonic accuracy and over 1% in backward transfer and achieves a new SoTA.", "source_url": "https:\/\/openalex.org\/W3217037607", "pdf_url": "https:\/\/arxiv.org\/pdf\/2112.12989", "doi": "10.25781\/kaust-d16h1", "handle": "", "repository_name": "arXiv (Cornell University)", "source_record_id": "W3217037607", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 423, "title": "Neural transfer learning for natural language processing", "title_ar": "", "title_en": "Neural transfer learning for natural language processing", "author": "Sebastian Ruder", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dublin Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; llm", "keywords": "", "abstract": "The current generation of neural network-based natural language processing models excels at learning from large amounts of labelled data. Given these capabilities, natural language processing is increasingly applied to new tasks, new domains, and new languages. Current models, however, are sensitive to noise and adversarial examples and prone to overfitting. This brittleness, together with the cost of attention, challenges the supervised learning paradigm. Transfer learning allows us to leverage knowledge acquired from related data in order to improve performance on a target task. Implicit transfer learning in the form of pretrained word representations has been a common component in natural language processing. In this dissertation, we argue that more explicit transfer learning is key to deal with the dearth of training data and to improve downstream performance of natural language processing models. We show experimental results transferring knowledge from related domains, tasks, and languages that support this hypothesis. We make several contributions to transfer learning for natural language processing: Firstly, we propose new methods to automatically select relevant data for supervised and unsupervised domain adaptation. Secondly, we propose two novel architectures that improve sharing in multi-task learning and outperform single-task learning as well as the state-of-the-art. Thirdly, we analyze the limitations of current models for unsupervised cross-lingual transfer and propose a method to mitigate them as well as a novel latent variable cross-lingual word embedding model. Finally, we propose a framework based on fine-tuning language models for sequential transfer learning and analyze the adaptation phase.", "source_url": "https:\/\/openalex.org\/W2979736514", "pdf_url": "", "doi": "10.13025\/17598", "handle": "", "repository_name": "Arrow@dit (Dublin Institute of Technology)", "source_record_id": "W2979736514", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 424, "title": "Natural language processing for resource-poor languages", "title_ar": "", "title_en": "Natural language processing for resource-poor languages", "author": "Long Duong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Melbourne", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2765462701", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Minerva Access (University of Melbourne)", "source_record_id": "W2765462701", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 425, "title": "Natural Language Processing Methods for Language Modeling", "title_ar": "", "title_en": "Natural Language Processing Methods for Language Modeling", "author": "Dávid Márk Nemeskey", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Hungary", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; transformers; image-segmentation; llm", "keywords": "", "abstract": "In this thesis we concentrated on two issues: how modern NLP (or traditional linguistic) techniques can improve language modeling, and how to improve the state of the art for Hungarian. Chapter 2 highlighted the problems of word-based language modeling for Hungarian and introduced the “gluten-free” format, a morphological segmentation algorithm that alleviates the adverse effects of the overabundance of word forms in the language. Chapter 3 proposed a novel method for evaluating multi-sense embeddings based on lexicographical resources. Chapter 5 gave an example for the opposite direction, when language models are used to improve the performance of an NLP system. We improved on the state of the art in Hungarian language modeling in several ways. First, we presented a set of language modeling benchmarks on three Hungarian corpora in Chapter 2. A preprocessed version of the Hungarian Webcorpus has been released to serve as a standard dataset for language model assessment. A new Hungarian corpus has been created in Chapter 4. Webcorpus 2.0 was compiled from Hungarian pages in the Common Crawl and the Hungarian Wikipedia. At 9 billion tokens, it is 3.5 times the size of the previous largest (commercial) corpus, and can serve as training data for large-scale language models. Finally, the emBERT module developed in Chapter 5 enables the integration of modern contextualized embedding-based classifiers into the e-magyar pipeline. Based on our preliminary Hungarian BERT model, the NP chunker outperforms the previous best system by 2.8% in F1 score. All resources (corpora, models and software alike) presented in the thesis are freely downloadable under permissive licenses.", "source_url": "https:\/\/openalex.org\/W3215499059", "pdf_url": "https:\/\/edit.elte.hu\/xmlui\/bitstream\/10831\/62063\/1\/Nemeskey_David_Ertekezes.pdf", "doi": "10.15476\/elte.2020.066", "handle": "", "repository_name": "Eötvös Loránd Tudományegyetem", "source_record_id": "W3215499059", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 426, "title": "Natural language processing for sentiment analysis", "title_ar": "", "title_en": "Natural language processing for sentiment analysis", "author": "Luke Camilleri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "nlp; sentiment-analysis", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3117751661", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3117751661", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 427, "title": "Natural language processing tools", "title_ar": "", "title_en": "Natural language processing tools", "author": "Андрій Олександрович Швидченко", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "1. Metinvest Digital. Що таке обробка природної мови (NLP) та як вона може використовуватися у бізнесі. URL: https:\/\/metinvest.digital\/ua\/page\/1052. 2. Wit.ai. Build Your First Wit App. URL: https:\/\/wit.ai\/docs\/quickstart", "source_url": "https:\/\/openalex.org\/W7197702553", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Electronic Institutional Repository of the National Aviation University of Ukraine (National Aviation University, Ukraine)", "source_record_id": "W7197702553", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 428, "title": "Negation in natural language processing", "title_ar": "", "title_en": "Negation in natural language processing", "author": "Hung Thinh Truong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Melbourne", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7199199210", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Minerva Access (University of Melbourne)", "source_record_id": "W7199199210", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 429, "title": "Natural language processing and information extraction", "title_ar": "", "title_en": "", "author": "Νικόλαος Στυλιανού", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "el", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "Το αντικείμενο της παρούσας διατριβής είναι η Επεξεργασία Φυσικής Γλώσσας και η Εξαγωγή Πληροφοριών από κείμενα. Η διατριβή ασχολείται με τα ακόλουθα ερευνητικά προβλήματα που αφορούν συγκεκριμένες εργασίες της Επεξεργασίας Φυσικής Γλώσσας και Εξαγωγής Πληροφοριών: α) βελτίωση της διαδικασίας λήψης κλινικών αποφάσεων μέσω της Αναγνώρισης Βιοϊατρικών Οντοτήτων, β) βελτιστοποίηση της Εξόρυξης Βιοϊατρικών Επιχειρημάτων, γ) αποδοτικότερη τεχνική Μοντελοποίησης Γλώσσας με χρήση απομακρυσμένων πληροφοριών, δ) ανάπτυξη εφαρμογών Επεξεργασίας Φυσικής Γλώσσας για επίλυση πραγματικών προβλημάτων. Αρχικά, παρουσιάζονται μια σειρά από καινοτόμες αρχιτεκτονικές για καλύτερη Αναγνώριση Βιοϊατρικών Οντοτήτων, στοχευμένες σε οντότητες Ιατρικής Βασισμένης στη Τεκμηρίωση. Αυτές οι σημασιολογικές οντότητες είναι πιο περιγραφικές από βιοϊατρικές οντότητες γενικών κατηγοριών, προσφέρουν χρήσιμες πληροφορίες κατά την δημιουργία σχεδίων θεραπείας καθώς και είναι πιο δύσκολο να αναγνωριστούν από μοντέλα Μηχανικής Μάθησης. Η αρχιτεκτονικές βαθιών νευρωνικών δικτύων που προτάθηκαν σκοπεύουν στη βελτίωση της απόδοσης της αναγνώρισης όλων των βιοϊατρικών οντοτήτων και παρέχουν πιο αποδοτική λύση στις εργασίες αυτές. Επιπλέον μελετάται η χρήση οντοτήτων Ιατρικής Βασισμένης στη Τεκμηρίωση για την εξαγωγή συμπερασμάτων από ιατρικές δημοσιεύσεις. Για την περαιτέρω βελτίωση της κλινικής πρακτικής, επεκτείνουμε τη προσέγγισή μας για εξαγωγή συμπερασμάτων για τη δημιουργία δομών συμπερασμάτων από ιατρικές δημοσιεύσεις. Παρουσιάζεται μία καινοτόμα προσέγγιση Εξόρυξης Βιοϊατρικών Επιχειρημάτων, η οποία χρησιμοποιεί οντότητες Ιατρικής Βασισμένης στη Τεκμηρίωση. Το τελικό σύστημα έχει αυξημένη απόδοση σε όλες της εργασίες της Εξόρυξης Επιχειρημάτων και δημιουργεί πιο περιγραφικές δομές συμπερασμάτων. Αξιοποιώντας τη σημασία των σημασιολογικών οντοτήτων, παρουσιάζουμε δύο μεθοδολογίες για την χρήση αναφορικών πληροφοριών στην Αναπαράσταση Γλώσσας. Συνδυάζοντας την Επίλυση Αναφορών με την Αναπαράσταση Γλώσσας, παρουσιάζουμε νέες αρχιτεκτονικές για την αποδοτική χρήση αναφορικών εκφράσεων και τη δημιουργία κρυμμένων αναπαραστάσεων των οντοτήτων που περιγράφονται. Τα τελικά μοντέλα αναπαράστασης γλώσσας έχουν καλύτερη απόδοση σε εργασίες Επεξεργασίας Φυσικής Γλώσσας, με μικρή αύξηση στην υπολογιστική πολυπλοκότητα. Τέλος, παρουσιάζονται μια σειρά από εφαρμογές εργασιών Επεξεργασίας Φυσικής Γλώσσας σε πραγματικά προβλήματα και μελετάται η πρόσθετη αξία των εφαρμογών αυτών. Οι εφαρμογές αυτές είναι, ένα σύστημα ανάλυσης συναισθημάτων σε πραγματικό χρόνο με τη χρήση τεχνολογιών εξαγωγής τοποθεσίας, μία δομή για την μετατροπή μεγάλων αποθετηρίων εγγράφων σε Γράφους Γνώσης και μία πλατφόρμα εφαρμογής αλγορίθμων Μηχανιης Μάθησης, ανοιχτού κώδικα, για την διασύνδεση της ερευνητικής κοινότητας και της ελεύθερης αγοράς.", "source_url": "https:\/\/openalex.org\/W3210355976", "pdf_url": "", "doi": "10.12681\/eadd\/50236", "handle": "", "repository_name": "", "source_record_id": "W3210355976", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 431, "title": "Natural Language Processing in de menselijke hersenen", "title_ar": "", "title_en": "Natural Language Processing in de menselijke hersenen", "author": "Helena Balabin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Belgium", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; medical-ai; transformers; llm; sentiment-analysis; machine-translation; speech-recognition", "keywords": "", "abstract": "Natural Language Processing (NLP) is a subfield of artificial intelligence focused on processing and interpreting human language. With the recent success of NLP approaches across a diverse range of application areas, such as machine translation and sentiment analysis, the question arises as to how far the seemingly human-like performances indeed resemble the processing of language in the human brain. Neurological studies of connected speech are central to providing deeper insights into the possible connection between language representations in the human brain and language models used in NLP. The collection of connected speech data in multiple cohorts of older adults will provide the basis for this work. By employing state-of-the-art NLP methods such as the Transformer, numerical feature representations will be generated for sentences and multi-sentence units extracted from textual transcriptions of the collected speech data. These NLP features will be compared to functional brain connectivity patterns to study their link to human language processing. Another aim of this work is to investigate whether NLP features based on connected speech from older adults can be used to differentiate between healthy aging, preclinical, and the early dementia stage of Alzheimer's disease. As a result, this work will potentially provide a deeper understanding of the connection between the derived NLP features and the aging brain and the possible existence of human brain counterparts of given NLP representations.", "source_url": "https:\/\/openalex.org\/W7162916085", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Lirias (KU Leuven)", "source_record_id": "W7162916085", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 432, "title": "The application of natural language processing technologies within the legislative process", "title_ar": "", "title_en": "The application of natural language processing technologies within the legislative process", "author": "Arkady Alexandrov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "The application of natural language processing technologies within the legislative process Keywords legislation; rational lawmaking; natural language processing; system theory of law; language of law; corpus analysis; network analysis Abstract This dissertation examines the potential of Natural Language Processing (NLP) in the legislative process. Its primary objective is to evaluate whether legal NLP methods and technologies can enhance the values of good law, such as clarity, consistency, coherence, efficiency etc. The work is interdisciplinary, situated at the intersection of legal theory, linguistics, and legal informatics. The first chapter establishes a theoretical framework based on modern concepts of rational law-making, the values of good law, andp a systemic approach to law as a complex communication system. Emphasis is placed on the processes of information exchange between lawgiver and addressees, which include both the a priori and a posteriori interpretation of law and the sharing of linguistic contexts. A systemic perspective on the issues of legal order consistency and coherence is presented, along with proposed principles for quantifying these attributes using empirical models. The empirical research employs a combination of corpus and network methods to analyze legal acts, their linguistic...", "source_url": "https:\/\/openalex.org\/W7135982295", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135982295", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 433, "title": "Continual Learning in Natural Language Processing", "title_ar": "", "title_en": "Continual Learning in Natural Language Processing", "author": "Min Zeng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W4412728352", "pdf_url": "", "doi": "10.14711\/thesis-hdl151243", "handle": "", "repository_name": "", "source_record_id": "W4412728352", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 434, "title": "Word Embeddings for Natural Language Processing", "title_ar": "", "title_en": "Word Embeddings for Natural Language Processing", "author": "Rémi Lebret", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Ecole Polytechnique Fédérale de Lausanne", "university_source": "repository_host", "college": "", "department": "", "country": "Switzerland", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; data-mining; predictive-analytics; cnn; llm; sentiment-analysis", "keywords": "", "abstract": "Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for capturing both syntactic and semantic information about words. Word embeddings, when used as the underlying input representation, have been shown to be a great asset for a large variety of natural language processing (NLP) tasks. Recent techniques to obtain such word embeddings are mostly based on neural network language models (NNLM). In such systems, the word vectors are randomly initialized and then trained to predict optimally the contexts in which the corresponding words tend to appear. Because words occurring in similar contexts have, in general, similar meanings, their resulting word embeddings are semantically close after training. However, such architectures might be challenging and time-consuming to train. In this thesis, we are focusing on building simple models which are fast and efficient on large-scale datasets. As a result, we propose a model based on counts for computing word embeddings. A word co-occurrence probability matrix can easily be obtained by directly counting the context words surrounding the vocabulary words in a large corpus of texts. The computation can then be drastically simplified by performing a Hellinger PCA of this matrix. Besides being simple, fast and intuitive, this method has two other advantages over NNLM. It first provides a framework to infer unseen words or phrases. Secondly, all embedding dimensions can be obtained after a single Hellinger PCA, while a new training is required for each new size with NNLM. We evaluate our word embeddings on classical word tagging tasks and show that we reach similar performance than with neural network based word embeddings. While many techniques exist for computing word embeddings, vector space models for phrases remain a challenge. Still based on the idea of proposing simple and practical tools for NLP, we introduce a novel model that jointly learns word embeddings and their summation. Sequences of words (i.e. phrases) with different sizes are thus embedded in the same semantic space by just averaging word embeddings. In contrast to previous methods which reported a posteriori some compositionality aspects by simple summation, we simultaneously train words to sum, while keeping the maximum information from the original vectors. These word and phrase embeddings are then used in two different NLP tasks: document classification and sentence generation. Using such word embeddings as inputs, we show that good performance is achieved in sentiment classification of short and long text documents with a convolutional neural network. Finding good compact representations of text documents is crucial in classification systems. Based on the summation of word embeddings, we introduce a method to represent documents in a low-dimensional semantic space. This simple operation, along with a clustering method, provides an efficient framework for adding semantic information to documents, which yields better results than classical approaches for classification. Simple models for sentence generation can also be designed by leveraging such phrase embeddings. We propose a phrase-based model for image captioning which achieves similar results than those obtained with more complex models. Not only word and phrase embeddings but also embeddings for non-textual elements can be helpful for sentence generation. We, therefore, explore to embed table elements for generating better sentences from structured data. We experiment this approach with a large-scale dataset of biographies, where biographical infoboxes were available. By parameterizing both words and fields as vectors (embeddings), we significantly outperform a classical model.", "source_url": "https:\/\/openalex.org\/W2540566554", "pdf_url": "", "doi": "10.5075\/epfl-thesis-7148", "handle": "", "repository_name": "Infoscience (Ecole Polytechnique Fédérale de Lausanne)", "source_record_id": "W2540566554", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 435, "title": "A generative chatbot with natural language processing", "title_ar": "", "title_en": "A generative chatbot with natural language processing", "author": "David Liebman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; generative-ai; rnn; transformers; llm; speech-recognition", "keywords": "", "abstract": "The goal in this thesis is to create a chatbot, a computer program that can respond verbally to a human in the course of simple day-to-day conversations. A deep learning neural network model called the Transformer is used to develop the chatbot. A full description of a Transformer is provided. The use of a few different Transformer-based Natural Language Processing models to develop the chatbot, including Generative Pre-Training 2 (GPT2), are shown. For comparison a Gated Recurrent Unit (GRU) based model is included. Each of these are explained below. The chatbot code is installed on a small device such as the Raspberry Pi with speech recognition and speech-to-text software. In this way a device that can carry out a verbal conversation with a human might be created. For the GRU-based model a Raspberry Pi 3B with 1GB RAM can be used. A Raspberry Pi 4B with 4GB of RAM is needed to run a chatbot with the GPT2.", "source_url": "https:\/\/openalex.org\/W3155268491", "pdf_url": "", "doi": "", "handle": "", "repository_name": "SUNY Digital Repository Support (State University of New York System)", "source_record_id": "W3155268491", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 436, "title": "Approaches to natural language processing in app development", "title_ar": "", "title_en": "Approaches to natural language processing in app development", "author": "Camran Djoweini ; Henrietta Hellberg", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "Natural language processing is an on-going field that is not yet fully established. A high demand for natural language processing in applications creates a need for good development-tools and different implementation approaches developed to suit the engineers behind the applications. This project approaches the field from an engineering point of view to research approaches, tools, and techniques that are readily available today for development of natural language processing support. The sub-area of information retrieval of natural language processing was examined through a case study, where prototypes were developed to get a deeper understanding of the tools and techniques used for such tasks from an engineering point of view. We found that there are two major approaches to developing natural language processing support for applications, high-level and low-level approaches. A categorization of tools and frameworks belonging to the two approaches as well as the source code, documentation and, evaluations, of two prototypes developed as part of the research are presented. The choice of approach, tools and techniques should be based on the specifications and requirements of the final product and both levels have their own pros and cons. The results of the report are, to a large extent, generalizable as many different natural language processing tasks can be solved using similar solutions even if their goals vary.", "source_url": "https:\/\/openalex.org\/W2941134809", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2941134809", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 437, "title": "Natural language processing for textual process descriptions", "title_ar": "", "title_en": "Natural language processing for textual process descriptions", "author": "Gustavo Cilleruelo Calderón", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "machine-learning; nlp", "keywords": "", "abstract": "Textual descriptions of processes are ubiquitous in organizations as a way to store, maintain and manage procedures. Formal notations for processes exist in fields like business process management, for example BPMN. With the rise of Natural Language Processing there is a growing interest on using these techniques to bridge the gap between textual processes and formal tools to analyze them. Nevertheless, an overview of the literature reveals that often the process datasets used are either small or too simplistic. We compile processes from three different sources to train classifiers with self-learning techniques, in order to leverage readily available unlabeled data. We train the Universal Sentence Encoder on these datasets, where bootstrapping greatly outperforms fully-supervised training. Furthermore, we build a framework to train and use these classifiers to detect processes on PDF sources.", "source_url": "https:\/\/openalex.org\/W3174237029", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W3174237029", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 438, "title": "Natural Language Processing in Perinatal Epidemiology", "title_ar": "", "title_en": "Natural Language Processing in Perinatal Epidemiology", "author": "Qiu‐Yue Zhong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Harvard University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp; medical-ai", "keywords": "", "abstract": "Suicide is one of the leading causes of maternal deaths. Early detection of pregnant women with suicidal behavior presents an important opportunity for directing suicide prevention efforts to those at high risk for suicide and, therefore, can help to prevent maternal mortality. The increasing utilization of electronic medical records (EMRs) has provided unprecedented opportunities for identifying pregnant women with suicidal behavior. However, suicidal behavior is often under-coded in EMRs. Studies have illustrated the clinical relevance of applying natural language processing (NLP) to identify patients with suicidal behavior; and this inspired our effort to develop algorithms specific to pregnant women, a population that is understudied when it comes to understanding the determinants and sequelae of suicidal behavior. We first examined the comparative performance of structured data using diagnostic codes vs. NLP of unstructured text for screening suicidal behavior among pregnant women in EMRs. We found that the use of NLP substantially improves the sensitivity of screening for suicidal behavior. However, the chart review confirmation rate for suicidal behavior was lower among women who did not have diagnostic codes for suicidal behavior but screened positive by NLP, as compared with women screened positive by diagnostic codes. We then developed classification algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by NLP in EMRs. Using expert-curated features, our algorithm achieved an area under the receiver operating characteristic curve of 0.83. By setting a PPV comparable to the PPV of diagnostic codes related to suicidal behavior (0.71), we obtained an estimated sensitivity of 0.34, a specificity of 0.96, and a NPV of 0.83. The algorithm resulted in a 11-fold increase in the number of pregnant women identified with suicidal behavior. Lastly, using the suicidal behavior algorithm, we examined the associations of antepartum suicidal behavior and psychiatric disorders, alone and in combination, with adverse outcomes in EMRs. We found that independent of psychiatric disorders, antepartum suicidal behavior was associated with increased odds of several adverse obstetric outcomes, but not adverse infant outcomes. The combination of suicidal behavior and psychiatric disorders showed higher odds of adverse infant outcomes and some adverse obstetric outcomes than either suicidal behavior or psychiatric disorders alone.", "source_url": "https:\/\/openalex.org\/W3151972365", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)", "source_record_id": "W3151972365", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 439, "title": "Natural Language Processing for Slang", "title_ar": "", "title_en": "Natural Language Processing for Slang", "author": "Zhewei Sun", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Toronto", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; self-semi-supervised; predictive-analytics; llm", "keywords": "", "abstract": "Slang is a common type of language that makes creative and highly flexible use of words. A basic problem that language users tackle is how to develop and interpret novel slang terms for communication in a community. This problem is relevant for natural language processing since new slang expressions often emerge in daily conversations and online social media. However, principled computational approaches to modeling slang are lacking, which presents key challenges to the effective natural language processing of slang. In this dissertation, I develop a computational framework that offers new methodologies for the automated generation, interpretation, and translation of English slang word usages, as well as for characterizing the principles in slang variation across language communities. My dissertation is organized into three main parts. The first part addresses the under-explored problem of slang semantic extension, namely how existing words in the lexicon take on new meanings in informal context. I develop a generative framework that combines contrastive learning with probabilistic models of semantic chaining to capture slang semantic extension. By leveraging dictionary-based resources of slang, I show how the learned semantic representations more accurately predict slang word choices compared to existing approaches that rely more exclusively on corpus data. The second part of my dissertation tackles the inverse problem of slang interpretation by applying these semantic representations to interpret and translate novel slang usages in natural text. I show how this approach provides better accuracy and sample efficiency in both slang interpretation and translation, in comparison to baseline contextualized language models. Finally, the third part of my dissertation investigates semantic variation of slang across different language communities focusing on a comparative study of US and UK. I show that models incorporating either communicative need or semantic chaining can predict the regional identity of slang usages. In summary, my dissertation contributes a principled framework for modeling the lexical semantics and usages of English slang, and it opens up future opportunities for the computational investigation and automated processing of informal language across a diverse set of communities and languages.", "source_url": "https:\/\/openalex.org\/W7133071557", "pdf_url": "https:\/\/utoronto.scholaris.ca\/bitstreams\/2f5619f5-5d4c-40a8-bf85-3e19e6c0bfa5\/download", "doi": "", "handle": "", "repository_name": "TSpace (University of Toronto)", "source_record_id": "W7133071557", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 440, "title": "Virtual assistant with natural language processing capabilities", "title_ar": "", "title_en": "Virtual assistant with natural language processing capabilities", "author": "Charline Mas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "During this thesis, an end-to-end solution was provided, creating and developing a Chatbot which, thanks to natural language processing techniques, is able to answer very complex questions, often requiring even more complex answers, in a well-defined area.", "source_url": "https:\/\/openalex.org\/W2919638601", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W2919638601", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 441, "title": "Applying natural language processing techniques to code", "title_ar": "", "title_en": "Applying natural language processing techniques to code", "author": "Aviel J. Stein ; Spiros Mancoridis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "Analyzing source code is becoming a helpful method as the amount of code and the number of coders increases. Developing software is a mentally strenuous activity because it involves understanding and formalizing solutions to complex problems. As solutions are adapted and the problem themselves change, the code changes with it. Traditionally, source code analysis (SCA) uses methods and tools that rely on the rigid structure of code to work, and while useful, are not always able to adapt to new information and code. By comparison, the natural language processing (NLP) field leverages naturally occurring patterns within natural language text to help people understand and interpret text robustly on a large scale. This works shows that methods from NLP can be applied to code to extract information automatically in broad and more adaptable ways. We show several examples of how techniques developed for NLP can be successfully applied to software to create source code tools that can help developers improve source code.", "source_url": "https:\/\/openalex.org\/W4285731393", "pdf_url": "", "doi": "10.17918\/00001100", "handle": "", "repository_name": "iDEA - Drexel University Institutional Repository", "source_record_id": "W4285731393", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 442, "title": "Neural Networks for Natural Language Processing", "title_ar": "", "title_en": "Neural Networks for Natural Language Processing", "author": "Mohamed Morchid", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "deep-learning; nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2995392739", "pdf_url": "", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W2995392739", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 443, "title": "Natural language processing and Information Science: interrelationships and contributions", "title_ar": "", "title_en": "Natural language processing and Information Science: interrelationships and contributions", "author": "Keli Rodrigues do Amaral Benin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "data-mining", "categories": "machine-learning; nlp; ai-applications; data-mining; ai-education", "keywords": "", "abstract": "Introduction: In recent decades, there has been an increase in the amount of information processed, stored and made available in documents, mainly made available in the digital environment. Due to the exponential increase in the amount of information, manual manipulation of information becomes impracticable, making it necessary to use mechanisms and tools that enable automatic processing of information. The main approach to text and language analysis by computational means is called Natural Language Processing (NLP). Objective: The objective of this research is to analyze the existing interrelationships between the field of Natural Language Processing and Information Science, describing the main contributions identified. Methodology: The research is characterized as having a qualitative and exploratory approach, as it seeks to identify and describe the existing interrelationships between the field of Natural Language Processing and Information Science. As for the methodological procedures, the research can be classified as bibliographical, based on the analysis of articles published in the BRAPCI and SCOPUS Databases, in the period 2012 and 2022, which address the interrelationship between Natural Language Processing and Life Science. Information. 68 articles were selected for analysis. After selecting articles, content analysis was carried out according to the teachings of Bardin (2011), where five categories were created (Basic and Conceptual Articles, Text Preprocessing; Semantic Analysis and Text Representation; Information Extraction and Text Mining; Topic Modeling and Text Classification) that allowed for qualitative analysis. Results: After analyzing the articles, it was possible to observe the contributions of the field of Natural Language Processing to the area of Information Science, these contributions are more significant for the area of information retrieval. Regarding the area of Information Science, it contributes to the field of Natural Language Processing, with the use of ontologies, taxonomy, thesauri, dictionaries, indexing and organization and representation of information and knowledge. Likewise, the contributions of Linguistics and Terminology to the PLN were identified, with the use of linguistic analyzes and terms. Final considerations: Given this, it was possible to analyze the interrelationships of the field of Natural Language Processing with the area of Information Science, in which the two themes are able to take advantage of the techniques and specificities of the other. Given the above, Information Science can and should take advantage of the computational tools developed within the scope of NLP research, using them in the cataloging processes and then retrieval in information centers, as well as in the creation of information representation models", "source_url": "https:\/\/openalex.org\/W7120874993", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/32098\/3\/processamentolinguagemnaturalcienciainformacao.pdf", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120874993", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 444, "title": "Synthesizing Creative Requirementswith Natural Language Processing", "title_ar": "", "title_en": "Synthesizing Creative Requirementswith Natural Language Processing", "author": "H.N. Wever", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Utrecht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp; generative-ai", "keywords": "", "abstract": "This thesis studies creativity in requirements engineering. First, a semi structured literature research explores the subject and studies how creativity in the field of requirements engineering is conceived. This research leads to three dimensions of creativity, usefulness, novelty and surprisingness, and uses these dimensions to assess the creativity of requirements. Next to this, natural language processing techniques are used to create a program that synthesizes requirements. This program uses semantic role labeling to apply combinational and exploratory creativity in order to synthesize creative requirements. Libraries and resources that are used to do this are PractNLPTools, SemLink, VerbNet, PropBank, and Sense2Vec. The effectiveness of the program is tested with an experiment. The developed program is used to synthesize creative requirements for two applications. The user stories of these applications are used to do so. One of the applications has additional user stories from similar applications in the domain as input to synthesize requirements. The synthesized requirements of the applications are assessed with a survey on the three identified dimensions of creativity.", "source_url": "https:\/\/openalex.org\/W2910598835", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Utrecht University Repository (Utrecht University)", "source_record_id": "W2910598835", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 445, "title": "Natural language processing in cross-media analysis", "title_ar": "", "title_en": "Natural language processing in cross-media analysis", "author": "Yonas Demeke Woldemariam", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Umeå University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; multimodal; video-analysis; sentiment-analysis; speech-recognition", "keywords": "", "abstract": "A cross-media analysis framework is an integrated multi-modal platform where a media resource containing different types of data such as text, images, audio and video is analyzed with metadata extractors, working jointly to contextualize the media resource. It generally provides cross-media analysis and automatic annotation, metadata publication and storage, searches and recommendation services. For on-line content providers, such services allow them to semantically enhance a media resource with the extracted metadata representing the hidden meanings and make it more efficiently searchable. Within the architecture of such frameworks, Natural Language Processing (NLP) infrastructures cover a substantial part. The NLP infrastructures include text analysis components such as a parser, named entity extraction and linking, sentiment analysis and automatic speech recognition. Since NLP tools and techniques are originally designed to operate in isolation, integrating them in cross-media frameworks and analyzing textual data extracted from multimedia sources is very challenging. Especially, the text extracted from audio-visual content lack linguistic features that potentially provide important clues for text analysis components. Thus, there is a need to develop various techniques to meet the requirements and design principles of the frameworks. In our thesis, we explore developing various methods and models satisfying text and speech analysis requirements posed by cross-media analysis frameworks. The developed methods allow the frameworks to extract linguistic knowledge of various types and predict various information such as sentiment and competence. We also attempt to enhance the multilingualism of the frameworks by designing an analysis pipeline that includes speech recognition, transliteration and named entity recognition for Amharic, that also enables the accessibility of Amharic contents on the web more efficiently. The method can potentially be extended to support other under-resourced languages.", "source_url": "https:\/\/openalex.org\/W2884821628", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DiVA at Umeå University (Umeå University)", "source_record_id": "W2884821628", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 446, "title": "Textual Analysis with Natural Language Processing", "title_ar": "", "title_en": "Textual Analysis with Natural Language Processing", "author": "Madeleine Janickyj", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Coventry University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7113220319", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Pure (Coventry University)", "source_record_id": "W7113220319", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 447, "title": "The conditions of visibility : threshold collapse and the production of subjects in computer vision", "title_ar": "", "title_en": "The conditions of visibility : threshold collapse and the production of subjects in computer vision", "author": "Sharon Lobo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "computer-vision; robotics; object-detection", "keywords": "", "abstract": "Aerial surveillance systems increasingly rely on computer vision to identify human presence in drone imagery, supporting forms of situational awareness used in security and monitoring contexts. This report examines how computer vision systems render human bodies visible within aerial imagery. It analyzes how visibility is produced through model architecture, data conditions, and decision thresholds. Using a YOLO-based detector applied to the VisDrone dataset, the report introduces controlled corruptions including noise, blur, darkening, and downsampling, along with variations in confidence thresholds. The results show that detection does not degrade smoothly. It exhibits threshold-like behavior where recall remains stable up to a point and then collapses abruptly. This collapse is not only caused by degraded input. Even when the model assigns non-zero confidence to potential detections, many are excluded by thresholding, creating a gap between internal representation and final output. As a result, bodies may be partially recognized yet absent in visible detections. These findings suggest that object detection operates as a system of conditional visibility. Visibility is produced through transformations and filtering operations that determine what counts as detectable. Detection systems establish the conditions under which bodies can appear, positioning object detection as a visual apparatus that constructs the subjects it identifies.", "source_url": "https:\/\/openalex.org\/W7214209376", "pdf_url": "", "doi": "10.26153\/tsw\/64827", "handle": "", "repository_name": "Texas Digital Library (University of Texas)", "source_record_id": "W7214209376", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 448, "title": "Automation of work processes using object detection in electrical installation images : Patch-based training and object detection", "title_ar": "", "title_en": "Automation of work processes using object detection in electrical installation images : Patch-based training and object detection", "author": "Alan Abdulrazzaq ; Björn Andersson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "computer-vision; ai-applications; object-detection; ai-industry", "keywords": "", "abstract": "This thesis investigates how artificial intelligence and computer vision can be utilized to automate the identification of installation symbols in digital electrical drawings for the company mbiz AB. In the electrical installation industry, planning and selfmonitoring are currently performed largely manually, which is both time-consuming and labor-intensive. A key technical challenge is that the symbols are extremely small relative to the overall size and high resolution of the drawings, complicating automated analysis. The study evaluates a methodology based on the object detection model YOLOv11 in combination with SAHI (Slicing Aided Hyper Inference), a technique that divides the drawing into smaller segments to preserve detail. The results show that the model achieved a Mean Average Precision (mAP) of 0.97, confirming that patch-based inference is an effective method for handling technical documents. The study resulted in a functional prototype that integrates visual detection with automated reading of drawing numbers and coordinate translation to PDF format. The conclusion is that the technology has significant potential to increase efficiency and quality assurance in future installation projects by reducing manual tasks.", "source_url": "https:\/\/openalex.org\/W7132371682", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7132371682", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 449, "title": "Tracking an object with a depth-vision camera", "title_ar": "", "title_en": "Tracking an object with a depth-vision camera", "author": "Josefína VLK", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "computer-vision; robotics; object-detection; video-analysis", "keywords": "", "abstract": "This thesis focuses on the design and implementation of a system for object detection and tracking using the Intel RealSense D435i depth camera. The main goal is to develop a system that can identify an object in input video stream and track its movement in space. The spatial data obtained from the tracking process are used to control a mobile robotic platform, the Husky A200, via the ROS 2 framework. The theoretical part provides an overview of computer vision methods and algorithms for object detection and tracking. Selected methods were implemented using the OpenCV library and experimentally compared. The results of this project may serve as a foundation for future developments in autonomous robotic platform control systems.", "source_url": "https:\/\/openalex.org\/W7128412113", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7128412113", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 450, "title": "Identification of errors in the 3D printing process using computer vision applications", "title_ar": "", "title_en": "Identification of errors in the 3D printing process using computer vision applications", "author": "Daniel Janovský", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; object-detection", "keywords": "", "abstract": "This thesis focuses on the integration and application of computer vision techniques in the field of 3D printing with the aim of automated detection of printing defects. The work analyzes the most common defects occurring during the 3D printing process and explores the possibilities of their identification using modern machine learning methods. A dataset for training the computer vision model was created based on data obtained from a database of images depicting various printing defects, which were subsequently categorized into specific classes. The dataset was manually annotated using the CVAT tool in COCO format, with individual defects labeled using polygonal annotations. In total, the dataset contains approximately 154 thousand annotated objects divided into five defect classes. Based on the prepared data, an object detection model utilizing the YOLOv12 architecture was trained, combining high inference speed with advanced image processing mechanisms. The model was trained using different hyperparameter settings, specifically with a fixed number of 100 epochs and a learning rate of 0.01, while the batch size was adjusted according to the input image resolution (256 for 320 px, 64 for 640 px, and 32 for 1080 px). Additionally, three input resolutions (320, 640, and 1080 px) were evaluated to optimize detection accuracy. The performance of the trained model was evaluated using standard metrics such as precision, recall, and mean Average Precision (mAP) at different IoU thresholds. The results showed that increasing the resolution led to an improvement in detection accuracy, with mAP50 increasing from approximately 0.63 (320 px) to 0.74 (640 px) and further to 0.77 (1080 px). A similar trend was observed for mAP50-95, which increased from 0.37 to 0.58, as well as for precision, which rose from 0.83 to 0.89, and recall, which improved from 0.56 to 0.73. The most significant improvement was observed when increasing the resolution from 320 px to 640 px.", "source_url": "https:\/\/openalex.org\/W7163860920", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7163860920", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 451, "title": "Computer vision for non-rigid object assembly automation: With applications in automotive wire harness assembly", "title_ar": "", "title_en": "Computer vision for non-rigid object assembly automation: With applications in automotive wire harness assembly", "author": "Hao Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Chalmers University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "ai-industry", "categories": "deep-learning; computer-vision; robotics; ai-applications; multimodal; ai-industry", "keywords": "", "abstract": "This thesis examines the role of computer vision in facilitating robotic automation for non-rigid object assembly, with a particular focus on wire harness assembly tasks during the automotive final assembly stage. Despite extensive research in this field, industrial adoption has remained limited. Accordingly, the thesis analyzes the challenges associated with applying computer vision to wire harness assembly automation and explores strategies to enable practical deployment in industrial environments.Employing both qualitative and quantitative methods, the research progresses from problem identification to artifact design, demonstration, and evaluation in laboratory and industrially relevant environments. The studies identify challenges at the object, scene, data, and operational levels. To address these challenges, three primary artifacts were developed and evaluated. First, a learning-based perception pipeline enables markerless detection of wire harness components. This demonstrates the feasibility of deep learning-based component recognition and highlights limitations when components possess highly similar or occluded visual features. Second, a robot-assisted pipeline for automated multi-view data acquisition and multimodal annotation substantially accelerates the preparation of computer vision datasets. This pipeline also supports the training and evaluation of learning-based perception methods for industrial applications. Third, a vision-based human–robot collaboration framework for wire harness installation significantly reduces localized physical discomfort while maintaining task success. However, this approach increases mental demand and cycle time, with the majority of the additional time attributable to robot execution.In summary, this thesis provides deployable methods and practical guidance for data-centric development, interaction design, and takt-time-oriented workflow optimization in non-rigid object assembly automation. It also demonstrates that, given current technological constraints, computer vision is most effective as a human-centered enabler of robot-assisted assembly rather than as a direct pathway to fully autonomous robotic assembly of non-rigid objects.", "source_url": "https:\/\/openalex.org\/W7160860637", "pdf_url": "https:\/\/research.chalmers.se\/publication\/551870\/file\/551870_Fulltext.pdf", "doi": "10.63959\/chalmers.dt\/5880", "handle": "", "repository_name": "Chalmers Research (Chalmers University of Technology)", "source_record_id": "W7160860637", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 452, "title": "Computer Vision for Aircraft Fuselage Defect Detection: A YOLO11-Based Approach", "title_ar": "", "title_en": "Computer Vision for Aircraft Fuselage Defect Detection: A YOLO11-Based Approach", "author": "Pedro Afonso Bruno Arroteia", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; transfer-learning; self-semi-supervised; predictive-analytics; object-detection; image-segmentation; ai-industry", "keywords": "", "abstract": "Aircraft fuselages are exposed to operational and environmental stresses that generate defects such as scratches, paint peeling, rust, and rivet damage. Conventional inspection techniques, though widely used, are labor-intensive, time-consuming, and dependent on inspector expertise, which can lead to variability and overlooked anomalies. These chal- lenges highlight the need for automated solutions that ensure consistency and efficiency in quality inspection processes. This thesis presents the development of a computer vision-based system for auto- mated aircraft fuselage defect detection, combining deep learning with a user-oriented interface. The system employs the YOLO11 object detection architecture, fine-tuned on a specialized dataset to detect and classify four defect types. To mitigate data scarcity and class imbalance, data augmentation and transfer learning were applied. The model was evaluated on training, validation, and testing sets, achieving 0.814 precision and 0.735 recall on the training set, and on the validation set 0.722 precision, 0.787 recall, 0.659 accuracy, mAP@0.5 of 0.82, and mAP@0.5:0.95 of 0.524. Beyond model training, a RESTful API and web-based Human–Machine Interface were developed, enabling inspection images to be uploaded and processed, with results presented as bounding boxes, defect types, and confidence scores. This integration reflects Industry 5.0 principles of human–AI collaboration by allowing technicians to interpret, adjust, and validate predictions. The results demonstrate the feasibility of an intelligent inspection system that enhances accuracy, efficiency, and transparency in aircraft maintenance workflows. While challenges such as dataset scarcity and background variability remain, the framework establishes a solid foundation for future work in semi-supervised learning, segmentation-based approaches, and real-time deployment on edge devices. By contributing both a validated detection model and an operational platform, this study advances the application of AI in aviation quality inspection and supports safer, more efficient maintenance practices.", "source_url": "https:\/\/openalex.org\/W7224044969", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7224044969", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 453, "title": "Design and implementation of a portable machine vision system for real-time object detection and auditory feedback", "title_ar": "", "title_en": "Design and implementation of a portable machine vision system for real-time object detection and auditory feedback", "author": "Themba M Sivate", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Durban University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; computer-vision; nlp; object-detection; video-analysis; speech-recognition", "keywords": "", "abstract": "A primary challenge faced by individuals with visual impairments is the difficulty or inability to perform object identification. While conventional aids such as magnifying spectacles may assist with near-field vision, individuals with reduced or absent sight often rely extensively on tactile exploration for object recognition. This reliance presents significant navigational challenges, particularly in dynamic environments such as roadways, where the increased risk of accidents is a major concern. To address this, this research proposes the development of a portable machine vision system designed to provide real-time auditory feedback regarding detected proximal objects, thereby assisting individuals with visual impairments in navigation. The design of the system prioritizes portability, reliability, modularity, and unobtrusiveness during typical operation. The hardware implementation of the proposed system consists of three key elements: a Single Board Computer (SBC), a wireless camera, and a Bluetooth-enabled earpiece. The experimental results demonstrate that the proposed system is capable of delivering real- time audio feedback of detected objects to visually impaired individuals. The system was evaluated in a real-life environment in both acceptable and poor lighting conditions. The efficacy of the proposed system in well-lit environments resulted in an average detection rate of 87.65%. However, in low-light scenes an average detection rate of 51% was observed because of the low image resolution. The minimum observed delay was 4.9 seconds, while the maximum was 10.2 seconds. This latency encompasses the duration required for image capture, processing, and audio translation. The latency can be mitigated by incorporating an integrated circuit with a dedicated Graphical Processing Unit (GPU), which is more proficient in handling machine learning and video processing tasks.", "source_url": "https:\/\/openalex.org\/W7165516628", "pdf_url": "https:\/\/openscholar.dut.ac.za\/bitstreams\/a73257ee-70d5-4c3e-b471-150f6c5fc153\/download", "doi": "10.51415\/10321\/6409", "handle": "", "repository_name": "DUT Open Scholar (Durban University of Technology)", "source_record_id": "W7165516628", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 454, "title": "Object detection", "title_ar": "", "title_en": "Object detection", "author": "Pavel Vítek", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "computer-vision; object-detection", "keywords": "", "abstract": "This paper deals with object detection where a human figure is used as a model. The introductory chapter defines terms detection and object. The second chapter describes the basic theory of computer vision , such as image processing and its parts. A substantial part is also focused on the Canny edge detection, upper body detection and pose detection. The third chapter describes the MATLAB integrated development environment in which the image processing functions are implemented. The last chapter is a practical demonstration of object detection. Different upper body detection methods are compared there. In the last subsection, two methods of determining the deviations based on the template are compared. Finally, all methods are compared with respect to the obtained results.", "source_url": "https:\/\/openalex.org\/W7135601709", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135601709", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 455, "title": "Biologically Inspired Efficiencies in Computer Vision and Audition", "title_ar": "", "title_en": "Biologically Inspired Efficiencies in Computer Vision and Audition", "author": "Mohammad Kazem Ebrahimpour", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; computer-vision; nlp; robotics; object-detection; video-analysis; speech-recognition", "keywords": "", "abstract": "Computer perception is one of the fundamental problems in artificial intelligence. Given an image or a recorded audio, a human can quickly recognize and detect objects based on image or sound or both. In computer vision, Object Detection is concerned with recognizing objects in images and drawing a bounding box around them. Researchers have been working on developing algorithms to recognize, detect, and segment objects\/scenes in images for decades. Numerous challenges make these problems significantly challenging in real-world scenarios, since objects usually appear in different conditions, such as viewpoints, scales, and with background noise, and they even may deform into different shapes, parts, or poses. Real-time object detection has many important applications, such as autonomous driving cars and video surveillance. In this dissertation, we approach visual understanding in the following ways: First, we utilize implicit information in trained neural networks to localize all objects of interest in an image using a sensitivity analysis approach. Second, we introduce a novel framework for object detection called “Ventral- Dorsal” Neural Networks, inspired by the structure of the human brain. Third, we expand the Ventral-Dorsal framework, focusing on attaining real-time performance needed for online applications. Forth, we compare human attention with deep neural network attention algorithms in order to understand whether neural network attention matches human attention. Also, auditory perception is crucial in artificial intelligence systems. Until recently, auditory object recognition pipelines were in need of substantial hand engineering for feature extraction. Engineered features need to be tuned for every individual problem. Also, some popular feature extraction methods are time-consuming, limiting real-time applications.Here we attempt to avoid these problems using end-to-end training. Due to the recent improvements in deep neural networks, we are able to eliminate feature learning by optimizing feature extraction and classification jointly in one network. In this dissertation, we approach the auditory object recognition problem in the following ways: we proposed a novel “end-to-end” deep neural network architecture that takes raw audio as input and maps it to class labels. We also applied our proposed architecture to a new dataset of infant vocalization sounds for further investigation.", "source_url": "https:\/\/openalex.org\/W3090086814", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W3090086814", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 456, "title": "Design and Experiment of a Computer Vision System of an Autonomous Robot", "title_ar": "", "title_en": "Design and Experiment of a Computer Vision System of an Autonomous Robot", "author": "Michael Kurnia ; Rusman Rusyadi ; Gembong Baskoro", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "computer-vision; robotics; object-detection", "keywords": "", "abstract": "The purpose of the thesis is to design and experiment a computer vision system for an autonomous robot. Research and discuss the hardware and software required for the system. Write the code for the servo motor control and image processing for object detection. The thesis will adapt to the Hardware and Software design method of research to implement the system. The thesis has gathered data of the designed system and a prototype has been built to support the data gathered within this thesis. In conclusion, the thesis has shown the development of a simple computer vision system, implementing an algorithm that is suitable for the system and is able to extract coordinates from the image processed.", "source_url": "https:\/\/openalex.org\/W3205809636", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3205809636", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 457, "title": "Localization of UAVs Using Computer Vision in a GPS-Denied Environment", "title_ar": "", "title_en": "Localization of UAVs Using Computer Vision in a GPS-Denied Environment", "author": "Ram Charan Aluri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; robotics; object-detection", "keywords": "", "abstract": "The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.", "source_url": "https:\/\/openalex.org\/W4406532067", "pdf_url": "https:\/\/digital.library.unt.edu\/ark:\/67531\/metadc1944237\/m2\/1\/high_res_d\/ALURI-THESIS-2022.pdf", "doi": "10.12794\/metadc1944237", "handle": "", "repository_name": "University of North Texas Digital Library (University of North Texas)", "source_record_id": "W4406532067", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 458, "title": "Automated Curbside Waste Collection using Advanced Computer Vision Strategies", "title_ar": "", "title_en": "Automated Curbside Waste Collection using Advanced Computer Vision Strategies", "author": "Mohammad Ali Lordifar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Guelph", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; ai-applications; cnn; object-detection; ai-industry", "keywords": "", "abstract": "Computer vision is an interesting and rapidly growing area of study that is often used in conjunction with artificial intelligence (AI) methods. The number of useful applications for computer vision techniques is vast, and includes object detection, analysis of human actions, autonomous vehicles, and target tracking.A local company has partnered with the Intelligent Control and Estimation Laboratory to work on further developing an automated waste collection system. Currently, the system utilizes an industrial-grade camera, mechatronics control box that integrates with waste collection trucks (e.g., robot arms), and customized AI software. The system relies heavily upon its ability to successfully and accurately detect a waste collection cart (e.g., garbage, recycling, or compost bin). The current methodology employs a significant amount of manual annotation or labelling of pictures to train the system to detect a cart; all of which is time consuming and costly. This research thesis implemented and further developed convolutional neural networks (CNN) and ‘you only look once’ (YOLO) object detection strategies. The application of CNN and YOLO to the automated waste collection system improved successful cart detection rates (about 98%), reduced false positives (less than 2%), and will improve operational safety and reliability when implemented in the real-time system.", "source_url": "https:\/\/openalex.org\/W3049386911", "pdf_url": "https:\/\/atrium.lib.uoguelph.ca\/bitstreams\/7abc16b0-85af-4178-a4a5-d0ec14beb1a9\/download", "doi": "", "handle": "", "repository_name": "The Atrium (University of Guelph)", "source_record_id": "W3049386911", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 459, "title": "Shoulder Keypoint-Detection from Object Detection", "title_ar": "", "title_en": "Shoulder Keypoint-Detection from Object Detection", "author": "Prince Kapoor", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Ottawa", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; cnn; rnn; object-detection; image-segmentation; ai-agriculture", "keywords": "", "abstract": "This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image analysis challenges. Due to the advent of deep learning, CNN had been used in almost all the computer vision applications and that is why there is utter need to understand the miniature details of these feature extractors and find out their pros and cons of each feature extractor meticulously. In order to perform our experimentation, we decided to explore an object detection task using a particular model architecture which maintains a sweet spot between computational cost and accuracy. The model architecture which we had used is LSTM-Decoder. The model had been experimented with different CNN feature extractor and found their pros and cons in variant scenarios. The results which we had obtained on different datasets elucidates that CNN plays a major role in obtaining higher accuracy and we had also achieved a comparable state-of-the-art accuracy on Pedestrian Detection Dataset. In extension to object detection, we also implemented two different model architectures which find shoulder keypoints. So, One of our idea can be explicated as follows: using the detected annotation from object detection, a small cropped image is generated which would be feed into a small cascade network which was trained for detection of shoulder keypoints. The second strategy is to use the same object detection model and fine tune their weights to predict shoulder keypoints. Currently, we had generated our results for shoulder keypoint detection. However, this idea could be extended to full-body pose Estimation by modifying the cascaded network for pose estimation purpose and this had become an important topic of discussion for the future work of this thesis.", "source_url": "https:\/\/openalex.org\/W2888395965", "pdf_url": "", "doi": "10.20381\/ruor-22271", "handle": "", "repository_name": "uO Research (University of Ottawa)", "source_record_id": "W2888395965", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 460, "title": "Advancing heat stress detection in dairy cows through machine learning and computer vision", "title_ar": "", "title_en": "Advancing heat stress detection in dairy cows through machine learning and computer vision", "author": "Shu, Hang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Liège", "university_source": "repository_host", "college": "", "department": "", "country": "Belgium", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; predictive-analytics; cnn; object-detection; image-segmentation; face-recognition; video-analysis; xai; ai-agriculture", "keywords": "", "abstract": "Heat stress detection in dairy cows has long been connected with production loss. However, the reduction in milk yield lags behind the exposure to heat stress events for about two days. Other stress responses, such as physiological and behavioural changes, are well documented to be activated by dairy cows in the earlier stage of heat stress compared with production loss. Among all candidate indicators, body surface temperatures (BST), respiration rate (RR), and relevant behaviours have been concluded to be the most appropriate indicators due to their high feasibility of acquisition and early response. Vision-based methods are promising for accurate measurements while adhering to animal welfare principles. Meanwhile, predictive models show a non-invasive alternative to obtain these data and can provide useful insights with their interpretations. Thus, this thesis aimed to provide non-invasive solutions to the detection of heat stress in dairy cows by using artificial intelligence techniques. The detailed research content and relevant conclusions are as follows: An automated tool based on improved UNet was proposed to collect facial BST from five facial landmarks (i.e., eyes, muzzle, nostrils, ears, and horns) on cattle infrared images. The baseline UNet model was improved by replacing the traditional convolutional layers in the decoder with Ghost modules and adding efficient channel attention modules. The improved UNet outperformed other comparable models with the highest mean Intersection of Union of 80.76% and a slightly slower but still good inference speed of 32.7 frames per second (FPS). Agreement analysis reveals small to negligible differences between the temperatures obtained automatically in the area of eyes and ears and the ground truth. A vision-based method was proposed to measure RR for multiple dairy cows lying on free stalls. The proposed method involved various computer vision tasks (i.e., instance segmentation, object detection, object tracking, video stabilisation, and optical flow) to obtain respiration-related signals and finally utilised Fast Fourier Transform to extract RR. The results show that the measured RR had a Pearson correlation coefficient of 0.945, a root mean square error (RMSE) of 5.24 breaths per minute (bpm), and an intraclass correlation coefficient of 0.98 compared with visual observation. The average processing time and FPS on 55 test video clips (mean ± standard deviation duration of 16 ± 4 s) was 8.2 s and 64, respectively. A deep learning-based model was proposed to recognise cow behaviours (i.e., drinking, eating, lying, standing-in, and standing-out) that are known to be influenced by heat stress. The YOLOv5s model was selected due to its ability to compress the weight size while maintaining accuracy. It had a mean average precision of 0.985 and an inference speed of 73 FPS. Further validation demonstrates the excellent capacity of the proposed model in measuring herd-level behavioural indicators, with an intraclass correlation coefficient of 0.97 compared with manual observation. Critical thresholds were determined by using piecewise regression models with environmental indicators as the predictors and animal-based indicators as the outcomes. An ambient temperature (Ta) threshold was determined at 26.1 °C when the automated measured mean eye temperature reached 35.3 °C. A Ta threshold of 23.6 °C and a temperature-humidity index (THI) threshold of 72 were determined when the automated measured RR reached 61.1 and 60.4 bpm, respectively. In addition, the test dairy herd began to change their standing and lying behaviour at the earliest Ta of 23.8 ℃ or THI of 68.5. Four machine learning algorithms were used to predict RR, vaginal temperature (VT), and eye temperature (ET) from 13 predictor variables from three dimensions: production, cow-related, and environmental factors. The artificial neural networks yielded the lowest RMSE for predicting RR (13.24 bpm), VT (0.30 ℃), and ET (0.29 ℃). The results interpreted with partial dependence plots and Local Interpretable Model-agnostic Explanations show that P.M. measurements and winter calving contributed most to high RR and VT predictions, whereas lying posture, high Ta, and low wind speed contributed most to high ET predictions. Based on these results, an integrative application of all the proposed measurement, prediction, and assessment methods has been suggested, wherein RGB and infrared cameras are used to measure animal-based indicators, and critical thresholds, along with model interpretation, are used to assess the heat stress state of dairy cows. This strategy ensures timely and thorough cooling of cows in all areas of the dairy farm, thereby minimising the negative impact of heat stress to the greatest extent.", "source_url": "https:\/\/openalex.org\/W7111984551", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Open Repository and Bibliography (University of Liège)", "source_record_id": "W7111984551", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 461, "title": "Advances in Decision Forests and Ferns with Applications in Deep Representation Learning for Computer Vision", "title_ar": "", "title_en": "Advances in Decision Forests and Ferns with Applications in Deep Representation Learning for Computer Vision", "author": "Zuo, Yan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; image-classification; object-detection; image-segmentation", "keywords": "", "abstract": "In recent years, deep learning has largely embedded itself within computer vision as an extremely useful tool for accomplishing a range of vision-based tasks such as image classification, object detection and semantic segmentation. With this surge in popularity in deep learning, more traditional computer vision methods such as decision forests have somewhat fallen out of favour in the community despite their benefits. This thesis hopes to bridge this gap; it focuses on advancing the ensemble methods of decision forests and ferns and incorporating them within deep learning frameworks for computer vision applications.", "source_url": "https:\/\/openalex.org\/W3165855881", "pdf_url": "", "doi": "10.26180\/5cec9c40848a3", "handle": "", "repository_name": "Figshare", "source_record_id": "W3165855881", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 462, "title": "Deep learning for small object detection", "title_ar": "", "title_en": "Deep learning for small object detection", "author": "Brais Bosquet", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; object-detection", "keywords": "", "abstract": "Small object detection has become increasingly relevant due to the fact that the performance of common object detectors falls significantly as objects become smaller. Many computer vision applications require the analysis of the entire set of objects in the image, including extremely small objects. Moreover, the detection of small objects allows to perceive objects at a greater distance, thus giving more time to adapt to any situation or unforeseen event.", "source_url": "https:\/\/openalex.org\/W3142604139", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W3142604139", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 463, "title": "Language Supervision for Computer Vision", "title_ar": "", "title_en": "Language Supervision for Computer Vision", "author": "Karan Desai", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Michigan", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; transfer-learning; few-zero-shot; multimodal; object-detection; image-segmentation; llm", "keywords": "", "abstract": "Representation learning lies at the core of modern Artificial Intelligence. In computer vision, labeled image datasets like ImageNet have been the standard choice for representation learning. Despite being empirically successful, this approach is expensive to scale due to labeling costs. Moreover, the representation quality is limited by the size and diversity of datasets and their associated label ontologies. My research explores using natural language supervision for computer vision. Using natural language allows us to go beyond fixed label ontologies and scale up to more general sources such as internet data. Toward this goal, my dissertation explores four problems -- (1) Learning representations: I propose one of the first methods for language-supervised visual learning that uses image captioning as the training objective, showing its efficacy compared to ImageNet-trained methods on downstream tasks like object detection and segmentation. (2) Scaling data: I explore social media as a rich source of high-quality image descriptions and curate a dataset of 12 million image-text pairs while ensuring responsible curation practices. (3) Understanding data: It is difficult to comprehend the diversity of visual concepts present in millions of image-text pairs. I posit that images and text naturally organize into a tree-like hierarchy and propose an approach for learning representations that capture this hierarchy using tools from hyperbolic geometry. (4) Transfer to downstream tasks: Large vision-language models show impressive zero-shot transfer capabilities on image-level tasks like classification and retrieval. However, their transferability to pixel-level tasks like object detection and segmentation has relied on expensive labeled mask annotations. I propose an object detector to efficiently transfer pre-trained vision models to segment and classify visual objects without any fine-tuning, unlike existing detectors that train using orders of magnitude more labeled masks to achieve high performance. In summary, my research affirms that using language supervision can drive the next leap of progress in computer vision and has immense utility in practical applications.", "source_url": "https:\/\/openalex.org\/W4398230580", "pdf_url": "", "doi": "10.7302\/22865", "handle": "", "repository_name": "Deep Blue (University of Michigan)", "source_record_id": "W4398230580", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 464, "title": "Towards Universal Object Detection", "title_ar": "", "title_en": "Towards Universal Object Detection", "author": "Zhaowei Cai", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "computer-vision; medical-ai; robotics; object-detection; medical-imaging", "keywords": "", "abstract": "Object detection is one of the most important and challenging research topics in computer vision. It is playing an important role in our everyday life and has many applications, e.g. surveillance, autonomous driving, robotics, drone, medical imaging, etc. The ultimate goal of object detection is a universal object detector that can work very well in any case under any condition like human vision system. However, there are multiple challenges on the universality of object detection, e.g. scale-variance, high-quality requirement, domain shift, computational constraint, etc. These will prevent the object detector from being widely used for various scales of objects, critical applications requiring extremely accurate localization, scenarios with changing domain priors, and diverse hardware settings. To address these challenges, multiple solutions have been proposed in this thesis. These include an efficient multi-scale architecture to achieve scale-invariant detection, a robust multi-stage framework effective for high-quality requirement, a cross-domain solution to extend the universality over various domains, and a design of complexity-aware cascades and a novel low-precision network to enhance the universality under different computational constraints. All these efforts have substantially improved the universality of object detection, and the advanced object detector can be applied to broader environments.", "source_url": "https:\/\/openalex.org\/W3000632482", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W3000632482", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 465, "title": "Computer vision Applied to horticultural Multifunctional Harvest Automation - lettuce", "title_ar": "", "title_en": "Computer vision Applied to horticultural Multifunctional Harvest Automation - lettuce", "author": "Flávio Roberto de Freitas Gonçalves", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; ai-applications; object-detection; ai-agriculture", "keywords": "", "abstract": "Agricultural automation has become significant in our country due to the need for domestic companies to compete properly with foreign companies and increased productivity and reduced losses. Using advanced technology features such as embedded electronic devices and precision farming techniques and process control, increased production is now possible. Horticultural crops are of great economic, social and food importance to the world's population. The current major challenge in the horticultural agricultural mechanization sector is harvesting. To this end, the development of a computer vision system employing image processing and artificial neural networks that allow the identification of vegetables and provide positioning parameters for a robotic actuator set for harvesting operations would meet this challenge. The objective of this work was to develop a detection system that through these resources enables the detection of vegetables and their positioning. For this purpose a capture apparatus was defined using model OV2640 serial cameras with ArduCam \/ Arduino control interface set in stereo vision configuration. The programming of the system was done in C # using the EMGU library using deep learning algorithms (YOLO) and depth metric algorithm by means of distance estimation map. As a result we have the definition of the apparatus where operating conditions were established and the performance of the image capture algorithms were analyzed, an image bank was collected, an artificial neural network was defined and training was performed with these images to identify the lettuce horticulture. , the object detection system was generated, the distance detection module was calibrated and lettuce detection tests were performed. Evaluating the results we concluded that it is possible to detect vegetables using the implemented system.", "source_url": "https:\/\/openalex.org\/W7120869170", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120869170", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 466, "title": "Privacy conscious computer vision", "title_ar": "", "title_en": "Privacy conscious computer vision", "author": "Jukka Yrjänäinen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; data-mining; object-detection; fairness", "keywords": "", "abstract": "This work describes the design and the implementation of the distributed computer vision solution for person tracking and counting in a public museum. The system consists of an edge device fleet that does send data to a cloud server for further analysis. The key design goal is the protection of the privacy of the persons being monitored. This is achieved by a system that does not send actual images to the server, instead detected persons are represented with a feature vector extracted from the images. Privacy sensitive image data is not stored or transmitted anywhere in the system. Device design consists of Raspberry Pi single-board computers equipped with a neural network acceleration hardware and a camera module. These devices are used to locate a person from the camera view with the object detection neural network. After object detection a re-identification neural network is applied to the found object to generate a feature vector representation. This vector is sent to the cloud server. Based on the feature vectors it is possible to associate detected people across multiple cameras and moments time. However, it is not possible to reconstruct the original image from the feature vector. The experiments and performance measurements with edge devices show that the simultaneous use of deep neural networks for object detection and feature generation using relatively low-cost hardware is feasible. The design recommendation based on the experiments is that the use of a dedicated HW accelerator for running all neural networks is preferred. The analysis also show that the variation of the accuracy and computational complexity of used neural networks offers a range of feasible performance trade-offs. The data analysis method in the server using only feature vector data for tracking and clustering is evaluated. The experiments with publicly available image dataset indicate that with the proposed approach it is possible to approximate person count with reasonable accuracy.", "source_url": "https:\/\/openalex.org\/W3120794583", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W3120794583", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 467, "title": "SYNTHETIC SCENE BASED OBJECT DETECTION: CAN YOU TRAIN NEURAL NETWORKS WITH SYNTHETICALLY GENERATED DATA TO RECOGNIZE REAL OBJECTS FOR MANUFACTURING?", "title_ar": "", "title_en": "SYNTHETIC SCENE BASED OBJECT DETECTION: CAN YOU TRAIN NEURAL NETWORKS WITH SYNTHETICALLY GENERATED DATA TO RECOGNIZE REAL OBJECTS FOR MANUFACTURING?", "author": "Gabriella Aiello", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Rhode Island", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; ai-applications; cnn; object-detection; image-segmentation; video-analysis; ai-industry", "keywords": "", "abstract": "Within the manufacturing industry, Artificial Intelligence (AI) has revolutionized the way manufacturers mass produce goods and has supported a more consumer driven focus. The ability of computers to understand digital images or videos is a key sensing capability that enables connected enterprise systems. Deep learning (DL) techniques used for computer vision tasks such as object detection reliably learn features without explicitly defining said features in the training process. Evaluating deep learning techniques for object detection applications can be broken into three steps: gathering\/processing data, model training\/evaluation and model deployment. This research focuses on the validity of using synthetically generated data to train convolutional neural networks (CNN) to recognize objects. An object detection pipeline was created to provide an infrastructure for new models to be created using 3D computer aided design (CAD) models of objects. The pipeline was built for research purposes but can be used for application purposes beyond testing models performance. A design of experiments was created to explore significant dataset parameters as well as hyperparameters used to train deep neural networks for object detection. Some significant factors explored were dataset size, inter-class variation, learning rate, number of epochs, and type of ma- chine learning model. Detectron2 was used to train was used to implement most object detection algorithms. Using optimized synthetically generated datasets and optimal hyperparameters, the object detection pipeline was capable of detecting objects bounding box and segmentation with average precision greater than 80%.", "source_url": "https:\/\/openalex.org\/W3134322164", "pdf_url": "https:\/\/digitalcommons.uri.edu\/cgi\/viewcontent.cgi?article=2894&context=theses", "doi": "10.23860\/thesis-aiello-gabriella-2020", "handle": "", "repository_name": "Digital Commons - URI (University of Rhode Island)", "source_record_id": "W3134322164", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 468, "title": "Real-Time Object Detection and Grasping for a Robotic Arm", "title_ar": "", "title_en": "Real-Time Object Detection and Grasping for a Robotic Arm", "author": "Thanh Thoi Nguyen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; object-detection", "keywords": "", "abstract": "Research and development with robotic arms have been explored for their potential to perform various physical tasks such as sorting items by colors and shapes.Prior work and research made advancements with robotic arm systems by combining them with deep neural networks (DNN) and computer-based vision to detect and interact with objects.These systems take a massive amount of time to deploy and develop due to time-consuming DNN training costs.This training cost staggers the development of robotic arms since computer vision needs a DNN to operate.Many of these systems rely on object detection to pick up items from a fixed camera overhead within their field of vision.Fixed overhead cameras compensate for the limitation of computer vision to properly locate and estimate the distances of the object in 3D space.Such that an overhead camera will assume detected objects will be on the surfaces directly in front.Research to estimate distance with computer vision relies on well-trained DNN to estimate accurately.Computer vision distance estimation has had yet to be successful with robotic arm systems.This project aims to reduce the training cost on DNN by using Transfer-Learning, to promptly deploy a well-trained DNN to a robotic arm system to detect an object and estimate its distance from a non-fixed field of vision to grasp objects in real-time.", "source_url": "https:\/\/openalex.org\/W4284885449", "pdf_url": "http:\/\/scholarworks.calstate.edu\/downloads\/nk322m021", "doi": "10.46569\/20.500.12680\/6t053n59p", "handle": "", "repository_name": "", "source_record_id": "W4284885449", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 469, "title": "Research on Efficient Vision-Based Hardware Architecture for Feature Representation and Object Detection", "title_ar": "", "title_en": "Research on Efficient Vision-Based Hardware Architecture for Feature Representation and Object Detection", "author": "Aiwen Luo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Hiroshima University", "university_source": "repository_host", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; object-detection", "keywords": "", "abstract": "Research on Efficient Vision-Based Hardware Architecture for Feature Representation and Object Detection (特徴表現及び物体検出のための効率的なビジョンベースのハードウェアアーキテク チャに関する研究)Machine learning (ML) is described by Arthur Samuel as the field of study that gives computers the ability to learn without being explicitly programmed.The methods for solving ML problem can be classified into two groups, i.e. supervised learning and unsupervised learning.Pattern recognition is a branch of ML that focuses on the recognition of patterns and regularities in data.Pattern recognition systems can be trained from labeled training data in supervised learning, but also can be used to discover previously unknown patterns in unsupervised learning when no labeled data are available.Feature representation is becoming an important prerequisite for building ML models to achieve high detection performance, in particular in vision-based applications.A wealth of feature-extraction techniques are being presented in this research field.To meet for the embedded systems with limited resources, this thesis developed a simplified scale -invariant speeded up robust features (SURF) approach, which can decrease object-matching complexity and enhance computational performance significantly, to extract the feature vectors in the image.Whereas, the histogram of oriented gradient (HOG) is a widely accepted feature for object detection, which attains high accuracy against changes of illumination of variously textured objects.Comparing to hardware solutions, the software technologies for feature extraction are more affected by computationally demanding algorithms so that the power dissipation of so ftware technologies is burdensome for mobile applications and the processing speed becomes a bottleneck in case of real-time (>30 frames\/s) object detection.Consequently, the achievable advantages of faster processing speed and lower power consumption lead to application-specific integrated circuit (ASIC) solutions.Therefore, a resource-efficient coprocessor for a simplified SURF descriptor, employing Haar-like wavelets as feature vectors (FVs), is developed and a prototype is fabricated in 180nm CMOS technology in this research.A pipelined hardware architecture with the low computational budget, that directly uses the serially-inputted pixel data without pre-storage, is", "source_url": "https:\/\/openalex.org\/W2964449452", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Hiroshima University Acedemic Information Repository (Hiroshima University)", "source_record_id": "W2964449452", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 470, "title": "Deep Learning Object Detection Models in Robotic Process Automation", "title_ar": "", "title_en": "Deep Learning Object Detection Models in Robotic Process Automation", "author": "Waltteri Vuorimaa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; robotics; object-detection", "keywords": "", "abstract": "In recent years, businesses have become increasingly aware of their need to automate previously manual business processes. Adoption rates of robotic process automation (RPA) tools have increased a lot, and this has generated a great deal of value for companies. But, alas, especially some older legacy applications are still extremely problematic from the point of view of RPA platforms, and they cannot often be interfaced in a same fashion as some more modern applications. This is especially lamentable, as it is often the older applications, which tend to be used in the more manual business processes. This thesis aims to build a foundation for building and evaluating commercializable computer vision (CV) object detection models, that resolve the issues faced by RPA applications that arise from the lack of operating system (OS) level user interface (UI) visual element data. This kind of models could be used as a basis for building completely new RPA tools, or for augmenting existing solutions. The research questions discussed by this study are as follows: 1. Is using machine learning models for the detection of user interface elements viable? 2. What are the implications to existing RPA applications? 3. Can we build a model that is able to generalize from data from another domain? This thesis discusses the theoretical background of object detection and machine learning, and presents three potential families of machine learning model architectures that could be used for the aforementioned tasks. These models were then trained and compared to the CV capabilities of a commercial RPA platform, UiPath. The results show that the most simple object detection models cannot compete with the current commercial offering. There is, however, a clear path to further research, and it is clear that building a object detection system for UI elements is viable. In addition, this thesis presents the first publicly available dataset of (manually) annotated desktop UI screenshot images, and a tool for generating artificial user interface data.", "source_url": "https:\/\/openalex.org\/W3154899117", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W3154899117", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 471, "title": "Enhancing Plant Disease Detection in Smart Agriculture Using Deep Learning Models and Advanced Data Augmentation Techniques", "title_ar": "", "title_en": "Enhancing Plant Disease Detection in Smart Agriculture Using Deep Learning Models and Advanced Data Augmentation Techniques", "author": "Nurbol Mukhametzhan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nazarbayev University", "university_source": "repository_host", "college": "", "department": "", "country": "Kazakhstan", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; few-zero-shot; cnn; ai-agriculture", "keywords": "", "abstract": "Deep learning for automatic detection of plant diseases has emerged as an innova- tive method to address agricultural productivity and food security issues. While deep CNNs have achieved near-ceiling performance on benchmark datasets under controlled conditions, the results do not necessarily translate to real-world agricultural environments. The reason for this is domain shift: models are typically trained on clean laboratory images. Field images include background clutter, illumination variations, occlusions, and inconsistent capture conditions [1, 2, 10, 11]. This thesis investigates cross-domain plant disease classification under a controlled- to-field setting using transfer-learned CNN architectures, namely EfficientNet-B0, MobileNetV3-Large, and ResNet-50 [21, 22, 20]. PlantVillage is used as the source domain for training, while PlantDoc is used as the target domain for evaluation and adaptation. The study evaluates a 12-class controlled-to-field transfer task using a harmonized subset of overlapping classes from both datasets. The implemented framework compares four training pipelines: P1, which uses preprocessing only and no augmentation; P2, which applies basic geometric and photometric augmentation; P3, which applies stronger environmental augmentation together with mixed-image regularization using MixUp and CutMix; and P4, which serves as an ablation study by applying P3’s environmental transforms without image mixing. [24, 26, 27]. The results show that source-domain validation performance does not translate into strong field performance. In the zero-shot cross-domain setting, where models are trained on PlantVillage and evaluated directly on PlantDoc without target-domain training, the best configuration reaches only 24.84% accuracy on the filtered PlantDoc FULL evaluation set of 1405 images, confirming a substantial lab-to-field generalization gap. When target-domain supervision is introduced through fine-tuning on the filtered PlantDoc training split and evaluation on the untouched filtered PlantDoc test split, performance improves substantially, reaching up to 59.32% accuracy and 0.5734 macro-F1. The results further show that basic augmentation is more reliable than the most aggressive augmentation setting for zero-shot transfer, while supervised fine-tuning on target-domain data provides the largest reduction in the deployment gap. Furthermore, a target-domain data efficiency ablation reveals that supervised adaptation scales logarithmically. Models recover the majority of their fine-tuned performance using only a fraction of the target dataset, demonstrating that bridging the lab-to-field gap does not strictly require massive field-collected datasets. Overall, this research studies plant disease recognition using field data. It shows that while domain adaptation improves robustness and makes models more deployable, augmenting the source training data alone is not sufficient to solve the controlled- to-field generalization problem.", "source_url": "https:\/\/openalex.org\/W7199181336", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Nazarbayev University Repository (Nazarbayev University)", "source_record_id": "W7199181336", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 472, "title": "Deep Learning for Plant Disease Detection Using Image Analysis", "title_ar": "", "title_en": "Deep Learning for Plant Disease Detection Using Image Analysis", "author": "Muhammad Butt", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; transfer-learning; cnn; xai; ai-agriculture", "keywords": "", "abstract": "The study explores the design and execution viability of deep learning algorithms to the automated system of identifying plant diseases using leaf images.The global agricultural sector is experiencing significant losses in crop production due to late or inappropriate diagnosis of diseases caused by diseases especially in areas that lack agronomic knowledge.Four convolutional neural network architectures were tested to reduce such a problem: one of my own baseline CNN, ResNet-50, Inception-v3, and EfficientNet-B0.Training of the models was done using the PlantVillage dataset with the help of transfer learning, data augmentation, and routine preprocessing operations.The model performance was measured based on the accuracy, the precision, the recall, the F1score and ROC-AUC.Robustness testing was done by the introduction of synthetic noise, changes in light, and background complexity to simulate conditions in the realworld field.Grad-CAM visualisations were also added to increase the clarity and transparency of the model decisions.With empirical findings, it has been shown that EfficientNet-B0 gives an optimal trade-off between classification accuracy and computational efficiency, making it suitable to be deployed on a mobile or edge platform.The research offers practical results to the scalable, explainable, and resourceefficient implementation of artificial intelligence systems in the area of disease management in agriculture.", "source_url": "https:\/\/openalex.org\/W7215477915", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/7d1433de-a71a-45fc-b741-2ce3d525322e\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215477915", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 473, "title": "Plant Disease Detection and Classification Using Deep Learning Techniques", "title_ar": "", "title_en": "Plant Disease Detection and Classification Using Deep Learning Techniques", "author": "Mazhar Iqbal", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; ai-agriculture", "keywords": "", "abstract": "The early and accurate detection of plant diseases is very important in protecting crop yield and ensuring food security.This thesis is going to explore how deep learning methods, especially Convolutional Neural Networks (CNNs) and ResNet-50, can help identify and classify diseases in Tomato, Potato, and Pepper plants.A dataset of over 38,000 images of both healthy and diseased leaf samples were collected.Different data augmentation techniques like rotation, shearing, zooming, and flipping were used to increase the efficiency of models and balance the number of images in each class.This research looks at how well a custom CNN and a fine-tuned ResNet-50 model perform on the three crops by checking their training, validation, and test results.The result shows that the CNN model's performance was reasonably well, and it achieved test accuracy of 94.12% (Tomato), 92.83% (Potato), and 91.98% (Pepper), while the ResNet-50 model has shown much better results, with an accuracy score of 99.37% and has shown a strong performance across all datasets.Diseases like Late Blight and Yellow Leaf Curl Virus, that are very difficult to visually distinguish but ResNet-50 was very precise at identifying these diseases.This work shows how deep learning is very effective in plant disease detection but also explores that these models could be used in real farming environments to help farmers take quick action and reduce crop losses.By decreasing dependence on manual inspection and helping in early disease detection, the research helps to support greener, technology-driven agriculture.", "source_url": "https:\/\/openalex.org\/W7215253019", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/396f0104-78e4-40da-ba0b-543e36d34d00\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215253019", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 474, "title": "An efficient deep learning approach to detect plant diseases using image data", "title_ar": "", "title_en": "An efficient deep learning approach to detect plant diseases using image data", "author": "Ahmad Anis ; Babli Saha ; Maisa Tarannum Srizee", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "BRAC University", "university_source": "repository_host", "college": "", "department": "", "country": "Bangladesh", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; predictive-analytics; cnn; transformers; ai-agriculture", "keywords": "", "abstract": "Plant diseases drastically impact on the yield and quality of crops and this is a great challenge to food security and sustainable agriculture in the world and more so in countries such as Bangladesh. There is need to therefore ensure the early and accurate diagnosis of plant diseases to enhance productivity and safeguard food supplies. Plant disease classification based on images has been highly successful with the swift development of deep learning, especially Convolutional Neural Networks (CNNs).In this thesis, a lightweight hybrid CNN-Transformer model is suggested as a classification of rice leaf disease. A number of pretrained CNN models, such as ResNet50, VGG16, InceptionV3, MobileNet V3, EfficientNetB0, and DenseNet121 are tested with transfer learning to determine the baseline performance. The dataset used in the study is the 23, 265 images of rice leaf in 10 different classes including healthy and diseased. To (hopefully) bypass the shortcomings of single-architecture models, the hybrid model proposed replaces local feature extraction with EfficientNetB0 and global contextual learning with TinyViT as well as a lightweight version of MobileNet. The experimental findings indicate that the hybrid method is better than single CNN and independent Transformer models as it has an accuracy of 98.04%with high precision, recall, and F1-score and low costs of computation. The results demonstrate the usefulness of lightweight CNN-Transformer models in real-time and resource-limited agricultural disease prediction and farm health crop management.", "source_url": "https:\/\/openalex.org\/W7202234903", "pdf_url": "https:\/\/dspace.bracu.ac.bd\/bitstreams\/270bad3b-32c1-4c00-9d85-93d3776dcfb2\/download", "doi": "", "handle": "", "repository_name": "BRAC University Institutional Repository (BRAC University)", "source_record_id": "W7202234903", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 475, "title": "Deep Learning-Based Early Detection and Classification of Plant Diseases Using Leaf Images", "title_ar": "", "title_en": "Deep Learning-Based Early Detection and Classification of Plant Diseases Using Leaf Images", "author": "Manish Saha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; object-detection; ai-agriculture", "keywords": "", "abstract": "Early and accurate detection of plant diseases is essential for improving crop yield and ensuring agricultural sustainability.This study proposes a deep learning-based approach for the automated detection and classification of plant diseases using leaf images.A custom Convolutional Neural Network (CNN) architecture is developed and evaluated against two stateof-the-art object detection models-Faster R-CNN and R-FCN.The proposed CNN model is trained and tested on a labeled dataset of diseased and healthy leaf images under varied lighting and background conditions.Performance is assessed using key evaluation metrics including accuracy, precision, recall, and F1-score.Experimental results reveal that the CNN model outperforms the benchmark models, achieving an accuracy of 94.80%, precision of 94.67%, recall of 94.73%, and an F1-score of 94.70%.These results confirm the model's robustness and generalization capability in realworld conditions.The approach offers a low-cost, scalable, and reliable solution for farmers and agricultural experts to identify plant diseases at an early stage, enabling timely intervention and reducing crop losses.This research contributes to the advancement of intelligent agricultural systems by integrating deep learning techniques for plant health monitoring.", "source_url": "https:\/\/openalex.org\/W7215313652", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/ed8770ae-8680-4f5b-9b6f-502ea8048e35\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215313652", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 476, "title": "Disease detection and mapping in olive groves using UAVs and deep learning for precision agriculture", "title_ar": "", "title_en": "Disease detection and mapping in olive groves using UAVs and deep learning for precision agriculture", "author": "Maurício Morais", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; medical-ai; robotics; ai-applications; object-detection; ai-agriculture", "keywords": "", "abstract": "This dissertation presents the implementation and validation of a cost-effective, Unmanned Aerial Vehicle (UAV) based system for automated detection and spatial mapping of olive knot disease in olive groves. Addressing the need for accessible and efficient plant disease monitoring in Precision Agriculture (PA), the proposed methodology leverages existing UAV imagery capabilities with lightweight Deep Learning (DL) models, specifically the You Only Look Once (YOLO) object detection architecture, to enable scalable and accurate detection. The academic contributions presented in this work have resulted in two peer-reviewed publications related to the dissertation topic, as detailed at the end of this document. An annotated dataset of UAV-acquired images was compiled, and several state-of-the-art YOLO object detection models were trained and evaluated under identical conditions. The best-performing model achieved a strong F1-score, demonstrating good results in detecting olive knot disease and accurately mapping its spatial distribution within the plantation. The workflow integrates spatial cross-referencing of detections with UAV flight path data and proximity analysis, enabling the assignment of disease detections to individual trees. An interactive map interface, developed using the Folium Python library, provides visualization of the disease distribution and supports practical grove management. The experimental results indicate that cost-effective UAVs and lightweight DL models can be effectively combined for plant disease detection and spatial analysis, offering a robust and scalable approach for real-world agricultural applications. Limitations regarding early symptom detection and image quality are discussed, and directions for future work are proposed.", "source_url": "https:\/\/openalex.org\/W7112960058", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7112960058", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 477, "title": "A Unified Framework for Distributed and Resource-Aware Plant Disease Detection Using Federated and Adaptive Hybrid Deep Learning", "title_ar": "", "title_en": "A Unified Framework for Distributed and Resource-Aware Plant Disease Detection Using Federated and Adaptive Hybrid Deep Learning", "author": "Ticauris S Stokes", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Southern Mississippi", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; deep-learning; medical-ai; robotics; responsible-ai; ai-applications; predictive-analytics; federated-learning; fairness; ai-agriculture; ai-smart-cities", "keywords": "", "abstract": "Plant disease detection in precision agriculture must operate under significant computational constraints, particularly in low-resource (LR) environments such as unmanned aerial vehicles (UAVs) and edge devices. While LR models enable efficient real-time inference, they often lack the representational capacity required to accurately detect complex disease patterns. In contrast, high-resource (HR) models achieve superior detection performance due to their deep architectures but are computationally expensive and unsuitable for direct deployment in resource-constrained settings. This fundamental trade-off between efficiency and accuracy presents a major challenge for practical agricultural monitoring systems. To address the distributed nature of agricultural data, federated learning (FL) is adopted to enable collaborative model training across multiple data sources without requiring centralized data aggregation. FL improves generalization and preserves data privacy; however, it does not resolve the inherent computational disparity between LR and HR models. Consequently, models trained under FL still face limitations when deployed in real-time LR environments. To bridge this gap, this work proposes a hybrid adaptive inference framework that integrates LR and HR models within a unified system. The framework dynamically selects between efficient LR inference and more accurate HR processing based on prediction confidence, enabling effective utilization of computational resources while maintaining high detection performance. Furthermore, a Bayesian optimization strategy is employed to determine optimal decision thresholds, ensuring a balanced trade-off between accuracy, processing time, and energy consumption. Experimental results demonstrate that the proposed framework significantly reduces computational cost while preserving high detection accuracy, enabling scalable and efficient plant disease detection in real-world UAV-based agricultural systems.", "source_url": "https:\/\/openalex.org\/W7162049655", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aquila Digital Community (University of Southern Mississippi)", "source_record_id": "W7162049655", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 478, "title": "Transfer Learning-Based EfficientNetB0 Model for Multi-Crop Plant Disease Classification", "title_ar": "", "title_en": "Transfer Learning-Based EfficientNetB0 Model for Multi-Crop Plant Disease Classification", "author": "Mebrahtu Yohannes", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Ethiopia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; ai-agriculture", "keywords": "", "abstract": "globally. Early and accurate diagnosis of diseases is key to reducing losses and improving agricultural practices. Traditional methods of plant disease diagnosis are known for being lengthy, cumbersome, and prone to human error. Recent developments in deep learning have shown promise in the automatic diagnosis and classification of plant diseases. Namely, convolutional neural networks (CNNs) perform quite well for such problems. The purpose of this study is to present an approach to plant disease classification that is based on the use of transfer learning and utilizes the Convolutional Neural Network architecture called EfficientNetB0. Transfer learning with ImageNet pretrained weights and an efficient architecture for feature extraction was chosen because of its ability to significantly lower the computational resources required and improve performance. Moreover, plant leaf images from different datasets were taken into account to provide better training. The model achieved 99.32 % training accuracy, 99.37% validation accuracy, and 99.32% test accuracy. It also had impressive convergence capability and low computational complexity, with around 4.7 million parameters and the size of the saved model at around 34 MB. It can be concluded from the findings that the transfer learning-based EfficientNetB0 model provides a great balance between accurate classification and efficiency, while improving the practicality of the system.", "source_url": "https:\/\/openalex.org\/W7212441753", "pdf_url": "", "doi": "10.82589\/muir-1230", "handle": "", "repository_name": "Mekelle University Institutional Repository (MU-IR)", "source_record_id": "W7212441753", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 479, "title": "Efficient deep learning for mobile weed classification", "title_ar": "", "title_en": "Efficient deep learning for mobile weed classification", "author": "Leonardo G. Sanches", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Federal University of Technology – Paraná", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "ai-agriculture", "categories": "deep-learning; computer-vision; medical-ai; ai-applications; ai-agriculture", "keywords": "", "abstract": "Agricultural production across regions faces several challenges in cultivation, including climate variability, pests, diseases, and weeds. Deep neural network models have proven efficient in addressing some of these challenges, for example, in identifying and classifying weeds. A practical way for a farmer to monitor the health of their crop is to take a picture of a plant using their mobile device’s camera and run an application on the device that analyzes the image using a neural network. If it identifies the invasive plant, the application can determine the weed type in the image. The problem with developing this type of application is that many deep learning models require significant computational power, making them unsuitable for mobile devices. To mitigate this problem, we developed a pipeline for evaluating deep learning models suitable for weed classification. Our solution uses a set of metrics to assess both computational efficiency and model performance. Furthermore, we use our pipeline to generate an optimized model that performs inference on mobile devices with performance comparable to that of traditional deep learning models.", "source_url": "https:\/\/openalex.org\/W7167994211", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/40711\/1\/deeplearningeficienteparaclassificacao.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)", "source_record_id": "W7167994211", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 480, "title": "Plant disease classification system using deep learning", "title_ar": "", "title_en": "Plant disease classification system using deep learning", "author": "Mohd Azizul Asmad Latip", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universiti Malaysia Sabah", "university_source": "repository_host", "college": "", "department": "", "country": "Malaysia", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; cnn; ai-cybersecurity; ai-agriculture", "keywords": "", "abstract": "In agriculture production, the unlimited and no disease plant product from farming become important things due this product is necessities in terms foods and mainly source for highly nutrient to community regardless country in the world. However, an increase in the human population requires an increase in agricultural production. Generally, the most important thing in agriculture that affects the quantity and quality of crops is plant diseases and make crop disease become a major threat to food security. In general, a farmer knows that his plant is attacked by a disease through direct vision. But, this process is sometimes inaccurate. Plant diseases are not only a threat to food security at the global scale, but can also have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops. With the development of machine learning technology, plant disease classification can be done automatically using deep learning. In deep learning, a convolutional neural network (CNN) is a class of deep neural networks, most commonly applied to analysing visual imaginary. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels that scan the hidden layers and translation invariance characteristics.", "source_url": "https:\/\/openalex.org\/W7198848850", "pdf_url": "https:\/\/eprints.ums.edu.my\/id\/eprint\/33302\/1\/PLANT%20DISEASE%20CLASSIFICATION%20SYSTEM%20USING%20DEEP%20LEARNING.24pages.pdf", "doi": "", "handle": "", "repository_name": "UMS Institutional Repository (Universiti Malaysia Sabah)", "source_record_id": "W7198848850", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 481, "title": "Deep learning detector for pests and plant disease recognition", "title_ar": "", "title_en": "Deep learning detector for pests and plant disease recognition", "author": "Oluwatimilehin Adekunle Ileladewa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; image-classification; object-detection; ai-agriculture", "keywords": "", "abstract": "Pests and diseases in plants have been a major challenge factor in the agricultural field. And developing a quick and accurate model could help in detecting pests and diseases in plants. Meanwhile, evolution in deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. However, in this project, deep learning techniques are used in developing a model for diseases and pest detection in plants, and then train and test the model before eventually integrating the model into a mobile application. The goal of this project is to develop a framework that can classify the class of a plant, and detect areas of the plant leaf already affected by diseases, and eventually deploy the framework on a mobile application. In order to find a suitable meta-architecture for the aim of the project, we use the combination of Single Shot MultiBox Detector and MobileNet (SSD MobileNet) where Single Shot MultiBox Detector (SSD) is the algorithm that takes a single shot to detect multiple objects within an image, and mobilenet is a neural network for recognition and classification. The system is trained using a large dataset containing different classes of both diseased and healthy images of plants from PlantVillage. Final results of the project reveal that our proposed system can recognize and detect various type of pests and diseases that have been trained in the model, with the ability to handle the complexity of a plant’s surrounding area.", "source_url": "https:\/\/openalex.org\/W3133549069", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3133549069", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 482, "title": "Plant recognition, detection, and counting with deep learning", "title_ar": "", "title_en": "Plant recognition, detection, and counting with deep learning", "author": "Pornntiwa Pawara", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Groningen \/ Centre for Information Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; image-classification; ai-education; ai-agriculture", "keywords": "", "abstract": "In agricultural and farm management, plant recognition, plant detection, and plant counting systems are crucial. We can apply these tasks to several applications, for example, plant disease detection, weed detection, fruit harvest system, and plant species identification. Plants can be identified by looking at their most discriminating parts, such as a leaf, fruit, flower, bark, and the overall plant, by considering attributes as shape, size, or color. However, the identification of plant species from field observation can be complicated, time-consuming, and requires specialized expertise. Computer vision and machine-learning techniques have become ubiquitous and are invaluable to overcome problems with plant recognition in research. Although these techniques have been of great help, image-based plant recognition is still a challenge. There are several obstacles, such as considerable species diversity, intra-class dissimilarity, inter-class similarity, and blurred resource images. Recently, the emerging of deep learning has brought substantial advances in image classification. Deep learning architectures can learn from images and notably increase their predictive accuracy. This thesis provides various techniques, including data augmentation and classification schemes, to improve plant recognition, plant detection, and plant counting system.", "source_url": "https:\/\/openalex.org\/W3118912755", "pdf_url": "https:\/\/pure.rug.nl\/ws\/files\/156115980\/Complete_thesis.pdf", "doi": "10.33612\/diss.156115978", "handle": "", "repository_name": "University of Groningen research database (University of Groningen \/ Centre for Information Technology)", "source_record_id": "W3118912755", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 483, "title": "Salient map image for plant disease using deep learning", "title_ar": "", "title_en": "Salient map image for plant disease using deep learning", "author": "Poey Poey Quah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; image-segmentation; ai-agriculture", "keywords": "", "abstract": "Plant diseases are a critical factor that impacts the yield and quality of crops and economics in the agricultural sector. This can shown in the incident of a fungal wheat disease in North Texas caused $250 million loss of revenue of the affected country in the year of 2001. Hence, there is essential in protecting the crops from diseases to ensure production quality and quantity. Early detection of the plant diseases is necessary, and it can help to prevent the spreading of the diseases by choosing an appropriate treatment for the plants. However, the process is often trailed by the lack of necessary infrastructure that offers simplicity in performing accurate classifications. Thus, rapid and accurate detection of plant disease through machine learning is essential to minimizing or averting this hardship. On top of that, the existing work does not segment the progression area of the disease on the leaf. In which, this area giving a lot of information on the disease. Especially to the pattern of disease symptoms that are very similar such as in the case of vegetable early and late blight is currently not given much consideration in the machine learning process. Hence, the objective of this project is to construct a salient map image that tracks the disease progression right from inception to manifestation following the pathological disease anatomy. Semantic segmentation with ConvolutionalNeuralNetwork (CNN) is used to construct the salient map image, through transfer learning with SegNet. In this project, 460 images of early blight and late blight diseases plants from PlantVillage dataset is used for the training and testing processes of the CNN. Next, the training parameters are fine-tuned in order to optimize the deep learning model accuracy. At the end of the project, the deep learning model will be able to segment the leaf image into several regions with the overall accuracy of 89.567% and overall IOU of 52.5448%. Also, although the transfer learning on FCN with same data-set and training parameters has slightly better performance with overall accuracy of 89.91% and IOU of 53.92%, its main drawbacks of long model training duration and consumption of huge memory size has made SegNet more preferable in this project. With the gradient map image generated, the pattern of each disease manifestation along the leaf surface can be tracked and quantified for better understanding and characterization based on their anatomy.", "source_url": "https:\/\/openalex.org\/W3213994822", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3213994822", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 484, "title": "Performance comparison of traditional machine learning and deep learning methods in plant disease detection", "title_ar": "", "title_en": "", "author": "Musa Çetinkaya", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2024, "language": "tr", "research_domain": "medical-ai", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; cnn; rnn; object-detection; ai-agriculture", "keywords": "", "abstract": "Geleneksel makine öğrenmesi (GMÖ) ve derin öğrenme (DÖ), yapay zeka alanında iki önemli yaklaşımdır. GMÖ, tahmine dayalı modeller oluşturmak için destek vektör makineleri (DVM), k-en yakın komşular, karar ağaçları gibi çeşitli algoritmalar kullanır. Bu yaklaşımda özellik çıkartımı, algoritmanın başarımını etkileyen en önemli unsurdur. GMÖ küçük veri kümelerinde başarılı sonuçlar elde ederken karmaşık veriler ve büyük ölçekli veri kümelerinde aynı başarıyı gösteremezler. Makine öğrenmesinin bir alt kümesi olan DÖ ise ham veriden hiyerarşik özellik temsillerini otomatik olarak öğrenmek için çok katmanlı sinir ağlarını kullanır. Finans, endüstri, tıp gibi alanlarda ve görüntü sınıflandırma, doğal dil işleme ve ses-tanıma işleme gibi çalışmalarda çok iyi sonuçlar elde ederken büyük miktarda veri ve hesaplama kaynağı gerektirir. Bu tez çalışması GMÖ ve DÖ modellerinin tahmin performansını karşılaştırmak amacıyla bitki hastalıklarının bilgisayar destekli teşhisini sunmaktadır. Bitki hastalıklarının tespiti, modern tarımda ürün kayıplarını en aza indirmek ve gıda güvenliğini sağlamak açısından çok önemlidir. Bitki hastalıklarının erken ve doğru bir şekilde belirlenmesi pestisit kullanımını azaltabilir, mahsul verimini artırabilir ve ürün kalitesini iyileştirebilir. Bu amaçla, çalışmada geleneksel yöntemlerden Lineer Regresyon, DVM ve Rastgele Orman ile DÖ yöntemlerinden Evrişimli Sinir Ağı, LSTM, YOLOv8 ve VGG16 algoritmalarının performansları karşılaştırılmıştır. Elde edilen sonuçlar GMÖ’de DVM için ile en yüksek olmak üzere ortalama ,9 doğruluk elde edilirken DÖ’de CNN için doğruluk en yüksek ve ortalama ,8 olarak elde edilmiştir.", "source_url": "https:\/\/openalex.org\/W7217036510", "pdf_url": "https:\/\/acikkaynak.bilecik.edu.tr\/bitstreams\/aceb2775-96b9-48c5-8354-231a1e200291\/download", "doi": "", "handle": "", "repository_name": "Bilecik Şeyh Edebali University Institutional Repository", "source_record_id": "W7217036510", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 486, "title": "Deep learning approaches for yield prediction and crop disease recognition", "title_ar": "", "title_en": "Deep learning approaches for yield prediction and crop disease recognition", "author": "Luning Bi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Iowa State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; ai-applications; predictive-analytics; optimization; cnn; rnn; transformers; gan; ai-agriculture", "keywords": "", "abstract": "The increase of the world population has brought significant challenges to the agriculture production system. Although mechanization has been realized in agriculture, many tasks (e.g., breeding, field inspection) are still labor-intensive and time-consuming. Therefore an automatic and intelligent solution is needed for the advancement of agricultural production. During this process, the biggest challenge is how to teach computers to understand the concepts in the real world. For example, an experienced expert can easily determine whether a plant is diseased or healthy. However, this may be challenging for the computer. Thus, the motivation of this dissertation study is to tackle these challenges in precision agriculture. This dissertation consists of four papers that propose different deep learning methods for the most challenging problems in agriculture. In the first paper, a genetic algorithm (GA)-assisted deep neural network was built for yield prediction using genetic information and environmental factors. In the global search phase, the GA was introduced to help determine the best initial weights of the neural network. In the local phase, random perturbation was used to avoid the local optimum. By using the proposed method, the root mean square error can be reduced by up to 10%. In the second paper, we proposed a generative adversarial network (GAN)-based approach to generate additional images for the classification of plant species and diseases using limited data. CNN was used as the basic network to classify species and diseases. GAN and label smoothing regularization (LSR) were combined to generate additional training images. Regular data augmentation techniques were also used to expand the dataset. The results showed that compared with using the real dataset only, the proposed method can improve the prediction accuracy by 6%. In the third paper, the potential of using satellite imagery for plant disease detection was explored. A gated recurrent units (GRU)-based model was presented for early detection of soybean sudden death syndrome (SDS) through time-series satellite imagery. The results showed that, compared to XGBoost and fully connected deep neural network (FCDNN), the GRU-based can improve the overall prediction accuracy by 7%. In addition, the proposed method can also be adapted to predict the future development of SDS. In the fourth paper, a transformer-based approach was proposed for soybean yield prediction using time-series camera images and seed treatments information. First, a vision transformer (ViT) base model was designed to extract features from the images. Then another transformer-based model was established to predict the yield using the time-series features. A case study was been conducted using a data set that was collected during the 2020 soybean-growing seasons in Canada. The experiment results show that compared to non-time series prediction and other baseline models, the proposed approach can reduce the mean squared error by 25%-40%. In conclusion, this dissertation aims to apply different state-of-art deep learning methods in agriculture. The study covers different topics, which range from yield prediction, species classification, to plant disease classification and prediction. At the model level, the application of linear models, tree-based methods, fully connected neural networks, convolutional neural networks, time-series models and transformers to different tasks have been investigated. In terms of the learning type, both unsupervised learning and supervised learning have been utilized. The experimental results have shown that appropriate deep learning methods can achieve better performance than traditional methods on specific tasks. Based on our work, more applications of deep learning techniques can be developed in the future.", "source_url": "https:\/\/openalex.org\/W7135318077", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Iowa State University Digital Repository (Iowa State University)", "source_record_id": "W7135318077", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 487, "title": "A deep learning model for early detection of maize diseases in Tanzania", "title_ar": "", "title_en": "A deep learning model for early detection of maize diseases in Tanzania", "author": "Flavia Mayo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; transformers; ai-agriculture", "keywords": "", "abstract": "Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of residents depending on it for their livelihood. Maize is the main and dominating food crop in the country, accounting for 45% of all farmland produce. Inevitably, its production is affected by diseases that, if detected early, could be easily treated. Maize Streak Virus (MSV) and Maize Lethal Necrosis (MLN) are commonly reported diseases that are often detected too late by farmers. This raised the need for a sophisticated method for early detection of these diseases to enable timely treatment. This study investigated the potential of developing a deep-learning model for the early detection of maize diseases in Tanzania. Imagery datasets were used for model training, they were collected through physical observation with the help of a plant pathologist from three regions of the country; Arusha, Kilimanjaro, and Manyara. Two models, a Convolutional Neural Network (CNN) and a Vision Transformer (ViT), were developed from scratch and classified into four classes: Healthy, MLN, MSV, and WRONG. The results revealed that the ViT model outperformed the CNN model, achieving validation accuracies of 0.931 and 0.9096 respectively. Despite the superior performance of the ViT model, the CNN model was selected for deployment in a mobile-based application due to its smaller size, lower memory, and small computational requirements. A quantitative research method using a survey questionnaire was employed to gather requirements for system development and validation. Validation of the model's performance through questionnaires administered to farmers and agricultural experts, yielding highly positive feedback. This enthusiastic reception highlights the potential impact of the developed application on improving early disease detection and enhancing maize productivity in Tanzania. Empowering stakeholders of agriculture to identify more effective methods of managing them before serious harm is done. Consequently, custodians of agriculture, such as the Ministry of Agriculture, organizations, and other companies, will have the potential to detect maize diseases early, henceforth adding up to the increase in the Gross Domestic Product (GDP) and guaranteeing the country’s food security.", "source_url": "https:\/\/openalex.org\/W4409342614", "pdf_url": "", "doi": "10.58694\/20.500.12479\/2917", "handle": "", "repository_name": "", "source_record_id": "W4409342614", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 488, "title": "TOWARDS RELIABLE PLANT PHENOTYPING WITH DEEP LEARNING AND REMOTE SENSING", "title_ar": "", "title_en": "TOWARDS RELIABLE PLANT PHENOTYPING WITH DEEP LEARNING AND REMOTE SENSING", "author": "Umme Fawzia Rahim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "ai-agriculture", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; ai-agriculture", "keywords": "", "abstract": "Quantitative measurement of plant components (e.g., leaves, inflorescences, flowers, and fruits) is of great interest in precision agriculture since it provides essential information for breeding research and guided agricultural management to enhance crop quality and productivity, as well as for yield estimation and adjusting marketing strategies.The conventional manual phenotyping is time and labor intensive, and often destructive, which remains as the major bottleneck to the advancement of precision and intelligent agriculture.Such restrictions encouraged the growth of high throughput plant phenotyping (HTPP), which is primarily based on sensing technologies and automated data retrieval and processing.These automatic approaches acquire significant amounts of data, primarily in the format of digital images, which need to be thoroughly processed.This is today possible due to the development of powerful image analysis technologies that can automatically identify objects in images using machine learning methods.Due to a range of environmental conditions, field phenotyping still faces significant challenges; therefore, the progress of HTPP capabilities for field conditions can be highly relevant to support digitalization of farm data and precision farming.This thesis introduces an automated image-based phenotyping system using advanced computer vision techniques (i.e., deep learning and multi-object tracking) and appropriate pre-and post-processing methods (i.e., image contrast enhancement, density map-based", "source_url": "https:\/\/openalex.org\/W7144386906", "pdf_url": "https:\/\/shizuoka.repo.nii.ac.jp\/record\/14937\/files\/K1255.pdf", "doi": "10.14945\/00029830", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7144386906", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 489, "title": "Deep Learning for autonomous intelligent agents", "title_ar": "", "title_en": "Deep Learning for autonomous intelligent agents", "author": "Fateme Fotouhi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "deep-learning; computer-vision; medical-ai; robotics; responsible-ai; ai-applications; multimodal; federated-learning; fairness; ai-agriculture", "keywords": "", "abstract": "Autonomous intelligent agents possess the capability to autonomously perceive their environment and make decisions based on their prior knowledge or learning experiences. The field of Deep Learning (DL) has witnessed significant advancements in recent years, leading to the development of diverse algorithms applicable in various domains. By leveraging these algorithms, intelligent agents can extract and learn higher-level features from data, enabling quick and efficient perception. These algorithms find utility across domains such as visual perception for agents, modular learning frameworks, and collaborative learning scenarios in distributed deep learning. Despite the remarkable progress in DL algorithms, several challenges persist, including limited training data, data imbalance, scalability concerns, and data privacy considerations. The ability of intelligent agents to interpret visual data holds immense importance in applications where human assessment of data is time-consuming and labor-intensive. An illustrative example of such an application is disease detection in agriculture, where manual execution of this task can consume hours or even days for a single field. Consequently, training agents through deep learning algorithms and deploying them on edge devices for inference can provide effective solutions to such challenges. Intelligent agents are not confined to executing visual perception alone; they can also acquire knowledge through modular deep learning frameworks. In certain applications, such as robotics, intelligent agents must make decisions based on diverse visual and linguistic inputs from their environment. This task becomes even more challenging when dealing with cluttered and complex environments or when multiple factors, such as object manipulation, come into play, surpassing simple navigation requirements. Moreover, intelligent agents can collaborate to train a consensus model within a distributed setting. In decentralized deep learning, agents privately update their local deep learning models using their individual data and achieve consensus through peer-to-peer communication. This approach eliminates the need for a central server as in Federated Learning (FL). However, communication overhead remains a challenge in decentralized learning. Recent studies have proposed communication-efficient methods, although the reduction in communication time often comes at the expense of significant accuracy loss, particularly in large-scale networks. Additionally, a pertinent question arises regarding the efficacy of decentralized learning methods in practical scenarios, such as anomaly detection in agriculture. Specifically when the complexity intensifies when we encounter the non-uniform distribution of various plant genotypes within a field. This report aims to address the aforementioned challenges in different facets of employing intelligent agents. Firstly, an autonomous framework for disease detection in agriculture is investigated, targeting issues such as insufficient training data and data imbalance. In this context, a comparative analysis is performed between manually collected Single Nucleotide Polymorphisms (SNPs) and DL-executed SNPs to evaluate the performance of DL models. Secondly, a modular vision-language framework is proposed to tackle the challenges presented by the ALFRED dataset. This modular framework comprises six distinct modules, designed to enhance the agent's task completion abilities within this dataset. Thirdly, a communication-efficient graph-based method is introduced for decentralized deep learning, aiming to reduce communication time while simultaneously preserving high accuracy, even in large-scale networks. Fourthly, an investigation is conducted into detecting anomalies in agricultural fields using decentralized deep learning. This research aims to demonstrate the efficacy and generalizability of decentralized learning compared to local learning approaches, particularly in scenarios involving distributed genotypic differences across the field. Lastly, concluding remarks are provided, and future research directions are discussed to further advance the utilization of intelligent agents.", "source_url": "https:\/\/openalex.org\/W4399730369", "pdf_url": "", "doi": "10.31274\/td-20240617-351", "handle": "", "repository_name": "", "source_record_id": "W4399730369", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 490, "title": "Android application for crop disease diagnosis using image processing and deep learning (Smart Agriculture).", "title_ar": "", "title_en": "Android application for crop disease diagnosis using image processing and deep learning (Smart Agriculture).", "author": "Munashe Brian Katsande", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Zimbabwe", "university_source": "repository_host", "college": "", "department": "", "country": "Zimbabwe", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; object-detection; ai-agriculture", "keywords": "", "abstract": "Plant diseases are a major threat to food security worldwide. An accurate and a faster approach to detection and diagnosis of diseases in crops will go a long way to help farmers save their crop and increase yield. Recent developments in smartphone technology and deep neural networks have allowed researchers to develop accurate and ease to use systems to help farmer in this regard. In this dissertation, we developed an android based cotton crop disease detector using deep convolutional networks and image processing. We made use of transfer learning using a pre trained Inceptionv3 model. Additional layers were added to the pretrained model and trained on our dataset. The trained model finally integrated into an android mobile app and experimental results on the developed model were able to achieve an average accuracy of 83%.", "source_url": "https:\/\/openalex.org\/W3201423842", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Zimbabwe Institutional Repository (University of Zimbabwe)", "source_record_id": "W3201423842", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 491, "title": "Plant Species Detection in Aerial and Satellite Images using Deep Learning", "title_ar": "", "title_en": "Plant Species Detection in Aerial and Satellite Images using Deep Learning", "author": "Anastasiia N. Safonova", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Granada", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "deep-learning; computer-vision; robotics", "keywords": "", "abstract": "It is unquestionable that the conservation of plant species is essential for the preservation of nature, climate, and human wellbeing.Classically, the task of conserving threatened plant species was generally done through direct field supervision by natural resources managers.Thanks to the advances in sensor technologies and also in aircrafts such as unmanned aerial vehicles (UAV), high resolution remote sensing (RS) data are becoming an important resource for monitoring threatened plant species in large areas.Such RS data are usually multi-spectral images, with three or more bands, of up to 3 cm\/pixel resolution, providing an orthogonal or quasi-orthogonal view of the considered plant species.This information may not be as complete as the information provided by natural images; however, it might be sufficient to monitor tree species located in very large areas with difficult access.", "source_url": "https:\/\/openalex.org\/W3183821534", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Institutional Repository of the University of Granada (University of Granada)", "source_record_id": "W3183821534", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 492, "title": "Detection of diseases in rice plant with deep learning methods and artificial network based NDVI values", "title_ar": "", "title_en": "", "author": "İrfan Ökten", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2022, "language": "tr", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "deep-learning; medical-ai; cnn", "keywords": "", "abstract": "Dünya çapında gıda tüketimi göz önüne alındığında pirincin önemli bir yere sahip olduğu görülür. Çeltik bitkisi, buğdaygiller ailesinden mısır ve buğdaydan sonra en fazla ekimi yapılan bitkidir. Çeltik üretiminde çeşitli zararlılardan ve hastalıklardan dolayı üretim kaybı yaşanmaktadır. Genellikle yaşanan bu kayıplara neden olan üç temel hastalık bulunmaktadır: yaprak patlaması (Leaf Blast - Pyricularia grisea), kahverengi lekeler (Brown Spot - Helminthosporium oryzae) ve beyaz noktalar (Hispa - Dicladispa armigera). Tüm bu hastalık belirtileri bitkinin yaprağında bulunmaktadır. Yaprak görüntülerinden bitki hastalığının otomatik bir şekilde teşhisi geliştirme aşamasında olan bir konudur. Bu tez kapsamında çeltik bitkisine ait bu üç hastalığı tespit etmek için derin öğrenme modellerinden Evrişimli Sinir Ağı (CNN: Convolution Neural Networks) kullanılmıştır. Çalışmada kullanılan 3341 adet çeltik bitkisi yaprağına ait RGB veri seti Kaggle sitesinden elde edilmiştir. Çeltik bitkisinin RGB yaprak görüntülerinden derin öğrenme modeli ile çeltik bitkisine ait üç önemli hastalık (Brown Spot, Leaf Blast ve Hispa) tespiti yüksek doğruluk oranlarıyla elde edilmiştir. Çeltik bitkisine ait hastalıkların erken tespiti için CNN modelinin deneyler sonucu bulunan en uygun hiper parametreler ile eğitimi gerçekleştirilmiştir. Ağın eğitimi için çeltik bitkisine ait RGB görüntüler kullanılarak ,78 oranında doğruluk elde edilmiştir. Tarım alanlarında ürünlerin sağlık durumlarının incelenmesinde genellikle multispektral kameralardan ve uydu görüntülerinden elde edilen Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI: Normalized Difference Vegetation Index) değerleri kullanılmaktadır. NDVI verilerinin elde edilmesinde kullanılan uzaktan algılama tekniklerinde yüksek maliyet ve hava durumunun uygunluğu öne çıkmaktadır. Diğer alternatif olan insansız hava araçlarına (İHA) entegre edilmiş multispektral kamera sistemlerinin maliyetlerinin yüksek olması ve uzman gereksinimine ihtiyaç duyulması bir başka sorundur. Bu tez çalışmasında ikinci olarak multispektral kameraların yerine standart RGB görüntü veren kamera sistemlerinden NDVI değerini tahmin etmek (nNDVI: Neural network-based Normalized Difference Vegetation Index) için yeni bir yapay sinir ağı modeli önerilmiştir. Önerilen ağın eğitim ve testlerinde kullanılan veri seti İsviçre'de bir tarım alanından ve Togo'da bir çiftlikten multispektral kamera ile alınan görüntülerden elde edilmiştir. Bu model sayesinde ,013 doğruluk oranı ile RGB görüntülerinden NDVI verileri elde edilmiştir. Son olarak bu tez kapsamında çeltik bitkisinin standart RGB görüntülerinden nNDVI verileri elde edilerek, bu veriler ile derin öğrenme modelinin eğitimi yapılmış ve ,97'lik bir doğruluk oranına ulaşılmıştır. Çeltik bitkisindeki yaprak görüntüleri ile hastalığının tespiti için elde edilen bu başarım oranı, yöntemin uygulanabilirliğini göstermektedir.", "source_url": "https:\/\/openalex.org\/W7217327925", "pdf_url": "https:\/\/acikkaynak.bilecik.edu.tr\/bitstreams\/7f64b62f-1f4b-408b-8fad-6e1b1f3cf42b\/download", "doi": "", "handle": "", "repository_name": "Bilecik Şeyh Edebali University Institutional Repository", "source_record_id": "W7217327925", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 493, "title": "Recognition of Plant Diseases Using Convolutional Neural Networks", "title_ar": "", "title_en": "Recognition of Plant Diseases Using Convolutional Neural Networks", "author": "Cyntia D. León-Lemus", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Western Institute of Technology and Higher Education", "university_source": "repository_host", "college": "", "department": "", "country": "Mexico", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; ai-agriculture", "keywords": "", "abstract": "Early detection of diseases in crops for human consumption is crucial for agricultural production in the world. Nowadays, Deep learning algorithms are used for the analysis and classification of images such as the convolutional neural network used to detect diseases in plants. Unfortunately, these algorithms still need a lot of computation to obtain results that could take hours, days, or maybe weeks for a prediction. This paper aims to provide an analysis of how the Wavelet transform in distributed platforms can provide competitive results with a fraction of the resources used comparatively in time and memory used to detect diseases in tomato leaves.", "source_url": "https:\/\/openalex.org\/W3089570666", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ITESO Institutional Repository (ReI) (Western Institute of Technology and Higher Education)", "source_record_id": "W3089570666", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 494, "title": "Deep Learning for Early Detection, Identification, and Spatiotemporal Monitoring of Plant Diseases Using Multispectral Aerial Imagery", "title_ar": "", "title_en": "Deep Learning for Early Detection, Identification, and Spatiotemporal Monitoring of Plant Diseases Using Multispectral Aerial Imagery", "author": "Joseph Mbugua", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Claremont Colleges", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; image-classification; ai-agriculture", "keywords": "", "abstract": "Production of food crops is hampered by the proliferation of crop diseases which cause huge harvest losses. Current crop-health monitoring programs involve the deployment of scouts and experts to detect and identify crop diseases through visual observation. These monitoring schemes are expensive and too slow to offer timely remedial recommendations for preventing the spread of these crop-damaging diseases. There is thus a need for the development of cheaper and faster methods for identifying and monitoring crop diseases. Recent advances in deep learning have enabled the development of automatic and accurate image classification systems. These advances coupled with the widespread availability of multispectral aerial imagery provide a cost-effective method for developing crop-diseases classification tools. However, large datasets are required to train deep learning models, which may be costly and difficult to obtain. Fortunately, models trained on one task can be repurposed for different tasks (with limited data) using transfer learning technique. The purpose of this research was to develop and implement an end-to-end deep learning framework for early detection and continuous monitoring of crop diseases using transfer learning and high resolution, multispectral aerial imagery. In the first study, the technique was used to compare the performance of five pre-trained deep learning convolutional neural networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) in classifying crop diseases for apples, grapes, and tomatoes. The results of the study show that the best performing crop-disease classification models were those trained on the VGG16 network, while those trained on the ResNet50 network had the worst performance. The other studies compared the performance of using transfer learning and different three-band color combinations to train single- and multiple-crop classification models. The results of these studies show that models combining red, near infrared, and blue bands performed better than models trained with the traditional visible spectral band combination of red, green, and blue. The worst performing models were those combining near infrared, green, and blue bands. This research recommends that further studies be undertaken to determine the best band combinations for training single- and multi-label classification models for both crops and plants and diseases that affect them.", "source_url": "https:\/\/openalex.org\/W3177001023", "pdf_url": "https:\/\/scholarship.claremont.edu\/cgi\/viewcontent.cgi?article=1237&context=cgu_etd", "doi": "10.5642\/cguetd\/218", "handle": "", "repository_name": "Scholarship - Claremont (Claremont Colleges)", "source_record_id": "W3177001023", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 495, "title": "Scaling up graph neural networks", "title_ar": "", "title_en": "Scaling up graph neural networks", "author": "Ningyi Liao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; transformers; gnn", "keywords": "", "abstract": "Recent years have witnessed the burgeoning of services based on data represented by graphs, which leads to rapid increase in the amount and complexity of such graph data. Graph Neural Networks (GNNs) are specialized neural models designed to represent and process graph data, and have emerged remarkably for their impressive performance on a wide range of graph learning tasks. Most of these models, however, are known to be difficult to apply to large-scale data. When the graph size increases, regular GNNs become impractical due to the prohibitive computational overhead and unsatisfying performance. Hence, understanding and enhancing the scalability of GNN models is a compelling and prominent task for their broader application in handling real-world graph data. In this thesis, we explore the scalability issue on different graph variants and GNN designs, uncover the theoretical groundings from the perspective of graph processing, and propose methods to scale up GNN to up to billion-scale graphs with top-tier performance. In the first part, we propose novel GNN model designs by enhancing graph data management techniques. Our examination reveals that the scalability bottleneck of GNN designs typically lies in repetitive graph-related computation and leads to a coupled overhead with data scale. To apply GNNs to large graphs, it is crucial to specifically managing the graph computation. Our first model, SCARA, is developed for efficiently computing graph embedding from the dimension of node features. The other model, LD2, is specialized for graphs under heterophily exhibiting distinct data distribution. Theoretical analyses are offered for both models to assess their approximation precision, graph expressiveness, and computational complexity. Experiment results demonstrate that both designs achieve state-of-the-art scalability performance in terms of computation speed and memory footprint without compromising accuracy. In the second part, we extend the idea of boosting graph computations to broader graph learning approaches. In HubGT, we examine the promising Transformer-based GNN model, analyze its scalability issues, and propose dedicated graph processing algorithms for fast and effective data retrieval. In GENTI, we introduce the strategy of decoupling and accelerating graph element computation for the subgraph learning problem, which has particular use in handling dynamic graph links. These applications highlight the utility of our intuition for scaling up graph learning through advanced graph management techniques, especially decoupling on the computational device layer, graph-oriented storage on the data structure layer, and approximation on the algorithmic layer. Both works showcase practical efficiency in processing larger graphs at a faster speed, significantly advancing the capabilities of these diverse graph learning approaches. In the third part, we dive deep into understanding the GNN scalability issue concerning graph data management. From the theoretical side, our Unifews framework innovatively integrates the graph signal processing framework with the graph sparsification mechanism to provide an interpretation of the approximate graph propagation schemes, which incorporate and generalize the scalable computations in SCARA and LD2. Our derivation establishes a new framework for characterizing graph sparsification as an approximation for the GNN learning process with a bounded error, offering solid theoretical support for a range of scalable GNN designs. From the empirical side, we conduct a benchmark study focusing on Spectral GNN models, which are unique for their scalable learning schemes. Based on our characterization and taxonomy, we formulate and analyze over 30 GNNs as spectral filters, including the SCARA and LD2 paradigms, with a unified and scalable implementation applicable to million-scale graphs. Within this framework, extensive evaluations are conducted with comprehensive metrics on time efficiency, memory footprint, and effectiveness, where novel observations and practical guidelines are exclusively available due to the benchmark scale and coverage. Challenging the prevailing belief, our benchmark reveals an intricate landscape regarding the effectiveness and efficiency of spectral graph filters, demonstrating the potential to achieve desirable performance through tailored spectral manipulation of graph data. In conclusion, this thesis aims to advance the design and implementation of scalable GNNs, especially by identifying key performance bottlenecks and underpinning graph management theories. Our contributions not only equip various graph learning systems with the capability to operate on real-world, billion-scale graphs, but also provide pioneering theoretical foundations and practical guidelines that inspire future research regarding GNN performance at large scale.", "source_url": "https:\/\/openalex.org\/W4416843743", "pdf_url": "", "doi": "10.32657\/10356\/204545", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4416843743", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 496, "title": "One-Class Learning for Graph Neural Networks", "title_ar": "", "title_en": "One-Class Learning for Graph Neural Networks", "author": "Marcos Paulo Silva Gôlo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidade de São Paulo", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; responsible-ai; gnn; xai", "keywords": "", "abstract": "Real-world problems often require identifying a single class of interest (e.g., normal patterns, relevant profiles, confirmed causal relations), while the non-interest space is broad, unknown, heterogeneous, and costly to label. This setting motivates One-Class Learning (OCL), which learns from interest examples and decides whether new instances belong to the target concept. Many of these problems are naturally modeled as graphs, since graphs explicitly represent relationships among entities. In parallel, Graph Neural Networks (GNNs) have become the main approach for learning graph representations by exploiting relational dependencies through neighborhood aggregation, and they are particularly relevant for heterogeneous data. Despite these advances, integrating OCL with GNNs still faces practical limitations: a significant portion of the literature relies on adapting multi-class datasets to one-class settings; unsupervised approaches often yield weak performance; two-step pipelines learn representations that are not customized with the OCL objective; hypersphere-based losses alone may lead to undesirable local minima; and interpretability is commonly handled via post-hoc projections. Moreover, within OCL for GNNs, important problems remain underexplored, such as edge classification and graph streams. To address these gaps, this thesis proposes end-to-end methods that align GNN representation learning with the OCL objective. The core idea is to combine the reconstruction loss of Graph Autoencoders with an OCL sphere loss, explicitly controlling the balance between terms and learning low-dimensional (2D\/3D) embeddings to provide interpretability during training. The contributions are organized into four main methods: OLGA for node classification on homogeneous graphs; OLGASTREAM for data streams on homogeneous graphs; ECHOLGA, which explores edge classification for causal discovery via heterogeneous hypergraph modeling; and FOLGA, which explores heterogeneous graphs by leveraging rewiring and graph fusion. Experimental results show that the proposed methods achieve competitive, and often superior, performance compared to representative baselines and state-of-the-art approaches, while also offering interpretability of the learning process, thus advancing the field of One-Class Learning for Graph Neural Networks.", "source_url": "https:\/\/openalex.org\/W7167506759", "pdf_url": "", "doi": "10.11606\/t.55.2026.tde-06072026-160904", "handle": "", "repository_name": "Digital Library of Theses and Dissertations (Universidade de São Paulo)", "source_record_id": "W7167506759", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 497, "title": "Efficient Graph Neural Networks for Predicting Chemistry", "title_ar": "", "title_en": "Efficient Graph Neural Networks for Predicting Chemistry", "author": "Teng Jiek See", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; predictive-analytics; gnn", "keywords": "", "abstract": "Graph Neural Networks (GNNs) are powerful deep learning models for predicting molecular properties but they require large datasets and significant computational resources. My research focuses on enhancing their efficiency and accuracy by improving how these models learn and process information internally. By inventing new model architectures and training strategies, I demonstrate that GNNs can achieve superior prediction accuracy while using either the same or even fewer computational resources. My work advances the development of efficient GNNs for chemistry prediction.", "source_url": "https:\/\/openalex.org\/W7203887660", "pdf_url": "", "doi": "10.26180\/33294069", "handle": "", "repository_name": "Monash University", "source_record_id": "W7203887660", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 498, "title": "Network anomaly detection with graph neural networks", "title_ar": "", "title_en": "Network anomaly detection with graph neural networks", "author": "Hamid Latif-Martínez", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; few-zero-shot; predictive-analytics; transformers; gnn; xai", "keywords": "", "abstract": "(English) Modern networks support critical applications with increasingly diverse, complex, and dynamic requirements. Ensuring their proper functioning is vital to maintaining the reliability and availability of these dependent services. Anomaly Detection (AD) plays a crucial role in preserving network performance by promptly identifying disruptions or degradations. In computer networks, the behavior of a node (e.g., a router) is heavily influenced by its surrounding elements. However, many traditional and Machine Learning (ML)-based methods often fail to exploit these structural correlations effectively, and in some cases ignore them entirely. While some existing approaches attempt to incorporate topological information into the AD task, they often do so suboptimally, losing key relational information. As a result, these systems struggle to achieve the robustness and adaptability required for deployment in dynamic, production environments. Graph Neural Networks (GNNs) have recently gained prominence for modeling tasks where spatial correlations are essential. Nevertheless, their application to AD in computer networks remains underexplored, despite their natural suitability for such structured environments. This thesis investigates new uses of GNNs for AD in networked systems, focusing on three representative scenarios: (i) Border Gateway Protocol (BGP) AD, where hijacks and misconfigurations cause structural changes; (ii) contextual AD, where anomalies depend on the behavior of neighboring entities; and (iii) zero-shot spatiotemporal modeling, enabling forecasting in data-scarce environments. In the first scenario, we leverage the raw BGP topology to design a model capable of generalizing across incidents while maintaining a low false-positive rate, an essential requirement for real-world deployment. We evaluate the model on well-known BGP incidents and extensive regular operation data to validate its robustness. For contextual AD, we propose two GNN-based models that uncover hidden relationships between flows in a backbone network. We validate these models on datasets containing both real and synthetic anomalies and show how the attention mechanisms provide interpretable visualizations of the learned contextual dependencies. Finally, we introduce a GNN-based spatiotemporal model trained on non-networking time series, capable of achieving zero-shot forecasting performance on network monitoring data. The model exploits attention mechanisms to learn and transfer spatial and temporal correlations across domains, outperforming state-of-the-art baselines. Moreover, the learned attention patterns suggest opportunities to simplify spatiotemporal representations, which is critical for scaling models to large production networks. These predictions can subsequently be used to perform AD, which is our main motivation to develop it. Collectively, the contributions of this thesis advance the use of Graph Neural Networks for Anomaly Detection in computer networks by systematically leveraging topological information to improve predictive performance and interpretability. Furthermore, the role of attention mechanisms for providing more interpretable results is also explored, opening the door to using them as a way to both understand the reasons behind the detected anomalies and to provide more transparency to the underlying dynamics in computer networks. (Català) Les xarxes modernes donen suport a aplicacions crítiques amb requisits cada vegada més diversos, complexos i dinàmics. Garantir el seu funcionament correcte és fonamental per mantenir la fiabilitat i disponibilitat dels serveis que en depenen. La Detecció d’Anomalies (AD, per les seves sigles en anglès) juga un paper clau en la preservació del rendiment de la xarxa, ja que permet identificar ràpidament interrupcions o degradacions. En les xarxes de computadors, el comportament d’un node (per exemple, un encaminador) està fortament influenciat pels elements del seu entorn. No obstant això, molts mètodes tradicionals i basats en l’aprenentatge automàtic (ML) no aprofiten de manera efectiva aquestes correlacions estructurals i, en alguns casos, les ignoren completament. Tot i que alguns enfocaments existents intenten incorporar informació topològica a la tasca de detecció d’anomalies, sovint ho fan de manera subòptima, perdent informació relacional clau. Això provoca que aquests sistemes tinguin dificultats per assolir la robustesa i adaptabilitat necessàries per ser desplegats en entorns de producció dinàmics. Les Xarxes Neuronals Gràfiques (GNNs) han guanyat protagonisme recentment per a tasques de modelatge on les correlacions espacials són essencials. Tanmateix, la seva aplicació a la detecció d’anomalies en xarxes de computadors encara està poc explorada, tot i la seva adequació natural per a entorns estructurats.", "source_url": "https:\/\/openalex.org\/W4413654066", "pdf_url": "", "doi": "10.5821\/dissertation-2117-440469", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W4413654066", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 499, "title": "Exploring Graph Neural Networks for Metagenomic Assembly Graphs", "title_ar": "", "title_en": "Exploring Graph Neural Networks for Metagenomic Assembly Graphs", "author": "Tiago Miguel Campos Amaral Gomes", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; data-mining; gnn", "keywords": "", "abstract": "Metagenomics has transformed microbial research by analyzing environmental DNA without the need for culturing. Metagenomic binning focuses on clustering the DNA fragments obtained from these environments based on their shared characteristics, providing insights into microbial community composition. Deep learning approaches offer a promising approach towards this outcome by leveraging graph representations of metagenomic data to improve clustering accuracy. However, binning remains challenging with long-read sequencing technologies. While long reads enable more comprehensive genomic coverage, their high error rates create obstacles that traditional binning algorithms struggle to overcome, limiting accurate genome reconstruction. In this thesis, we develop a framework for metagenomic binning of long-read sequencing data, integrating Graph Neural Networks (GNNs) with graph-based error correction techniques. We leverage the MetaFlye assembler to construct assembly graphs from errorprone long-read data and incorporate a GNN model based on GraphSAGE for unsupervised clustering, aiming to accurately and efficiently group DNA fragments into taxonomic bins. Additionally, we explore and evaluate graph-based error correction methodologies designed to refine the graph structure by reducing erroneous connections and reinforcing biologically meaningful ones, thereby improving the quality of metagenomic binning. Through this research, we seek to develop computational methods that enhance the accuracy and reliability of metagenomic analysis.", "source_url": "https:\/\/openalex.org\/W7128567491", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7128567491", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 500, "title": "Using spatial data in graph neural networks", "title_ar": "", "title_en": "Using spatial data in graph neural networks", "author": "Artem Kopyl", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; medical-ai; predictive-analytics; gnn", "keywords": "", "abstract": "This thesis investigates how relational structure can be introduced into tabular datasets by constructing graphs from spatial features and applying graph neural networks (GNNs) to enhance predictive modelling. A modular tool, Enhancer, was developed to automate this process, supporting configurable edge creation strategies, applying GNNs, and providing structured evaluation. Four edge creation strategies were proposed and evaluated across synthetic and real-world datasets. Experiments show that when spatial relationships correlate with the target variable, GNNs significantly outperform standard multilayer perceptrons. In real-world data, the benefit is less consistent, emphasising the importance of appropriate edge construction. The system also provides training, diagnostics and consistent comparison of results. The work introduces a flexible pipeline for graph-based data enhancement and shows its effectiveness when meaningful spatial structure is present, offering a base for future extensions such as larger datasets or adaptive graph construction.", "source_url": "https:\/\/openalex.org\/W7135634576", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135634576", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 501, "title": "Quantization on Graph Neural Networks for Image Classification", "title_ar": "", "title_en": "Quantization on Graph Neural Networks for Image Classification", "author": "Rithik Reddy Katpally", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; cnn; gnn; image-classification", "keywords": "", "abstract": "Quantization has become a key approach for reducing storage and computational demands of deep neural networks while maintaining high accuracy. Although 8-bit quantization is well-established for convolutional architectures such as ResNet50 and MobileNetV2, its application to graph-based vision models remains underexplored. In this work, we extend quantization-aware training to Vision Graph Neural Networks (ViGs) and conduct comparisons with quantized CNNs on the CIFAR-100 dataset. To ensure parity, all models have same training hyperparameters such as learning rate, batch size, optimizer, number of epochs. We used numerous techniques to preserve performance for low-bit precision. First, Pauta Quantization clips activation outliers based on statistical thresholds. Next, Attention Quantization Distillation (AQD) encourages the quantized network to mimic channel-wise attention patterns from full-precision activations to retain feature representations. Finally, Stochastic Quantization Distillation (SQD) injects randomness into the quantization process to make it more robust. Comprehensive experiments on CIFAR-100 reveal that 8-bit quantization delivers an excellent trade-off between efficiency and accuracy across both architectures. Notably, our quantized ViGs almost matches the performance of their ResNet50 and MobileNetV2 for top-1 accuracy, while also making similar inference and memory footprint. These findings demonstrate that quantized Vision GNNs are a practical alternative to CNNs for deployment on resource-constrained devices. Future research will explore fine-tuning of AQD and other distillation parameters.", "source_url": "https:\/\/openalex.org\/W4413638221", "pdf_url": "", "doi": "10.31979\/etd.d5rw-djyy", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4413638221", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 502, "title": "Program Recognition using Graph Neural Networks", "title_ar": "", "title_en": "Program Recognition using Graph Neural Networks", "author": "Markus Puura", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; predictive-analytics; cnn; gnn; llm", "keywords": "", "abstract": "Program recognition is a fundamental problem in computer science that has been studied for decades. A well-known subproblem is code clone detection, which aims to identify duplicated code fragments, ranging from exact copies to segments that perform the same computation with different syntax or structure. Historically, this task has been approached using mathematical and structural methods. More recently, with the rise of large language models (LLMs), syntactic approaches based on natural language processing have gained popularity. However, these models tend to struggle with capturing semantic similarities, as they are highly sensitive to syntax and often tied to specific programming languages. To address these limitations, recent research has turned toward graph-based representations of code, often combining control-flow and data-flow relationships, to better reflect the program's semantics. This allows for the use of graph kernels or graph neural networks to learn from such structured representations and make predictions on unseen code.In this work, we represent programs using data dependency graphs to capture semantic behavior, and we train a Siamese Graph Convolutional Network (GCN) to learn embeddings that reflect program similarity. This Siamese architecture allows the model to compare pairs of graphs and learn to distinguish semantically similar from dissimilar programs. State-of-the-art methods often rely on datasets composed of independently written implementations of the same algorithm. However, such datasets are often small, potentially noisy, and lack precise semantic guarantees. To overcome these limitations, we propose a method to automatically generate training datasets by applying compilation, obfuscation, and mutation techniques to source code. This process allows us to produce large quantities of semantically equivalent code variants, which are then used to train our Siamese GCN for program recognition.", "source_url": "https:\/\/openalex.org\/W7208234652", "pdf_url": "", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W7208234652", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 503, "title": "Fake news detection with Graph Neural Networks", "title_ar": "", "title_en": "Fake news detection with Graph Neural Networks", "author": "Go, Jin Ho", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "The rapid advancement of technology and the accelerating dissemination of information have contributed to a significant increase in the prevalence of fake news, defined as mislead- ing or deliberately deceptive news articles. This proliferation poses a significant challenge, as it can erode trust in institutions and the media, incite violence and hatred, influence elections and public policy, discourage civic engagement, and fracture social cohesion. Therefore, developing e\"ective and e!cient strategies for detecting and intercepting on- line fake news is crucial. This thesis makes three research contributions focused on fake news detection, each employing Graph Neural Networks (GNNs) and leveraging diverse graph data features to model social network. The first contribution addresses the evolving relationships between various entity types within a social network over time. We model the social network as a Dynamic Hetero- geneous Information Network (DHIN), where \"dynamic\" captures temporal changes and \"heterogeneous\" represents multiple node and edge types. To mitigate the high compu- tational cost typically associated with DHINs, we designed a GNN model incorporating a dynamic update mechanism. This approach significantly reduces the training time re- quired for DHINs by 35% to 45% compared to existing techniques. The second contribution focuses on early-stage fake news detection by examining prop- agation patterns during the initial phases of news dissemination. We developed a GNN architecture that separately trains a homogeneous information network (representing news propagation) and a heterogeneous information network (representing the broader social context). A fusion mechanism integrates the representations from both networks at desig- nated intervals. Findings demonstrate that early-stage propagation patterns o\"er strong indicators of a news item’s veracity. Within the initial two-hour period, our model achieves approximately 80% accuracy on real-world datasets. The third contribution investigates the largely unexplored influence of the author-topic relationship on news veracity. We utilize author-topic modelling to establish these re- lationships and represent them using hypergraphs, which capture complex interactions among multiple vertices. Furthermore, by establishing relationships between hyperedges, we model higher-order interactions without introducing additional nodes. On datasets containing author information, our model demonstrates outstanding performance com- pared to existing methods. Consequently, a significant deviation in an author’s observed topic distribution from their expected distribution, as derived from the author-topic rela- tionship, can serve as a strong signal for fake news detection.", "source_url": "https:\/\/openalex.org\/W7106819264", "pdf_url": "https:\/\/unsworks.unsw.edu.au\/bitstreams\/1b056a5d-aa7f-4879-8058-572735bd50b3\/download", "doi": "10.26190\/unsworks\/31844", "handle": "", "repository_name": "UNSWorks (UNSW Sydney)", "source_record_id": "W7106819264", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 504, "title": "Extending Graph Neural Networks with Global Features", "title_ar": "", "title_en": "", "author": "Dragos-Andrei Brasoveanu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2026, "language": "de", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "Wir untersuchen die Verwendung globaler Graphmerkmale, um die Ausdruckskraft und die Vorhersageleistung von graph neural networks (GNNs) zu verbessern. Während Message Passing Neural Networks (MPNNs) ein gängiger Ansatz für das Lernen von Graphrepräsentationen sind, sind sie in ihrer Ausdruckskraft beschränkt. Diese Einschränkung beeinträchtigt die Universalität von MPNNs und folglich die Arten von Grapheneigenschaften, die sie darstellen können.Um diese Herausforderungen anzugehen, schlagen wir vor, expressive globale Graphmerkmale in eine Basis-GNN-Architektur zu integrieren. Im Gegensatz zu herkömmlichen Ansätzen, die die Vorhersageleistung entweder durch Änderungen im Message Passing oder durch Modifikation der Graphen (z. B. Hinzufügen zusätzlicher Knotenmerkmale) verbessern, konzentriert sich unsere Methode auf globale Graphmerkmale, die die gesamte Graphstruktur beschreiben. Diese globalen Merkmale, die in Bereichen wie der Chemoinformatik gut etabliert, jedoch in der GNN Literatur oft übersehen sind, können verwendet werden, um wichtige Aspekte des Graphen zu erfassen, die von MPNNs nicht ausgedrückt werden können, wie etwa den Graphendurchmesser oder den längsten Zyklus.Wir ergänzen die von GNNs gelernten Embeddings mit ausgewählten globalen Graphmerkmalen und deren Einfluss auf die Ausdruckskraft und Modellleistung. Wir zeigen, dass 19 der 23 untersuchten globalen Merkmale, wie der Wiener- oder der Hosoya-Index, nachweislich die Ausdruckskraft von MPNNs erhöhen. Unsere empirischen Studien belegen, dass die Integration globaler Graphmerkmale die Vorhersagekraft von GNNs bei molekularen Benchmark-Datensätzen verbessern kann, was auf eine vielversprechende Richtung zur Steigerung der Effektivität von GNN-basierten Modellen hinweist.", "source_url": "https:\/\/openalex.org\/W7138864784", "pdf_url": "https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/227048\/1\/Brasoveanu%20Dragos-Andrei%20-%202026%20-%20Extending%20Graph%20Neural%20Networks%20with%20Global...pdf", "doi": "10.34726\/hss.2026.120129", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W7138864784", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 505, "title": "Descriptive Complexity and Graph Neural Networks", "title_ar": "", "title_en": "", "author": "Matias Selin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "fi", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "Koneoppimisen sisällä kehittynyt syväoppiminen on erityisesti viimeisen vuosikymmenen aikana noussut yhdeksi merkittävimmistä tämänhetkisistä tutkimusaiheista [68]. Suuri osa kehityksestä on kuitenkin perustunut kroonisen epäluotettavaan empiiriseen tutkimukseen samalla kun aiheen matemaattinen teoria on jäänyt pienemmälle huomiolle [40]. Erityistapauksessa, jossa käsiteltävänä on graafimuotoinen data, ovat graafineuroverkot [33, 31] saavuttaneet merkittävän suosion huolimatta siitä, että niiden teoreettiset perusominaisuudet ovat pitkään jääneet epämääräisiksi. Esimerkiksi näiden mallien ilmaisuvoimaa, eli niiden kykyä ilmaista eri funktioita syötetyllä datalla, on vasta hiljattain ymmärretty paremmin [11]. Yhdeksi lupaavaksi lähestymistavaksi tässä on osoittautunut kuvaileva vaativuusteoria, jossa tavoitteena on löytää näitä malleja karakterisoivia logiikoita [41]. Tässä tutkielmassa käsittelen kuvailevan vaativuusteorian soveltamista graafineuroverkkoihin. Luvun 1 johdannon jälkeen aloitan tutkielman varsinaisen sisällön luvussa 2 kattavalla kirjallisuuskatsauksella aiheen historiasta, minkä jälkeen kuvailen yksityiskohtaisesti luvussa 3 miten Ahvosen ja muiden [8] rekurrenttien liukuluvuilla laskevien graafineuroverkkojen karakterisaatio laskevalla modaalisubstituutiokalkyylillä toimii. Kuvailen myös, miten globaalin lukemisen lisääminen graafineuroverkkoihin voidaan simuloida logiikan puolella tätä vastaavalla globaalilla laskevalla modaalitimantilla. Luvussa 4 tarkastelen erästä tunnettua graafineuroverkkojen erotteluvoiman rajaa eli Weisfeilerin ja Lemanin algoritmia [86, 61] sekä sitä, millä keinoin sen yli voidaan päästä. Eri vaihtoehdoista tehokkain on Saton ja muiden [77] kehittämä noodirandomointi, jossa – hieman yksinkertaistettuna – jokaiselle noodille asetetaan yksilöllinen tunniste. Osoitan uutena tuloksena, että Ahvosen ja muiden [8] karakterisaatiotulosta voidaan helposti laajentaa noodirandomoituun tapaukseen sallimalla logiikassa propositiosymbolien kvantifiointi. Tutkielman lopuksi tutkin tätä uutta logiikkaa tarkemmin muun muassa vertailemalla sitä erääseen sen muunnokseen, joka poikkeaa siitä syntaktisesti vain hieman mutta osoittautuu muilta ominaisuuksiltaan varsin erilaiseksi. Tämän luvun tulokset perustuvat yhteistyöhön Antti Kuusiston kanssa.", "source_url": "https:\/\/openalex.org\/W7133053626", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/232168\/2\/SelinMatias.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133053626", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 506, "title": "Scalability and interpretability of graph neural networks for small molecules", "title_ar": "", "title_en": "Scalability and interpretability of graph neural networks for small molecules", "author": "Manjot. Sangha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Massachusetts Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; responsible-ai; ai-applications; transfer-learning; predictive-analytics; gnn; xai; ai-smart-cities", "keywords": "", "abstract": "In this thesis I examine the use of graph neural networks for prediction tasks in chemistry with an emphasis on interpretable and scalable methods. I propose a novel kernel-inspired graph neural network architecture, called a subgraph matching neural network (SMNN), which is designed to have all feature representations and weights be human interpretable. I show that this network can achieve competitive performance with common graph neural network baselines. I also show that the network is capable of learning features that allow for transfer learning to larger molecules with significantly better performance than some baselines. This provides evidence the network is learning chemically useful representations. I then propose a framework for defining graph pooling operations to improve the scalability of graph neural networks with molecule size. I empirically examine some examples of these graph pooling layers and show that they can provide a significant speed-up without hurting accuracy, and even improving accuracy in some cases. Finally an instance of the SMNN network with a pooling layer is shown to achieve state-of-the-art accuracy on the Harvard Clean Energy Project dataset.", "source_url": "https:\/\/openalex.org\/W2962495078", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace@MIT (Massachusetts Institute of Technology)", "source_record_id": "W2962495078", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 507, "title": "Graph Neural Networks for Circuit Clustering and Partitioning", "title_ar": "", "title_en": "Graph Neural Networks for Circuit Clustering and Partitioning", "author": "A R Soroush", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Guelph", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; data-mining; predictive-analytics; gnn", "keywords": "", "abstract": "Circuit clustering and circuit partitioning are essential tasks in the Very Large Scale Integration (VLSI) design flow and are usually solved using algorithmic techniques. Graph Neural Networks (GNNs), a branch of artificial neural networks, leverage the predictive power of deep learning on graph-structured data. Given the circuits' graphical nature, GNNs are increasingly applied to various VLSI design tasks. This research investigates applying GNNs to solve circuit clustering and partitioning. Moreover, a hierarchical framework is suggested, linking the clustering GNN to the partitioning GNN, where information gained from clustering GNN enriches the initial dataset before being fed to the partitioning GNN. The proposed method motivates further exploration of preceding a clustering GNN to other GNNs addressing other VLSI layout design problems (e.g. placement), enhancing the node features. Furthermore, the proposed linkage between two distinct GNNs can be extended to other GNNs that solve consecutive tasks in VLSI system design.", "source_url": "https:\/\/openalex.org\/W7204615144", "pdf_url": "https:\/\/atrium.lib.uoguelph.ca\/bitstreams\/6fc219c1-974a-4906-86a9-059e815dcc21\/download", "doi": "", "handle": "", "repository_name": "The Atrium (University of Guelph)", "source_record_id": "W7204615144", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 508, "title": "Meta Learning for Graph Neural Networks", "title_ar": "", "title_en": "Meta Learning for Graph Neural Networks", "author": "Rohan Dhamdhere", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rochester Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; nlp; optimization; cnn; gnn; image-classification; image-segmentation; video-analysis", "keywords": "", "abstract": "Deep learning has enabled incredible advances in pattern recognition such as the fields of computer vision and natural language processing. One of the most successful areas of deep learning is Convolutional Neural Networks (CNNs). CNNs have helped improve performance on many difficult video and image understanding tasks but are restricted to dense gridded structures. Further, designing their architectures can be challenging, even for image classification problems. The recently introduced graph CNNs can work on both dense gridded structures as well as generic graphs. Graph CNNs have been performing at par with traditional CNNs on tasks such as point cloud classification and segmentation, protein classification and image classification, while reducing the complexity of the network. Graph CNNs provide an extra challenge in designing architectures due to more complex weight and filter visualization of generic graphs. Designing neural network architectures, yielding optimal performance, is a laborious and rigorous process. Hyperparameter tuning is essential for achieving state of the art results using specific architectures. Using a rich suite of predefined mutations, evolutionary algorithms have had success in delivering a high-quality population from a low-quality starter population. This thesis research formulates the graph CNN architecture design as an evolutionary search problem to generate a high-quality population of graph CNN model architectures for classification tasks on benchmark datasets.", "source_url": "https:\/\/openalex.org\/W2923863837", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RIT Scholar Works (Rochester Institute of Technology)", "source_record_id": "W2923863837", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 509, "title": "Anomaly Detection Using Graph Neural Network", "title_ar": "", "title_en": "Anomaly Detection Using Graph Neural Network", "author": "Bishal Lakha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Boise State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; transformers; gnn; object-detection; ai-cybersecurity", "keywords": "", "abstract": "Detecting malicious behavior is becoming increasingly crucial as the internet becomes more prevalent. This problem can be formulated as an anomaly detection task on provenance data, where attacks are detectable as anomalies in the behavior of the system. The availability of system-level data in comparison to network data is quite limited and so is the research carried out on system-level logs. However, monitoring the operating system's processes during program execution and identifying anomalous behavior in system calls can be beneficial since it can provide broad coverage and generality, as a variety of malicious applications could be identified. Furthermore, logs like system processes and events are provenance data- a graph that describes the relationship between all the elements that contributed to the creation of the data, making use of a Graph Neural Network (GNN) better suited for the task. Moreover, such data may contain metadata, which in general tends to be complex and make feature engineering more difficult resulting in limited usage of such features. In this thesis, we address these issues by first utilizing the graph-like structure of logs, in which processes enact events and generate additional processes. Then we use a graph neural network to create representations of each event, encoding information about their neighboring events in a way that is unsupervised. The second is to make use of complex features such as command arguments which vary widely and cannot be used in the presented format as features in typical machine learning algorithms. If these features are instead encoded using a system composed of transformer and Variational Auto Encoder models, they can then be used in other algorithms such as a GNN or anomaly detector. These two approaches combined improve anomaly detection AUCROC for the BETH dataset by around 8 percent as compared to the manually engineered features alone.", "source_url": "https:\/\/openalex.org\/W4391694518", "pdf_url": "", "doi": "10.18122\/td.2065.boisestate", "handle": "", "repository_name": "ScholarWorks (Boise State University)", "source_record_id": "W4391694518", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 510, "title": "Application of Graph Neural Networks for biomedical data", "title_ar": "", "title_en": "Application of Graph Neural Networks for biomedical data", "author": "Ma, Mengru", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat de Barcelona", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "deep-learning; medical-ai; cnn; gnn", "keywords": "", "abstract": "The rapid advancement of biomedical data collection technology has enabled compre- hensive data analysis, but traditional meth- ods often struggle with complex and irregu- lar data. While Convolutional Neural Net- works (CNNs) have improved biomedical im- age analysis, they are less effective for non- Euclidean data like graphs. In contrast, Graph Neural Networks (GNNs) offer a promising so- lution by capturing complex relationships and topological structures in graph data. This final work provides an overview of GNN applications and challenges in biomedical data analysis. Core GNN models, including Graph Convolutional Networks (GCN), Graph Atten- tion Networks (GAT), and GraphSAGE, are explained and then implemented through two detailed case studies, demonstrating their fea- sibility and effectiveness. Overall, this work highlights the potential of GNNs in bioinformatics, providing robust sup- port for analyzing complex biomedical data and offering insights for future research and development in this field", "source_url": "https:\/\/openalex.org\/W7112999733", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona)", "source_record_id": "W7112999733", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 511, "title": "Graph Neural Networks for Natural Language Processing", "title_ar": "", "title_en": "Graph Neural Networks for Natural Language Processing", "author": "Kunze Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; nlp; self-semi-supervised; predictive-analytics; gnn", "keywords": "", "abstract": "By constructing graph-structured data from the input data, Graph Neural Network (GNN) enhances the performance of numerous Natural Language Processing (NLP) tasks. In this thesis, we mainly focus on two aspects of NLP: text classification and knowledge graph completion. TextGCN shows excellent performance in text classification by leveraging the graph structure of the entire corpus without using any external resources, especially under a limited labelled data setting. Two questions are explored: (1) Under the transductive semi-supervised setting, how to utilize the documents better and learn the complex relationship between nodes. (2) How to transform TextGCN into an inductive model and also reduce the time and space complexity? In detail, firstly, a comprehensive analysis was conducted on TextGCN and its variants. Secondly, we propose ME-GCN, a novel method for text classification that utilizes multi-dimensional edge features in a graph neural network (GNN) for the first time. It uses the corpus-trained word and document-based edge features for semi-supervised classification and has been shown to be effective through experiments on benchmark datasets under the limited labelled data setting. Thirdly, InducT-GCN, an inductive framework for GCN-based text classification that does not require additional resources is introduced. The framework introduces a novel approach to make transductive GCN-based text classification models inductive, improving performance and reducing time and space complexity. Most existing work for Temporal Knowledge Graph Completion (TKGC) overlooks the significance of explicit temporal information and fails to skip irrelevant snapshots based on the entity-related relation in the query. To address this, we introduced Re-Temp (Relation-Aware Temporal Representation Learning), a model that leverages explicit temporal embedding and a skip information flow after each timestamp to eliminate unnecessary information for prediction.", "source_url": "https:\/\/openalex.org\/W7126651815", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Sydney eScholarship Repository (The University of Sydney)", "source_record_id": "W7126651815", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 513, "title": "Track reconstruction using graph neural networks", "title_ar": "", "title_en": "Track reconstruction using graph neural networks", "author": "Cruz Carpio, Carlos Andrés", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cantabria", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; gnn; object-detection", "keywords": "", "abstract": "After more than a decade of data-taking of the experiments at the Large Hadron Collider at CERN, no evidence of New Physics has been found. A new accelerator, the High-Luminosity Large Hadron Collider is currently under construction, increasing the instantaneous luminosity by one order of magnitud. The new accelerator conditions generate a challenging environment for the detectors at the LHC. One of the most important difficulties that the CMS experiment will find is the extremely large hit occupancy in the tracker and the huge combinatorial problem associated to the track reconstruction. This is even more difficult for displaced topologies emerging from potential new, long-lived particles, where the traditional seeding methods cannot be used. This work explores the use of Graph Neural Networks as an alternative procedure to identify tracks in a detector. A simple track generator has been developed together with a simplified model of the CMS tracker, and the resulting tracks and hits are reconstructed using a dedicated GNN algorithm trained with a number of example tracks. The performance of the algorithm is tested to be higher than 80% even for 500 simultaneous tracks.", "source_url": "https:\/\/openalex.org\/W7110671432", "pdf_url": "https:\/\/repositorio.unican.es\/xmlui\/bitstream\/10902\/35392\/1\/CruzCarpioCarlosAndres.pdf", "doi": "", "handle": "", "repository_name": "UCrea (University of Cantabria)", "source_record_id": "W7110671432", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 514, "title": "A study of Graph Neural Networks and Graph Attention Networks for Node Classification", "title_ar": "", "title_en": "A study of Graph Neural Networks and Graph Attention Networks for Node Classification", "author": "Yash Bhargava", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "National College of Ireland", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; medical-ai; transformers; gnn", "keywords": "", "abstract": "Graph representations have received considerable attention as they can capture complex data relationships represented as nodes and edges, such as molecular structures, social network analysis, healthcare, and citation networks, etc. Due to this diversity of application areas, finding a suitable network representation to effectively handle complex structures, such as high dimensional term document matrices where the relationship between the nodes and edges is complex is difficult. In this study, the authors have focused on two prominent neural network representations, namely Graph Neural Networks (GNNs) and Graph Attention Networks. These two well-known neural graph architecture are designed to improve the performance of baseline GNN architectures. A range of models are proposed, and were trained and tested on the CORA dataset which consisted of research papers and citations. While the two type of neural models can handle graph based representations, it is not known a priori which one is the most suitable one for node classification. It was found that Graph Attention Networks based on attention mechanisms outperformed all proposed architectures with an accuracy of 73.8%. An accuracy that is even better than the model found in Keras. The major findings of this study show that the graph neural network models based on attention mechanisms were better than the simple GNN node classifiers and also set new targets for effectively handling complex graph-structured data in various applications such as social network analysis and citation networks.", "source_url": "https:\/\/openalex.org\/W7127208576", "pdf_url": "", "doi": "", "handle": "", "repository_name": "TRAP@NCI (National College of Ireland)", "source_record_id": "W7127208576", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 515, "title": "Graph neural networks and their application to social network analysis", "title_ar": "", "title_en": "Graph neural networks and their application to social network analysis", "author": "Marek Behún", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "computer-vision", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; gnn", "keywords": "", "abstract": "Recently, the research on Graph Neural Networks (GNNs) made it possi- ble to apply deep learning techniques to graph-structured data. In this thesis, we explore the application of GNNs to Social Network Analysis (SNA). We build and compare deep learning models for the prediction of hotel review ratings, hotel classes, and hotel scores on data scraped from the Tripadvisor website. We consider the resulting models precise enough to be used by rec- ommender systems. A non-trivial part of this thesis is also the description of the theory behind GNNs and visualization techniques for high-dimensional data. We also provide software suitable for further experimentation on this topic.", "source_url": "https:\/\/openalex.org\/W7135806338", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135806338", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 516, "title": "Graph neural networks for improvement recommender system", "title_ar": "", "title_en": "Graph neural networks for improvement recommender system", "author": "Tran, Trung Tin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; data-mining; gnn", "keywords": "", "abstract": "The field of recommender systems is a significant branch of data mining, with extensive applications in both research and practical contexts. These systems leverage user and item interaction data to generate personalized item recommendations, enhancing user experiences in e-commerce by promoting items that align with user preferences. Beyond e-commerce, recommender systems are also applied in recommending books, movies, courses, and websites. User recommendations are primarily determined by the similarity between users, assessed using techniques such as collaborative filtering (CF), matrix factorization (MF), and singular value decomposition (SVD). These techniques typically rely on matrices of past user ratings. In graph data mining, interactions between users and items can be represented as a graph, enabling more extensive and effective interaction filtering techniques. Graph Neural Network (GNN) models are crucial for representing and extracting insights from graph data. Social relationships significantly influence shopping habits, with recommendations from friends often having a greater impact than those from other sources. Integrating social connections with user similarity, based on shared shopping patterns, presents challenges due to the noise and variability in different datasets. This complexity is further amplified when considering relationships between items, such as geographic location, product type, buyer comments, and visit time sessions. Summarized, the integration of advanced techniques such as GNNs and the consideration of social connections and item relations offer promising advancements in the effectiveness of recommender systems, despite the inherent challenges.", "source_url": "https:\/\/openalex.org\/W7111980195", "pdf_url": "https:\/\/dspace.vsb.cz\/bitstreams\/53bf97bd-4960-4c1a-9be0-3367945bfc2b\/download", "doi": "", "handle": "", "repository_name": "DSpace VŠB-TUO (VŠB-TUO)", "source_record_id": "W7111980195", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 517, "title": "Personalized learning using graph neural network and knowledge graph", "title_ar": "", "title_en": "Personalized learning using graph neural network and knowledge graph", "author": "Shing Chun Yip", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; gnn", "keywords": "", "abstract": "HKUST Electronic Theses Personalized learning using graph neural network and knowledge graph by Shing Chun Yip thesis 2020 1 online resource (ix, 32 pages) : illustrations (some color) Personalized learning is designed to serve as a customization tool for providing", "source_url": "https:\/\/openalex.org\/W4321434150", "pdf_url": "", "doi": "10.14711\/thesis-991012936267503412", "handle": "", "repository_name": "", "source_record_id": "W4321434150", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 518, "title": "COMBINING CONVOLUTIONAL NEURAL NETWORKS AND GRAPH NEURAL NETWORKS FOR IMAGE CLASSIFICATION", "title_ar": "", "title_en": "COMBINING CONVOLUTIONAL NEURAL NETWORKS AND GRAPH NEURAL NETWORKS FOR IMAGE CLASSIFICATION", "author": "Vivek Trivedy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Temple University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "deep-learning; computer-vision; cnn; gnn; image-classification", "keywords": "", "abstract": "Convolutional Neural Networks (CNNs) have dominated the task of imageclassification since 2012. Some key components of their success are that the underlying architecture integrates a set inductive biases such as translational invariance and the training computation can be significantly reduced by employing weight sharing. CNNs are powerful tools for generating new representations of images tailored to a particular task such as classification. However, because each image is passed through the network independent of other images, CNNs are not able to effectively aggregate information between examples. In this thesis, we explore the idea of using Graph Neural Networks (GNNs) in conjunction with CNNs to produce an architecture that has both the representational capacity of a CNN and the ability to aggregate information between examples. Graph Neural Networks apply the concept of convolutions directly on graphs. A result of this is that GNNs are able to learn from the connections between nodes. However, when working with image datasets, there is no obvious choice on how to construct a graph. There are certain heuristics such as ensuring homophily that have empirically been shown to increase the performance of GNNs. In this thesis, we apply different schemes of constructing a graph from image data for the downstream task of image classification and experiment with settings such as using multiple feature spaces and enforcing a bipartite graph structure. We also propose a model that allows for end to end training using CNNs and GNNs with proxies and attention that improves classification accuracy in comparison to a regular CNN.", "source_url": "https:\/\/openalex.org\/W3174918721", "pdf_url": "", "doi": "", "handle": "", "repository_name": "TUScholarShare (Temple University)", "source_record_id": "W3174918721", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 519, "title": "Power Flow Optimization with Graph Neural Networks", "title_ar": "", "title_en": "Power Flow Optimization with Graph Neural Networks", "author": "Jonas Berg Hansen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "deep-learning; ai-applications; gnn; ai-smart-cities", "keywords": "", "abstract": "Power flow analysis is an important tool in power engineering for planning and operating power systems. The standard power flow problem consists of a set of non-linear equations, which are traditionally solved using numerical optimization techniques, such as the Newton-Raphson method. However, these methods can become computationally expensive for larger systems, and convergence to the global optimum is usually not guaranteed. In recent years, several methods using Graph Neural Networks (GNNs) have been proposed to speed up the computation of the power flow solutions, without making large sacrifices in terms of accuracy. This class of models can learn localized features that are independent from a global graph structure. Therefore, by representing power systems as graphs these methods can, in principle, generalize to systems of different size and topology. However, most of the current approaches have only been applied to systems with a fixed topology and none of them were trained simultaneously on systems of different topology. Hence, these models are not fully shown to generalize to widely different systems or even to small perturbations of a given system. In this thesis, several supervised GNN models are proposed to solve the power flow problem, using established GNN blocks from the literature. These GNNs are trained on a set of different tasks, where the goal is to study the generalizability to both perturbations and completely different systems, as well as comparing performance to standard Multi-Layered Perceptron (MLP) models. The experimental results show that the GNNs are comparatively successful at generalizing to widely different topologies seen during training, but do not manage to generalize to unseen topologies and are not able to outperform an MLP on slight perturbations of the same energy system. The study presented in this thesis allowed to draw important insights about the applicability of GNN as power flow solvers. In the conclusion, several possible ways for improving the GNN-based solvers are discussed.", "source_url": "https:\/\/openalex.org\/W3200352710", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W3200352710", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 520, "title": "Continuous control for robot based on deep reinforcement learning", "title_ar": "", "title_en": "Continuous control for robot based on deep reinforcement learning", "author": "Shansi Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning; robotics", "keywords": "", "abstract": "results of redundant manipulator . . . . . . . .", "source_url": "https:\/\/openalex.org\/W2973229164", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/90191\/1\/thesis-final.pdf", "doi": "10.32657\/10356\/90191", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2973229164", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 521, "title": "Learning-based multi-robot collaboration for intelligent and adaptive robotic systems", "title_ar": "", "title_en": "Learning-based multi-robot collaboration for intelligent and adaptive robotic systems", "author": "Yutong Du", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "ai-industry", "categories": "reinforcement-learning; robotics; ai-applications; ai-industry", "keywords": "", "abstract": "The concepts of Artificial Intelligence (AI) and Digital Twins are playing a more and more important role in industrial manufacturing, which can be adopted to empower more collaborative and adaptive multi-robot systems to replace humans in finishing some repetitive tasks. Existing studies are more focused on static environments or solutions that rely on centralized coordination. This thesis carried out two case studies as part of the academic initiative called \"Conscious Agents as a Foundation for a Collaborative Factory of the Future.\". The initial study presents an environment for a manufacturing scenario, and an observation algorithm that records the activity of an autonomous robot executing pick-and-place operations and merges this knowledge with reinforcement learning for motor control and decision support. The subsequent study proposes a method of decentralized learning in a dynamic multi-robot collaborative system. Tests confirm that the suggested learned policy allows robots to avoid collisions while working with another controller by the same policy, and also ensures the performance of independent work. This work contributes to the understanding of how decentralized learning can enhance autonomy and cooperation in industrial robotic systems.", "source_url": "https:\/\/openalex.org\/W7125355300", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W7125355300", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 522, "title": "Intelligent Agent for Music Recommendation in Multimedia Robotic Systems Using Machine-Learning Algorithms", "title_ar": "", "title_en": "", "author": "Максим Тимурович Іванніков", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2025, "language": "uk", "research_domain": "robotics", "research_subdomain": "ai-education", "categories": "robotics; ai-applications; ai-education", "keywords": "", "abstract": "Іванніков. М. Т. Інтелектуальний агент для підбору музичних рекомендацій у мультимедійних робототехнічних системах із застосуванням алгоритмів машинного навчання. КПІ ім. Сікорського, Київ, 2025. Проєкт містить 85 сторінок тексту, 28 рисунків, 2 таблиці, посилання на 28 літературних джерела, а також демонстраційний вебзастосунок із повним програмним кодом (37 файлів). Об`єкт дослідження: інтелектуальний агент для підбору музичних рекомендацій. Предмет дослідження: інтелектуальний агент для підбору музичних рекомендацій. Мета дипломного проєкту: створення інтелектуального агента, що автоматично знаходить схожі пісні відповідно до вподобань користувача. У першому розділі проаналізовано предметну область персоналізованого аудіоконтенту, окреслено глобальні тенденції ринку стримінгу, роль інтелектуальних агентів у мультимедійних робототехнічних системах, технічні виклики edge-AI та етичні аспекти формування рекомендацій; на підставі цього сформульовано вимоги до алгоритмів, здатних працювати з обмеженими ресурсами й мінімальною історією прослуховувань. У другому розділі виконано детальний огляд п’яти провідних музичних сервісів (Spotify, Apple Music, YouTube Music, Amazon Music, Deezer), порівняно їхні підходи до cold-start, прозорості та контекстної чутливості; виявлено сильні й слабкі сторони існуючих рішень, що стало підґрунтям для вибору власної стратегії рекомендацій. У третьому розділі розглянуто основні методи рекомендації музичного контенту — колаборативну й контент-орієнтовану фільтрацію, гібридні схеми та моделі глибокого навчання; проаналізовано їх переваги та обмеження в умовах робототехнічних платформ і обґрунтовано вибір легковагового алгоритму k-nearest neighbors як оптимального рішення. У четвертому розділі спроєктовано систему: визначено функціональні й нефункціональні вимоги, побудовано клієнт-серверну архітектуру, описано моделі бази даних, схему взаємодії UI з бекендом та принципи модульності й масштабованості; наведено вибір технологій (Flask, React 18 + Vite, SQLAlchemy) і продемонстровано діаграми прецедентів, компонентів та станів. У п’ятому розділі реалізовано систему: докладно описано структуру клієнтської та серверної частин, алгоритм препроцесингу ознак, сам k-NN-агент з підтримкою гарячого перезавантаження, механізм автоматичного тюнінгу гіперпараметрів і результати його використання, що підтвердили поліпшення продуктивності моделі.", "source_url": "https:\/\/openalex.org\/W7200579210", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Електронний архів наукових та освітніх матеріалів КПІ ім. Ігоря Сікорського (КПІ ім. Ігоря Сікорського)", "source_record_id": "W7200579210", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 523, "title": "Deep Learning-Based Vision System for Intelligent Object Detection and Robotic Sorting Solution", "title_ar": "", "title_en": "Deep Learning-Based Vision System for Intelligent Object Detection and Robotic Sorting Solution", "author": "Anastasiia Stepanova", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; object-detection; image-segmentation; ai-industry", "keywords": "", "abstract": "Manufacturing and production enterprises encounter digital development in the scope of Industry 4.0. Car workshops as part of the automotive industry aftermarket are in the transition from manual labour to automation and robotics solutions that aim to facilitate the workspace productivity by decreasing workers’ fatigue and errors. Automotive industry expects efficiency increase by 30% due to the deployment of autonomous robotics in both production and operation. Machine vision serving as the perceptive brain for automation systems allows robots to navigate in the unstructured environments of car workshops. Recent advancements in deep neural networks allow models to learn hierarchical features and patterns extracted from visual data and hence contribute to automated solutions in car workshops’ unstructured environments that present a certain challenge for machine learning. This thesis does not aim to replace human workers, but to provide aid in necessary but secondary task of the post-work tool sorting and arranging in a car service garage. In this project, a deep learning-based vision system was developed and evaluated. The primary task of the project included automatic identification, instance segmentation, and pose estimation of metallic wrenches in automotive workshop environment. Metallic tools represent a certain machine vision challenge due to the high specular reflection that causes significant segmentation mask fragmentation and affects performance of 3D cameras. This issue was addressed by utilizing a deeplearning-based keypoint detection as geometric anchors to interpolate and repair fragmented masks. The vision framework utilizes custom trained Detectron2 segmentation framework. The primary technical contribution of this thesis is the developed keypoint-guided mask reconstruction algorithm. Unlike standard frameworks that encounter fragmented segmentation masks on reflective objects, this system exploits detected semantic keypoints as stable geometric anchors that are further utilized to interpolate the mask and repair incomplete visual data. In this system, Zivid2 M70 industrial 3D camera was used for perception task to ensure high precision of depth values. The developed vision system was experimentally validated through three testing cases: static repeatability, rotational accuracy, and robustness under occlusion. The test results have shown high system stability: angular standard deviation of 0.17° and depth variance of 0.02 mm. The system achieved MAE of 1.33° and RMSE of 1.69° in a 180° rotational sweep. Furthermore, the developed algorithm to reconstruct fragmented masks has shown precise orientation calculation in occluded scenes with angular variance of 0.08°. Therefore, the proposed vision system provides accurate and reliable data for robotic manipulation and automated tool arranging tasks under unstructured conditions of car workshops.", "source_url": "https:\/\/openalex.org\/W7164869336", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7164869336", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 524, "title": "Autonomous inspection of composite materials with multiple intelligent robots through thermal imaging and machine learning", "title_ar": "", "title_en": "Autonomous inspection of composite materials with multiple intelligent robots through thermal imaging and machine learning", "author": "Andrew Loeliger", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "machine-learning", "research_subdomain": "", "categories": "machine-learning; robotics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7202281563", "pdf_url": "", "doi": "10.48730\/nnc7-r936", "handle": "", "repository_name": "STAX", "source_record_id": "W7202281563", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 525, "title": "Robot Learning Through World Models", "title_ar": "", "title_en": "Robot Learning Through World Models", "author": "Baheyeldin Tharwat Moustafa Abdelrazek", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; computer-vision; robotics; responsible-ai; self-semi-supervised; predictive-analytics; video-analysis; fairness", "keywords": "", "abstract": "Embodied AI introduces a new generation of intelligent agents capable of closed-loop perception and control in real-world environments. However, despite recent progress in robot learning, Most current approaches to robot manipulation are heavily based on supervised learning frameworks. Methods like imitation learning require a large number of examples to succeed in prediction and control. These examples are usually collected by teleoperation, which is expensive to collect, difficult to scale, and prone to bias, limiting the scalability and generalizability of these systems across tasks, environments, and embodiments. On the other hand, humans accumulate knowledge and experience by building internal models of how the world around them works. These world models allow them to predict the future outcomes of their potential actions, reducing the trial and error in the real world needed to learn successful behaviors. This thesis focuses on the problem of equipping robots with the ability to understand the world, learn action representations, imagine the future, and make decisions based on their predictions. We introduce methods for both pretraining and post-training of imitation learning models using world models, where we improve these models without any physical robot interactions. Pretraining in a self-supervised way, by learning latent action representations from large-scale unlabeled human video data, sourced from the Internet, performing actions with everyday objects. Then, training on downstream tasks of a target embodiment using a paired image action dataset using supervised learning. Finally, post-training in the sense of test-time decision-making, given the trained imitation learning model, a learned world model predicts different future states, and a reward model evaluates these imagined outcomes to select the highest-value sequence for action execution. Our experiments show that the proposed methods outperform previous approaches without world models on LIBERO and CALVIN benchmarks and also on real-world experiments, not only by requiring fewer examples to learn, but also by achieving higher performance. The proposed methods open up the potential for building foundation models for robotics by pretraining large models on much larger web-scale video data, and that are able to think and reason in their own imagination to adapt to environments.", "source_url": "https:\/\/openalex.org\/W7164150232", "pdf_url": "", "doi": "10.82419\/356", "handle": "", "repository_name": "", "source_record_id": "W7164150232", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 526, "title": "Intelligent Fault Diagnosis of Wheeled Mobile Robots", "title_ar": "", "title_en": "Intelligent Fault Diagnosis of Wheeled Mobile Robots", "author": "Xiaoxue Mei", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "fairness", "categories": "deep-learning; medical-ai; robotics; responsible-ai; ai-applications; fairness; ai-industry", "keywords": "", "abstract": "This research focuses on improving the reliability of intelligent robots through artificial intelligence–based fault diagnosis. By analysing multi-sensor data, advanced deep learning methods are developed to enable robots to automatically detect and identify faults under complex and dynamic working conditions. The research further introduces privacy-preserving collaborative learning among multiple robots and efficient AI models suitable for real-world deployment. This work contributes to safer, more reliable, and intelligent robot–human collaboration in industrial and service environments.", "source_url": "https:\/\/openalex.org\/W7141181167", "pdf_url": "", "doi": "10.25916\/sut.31856386", "handle": "", "repository_name": "Swinburne figshare (Swinburne University of Technology)", "source_record_id": "W7141181167", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 527, "title": "Robots Need Some Education: On the complexity of learning in evolutionary robotics", "title_ar": "", "title_en": "Robots Need Some Education: On the complexity of learning in evolutionary robotics", "author": "Fuda van Diggelen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; robotics; responsible-ai; ai-applications; optimization; fairness; ai-education", "keywords": "", "abstract": "Evolutionary Robotics and Robot Learning are two fields in robotics that aim to automatically optimize robot designs. The key difference between them lies in what is being optimized and the time scale involved. Evolutionary Robotics is a field that applies evolutionary computation techniques to evolve the morphologies or controllers, or both. Robot Learning, on the other hand, involves any learning technique aimed at optimizing a robot's controller in a given morphology. In terms of time scales, evolution occurs across multiple generations, whereas learning takes place within the 'lifespan' of an individual robot. The long-term goal of Evolutionary Robotics is to create adaptive systems where a population of robots evolve autonomously, optimizing both their physical structure and control system through an evolutionary process. In the context of evolution, adaptability is a trait of the population. Conversely, when it comes to learning, it is the robots' controller that exhibits adaptability. In the end, both forms of adaptation aim to enhance the robots' task performance. Integrating Robot Learning with Evolutionary Robotics seems like a natural fit for improving robot design. Unfortunately, integration requires the careful design of suitable learning algorithms in the context of evolutionary robotics. The effects of introducing learning into the evolutionary process are not well-understood and can thus be tricky. This thesis investigates these intricacies and presents several learning algorithms developed for an Evolutionary Robotics context. My dissertation is structured into three parts: Part I - investigates the complex interaction between learning algorithms and evolutionary processes, provides statistical tools for evaluating different learning algorithms, and explores the dynamics of optimization and the reality gap. The interactions present a counterintuitive finding: learning can negatively affect the evolutionary process. Learning can bias evolution by converging to simple designs that learn quickly, overfitting simulators and exacerbating the reality gap. Part II - investigates model-agnostic learning methods for autonomous robots. Evolutionary algorithms can produce arbitrary robot designs for environments with minimal prior knowledge. This presents a challenging requirement for learning. Nevertheless, robots are expected to be able to autonomously perform tasks 'in the wild'. Here, I cover continuous self-modeling for adaptive feedback control and rapid skill acquisition for quickly learning locomotion. Part III - explores how learning can be extended beyond individual robot. In group settings, swarms of robots can obtain abilities beyond that of any individual robot inside. Here, I demonstrate how such emergent capabilities can be learned and used to solve complex tasks, both in homogeneous and heterogeneous populations of robots. Overall, this thesis offers a comprehensive analysis of Robot Learning within the context of Evolutionary Robotics presented as a collection of peer-reviewed works. Each part of the dissertation combines rigorous theoretical analysis with practical hardware implementations, demonstrating the validity of the ideas presented. As a result, my thesis provides unique insights into how an evolving population of robots can effectively integrate learning. From the 'birth' of a robot, learning about its environment, to its 'adulthood' as a functioning member of a robotic society.", "source_url": "https:\/\/openalex.org\/W4408010328", "pdf_url": "", "doi": "10.5463\/thesis.1004", "handle": "", "repository_name": "", "source_record_id": "W4408010328", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 528, "title": "Decision tree learning for intelligent mobile robot navigation", "title_ar": "", "title_en": "Decision tree learning for intelligent mobile robot navigation", "author": "G.H. Shah Hamzei", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Loughborough University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "generative-ai", "research_subdomain": "ai-smart-cities", "categories": "generative-ai; robotics; expert-systems; ai-applications; ai-smart-cities", "keywords": "", "abstract": "The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of \"hostility\". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed.", "source_url": "https:\/\/openalex.org\/W7204433763", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Loughborough University Research Repository (Loughborough University)", "source_record_id": "W7204433763", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 529, "title": "Knowledge Representation and Automated Task Planning for Intelligent Robots through Spatial Temporal Logic and Learning", "title_ar": "", "title_en": "Knowledge Representation and Automated Task Planning for Intelligent Robots through Spatial Temporal Logic and Learning", "author": "Zhiyu Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; computer-vision; generative-ai; robotics; video-analysis", "keywords": "", "abstract": "Intelligent robots have emerged as a hot research topic in the interdisciplinary study of control theory, robotics, and computer science as it demonstrates huge potential in fundamentally benefiting our society. The development of intelligent robots with the ability to independently solve a new task is an important yet challenging research problem. This work devotes to proposing an integrated system for such intelligent robots through three major objectives. First, we propose a new spatial temporal logic with a sound and complete deduction system for knowledge representation and reasoning in intelligent robots. The proposed logic achieves a balance between expressiveness and tractability for intelligent robots since the satisfiability of the proposed logic is decidable. We propose two specification mining algorithms to generate spatial temporal logic formulas from video and unstructured text data. The obtained formulas are used to construct a domain theory storing necessary knowledge for task planning. Second, we develop automatic task planning with an interactive proposer and verifier for intelligent robots based on the domain theory in spatial temporal logic. The proposer is used to generate ordered actions through forward graph expansion and backward solution extraction, and the verifier is used to verify the results from the proposer and generate executable task plans. We further study the scenario where the domain theory for task planning is incomplete by integrating the task planning algorithm with machine learning systems. The proposed framework gives intelligent robots the ability to actively seeking knowledge online based on missing information. Third, we study a motion planning problem for intelligent robots under formal specifications of the proposed spatial temporal logic. We develop a control synthesis algorithm for the formal specifications by proposing a two-layer MPC framework. The high-layer transfers the global formal specifications into a sequence of sub-formulas and the low-layer solves a distributed MPC using an MILP solver. We show that the proposed framework is sound and recursively feasible.", "source_url": "https:\/\/openalex.org\/W3202693723", "pdf_url": "", "doi": "10.7274\/1831cj85f59", "handle": "", "repository_name": "Figshare", "source_record_id": "W3202693723", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 530, "title": "Mobile Robot Learning", "title_ar": "", "title_en": "Mobile Robot Learning", "author": "Gregory Kahn", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; computer-vision; reinforcement-learning; robotics; predictive-analytics", "keywords": "", "abstract": "In order to create mobile robots that can autonomously navigate real-world environments, we need generalizable perception and control systems that can reason about the outcomes of navigational decisions. Learning-based methods, in which the robot learns to navigate by observing the outcomes of navigational decisions in the real world, offer considerable promise for obtaining these intelligent navigation systems. However, there are many challenges impeding mobile robots from autonomously learning to act in the real-world, in particular (1) sample-efficiency---how to learn using a limited amount of data? (2) supervision---how to tell the robot what to do? and (3) safety---how to ensure the robot and environment are not damaged or destroyed during learning?In this thesis, we will present deep reinforcement learning methods for addressing these real world mobile robot learning challenges. At the core of these methods is a predictive model, which takes as input the current robot sensors and predicts future navigational outcomes; this predictive model can then be used for planning and control. We will show how this framework can address the challenges of sample-efficiency, supervision, and safety to enable ground and aerial robots to navigate in complex indoor and outdoor environments.", "source_url": "https:\/\/openalex.org\/W3161107037", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W3161107037", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 531, "title": "A Distributed Reinforcement Learning based Intelligent Routing and Beamforming Design for Robot-assisted Mobile Ad-hoc Network under Uncertain and Complex Environment", "title_ar": "", "title_en": "A Distributed Reinforcement Learning based Intelligent Routing and Beamforming Design for Robot-assisted Mobile Ad-hoc Network under Uncertain and Complex Environment", "author": "Ming Feng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning; robotics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3136296835", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3136296835", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 532, "title": "Toward Robot Learning for Fine Manipulation", "title_ar": "", "title_en": "Toward Robot Learning for Fine Manipulation", "author": "Jinda Cui", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "ai-industry", "categories": "reinforcement-learning; robotics; ai-applications; ai-industry", "keywords": "", "abstract": "People may think robots are capable when asked their impressions. Indeed, at the moment one is reading, hundreds and thousands of industrial robots are moving heavy car chassis in automobile factories and palletizing big piles of packages in e-commerce warehouses. However, if robots are so capable and helpful, why do we see so few robotic applications outside of highly structured industrial environments, in domestic environments such as our homes and businesses?After decades of research and development, robots might have been more advantageous than humans in mechanical capacity and precision, but they are still far less intelligent. Robots programmed through traditional model-based engineering are still much in awe of the ubiquitous variations in unstructured domestic environments.We argue that robots' manipulation skills must be adaptive and actively compliant to reach human-level performance and enable their wide adoption in unstructured domestic environments. We illustrate our attempts to bring the gap closer between robots' and humans' manipulation skills in the work presented in this dissertation.First, we explain why we think learning-based approaches are promising in closing the skill gap and survey the state-of-the-art in learned robot manipulation. We categorize the challenges in unstructured domestic environments for robot manipulation and analyze various learning techniques used in existing literature through the lens of adaptability.We then attempt to push robot manipulation a step forward into domestic environments with cost and adaptability in mind. We introduce a novel, optimization-based resolved velocity controller to serve as the motion controller in manipulation tasks. The proposed velocity controller considers the robot's mechanical limits and allows the robot to behave optimally and safely in and around singular configurations.Next, we present the model-free reinforcement learning of a force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our system demonstrates the ability to perform non-prehensile, dexterous manipulation tasks without models of the manipulated objects and shows robustness to uncertain geometry.Finally, the resolved velocity controller and the learned force controller are combined in a parallel force and velocity control framework to learn fine manipulation primitive skills (such as box sliding and tilting with uncertainty in initial contact). Exploration and exploitation are essential for robots to learn in the training phase. We show that such behavior can also be learned and used after deployment to enhance the adaptability of fine manipulation skills. We highlight the challenges in learning fine manipulation skills and the effectiveness of our approach with two case studies. The learned fine manipulation primitive skills have good success rates in assigned tasks and demonstrate strong adaptability to variations in object and contact properties such as dimensions, weight, and contact friction coefficients.Our results suggest that affordable personal robots with fine manipulation skills and human-like adaptability can be developed in the future.", "source_url": "https:\/\/openalex.org\/W7133009403", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Lehigh Preserve", "source_record_id": "W7133009403", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 533, "title": "Design and development of an intelligent mobility robot", "title_ar": "", "title_en": "Design and development of an intelligent mobility robot", "author": "Kun Huang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; robotics; expert-systems; ai-applications; predictive-analytics; ai-industry; ai-smart-cities", "keywords": "", "abstract": "To meet the demand of short-distance transportation, a novel type of intelligent mobility robot is proposed by this thesis. The robot features high portability, reliable safety and multiple control and interaction methods, including posture control and remote interaction. The robot has wide range of application prospects in both industrial production and daily life, such as travel, services, entertainment, etc. To realize posture control of the robot, a complete set of posture perception system is designed and developed. Firstly, the pressures on different sections are real-time acquired by specific sensors and corresponding detection circuit. Then based on the pressure distributions and statistic information, the posture is perceived by a series of processing algorithms, including sampling a reference posture when the user is standing stably and calculating current user posture by an non-linear mapping. Next, the posture is quantitatively described as the inclination angles of the body gravity center in two dimensions. Moreover, two machine learning are applied to further improve the accuracy of posture detection, including parameter regression method and Gaussian Process Dynamic Model. In addition, some auxiliary sensors are also applied to ensure the robustness and reliability through fusion of multi-sensor information. At last, with the help of hybrid neural network, some interaction via posture patterns are achieved. This thesis also analyzes the planar kinematics of the intelligent mobility robot, considering its steering mechanisms, suspension systems, and differential speed of wheel pairs. The motion planning algorithm outputs desired motion parameters of the robot, such as longitudinal speed and turning curvature, based on both user input and kinematic constrains. To control the motion of the robot, the distribute control strategy as well as fuzzy logic control algorithm are applied to coordinately assign appropriate control amount to multiple actuators of different types. The design and control methods of actuators are discussed at last. This thesis designs and implements the Field Oriented Control to drive the Permanent Magnet Synchronous Motor and to control its speed and toque. The FOC system improves energy efficiency and decreases the operation noise and toque ripple. Meanwhile, a kind of brake device is also designed by the thesis to provide reliable and timely braking performance, wherein the braking force is adjustable.", "source_url": "https:\/\/openalex.org\/W2939709170", "pdf_url": "", "doi": "10.14711\/thesis-991012554966503412", "handle": "", "repository_name": "", "source_record_id": "W2939709170", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 534, "title": "Representation Learning and Semantics for Robotic Vision", "title_ar": "", "title_en": "Representation Learning and Semantics for Robotic Vision", "author": "Benjamin J. Meyer", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "machine-learning; computer-vision; robotics", "keywords": "", "abstract": "Vision is a powerful tool in enabling intelligent robotic systems to safely and effectively perform useful tasks. An important precursor to well-considered robotic action is a semantic understanding of the environment, which can be facilitated by machine learning techniques. Recent advances in the field of machine learning for computer vision seldom make considerations for robotic applications. In this thesis, important problem domains for robotic vision are identified and explored. Machine learning methodologies are developed with motivation drawn from the needs of a robotic vision system.", "source_url": "https:\/\/openalex.org\/W2971091151", "pdf_url": "", "doi": "10.26180\/5d529eb5a225b", "handle": "", "repository_name": "Figshare", "source_record_id": "W2971091151", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 535, "title": "Interactive Machine Learning for Assistive Robots", "title_ar": "", "title_en": "Interactive Machine Learning for Assistive Robots", "author": "Dorothea Koert", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Technical University of Darmstadt", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; robotics; ai-applications; predictive-analytics; ai-industry; ai-smart-cities", "keywords": "", "abstract": "Intelligent assistive robots can potentially support elderly persons and caregivers in their everyday lives and facilitate a closer man and machine collaboration as an essential part of the yet to come 5-th industrial revolution. In contrast to classical robotic applications where robots were mostly designed for repetitive tasks, assistive robots will face a variety of different tasks in close contact with everyday users. In particular, it is difficult to foresee the variety of applications beforehand since they depend on a person's individual needs and preferences. This renders preprogramming of all tasks for assistive robots difficult and gives need to explore methods of how robots can learn new tasks at hand during deployment time. Learning from and during direct interaction with humans provides hereby a potentially powerful tool for an assistive robot to acquire new skills and incorporate prior human knowledge during the exploration of novel tasks. Such an interactive learning process can not only help the robot to acquire new skills or profit from human prior knowledge but also facilitates the participation of inexperienced users or coworkers which can lead to a higher acceptance of the robot. However, while on the one hand human presence and assistance can be beneficial during the learning process, on the other hand, close contact with inexperienced users also imposes challenges. In shared workspaces or in close contact with everyday users a robot should be able to adapt learned skills to achieve as little disturbance of humans as possible. It becomes also important to evaluate human preferences about such adaptation strategies, their understanding of interactive learning processes and different ways for human input into learning. To come closer to the goal of intelligent assistive robots is therefore important to develop novel interactive learning methods and evaluate them in different robotic applications. This thesis focusses on three main challenges related to the development of assistive intelligent robots and their interaction with everyday users. The different parts of the thesis contribute not only novel theoretical methods but additionally also evaluations on different robotic tasks with users, that had zero or only little prior experience with robots. The first challenge is to enable robots to learn cooperative skills from a potentially open-ended stream of human demonstrations in an incremental fashion. While learning new skills from human demonstrations has already been exploited in the literature it remains challenging to learn skill libraries from incrementally incoming demonstrations and when the total number of skills is not known beforehand. Therefore, in the first part of the thesis, we introduce an approach for online and incremental learning of a library for collaborative skills. Here, we follow a Mixture of Experts based approach and incrementally learn a library of collaborative skills and a gating model from coupled human-robot trajectories. Once trained, the gating model can decide which skill to choose as an appropriate response to a human motion, based on prior demonstrations and activate the corresponding robot skill. In contrast to existing batch learning methods, our method does not require the total number of skills to be known a priori and can learn new skills as well as update existing skills from multiple human demonstrations. The cooperative skills are represented as Probabilistic Interaction Primitives which can capture variance and inherent correlations in the demonstrations. We evaluate our method with different human subjects in a task where a robot assists the subjects in making a salad. We also evaluate hereby how learned skills transfer between different subjects. Second, intelligent assistive robots should be able to adapt learned skills to humans when working in close contact or shared workspaces. For Probabilistic Movement Primitives (ProMPs), which were chosen as a skill representation in this thesis, such methods for online adaptation were missing in the literature so far. Hereby, it is in particular important to also evaluate the perceived level of safety and comfort of humans according to different adaptation strategies. To this end, we present two methods for online adaptation of learned skills in a shared workspace setting. Here, we introduce two novel online adaptation methods for ProMPs, namely spatial deformation and temporal scaling. Spatial deformation avoids collisions by dynamically changing the shape of the movement primitive, while at the same time staying close to the demonstrated motions. In temporal scaling, we adapt the ProMP's velocity profile to avoid time-dependent collisions. To achieve intention aware adaptation in shared workspaces we combine both methods with a goal-directed prediction model for human motions. This prediction model can also be learned online from human motions.", "source_url": "https:\/\/openalex.org\/W3128545355", "pdf_url": "https:\/\/tuprints.ulb.tu-darmstadt.de\/14184", "doi": "10.25534\/tuprints-00014184", "handle": "", "repository_name": "TUbilio (Technical University of Darmstadt)", "source_record_id": "W3128545355", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 536, "title": "Socially intelligent robots that understand and respond to human touch", "title_ar": "", "title_en": "Socially intelligent robots that understand and respond to human touch", "author": "Merel M. Jung", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "machine-learning; medical-ai; robotics", "keywords": "", "abstract": "Touch is an important nonverbal form of interpersonal interaction which is used to communicate emotions and other social messages. As interactions with social robots are likely to become more common in the near future these robots should also be able to engage in tactile interaction with humans. Therefore, the aim of the research presented in this dissertation is to work towards socially intelligent robots that can understand and respond to human touch. To become a socially intelligent actor a robot must be able to sense, classify and interpret human touch and respond to this in an appropriate manner. To this end we present work that addresses different parts of this interaction cycle. The contributions of this dissertation are the following. We have made a touch gesture dataset available to the research community and have presented benchmark results. Furthermore, we have sparked interest into the new field of social touch recognition by organizing a machine learning challenge and have pinpointed directions for further research. Also, we have exposed potential difficulties for the recognition of social touch in more naturalistic settings. Moreover, the findings presented in this dissertation can help to inform the design of a behavioral model for robot pet companions that can understand and respond to human touch. Additionally, we have focused on the requirements for tactile interaction with robot pets for health care applications.", "source_url": "https:\/\/openalex.org\/W2744115739", "pdf_url": "https:\/\/ris.utwente.nl\/ws\/files\/12836170\/Dissertation_Merel_Jung.pdf", "doi": "10.3990\/1.9789036543644", "handle": "", "repository_name": "", "source_record_id": "W2744115739", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 537, "title": "Skill Learning for Industrial Robot Manipulators", "title_ar": "", "title_en": "Skill Learning for Industrial Robot Manipulators", "author": "Te Tang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; robotics; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "Industrial robots have been kept upgraded for decades to achieve extraordinary accuracy, speed, and repeatability. However, even the most advanced manipulator today is functioning as a programmable machine, instead of an intelligent agent. This deficiency of intelligence restricts robots from broader applications. To meet the increasing demand for automation, it is essential to make industrial robots more skillful and intelligent. Under this background, the objective of this dissertation is to develop generic and efficient methodologies to teach robots novel skills. Three major approaches including model-based learning, model-free learning and analogy-based learning are discussed and explored. A series of skills such as assembly, grasping, tracking and motion planning, have been successfully taught to industrial manipulators and evaluated by experiments.As the name implies, model-based learning tries to formulate skills analytically based on physical models. In Chapter 2, an auto-alignment skill is developed for robotic assembly by constructing a novel contact model. Robots are enabled to predict tilt angles between assembly parts from force\/torque measurement and perform fine assembly from large misalignment conditions. With this skill model, traditional procedures such as the installation of positioning fixtures and manual alignment can be skipped, which saves tremendous preparation efforts for robotic assembly.However, not always system models can be constructed, especially for those complicated scenarios. The model-free approach is then developed to learn control policies by regressing general parametric functions. In Chapter 3, a compliant robotic force controller is learned from human demonstration. A human operator holds a specially-designed handle and demonstrates to robots the compliant insertions. The Gaussian mixture regression is introduced to fit motion patterns from measured data. This approach enables to transfer the compliant assembly skill from humans to robots efficiently and intuitively. Besides model-based and model-free learning, an analogy-based leaning approach is proposed in Chapter 4. The distinct idea is that instead of pursuing a control policy either constructed by models or regressed from data, we discover the correlation between scenarios, i.e., analogy. The past scenario that bears a strong similarity with the current one will be identified, and a mapping function between these two scenes will be constructed. Applying the mapping function, the past problem-solving action can be transferred to a new one that is feasible for the current scenario. This analogy-based approach has been implemented in multiple industrial tasks, and taught robots various skills such as grasping versatile objects (Chapter 5), tracking and manipulating deformable objects (Chapter 6) and efficient motion re-planning for similar scenarios (Chapter 7).", "source_url": "https:\/\/openalex.org\/W2902534828", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W2902534828", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 538, "title": "Intelligent Robotic Recycling of Flat Panel Displays", "title_ar": "", "title_en": "Intelligent Robotic Recycling of Flat Panel Displays", "author": "Adam Sanderson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Waterloo", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; ai-applications; cnn; object-detection; image-segmentation; ai-industry", "keywords": "", "abstract": "As the population and prosperity continue to rise the demand for high-tech products is rapidly increasing. Displays are a huge part of this market with millions being created each year. We have a finite amount of resources on this planet and it is imperative that we properly dispose of our devices. In this thesis an intelligent robotic workcell for dismantling and recycling of flat panel displays is proposed and tested. This system utilizes industrial robots and open source tools to process a flat panel display (FPD) with the goal of removal of cold compact fluorescent tubes (CFLs). These are removed so the FPD can be shredded and properly recycled without the mercury present in the CFLs contaminating the materials or harming workers. This system utilizes many new and innovative techniques including deep learning algorithms for object detection and image segmentation. These deep learning techniques specifically Faster R-CNN for object detection and DeepLab for image segmentation have been shown to be extremely robust and capable in this application. The system was tested both with a real world system and with simulated geometry with a live vision feed. During simulation, the system was capable of processing flat panel FPDs in between 110 and 230 seconds per unit. It has been calculated using the simulation results, that this system can be profitable and has an approximate payback period of between 0.19 and 4.87 years depending on the material being fed into the system. The large range is due to the difference in value between monitor and TV style FPDs. The monitors have far less value than the TVs and are far more difficult to process.", "source_url": "https:\/\/openalex.org\/W2947070151", "pdf_url": "https:\/\/uwspace.uwaterloo.ca\/bitstreams\/c1b45110-475c-469e-ab35-3d38398d358d\/download", "doi": "", "handle": "", "repository_name": "UWSpace (University of Waterloo)", "source_record_id": "W2947070151", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 539, "title": "Towards Probabilistic Generative Models for Socially Intelligent Robots", "title_ar": "", "title_en": "Towards Probabilistic Generative Models for Socially Intelligent Robots", "author": "Xavier Alameda-Pineda", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; robotics; video-analysis; speech-recognition", "keywords": "", "abstract": "Socially intelligent robots represent a scientific and technological challenge that needs significant contributions at the cross-roads of machine learning, computer vision, audio processing and robotics to be addressed. Naturally, given that the communication modalities most commonly used in human interaction are audio and video, audio-visual fusion plays is a skill to be mastered for robots to exhibit social intelligence. The scenarios and tasks to be addressed in social robotics, require learning methods operating in unsupervised settings and handling the uncertainty in perception and in action. In addition, the developed tools cannot disregard the recent advances and breakthrough in representation learning brought by deep neural architectures. Consequently, combinations of probabilistic models – able to account for uncertainty – and deep neural networks – learning powerful data representations – seem the most appropriate methodological framework to develop social intelligence for robotic platforms. In this manuscript, we motivate the interest of developing machine learning methods exploiting audio-visual data to endow robots with social intelligence. We present the foundations of probabilistic generative models and discuss in detail seven recent contributions. Each of them addresses a different task, useful to acquire social intelligence, and are developed under the large framework of deep probabilistic models – combinations of deep neural networks and probabilistic models. Several future research opportunities as well as my personal scientific ambition for the next years conclude the manuscript.", "source_url": "https:\/\/openalex.org\/W4392497979", "pdf_url": "", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W4392497979", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 540, "title": "Robot Learning for Muscular Systems", "title_ar": "", "title_en": "Robot Learning for Muscular Systems", "author": "Dieter Büchler", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Technical University of Darmstadt", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; reinforcement-learning; robotics; ai-applications; predictive-analytics; ai-agriculture; ai-industry; ai-smart-cities", "keywords": "", "abstract": "Today's robots are capable of performing many tasks that tremendously improve human lives. For instance, in industrial applications, robots move heavy parts very quickly and precisely along a predefined path. Robots are also widely used in agriculture or domestic applications like vacuum cleaning and lawn mowing. However, in more general settings, the gap between human abilities and what current robots deliver is still not bridged, such as in dynamic tasks. Like table tennis with anthropomorphic robot arms, such tasks require the execution of fast motions that potentially harm the system. Optimizing for such fast motions and being able to execute them without impairing the robot still pose difficult challenges that, so far, have not been met. Humans perform dynamic tasks relatively easy at high levels of performance. Can we enable comparable perfection on kinematically anthropomorphic robots? This thesis investigates whether learning approaches on more human-like actuated robots bring the community a step closer towards this ambitious goal. Learning has the potential to alleviate control difficulties arising from fast motions and more complex robots. On the other hand, an essential part of learning is exploration, which forms a natural trade-off with robot safety, especially at dynamic tasks. This thesis's general theme is to show that more human-like actuation enables exploring and failing directly on the real system while attempting fast and risky motions. In the first part of this thesis, we develop a robotic arm with four degrees of freedom and eight pneumatic artificial muscles (PAM).Such a system is capable of replicating desired behaviors as seen in human arm motions: 1) high power-to-weight ratios, 2) inherent robustness due to passive compliance and 3) high-speed catapult-like motions as possible with fast energy release. Rather than recreating human anatomy, this system is designed to simplify control than previously designed pneumatic muscle robots. One of the main insights is that a simple PID controller is sufficient to control this system for slow motions accurately. When exploring fast movements directly on the real system, the antagonistic actuation avoids damages to the system. In this manner, the PID controller's parameters and additional feedforward terms can be tuned automatically using Bayesian optimization without further safety considerations. Having such a system and following our goal to show the benefits of the combination of learning and muscular systems, the next part's content is to learn a dynamics model and use it for control. In particular, the goal here is to learn a model purely from data as analytical models of PAM-based robots are not sufficiently good. Nonlinearities, hysteresis effects, massive actuator delay, and unobservable dependencies like temperature make such actuators' modeling especially hard. We learn probabilistic forward dynamics models using Gaussian processes and, subsequently, employ them for control to address this issue. However, Gaussian processes dynamics models cannot be set-up for our musculoskeletal robot as for traditional motor-driven robots because of unclear state composition, etc. In this part of the thesis, we empirically study and discuss how to tune these approaches to complex musculoskeletal robots. For the control part, introduce Variance Regularized Control (VRC) that tracks a desired trajectory using the learned probabilistic model. VRC incorporates the GP's variance prediction as a regularization term to optimize for actions that minimize the tracking error while staying in the training data's vicinity. In the third part of this thesis, we utilized the PAM-based robot to return and smash table tennis balls that have been shot by a ball launcher. Rather than optimizing the desired trajectory and subsequently track it to hit the ball, we employ model-free Reinforcement Learning to learn this task from scratch. By using RL with our system, we can specify the table tennis task directly in the reward function. The RL agent also applies the actions directly on the low-level controls (equivalent to the air pressure space) while robot safety is assured due to the antagonistic actuation. In this manner, we allow the RL agent to be applied to the real system in the same way as in simulation. Additionally, we make use of the robustness of PAM-driven robots by letting the training run for 1.5 million time steps 14 hours. We introduce a semi sim and real training procedure in order to avoid training with real balls. With this solution, we return 75% of all incoming balls to the opponent's side of the table without using real balls during training. We also learn to smash the ball with an average ball speed of 12 m\\s (5 m\\s for the return task) after the hit while sacrificing accuracy (return rate of 29%). In summary, we show that learning approaches to control of muscular systems can lead to increased performance in dynamic tasks.", "source_url": "https:\/\/openalex.org\/W3120428840", "pdf_url": "https:\/\/tuprints.ulb.tu-darmstadt.de\/17210\/1\/phdthesis_buechler.pdf", "doi": "10.25534\/tuprints-00017210", "handle": "", "repository_name": "TUbilio (Technical University of Darmstadt)", "source_record_id": "W3120428840", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 541, "title": "TOWARDS TRUSTWORTHY NATURAL LANGUAGE PROCESSING", "title_ar": "", "title_en": "TOWARDS TRUSTWORTHY NATURAL LANGUAGE PROCESSING", "author": "HANNAH AUGHT SCHWARTZ BROWN", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "fairness", "categories": "nlp; responsible-ai; fairness", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7163603768", "pdf_url": "", "doi": "", "handle": "", "repository_name": "National University of Singapore", "source_record_id": "W7163603768", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 542, "title": "Natural language processing for business documents", "title_ar": "", "title_en": "", "author": "Ελευθέριος-Παναγιώτης Λούκας", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "el", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; few-zero-shot; rnn; transformers; llm", "keywords": "", "abstract": "Η Επεξεργασία Φυσικής Γλώσσας (NLP) για επιχειρηματικά και χρηματοοικονομικά κείμενα (Hahn et al., 2018; Chen et al., 2022) αποτελεί ένα ταχέως αναπτυσσόμενο ερευνητικό πεδίο που εφαρμόζει υπολογιστικές τεχνικές σε κείμενα όπως εταιρικά έγγραφα, αναφορές αναλυτών και οικονομικές ειδήσεις. Τα έγγραφα αυτά παρουσιάζουν μοναδικές προκλήσεις λόγω του εξειδικευμένου λεξιλογίου (El-Haj et al., 2019), του κρίσιμου ρόλου των αριθμητικών δεδομένων, των ιδιαίτερων συντακτικών δομών και της σημασιολογίας του εκάστοτε πεδίου. Οι δυσκολίες αυτές εντείνονται από ευρύτερα προβλήματα, όπως η επεξεργασία μεγάλου όγκου μη δομημένων δημόσιων δεδομένων και η οικονομικά αποδοτική ανάπτυξη γλωσσικών μοντέλων, ιδίως για οργανισμούς με περιορισμένους πόρους όπως οι small-to-medium enterprises (SMEs). Η αντιμετώπιση αυτών των προκλήσεων είναι κρίσιμη για εφαρμογές που εκτείνονται από την ανίχνευση απάτης (Goel and Gangolly, 2012), τη long-form summarization (Cao et al., 2024) και την εξαγωγή πληροφορίας έως τη χρηματοοικονομική question answering (Maia et al., 2018). Η παρούσα διατριβή στοχεύει στην προώθηση της εφαρμοσμένης NLP και της αξιοποίησης επιχειρηματικών εγγράφων σε πραγματικές συνθήκες, αντιμετωπίζοντας σύγχρονες βιομηχανικές προκλήσεις σε διαφορετικά επίπεδα της τεχνητής νοημοσύνης, και πιο συγκεκριμένα στο επίπεδο των δεδομένων, των εφαρμογών και της ανάπτυξης (deployment), με σταθερή έμφαση σε περιβάλλοντα περιορισμένων πόρων. Διατυπώνουμε τρία βασικά ερευνητικά ερωτήματα: (1) Πώς μπορούν τα ανοικτής πρόσβασης, μη δομημένα επιχειρηματικά έγγραφα να αξιοποιηθούν αποτελεσματικά για NLP; (2) Πώς μπορούν οι σύγχρονες μέθοδοι NLP που βασίζονται σε deep learning (DL) να προσαρμοστούν και να επεκταθούν ώστε να δημιουργούν επιχειρηματική αξία σε εργασίες όπως το automatic document tagging, λαμβάνοντας υπόψη τις ιδιαιτερότητες της χρηματοοικονομικής γλώσσας και την έντονη εξάρτησή της από αριθμητικά δεδομένα; (3) Για τις συνήθεις βιομηχανικές εργασίες text classification, ποιες προσεγγίσεις είναι οι πιο ακριβείς και οικονομικά αποδοτικές σε συνθήκες περιορισμένων πόρων; Το τρίτο ερώτημα μελετάται μέσω μιας πραγματικής εφαρμογής οικονομικά αποδοτικής αναγνώρισης πρόθεσης σε διαλόγους πελατών, με σύγκριση μεταξύ BERT-based models και Large Language Models (LLMs) και με βελτιστοποίηση του κόστους ανάπτυξης των LLMs. Για την αντιμετώπιση των παραπάνω, αρχικά εστιάζουμε στον «εκδημοκρατισμό» της πρόσβασης στα επιχειρηματικά έγγραφα μέσω της ανάπτυξης του EDGAR-CORPUS, του μεγαλύτερου δημόσια διαθέσιμου financial NLP corpus στα Αγγλικά, των domain-specific word embeddings EDGAR-W2V, τα οποία υπερτερούν των εναλλακτικών προσεγγίσεων, καθώς και του EDGAR-CRAWLER, ενός open-source εργαλείου εξαγωγής χρηματοοικονομικών δεδομένων με εκατοντάδες χρήστες από τον ακαδημαϊκό χώρο, τη FinTech κοινότητα και την ανάπτυξη web εφαρμογών. Στη συνέχεια, εισάγουμε το XBRL tagging ως μια πραγματική εργασία NLP, όπου δημιουργούμε το FiNER-139, το πρώτο dataset για το συγκεκριμένο πρόβλημα, και δείχνουμε ότι τα LSTM μοντέλα μπορούν να υπερέχουν έναντι του BERT λόγω περιορισμών της αρχιτεκτονικής των transformers και της τυπικής τεχνικής tokenization σε σχέση με τον κατακερματισμό αριθμητικών tokens. Μετά από εκτενή αξιολόγηση διαφορετικών μεθόδων, προτείνουμε μια νέα τεχνική tokenization με χρήση pseudo-tokens για transformer μοντέλα, η οποία βελτιώνει σημαντικά την απόδοση σε numeric-first tasks, οδηγώντας στην κυκλοφορία νέων state-of-the-art BERT μοντέλων (SEC-BERT) ειδικά σχεδιασμένων για το χρηματοοικονομικό πεδίο. Τέλος, για την οικονομικά αποδοτική αναγνώριση πρόθεσης, πραγματοποιούμε μια εκτενή συγκριτική μελέτη στο Banking77 dataset (Casanueva et al., 2020), δείχνοντας ότι μικρότερα BERT-based models μπορούν να είναι πιο αποδοτικά και οικονομικά από τα LLMs, απαιτώντας μόνο ελαφρώς περισσότερα επισημασμένα δεδομένα εκπαίδευσης. Παράλληλα, παρουσιάζουμε το “Dynamic Few-Shot Prompting”, μια προσέγγιση βασισμένη σε Retrieval-Augmented Generation (RAG) που μειώνει δραστικά το κόστος inference των LLMs διατηρώντας υψηλή ακρίβεια, και διερευνούμε τη χρησιμότητα της synthetic data generation. Η διατριβή καθιστά δημόσια διαθέσιμους βασικούς πόρους: το EDGAR-CORPUS, τα EDGAR-W2V embeddings, το λογισμικό EDGAR-CRAWLER, το dataset FiNER-139, την οικογένεια μοντέλων SEC-BERT, καθώς και ένα επιμελημένο υποσύνολο του Banking77 dataset, το οποίο περιλαμβάνει expert-selected παραδείγματα για κάθε intent class και το οποίο αποδεικνύεται κρίσιμο για την επίτευξη υψηλής απόδοσης σε few-shot learning σενάρια.", "source_url": "https:\/\/openalex.org\/W4417302042", "pdf_url": "", "doi": "10.12681\/eadd\/60507", "handle": "", "repository_name": "", "source_record_id": "W4417302042", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 543, "title": "Privacy preserving methods for Natural Language Processing", "title_ar": "", "title_en": "Privacy preserving methods for Natural Language Processing", "author": "Plant, Richard", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Edinburgh Napier University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; responsible-ai; ai-applications; llm; fairness; ai-cybersecurity", "keywords": "", "abstract": "This work investigates the effects of personal data privacy risk and other potential types of attack that threaten privacy or effective learning in the NLP domain, including private information re-identification, membership inference, and data poisoning. Through a survey of the extant literature a comprehensive set of extant attacks on NLP models that pose a threat to privacy have been identified, as well as leading ideas on reducing their impact. This work demonstrates the risk of specific attacks to popular NLP models, as well as carrying out a rigorous empirical evaluation of the impact of proposed mitigation strategies. This thesis proposes a set of privacy-preserving defences for machine learning when applied to the natural language domain, especially applied to large pre-trained language models. This includes several approaches based on local differential privacy, that is, applying a transformation to the data before it is processed that makes breaching privacy more difficult while preserving the utility of the set for learning, as well as other approaches based on adversarial training, such as Gradient Reversal and Cross-Gradient Training. In addition, this research includes the development and empirical demonstration of the effectiveness of a hybrid LDP\/adversarial approach on reducing re-identification risk for language models, as well as similar hybrid and combined approaches for reducing membership inference attack risk.", "source_url": "https:\/\/openalex.org\/W7104385337", "pdf_url": "", "doi": "10.17869\/enu.2025.4843658", "handle": "", "repository_name": "Edinburgh Napier Research Repository (Edinburgh Napier University)", "source_record_id": "W7104385337", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 544, "title": "Coreference resolution in natural language processing", "title_ar": "", "title_en": "Coreference resolution in natural language processing", "author": "Ruicheng Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; expert-systems; predictive-analytics; rnn; transformers; sentiment-analysis", "keywords": "", "abstract": "Coreference Resolution (CR), a key task in Natural Language Processing, involves identifying expressions that refer to the same entity or event. This thesis presents a detailed survey of CR models from the past decade, intro- duces a novel Multi-task Learning (MTL) model for the Gold-two-mention CR task, and explores CR’s application in financial sentiment analysis. The models surveyed are classified into feature-based, neural networks (includ- ing multilayer perceptrons and recurrent neural networks), knowledge-based, and transformer-based frameworks. The new MTL model simultaneously ad- dresses mention identification and linking in gold-two-mention style CR, us- ing a dynamic weight balancing mechanism to optimize task-specific weights during training. This model achieves state-of-the-art results on three bench- mark datasets. Furthermore, the thesis integrates advanced CR techniques with domain-specific models such as FinBERT to improve sentiment anal- ysis in financial texts. Rigorous empirical methods, including Propensity Score Matching and multivariate analysis, assess the impact of sentiments on financial outcomes, validating the framework’s predictive capabilities in financial sentiment analysis. This research not only improves CR method- ologies but also demonstrates their practical relevance in financial analytics, encouraging further interdisciplinary studies.", "source_url": "https:\/\/openalex.org\/W7108096065", "pdf_url": "", "doi": "10.32657\/10356\/204483", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W7108096065", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 545, "title": "Sociolinguistically Driven Approaches for Just Natural Language Processing", "title_ar": "", "title_en": "Sociolinguistically Driven Approaches for Just Natural Language Processing", "author": "Su Lin Blodgett", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Massachusetts Amherst", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "fairness", "categories": "machine-learning; nlp; responsible-ai; ai-applications; fairness; ai-education", "keywords": "", "abstract": "Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at this intersection: i) How can we conceptualize harms arising from NLP systems?, and ii) How can we quantify such harms? We propose the following contributions. First, we contribute a model in order to collect the first large dataset of African American Language (AAL)-like social media text. We use the dataset to quantify the performance of two types of NLP systems, identifying disparities in model performance between Mainstream U.S. English (MUSE)- and AAL-like text. Turning to the landscape of bias in NLP more broadly, we then provide a critical survey of the emerging literature on bias in NLP and identify its limitations. Drawing on work across sociology, sociolinguistics, linguistic anthropology, social psychology, and education, we provide an account of the relationships between language and injustice, propose a taxonomy of harms arising from NLP systems grounded in those relationships, and propose a set of guiding research questions for work on bias in NLP. Finally, we adapt the measurement modeling framework from the quantitative social sciences to effectively evaluate approaches for quantifying bias in NLP systems. We conclude with a discussion of recent work on bias through the lens of style in NLP, raising a set of normative questions for future work.", "source_url": "https:\/\/openalex.org\/W3159454188", "pdf_url": "", "doi": "10.7275\/20410631", "handle": "", "repository_name": "ScholarWorks@UMassAmherst (University of Massachusetts Amherst)", "source_record_id": "W3159454188", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 546, "title": "Natural Language Processing for Discussion Platforms", "title_ar": "", "title_en": "Natural Language Processing for Discussion Platforms", "author": "Maike Katrin Behrendt", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; medical-ai; transfer-learning; transformers; llm", "keywords": "", "abstract": "Online participation platforms and digital discussion spaces have become central arenas for public discourse, particularly in times of crisis such as the COVID-19 pandemic. While these environments expand opportunities for civic engagement, they are frequently characterized by incivility, misinformation, and unstructured exchanges. Drawing on the concept of deliberation, defined by the core dimensions of rationality, civility, interactivity and equality, this thesis investigates how Natural Language Processing (NLP) methods can be leveraged to foster more respectful, reasoned, and inclusive online discussions. The dissertation addresses five central aspects: (i) the current approaches in applying NLP to online discussion platforms, (ii) the optimization of smaller language models for efficient real-time deployment, (iii) the integration of NLP-based interventions into discussion platforms, (iv) user perceptions of Artificial Intelligence (AI)-supported moderation and assistance, and (v) the measurable effects of such interventions on discussion quality. First, a systematic review provides a comprehensive overview of existing NLP applications in political and civic online discussions, identifying established approaches and tasks that could potentially be used to support discussions (see Chapter 4). Second, the thesis develops and evaluates methods to improve the efficiency and effectiveness of small Transformer-based models, particularly Bidirectional Encoder Representations from Transformers (BERT), for tasks relevant to online deliberation. This includes adapting pre-training strategies for argument similarity detection (ArgueBERT, see Chapter 5) and optimizing fine-tuning procedures for classification performance (MaxPoolBERT, see Chapter 6). In addition, the Additive deliberative Quality score with Adapters (AQuA) is introduced to assess the deliberative quality of user contributions (see Chapter 7). This model, along with a comment recommendation module based on stance detection, has been integrated into an online discussion platform and tested in a large-scale, real-time experiment (see Chapter 8). The findings provide insights into the technical integration of AI-supported tools into online discussion platforms, as well as into users’ perceptions of such systems and their observed effects in a specific participatory context. By combining perspectives from computer science, political science, and communication science, this interdisciplinary work contributes to the development of AI-supported tools that aim to support deliberative processes in digital public spheres.", "source_url": "https:\/\/openalex.org\/W7213477619", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Univ. Duesseldorf: Duesseldorfer Dokumenten- und Publikationsserver", "source_record_id": "W7213477619", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 547, "title": "Weak Supervision and Active Learning for Natural Language Processing", "title_ar": "", "title_en": "Weak Supervision and Active Learning for Natural Language Processing", "author": "MING LIU", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "deep-learning; computer-vision; nlp", "keywords": "", "abstract": "Deep neural models in natural language processing rely on large amounts of labeled data. In the real world, annotation can be expensive and time consuming. In this project, the aim is to learn good deep neural models with a minimum amount of labeled data. We take multiple strategies to achieve this goal: we use structured information from the data, incorporate prior knowledge for the model and learn an active learning policy for annotation. Experiments on simulated and real word tasks show these strategies are useful and effective.", "source_url": "https:\/\/openalex.org\/W3124832285", "pdf_url": "https:\/\/figshare.com\/articles\/Weak_Supervision_and_Active_Learning_for_Natural_Language_Processing\/8396834", "doi": "10.26180\/5d1ab5f09f24b", "handle": "", "repository_name": "Figshare", "source_record_id": "W3124832285", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 548, "title": "News similarity with natural language processing", "title_ar": "", "title_en": "", "author": "Álvaro Parafita Martínez", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat de Barcelona", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2016, "language": "ca", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Jordi Vitrià i Marca", "source_url": "https:\/\/openalex.org\/W2336689562", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona)", "source_record_id": "W2336689562", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 549, "title": "Representation learning for natural language processing", "title_ar": "", "title_en": "", "author": "Ana Pribanić", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Zagreb Faculty of Humanities and SocialSciences", "university_source": "repository_host", "college": "", "department": "", "country": "Croatia", "governorate": "", "publication_year": 2021, "language": "sl", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; nlp; transformers", "keywords": "", "abstract": "Obrada prirodnog jezika postaje sve aktivnijim područjem istraživanja. Razvoj tehnika reprezentativnog učenja od lokalnih do distribuiranih reprezentacija omogućio je tehnologijama obrade prirodnog jezika bolje razumijevanje semantičkog značenja i konteksta riječi i rečenica na prirodnim jezicima. Veliki napredak ostvaren je dvosmjernim treniranjem modela transformera čija je izvedba prikazana u ovom radu vizualizacijom skupa podataka IMDB recenzija na hrvatskom i engleskom jeziku.", "source_url": "https:\/\/openalex.org\/W7134427567", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ODRAZ (University of Zagreb Faculty of Humanities and SocialSciences)", "source_record_id": "W7134427567", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 550, "title": "Word2vec as the approach in natural language processing", "title_ar": "", "title_en": "", "author": "О. І. Онуфрик", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Vinnytsia National Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2020, "language": "uk", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "За останнє десятиліття штучний інтелект зробив великий крок у розвитку. Сучасна людина щодня зустрічається з ним в побуті. Штучний інтелект складається з багатьох напрямків, таких як – комп’ютерний зір, розпізнавання мови, тексту, тощо. Алгоритми обробки природньої мови займають значну нішу в галузі штучного інтелекту, зокрема такі компанії, як Google, Apple and Amazon використовують їх в своїх голосових помічниках.", "source_url": "https:\/\/openalex.org\/W7213704912", "pdf_url": "https:\/\/ir.lib.vntu.edu.ua\/bitstream\/123456789\/30645\/1\/10735-38228-1-SM.pdf", "doi": "", "handle": "", "repository_name": "Repository of Vinnytsia National Technical University (Vinnytsia National Technical University)", "source_record_id": "W7213704912", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 551, "title": "Natural Language Processing in Clinical Domain", "title_ar": "", "title_en": "", "author": "Lukáš Radovanský", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Czech Technical University in Prague", "university_source": "repository_host", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2022, "language": "cs", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp; medical-ai", "keywords": "", "abstract": "Tato bakalářská práce prozkoumává možnosti nejnovějších technologií v oblasti zpracování přirozeného jazyka a jejich použití v doméně tabakismu a odvykání kouření. Práce popisuje zpracování přirozeného jazyka v dané doméně a to zejména v kontextu technologie chatbot. Shrnuje historii, slabé a silné stránky této technologie, existující aplikace založené na této technologii a také její použití a efektivitu v oblasti tabakismu a odvykání kouření. Jedním z výstupů práce je základní demo verze chatbota pro českou Národní linku pro odvykání, který bude v budoucnu k dispozici klientům linky na jejích webových stránkách. Chatbot byl vyvíjen pod odborným dohledem zaměstnanců Národní linky pro odvykání, dle jejich požadavků a na základě jejich zpětné vazby.", "source_url": "https:\/\/openalex.org\/W7200669080", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Czech Technical University Digital Library (Czech Technical University in Prague)", "source_record_id": "W7200669080", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 552, "title": "Natural language processing for digital advertising", "title_ar": "", "title_en": "Natural language processing for digital advertising", "author": "Yiping Jin ; Dittaya Wanvarie", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Thailand", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; transformers", "keywords": "", "abstract": "Advertising is not only a marketing or sales activity but a particular form of two-way communication.�In this thesis,�we propose to apply the two main subtasks of natural language processing (NLP), namely natural language understanding (NLU) and natural language generation (NLG), to digital advertising to enhance the effectiveness of advertising. We apply weakly-supervised text classification to rapidly build text classifiers for contextual advertising (Jin et al. 2022). The method requires a handful of labeled keywords instead of a large corpus of labeled documents and can be easily transferred to new domains. We further evaluate the weakly-supervised models using unsupervised error estimation and perform automatic keyword selection (Jin et al., 2021a). Unsupervised error estimation is essential because no labeled development dataset is available in real-world problems where weakly-supervised text classification methods are applied. Finally, we tap on a state-of-the-art sequence-to-sequence Transformer model to generate cohesive and diverse advertising slogans from a short company description (Jin et al., In press). We prevent the model from hallucinating unsupported information using entity masking and generate diverse and catchy slogans using conditional training.", "source_url": "https:\/\/openalex.org\/W4386790576", "pdf_url": "http:\/\/cuir.car.chula.ac.th\/bitstream\/123456789\/79815\/1\/6173105023.pdf", "doi": "10.58837\/chula.the.2021.121", "handle": "", "repository_name": "Chula Digiverse", "source_record_id": "W4386790576", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 553, "title": "Expert System for Pregnant Women by Natural Language Processing", "title_ar": "", "title_en": "Expert System for Pregnant Women by Natural Language Processing", "author": "สันทัด พรหมแท่น", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Prince of Songkla University", "university_source": "repository_host", "college": "", "department": "", "country": "Thailand", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp; medical-ai; expert-systems", "keywords": "", "abstract": "The antenatal care is basic step. It is essentials for health of pregnant women. The first step into the women pregnant have a healthy, beside the fetus have a healthy. The Healthy Pregnant women must be closely and continuously care by the expert. The steps of antenatal care included: 1) health assessment 2) report of Assessment result 3) health care recommendations The problem of antenatal care included: (1) a number of experts is insufficient for pregnant women. (2) New graduate nurses lack experience in health care recommendation (3) Pregnant women do not understand the information in mother and child health handbook. The objective of this research is to studied the technique of Natural Language Processing (NLP) to health questions sentences analysis by getting user input and used the technique of Rule Base to report of Assessment result. Meanwhile, to health care recommendations and develop an expert - based application to enhance the health of pregnant women by using a Chatbot. The results of this study, found that with The Rule Base technique was used to define 58 rules. The technique of Natural Language Processing (NLP) was also used to analyze and find the intent of the sentence to reply to the user with an accuracy of 92.16 %. The result of the evaluation to understanding data communications in application, found that a sample of 27 in 30 (90 percent) had understanding data communications. The result of the evaluation knowledge score before and after of new graduate nurses use of the application were statistically significant differences between the pre-test and post-test (Z = -2.989, p = 001) so the post-test scores (X= 9.3846, SD = 96077) higher than the pre-test scores (X= 7.7692, SD = 1.96443).", "source_url": "https:\/\/openalex.org\/W7199381325", "pdf_url": "", "doi": "", "handle": "", "repository_name": "PSU Knowledge Bank (Prince of Songkla University)", "source_record_id": "W7199381325", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 554, "title": "Representation learning for natural language processing", "title_ar": "", "title_en": "Representation learning for natural language processing", "author": "Chunting Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Hong Kong", "university_source": "repository_host", "college": "", "department": "", "country": "Hong Kong", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; nlp; predictive-analytics; cnn; rnn; sentiment-analysis", "keywords": "", "abstract": "Many applications in natural language processing (NLP) depend on extracting compact and informative features from unstructured texts. Distributed representations encode data as continuous vectors, by constructing a projection from semantics to points in high dimensional spaces. As an effective and efficient way of learning such representations, neural network based models have obtained great successes in various NLP applications. For different NLP scenarios, different types of neural networks can be carefully assembled to mimic human's behavioral patterns of understanding the real world or to exploit the semantics based on linguistic composition. This thesis focuses on representation learning at different granularities; different neural network models are designed that embed features of words, sentences and articles. Learned representations are applied in three selected natural language applications: sentiment classification, text classification and reading comprehension. The first part of this work learns word representations with a shallow neural network, in which global topical knowledge is incorporated into local contexts when a word interacts with its context words. The learned word vectors are evaluated on three tasks: word analogical reasoning, word similarity task and text classification. Experiments show that word embeddings are enhanced in terms of preserving their semantic information together with category knowledge. The second part of this work proposes a model called \\\\C-LSTM\" which can learn sentence representations through combining a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network. The input to the LSTM network is sequential n-gram features extracted by the CNN, and thus at each time step the input has integrated a wide range of context information. Experiments on sentiment classification and question-type classification tasks demonstrate the remarkable performance of C-LSTM as compared with state-of-the-art models. The last part of this work proposes a context aware attention neural network model for reading comprehension. Two separate convolutional neural networks are employed to encode the articles, and the max pooling result of one CNN is used to attentively read the other CNN output. The attended representation of the article together with the query representation are used to predict the answer. Extensive experimental analyses have verified its effectiveness in extracting informative n-grams that match the query or are helpful to answer inference.", "source_url": "https:\/\/openalex.org\/W2558292009", "pdf_url": "", "doi": "10.5353\/th_b5807307", "handle": "", "repository_name": "The HKU Scholars Hub (University of Hong Kong)", "source_record_id": "W2558292009", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 555, "title": "Demographic-Aware Natural Language Processing", "title_ar": "", "title_en": "Demographic-Aware Natural Language Processing", "author": "Aparna Garimella", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Michigan", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; nlp; predictive-analytics; speech-recognition", "keywords": "", "abstract": "The underlying traits of our demographic group affect and shape our thoughts, and therefore surface in the way we express ourselves and employ language in our day-to-day life. Understanding and analyzing language use in people from different demographic backgrounds help uncover their demographic particularities. Conversely, leveraging these differences could lead to the development of better language representations, thus enabling further demographic-focused refinements in natural language processing (NLP) tasks. In this thesis, I employ methods rooted in computational linguistics to better understand various demographic groups through their language use. The thesis makes two main contributions. First, it provides empirical evidence that words are indeed used differently by different demographic groups in naturally occurring text. Through experiments conducted on large datasets which display usage scenarios for hundreds of frequent words, I show that automatic classification methods can be effective in distinguishing between word usages of different demographic groups. I compare the encoding ability of the utilized features by conducting feature analyses, and shed light on how various attributes contribute to highlighting the differences. Second, the thesis explores whether demographic differences in word usage by different groups can inform the development of more refined approaches to NLP tasks. Specifically, I start by investigating the task of word association prediction. The thesis shows that going beyond the traditional ``one-size-fits-all'' approach, demographic-aware models achieve better performances in predicting word associations for different demographic groups than generic ones. Next, I investigate the impact of demographic information on part-of-speech tagging and syntactic parsing, and the experiments reveal numerous part-of-speech tags and syntactic relations, whose predictions benefit from the prevalence of a specific group in the training data. Finally, I explore demographic-specific humor generation, and develop a humor generation framework to fill-in the blanks to generate funny stories, while taking into account people's demographic backgrounds.", "source_url": "https:\/\/openalex.org\/W3139872221", "pdf_url": "https:\/\/deepblue.lib.umich.edu\/bitstreams\/de2aac6e-ca29-4c11-b396-29d819be092d\/download", "doi": "", "handle": "", "repository_name": "Deep Blue (University of Michigan)", "source_record_id": "W3139872221", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 556, "title": "Data Augmentation methods in natural language processing.", "title_ar": "", "title_en": "", "author": "Taynan Maier Ferreira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidade de São Paulo", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2021, "language": "pt", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "nlp; sentiment-analysis", "keywords": "", "abstract": "Métodos de aumento de dados (AD) -uma família de técnicas desenhada para a geração de dados de treino sintéticos -têm demonstrado resultados notáveis em diversas tarefas de Aprendizado Profundo e Aprendizado de Máquina.Apesar de sua adoção ampla e bem-sucedida dentro da comunidade de visão computacional, técnicas de AD desenhados para tarefas de Processamento de Linguagem Natural (PLN) têm demonstrado avanço muito mais lento e limitado sucesso em ganho de desempenho.Como consequência, com a exceção da adoção de Back-Translation em tarefas de tradução, essas técnicas não tem sido exploradas tão profundamente e de forma ampla pela comunidade de PLN.Não há uma visão unificada ou análise comparativa entre os vários métodos de AD disponíveis.Além disso, ainda não se tem um entendimento prático adequado sobre o relacionamento entre AD e diversos outros aspectos importantes do desenho de um modelo, como dados de treino e parâmetros de regularização.Nesse trabalho, realizamos um profundo estudo de técnicas de AD em PLN, comparando seus desempenhos relativos sob diferentes cenários em tarefas de Análise de Sentimentos.Também propomos Deep Back-Translation, uma nova técnica de AD para PLN.Nós realizamos uma análise qualitativa e quantitativa do dado sintético, avaliamos seu ganho de desempenho e comparamos todos esses aspectos com procedimentos prévios de AD.", "source_url": "https:\/\/openalex.org\/W3214297092", "pdf_url": "http:\/\/www.teses.usp.br\/teses\/disponiveis\/3\/3141\/tde-04112021-162156\/publico\/TaynanMaierFerreiraCorr21.pdf", "doi": "10.11606\/d.3.2021.tde-04112021-162156", "handle": "", "repository_name": "Digital Library of Theses and Dissertations (Universidade de São Paulo)", "source_record_id": "W3214297092", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 557, "title": "Natural language processing as autoregressive generation", "title_ar": "", "title_en": "Natural language processing as autoregressive generation", "author": "Xiang Lin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; responsible-ai; predictive-analytics; llm; machine-translation; fairness", "keywords": "", "abstract": "The advances in deep learning have led to great achievements in many Natural Language Processing (NLP) tasks. With the nature of language, i.e., sequential data, most NLP tasks can be framed into the sequence learning framework, such as text generation. As one of the most important foundations for modern NLP techniques, autoregressive generation models have achieved dominant performance in a great deal of NLP tasks. Therefore, this thesis emphasizes improving the autoregressive generation model for different NLP tasks. While many tasks can naturally fit into the sequence learning framework, some of them, e.g., building discourse parsing tree, require sophisticated designs to fit into neural models. Therefore, this thesis firstly emphasizes a novel unified framework for discourse parsing, which builds a discourse tree in a top-down depth-first manner, and it frames the task as an autoregressive generation task with the goal of each step being the prediction of the node position given a piece of text. The proposed approach is proven effective with extensive empirical experiments. In addition, I extend the above framework by proposing a hierarchical decoder, which leverages the information from parents and siblings of the nodes that are currently processed. The proposed decoder utilizes the nature of the tree structure and further improves the experiment performance on both discourse parsing and dependency parsing tasks. On the other hand, the de facto strategies, i.e., cross entropy loss and teacher forcing, for training the autoregressive generation models have been shown problematic in certain aspects. For example, cross entropy loss, which is one of the widely leveraged training objective functions, often leads to text degeneration in text generation, and teacher forcing suffers from the exposure bias problem, where there exists a mismatch between the training and testing setup. For text degeneration, I introduce a class of diminishing attentions, which enforces the submodularity of the coverage calculated by cross attention in the sequence-to-sequence model. The proposed diminishing attentions achieve notable improvement on several neural text generation tasks, including text summarization, machine translation, and image paragraph generation. Further, I propose a novel training objective, ScaleGrad, to replace cross entropy, which significantly reduces the degeneration problem in different text generation tasks. In fact, ScaleGrad can be extended to problems beyond text degeneration. It provides wide flexibility to inject different inductive biases into the text generation model by directly modifying the gradient information in the output layer. Next, for the exposure bias problem, this thesis introduces a novel type of scheduled sampling based on training accuracy, which requires only minimal hyper-parameter tuning compared to existing scheduled sampling methods. Additionally, a novel imitation loss is proposed to further enforce the model’s generative behavior to match the teacher-forced behavior. Moreover, this thesis demonstrates that reducing exposure bias can improve the robustness of language models against repetition and toxic errors.", "source_url": "https:\/\/openalex.org\/W4382929605", "pdf_url": "", "doi": "10.32657\/10356\/168487", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4382929605", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 558, "title": "Domain adaptation in Natural Language Processing", "title_ar": "", "title_en": "Domain adaptation in Natural Language Processing", "author": "Marina Sedinkina", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Ludwig-Maximilians-Universität München", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; transfer-learning; predictive-analytics; sentiment-analysis", "keywords": "", "abstract": "Domain adaptation has received much attention in the past decade. It has been shown that domain knowledge is paramount for building successful Natural Language Processing (NLP) applications. To investigate the domain adaptation problem, we conduct several experiments from different perspectives. First, we automatically adapt sentiment dictionaries for predicting the financial outcomes “excess return” and “volatility”. In these experiments, we compare manual adaptation of the domain-general dictionary with automatic adaptation, and manual adaptation with a combination consisting of first manual, then automatic adaptation. We demonstrate that automatic adaptation performs better than manual adaptation, namely the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting excess return and volatility. Furthermore, we perform qualitative and quantitative analyses finding that annotation based on an expert’s a priori belief about a word’s meaning is error-prone – the meaning of a word can only be recognized in the context that it appears in. Second, we develop the temporal transfer learning approach to account for the language change in social media. The language of social media is changing rapidly – new words appear in the vocabulary, and new trends are constantly emerging. Temporal transfer-learning allows us to model these temporal dynamics in the document collection. We show that this method significantly improves the prediction of movie sales from discussions on social media forums. In particular, we illustrate the success of parameter transfer, the importance of textual information for financial prediction, and show that temporal transfer learning can capture temporal trends in the data by focusing on those features that are relevant in a particular time step, i.e., we obtain more robust models preventing overfitting. Third, we compare the performance of various domain adaptation models in low-resource settings, i.e., when there is a lack of large amounts of high-quality training data. This is an important issue in computational linguistics since the success of NLP applications primarily depends on the availability of training data. In real-world scenarios, the data is often too restricted and specialized. In our experiments, we evaluate different domain adaptation methods under these assumptions and find the most appropriate techniques for such a low-data problem. Furthermore, we discuss the conditions under which one approach substantially outperforms the other. Finally, we summarize our work on domain adaptation in NLP and discuss possible future work topics.", "source_url": "https:\/\/openalex.org\/W3164246911", "pdf_url": "", "doi": "10.5282\/edoc.27831", "handle": "", "repository_name": "Elektronische Hochschulschriften der LMU München (Ludwig-Maximilians-Universität München)", "source_record_id": "W3164246911", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 559, "title": "Exploring reinforcement learning in natural language processing", "title_ar": "", "title_en": "", "author": "M.C. Godino González ; Joan Francesc", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "ca", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; nlp; reinforcement-learning; rnn; transformers", "keywords": "", "abstract": "El Procesament del Llenguatge Natural és el conjunt de tasques que tracten amb el llenguatge \"humà\". En aquest treball ens centrem en la traducció automàtica, una d'aquestes tasques, que consisteix en traduir automàticament d'un idioma a un altre. La principal forma d'afrontar aquesta tasca és mitjançant l'ús de tècniques de aprenentatge profund, particularment d'aprenentatge supervisat. Aquest treball té com objectiu explorar l'aplicació de tècniques de aprenentatge per reforç a la traducció automàtica. Experimentem amb dos datasets d'extremadament baixos recursos (IWSLT14 alemany-anglès i FLoRes nepalès-anglès) així com amb dues arquitectures d'aprenentatge profund diferents: el Transformer i una red bi-LSTM. Hem observat que les tècniques habituals d'aprenentatge per reforç aplicades al Transformer requereixen molta memòria de la GPU i per tant no hem pogut completar exitosament l'entrenament dels models. No obstant, hem obtingut resultats positius als experiments amb bi-LSTM.", "source_url": "https:\/\/openalex.org\/W3180701530", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W3180701530", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 560, "title": "Evaluation of mpi4py for Natural Language Processing Scenarios", "title_ar": "", "title_en": "Evaluation of mpi4py for Natural Language Processing Scenarios", "author": "Manvi Saxena", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3146844420", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3146844420", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 561, "title": "Evolutionary game theoretic models for natural language processing", "title_ar": "", "title_en": "Evolutionary game theoretic models for natural language processing", "author": "Rocco Tripodi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Università Ca' Foscari Venezia", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "self-semi-supervised", "categories": "machine-learning; nlp; ai-applications; self-semi-supervised; data-mining; optimization; ai-education", "keywords": "", "abstract": "This thesis is aimed at discovering new learning algorithms inspired by principles of biological evolution, which are able to exploit relational and contextual information, viewing clustering and classification problems in a dynamical system perspective. In particular, we have investigated how game theoretic models can be used to solve different Natural Language Processing tasks. Traditional studies of language have used a game-theoretic perspective to study how language evolves over time and how it emerges in a community but to the best of our knowledge, this is the first attempt to use game-theory to solve specific problems in this area. These models are based on the concept of equilibrium, a state of a system, which emerges after a series of interactions among the elements, which are part of it. Starting from a situation in which there is uncertainty about a particular phenomenon, they describe how a disequilibrium state resolves in equilibrium. The games are situations in which a group of objects has to be classified or clustered and each of them has to choose its collocation in a predefined set of classes. The choice of each one is influenced by the choices of the other and the satisfaction that a player has, about the outcome of a game, is determined by a payoff function, which the players try to maximize. After a series of interactions the players learn to play their best strategies, leading to an equilibrium state and to the resolution of the problem. From a machine-learning perspective this approach is appealing, because it can be employed as an unsupervised, semi-supervised or supervised learning model. We have used it to resolve the word sense disambiguation problem. We casted this task as a constraint satisfaction problem, where each word to be disambiguated is con- strained to choose the most coherent sense among the available, according to the sense that the words around it are choosing. This formulation ensures the mainte- nance of textual coherence and has been tested against state-of-the-art algorithms with higher and more stable results. We have also used a game theoretic formulation, to improve the clustering results of dominant set clustering and non-negative matrix factorization technique. We evaluated our system on different document datasets through different approaches, achieving results, which outperform state-of-the-art algorithms. This work opened new perspectives in game theoretic models, demonstrating that these approaches are promising and that they can be employed also for the resolution of new problems.", "source_url": "https:\/\/openalex.org\/W2509969451", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ARCA (Università Ca' Foscari Venezia)", "source_record_id": "W2509969451", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 562, "title": "Leveraging Measurement Theory for Natural Language Processing Research", "title_ar": "", "title_en": "Leveraging Measurement Theory for Natural Language Processing Research", "author": "Qixiang Fang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Utrecht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; responsible-ai; llm; fairness", "keywords": "", "abstract": "This dissertation explores the intersection of natural language processing (NLP) and measurement theory. NLP is a field aimed at enabling machines to process and generate human languages, such as English, German, and Mandarin. These languages are complex, diverse, and full of irregularities, making them challenging for machines to handle compared to structured artificial languages, like programming languages. NLP research ranges from simple tasks, like word frequency analysis, to complex ones involving the understanding and generation of human language. Measurement theory, traditionally applied in social sciences, addresses how we measure various properties scientifically. Key concepts include construct validity, which examines whether a measure accurately represents what it intends to measure, and reliability, which focuses on the consistency of a measure across different conditions. This dissertation argues that many challenges in NLP relate to measurement issues and suggests that principles from measurement theory can help address these challenges, particularly by providing tools to evaluate and improve the quality of NLP models. The structure of the dissertation is as follows: Chapter 2 offers background on NLP and measurement theory, covering essential text representation techniques in NLP, the history of measurement theory, and recent discussions on its unification across fields. Chapters 3-5 apply measurement theory to evaluate NLP models: Chapter 3 explores the reliability of gender bias measures in NLP by using classical reliability estimators from social sciences. Chapter 4 adapts a construct validity testing framework to assess the quality of text representations for social science constructs. Chapter 5 introduces a psychometric-based benchmarking approach to evaluate large language models, demonstrated through a case study on eighth-grade math proficiency. Chapters 6-7 focus on using measurement theory to improve NLP model performance: Chapter 6 presents a framework for designing user models based on measurement principles, achieving better-quality user representations than current methods. Chapter 7 examines how integrating human values into model training can enhance models’ ability to recognize values in human arguments. Chapters 8 and 9 reflect on current NLP research challenges and propose future directions: Chapter 8 identifies challenges in text-based personality computing, offering potential solutions and avenues for research. Chapter 9 concludes with a summary of the dissertation’s findings and suggests future work at the intersection of measurement theory and NLP. This work underscores the potential of measurement theory to enhance NLP research by offering frameworks for evaluating and designing more reliable and valid models. By integrating these approaches, the dissertation aims to bridge NLP and measurement theory, advancing NLP's capability to address complex measurement challenges.", "source_url": "https:\/\/openalex.org\/W4404301181", "pdf_url": "", "doi": "10.33540\/473", "handle": "", "repository_name": "Utrecht University Repository (Utrecht University)", "source_record_id": "W4404301181", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 563, "title": "Modelling the machines' own language with natural language processing", "title_ar": "", "title_en": "", "author": "Ali DAYAN", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2022, "language": "tr", "research_domain": "nlp", "research_subdomain": "", "categories": "nlp", "keywords": "", "abstract": "Doğadaki tüm canlılar birbirleri ile haberleşmek için çeşitli yöntemler kullanmaktadır. Dillerin oluşumu ve gelişimi hakkında hala yeterli bilgi bulunmamaktadır. Dillerin oluşumu ve doğal dil işleme süreçleri ile ilgili geçmişten günümüze kadar çeşitli çalışmalar yapılmış olup bu çalışmalar farklı metot, farklı analiz ve farklı yaklaşımlar içermektedir. Makinelerin kendi dillerini üretebilmesi için yaklaşım geliştirilmesi, böylece insansı makinelerin oluşturulma sürecinde bu makinelerin de tıpkı insanların sahip olduğu gibi kendi dilleri ile iletişim kurabilme süreçlerine katkıda bulunulmak, doğal dillerin oluşumu sürecinde de dil bilimcilere yol göstermesi, evrişimli sinir ağlarını kullanarak canlıları ayırt etme ve sınıflandırma yetilerini, yeni sesler türetme yetisini kazanması hedeflenmiştir. Görüntü işleme algoritmalarının ses işlemede kullanılarak daha etkin ses analizlerinin yapılmasının sağlanması ve ses işlemede yeni model geliştirilmesi konularına odaklanılmıştır. İnsanlar yüzyıllardır dillerin oluşumu ve gelişimi üzerine çalışmalar yapmaktadır. Diller sayesinde insanlar birbirleri ile etkileşimde bulunabilmektedir. Çalışma ile bu yetinin makinelere kazandırılması sağlanması için modeller oluşturulmuştur. Çalışmada var olan doğal dil işleme modelleri ile ses işleme modelleri kullanılmıştır. Öncelikle makineye günümüzde var olan alfabelerden hangisini kullanmak istediği karar verdirtilmiş ardından da Yenilemeli Yapay Sinir Ağları, Mel Frekans Cepstral Katsayısı ve Dinamik Zaman Çözgü yaklaşımı modellerinin birlikte kullanılması ile canlıların seslerine benzer sesler türetilmiş ve canlılar bu sesler ile adlandırılmıştır. Çalışma sonucunda, makinelerin kendi dillerini üretebilmesi için yaklaşım geliştirilmiştir. Böylece insansı makinelerin oluşturulma sürecinde bu makinelerin de tıpkı insanların sahip olduğu gibi kendi dilleri ile iletişim kurabilme süreçlerine katkıda bulunulmuştur. Doğal dillerin oluşumu sürecinde de dil bilimcilere yol göstermesi hedeflenmiştir.", "source_url": "https:\/\/openalex.org\/W7217499380", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Beykent University Repository", "source_record_id": "W7217499380", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 564, "title": "Self- and semi-supervised machine learning techniques for discovering novel ocular and neuroimaging biomarkers of Alzheimer's disease", "title_ar": "", "title_en": "Self- and semi-supervised machine learning techniques for discovering novel ocular and neuroimaging biomarkers of Alzheimer's disease", "author": "Charlene Z. L. Ong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; responsible-ai; self-semi-supervised; predictive-analytics; cnn; gnn; multimodal; image-segmentation; gan; medical-imaging; xai", "keywords": "", "abstract": "There is increasing prevalence of Alzheimer’s disease worldwide. To manage the progression of Alzheimer’s disease, biomarkers can be utilized to identify patients with the disease or predict subsequent progression. While neuroimaging biomarkers have been long established to visualize structural changes associated with Alzheimer’s disease, there are other promising biomarkers such as retinal imaging biomarkers which offer the possibility of detecting early pathophysiological changes and are non-invasive and widely available. Nevertheless, the link between retinal imaging biomarkers to Alzheimer’s disease remains unclear and there is much difficulty in procuring datasets of patients for investigation, in particular longitudinal datasets. At the same time, advances in deep learning also offer much promise for furthering our understanding of the link between ophthalmic biomarkers and dementia. Self-supervised learning techniques offer potential for learning new biomarkers which might not have been defined previously. However, there is often a lack of sufficient ground truth annotations, hence hampering the application of deep learning models. In light of this, the work in this thesis aims to address these issues by exploring the use of self or semi-supervised learning approaches and leveraging on both neuroimaging and ophthalmic imaging. By utilizing unlabeled datasets, self or semi-supervised learning could potentially improve the model performance on a range of tasks, such as segmentation, disease classification, and prognosis prediction. More importantly, self-supervised learning techniques could uncover novel biomarkers in a data-driven manner. Some of these methods include generative adversarial networks, graph neural networks, and masked autoencoders, which are explored in this study for biomarker discovery. Firstly, the challenge of lack of sufficient ground truth annotation to develop models for retinal layer segmentation is investigated. Retinal layer segmentation is critical for quantifying the retinal layer thickness, which has been shown to be a useful diagnostic biomarker for age-related macular degeneration and a potential biomarker for Alzheimer’s disease. While there have been existing approaches developed which reduce annotation need, we propose to develop a deep learning approach using generative adversarial learning that utilizes a very limited number of labels for segmentation. We demonstrated that our approach showed comparable or superior performance to other state-of-the-art approaches, particularly with limited labels. In addition, we also evaluated our approach under various noisy conditions and showed it was robust in most noisy conditions. Secondly, current dementia diagnosis is based primarily on clinical assessment. Neuroimaging in the form of magnetic resonance imaging is used to provide additional granularity. It will hence be practical to explore neuroimaging biomarkers as a value add to clinical assessment, especially in a pre-dementia cohort. Our proposed approach uses a graph convolutional neural network to combine both clinical and non-clinical features such as neuroimaging for the classification of cognitive syndromes. It integrates a Cross Attention Module to highlight the salient features in the non-clinical features, attaining the highest accuracy and Matthew’s correlation coefficient compared to existing state-of-the-art methods. In addition, by investigating the feature interpretability via Integrated Gradients, several nonclinical features, such as cerebrospinal fluid volume, right hippocampal fissure volume, right frontal pole thickness, and left pallidum volume were identified as key features for the classification. Thirdly, we explore the utility of ophthalmic biomarkers as prognostic biomarkers for cognitive decline in Alzheimer’s disease patients. In parallel, we explore the possibility of combining both ophthalmic and neuroimaging features to form multimodal features. The application of these multimodal features is investigated in a clinical trial dataset consisting of participants with mild-to-moderate Alzheimer’s disease. To learn key feature representations associated with the ophthalmic images, we utilize self-supervised learning via masked autoencoders to extract feature representations. For neuroimaging, due to the availability of established neuroimaging software, we utilize standard preprocessing pipelines to obtain these neuroimaging features. We investigated the combination of these features using a graph convolutional network model for the prediction of cognitive decline in Alzheimer’s disease and showed initial promising results with the addition of optical coherence tomography data.", "source_url": "https:\/\/openalex.org\/W7127051058", "pdf_url": "", "doi": "10.32657\/10356\/205534", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W7127051058", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 565, "title": "Semi-Self-Supervised Learning for Deep Models: Reducing Annotation Costs in Dense-Pattern Agricultural Image Analysis", "title_ar": "", "title_en": "Semi-Self-Supervised Learning for Deep Models: Reducing Annotation Costs in Dense-Pattern Agricultural Image Analysis", "author": "Najafian, Keyhan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Saskatchewan", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; ai-applications; transfer-learning; self-semi-supervised; object-detection; image-segmentation; video-analysis; ai-agriculture", "keywords": "", "abstract": "Most of the success of deep learning in computer vision depends on supervised learning (SL) and large-scale, manually annotated datasets. Creating such datasets is a major bottleneck due to the substantial cost and labor required, particularly for pixel-level tasks on images with dense and irregular object distributions, as commonly encountered in precision agriculture. While existing semi- and self-supervised methods attempt to reduce this dependency by requiring minimal to no annotation, they still require massive, difficult-to-acquire unlabeled datasets. Self-supervised learning typically involves pretraining on extensive unlabeled data to learn generalizable representations, followed by fine-tuning on task-specific labeled samples, whereas semi-supervised learning combines a small set of labeled data with larger unlabeled collections. To mitigate these challenges, this dissertation introduces a semi-self-supervised learning paradigm that integrates data synthesis, domain adaptation, and spatiotemporal modeling to develop high-performing models across various tasks. Specifically, our paradigm leverages high-frame-rate unannotated video, supplemented with minimal human-annotated static images and video frames, to create large-scale annotated datasets more efficiently than conventional data collection. The produced data includes synthetic training samples, which are rendered through data simulation (synthetic data), and automatically labeled real samples (generated data), where all labels originate from the initial minimal human annotations, thus eliminating the need for further manual labeling and directly addressing the data scarcity problem. We demonstrate the efficacy of our proposed semi-self-supervised paradigm through four key studies on progressively more challenging computer vision tasks within the demanding context of precision agriculture. The framework is first applied to object detection, where a computational synthesis method creates large-scale bounding-box annotated datasets from minimal manually annotated video frames, achieving robust performance of over 80%. Extending this to the pixel-level, the method generates high-quality semantic masks for scenes characterized by dense and overlapping objects, reaching a 91% Dice score with merely 19 annotated video frames and static images. For the more granular task of instance segmentation, our approach enables scalable instance-level object analysis, obtaining over 90% accuracy. Finally, by integrating optical flow and spatiotemporal modeling, our method effectively segments objects in complex video sequences with minimal supervision, outperforming conventional approaches. Together, these studies validate the framework’s ability to deliver high-performing models across diverse and complex tasks while drastically reducing manual labeling effort. These studies establish that our proposed semi-self-supervised paradigm offers a transformative approach for developing DL models. By effectively leveraging video data and supervision derived from minimal human annotations, coupled with domain adaptation, this methodology demonstrates significant scalability and generalizability. It successfully enables instance-level and pixel-level reasoning even with limited annotation, confirming its potential to overcome annotation bottlenecks and advance DL applications in complex, data-constrained real-world scenarios, particularly within the field of precision agriculture.", "source_url": "https:\/\/openalex.org\/W7113662752", "pdf_url": "", "doi": "", "handle": "", "repository_name": "HARVEST (University of Saskatchewan)", "source_record_id": "W7113662752", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 566, "title": "From unsupervised to semi-supervised : Leveraging self-supervised embeddings with partial anomaly supervision for tabular anomaly detection", "title_ar": "", "title_en": "From unsupervised to semi-supervised : Leveraging self-supervised embeddings with partial anomaly supervision for tabular anomaly detection", "author": "Jesper Lampinen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dalarna University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; transfer-learning; self-semi-supervised; predictive-analytics; object-detection", "keywords": "", "abstract": "Self-supervised learning (SSL) has shown promise for extracting useful representations from unlabelled tabular data, but recent work has found that SSL embeddings do not improve unsupervised anomaly detection. This thesis investigates whether these limitations can be overcome by pairing SSL representations with semi-supervised detector heads, a direction identified as future work by Mai et al. (2024). Using SCARF as the SSL method, embeddings from a frozen, contrastively pre-trained encoder are passed to four downstream models: Isolation Forest and One-Class SVM in the unsupervised setting, and self-training with XGBoost and logistic regression at label budgets of 1%, 5%, 10%, and 20%. Raw and PCA-transformed features serve as baselines. Experiments are conducted on three tabular benchmark datasets from ADBench (Cardiotocography, Shuttle, SpamBase), across five random seeds. The results show that SCARF embeddings consistently underperform raw and PCA features across all datasets, detectors, and supervision levels. Semi-supervision improves detection for all feature conditions, but the improvement is proportionally similar across conditions, and the performance gap between SCARF and baselines does not close as more labels are introduced. These findings extend the negative results of Mai et al. into the semi-supervised regime, suggesting that the contrastive pre-training objective of SCARF, applied without supervised fine-tuning, does not produce representations sufficiently aligned with the anomaly detection task to benefit from semi-supervised detector heads.", "source_url": "https:\/\/openalex.org\/W7165072400", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Diva portal (Dalarna University Library)", "source_record_id": "W7165072400", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 567, "title": "SELF-SUPERVISED LEARNING FOR REFRIGERATION SYSTEM MONITORING IN IIOT", "title_ar": "", "title_en": "SELF-SUPERVISED LEARNING FOR REFRIGERATION SYSTEM MONITORING IN IIOT", "author": "Xavier Alexander Lopez", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "machine-learning", "research_subdomain": "self-semi-supervised", "categories": "machine-learning; expert-systems; self-semi-supervised", "keywords": "", "abstract": "This thesis evaluates self-supervised, semi-supervised, supervised, and rule-based machine learning approaches for anomaly detection in low-frequency LoRaWAN refrigeration monitoring across 84 sensors.", "source_url": "https:\/\/openalex.org\/W7160421932", "pdf_url": "", "doi": "10.25394\/pgs.32129659", "handle": "", "repository_name": "Purdue", "source_record_id": "W7160421932", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 568, "title": "Improving Human Behavior Understanding with Semi-supervised Learning", "title_ar": "", "title_en": "Improving Human Behavior Understanding with Semi-supervised Learning", "author": "Bruno Manuel Degardin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; responsible-ai; self-semi-supervised; cnn; transformers; multimodal; video-analysis; gan; diffusion; fairness", "keywords": "", "abstract": "Recent advancements in Computer Vision (CV) and Machine Learning (ML) have fundamentally revolutionized the field of human behaviour understanding, enabling intelligent systems to move beyond simple motion detection to the interpretation of complex, semantic actions from vast streams of visual data. This paradigm shift has been driven by the exponential growth of Deep Learning algorithms, particularly the evolution from 3D Convolutional Neural Networks (3D CNNs) to modern Vision Transformers (ViTs) , which have achieved remarkable success in modeling long-range spatiotemporal dependencies in RGB videos and multimodal signals. However, despite their impressive performance on controlled benchmarks, the deployment of these pixel-based models in real-world scenarios remains heavily constrained. Their reliance on high-dimensional visual data often incurs prohibitive computational costs that hinder real-time application on edge devices. Furthermore, these methods exhibit significant sensitivity to background clutter and viewpoint variations , and the processing of raw video raises critical privacy concerns by capturing unnecessary appearance details such as identity and clothing. Maximizing the potential of this modality presents a significant set of challenges that hinder widespread deployment. The heavy reliance on massive, high-quality annotated datasets creates a prohibitive bottleneck for supervised learning, while existing generative models used for data augmentation often lack physical plausibility or controllability. Furthermore, self-supervised methods borrowed from video domains frequently fail to capture the fine-grained dynamics of skeletal motion, and current evaluation metrics for synthetic data struggle to distinguish between realistic kinematics and artifacts. Addressing these limitations requires a fundamental shift toward more label-efficient, physically aware, and generative approaches to behaviour analysis. In this thesis, we proposed a comprehensive suite of novel solutions to mitigate these problems by exploiting semi-supervised and self-supervised learning paradigms, as well as advanced generative modeling. First, we addressed the limitations of temporal reasoning in Graph Convolutional Networks (GCNs) by proposing REGINA, a reasoning- augmented architecture. This method injects explicit temporal self-similarity cues into the network through a learnable temporal adjacency matrix, allowing the model to adaptively optimize its receptive fields to capture long-range dependencies that standard convolutions often miss. Addressing data scarcity, we proposed Kinetic-GAN, a generative adversarial network that scales action conditioning by an order of magnitude over prior works. By generating actions directly from a disentangled latent space using a mapping network and stochastic noise injection, it synthesizes diverse, high-fidelity global body movements across more than 100 action classes. We further advanced representation learning without labels through ATOM, a self-supervised strategy that leverages atomic motion representation learning. By decomposing actions into short, fine-grained primitives and training the backbone to recover their temporal order, ATOM learns robust features from the intrinsic structure of motion, transferring effectively to supervised tasks without the computational burden of heavy reconstruction heads. To bridge the gap between generation and recognition, we developed an intrinsic, kinematics-aware quality assessment framework. Recognizing that conventional distribution metrics fail to capture physical validity, this framework utilizes per-sample kinematic indicators within a weaklysupervised discriminator to iteratively filter synthetic samples, ensuring only physically plausible data is used to boost downstream performance. Finally, pushing the boundaries of controllable synthesis, we proposed GLDM, a Spatiotemporal Graph Latent Diffusion Model. GLDM projects skeleton sequences into a compact, graph-aligned latent space and performs diffusion with graph-aware updates. Crucially, by integrating cross-attention mechanisms, GLDM enables precise multi-modal conditioning, allowing generation to be guided not just by class labels but also by natural language prompts, achieving generalization beyond training data, unconstrained temporal flexibility and solving the mode collapse issues inherent to adversarial approaches. Results obtained by the proposed methods show that it is possible to improve HBU systems regarding label efficiency, generative controllability, and physical plausibility, while still obtaining state-of-the-art results. All proposed methods were evaluated on several large-scale benchmarks, demonstrating that robust behaviour understanding can be achieved with significantly reduced reliance on manual annotation.", "source_url": "https:\/\/openalex.org\/W7225277943", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7225277943", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 569, "title": "Efficient Rare Disease Detection with Self-Supervised and Transfer Learning", "title_ar": "", "title_en": "Efficient Rare Disease Detection with Self-Supervised and Transfer Learning", "author": "Miftahul Jannat", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; self-semi-supervised; predictive-analytics; cnn; medical-imaging", "keywords": "", "abstract": "Accurate diagnosis of rare diseases by analyzing medical images is often faced with the problem of limited availability of annotated datasets, as the manual annotation process, being time-consuming, requires expert knowledge in addition to a significant time investment.The research explores the use of Self-Supervised Learning (SSL) combined with Transfer Learning as a method to reduce the dependency on labeled data without compromising diagnostic performance.The approach designed involves pretraining convolutional neural networks using large unlabeled datasets through self-supervised learning (SSL) methods.For this purpose, the CheXpert dataset of over 200000 X-ray images is employed.To enable the model to imbibe generalizable features from the unlabeled pictures, several SSL pretext tasks are applied, namely rotation prediction, jigsaw puzzles, as well as contrastive learning (SimCLR).Following the pretraining process, the models are fine-tuned using the ISIC 2018 dataset, which contains annotated dermoscopic images for the task of skin lesion classification.Feature extraction and classification performance are evaluated using different architectures, such as ResNet-50.Finetuning is performed using different percentages of labeled data (10%, 25%, 50%, and 100%) to evaluate the efficiency of annotations in semi-supervised learning (SSL).The performance and models have been assessed with respect to Accuracy, Precision, Recall, and F1 Score.From the results, it appears that SSL-pretrained models using SimCLR outperformed fully supervised models even when they were trained on significantly fewer labeled samples.It is astonishing that models which were trained with only 50% of the labeled data actually achieved over 75% accuracy in comparison to those trained on the complete dataset.This work demonstrates that the semi-supervised learning (SSL) approach made possible through transfer learning is a highly effective and efficient in terms of labeling resources for detecting rare diseases in medical images.", "source_url": "https:\/\/openalex.org\/W7215667449", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/9b495e9a-88c8-4914-8f52-550745585eb9\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215667449", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 570, "title": "Explainable Supervised and Semi-Supervised Learning for Breast Cancer Risk Prediction from Questionnaires: A Study on BCSC and UAE Datasets", "title_ar": "", "title_en": "Explainable Supervised and Semi-Supervised Learning for Breast Cancer Risk Prediction from Questionnaires: A Study on BCSC and UAE Datasets", "author": "Omar Ahmad Alsarookh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "American University of Sharjah", "university_source": "repository_host", "college": "", "department": "", "country": "United Arab Emirates", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; medical-ai; responsible-ai; self-semi-supervised; predictive-analytics; medical-imaging; xai", "keywords": "", "abstract": "Breast cancer is one of the most prevalent cancers globally and remains a leading cause of death among women. While mammography helps detect existing abnormalities, it offers limited insight into the future risk of developing cancer. This highlights the need for proactive risk assessment which enables early intervention before symptoms appear. This thesis explores supervised and semi-supervised machine learning methods to classify women into low and high-risk groups using environmental and lifestyle features. Three datasets were utilized: the labeled Breast Cancer Surveillance Consortium (BCSC) Risk Estimation dataset, the unlabeled BCSC Risk Factors dataset, and a labeled private region-specific dataset from University Hospital Sharjah (UHS) in the UAE. In the supervised approach, multiple models were evaluated on the BCSC Risk Estimation dataset including XGBoost, TabNet, Random Forest, and Logistic Regression. XGBoost achieved the best performance with an F1-score of 0.93. A separate region-specific supervised model was also developed using the UHS dataset, with XGBoost again performing best (F1-score: 0.85). Since labeled medical data is often scarce and expensive to obtain, semi-supervised learning allows us to leverage large volumes of unlabeled data to improve model performance and generalization. In the semi-supervised approach, two techniques were used on the unlabeled BCSC Risk Factors dataset: Label Spreading, a graph-based method, and Self-Training, where the classifier iteratively labels data based on confidence thresholds. Pseudo-labeled samples from both methods were combined with labeled data to retrain classifiers. The best semi-supervised model (Self-Training with XGBoost) achieved an F1-score of 0.91. Generalizability was evaluated by testing the BCSC-trained models on the UHS dataset. The supervised BCSC model achieved an F1-score of 0.92, while the semi-supervised model’s F1-score dropped to 0.81 when tested on UHS data, which highlights domain shift and pseudo-label noise. SHAP and LIME were used for explainability, confirming the influence of key risk factors such as breast density, family history, and BMI. These results validate the feasibility of integrating supervised and semi-supervised approaches for proactive, population-specific breast cancer risk assessment.", "source_url": "https:\/\/openalex.org\/W7215125748", "pdf_url": "", "doi": "10.82366\/568", "handle": "", "repository_name": "DSpace at AUS (American University of Sharjah)", "source_record_id": "W7215125748", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 571, "title": "Semi-supervised learning using pseudo-labels: A case study in Northern Sámi ASR", "title_ar": "", "title_en": "Semi-supervised learning using pseudo-labels: A case study in Northern Sámi ASR", "author": "Pal, Priyanshi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; nlp; ai-applications; transfer-learning; self-semi-supervised; speech-recognition; ai-education", "keywords": "", "abstract": "While Self-Supervised Learning has advanced Automatic Speech Recognition (ASR) for low-resource languages, limited labeled data remains a bottleneck, often preventing reasonable downstream performance. In line with this requirement, Semi-Supervised Learning approaches have gained prominence as providing a means to data-efficient methods that can be helpful, even with limited supervised data. This work investigates the usefulness of semi-supervised learning, specifically Pseudo-Labeling with self-supervised speech models for Northern Sámi, an under-resourced agglutinative language. Within a teacher–student framework, pseudo-labels are incorporated using two strategies: (a) filtering them through WER-based agreement between the models, and (b) using all pseudo-labels generated by a teacher model. Results show that pseudo-labels from the larger teacher foundation model can improve the downstream performance of a smaller one without additional labeled data, yielding 33.30–3.84% relative character error rate reduction (CERR) across three out-of-distribution test sets. The smaller student model is evaluated in a supervised setting (20 hours of labeled data) and semi-supervised settings with 20, 26 and 75 hours of pseudo-labeled data. Notably, the student model can benefit as much, or even more, from pseudo-labels as from labeled data. Further gains can be achieved when both annotated and pseudo-labels are combined in a two-stage fine-tuning process, rather than mixing all data in a single training phase.", "source_url": "https:\/\/openalex.org\/W7115821482", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W7115821482", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 572, "title": "Transformers and contrastive semi-supervised learning for medical image segmentation", "title_ar": "", "title_en": "Transformers and contrastive semi-supervised learning for medical image segmentation", "author": "Qianying Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Glasgow", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; self-semi-supervised; cnn; transformers; image-segmentation; medical-imaging; fairness; ai-education", "keywords": "", "abstract": "Medical Image Semantic Segmentation (MISS), the process of assigning a semantic label to each pixel in an image, is a foundational task in computational medicine, critical for quantitative diagnostics and treatment planning. However, developing robust MISS models faces two intertwined challenges. First, there is an architectural dilemma: Convolutional Neural Networks (CNNs), like U-Net, excel at learning local features but are limited by their receptive fields, failing to capture global context essential for segmenting organs with large deformations. Conversely, Vision Transformers (ViTs) effectively model long-range dependencies but lack the inductive biases of CNNs, leading to poor generalization on the small datasets typical in medicine without extensive pre-training. Second, the prohibitive cost and expertise required for creating pixel-level annotations create a severe data scarcity bottleneck. While Semi-Supervised Learning (SSL) aims to mitigate this by leveraging unlabeled data, existing methods often fail to learn high-level semantic relations and are susceptible to confirmation bias from noisy pseudo-labels, class imbalance, and suboptimal contrastive sample selection. This thesis presents a comprehensive investigation to systematically address these challenges, delivering a cohesive suite of novel deep learning frameworks. The contributions are four-fold: First, to resolve the architectural trade-off, this work introduces CS-Unet, a pure Transformer network built upon a U-Net-like architecture. Its core innovation is the Convolutional Swin Transformer (CST) block, which integrates convolutions directly within the Multi-Head Self-Attention and Feed-Forward Network modules. This design imbues the Transformer with inherent localized spatial context and strong inductive biases, enabling it to efficiently learn both local and global features. Without pre-training, CS-Unet outperforms existing Transformer and CNN-based models on multi-organ and cardiac datasets, achieving state-of-the-art performance with fewer parameters. Second, to address data scarcity, a novel Multi-Scale Cross Supervised Contrastive Learning (MCSC) framework for SSL is developed. MCSC jointly trains CNN and Transformer models, using a cross-teaching paradigm where each network provides pseudo-labels for the other. Crucially, it moves beyond simple output consistency by applying a contrastive loss to feature maps at multiple scales, enforcing hierarchical semantic consistency. To handle the class imbalance endemic to medical imaging, a class-prevalence-aware loss is used to ensure features for infrequent classes are learned robustly. Third, to fortify SSL against noisy pseudo-labels, a certainty-guided contrastive learning strategy is proposed. This approach mitigates the impact of inaccurate pseudo-labels by using a certainty metric to guide the selection of samples for contrastive learning. The framework’s computational efficiency is enhanced through novel sampling strategies that select a few representative samples for contrasting, and a negative memory bank is used to increase sample diversity and eliminate dependence on batch size. Fourth, this thesis introduces a new paradigm for SSL by leveraging external anatomical priors through the Contrastive Cross-Teaching with Registration (CCT-R) framework. CCT-R is the first method to integrate spatial registration transforms into the learning process. It features two novel modules: a Registration Supervision Loss (RSL), which uses transforms between labeled and unlabeled volumes to generate an additional, highly reliable source of pseudo-labels, and Registration-Enhanced Positive Sampling (REPS), which uses registration to identify anatomically-corresponding positive pairs across volumes for contrastive learning. Overall, these contributions provide a powerful toolkit that significantly alleviates the annotation bottleneck in medical AI. The proposed methods demonstrate state-of-the-art performance on challenging segmentation benchmarks, delivering a pathway to develop accurate, data-efficient models for real-world clinical applications and opening new avenues for research into fusing geometric priors with semantic segmentation.", "source_url": "https:\/\/openalex.org\/W7127166515", "pdf_url": "", "doi": "10.5525\/gla.thesis.85734", "handle": "", "repository_name": "Enlighten: Theses (The University of Glasgow)", "source_record_id": "W7127166515", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 573, "title": "Segmentation Label Generation Based on Self-Supervised and Semi-Supervised Learning", "title_ar": "", "title_en": "Segmentation Label Generation Based on Self-Supervised and Semi-Supervised Learning", "author": "Berna de Araújo Nogueira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; computer-vision; self-semi-supervised; image-segmentation", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7111104835", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7111104835", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 574, "title": "Self and semi-supervised Learning for vehicle make and model recognition in images", "title_ar": "", "title_en": "Self and semi-supervised Learning for vehicle make and model recognition in images", "author": "Ricardo Nuno Alves Teixeira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "transfer-learning", "categories": "machine-learning; computer-vision; ai-applications; transfer-learning; few-zero-shot; self-semi-supervised; ai-education", "keywords": "", "abstract": "Recent advances in computer vision have demonstrated remarkable success in general object recognition tasks. However, fine-grained visual classification remains challenging due to subtle inter-class variations and the requirement for extensive labeled datasets. While self-supervised learning has shown promise in learning rich representations from unlabeled data, its application to specialized domains like fine-grained recognition has been less explored. This study explores various self-supervised learning architectures, such as a contrastive learning method and a teacher-student framework , namely SimCLR and DINO respectively, to evaluate their effectiveness in fine-grained classification tasks. Specifically, it focuses on the application of self and semisupervised learning techniques to vehicle make and model recognition in image datasets with few labels, providing a comprehensive analysis of their performance for this specialized classification challenge. We propose a multi-stage framework that leverages a backbone pretrained on the ImageNet dataset, followed by self-supervised fine-tuning on the Stanford Cars dataset, finalizing with few-shot classification with a linear classifier, also on Stanford Cars, to evaluate representation quality. Our experiments reveal significant challenges in domain transfer from general to specialized visual domains. We observe catastrophic forgetting when adapting models from broad object recognition to fine-grained automotive features, particularly pronounced with limited data availability. This work highlights critical research directions for improving domain adaptation in self-supervised learning, particularly for fine-grained applications with data constraints.", "source_url": "https:\/\/openalex.org\/W7124516569", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7124516569", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 575, "title": "Self-Supervised Learning for Geometry", "title_ar": "", "title_en": "Self-Supervised Learning for Geometry", "author": "Huangying Zhan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Adelaide", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; self-semi-supervised; predictive-analytics; video-analysis; ai-smart-cities", "keywords": "", "abstract": "This thesis focuses on two fundamental problems in robotic vision, scene geometry understanding and camera tracking. While both tasks have been the subject of research in robotic vision, numerous geometric solutions have been proposed in the past decades. In this thesis, we cast the geometric problems as machine learning problems, specifically, deep learning problems. Differ from conventional supervised learning methods that using expensive annotations as the supervisory signal, we advocate for the use of geometry as a supervisory signal to improve the perceptual capabilities in robots, namely Geometry Self-supervision. With the geometry self-supervision, we allow robots to learn and infer the 3D structure of the scene and ego-motion by watching videos, instead of expensive ground-truth annotation in traditional supervised learning problems. Followed by showing the use of geometry for deep learning, we show the possibilities of integrating self-supervised models with traditional geometry-based methods as a hybrid solution for solving the mapping and tracking problem. We focus on an end-to-end mapping problem from stereo data in the first part of this thesis, namely Deep Stereo Matching. Stereo matching is one of the oldest problems in computer vision. Classical approaches to stereo matching typically rely on handcrafted features and a multiple-step solution. Recent deep learning methods utilize deep neural networks to achieve end-to-end trained approaches while significantly outperforming classic methods. We propose a novel data acquisition pipeline using an untethered device (Microsoft HoloLens) with a Time-of-Flight (ToF) depth camera and stereo cameras to collect real-world data. A novel semi-supervised method is proposed to train networks with ground-truth supervision and self-supervision. The large scale real-world stereo dataset with semi-dense annotation and dense self-supervision allow our deep stereo matching network to generalize better when compared to prior arts. Mapping and tracking using a single camera (Monocular) is a harder problem when compared to that using a stereo camera due to varies well-known challenges. In the second part of this thesis, We decouple the problem into single view depth estimation (mapping) and two view visual odometry (tracking) and propose a self-supervised framework, namely SelfTAM, which jointly learns the depth estimator and the odometry estimator. The self-supervised problem is usually formulated as an energy minimization problem consist of an energy of data consistency in multi-view (e.g. photometric) and an energy of prior regularization (e.g. depth smoothness prior). We strengthen the supervision signal with a deep feature consistency energy term and a surface normal regularization term. Though our method trains models with stereo sequence such that a real-world scaling factor is naturally incorporated, only monocular data is required in the inference stage. In the last part of this thesis, we revisit the basics of visual odometry and explore the best practice to integrate deep learning models with geometry-based visual odometry methods. A robust visual odometry system, DF-VO, is proposed. We use deep networks to establish 2D-2D\/3D-2D correspondences and pick the best correspondences from the dense predictions. Feeding the high-quality correspondences into traditional VO methods, e.g. Epipolar Geometry and Prospective-n-Points, we can solve visual odometry problem within a more robust framework. With the proposed self-supervised training, we can even allow the models to perform online adaptation in the run-time and take a step toward a lifelong learning visual odometry system.", "source_url": "https:\/\/openalex.org\/W3132493307", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Adelaide Research & Scholarship (AR&S) (University of Adelaide)", "source_record_id": "W3132493307", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 576, "title": "Optimization Methods for Semi-Supervised Learning", "title_ar": "", "title_en": "Optimization Methods for Semi-Supervised Learning", "author": "Edward Cheung", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Waterloo", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "self-semi-supervised", "categories": "machine-learning; medical-ai; self-semi-supervised; predictive-analytics; optimization", "keywords": "", "abstract": "The goal of this thesis is to provide efficient optimization algorithms for some semi-supervised learning (SSL) tasks in machine learning. For many machine learning tasks, training a classifier requires a large amount of labeled data; however, providing labels typically requires costly manual annotation. Fortunately, there is typically an abundance of unlabeled data that can be easily collected for many domains. In this thesis, we focus on problems where an underlying structure allows us to leverage the large amounts of unlabeled data, while only requiring small amounts of labeled data. In particular, we consider low-rank matrix completion problems with applications to recommender systems, and semi-supervised support vector machines (S3VM) to solve binary classification problems, such as digit recognition or disease classification. For the first class of problems, we study convex approximations to the low-rank matrix completion problem. Instead of restricting the solution space to low-rank matrices, we use the trace norm as a convex surrogate. Unfortunately, many trace norm minimization algorithms scale very poorly in practice since they require a full singular value decomposition (SVD) at each iteration. Recently, there has been renewed interest in the trace norm constrained problem utilizing the Frank-Wolfe algorithm, which only requires calculating the leading singular vector pair, providing an order of magnitude improvement on the iteration complexity. However, the Frank-Wolfe algorithm empirically has very slow convergence and in practice yields high-rank solutions, which greatly increases computational costs. To address this issue, we investigate a rank-drop step for Frank-Wolfe, which solves a subproblem specifically designed to decrease the rank of the iterate, ensuring that the Frank-Wolfe algorithm converges along a low-rank path. We show that this rank-drop subproblem can be decomposed into two cases, where each subproblem can be solved efficiently and we guarantee that the iterates remain feasible, preserving the projection-free property of Frank-Wolfe. Next we show that these ideas can be used to provide scalable algorithms for simultaneously sparse and low-rank matrix completion problems. We extend the Frank-Wolfe analysis to accommodate nonsmooth objectives, which can be used to solve the simultaneously sparse and low-rank problem. We replace the traditional linear approximation used in Frank-Wolfe by a uniform affine approximation to better address poor local approximations given by the first-order Taylor approximation. We show that this naturally leads to a sequence of smooth functions that uniformly converges to the original nonsmooth objective, allowing for a careful balance between approximation quality and convergence that is closely related to the step sizes of the Frank-Wolfe algorithm. We apply this algorithm to solve sparse covariance estimation problems, graph link prediction, and robust matrix completion problems. Finally, we propose a variant of self-training for the semi-supervised binary classification problem by leveraging ideas from S3VM. To address common issues associated with self-training, such as error propagation and label imbalances, we proposed an adaptive scheme using the functional margin of S3VM to construct a confidence measure. The confidence score is used to create rules to adapt the optimization problems to incorporate label uncertainty and class imbalances. Moreover, we show that the incremental training approach leverages warm-starts very well, leading to much faster training than standard S3VM methods alone, with much stronger empirical performance on imbalanced datasets.", "source_url": "https:\/\/openalex.org\/W2803599927", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UWSpace (University of Waterloo)", "source_record_id": "W2803599927", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 577, "title": "Semi-supervised learning for classification of Nordic news articles", "title_ar": "", "title_en": "Semi-supervised learning for classification of Nordic news articles", "author": "Nils Magne Fossåen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "self-semi-supervised", "categories": "machine-learning; self-semi-supervised", "keywords": "", "abstract": "Semi-supervised learning defines the techniques that fall in between supervised and unsupervised learning. It is commonly used in classification settings where one has a lesser amount of labeled data compared to unlabeled. The goal is to extract extra learning from the unlabeled data to improve on the supervised classification. We will explore some of the approaches to semi-supervised learning to improve on the classification of Nordic news articles in the corpus provided. We will be exploring the methods of self-training in several different configurations and methods of feature extraction and engineering. We will also provide some background and baseline using common supervised methods for improving results as well as different document representations like word-embedding so that we will be able to compare and put our semi-supervised results in relation to these methods. We will see that while some of the methods explored did not succeed, others did and in relation to some of the supervised methods their performance is comparable. We will also see some promising approaches for countering the imbalance problem when considering confident pseudo-labels.", "source_url": "https:\/\/openalex.org\/W3089233220", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W3089233220", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 578, "title": "Safe Semi-Supervised Learning", "title_ar": "", "title_en": "Safe Semi-Supervised Learning", "author": "Andrea Bertazzi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "generative-ai", "research_subdomain": "self-semi-supervised", "categories": "machine-learning; generative-ai; self-semi-supervised", "keywords": "", "abstract": "Semi-supervised algorithms have been shown to possibly have a worse performance than the corresponding supervised model. This may be due to a violation of the assumptions on the data that are introduced in most classification systems. We study an approach that was previously shown to have guarantees of improvement for the LDA classifier in terms of log-likelihood on the full data-set of labeled and unlabeled observations. This method is based on two key concepts: contrast and pessimism. We extend this approach to a broader class of probabilistic generative models, in which the class conditional distributions can be modeled with any parametric class that belongs to the exponential family. In this case, we prove that the classifier is never worse and, under mild assumptions, strictly improves the log-likelihood on the complete data-set. The case of Gaussian densities is analyzed in detail, both for LDA and QDA. Moreover, we study this method in the case of least squares classification. In terms of square loss we prove the contrastive pessimistic classifier is guaranteed not to degrade the performance and, with a further requirement on the data, it strictly outperforms the supervised model. Finally, we apply contrast and pessimism to the task of parameter estimation of a multivariate Gaussian density in a missing data framework. We fully characterize the case of a missing block of data and we show that a strictly increased likelihood on the complete data-set is obtained for any monotone sample. In other terms, the contrastive pessimistic estimates are guaranteed to fit better the complete data-set composed of both observed and hidden components.", "source_url": "https:\/\/openalex.org\/W2996176234", "pdf_url": "https:\/\/repository.tudelft.nl\/file\/File_bd7b1eec-f0aa-4e2d-9825-87b65336c01f", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2996176234", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 579, "title": "Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams", "title_ar": "", "title_en": "Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams", "author": "Sean Louis Alan Floyd", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Ottawa", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; self-semi-supervised; predictive-analytics; object-detection", "keywords": "", "abstract": "In this thesis, learning refers to the intelligent computational extraction of knowledge from data. Supervised learning tasks require data to be annotated with labels, whereas for unsupervised learning, data is not labelled. Semi-supervised learning deals with data sets that are partially labelled. A major issue with supervised and semi-supervised learning of data streams is late-arriving or missing class labels. Assuming that correctly labelled data will always be available and timely is often unfeasible, and, as such, supervised methods are not directly applicable in the real world. Therefore, real-world problems usually require the use of semi-supervised or unsupervised learning techniques. For instance, when considering a spam detection task, it is not reasonable to assume that all spam will be identified (correctly labelled) prior to learning. Additionally, in semi-supervised learning, \"the instances having the highest [predictive] confidence are not necessarily the most useful ones\" [41]. We investigate how self-training performs without its selective heuristic in a streaming setting. This leads us to our contributions. We extend an existing concept drift detector to operate without any labelled data, by using a sliding window of our ensemble's prediction confidence, instead of a boolean indicating whether the ensemble's predictions are correct. We also extend selective self-training, a semi-supervised learning method, by using all predictions, and not only those with high predictive confidence. Finally, we introduce a novel windowing type for ensembles, as sliding windows are very time consuming and regular tumbling windows are not a suitable replacement. Our windowing technique can be considered a hybrid of the two: we train each sub-classifier in the ensemble with tumbling windows, but delay training in such a way that only one sub-classifier can update its model per iteration. We found, through statistical significance tests, that our framework is (roughly 160 times) faster than current state of the art techniques, and achieves comparable predictive accuracy. That being said, more research is needed to further reduce the quantity of labelled data used for training, while also increasing its predictive accuracy.", "source_url": "https:\/\/openalex.org\/W2954664475", "pdf_url": "", "doi": "10.20381\/ruor-23520", "handle": "", "repository_name": "uO Research (University of Ottawa)", "source_record_id": "W2954664475", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 581, "title": "Semi-Supervised Self-Organizing Maps with Time-Varying Structures for Clustering and Classification", "title_ar": "", "title_en": "Semi-Supervised Self-Organizing Maps with Time-Varying Structures for Clustering and Classification", "author": "Pedro H. M. Braga", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "self-semi-supervised", "categories": "machine-learning; deep-learning; self-semi-supervised; data-mining", "keywords": "", "abstract": "In recent years, the advances in technology have produced datasets of increasing size, not only regarding the number of samples but also the number of features. Unfortunately, despite these advances, creating a sufficiently large amount of properly labeled data with enough examples for each class is not an easy task. Organizing and labeling such data is challenging, expensive, and time-consuming. Also, it is usually done manually, and people can label with different formats and styles, incorporating noise and errors to the dataset. Hence, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Therefore, at the current stage of research, it is of great importance to put forward semi-supervised learning models aiming to combine both types of data, in order to benefit from the distinct information they can provide, to obtain better performances of both clustering and classification tasks, that would expand the range of machine learning applications. Moreover, it is also important to develop methods that are easy to parameterize in a way that become robust to the different characteristics of the data at hand. In this sense, the Self-Organizing Maps (SOM) can be considered as good options to address such objectives. It is a biologically inspired neural model that uses unsupervised and incremental learning to produce prototypes of the input data. However, such an unsupervised characteristic makes it unfeasible for SOM to execute Semi-Supervised Learning. In that way, this Dissertation presents some new proposals based on SOM to perform Semi-Supervised learning tasks for both clustering and classification. It is done by introducing to SOM the standard concepts of Learning Vector Quantization (LVQ), which can be seen as its supervised counterpart, to build hybrid approaches. Such proposals can dynamically switch between the two types of learning at training time, according to the availability of labels and automatically adjust themselves to the local variance observed in each data cluster. In the course of this work, the experimental results show that the proposed models can surpass the performance of other traditional methods not only in terms of classification but also regarding clustering quality. It also enhances the range of possible applications of a SOM and LVQ-based models by combining them with recent and promising techniques from Deep Learning to solve more complex problems commonly found in such field.", "source_url": "https:\/\/openalex.org\/W3119985271", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Americanae (AECID Library)", "source_record_id": "W3119985271", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 582, "title": "Study of Semi-supervised Deep Learning Methods on Human Activity Recognition Tasks", "title_ar": "", "title_en": "Study of Semi-supervised Deep Learning Methods on Human Activity Recognition Tasks", "author": "Shiping Song", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; nlp; self-semi-supervised; predictive-analytics; rnn", "keywords": "", "abstract": "This project focuses on semi-supervised human activity recognition (HAR) tasks, in which the inputs are partly labeled time series data acquired from sensors such as accelerometer data, and the outputs are predefined human activities. Most state-of-the-art existing work in HAR area is supervised now, which relies on fully labeled datasets. Since the cost to label the collective instances increases fast with the increasing scale of data, semi-supervised methods are now widely required. This report proposed two semi-supervised methods and then investigated how well they perform on a partly labeled dataset, comparing to the state-of-the-art supervised method. One of these methods is designed based on the state-of-the-art supervised method, Deep-ConvLSTM, together with the semi-supervised learning concepts, self-training. Another one is modified based on a semi-supervised deep learning method, LSTM initialized by seq2seq autoencoder, which is firstly introduced for natural language processing. According to the experiments on a published dataset (Opportunity Activity Recognition dataset), both of these semi-supervised methods have better performance than the state-of-the-art supervised methods.", "source_url": "https:\/\/openalex.org\/W3036519956", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3036519956", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 583, "title": "Semi-supervised learning methods for unsupervised domain adaptation in medical imaging segmentation", "title_ar": "", "title_en": "Semi-supervised learning methods for unsupervised domain adaptation in medical imaging segmentation", "author": "Pedro L. Ballester", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; computer-vision; medical-ai; transfer-learning; self-semi-supervised; predictive-analytics; image-segmentation; medical-imaging", "keywords": "", "abstract": "Machine learning applications make several assumptions regarding the scenario where they are employed. One common assumption is that data distribution in the test environment follows the same distribution of the training set. This assumption is systematically broken in most real-world scenarios; the difference between these distributions is commonly known as domain shift. Unsupervised domain adaptation aims at suppressing this problem by leveraging knowledge with unlabeled data from the test environment. One of the most sensitive fields for domain shift is medical imaging. Due to the heterogeneity in data distributions from scanners, models tend to vary in predictive performance when dealing with images from scanners with no examples in the training set. We propose two contributions in this work. First, we introduce the use of self-ensembling domain adaptation in the field of medical imaging segmentation in a spinal cord grey matter segmentation task. Next, based on the success of self-ensembling, we adapt two other recent work from the semi-supervised learning literature to the same task, namely, unsupervised data augmentation and MixMatch. We conduct ablation studies and other experiments in order to understand the behavior of each method and compare their best results. The results show improvements over training models in a supervised learning fashion and demonstrate that recent semi-supervised learning methods are promising for domain adaptation in medical imaging segmentation.", "source_url": "https:\/\/openalex.org\/W2992030155", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W2992030155", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 584, "title": "Semi-Supervised Learning with Unlabeled data: from Centralized to Distributed Systems", "title_ar": "", "title_en": "Semi-Supervised Learning with Unlabeled data: from Centralized to Distributed Systems", "author": "Nan Yang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; self-semi-supervised; cnn; image-classification; federated-learning; fairness", "keywords": "", "abstract": "The rapid increase in data generated by edge devices and IoT technologies demands efficient management solutions, especially in terms of cost and infrastructure. Key challenges include expensive data labeling, requiring significant human resources, and ensuring data privacy and security, with the risk of information leakage during transmission. The limited availability of labeled data versus the exponential growth of new data presents challenges for maintaining accuracy and efficiency in data-driven models. High costs of data annotation, especially involving subject-matter experts, limit model training effectiveness. Privacy concerns are heightened due to edge devices' interaction with sensitive user data, making the affordability of data labeling and data privacy protection at the edge crucial issues. To tackle these challenges, we proposed Data Augmentation Random Padding to increase effective data for model training in CNNs, enhancing image classification accuracy. Despite limitations, we developed Semi-Supervised Learning (SSL) method \"AdaptMatch\" to utilize a large amount of unlabeled data and a small number of labeled data in centralized learning, improving learning speed and reducing label bias. To address the inadequacies of centralized data learning, we introduced a decentralized SSL method, Federated Incremental Learning (FedIL), for learning on edge devices while protecting privacy. However, FedIL's lower training efficiency and struggles with data imbalance led to the development of Federated Masked Autoencoder (FedMAE), which is also a decentralized semi-supervised learning method based on self-supervised learning. FedMAE enables asynchronous training of large-scale unlabeled images in federated learning, outperforming existing methods in handling highly imbalanced data.", "source_url": "https:\/\/openalex.org\/W7126832066", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Sydney eScholarship Repository (The University of Sydney)", "source_record_id": "W7126832066", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 586, "title": "Semi-Supervised Image Segmentation of Medical Data", "title_ar": "", "title_en": "Semi-Supervised Image Segmentation of Medical Data", "author": "Ove Nicolai Dalheim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; self-semi-supervised; data-mining; cnn; image-segmentation", "keywords": "", "abstract": "Bladder cancer is the fourth most common cancer type in Norway, and tenth most common on a global scale. More and more tissue samples are sent to pathologists labs, increasing the workload and affecting the waiting time for patients. The corresponding increase is not seen in number of pathologists. Digitization and scanning of the tissue samples unveil the world of computational pathology, along with the many possibilities within it. Supervised approaches have been proposed within deep learning earlier, however, many express the lack of labeled data as a source for performance degradation. Convolutional neural networks have proven effective on image processing within the field of medicine. Some work based on semi-supervised approaches within computational pathology has been published in the recent years, however, most researchers are exploring supervised methods. This thesis proposes several methods to tile-wise segment histological images of bladder cancer into six different classes background, blood, damaged tissue, muscle tissue, stroma tissue and urothelium. A multiscale model is fed the tile at three different levels of magnification, and inherits capabilities from the VGG16 network through transfer learning. The proposed methods are either based on semi-supervised learning utilizing probability or clustering within a self-training process, or based on a combination of both expert and non-expert annotations. The dataset used in this thesis consists of about 360 whole-slide images, of which only 37 contain regions annotated by a pathologist, allowing for only about 10 % of the dataset to be utilized for supervised methods. Through the course of this thesis, a total of 145 new whole-slide images were introduced during training through semi-supervised learning, utilizing about 50 % of the dataset through various methods. The non-expert approach increased the F1-score for the muscle tissue class with 9.18 % from an initial 79.42 %, and the cluster-baser approach saw an increase of 1.38 % in accuracy from the initial 94.61 %. The method involving non-expert annotations outperformed both semi-supervised techniques with regards to segmentation, as a semi-supervised method will introduce new uncertain features to a deep learning model. This will have a strong impact on sensitive classes that are poorly represented in the training dataset. That said, a significant improvement is seen by using semi-supervised techniques as well, and scored better than non-expert with regards to classification.", "source_url": "https:\/\/openalex.org\/W3087951694", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W3087951694", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 587, "title": "Semi-supervised learning for plankton image classification", "title_ar": "", "title_en": "Semi-supervised learning for plankton image classification", "author": "Maram Hamid", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "LUT University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; self-semi-supervised; cnn; image-classification", "keywords": "", "abstract": "The production of big data from plankton populations has become feasible with the use of imaging devices. This opens up the possibility of testing key characteristics in planktonic systems. The manual labeling of images is complicated as well as time-consuming. On the other hand, semi-supervised learning makes it possible to automate the process by performing the classification of large unlabeled data with the available labeled data. The classification of plankton images using semi-supervised methods including label propagation, label spreading, and Pseudo labeling utilizing CNN based image features has been implemented in this study. The comparison of the accuracy of the semi-supervised methods and the CNN method using different datasets shows that the semi-supervised learning methods were more accurate than the baseline method where the amount of data available to train the model was sufficient and fully supervised CNN was able to extract the correct features. However, sometimes the semi-supervised learning methods have not been more accurate than CNN, when CNN has struggled to find the right image features.", "source_url": "https:\/\/openalex.org\/W3037808886", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LUTPub (LUT University)", "source_record_id": "W3037808886", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 588, "title": "Utilizing unlabeled data in cell type identification : A semi-supervised learning approach to classification", "title_ar": "", "title_en": "Utilizing unlabeled data in cell type identification : A semi-supervised learning approach to classification", "author": "Thijs Quast", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Linkoping University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "self-semi-supervised", "categories": "machine-learning; self-semi-supervised", "keywords": "", "abstract": "Recent research in bioinformatics has presented multiple cell type identification meth- dologies using single cell RNA sequence data (scRNA-seq). However, a consensus on which cell typing methodology consistently demonstrates superior performance remains absent. Additionally, very few studies approach cell type identification through a semi- supervised learning study, whereby the information in unlabeled data is leveraged to train an enhanced classifier. This paper presents cell annotation methodologies through self- learning and graph-based semi-supervised learning, in both raw count scRNA-seq data as well as in a latent embedding. I find that a self-learning framework enhances perfor- mance compared to a solely supervised learning classifier. Additionally, modelling on the latent data representations consistently outperforms modelling on the original data. The results show an overall accuracy of 96.12%, whereas additional models achieve an average precision rate of 95.12% and an average recall rate of 94.40%. The semi-supervised learn- ing approaches in this thesis compare favourable to scANVI in terms of accuracy, average precision rate, average recall rate and average f1-score. Moreover, results for alternative scenarios, in which cell types among training and test data do not perfectly overlap, are reported in this thesis.", "source_url": "https:\/\/openalex.org\/W3046627445", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DiVA (Linkoping University)", "source_record_id": "W3046627445", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:50", "updated_at": "2026-11-15 16:52:50" }, { "id": 589, "title": "Adversarial signals against discriminative and generative models for data protections", "title_ar": "", "title_en": "Adversarial signals against discriminative and generative models for data protections", "author": "Mingzhi Lyu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "diffusion", "categories": "machine-learning; computer-vision; generative-ai; responsible-ai; ai-applications; transfer-learning; self-semi-supervised; diffusion; fairness; ai-cybersecurity", "keywords": "", "abstract": "In recent years, discriminative and generative models have been widely adopted across a broad range of applications—including classification, detection, image synthesis, and editing—significantly enhancing productivity and creativity in various domains. However, their proliferation also introduces critical societal challenges such as copyright infringement, misinformation, and privacy violations. Protecting visual data from unauthorized manipulation and model training has thus become an urgent and essential task. Although adversarial attacks have been explored as protection strategies, achieving transferable and generalizable attacks remains a major open challenge due to the diversity of model architectures and task-specific training procedures. In response, this thesis systematically analyzes the mechanisms underlying discriminative and generative models and proposes a series of attack and defense strategies aimed at either protecting visual data or exposing the vulnerabilities of existing methods to inform future research. For discriminative models, data availability attacks (DAAs) have been widely adopted to protect copyrighted data against unauthorized training, by poisoning training data to degrade the performance of downstream models. However, existing DAAs often suffer from limited robustness or poor image quality. To address this, we introduce Imperfect Restoration Poisoning (IRP), a novel poisoning technique designed for supervised learning (SL), which achieves strong attack performance while preserving high image fidelity. Through extensive comparisons of IRP with eight baselines, coupled with evaluations alongside five representative defense methods, we showcase the superiority of IRP. In addition to proposing improved DAAs, we investigate the defense side, especially how to mitigate the impact of data poisoning. While Self-Supervised Learning (SSL) has been considered a promising paradigm to mitigate poisoning due to its objective mismatch with supervised attacks, our experiments across multiple poison types and datasets (CIFAR-10 and ImageNet-100) reveal that SSL often performs poorly when trained on poisoned data. To address this and further expose the limit of existing DAAs, we introduce VESPR (Vulnerability Exploitation of Supervised Poisoning for Robust SSL), which introduce adversarial training on SL to obfuscate poison features and guide robust feature learning for SSL. VESPR outperforms six existing defenses across seven poisoning strategies, revealing a research gap in understanding and improving poisoning robustness in discriminative models. Beyond classification models, this thesis also investigates visual data protection against unauthorized training in generative models, particularly with diffusion models (DMs) fine-tuned via techniques such as Textual Inversion (TI). We identify critical vulnerabilities in existing generative DAAs through a series of analyses. First, we propose Semantic Sensitivity Maps (SSM) to visualize the influence of poisoning on token-level text embeddings during TI training. Second, we observe that DMs exhibit non-uniform learning behavior across timesteps, with DAAs primarily injecting poison signals at early (low-noise) stages—leaving high-timestep stages insufficiently defended. Third, we show that poisoned signals shift attention away from semantically relevant regions, impairing embedding quality. Based on these insights, we introduce Safe-Zone Training (SZT), a novel training strategy that mitigates poisoning in DMs. SZT comprises 3 key components: (1) JPEG compression to weaken high-frequency poison signals, (2) restriction to higher timesteps during textual inversion training to avoid adversarial signal at lower timesteps, and (3) loss masking to constrain learning to target regions. Our results demonstrate that SZT effectively neutralizes several state-of-the-art DAAs, revealing a significant research gap for poisoning on generative models in the future. Apart from training-time protection, we extend the study to inference-time adversarial attacks to prevent AI-driven visual manipulations. In some tasks, such as visual watermarking removal, some prior adversarial attack methods are typically constrained to specific types of target models and operate in white-box settings, limiting their applicability. Within the context of visual watermarking removal, multiple studies have revealed that watermarks are vulnerable to both inpainting-based removers and blind watermark removers. In the thesis, a novel black-box method is proposed, named Adversarial Watermark Defender with Attribution-Guided Perturbation (AWD-AGP), which is the first attack defending against both types of watermark removers. The key innovation for superior transferability lies in leveraging the mechanistic differences between inpainting-based and blind removal models.", "source_url": "https:\/\/openalex.org\/W7127049003", "pdf_url": "", "doi": "10.32657\/10356\/205523", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W7127049003", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 590, "title": "Discriminator-driven diffusion generative adversarial network for synthetic medical image generation", "title_ar": "", "title_en": "Discriminator-driven diffusion generative adversarial network for synthetic medical image generation", "author": "Dennis Chia Yin Lim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; generative-ai; medical-ai; gan; medical-imaging", "keywords": "", "abstract": "This research project addresses the significant challenge of class imbalance in medical deep learning classification models, particularly when the abnormal class is underrepresented. Class imbalance can severely affect the classification performance of deep learning models, potentially leading to patient misdiagnosis and impeding clinician trust in AI adoption. In healthcare, class imbalance arises due to various factors, including the rarity of diseases or medical scenarios and data insufficiency for specific classes within large datasets. Existing solutions, such as traditional data augmentation and Generative Adversarial Networks (GANs) like DCGAN, as well as Conditional GANs like Pix2Pix and Stable Diffusion, have attempted to mitigate this issue, but with limitations.", "source_url": "https:\/\/openalex.org\/W7131907591", "pdf_url": "", "doi": "10.25916\/sut.31368169", "handle": "", "repository_name": "Swinburne figshare (Swinburne University of Technology)", "source_record_id": "W7131907591", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 591, "title": "Generative modelling under epistemic uncertainty", "title_ar": "", "title_en": "Generative modelling under epistemic uncertainty", "author": "Muhammad Mubashar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Oxford Brookes University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; robotics; predictive-analytics; llm; gan; diffusion", "keywords": "", "abstract": "The deployment of Deep Learning in safety-critical domains is hindered by pathological overconfidence and an inability to distinguish between aleatoric uncertainty (data ambiguity) and epistemic uncertainty (lack of knowledge). This thesis addresses these limitations by establishing a rigorous framework for Random-Set Deep Learning, shifting from point-estimate probabilities to belief functions over the power set of outcomes. Enabled by a novel Budgeting strategy that ensures scalability, this framework allows models to explicitly represent ignorance. While we validate these principles through Random-Set Neural Networks (RS-NN) for classification and a Unified Evaluation Framework, the primary contribution of this work lies in re-imagining Generative AI under epistemic uncertainty. We introduce Random-Set Large Language Models (RS-LLMs), which predict belief functions over token sets to quantify second-order uncertainty, thereby providing a robust mechanism for hallucination detection. Furthermore, we propose Epistemic Generative Adversarial Networks (GANs) and Epistemic Diffusion Models. By modeling the uncertainty of the generation process itself via second-order distributions, these architectures significantly mitigate mode collapse and enhance sample diversity compared to standard baselines. Finally, we demonstrate the practical utility of these generative capabilities in Autonomous Driving. We introduce the ROAD-INTENT dataset and utilize Random-Set Vision Language Models (RS-VLMs) to automate the annotation of actor intent with calibrated uncertainty scores, paving the way for safer, self-aware AI systems.", "source_url": "https:\/\/openalex.org\/W7135169854", "pdf_url": "", "doi": "10.24384\/j47e-p788", "handle": "", "repository_name": "Radar (Oxford Brookes University)", "source_record_id": "W7135169854", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 592, "title": "Towards data efficient generative models", "title_ar": "", "title_en": "Towards data efficient generative models", "author": "Zhaoyu Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Queen's University Belfast", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "gan", "categories": "machine-learning; computer-vision; generative-ai; gan; diffusion", "keywords": "", "abstract": "The past decade has seen significant advancements in generative models. As one of the most fundamental machine learning and computer vision tasks, generative models aim to produce realistic-looking synthetic samples by learning from real samples. However, training generative models from scratch commonly requires large amounts of data, which can be costly, time-consuming, or even impractical to collect and clean for specific domains. To address this, data-efficient generative models, which are designed to train with limited data from scratch, have gained significant attention. In this thesis, we focus on the theory and algorithms for training data-efficient generative models from scratch using two widely used generative models, i.e., generative adversarial networks (GANs) and diffusion models (DMs). For GANs, we address the challenges in data-efficient training from three different theoretical perspectives: the unfair min-max game, the overfitting of discriminator ($D$) and the leaking of augmentations problem. For DMs, we address the data efficiency challenge by better minimizing the total denoising score matching error during training. Firstly, we apply information game theory to analyze the GANs' min-max game and unveil a novel perspective on GAN training: the min-max game in existing GANs training is unfair. Based on this, we present a novel GAN called Information Gap GAN (IGGAN) to enhance the min-max game in GANs training. Specifically, IGGAN comprises a generator ($G$) and two discriminators ($D_1$ and $D_2$), each applying distinct data augmentation (DA) techniques. The information gap caused by various DA methods between $D_1$ and $D_2$ can alter the information received by each player in the min-max game, leading to incomplete information for all three players—$G$, $D_1$, and $D_2$—in IGGAN. As a result, IGGAN outperforms other existing methods, achieving better Inception Scores and lower Fréchet Inception Distances. Secondly we introduce a straightforward yet effective approach named Dual Adaptive Noise Injection (DANI) to mitigate the overfitting of $D$ in DE-GANs training. Specifically, DANI incorporates two adaptive strategies: adaptive injection probability and adaptive noise strength. By dynamically adjusting these factors, DANI effectively alleviates the overfitting of $D$. The strong performance and generalization ability of DANI demonstrate its effectiveness in improving GAN training under limited data conditions. Thirdly, we first illustrate that applying Data augmentation (DA) in DE-GANs can introduce out-of-distribution samples, leading to the undesirable leaking of augmentation issue in DE-GANs training. To address this, we present a straightforward yet powerful technique named Adaptive Negative Data Augmentation (ANDA) for DE-GANs. Specifically, ANDA enhances the augmented distribution of generated data by incorporating the augmented distribution of negative real data, which is generated by applying Negative Data Augmentation (NDA) to real data. This approach allows potential leaking samples to be presented as ``fake” instances to the discriminator in an adaptive manner, preventing the generator ($G$) from learning these samples and improving overall performance. We demonstrate that ANDA is effective in mitigating the leaking of augmentation issue. Finally, we focus on training diffusion models with limited data. We present a novel theoretical insight for diffusion models that two factors, i.e., the denoiser function hypothesis space and the number of training samples, can affect the denoising score matching error of all training samples. Based on this theoretical insight, it is clearly evident that the total denoising score matching error is hard to be minimized within the denoiser function hypothesis space of existing methods when training diffusion models with limited data. To address this, we design a new diffusion model with limited data called Limited Data Diffusion (LD-Diffusion), which consists of two main components: a compressing model and a novel mixed augmentation with fixed probability (MAFP) strategy. Comprehensive experiments show that LD-Diffusion greatly enhances the training of diffusion models with limited data, achieving state-of-the-art performance compared to other diffusion models.", "source_url": "https:\/\/openalex.org\/W7215036991", "pdf_url": "", "doi": "10.17034\/32826059", "handle": "", "repository_name": "Research Portal (Queen's University Belfast)", "source_record_id": "W7215036991", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 593, "title": "Generative Models from the Inside: a journey through properties and applications", "title_ar": "", "title_en": "Generative Models from the Inside: a journey through properties and applications", "author": "Barbara Toniella Corradini", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Florence", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; computer-vision; nlp; generative-ai; transformers; image-segmentation; llm; gan; diffusion", "keywords": "", "abstract": "Generative Artificial Intelligence (Gen-AI) is a rapidly evolving field focused on developing models that can generate realistic and novel content, from text to images. Recent advancements such as ChatGPT and DALL-E have revolutionized domains like Natural Language Processing and Computer Vision by creating models capable of producing highly realistic and coherent outputs. This increase in generative capabilities has been driven by foundational models like Transformers, Generative Adversarial Networks (GANs), and Diffusion Models, which have expanded the boundaries of what machines can create. This thesis addresses open challenges in the field, including optimizing computational efficiency, analyzing the latent spaces of generative models, and extracting valuable insights from pre-trained models. The contributions of this work include the development of novel recursive architectures, the application of topological data analysis to better understand GAN training dynamics, and the use of diffusion models for training-free segmentation, highlighting the increasing versatility of generative models across diverse applications. The future of Gen-AI is promising, with numerous opportunities yet to be explored. While progress has been significant, much remains to be learned, and further exploration will be essential to fully realize the potential of these models. We hope that this thesis will provide a meaningful contribution to inspire a deeper understanding and development of generative modelling.", "source_url": "https:\/\/openalex.org\/W7213486992", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Florence Research (University of Florence)", "source_record_id": "W7213486992", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 594, "title": "Generative diffusion models for wireless communication networks: detection, recovery, and decision-making", "title_ar": "", "title_en": "Generative diffusion models for wireless communication networks: detection, recovery, and decision-making", "author": "Changyuan Zhao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "diffusion", "categories": "machine-learning; computer-vision; generative-ai; robotics; ai-applications; predictive-analytics; diffusion; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "Modern wireless communication systems are increasingly required to operate under uncertain and heterogeneous conditions, spanning signal inference under unknown noise distributions, reliable transmission in the presence of adversarial interference, and adaptive spectrum management in dynamic environments. While classical approaches have been developed for each of these settings individually, they typically rely on simplifying statistical assumptions that become harder to satisfy as deployment environments grow more complex. This thesis proposes a unified perspective: these three challenges, despite arising at different layers of the communication stack, are all instances of the same fundamental problem of conditional generation, namely, learning and sampling from the posterior distribution p(x \\ y) of a wireless variable given an observation. Under this view, a generative model captures prior knowledge about the signal or environment, the wireless system specifies the likelihood, and their combination through the posterior score drives inference, recovery, and control within a single probabilistic framework. Three contributions instantiate this perspective at progressively larger operational scales, each showing how conditional generation addresses limitations that are difficult to handle using purely model-based or discriminative approaches. In Chapter 3, the conditional generation perspective is applied at the signal level, addressing regression, classification, and multiple-input multiple-output (MIMO) detection tasks where the noise distribution is unknown or non-Gaussian. This chapter replaces direct prediction with posterior-based inference, enabling the diffusion mechanism to act as a universal scoring tool rather than a sampler. The Supervised Score-Based Model (SSM) reinterprets score-based inference as a noise-free gradient descent process, establishing a formal equivalence with gradient boosting and enabling deterministic, stable predictions without the stochastic overhead of generative models. The Maximum-Likelihood Estimation Inference (MLEI) framework repurposes the diffusion evidence lower bound as a distribution-free likelihood proxy, enabling maximum-likelihood-equivalent MIMO detection without any parametric assumption on the noise. Together, these contributions demonstrate that the diffusion mechanism extends naturally beyond generation to serve as a principled inference engine. In Chapter 4, the scale advances to the physical layer, addressing wireless image transmission under jamming and spoofing attacks. This chapter reformulates physical-layer security as a posterior sampling problem under a corrupted observation model, where the attack structure is encoded directly into the likelihood term. The SecDiff framework decomposes adversarial decoding into two attack-specific likelihoods: a masked inpainting constraint for subcarrier jamming, and an expectation-maximisation (EM) channel refinement loop for pilot spoofing. This enables plug-and-play adversarial recovery without retraining the underlying codec and without prior knowledge of the attack, establishing that the conditional generation viewpoint remains effective under structured, non-stochastic perturbations. In Chapter 5, the conditional generation perspective reaches the network level, addressing unmanned aerial vehicle (UAV)-based spectrum sensing and trajectory planning in dynamic, partially observable environments. This chapter reformulates spectrum cartography and multi-agent trajectory optimisation as two coupled conditional generation problems over complementary target distributions: the full radio power map given sparse sensor observations, and the optimal UAV action given the inferred spectrum state. A masked autoencoder reconstructor and a conditional diffusion policy are jointly optimised through the reconstruction loss, enabling perception and control to reinforce each other within a single generative formalism. This establishes that the conditional generation principle can scale from point-wise signal inference to multi-agent policy generation. In summary, the three chapters support a unified perspective that connects task-specific model-based and discriminative designs with posterior-driven generative intelligence. By reformulating bespoke estimators, classifiers, and controllers as task-specific conditional generation problems, this thesis provides a scalable and principled foundation for wireless intelligence. Building on this foundation, Chapter 6 identifies future research directions in which the same conditional generation principle can be extended further, including richer environment modelling for cognitive wireless agents, more energy-efficient posterior sampling for latency-constrained deployments, and tighter integration of spectrum perception and decision-making in next-generation low-altitude networks.", "source_url": "https:\/\/openalex.org\/W7212335836", "pdf_url": "", "doi": "10.32657\/10356\/221595", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W7212335836", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 595, "title": "Synthesizing High B-Value Diffusion-Weighted Imaging of the Prostate using Diffusion Models", "title_ar": "", "title_en": "Synthesizing High B-Value Diffusion-Weighted Imaging of the Prostate using Diffusion Models", "author": "Pedro Rodrigues Parracho", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; generative-ai; medical-ai; image-segmentation; gan; diffusion; medical-imaging", "keywords": "", "abstract": "Prostate cancer (PCa) remains among the most prevalent and deadly cancers among men worldwide, with rising incidence rates placing increasing pressure on healthcare systems. Magnetic Resonance Imaging (MRI), and more specifically, Diffusion-Weighted Imaging (DWI), has proven to be an ideal diagnostic tool in the early detection of PCa. However, acquisition of high b-value DWI, which is critical in detecting diffusion-restricted lesions, is challenging due to the prolonged scan times and susceptibility to noise and motion artifacts. This dissertation explores the clinical practicability of generating high b-value prostate DWI from low b-value prostate DWI inputs using generative artificial intelligence (AI) models with the aim of decreasing acquisition times and artifact susceptibility. Three generative models were tested: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs). Each model was conditioned to take any low b-value DWI and generate a resulting DWI at any requested high b-value. Quantitative evaluation was conducted with image similarity metrics, comparing the generated DWI with the real acquired DWI. Diagnostic performance was also quantified with a deep learning lesion segmentation model in an attempt to determine diagnostic preservation in the generated DWI. The DDPM architecture was the most effective generative architecture, with the best performance in quantitative scores, in addition to high visual realism in maintaining diffusion effects. The generated images also preserved the majority of the diagnostic accuracy of the original high b-value scans, with preservation of lesion detection rates and localisation of lesions. The results verify that diffusion models are the most adequate architecture for low-to-high b-value DWI generation and can retain diagnostic performance. This study proved that generative AI can significantly enhance MRI workflows, reducing scan times, and facilitating scalable PCa screening programs, especially in resource-constrained environments, paving the way for more accessible diagnostic pathways.", "source_url": "https:\/\/openalex.org\/W7224044743", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7224044743", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 596, "title": "Non-Markovian Modelling of Limit Order Books with Generative Adversarial Networks", "title_ar": "", "title_en": "Non-Markovian Modelling of Limit Order Books with Generative Adversarial Networks", "author": "Navindu Subuddhi Nawurunna Palliyaguruge", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "generative-ai", "research_subdomain": "gan", "categories": "machine-learning; generative-ai; predictive-analytics; gan", "keywords": "", "abstract": "This thesis studies the use of Wasserstein GAN with Gradient Penalty and Temporal Attention augmented Bilinear Layers (TABL) to generate synthetic limit order book (LOB) data. The primary goal is to assess whether a non-Markovian conditioning of future snapshots on LOB history improves realism and mid-price movement prediction relative to a Markovian baseline. The proposed W-GAN models learn price dynamics implicitly from historical order flow, without relying on explicit mid-price movement labels. Results demonstrate that non-Markovian models consistently generate more realistic LOB snapshots and achieve higher F1 scores in mid-price movement classification than the baseline model. Among them, a history length n =30 performed the best in matching stylised facts and mid-price movements. They also incorporate LOB snapshots with empty queues in training, and learns to reproduce empty queues, but less frequently than in real data. Computational constraints required simplifying the model outputs, resulting in minor compromises to the quality of the generated samples. These findings highlight the benefits of non-Markovian conditioning in LOB generation and suggest promising directions for future research on model stability, scalability, and cross-asset generalisation.", "source_url": "https:\/\/openalex.org\/W7134871345", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/233187\/2\/NawurunnaPalliyagurugeNavindu.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7134871345", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 597, "title": "Image restoration and representation with deep generative models", "title_ar": "", "title_en": "Image restoration and representation with deep generative models", "author": "Yufei Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "computer-vision; generative-ai; responsible-ai; ai-applications; image-classification; image-segmentation; gan; diffusion; fairness; ai-industry", "keywords": "", "abstract": "A fundamental challenge in the realm of computer vision lies in accurately modeling\/characterizing image distributions. For example, in high-level vision tasks, better representations of images in the latent space can significantly enhance downstream processes such as image classification and segmentation. Similarly, in image restoration, a more accurate model for the distributions of clean images conditioned on degraded images can yield results with better perceptual quality. However, modeling these distributions is highly challenging due to the high dimensionality of both images and their latent codes. In a relatively parallel research avenue, deep generative models have made remarkable strides, aiming to implicitly or explicitly model image distributions. These models offer potent tools for handling intricate distributions. However, directly applying these models is unfeasible and leads to suboptimal performances due to different domain priors and task formulations. The challenge of seamlessly and effectively integrating generative models into image restoration and representation tasks, along with incorporating task-specific priors, remains an open area for further exploration. This thesis focuses on exploring the potential of deep generative models in image restoration and representation tasks, spanning from high-level vision tasks, i.e., image classification, to low-level vision tasks, i.e., image compression and restoration. Firstly, for the image classification task, we introduce a novel variational inference framework to obtain a latent representation with enhanced generalization ability. We implicitly model the posterior distributions of images given their latent codes using a generative adversarial network to disentangle domain-invariant features from the provided training data. Subsequently, instead of implicitly modeling the posterior distributions, we explore the possibility of explicitly modeling them. Utilizing the power of Normalizing Flow, a generative model capable of obtaining the exact likelihood of a given sample, we apply it to the task of low-light image enhancement (LLIE), considering its inductive bias. While the flow-based model achieves promising results, the performance is constrained by the intrinsic limitations in the design of normalizing flow. Therefore, we delve into seamlessly integrating the prior knowledge of the LLIE task in raw image space into a diffusion framework to overcome the limitations in model designs. Although our diffusion-based solution has promising results, it still has two main limitations: first, even with the unique advantages of enhancement in raw space, the significant storage overhead of raw images hampers its application. Consequently, in the subsequent two works, we explore joint compression by accurately modeling the latent distributions of images and leveraging the capabilities of autoregressive models to further enhance coding efficiency. Second, while the proposed work shortens the inference path from tens or hundreds to only three steps compared with commonly used diffusion models, it still requires iterative evaluations and causes inference overhead. In the last work, we propose an acceleration strategy incorporating a distillation strategy with a novel self-consistency loss. We achieve SOTA performance in only one inference step in the super-resolution task, where the conditional distribution is relatively more complex. In summary, the thesis makes three primary contributions. First, we showcase the effectiveness and unique advantages of generative-based image restoration and representation methods. Second, we propose diverse approaches to seamlessly integrate the capabilities of deep generative models with the domain knowledge specific to image restoration and representation tasks. To validate the effectiveness of these proposed methods, extensive experiments are conducted across multiple datasets. The experimental results unequivocally demonstrate that our methods outperform prior state-of-the-art models. The efforts and achievements presented in this thesis not only underscore the practical capabilities of image restoration and representation techniques but also provide fundamental support for future research and applications within the industry.", "source_url": "https:\/\/openalex.org\/W4408105674", "pdf_url": "", "doi": "10.32657\/10356\/182543", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4408105674", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 598, "title": "On large deviations in probabilistic deep learning and generative modeling", "title_ar": "", "title_en": "On large deviations in probabilistic deep learning and generative modeling", "author": "Nilsson, Viktor", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; medical-ai; responsible-ai; predictive-analytics; cnn; image-segmentation; llm; gan; diffusion; xai", "keywords": "", "abstract": "The topic of this thesis is the use of probabilistic methods in machine learning. These play a foundational role in motivating and defining machine learning algorithms, as well as in explaining why, and how well, the algorithms work. During the years in which this work was conducted (2020-2025), machine learning has gone from delivering a handful of impressive demonstrable results to becoming a staple of modern developed society, with products such as ChatGPT, etc., having capacity and applicability far beyond what anyone in the field expected ten years ago. Explaining why the modern methods work so well, despite their conceptual simplicity and elegance, requires both empirical and theoretical studies. This thesis has both parts, while the emphasis is on theory. The first part of the thesis, Papers A-C, concerns the implementation and analysis of novel methodologies in deep learning, whereas Papers D-F concern purely theoretical large deviations results for machine learning adjacent models. The main thread is the application of mathematical tools from probability theory and statistics, such as the theory of large deviations and empirical process theory, to the understanding and improvement of machine learning methodology. In Paper A, which presents the most applied direction pursued in the thesis, a deep probabilistic network model is applied to a task from the field of clinical radiation therapy, namely dose prediction, where a value of radiation dose target shall be assigned to each pixel\/voxel of a human tissue based on a CT-image. The developed probabilistic model is based on mixture density networks. It is empirically demonstrated that a convolutional U-net can learn a satisfactory mixture distribution in each pixel. To the best of our knowledge, this is the first implementation of mixture density networks working on images with a convolutional architecture. In Paper B, we construct and implement a new method called REMEDI for entropy estimation of continuous distributions using deep neural networks. Further, using empirical process theory, it is shown that such an estimator has a consistency property, ensuring that it has the theoretical capacity to estimate the entropy to arbitrary precision. The method is based on the celebrated Donsker-Varadhan lemma, a well-known fact from the theory of large deviations. The applicability of the method is demonstrated on distributions in moderate dimension, as well as on the task of training model in the information bottleneck framework, with satisfactory performance. In Paper C, we explore the integration of non-parametric model components into the flow-matching framework. This is done by learning a heavily compressed latent representation of images in the training dataset, which are then used as conditioning variables for the vector field network. Effectively, these can be seen as synthetic, continuous labels. The gain is a more efficient learning process, compared to baseline models, and more interpretable sampling. It is demonstrated that with sufficient compression, overfitting can be avoided, and diversity among samples attained, despite conditioning on training samples. Arguably, the most impactful area in machine learning is generative modeling. One part of the thesis deals with applying the theory of large deviations to two of its major methods, generative adversarial networks (GAN), and diffusion models, in particular Schrödinger bridges. The goal here is to prove large deviation principles, for certain sequences of probability measures associated with the models. In both cases, this allows a strong characterization of the convergence of these models, under varying certain model parameters, toward an idealized description of their behavior, often carrying a well-understood mathematical structure. In Schrödinger bridges, this idealized limit model consists of a dynamical optimal transport plan. This tells us that when varying the parameter in question, which is the reference noise level, or, as is often equivalent, the level of entropic regularization, the plans converge rapidly toward optimal transport behavior, justifying the interpretation of weakly regularized Schrödinger bridges as approximate optimal transport plans. Since Schrödinger bridges (or entropically regularized optimal transport plans) have nicer computational properties than optimal transport, they are often used in its place, and it is therefore important to understand how close this connection is. The large deviation principles derived here, applicable to several popular deep generative models, thus contribute to this understanding. Stating and proving such large deviations results are the contents of Papers E and F. For GANs, we consider recently developed particle systems making up cohorts of networks for the generative task, in Paper D.", "source_url": "https:\/\/openalex.org\/W7112536257", "pdf_url": "https:\/\/kth.diva-portal.org\/smash\/get\/diva2:2015506\/FULLTEXT02", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7112536257", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 599, "title": "Generative modeling for ergodic systems", "title_ar": "", "title_en": "Generative modeling for ergodic systems", "author": "Claudia Drygala", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "deep-learning; generative-ai; cnn; gan; diffusion", "keywords": "", "abstract": "Chaotic dynamical systems are characterized by strong nonlinearity, aperiodic long-term behavior, and high sensitivity to initial conditions, which make their numerical simulation extremely demanding, especially in engineering-relevant applications such as turbulent flows. Although ergodic theory provides a statistical framework for describing such systems, resolving their dynamics through fine-grid simulations of ODEs and PDEs remains computationally expensive, while reduced-order closures often introduce substantial modeling error. This thesis investigates deep generative learning as an alternative approach for the synthetic modeling of invariant state snapshots of chaotic systems, with a particular focus on generative adversarial networks (GAN). By combining principles from ergodic theory with generative learning, this work establishes for the first time that GAN training converges mathematically for ergodic systems in the limit of long observation times. This result provides a rigorous theoretical foundation for the use of GAN in modeling chaotic systems. Building on this foundation, the thesis further shows that statistics computed from GAN-generated snapshots converge to those obtained from simulations, uniformly over all uniformly bounded functions. Together, these findings establish a solid framework for evaluating generative models of chaotic dynamics through physics-based statistical measures rather than explicitly embedding such measures into the training loss. The proposed methodology is applied to a hierarchy of systems of increasing complexity, including the Lorenz attractor, the double pendulum, the Kuramoto–Sivashinsky equation, and turbulent flow configurations such as flow around a cylinder and flow under periodic wake impact. Across these cases, deep convolutional GAN are shown to reproduce snapshots of chaotic and turbulent states with high fidelity, using relatively small training datasets and without incorporating physical boundary conditions. At the same time, they offer substantial computational advantages, generating new snapshots within seconds compared with the days often required for Large Eddy Simulations. The thesis also demonstrates that conditional GAN enable controlled generation and strong generalization across unseen flow configurations, even when the available training data is heavily reduced. In addition, it presents a comparative study of GAN, variational autoencoders, and denoising diffusion probabilistic models on both simulated LES data and experimental PIV data. Among these models, GAN provide the best overall trade-off between accuracy and efficiency, while diffusion models show strong potential for future data-rich applications. Finally, the work takes a first step beyond invariant snapshots toward spatio-temporal generative modeling of turbulence, opening promising directions for future research on time-dependent chaotic flow dynamics.", "source_url": "https:\/\/openalex.org\/W7214885496", "pdf_url": "", "doi": "10.14279\/depositonce-26707", "handle": "", "repository_name": "DepositOnce", "source_record_id": "W7214885496", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 600, "title": "High-resolution synthesis across domains: a wavelet-driven approach to generative modeling", "title_ar": "", "title_en": "High-resolution synthesis across domains: a wavelet-driven approach to generative modeling", "author": "SIGILLO, LUIGI", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Sapienza University of Rome", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; generative-ai; medical-ai; ai-applications; gan; diffusion; medical-imaging; ai-industry", "keywords": "", "abstract": "Deep generative modeling is revolutionizing visual data synthesis, from creative industry applications to tools for scientific discovery in fields like medical imaging and remote sensing. In image synthesis, deep learning models like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs) help create novel, realistic, and controllable visual content. In specialized applications, these models can augment scarce data for medical analysis or enhance imagery for remote sensing tasks. A crucial challenge in applying deep learning to image synthesis is overcoming the persistent trade-offs between resolution, fidelity, and computational efficiency. While generative models have advanced significantly, they often struggle with generalization across diverse domains and data modalities. Conventional methods tend to scale poorly to ultra-high resolutions (UHR), leading to artifacts like repeated structures or blurred textures. Furthermore, these models often apply uniform refinement processes across all spatial regions, disregarding local frequency variations and failing to optimally allocate supervision to areas of different visual complexity. Wavelet transforms, particularly Discrete Wavelet Transforms (DWT) and their hypercomplex extension, Quaternion Wavelet Transforms (QWT), have shown promising results in multi-scale signal analysis. These methods operate by decomposing images into a hierarchy of frequency sub-bands, capturing both global structure and fine-grained details. This formulation endows models with the ability to leverage sparse representations and dimensionality reduction. However, their potential to reshape feature representation, inform conditioning, and adapt training objectives in a model-agnostic fashion had not been comprehensively exploited. In this thesis, we explore this concept and exploit the wavelet-driven learning paradigm to overcome the aforementioned shortcomings of traditional generative models. We leverage multi-scale analysis to make models inherently aware of frequency and spatial information. We first design a structure-aware GAN, StawGAN, tailored for cross-domain infrared-to-RGB image translation. Building on this foundation, we develop specialized diffusion models for domain-specific tasks, including high-fidelity maritime image super-resolution and efficient EEG-to-image synthesis. Moving beyond these conventional generative approaches, we introduce a series of wavelet-driven architectures that explicitly incorporate multi-scale signal representations. Among them is QUAVE, a novel framework leveraging quaternion wavelet transforms (QWT) to enhance feature extraction and improve generalization in medical imaging. Expanding on these insights, we also pioneer several wavelet-based super-resolution models: a QWT-conditioned diffusion model, a metadata- and wavelet-aware architecture for satellite imagery, and a highly efficient hybrid framework, Wavelet Diffusion GAN, that combines the strengths of GANs and diffusion processes. Finally, we address a direct extension of these works, focusing on high-fidelity synthesis. We culminate our investigation with a Latent Wavelet Diffusion (LWD) framework, a general and lightweight solution that enables existing latent diffusion and flow matching models to achieve UHR (up to 4K) synthesis without architectural modifications or additional inference costs. Through extensive experiments across various generative tasks involving different domains and data modalities, we have thoroughly explored the wavelet-driven paradigm while addressing scenario-specific challenges in fidelity, efficiency, and generalization, advancing research in this field.", "source_url": "https:\/\/openalex.org\/W7113426803", "pdf_url": "", "doi": "", "handle": "", "repository_name": "IRIS Research product catalog (Sapienza University of Rome)", "source_record_id": "W7113426803", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 601, "title": "Generative and geometric deep learning for 3D genome organization and biomolecular interaction modeling", "title_ar": "", "title_en": "Generative and geometric deep learning for 3D genome organization and biomolecular interaction modeling", "author": "Yanli Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Missouri", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; generative-ai; medical-ai; predictive-analytics; optimization; gnn; llm; gan; diffusion", "keywords": "", "abstract": "The three-dimensional organization of the genome and the interactions between biomolecules are fundamental to understanding cellular function and disease. Recent advances in sequencing technologies and structural biology have generated unprecedented amounts of biological data, creating new opportunities to study these processes. However, these data are often sparse, noisy, and highly complex, making it difficult to accurately model biological systems using conventional computational methods. This dissertation explores how generative and geometric deep learning can address these challenges by learning meaningful biological representations from high-dimensional genomic and structural data. The first part of this dissertation focuses on modeling three-dimensional genome organization from single-cell Hi-C data. Because single-cell Hi-C experiments capture only a small fraction of chromatin interactions, computational methods are essential for recovering missing information and revealing genome architecture. I first developed a deep residual generative adversarial network to enhance sparse single-cell Hi-C contact maps by recovering biologically meaningful chromatin interactions. Building on this work, I proposed HiCDiff, a diffusion model that iteratively denoises chromatin contact maps and produces more accurate representations of genome organization. These improved contact maps provide a stronger foundation for downstream analyses and structural modeling. To further characterize genome architecture, I developed an SO(3)-equivariant graph neural network that reconstructs three-dimensional chromosome structures directly from single-cell Hi-C data. By incorporating geometric equivariance into the learning process, the model captures the spatial relationships among genomic loci and reconstructs chromosome conformations with improved accuracy and biological consistency. The second part of this dissertation extends geometric deep learning to biomolecular interaction modeling. I developed MPBind, a multitask framework for predicting protein binding sites by integrating protein language model embeddings with equivariant graph neural networks. This approach combines evolutionary information encoded in protein sequences with three-dimensional structural features, enabling accurate prediction of diverse protein interactions while achieving strong generalization across previously unseen proteins. Although these studies address different biological problems, they share a common theme: combining generative models with geometry-aware neural networks to learn from complex biological data. Generative deep learning improves the quality of sparse and noisy experimental measurements, while geometric deep learning enables models to capture the structural principles underlying biological systems. Together, the methods presented in this dissertation provide a unified framework for studying genome organization and biomolecular interactions, demonstrating how modern artificial intelligence can advance computational genomics and structural biology and support future discoveries in biology and precision medicine.", "source_url": "https:\/\/openalex.org\/W7224088771", "pdf_url": "", "doi": "10.32469\/10355\/113055", "handle": "", "repository_name": "MOspace Institutional Repository (University of Missouri)", "source_record_id": "W7224088771", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 602, "title": "Music Generation with Generative Adversarial Networks", "title_ar": "", "title_en": "Music Generation with Generative Adversarial Networks", "author": "Li, Yupeng ; Linberg, Jonatan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Jönköping University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "generative-ai", "research_subdomain": "gan", "categories": "deep-learning; generative-ai; gan", "keywords": "", "abstract": "At present, state-of-the-art deep learning music generation systems require a lot time and hardware resources to develop. This means that they are almost exclusively available to large companies. In order to reduce these requirements, more efficient techniques and methods need to be utilised. This project aims to investigate various approaches by developing a music generation system using generative adversarial networks, comparing different techniques and their effect on the system's performance. Our results show the difficulties in generating music in a more resource-constrained environment. We find that structuring the input space with conditional model constraints improves the systems' ability to conform to musical standards. The results also indicate the importance of a patch-based discriminator for evaluating the texture of the generated music. Finally, we propose a similarity loss as a way of reducing mode collapse in the generator, thus stabilising the training process.", "source_url": "https:\/\/openalex.org\/W7112384551", "pdf_url": "https:\/\/hj.diva-portal.org\/smash\/get\/diva2:1787759\/FULLTEXT01", "doi": "", "handle": "", "repository_name": "Jönköping University Publications (Jönköping University)", "source_record_id": "W7112384551", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 603, "title": "Generative Adversarial Networks for Classic Cryptanalysis", "title_ar": "", "title_en": "Generative Adversarial Networks for Classic Cryptanalysis", "author": "Deanne Charan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "generative-ai", "research_subdomain": "gan", "categories": "machine-learning; deep-learning; generative-ai; gan", "keywords": "", "abstract": "The necessity of protecting critical information has been understood for millennia. Although classic ciphers have inherent weaknesses in comparison to modern ciphers, many classic ciphers are extremely challenging to break in practice. Machine learning techniques, such as hidden Markov models (HMM), have recently been applied with success to various classic cryptanalysis problems. In this research, we consider the effectiveness of the deep learning technique CipherGAN---which is based on the well- established generative adversarial network (GAN) architecture---for classic cipher cryptanalysis. We experiment extensively with CipherGAN on a number of classic ciphers, and we compare our results to those obtained using HMMs.", "source_url": "https:\/\/openalex.org\/W4206278187", "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=2034&context=etd_projects", "doi": "10.31979\/etd.h2mh-uh52", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4206278187", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 605, "title": "Synthetic Ultrasound Video Generation with Generative Adversarial Networks", "title_ar": "", "title_en": "Synthetic Ultrasound Video Generation with Generative Adversarial Networks", "author": "Daniil Kulik", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; cnn; video-analysis; gan; medical-imaging", "keywords": "", "abstract": "Ultrasound is a non-invasive, radiation-free portable imaging modality that offers real-time diagnostics in different clinical settings.Ultrasound video analysis can benefit from the rise of AI-powered applications in healthcare.However, access to ultrasound data remains the main challenge for the development of state-of-the-art machine learning models.Synthetic data generation can provide various benefits to ultrasound imaging analysis.Namely, generating ultrasound videos with specific characteristics allows for better training and testing of machine learning models.This work proposes a generative adversarial network for conditional ultrasound video generation.We conduct a thorough quantitative and qualitative evaluation of the network.Additionally, we show the added value of using the synthetic video for data augmentation in a downstream task.Extensive experiments on the EchoNet-Dynamic dataset demonstrate that the proposed model achieves an FID score of 126 and an FVD score of 233 and can be used in data augmentation tasks in small data scenarios.Contents 7 Conclusion and Future Work 70 vi List of Tables 3.1 Prior work in ultrasound image and video generation with GANs. .5.1 Configuration of the image discriminator. . . . . . . . . . . . . . . .5.2 Configuration of the video discriminator. . . . . . . . . . . . . . . .5.3 Configuration of the transposed convolutional generator. . . . . . .5.4 Configuration of the subpixel generator. . . . . . .", "source_url": "https:\/\/openalex.org\/W4389205759", "pdf_url": "https:\/\/repository.library.carleton.ca\/downloads\/w0892c38h", "doi": "10.22215\/etd\/2023-15790", "handle": "", "repository_name": "Carleton University's Institutional Repository (MacOdrum Library, Carleton University)", "source_record_id": "W4389205759", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 606, "title": "Privacy Attacks and Protection in Generative Models", "title_ar": "", "title_en": "Privacy Attacks and Protection in Generative Models", "author": "HU, Hailong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Luxembourg", "university_source": "repository_host", "college": "", "department": "", "country": "Luxembourg", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "generative-ai", "research_subdomain": "gan", "categories": "generative-ai; responsible-ai; ai-applications; gan; diffusion; fairness; ai-cybersecurity", "keywords": "", "abstract": "Recent years have witnessed the tremendous success of generative models in data synthesis. Typically, a well-trained model itself and its training set constitute key assets for model owners, which allows technology companies to gain a leading position in the global market. However, privacy is a key consideration in deploying state-of-the-art generative models in practice. On the one hand, the exposure of model privacy can lead to the compromise of the intellectual property rights of legitimate model owners, which consequently affects the market share of companies. On the other hand, the disclosure of training data, especially when it includes personal information, constitutes a direct infringement of data privacy, which severely leads to legal sanctions for companies. Indeed, the advent of emerging generative models critically necessitates novel privacy analysis and protection techniques to ensure the confidentiality of cutting-edge models and their training data. To solve these challenges, this dissertation investigates several new privacy attacks and protection methods for generative models from the perspective of model privacy and data privacy. In addition, this dissertation also explores a new mode that leverages existing pre-trained generative models to study the security vulnerabilities of discriminative models, which provides a fresh angle to apply generative models to the risk analysis of discriminative models. This dissertation is organized into three parts. In the first part, i.e. model privacy in generative models, I develop new model extraction attacks to steal generative adversarial networks (GANs). The evaluations show that preventing model extraction attacks against GANs is difficult but protecting GANs through verifying the ownership can be a deterrence against malicious adversaries. Thus, I further propose an ownership protection method to safeguard GANs, which can effectively recognize these stolen models constructed from physical stealing and model extraction. In the second part, i.e. data privacy in generative models, I develop two types of membership inference attacks against diffusion models, and the proposed loss-based method reveals the relationship between membership inference risks and the generative mechanism of diffusion models. I also investigate property inference risks in diffusion models and propose the first property aware sampling method to mitigate this attack, which bears the benefits of being plug-in and model-agnostic. In the third part, i.e. applications of generative models, I propose a new type of out-of-distribution (OOD) attack by leveraging off-the-shelf pre-trained GANs, which demonstrates that GANs can be utilized to directly construct samples to fool classification models and evade OOD detection. Taken together, this dissertation primarily provides new privacy attacks and protection methods for generative models and can contribute to a deeper and more comprehensive understanding of the privacy of generative artificial intelligence.", "source_url": "https:\/\/openalex.org\/W7110694972", "pdf_url": "https:\/\/orbilu.uni.lu\/bitstream\/10993\/58928\/1\/Dissertation_HailongHU.pdf", "doi": "", "handle": "", "repository_name": "Open Repository and Bibliography (University of Luxembourg)", "source_record_id": "W7110694972", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 607, "title": "Brain tumor segmentation via generative adversarial network", "title_ar": "", "title_en": "Brain tumor segmentation via generative adversarial network", "author": "Kevin Xie", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Illinois at Urbana-Champaign", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; computer-vision; generative-ai; medical-ai; predictive-analytics; image-segmentation; gan; medical-imaging", "keywords": "", "abstract": "With the development in technology and the complication of tumors presented in human brains, magnetic resonance imaging (MRI) has been an essential tool that physicians rely on to look at the structure of brain tissue. The ability to distinguish between normal tissue and tumor tissue has become the key to effective medical diagnosis and treatments. This thesis discusses developing machine learning techniques that will effectively segment the different parts of an MR image, namely the whole tumor, the tumor core and the enhancing tumor core. Using the dataset provided by the Perelman School of Medicine’s Center for Biomedical Image Computing & Analytics from University of Pennsylvania, a generative adversarial network (GAN) takes in imperfect predictions from other tumor segmentation networks and outputs more accurate and realistic predictions that resemble the ground truth. After our analysis, we see that the improvements that resulted from using a Pix2Pix GAN are shown in some cases, but the effects are unstable. Therefore, we conclude that fundamental changes in our GAN network are necessary to achieve more accurate results, and further research should be done on exploring the effects of using a Vox2Vox GAN.", "source_url": "https:\/\/openalex.org\/W3129104999", "pdf_url": "https:\/\/www.ideals.illinois.edu\/items\/132218\/bitstreams\/439091\/data.pdf", "doi": "", "handle": "", "repository_name": "Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)", "source_record_id": "W3129104999", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 608, "title": "Generative Adversarial Networks Based Reconstruction and Restoration of Cultural Heritage", "title_ar": "", "title_en": "Generative Adversarial Networks Based Reconstruction and Restoration of Cultural Heritage", "author": "Nesreen Hamadallah Jboor", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Qatar University", "university_source": "repository_host", "college": "", "department": "", "country": "Qatar", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "gan", "categories": "machine-learning; computer-vision; generative-ai; data-mining; gan", "keywords": "", "abstract": "Cultural heritage takes an important part in defining the identity and the history of a civilization or a nation. Valuing and preserving this heritage is thus a top priority for governments and heritage institutions. Through this paper, we present an image completion (inpainting) approach adapted for the curation and the completion of damaged artwork. Our approach uses a set of machine learning techniques such as Generative Adversarial Networks which are among the most powerful generative models that can be trained to generate realistic data samples. As we are focusing mostly on visual cultural heritage, the pipeline of our framework has many optimizations such as the use of clustering to optimize the training of the generative part to ensure a better performance across a variety of cultural data categories. The experimental results of our framework were validated on cultural dataset of paintings collected from Wiki-Art and the Rijksmuseum. We used the divide-and-conquer strategy by clustering the training data into different small clusters containing similarly looking images to train smaller Specialized DCGANs. The training has been made on five painting categories containing 2000 paintings each, which took an average of 6.1 training hours. Training the Specialized DCGAN on 1200 paintings from one of the clusters took 3.4 training hours. The inpainting results of the Specialized DCGANs are clearly better in quality than the results of a DCGAN trained on mixture of paintings or on painting category.", "source_url": "https:\/\/openalex.org\/W3004698630", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Qatar University QSpace (Qatar University)", "source_record_id": "W3004698630", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 609, "title": "Relaxed Wasserstein, Generative Adversarial Networks, Variational Autoencoders and their applications", "title_ar": "", "title_en": "Relaxed Wasserstein, Generative Adversarial Networks, Variational Autoencoders and their applications", "author": "Nan Yang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "gan", "categories": "deep-learning; computer-vision; generative-ai; gan", "keywords": "", "abstract": "Statistical divergences play an important role in many data-driven applications. Two notable examples are Distributionally Robust Optimization (DRO) problems and Generative Adversarial Networks (GANs).In the first section of my dissertation, we propose a novel class of statistical divergence called Relaxed Wasserstein (RW) divergence, which combines Wasserstein distance and Bregman divergence. We begin with its strong probabilistic properties, and then to illustrate its uses, we introduce Relaxed Wasserstein GANs (RWGANs) and compare it empirically with several state-of-the-art GANs in image generation. We show that it strikes a balance between training speed and image quality. We also discuss the potential use of Relaxed Wasserstein to construct ambiguity sets in DRO problems.In the second section of my dissertation, we show the application of another type of generative neural network, the Variational AutoEncoder (VAE), to metagenomic binning problems in bioinformatics. Shotgun sequencing is used to produce short reads from DNA sequences in a sample from a microbial community, which could contain thousands species of discovered or unknown microbes. The short reads are then assembled by connecting overlapping subsequences and thus forming longer sequences called contigs. Metagenomic binning is the process of grouping contigs from multiple organisms based on their genomes of origin. We propose a new network structure called MetaAE, which combines compositional and reference-based information in a nonlinear way. We show that this binning algorithm improves the performance of state-of-the-art binners by 20\\\\% on two independent synthetic datasets.", "source_url": "https:\/\/openalex.org\/W3205833864", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W3205833864", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 610, "title": "Improve the efficiency of conditional generative models", "title_ar": "", "title_en": "Improve the efficiency of conditional generative models", "author": "Yanwu Xu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Boston University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; computer-vision; generative-ai; medical-ai; transfer-learning; video-analysis; gan; diffusion; medical-imaging", "keywords": "", "abstract": "Deep generative models have undergone significant advancements, enabling the production of high-fidelity data across various fields, including computer vision and medical imaging. The availability of paired annotations facilitates a controllable generative process through conditional generative models, which capture the conditional distribution P(X|Y), where X represents high-dimensional data and Y denotes the associated annotation. This controllability makes conditional generative models more preferable than ordinary generative models, which only model P(X). For instance, the latest generative AI techniques within the Artificial Intelligence Generated Content (AIGC) realm have unlocked the potential for flexible image and video generation\/editing based on text descriptions or “prompts.” Additionally, generative AI has enhanced model efficiency by supplementing datasets with synthesized data in scenarios where annotations are unavailable or imprecise. Despite these capabilities, challenges persist in ensuring efficient training of conditional generative models. These challenges include: 1) capturing the intricate relationship between data and its annotations, which can introduce biases; 2) higher computational resource requirements during training and inference compared to discriminative models; and 3) the need to balance speed and quality at inference time. To address these challenges, this thesis introduces four models aimed at enhancing the training and inference efficiency of conditional generative models without compromising quality. The first method focuses on conditional Generative Adversarial Networks (cGANs), proposing a novel training objective to improve stability and diversity in synthetic data generation. The second method involves a hybrid generative model that combines GANs and Diffusion-based models to alleviate the unstable training of GANs and accelerate the denoising process. The third model introduces a fine-tuning framework that utilizes pre-trained diffusion parameters for high-fidelity, fast sampling, and quick adaptation during training. The final method presents a super-efficient 3D diffusion model for high-fidelity 3D CT synthesis, addressing the efficiency and quality gap in current models. These methods collectively tackle the efficiency of generative models and enhance generative quality in both computer vision and medical domains, suggesting sustainable solutions for the future of generative AI.", "source_url": "https:\/\/openalex.org\/W7126296160", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OpenBU (Boston University)", "source_record_id": "W7126296160", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 612, "title": "Deformable Image Registration Using Attentional Generative Adversarial Networks", "title_ar": "", "title_en": "Deformable Image Registration Using Attentional Generative Adversarial Networks", "author": "Hanchong Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; predictive-analytics; cnn; gan; medical-imaging", "keywords": "", "abstract": "Deformable image registration is a fundamental process that aims to estimate non-linear spatial correspondence between input images. Medical image registration serves wildly on clinical treatment evaluation, monitoring disease and tracking disease. Conventional registration algorithms iteratively optimize a similarity function for each pair of images, which result in long registration time. Learning based registration method typically use convolutional neural networks to learn features automatically during training and register an image pair in one shot. Generative adversarial network is a novel structure that involves a generator and a discriminator, where the discriminator encourages the former to generate better results. Predicting deformation between brain magnetic resonance images is a complicated task because of their high-dimensional non-linear transform. A generative model leveraging is proposed using an attentional mechanism to estimate the complicated deformation field, and train the model using perceptual cyclic constraints. As an unsupervised method, our model dose not need any labels for training. Experimental results show quantitative evidence that the proposed method can predict reliable deformation field at a fast speed.", "source_url": "https:\/\/openalex.org\/W3124900387", "pdf_url": "https:\/\/ucalgary.scholaris.ca\/bitstreams\/de143ab7-7823-45a9-bfac-1a2793663276\/download", "doi": "10.11575\/prism\/38593", "handle": "", "repository_name": "", "source_record_id": "W3124900387", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 613, "title": "Dual-Attention Generative Adversarial Network and Flame and Smoke Analysis", "title_ar": "", "title_en": "Dual-Attention Generative Adversarial Network and Flame and Smoke Analysis", "author": "Yuchuan Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Ottawa", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; expert-systems; predictive-analytics; transformers; image-segmentation; video-analysis; gan", "keywords": "", "abstract": "Flame and smoke image processing and analysis could improve performance to detect smoke or fire and identify many complicated fire hazards, eventually to help firefighters to fight fires safely. Deep Learning applied to image processing has been prevailing in recent years among image-related research fields. Fire safety researchers also brought it into their studies due to its leading performance in image-related tasks and statistical analysis. From the perspective of input data type, traditional fire research is based on simple mathematical regressions or empirical correlations relying on sensor data, such as temperature. However, data from advanced vision devices or sensors can be analyzed by applying deep learning beyond auxiliary methods in data processing and analysis. Deep Learning has a bigger capacity in non-linear problems, especially in high-dimensional spaces, such as flame and smoke image processing. We propose a video-based real-time smoke and flame analysis system with deep learning networks and fire safety knowledge. It takes videos of fire as input and produces analysis and prediction for flashover of fire. Our system consists of four modules. The Color2IR Conversion module is made by deep neural networks to convert RGB video frames into InfraRed (IR) frames, which could provide important thermal information of fire. Thermal information is critically important for fire hazard detection. For example, 600 °C marks the start of a flashover. As RGB cameras cannot capture thermal information, we propose an image conversion module from RGB to IR images. The core of this conversion is a new network that we innovatively proposed: Dual-Attention Generative Adversarial Network (DAGAN), and it is trained using a pair of RGB and IR images. Next, Video Semantic Segmentation Module helps extract flame and smoke areas from the scene in the RGB video frames. We innovated to use synthetic RGB video data generated and captured from 3D modeling software for data augmentation. After that, a Video Prediction Module takes the RGB video frames and IR frames as input and produces predictions of the subsequent frames of their scenes. Finally, a Fire Knowledge Analysis Module predicts if flashover is coming or not, based on fire knowledge criteria such as thermal information extracted from IR images, temperature increase rate, the flashover occurrence temperature, and increase rate of lowest temperature. For our contributions and innovations, we introduce a novel network, DAGAN, by applying foreground and background attention mechanisms in the image conversion module to help reduce the hardware device requirement for flashover prediction. Besides, we also make use of combination of thermal information from IR images and segmentation information from RGB images in our system for flame and smoke analysis. We also apply a hybrid design of deep neural networks and a knowledge-based system to achieve high accuracy. Moreover, data augmentation is also applied on the Video Semantic Segmentation Module by introducing synthetic video data for training. The test results of flashover prediction show that our system has leading places quantitative and qualitative in terms of various metrics compared with other existing approaches. It can give a flashover prediction as early as 51 seconds with 94.5% accuracy before it happens.", "source_url": "https:\/\/openalex.org\/W3204711149", "pdf_url": "", "doi": "10.20381\/ruor-26991", "handle": "", "repository_name": "uO Research (University of Ottawa)", "source_record_id": "W3204711149", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 614, "title": "Exploration and Exploitation in Reinforcement Learning", "title_ar": "", "title_en": "Exploration and Exploitation in Reinforcement Learning", "author": "Felix Adelsbo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "In reinforcement learning there exists a dilemma of exploration versus exploitation. This has led to the development of methods that have di↵er- ent approaches to this. Using di↵erent types of methods, and modifying them in di↵erent ways can lead to di↵erent results. Knowledge of how di↵erent methods work can give knowledge of what should be used in aspecific case. Two ways that methods can be modified are change in adjustable pa- rameters and change in the number of steps of random actions at thebeginning. How much these two modifications e↵ect the results in a spe- cific environment may di↵er a lot, and can be a very critical thing to consider for certain results. The goal of this study is to answer the question of how the performance of the di↵erent methods such as random, greedy, ✏-greedy, ✏-decreasingand Softmax is a↵ected by di↵erent values of their adjustable parameters,and by the number of steps of random actions at the beginning. Thesimulations and a comparative analysis are conducted for the case of an inverted pendulum with a vertical pole placed on a moving cart.", "source_url": "https:\/\/openalex.org\/W2895974005", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2895974005", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 615, "title": "Curriculum learning in reinforcement learning", "title_ar": "", "title_en": "Curriculum learning in reinforcement learning", "author": "Sanmit Narvekar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; reinforcement-learning; transfer-learning", "keywords": "", "abstract": "In recent years, reinforcement learning (RL) has been increasingly successful at solving complex tasks. Despite these successes, one of the fundamental challenges is that many RL methods require large amounts of experience, and thus can be slow to train in practice. Transfer learning is a recent area of research that has been shown to speed up learning on a complex task by transferring knowledge from one or more easier source tasks. Most existing transfer learning methods treat this transfer of knowledge as a one-step process, where knowledge from all the sources are directly transferred to the target. However, for complex tasks, it may be more beneficial (and even necessary) to gradually acquire skills over multiple tasks in sequence, where each subsequent task requires and builds upon knowledge gained in a previous task. This idea is pervasive throughout human learning, where people learn complex skills gradually by training via a curriculum. The goal of this thesis is to explore whether autonomous reinforcement learning agents can also benefit by training via a curriculum, and whether such curricula can be designed fully autonomously. In order to answer these questions, this thesis first formalizes the concept of a curriculum, and the methodology of curriculum learning in reinforcement learning. Curriculum learning consists of 3 main elements: 1) task generation, which creates a suitable set of source tasks; 2) sequencing, which focuses on how to order these tasks into a curriculum; and 3) transfer learning, which considers how to transfer knowledge between tasks in the curriculum. This thesis introduces several methods to both create suitable source tasks and automatically sequence them into a curriculum. We show that these methods produce curricula that are tailored to the individual sensing and action capabilities of different agents, and show how the curricula learned can be adapted for new, but related target tasks. Together, these methods form the components of an autonomous curriculum design agent, that can suggest a training curriculum customized to both the unique abilities of each agent and the task in question. We expect this research on the curriculum learning approach will increase the applicability and scalability of RL methods by providing a faster way of training reinforcement learning agents, compared to learning tabula rasa.", "source_url": "https:\/\/openalex.org\/W2742132707", "pdf_url": "", "doi": "10.26153\/tsw\/13849", "handle": "", "repository_name": "Texas Digital Library (University of Texas)", "source_record_id": "W2742132707", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 616, "title": "Knowledge transfer in reinforcement learning", "title_ar": "", "title_en": "", "author": "Ανέστης Φαχαντίδης", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "el", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "To αντικείμενο της διατριβής είναι η μελέτη μεθόδων Μεταφοράς Μάθησης (ΜΜ) για πράκτορες Ενισχυτικής Μάθησης (ΕΜ). Παρουσιάζονται νέοι αλγόριθμοι μεταφοράς μάθησης οι οποίοι είτε παρουσιάζουν καλύτερες επιδόσεις, σε σύγκριση με τις μεθόδους αιχμής από τη σχετική βιβλιογραφία, είτε καθιστούν δυνατή την αυτόνομη μεταφορά μάθησης σε περιπτώσεις όπου παραδοσιακά χρειαζόταν ανθρώπινη παρέμβαση.Αρχικά παρουσιάζονται καινοτομικές μέθοδοι για τη μεταφορά μοντέλων σε πράκτορες ΕΜ με σκοπό την μέγιστη αλλά και πολλαπλή αξιοποίηση τους. Τα μοντέλα των συναρτήσεων μετάβασης και ανταμοιβής μιας εργασίας πηγής μεταφέρονται σε μία σχετική, αλλά διαφορετική, εργασία στόχο. Ο αλγόριθμος μάθησης της εργασίας στόχου είναι υβριδικός, και εφαρμόζει ταυτόχρονα απευθείας μάθηση και μάθηση βασισμένη σε μοντέλο.Στη συνέχεια, μελετάται το μοντέλο μεταφοράς μάθησης με παροχή περιορισμένου αριθμού συμβουλών ενέργειας. Η διατριβή επικεντρώνεται σε πράκτορες δασκάλους που παρέχουν συμβουλές ενεργειών σε ετερογενείς πράκτορες μαθητές.Αρχικά, εξετάζονται μια σειρά από κρίσιμους παράγοντες που επηρεάζουν την ποιότητα των συμβουλών όπως η μέση επίδοση του ίδιου του δασκάλου στην εργασία, η διακύμανση της επίδοσης του κ.α. Τα αποτελέσματα των πειραμάτων δείχνουν τη σημασία του συντελεστή μεταβλητότητας (CV) της επίδοσης του δασκάλου ως στατιστικό εκτιμητή για την επιλογή πολιτικών παροχής συμβουλών.Στη συνέχεια, παρουσιάζεται μια σειρά από νέους αλγόριθμους για το πρόβλημα της κατανομής των συμβουλών και αξιολογούνται σε δύο σύνθετα ηλ. παιχνίδια: StarCraft και Pac-Man. Παρουσιάζεται επίσης μια καινοτόμα μέθοδος για την μάθηση μιας πολιτικής κατανομής συμβουλών. Επιπλέον, υποστηρίζεται ότι το πρόβλημα παροχής συμβουλών ενέργειας είναι υπο-περίπτωση ενός γενικότερου προβλήματος μάθησης. Προτείνουμε και μορφοποιούμε μια νέα υπο-κατηγορία προβλημάτων ενισχυτικής μάθησης την Ενισχυτική Μάθηση Περιορισμένης Αξιοποίησης (Constrained Exploitation Reinforcement Learning). Τα πειραματικά αποτελέσματά δείχνουν ότι η χρήση των προτεινόμενων μεθόδων είναι εφικτή και αποτελεσματική ακόμη και σε πιο σύνθετες εργασίες.Στη συνέχεια η διατριβή ασχολείται με μεθόδους για την αυτόνομη ΜΜ για εργασίες που έχουν σημαντικές διαφορές μεταξύ τους. Οι εργασίες αυτές μπορεί να διαφέρουν όχι μόνο ως προς τις συναρτήσεις μετάβασης και ανταμοιβών, αλλά και ως προς το χώρο ενεργειών και καταστάσεων τους.Αρχικά, περιγράφεται μία νέα μέθοδος με την ονομασία Value-Addition για τη μεταφορά συναρτήσεων αξίας που βασίζεται στις αξίες ενεργειών της εργασίας πηγής για την αυτόνομη επιλογή της συνάρτησης αντιστοίχισης των εργασιών.Στη συνέχεια περιγράφεται μια νέα μέθοδος με την ονομασία COMBREL για τη μεταφορά εμπειριών. Η συγκεκριμένη μέθοδος κάνει χρήση ενός πιθανοτικού μοντέλου. Συγκεκριμένα, προτείνεται μια νέα θεωρητική οπτική κατά την οποία το πρόβλημα πολλαπλών αντιστοιχίσεων μεταξύ δύο εργασιών είναι ισοδύναμο με το πρόβλημα πολλαπλών εργασιών καταδεικνύοντας τη σχέση μεταξύ δύο προβλημάτων που θεωρούταν προηγουμένως διαφορετικά. Ακολούθως, η νέα οπτική επιτρέπει τη μεταφορά ερευνητικών αποτελεσμάτων μεταξύ των δύο ερευνητικών προβλημάτων.Τέλος παρουσιάζονται μια σειρά από εφαρμογές της ΕΜ και των μεθόδων ΜΜ που παρουσιάστηκαν στα προηγούμενα κεφάλαια. Από μεθόδους παροχής συμβουλών ενέργειας σε ανθρώπους μέχρι τη μεταφορά μάθησης μεταξύ ρομπότ με διαφορετικούς βαθμούς ελευθερίας ή\/και αρθρώσεις και την μεταφορά μεταξύ ετερογενών πρακτόρων σε ηλ. παιχνίδια, επαληθεύεται η δυνατότητα των προτεινόμενων μεθόδων να επιλύσουν πιο σύνθετα προβλήματα.", "source_url": "https:\/\/openalex.org\/W2808217720", "pdf_url": "", "doi": "10.12681\/eadd\/38511", "handle": "", "repository_name": "", "source_record_id": "W2808217720", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 617, "title": "Reinforcement learning at the edge of chaos", "title_ar": "", "title_en": "Reinforcement learning at the edge of chaos", "author": "Rory Young", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Glasgow", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; reinforcement-learning; transfer-learning; predictive-analytics", "keywords": "", "abstract": "Reinforcement learning offers a powerful data-driven framework for solving complex control tasks. However, despite strong empirical performance in simulated domains, reinforcement learning methods remain rarely deployed in real-world systems. We argue that a key reason for this is because the learned control policies do not account for the predictability of the closed-loop control interaction. Consequently, these policies frequently induce chaotic state dynamics, where small changes to the control system can lead to dramatic and unpredictable long-term behaviour. This unpredictability poses a critical barrier for realworld deployment, as chaotic behaviour can lead to potentially unsafe outcomes. To address this issue, we propose two methods for embedding predictability into the training process. First, we introduce a Lyapunov-based regularisation method that constrains the divergence of predicted trajectories by penalising the predicted Maximal Lyapunov Exponent during policy updates. This regularisation encourages the controller to generate smoother, more predictable closed-loop dynamics and introduces a tuneable trade-off between task performance and predictability. Second, we develop a fine-tuning framework that updates pre-trained policies with a stabilising intrinsic reward function. This approach improves global stability without retraining from scratch and is compatible with any existing reinforcement learning controllers. Together, these contributions tackle a key limitation in current reinforcement learning practice: without guaranteed predictability, even high-performing policies remain unsuitable for real-world deployment. By explicitly shaping the dynamics of closed-loop behaviour, this work advances deep reinforcement learning towards robust and reliable control in real-world control systems.", "source_url": "https:\/\/openalex.org\/W7165029826", "pdf_url": "https:\/\/theses.gla.ac.uk\/86020\/1\/2026YoungPhD.pdf", "doi": "10.5525\/gla.thesis.86020", "handle": "", "repository_name": "Enlighten: Theses (The University of Glasgow)", "source_record_id": "W7165029826", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 618, "title": "Safe Exploration in Reinforcement Learning", "title_ar": "", "title_en": "Safe Exploration in Reinforcement Learning", "author": "Huy Nguyen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "machine-learning; reinforcement-learning; robotics", "keywords": "", "abstract": "Reinforcement Learning is a machine learning approach where the agent learns to make optimal decisions through trial and error, interacting with an environment to maximize cumulative reward. However, in safety critical domains like space exploration or autonomous driving, trial and error is not a viable method. Hence, safe exploration methods were introduced by many researchers, aiming to help the practical use of reinforcement learning in real-world scenarios. This thesis compares the different approaches by researchers, evaluates their methods and investigates the difficulties within the field", "source_url": "https:\/\/openalex.org\/W7172078631", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W7172078631", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 619, "title": "Nonparametric General Reinforcement Learning", "title_ar": "", "title_en": "Nonparametric General Reinforcement Learning", "author": "Jan Leike", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; reinforcement-learning; predictive-analytics", "keywords": "", "abstract": "Reinforcement learning problems are often phrased in terms of Markov decision processes (MDPs). In this thesis we go beyond MDPs and consider reinforcement learning in environments that are non-Markovian, non-ergodic and only partially observable. Our focus is not on practical algorithms, but rather on the fundamental underlying problems: How do we balance exploration and exploitation? How do we explore optimally? When is an agent optimal? We follow the nonparametric realizable paradigm: we assume the data is drawn from an unknown source that belongs to a known countable class of candidates. First, we consider the passive (sequence prediction) setting, learning from data that is not independent and identically distributed. We collect results from artificial intelligence, algorithmic information theory, and game theory and put them in a reinforcement learning context: they demonstrate how an agent can learn the value of its own policy. Next, we establish negative results on Bayesian reinforcement learning agents, in particular AIXI. We show that unlucky or adversarial choices of the prior cause the agent to misbehave drastically. Therefore Legg-Hutter intelligence and balanced Pareto optimality, which depend crucially on the choice of the prior, are entirely subjective. Moreover, in the class of all computable environments every policy is Pareto optimal. This undermines all existing optimality properties for AIXI. However, there are Bayesian approaches to general reinforcement learning that satisfy objective optimality guarantees: We prove that Thompson sampling is asymptotically optimal in stochastic environments in the sense that its value converges to the value of the optimal policy. We connect asymptotic optimality to regret given a recoverability assumption on the environment that allows the agent to recover from mistakes. Hence Thompson sampling achieves sublinear regret in these environments. AIXI is known to be incomputable. We quantify this using the arithmetical hierarchy, and establish upper and corresponding lower bounds for incomputability. Further, we show that AIXI is not limit computable, thus cannot be approximated using finite computation. However there are limit computable ε-optimal approximations to AIXI. We also derive computability bounds for knowledge-seeking agents, and give a limit computable weakly asymptotically optimal reinforcement learning agent. Finally, our results culminate in a formal solution to the grain of truth problem: A Bayesian agent acting in a multi-agent environment learns to predict the other agents' policies if its prior assigns positive probability to them (the prior contains a grain of truth). We construct a large but limit computable class containing a grain of truth and show that agents based on Thompson sampling over this class converge to play ε-Nash equilibria in arbitrary unknown computable multi-agent environments.", "source_url": "https:\/\/openalex.org\/W2558819991", "pdf_url": "https:\/\/arxiv.org\/pdf\/1611.08944", "doi": "10.25911\/5d76346c2e2be", "handle": "", "repository_name": "arXiv (Cornell University)", "source_record_id": "W2558819991", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 620, "title": "Curiosity-based Reinforcement Learning", "title_ar": "", "title_en": "Curiosity-based Reinforcement Learning", "author": "GIANLUCA MANNONI", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Pisa", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "multimodal", "categories": "deep-learning; computer-vision; reinforcement-learning; multimodal", "keywords": "", "abstract": "The thesis initially focused on the study of the main Reinforcement Learning methods, highlighting their principal limitations and their temporal evolution. Subsequently, the sparse reward problem was analyzed in detail, which is associated with the lack of updates of the current policy due to the low frequency of feedback provided by the environment. To address this issue, several curiosity-based methods proposed in the literature were examined. Among these, two different methodologies were implemented and experimentally compared with the selected Reinforcement Learning algorithm in applications characterized by sparse rewards and increasing levels of complexity. Three different types of tasks were therefore considered: the control of an inverted pendulum, a manipulation task based on visual observations only, and a navigation task in the presence of obstacles, which combines visual observations with other types of sensory information. In the first experiment, it was observed that the integration of curiosity modules does not provide significant benefits, while incurring a higher computational cost. In the second experiment, instead, the contribution of the modules appears to be more evident. It was therefore concluded that curiosity-based methods are determinant for achieving a performance improvement in the specific application scenario. In the final experiment, it was observed that the introduction of multimodal observations led to a marked deterioration in the performance of one of the two methods, mainly due to the higher sensitivity of the module to visual observations. This behavior can be attributed to a component fixed at the beginning of the training process, which is unable to adapt the relative importance of the received observations. Conversely, the second method updates the internal feature extractor of the model, thereby avoiding this limitation and ensuring, also in this case, superior performance compared to the other evaluated methods.", "source_url": "https:\/\/openalex.org\/W7142563277", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Electronic Theses and Dissertations Repository (University of Pisa)", "source_record_id": "W7142563277", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 621, "title": "Learning comparison: Reinforcement Learning vs Inverse Reinforcement Learning : How well does inverse reinforcement learning perform in simple markov decision processes in comparison to reinforcement learning?", "title_ar": "", "title_en": "Learning comparison: Reinforcement Learning vs Inverse Reinforcement Learning : How well does inverse reinforcement learning perform in simple markov decision processes in comparison to reinforcement learning?", "author": "P. Izquierdo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "This research project elaborates a qualitative comparison between two different learning approaches, Reinforcement Learning (RL) and Inverse Reinforcement Learning (IRL) over the Gridworld Markov D ...", "source_url": "https:\/\/openalex.org\/W3045465479", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3045465479", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 622, "title": "Reinforcement learning for argumentation", "title_ar": "", "title_en": "Reinforcement learning for argumentation", "author": "Sultan Alahmari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; reinforcement-learning; transfer-learning", "keywords": "", "abstract": "Argumentation as a logical reasoning approach plays an important role in improving communication, increasing agree-ability, and resolving conflicts in multi-agent-systems (MAS). The present research aims to explore the effectiveness of argumentation in reinforcement learning of intelligent agents in terms of, outperforming baseline agents, learning transfer between argument graphs, and improving relevance and coherence of dialogue quality. This research developed `ARGUMENTO+' to encourage a reinforcement learning agent (RL agent) playing abstract argument game for improving performance against different baseline agents by using a newly proposed state representation in order to make each state unique. When attempting to generalise this approach to other argumentation graphs, the RL agent was not able to effectively identify the argument patterns that are transferable to other domains. In order to improve the effectiveness of the RL agent to recognise argument patterns, this research adopted a logic-based dialogue game approach with richer argument representations. In the DE dialogue game, the RL agent played against hard-coded heuristic agents and showed improved performance compared to the baseline agents by using a reward function that encourages the RL agent to win the game with minimum number of moves. This also allowed the RL agent to adopt its own strategy, make moves, and learn to argue. This thesis also presents a new reward function that makes the RL agent's dialogue more coherent and relevant than its opponents. The RL agent was designed to recognise argument patterns, i.e. argumentation schemes and evidence support sources, which can be related to different domains. The RL agent used a transfer learning method to generalise and transfer experiences and speed up learning.", "source_url": "https:\/\/openalex.org\/W3091679651", "pdf_url": "http:\/\/etheses.whiterose.ac.uk\/27361\/1\/Alahmari_106031396_Thesis.pdf", "doi": "", "handle": "", "repository_name": "White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)", "source_record_id": "W3091679651", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 623, "title": "Learning Tetris with reinforcement learning", "title_ar": "", "title_en": "Learning Tetris with reinforcement learning", "author": "Ioannis Tsirovasilis ; Ιωάννης Τσιροβασίλης", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Piraeus", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; computer-vision; reinforcement-learning; optimization; video-analysis", "keywords": "", "abstract": "The last decades, scientists have expressed an increasing interest for the game Tetris and more precisely for efficient algorithms that can score the most in-game points. A plethora of approaches have been tried out, including genetic algorithms, linear programming, cross-entropy and natural policy gradient, but none of them competes with experts players playing under no time pressure. In recent years, scientists have started applying reinforcement learning in Tetris as it displays effective results in adapting to video game environments, exploit mechanisms and deliver extreme performances. Current thesis aims to introduce Memory Based Learning, a reinforcement learning algorithm which uses a memory that helps in the training process by replaying past experiences.", "source_url": "https:\/\/openalex.org\/W3214945965", "pdf_url": "", "doi": "10.26267\/unipi_dione\/1314", "handle": "", "repository_name": "Dione (University of Piraeus)", "source_record_id": "W3214945965", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 624, "title": "Reinforcement learning for telescope optimisation", "title_ar": "", "title_en": "Reinforcement learning for telescope optimisation", "author": "Curtly Blows", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cape Town", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "machine-learning; reinforcement-learning", "keywords": "", "abstract": "Reinforcement learning is a relatively new and unexplored branch of machine learning with a wide variety of applications. This study investigates reinforcement learning and provides an overview of its application to a variety of different problems. We then explore the possible use of reinforcement learning for telescope target selection and scheduling in astronomy with the hope of effectively mimicking the choices made by professional astronomers. This is relevant as next-generation astronomy surveys will require near realtime decision making in response to high-speed transient discoveries. We experiment with and apply some of the leading approaches in reinforcement learning to simplified models of the target selection problem. We find that the methods used in this study show promise but do not generalise well. Hence while there are indications that reinforcement learning algorithms could work, more sophisticated algorithms and simulations are needed.", "source_url": "https:\/\/openalex.org\/W3009177914", "pdf_url": "https:\/\/open.uct.ac.za\/bitstreams\/96eb8ff8-4cd1-4c08-b3d6-ddfe175ad417\/download", "doi": "", "handle": "", "repository_name": "Open University of Cape Town (University of Cape Town)", "source_record_id": "W3009177914", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 625, "title": "Reinforcement Learning in Industrial Applications", "title_ar": "", "title_en": "Reinforcement Learning in Industrial Applications", "author": "Niklas Kotarsky ; Eric Bergvall", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Lund University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "ai-industry", "categories": "reinforcement-learning; ai-applications; ai-industry", "keywords": "", "abstract": "Although reinforcement learning has gained great success in computer games, there are only few yet known implementations in ndustrial applications. This despite the fact that reinforcement learning offers interesting methods to optimise the control of nonlinear processes. In this thesis we have used two model free reinforcement learning algorithms (PPO and DDPG) to control three different simulations of industrial processes, the simplified Tennessee Eastman, original Tennessee Eastman and the Haldex brake valve. Both reinforcement learning algorithms could in almost all cases learn to reach a set point. In addition, hyperparameters were found to have a high impact on training performance. In conclusion, our tests indicate that the model free reinforcement learning algorithms are basically capable of controlling industrial processes. Python code for the PPO algorithm applied to the Original Tennesse Eastman process can be found at Github. 1 1 https:\/\/github.com\/Heigke\/Reinforcement-Learning-In-Industrial-Applications", "source_url": "https:\/\/openalex.org\/W3120024399", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Lund University Publications Student Papers (Lund University)", "source_record_id": "W3120024399", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 626, "title": "Absraction for Efficient Reinforcement Learning", "title_ar": "", "title_en": "Absraction for Efficient Reinforcement Learning", "author": "Alexander Telfar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "fairness", "categories": "reinforcement-learning; responsible-ai; fairness", "keywords": "", "abstract": "Successful reinforcement learning requires large amounts of data, compute, and some luck. We explore the ability of abstraction(s) to reduce these dependencies. Abstractions for reinforcement learning share the goals of this abstract: to capture essential details, while leaving out the unimportant. By throwing away inessential details, there will be less to compute, less to explore, and less variance in observations. But, does this always aid reinforcement learning? More specifically, we start by looking for abstractions that are easily solvable. This leads us to a type of linear abstraction. We show that, while it does allow efficient solutions, it also gives erroneous solutions, in the general case. We then attempt to improve the sample efficiency of a reinforcment learner. We do so by constructing a measure of symmetry and using it as an inductive bias. We design and run experiments to test the advantage provided by this inductive bias, but must leave conclusions to future work.", "source_url": "https:\/\/openalex.org\/W4205824659", "pdf_url": "https:\/\/openaccess.wgtn.ac.nz\/articles\/thesis\/Absraction_for_Efficient_Reinforcement_Learning\/17147651\/2\/files\/31706501.pdf", "doi": "10.26686\/wgtn.17147651", "handle": "", "repository_name": "Open Access Victoria University of Wellington | Te Herenga Waka (Figshare)", "source_record_id": "W4205824659", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 627, "title": "Reinforcement Learning and Dynamical Systems", "title_ar": "", "title_en": "Reinforcement Learning and Dynamical Systems", "author": "Björn Lindenberg", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "deep-learning; medical-ai; reinforcement-learning; robotics", "keywords": "", "abstract": "This thesis concerns reinforcement learning and dynamical systems in finite discrete problem domains. Artificial intelligence studies through reinforcement learning involves developing models and algorithms for scenarios when there is an agent that is interacting with an environment. By taking actions the agent may induce changes in the observed environment, where a modeled reward system reinforces correct behavior through learning algorithms. Reinforcement learning can be used in a wide variety of different domains, such as finance, robotics, games, routing and health care. However as the subject matures there is an increasing need to more heavily rely on advanced concepts in mathematics and deep learning to further our understanding of existing problems and find new algorithmic insights. Discrete dynamical systems arise in virtually any setting as soon as there is a set of elements subjected to iteration by a defining function. The function may be seen to represent the passing of time or to define the rules for state transitions. If the set of elements is finite but very large then we may find applications in several different fields such as operations research, cryptography and biology, where understanding properties of the structure and long-term behavior without explicit enumeration is key. In Paper I we extend the model of categorical reinforcement learning with a group-aided training procedure involving multiple agents. By having the agents learn through shared distributional information but act independently we argue for an accelerated learning process. We empirically show that the procedure may lead to much more robust learning, stronger individual agent performance and good ensemble efficiency. In Paper II we continue to build upon distributional reinforcement learning for finite Markov processes. A common approach among algorithms is to apply transformations on agent returns for stability and flexibility over a variety of different tasks. We show that one of the most successful methods may not work for a stochastic process. As a solution we introduce a new distributional operator that handles a large class of transformations with guaranteed theoretical convergence. We also propose an approximating single-actor algorithm based on these novel insights, which when tested achieves state-of-the-art performance compared to similar algorithms. In Paper III we focus on the issue of efficient exploration in reinforcement learning by studying the regret that a learning algorithm might have versus an optimal policy. Specifically, in the paper we derive a Bayesian regret bound for Thompson sampling on linear multi-armed bandits with Gaussian reward models when we have environment uncertainty described by a set of multivariate normal-gamma distributions. In Paper IV we derive sharp bounds on the number of iterations needed for any linear finite dynamical system to stabilize on an inner set of cycles. These bounds may be used in cycle analysis and criterion tests to understand the long-term behavior of extremely large systems.", "source_url": "https:\/\/openalex.org\/W4376641259", "pdf_url": "https:\/\/lnu.diva-portal.org\/smash\/get\/diva2:1756782\/FULLTEXT01", "doi": "10.15626\/lud.494.2023", "handle": "", "repository_name": "", "source_record_id": "W4376641259", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 628, "title": "Reinforcement Learning Across Timescales", "title_ar": "", "title_en": "Reinforcement Learning Across Timescales", "author": "Siddharth Ravi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "This project addresses a fundamental problem faced by many reinforcement learning agents. Commonly used reinforcement learning agents can be seen to have deteriorating performances at increasing frequencies, as they are unable to correctly learn the ordering of expected returns for actions that are applied. We call this the disappearing reinforcements problem. Moreover, truly multi-task reinforcement learning is only possible when agents are able to operate across frequencies, as different platforms operate at different frequencies. Most algorithms from control theory working on similar tasks, on the other hand, show improved performances when their operating frequencies are increased. This suggests that addressing disappearing reinforcements should enable reinforcement learning agents to have improved performance and generalization ability across timescales and tasks. In this project, we show that disappearing reinforcements is an effect seen independent of the function approximator used in reinforcement learning, and is instead of a more fundamental nature. We explore both theoretically and empirically the relationship between agents and their performances at increasing frequencies. We show that two specific types of agents from literature address the problem, and we benchmark the agents' performances with novel benchmarking measures inspired from control theory. Finally, we create a novel agent we call the dueling advantage learner, by combining both approaches from the state-of-the-art. We then benchmark the different agents across frequencies and tasks, and our agent is seen to outperform each of the individual approaches on the majority of the tasks.", "source_url": "https:\/\/openalex.org\/W3044841239", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W3044841239", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 629, "title": "Efficiency Comparison Between Curriculum Reinforcement Learning a Reinforcement Learning Using ML-Agents.", "title_ar": "", "title_en": "Efficiency Comparison Between Curriculum Reinforcement Learning a Reinforcement Learning Using ML-Agents.", "author": "Marco Tabell Johnsson ; Ala Jafar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "Efficiency Comparison Between Curriculum Reinforcement Learning & Reinforcement Learning Using ML-Agents.", "source_url": "https:\/\/openalex.org\/W3102154282", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3102154282", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 630, "title": "Reinforcement Learning for Generative Art", "title_ar": "", "title_en": "Reinforcement Learning for Generative Art", "author": "Jieliang Luo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "generative-ai", "research_subdomain": "", "categories": "machine-learning; generative-ai; reinforcement-learning; robotics", "keywords": "", "abstract": "Reinforcement learning (RL) is an efficient class of sequential decision-making algorithms that have achieved remarkable success in a broad range of applications, such as robotic manipulations, strategic games, or autonomous driving. The most well-known example of reinforcement learning is AlphaGo, a computer program that plays the board game Go and outperforms top human Go players. Unlike other two major machine learning categories, supervised learning and unsupervised learning, in which media artists are actively engaged, reinforcement learning has yet to result in many creative applications. Generative art is usually driven, in whole or in part, by autonomous systems that are derived from a set of rules. Interestingly, an RL policy can be seen as an autonomous system where the rules are learned by interacting with its environment. Regardless of its initial purpose, reinforcement learning has the potential to expand the boundary of generative art. However, a formal process of applying reinforcement learning to generative art does not yet exist and the current RL tools require an in-depth understanding of RL concepts. To bridge the gap, the first part of the dissertation introduces a conceptual framework to adapt reinforcement learning for generative art. The framework proposes a term RL-based generative art to denote a novel form of generative art of which the use of RL agents is the key element. The creative process of RL-based generative art and possible emergent behaviors are discussed in the framework. This leads to a discussion of several author's related practices on generative art, deep-learning art, and reinforcement learning. Those practices are critical for understanding the conceptual and technical details of each component in order to construct the framework. The second part introduces RL5, a JavaScript library for rapidly prototyping RL environments and training RL policies in web browsers. The library combines RL algorithms and RL environments into one framework and is fully compatible with p5.js. RL5 is developed with a particular focus on simplicity to favor (re)usability of RL algorithms and development of RL environments. Specifically, the library implemented three RL algorithms, Tabular Q-learning, REINFORCE, and DDPG, to cover all the three families of model-free RL, and nine RL environments that six of them address autonomous agents in steering behaviors, which can be used as building blocks for complex systems. Finally, the author demonstrates four different use cases of how to apply RL5 for pedagogical and creative applications.", "source_url": "https:\/\/openalex.org\/W3024023292", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W3024023292", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 631, "title": "Discovery in Reinforcement Learning", "title_ar": "", "title_en": "Discovery in Reinforcement Learning", "author": "Vivek Veeriah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Michigan", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; computer-vision; reinforcement-learning; robotics; predictive-analytics", "keywords": "", "abstract": "This thesis focuses on Reinforcement Learning (RL) which considers an agent that makes sequen- tial decisions in an environment to maximize its reward. Recent breakthroughs on domains such as Go, Atari games, and Robotics are a result of combining advances from Deep Learning with RL algorithms. This combination of RL with deep neural networks has moved the field from hand- designing feature representations to learning features directly from raw data (e.g., pixel images). Despite these advancements, RL practitioners still need to define what predictive knowledge an agent needs to learn, instead of the agent autonomously discovering such knowledge. Developing the ability to discover what predictive knowledge is useful to learn about could allow the RL agents to learn more efficiently. In this thesis, we identify some of the challenges towards designing agents with the aforementioned ability, and propose and evaluate methods to address them. In the first part of this thesis, we present an approach for discovering multiple subgoals to learn optimal policies from a single stream of experience. We show that this approach is a useful pre- training procedure and as a source of auxiliary task updates when there is a main task of interest. In the second part of this thesis, we present three approaches to discover useful knowledge of various forms to maximize the agent’s task performance. The first one introduces an architecture and an associated meta-gradient algorithm to discover predictive questions about the agent’s ex- perience to drive representation learning in RL agents. We show that it helps in faster learning when used as auxiliary tasks. The second work introduces another meta-gradient approach to dis- cover temporal-abstractions in the form of options that enables a hierarchical RL agent to learn faster on new, unseen tasks. The third one presents an algorithm to select a small number of affor- dances in the form of actions or options from a continuous space to improve the performance of a model-based planning agent on hierarchical tasks. The final part studies the problem of learning and maintaining accurate knowledge in the form of option-conditional predictions in a partially-observable environment. Specifically, we implement and empirically demonstrate the thought experiment by Ring [2021], which provided a detailed blueprint of how high-level, abstract knowledge could potentially be represented as layered pre- dictions of agent’s sensorimotor stream in navigation environments. In addition to this empirical demonstration, we introduce an approach to discover those option-conditional predictions which were previously hand-defined and demonstrate the feasibility of this approach.", "source_url": "https:\/\/openalex.org\/W4294885165", "pdf_url": "", "doi": "10.7302\/6337", "handle": "", "repository_name": "Deep Blue (University of Michigan)", "source_record_id": "W4294885165", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 632, "title": "Reinforcement Learning for 5G Handover", "title_ar": "", "title_en": "Reinforcement Learning for 5G Handover", "author": "Maxime Bonneau", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning; reinforcement-learning", "keywords": "", "abstract": "The development of the 5G network is in progress, and one part of the process that needs to be optimised is the handover. This operation, consisting of changing the base station (BS) providing data to a user equipment (UE), needs to be efficient enough to be a seamless operation. From the BS point of view, this operation should be as economical as possible, while satisfying the UE needs. In this thesis, the problem of 5G handover has been addressed, and the chosen tool to solve this problem is reinforcement learning. A review of the different methods proposed by reinforcement learning led to the restricted field of model-free, off-policy methods, more specifically the Q-Learning algorithm. On its basic form, and used with simulated data, this method allows to get information on which kind of reward and which kinds of action-space and state-space produce good results. However, despite working on some restricted datasets, this algorithm does not scale well due to lengthy computation times. It means that the agent trained can not use a lot of data for its learning process, and both state-space and action-space can not be extended a lot, restricting the use of the basic Q-Learning algorithm to discrete variables. Since the strength of the signal (RSRP), which is of high interest to match the UE needs, is a continuous variable, a continuous form of the Q-learning needs to be used. A function approximation method is then investigated, namely artificial neural networks. In addition to the lengthy computational time, the results obtained are not convincing yet. Thus, despite some interesting results obtained from the basic form of the Q-Learning algorithm, the extension to the continuous case has not been successful. Moreover, the computation times make the use of reinforcement learning applicable in our domain only for really powerful computers.", "source_url": "https:\/\/openalex.org\/W2757547886", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2757547886", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 633, "title": "Intelligent Bomberman with reinforcement learning", "title_ar": "", "title_en": "Intelligent Bomberman with reinforcement learning", "author": "T. Ngo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning; reinforcement-learning", "keywords": "", "abstract": "Bomberman is a strategical, maze-based game where the players defeat their enemies by placing a multi-direction count-down bomb that would explode and destroy obstacles and other players. In this paper, a simplified version of Bomberman is implemented in Java, where different controlled agents are placed. Each agent represents one of five reinforcement learning methods: Q-Learning, Sarsa, Double Q-Learning, and Deep Q Neural Network with two state representations: 5-tiles information and complete information. Then, we investigate whether a specific reinforcement learning method can successfully learn to play Bomberman efficiently by evaluating them with ad-hoc agents and finally against each other. The configuration of 5-tiles information with Sarsa archives the best overall quantitative results.", "source_url": "https:\/\/openalex.org\/W3177618102", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3177618102", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 634, "title": "Reinforcement learning for robotic manipulation", "title_ar": "", "title_en": "Reinforcement learning for robotic manipulation", "author": "Isac Arnekvist", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning; robotics", "keywords": "", "abstract": "Reinforcement learning was recently successfully used for real-world robotic manipulation tasks, without the need for human demonstration, usinga normalized advantage function-algorithm (NAF). Limi ...", "source_url": "https:\/\/openalex.org\/W2767038680", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2767038680", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 635, "title": "Reinforcement Learning for Circuit Partitioning", "title_ar": "", "title_en": "Reinforcement Learning for Circuit Partitioning", "author": "Ka Chuen Cheng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Guelph", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "In the field of Electronic Design Automation (EDA), dividing a complex circuit into smaller subcircuits is a critical step. This process, known as circuit partitioning, minimizes the connections needed between these subcircuits while dividing the overall design. This can simplify the problem, and further enhance the performance and efficiency of other EDA tasks, such as placement and routing. A popular method for circuit partitioning is the Fiduccia-Mattheyses-Sanchis (FMS) algorithm, which is a local search heuristic. While it is efficient, the FMS algorithm has two weaknesses: it relies heavily on a good initial solution and can become caught in suboptimal solutions. This research explores various methods to construct better initial solutions for circuit partitioning. As well, it investigates the application of reinforcement learning (RL) techniques to overcome the local minima problem inherent in the FMS algorithm, thereby achieving higher-quality partitions.", "source_url": "https:\/\/openalex.org\/W7204611933", "pdf_url": "https:\/\/atrium.lib.uoguelph.ca\/bitstreams\/58895cdc-70da-492a-bebc-98d210b65dc3\/download", "doi": "", "handle": "", "repository_name": "The Atrium (University of Guelph)", "source_record_id": "W7204611933", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 636, "title": "Multimodal perception for autonomous driving", "title_ar": "", "title_en": "Multimodal perception for autonomous driving", "author": "Beltrán de la Cita, Jorge", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Carlos III University of Madrid", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; ai-applications; multimodal; object-detection; ai-industry; ai-smart-cities", "keywords": "", "abstract": "Autonomous driving is set to play an important role among intelligent transportation systems in the coming decades. The advantages of its large-scale implementation –reduced accidents, shorter commuting times, or higher fuel efficiency– have made its development a priority for academia and industry. However, there is still a long way to go to achieve full self-driving vehicles, capable of dealing with any scenario without human intervention. To this end, advances in control, navigation and, especially, environment perception technologies are yet required. In particular, the detection of other road users that may interfere with the vehicle’s trajectory is a key element, since it allows to model the current traffic situation and, thus, to make decisions accordingly. The objective of this thesis is to provide solutions to some of the main challenges of on-board perception systems, such as extrinsic calibration of sensors, object detection, and deployment on real platforms. First, a calibration method for obtaining the relative transformation between pairs of sensors is introduced, eliminating the complex manual adjustment of these parameters. The algorithm makes use of an original calibration pattern and supports LiDARs, and monocular and stereo cameras. Second, different deep learning models for 3D object detection using LiDAR data in its bird’s eye view projection are presented. Through a novel encoding, the use of architectures tailored to image detection is proposed to process the 3D information of point clouds in real time. Furthermore, the effectiveness of using this projection together with image features is analyzed. Finally, a method to mitigate the accuracy drop of LiDARbased detection networks when deployed in ad-hoc configurations is introduced. For this purpose, the simulation of virtual signals mimicking the specifications of the desired real device is used to generate new annotated datasets that can be used to train the models. The performance of the proposed methods is evaluated against other existing alternatives using reference benchmarks in the field of computer vision (KITTI and nuScenes) and through experiments in open traffic with an automated vehicle. The results obtained demonstrate the relevance of the presented work and its suitability for commercial use.", "source_url": "https:\/\/openalex.org\/W2964186846", "pdf_url": "", "doi": "", "handle": "", "repository_name": "e-Archivo (Carlos III University of Madrid)", "source_record_id": "W2964186846", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 637, "title": "Development of deep learning based perception module for autonomous driving", "title_ar": "", "title_en": "", "author": "Mustafa Alper Balım", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2025, "language": "tr", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning; robotics", "keywords": "", "abstract": "Otonom araçlar, günümüzde güvenli, konforlu ve sürdürülebilir ulaşım çözümleri sunma potansiyeli sayesinde hızla gelişmekte ve geleceğin mobilite vizyonunu şekillendirmektedir. Otonom araçların temelini oluşturan yazılım ve donanım mimarisi; sensör donanımları, veri işleme altyapısı ve karar verme algoritmalarını içeren bütüncül bir yapıdan oluşmaktadır. Bu mimaride LiDAR, kamera ve RADAR gibi sensörlerin entegrasyonu ve yüksek hesaplama kapasiteli işlemciler, çevresel algılamanın doğruluğunu doğrudan etkilemektedir. Otonom sistemlerde en kritik katmanlardan biri olan algılama katmanı, çevredeki nesneleri doğru ve hızlı şekilde tespit ederek sürüş kararlarının temelini oluşturur. Bu katmanın başarımı; kullanılan sensörlerin çözünürlüğü, kanal sayısı, veri işleme teknikleri ve öğrenme algoritmalarının başarısına bağlı olarak şekillenir. Ancak, derin öğrenme tabanlı 3B nesne tespiti modelleri, eğitim ve test veri kümeleri arasındaki farklılıklardan kaynaklanan alan farkı (domain shift) nedeniyle farklı senaryolarda ciddi performans düşüşleri yaşamaktadır. Bu çalışmada, alan farkı problemini çözmek amacıyla altı farklı modern alan adaptasyonu yöntemi (UDA, Bi3D, CLUE, ST3D, SESS ve Uni3D) sistematik olarak değerlendirilmiş ve karşılaştırılmıştır. Çalışmalarda, açık (public) veri kümeleri olan Waymo ve nuScenes'in yanı sıra, KARSAN prototip otonom minibüsü ile toplanan yüksek çözünürlüklü gerçek dünya veri seti ve farklı LiDAR konfigürasyonlarına sahip özel bir simülasyon veri seti kullanılmıştır. Deneysel sonuçlar, SESS ve Uni3D yöntemlerinin, alan farkına bağlı performans kayıplarını önemli ölçüde azalttığını ve yüksek genel doğruluk elde ettiğini ortaya koymuştur. Ayrıca, LiDAR çözünürlüğü ve kanal sayısının performans üzerindeki etkileri detaylı şekilde analiz edilerek sensör konfigürasyonlarının sistem başarımına katkıları gösterilmiştir. Sonuç olarak, bu tez çalışması, otonom araçlarda güvenli ve genellenebilir algılama katmanı geliştirilmesine katkı sağlayacak kapsamlı bir alan adaptasyonu kıyaslaması sunmaktadır. Bulguların, hem akademik araştırmalarda hem de endüstriyel uygulamalarda önemli pratik faydalar sunacağı öngörülmektedir.", "source_url": "https:\/\/openalex.org\/W7218252149", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Bursa Technical University Institutional Repository", "source_record_id": "W7218252149", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 638, "title": "Deep Learning Based Perception on Radar Data for Autonomous Driving", "title_ar": "", "title_en": "Deep Learning Based Perception on Radar Data for Autonomous Driving", "author": "Meyer, Michael", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Christian-Albrechts-Universität zu Kiel", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; cnn; gnn; object-detection", "keywords": "", "abstract": "This thesis investigates perception tasks using automotive radar sensors for autonomous driving. Compared to cameras and lidars, radar sensors offer unique advantages including weather robustness and direct velocity measurements. We specifically focus on leveraging range-beam-doppler tensors as input for deep neural networks, a data representation that contains the full radar signal rather than the commonly used sparse radar pointclouds. The thesis investigates how range-beam-doppler tensors can be processed by neural networks through the exemplary task of scene classification and evaluates the effectiveness of complex-valued convolutions in processing radar data. Our findings show that while scene classification is achievable solely based on radar data, complex-valued convolutions do not yield significant benefits in this context, and the magnitude information alone suffices for this perception task. Furthermore, we investigate how polar radar tensors can be efficiently processed in convolutional neural networks. This leads to the development of radar 3D object detection model using graph neural networks, which improved detection performance by more than 8.9% compared to a model without graph convolutions. We demonstrate that ego-motion compensation, while more complex for range-beam-doppler tensors than pointclouds, is essential for accurate object detection, yielding an 8.7% improvement in average precision. We also demonstrate that discrete autoencoders can effectively compress range-beam-doppler data while maintaining sufficient information for multi-sensor fusion tasks. In comparing radar tensor and pointcloud approaches, we find that current pointcloud-based models achieve slightly superior detection performance. However, our research has significantly narrowed the performance gap between these two data representations and suggests that the range-beam-doppler data level holds considerable promise for future improvements.", "source_url": "https:\/\/openalex.org\/W7110640169", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Multimedialen Archiv und Publikationsserver der Christian-Albrechts-Universität zu Kiel (Christian-Albrechts-Universität zu Kiel)", "source_record_id": "W7110640169", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 639, "title": "Deep Learning-Based Perception Systems for Advancing Autonomous Driving and Road Safety", "title_ar": "", "title_en": "Deep Learning-Based Perception Systems for Advancing Autonomous Driving and Road Safety", "author": "Rahul Vijay Soans", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning; robotics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7207742820", "pdf_url": "", "doi": "10.34382\/0002005531", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7207742820", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 640, "title": "Research on Deep Learning-based Multi-vehicle Cooperative Perception System for Autonomous Driving", "title_ar": "", "title_en": "Research on Deep Learning-based Multi-vehicle Cooperative Perception System for Autonomous Driving", "author": "Yalong Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning; robotics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7207790538", "pdf_url": "", "doi": "10.18952\/0002000986", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7207790538", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 641, "title": "Deep Learning-based Radar Camera Fusion for Perception in Autonomous Driving", "title_ar": "", "title_en": "Deep Learning-based Radar Camera Fusion for Perception in Autonomous Driving", "author": "Huawei Sun ; Lorenzo Servadei ; Technische Universität München", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning; robotics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7233531946", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W7233531946", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 642, "title": "Multi-Scale Deep Learning Frameworks for Trajectory Prediction, Scene Understanding, and Perception in Autonomous Driving", "title_ar": "", "title_en": "Multi-Scale Deep Learning Frameworks for Trajectory Prediction, Scene Understanding, and Perception in Autonomous Driving", "author": "Xingnan Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Concordia University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; robotics; responsible-ai; ai-applications; predictive-analytics; rnn; transformers; xai; fairness; ai-smart-cities", "keywords": "", "abstract": "Reliable autonomous vehicle operation at urban intersections requires competence across the prediction–perception stack, yet current deep learning models underuse the structural priors that govern real-world driving. This thesis argues that incorporating structural priors at multiple scales improves the accuracy, interpretability, and robustness of autonomous driving models, through five interconnected studies. Chapter 3 introduces a Turn-Aware LSTM that encodes turning intent via cumulative heading change. On UAV footage from a signalised intersection in Châteauguay, Québec, it reduces Final Displacement Error by 15–20% for turning manoeuvres at a 3-second horizon. Chapter 4 establishes lane graph conditioning as an architecture-flexible inductive bias for trajectory prediction. A lightweight module (under 50,000 parameters), evaluated on 89,258 Waymo scenarios, reduces ADE by 9.3% at 3 s and, at 8 s, minADE by 26.6%, minFDE by 32.6%, and miss rate by 42.7%. Chapter 5 recovers real-world locations for anonymised scenarios by matching intersection topologies to OpenStreetMap via WayGraph's 48-dimensional star pattern descriptor. Across 56,797 scenarios it achieves 90% top-1 accuracy under synthetic OSM-to-OSM matching (33–55% under realistic noise), and a geographic audit reveals fourfold under-sampling of signalised intersections (4.3% vs. 17.8% city-wide). Chapter 6 develops a spatial attention visualisation framework that projects Transformer attention onto bird's-eye-view scenes. Applied to MTR-Lite, it identifies a tunnel vision pattern: failed predictions are associated with lower attention entropy (5.72 vs. 5.94 bits) and elevated self-attention; cyclist targets show higher miss rates (88.1% vs. 54.0%). Left-turn manoeuvres trigger a general agent-to-map attention shift (−19.9% vehicle attention, p < 0.001), suggesting possible VRU-related attention vulnerabilities, though the cyclist-specific interaction did not reach significance (p = 0.51, n = 17) and requires validation on larger samples. Chapter 7 shows that viewpoint diversity suppresses false positives via dual-camera LiDAR fusion with vehicle- and drone-mounted cameras. Across 2,600 CARLA frames and ten seeds, symmetric late fusion improves mAP by +0.92 percentage points (p = 0.001) and reduces false positives by 13% on a representative seed. These studies support the thesis argument across five scales—kinematic, road topology, geographic, attentional, and sensor geometry—and outline future research directions.", "source_url": "https:\/\/openalex.org\/W7221486811", "pdf_url": "https:\/\/spectrum.library.concordia.ca\/id\/eprint\/997687\/6\/Zhou_PhD_F2026.pdf", "doi": "", "handle": "", "repository_name": "Spectrum Research Repository (Concordia University)", "source_record_id": "W7221486811", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 643, "title": "Achieving Robust Autonomous Driving through Multi-modality Deep Reinforcement Learning", "title_ar": "", "title_en": "Achieving Robust Autonomous Driving through Multi-modality Deep Reinforcement Learning", "author": "Kritsanaphong Kittithammo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "multimodal", "categories": "deep-learning; computer-vision; reinforcement-learning; robotics; expert-systems; multimodal", "keywords": "", "abstract": "Autonomous driving systems rely heavily on sensor inputs to perceive and navigate their environment.However, individual sensors such as cameras or LiDAR often experience performance degradation in adverse weather conditions, which can lead to unreliable perception and unsafe control decisions.This limitation presents a critical challenge for deep reinforcement learning (DRL)-based driving agents, which typically depend on a single sensor modality.To address this issue, we propose a multi-modal DRL framework that integrates camera and LiDAR observations to achieve robust autonomous driving across varying weather conditions.The proposed method trains two independent DRL agents in the CARLA simulator: one that uses camera images and another that uses LiDAR point-cloud data.A shared reward mechanism encourages cooperative learning, while a fuzzy-inspired Rule-Based Action Fusion model combines the actions of both agents using weatherconditioned weighting.The fusion ratios are tuned for different weather conditions to improve reliability.We evaluate the proposed framework under three conditions -sunny, rainy, and foggy-and compare it with camera-only and LiDAR-only baselines trained under the same settings.Experimental results demonstrate that the multi-modal fusion approach significantly outperforms single sensor agents across all weather scenarios.In sunny conditions, the fusion model achieves an average reward of 11.99, compared with 2.07 and 8.25 for the camera and LiDAR agents, respectively.In rain and fog, where sensor degradation is more pronounced, the fusion model maintains strong performance with rewards of 10.64 and 11.51, while single-modality agents show substantial declines.These findings demonstrate that combining complementary sensor modalities can improve the stability, safety, and reliability of autonomous driving systems.This research contributes to the development of robust multi-sensor DRL methods capable of operating under diverse real-world conditions.", "source_url": "https:\/\/openalex.org\/W7207912773", "pdf_url": "https:\/\/uec.repo.nii.ac.jp\/record\/2001058\/files\/2331175.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7207912773", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 644, "title": "Uncertainty-aware perception, localization and mapping for autonomous driving", "title_ar": "", "title_en": "Uncertainty-aware perception, localization and mapping for autonomous driving", "author": "Kshitij Sirohi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitätsbibliothek Freiburg", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; robotics; predictive-analytics; cnn; image-segmentation", "keywords": "", "abstract": "Advances in deep learning have revolutionized scene understanding in recent years, achieving unprecedented performance levels. As a result, nearly all autonomous vehicle perception systems now rely on learning-based Deep Neural Networks (DNNs). These methods are trained on curated datasets and evaluated on test sets using task-specific metrics. This approach faces two significant limitations. First, the training and test datasets typically share similar data distributions and conditions, which may differ substantially from those encountered during real-world deployment. Real-world scenarios often present objects and situations absent from the training data. Second, evaluation metrics frequently focus solely on task-specific performance and fail to account for the network's confidence in the evaluation. Consequently, these methods tend to produce deterministic or overly confident outputs without a reliable uncertainty estimation, posing safety risks in critical applications such as autonomous driving. Moreover, the absence of uncertainty estimations in modern perception systems complicates their integration with classical probabilistic methods, that forms the foundation of successful autonomous systems across various applications, particularly in downstream tasks like localization and mapping. In this thesis, we focus on developing perception methods that enable real-time uncertainty estimation and leverage this uncertainty to integrate perception information with probabilistic approaches for localization and mapping. First, we introduce a new perception task that aims to achieve uncertainty-aware holistic scene understanding. To facilitate this, we provide a formal definition of novel metrics designed for holistic, uncertainty-aware perception evaluation, along with a DNN-based architecture tailored to perform this task using camera images. Next, we focus on the segmentation of 3D LiDAR point clouds, given the indispensable role the LiDAR sensor plays for 3D scene understanding in an autonomous vehicle. As the LiDAR point cloud brings challenges like sparse and unstructured data, we develop specialized methods to leverage 3D information from LiDAR point cloud into 2D convolutional neural networks and set the new benchmark for LiDAR panoptic segmentation. Furthermore, we present the first method that combines holistic perception capabilities with uncertainty estimation for LiDAR point clouds. By leveraging both the 3D spatial information of the point cloud and prediction uncertainties, our approach resolves ambiguities in segmenting challenging objects, significantly improving perception performance. Finally, we focus on the downstream tasks by utilizing the perception uncertainty as a bridge between modern perception and probabilistic approaches for localization and mapping. Especially in the final chapter, we showcase that utilizing perception uncertainties can improve mapping and localization performance. Moreover, our approach also propagates the perception uncertainties into the final mapping and localization results, thus providing their respective uncertainties. Our methods demonstrate that high-accuracy, uncertainty-aware perception based on deep learning is achievable, and perception uncertainty is essential for a robust downstream performance.", "source_url": "https:\/\/openalex.org\/W7147585983", "pdf_url": "", "doi": "10.6094\/unifr\/279476", "handle": "", "repository_name": "FreiDok plus (Universitätsbibliothek Freiburg)", "source_record_id": "W7147585983", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 645, "title": "Constraint-Guided Multimodal Learning for End-to-End Autonomous Driving", "title_ar": "", "title_en": "Constraint-Guided Multimodal Learning for End-to-End Autonomous Driving", "author": "Maoning Ge ; Maoning GE", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; robotics; ai-applications; predictive-analytics; multimodal; ai-smart-cities", "keywords": "", "abstract": "Autonomous driving in complex urban environments requires not only accurat e perception and prediction, but also strict adherence to physical constra ints and safety requirements.While recent deep learning approaches and Vi sion-Language-Action (VLA) models have significantly advanced the capabili ties of data-driven systems, they often overlook vehicle dynamics and envi ronmental constraints, which may lead to physically infeasible or unsafe b ehaviors.This thesis investigates constraint-guided multimodal learning f or autonomous driving, with a focus on integrating explicit physical const raints into trajectory prediction and end-to-end control.", "source_url": "https:\/\/openalex.org\/W7162977153", "pdf_url": "https:\/\/nagoya.repo.nii.ac.jp\/record\/2014422\/files\/k15729_abstract.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7162977153", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 646, "title": "Hybrid Optimization and Learning-Based Techniques for Autonomous Driving", "title_ar": "", "title_en": "Hybrid Optimization and Learning-Based Techniques for Autonomous Driving", "author": "Kürsat Petek", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitätsbibliothek Freiburg", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; self-semi-supervised; predictive-analytics; multimodal; image-segmentation; video-analysis; ai-smart-cities", "keywords": "", "abstract": "Autonomous driving has progressed rapidly in recent years, driven largely by advances in machine learning, computer vision, and probabilistic optimization methods. Despite this progress, the deployment of autonomous systems in complex, safety-critical real- world settings such as urban driving, dynamic traffic scenes, or large-scale outdoor environments remains a major open challenge. Real-world autonomy requires systems that can calibrate and localize themselves reliably, reason about their surroundings with minimal human supervision, and continuously adapt to changing environments. This thesis develops a set of methods that jointly address these challenges by combining geometric optimization with learning-based perception, and by designing label-efficient and self-supervised systems that scale to real-world deployment. In the first part, we study hybrid optimization methods for calibration and localiza- tion, focusing on the tight coupling of classical geometry with learned perception cues. We introduce a fully automatic camera–LiDAR calibration framework that removes the need for targets, initial guesses, or manual intervention by jointly exploiting motion cues and deep 2D–3D correspondences. Building on this idea, we propose a sparse-map localization system that integrates uncertainty-aware semantic predictions into a pose graph, enabling robust operation even in highly occluded urban environments with minimal map information. The second part focuses on online semantic Bird’s-Eye-View (BEV) mapping, where the goal is to infer a semantic map of the surrounding scene directly from frontal-view images in real time. We first investigate multi-modal fusion through BEVCar, which combines cameras and radar to obtain long-range, all-weather BEV maps using low- cost sensor setups. We then reduce the reliance on BEV annotations with SkyEye, a self-supervised method that learns BEV semantics from monocular images while using only frontal-view semantic labels as supervision. Finally, LetsMap removes this remaining supervision by jointly learning geometry and scene representation from raw RGB videos alone, achieving state-of-the-art label-efficient BEV mapping with only a tiny fraction of annotated BEV data. The third part addresses the challenge that modern perception systems rely on thousands of densely annotated images which is a major bottleneck for deploying autonomy at scale. We study how to drastically reduce this dependence by developing label-efficient and continually adapting panoptic segmentation methods that can be trained from data obtained within hours rather than months. SPINO shows that combining foundation-model features with only a handful of labeled images is sufficient to produce high-quality panoptic pseudo-labels at scale. PASTEL extends this idea with normalized-cut–based instance separation and targeted self-training, achieving even stronger performance under strict annotation budgets. Finally, CoDEPS enables continual test-time learning for both depth and panoptic segmentation through a geometry-aware mixing strategy and replay-based stabilization, allowing models to adapt to new environments during deployment without forgetting previous ones. Across these contributions, the thesis shows that combining geometric structure with modern learning techniques enables perception systems that are more robust, more data-efficient, and more adaptable than traditional supervised approaches. We demonstrate these methods extensively on diverse real-world driving datasets and robotic platforms, highlighting their potential to bring scalable, reliable autonomy closer to widespread deployment.", "source_url": "https:\/\/openalex.org\/W7196982350", "pdf_url": "", "doi": "10.6094\/unifr\/285382", "handle": "", "repository_name": "FreiDok plus (Universitätsbibliothek Freiburg)", "source_record_id": "W7196982350", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 647, "title": "Interaction-Aware Maneuver and Lane Change Prediction Using Deep Learning Architectures for Autonomous Driving", "title_ar": "", "title_en": "Interaction-Aware Maneuver and Lane Change Prediction Using Deep Learning Architectures for Autonomous Driving", "author": "Petrit Rama", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Kaiserslautern University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; robotics; responsible-ai; ai-applications; predictive-analytics; rnn; transformers; gnn; multimodal; xai; ai-smart-cities", "keywords": "", "abstract": "Maneuver prediction is a critical component of decision-making in autonomous driving, particularly in complex urban environments where traffic participants continuously interact under uncertainty. Accurate and interpretable prediction of future driving maneuvers, such as lane changes, is essential for safe and anticipatory planning. This thesis investigates interaction-aware maneuver prediction from the ego-vehicle perspective using deep learning-based approaches that integrate spatial reasoning, temporal modeling, and multi-modal perception. The research presents a progression of architectures that address maneuver and lane change prediction at increasing levels of complexity. First, interaction-aware models based on graph neural networks (GNNs) and recurrent neural networks (RNNs) jointly capture spatial interactions between traffic agents and the temporal evolution of driving behavior for scene understanding. Building on this, a cognitively inspired two-layer hierarchical decision-making framework separates planned maneuvers from stimuli and cause reasoning to improve interpretability. Subsequently, a deep multi-modal architecture based on Edge-Featured Graph Attention Networks (EGATs) is developed for lane change prediction, integrating visual perception, spatial interaction graphs, ego-vehicle state signals, and lane-level information. To support this research, a new in-house multi-modal dataset, WylonSet, is introduced and later extended to WylonSet++, providing high-quality annotations for lane change maneuvers and, in its extended version, for maneuver phases in diverse urban traffic scenarios. Finally, a transformer-based multi-modal architecture is proposed for multi-horizon lane change and phase prediction. The model employs attention-based fusion across modalities and incorporates uncertainty estimation at the modality, fusion, and prediction levels, enabling interpretable and future-aware behavior prediction. Experiments on four real-world driving datasets show that interaction-aware, multi-modal, and attention-based architectures consistently improve prediction accuracy and temporal consistency compared with uni-modal and non-interaction-aware variants. The transformer-based architecture predicts lane change maneuvers and their phases across horizons of up to 3 s, achieving an overall macro F1-score above 97% on WylonSet++ and detecting lane change events on average about 1 s before their onset. Overall, this thesis contributes new models, datasets, and evaluation methodology for maneuver and lane change prediction, providing a foundation for interpretable and deployment-oriented decision-making systems in autonomous driving.", "source_url": "https:\/\/openalex.org\/W7222983659", "pdf_url": "", "doi": "10.26204\/kluedo\/13713", "handle": "", "repository_name": "Publication Server of Kaiserslautern University of Technology (Kaiserslautern University of Technology)", "source_record_id": "W7222983659", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 648, "title": "Safety of Machine Learning Systems in Autonomous Driving", "title_ar": "", "title_en": "Safety of Machine Learning Systems in Autonomous Driving", "author": "Fadi Al-Khoury", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; robotics; responsible-ai; ai-applications; speech-recognition; fairness; ai-industry", "keywords": "", "abstract": "Machine Learning, and in particular Deep Learning, are extremely capable tools for solving problems which are difficult, or intractable to tackle analytically. Application areas include pattern recognition, computer vision, speech and natural language processing. With the automotive industry aiming for increasing amount of automation in driving, the problems to solve become increasingly complex, which appeals to the use of supervised learning methods from Machine Learning and Deep Learning. With this approach, solutions to the problems are learned implicitly from training data, and inspecting their correctness is not possible directly. This presents concerns when the resulting systems are used to support safety-critical functions, as is the case with autonomous driving of automotive vehicles. This thesis studies the safety concerns related to learning systems within autonomous driving and applies a safety monitoring approach to a collision avoidance scenario. Experiments are performed using a simulated environment, with a deep learning system supporting perception for vehicle control, and a safety monitor for collision avoidance. The related operational situations and safety constraints are studied for an autonomous driving function, with potential faults in the learning system introduced and examined. Also, an example is considered for a measure that indicates trustworthiness of the learning system during operation.", "source_url": "https:\/\/openalex.org\/W2770758792", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2770758792", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 649, "title": "Static and Dynamic Affordance Learning in Vision-based Direct Perception for Autonomous Driving", "title_ar": "", "title_en": "Static and Dynamic Affordance Learning in Vision-based Direct Perception for Autonomous Driving", "author": "Jean Marie Uwabeza Vianney", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Waterloo", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles are using the mediated perception approach for path planning and control, which highly rely on high-definition 3D maps and real-time sensors. Recent research efforts aim to substitute the massive HD maps with coarse road attributes. In this thesis, We follow the direct perception-based method to train a deep neural network for affordance learning in autonomous driving. The goal and the main contributions of this thesis are in two folds. Firstly, to develop the affordance learning model based on freely available Google Street View panoramas and Open Street Map road vector attributes. Driving scene understanding can be achieved by learning affordances from the images captured by car-mounted cameras. Such scene understanding by learning affordances may be useful for corroborating base-maps such as HD maps so that the required data storage space is minimized and available for processing in real-time. We compare capability in road attribute identification between human volunteers and the trained model by experimental evaluation. The results indicate that this method could act as a cheaper way for training data collection in autonomous driving. The cross-validation results also indicate the effectiveness of the trained model. Secondly, We propose a scalable and affordable data collection framework named I2MAP (image-to-map annotation proximity algorithm) for autonomous driving systems. We built an automated labeling pipeline with both vehicle dynamics and static road attributes. The data collected and annotated under our framework is suitable for direct perception and end-to-end imitation learning. Our benchmark consists of 40,000 images with more than 40 affordance labels under various day time and weather even with very challenging heavy snow. We train and evaluate a ConvNet based traffic flow prediction model for driver warning and suggestion under low visibility condition.", "source_url": "https:\/\/openalex.org\/W3004650162", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UWSpace (University of Waterloo)", "source_record_id": "W3004650162", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 650, "title": "Distributed Intelligence for 3D Object Tracking in Autonomous Vehicles, A Multi-Sensor Approach to Object Tracking and Planning for Self-Driving Cars, Real-Time 3D Perception and Path Planning in Autonomous Systems,,Vision-Based Localization and Distributed Architecture for Driverless Cars,A Hybrid Deep Learning Framework for Object Detection and Trajectory Planning in Autonomous Vehicles", "title_ar": "", "title_en": "Distributed Intelligence for 3D Object Tracking in Autonomous Vehicles, A Multi-Sensor Approach to Object Tracking and Planning for Self-Driving Cars, Real-Time 3D Perception and Path Planning in Autonomous Systems,,Vision-Based Localization and Distributed Architecture for Driverless Cars,A Hybrid Deep Learning Framework for Object Detection and Trajectory Planning in Autonomous Vehicles", "author": "Manas Madine", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Massachusetts Amherst", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; robotics; ai-applications; predictive-analytics; object-detection; medical-imaging; ai-industry; ai-smart-cities", "keywords": "", "abstract": "Abstract-I: Autonomous Navigating car : To make car move as fast as possible and with absolutely no collisions. And Controlling a car with high dimensional sensory inputs and Driving a car autonomously around a racetrack with obstacles like pedestrians and other vehicles and even traffic rules. We created a deep Q-network (DQN) agent to perform the task of autonomous car driving from raw sensory inputs. We evaluated our agent’s performance against several standard agents in a racing simulation environment (CARLA). Our results indicate that our DQN agent is capable of successfully controlling a car to navigate around a simulation environment(CARLA). Abstract-II: 3D Object Tracking in Autonomous Driving: we developed a stereo-camera based 3D multi Vehicle tracking System. The main objective of this system is to accurately predict location and orientation of vehicles from stereo camera data. It has three modules: a 2D object detection network, 3D position extraction and 3D object correlation. This system utilizes the Kalman filtering to improve the robustness of missed detections Abstract-III: trajectory planning: The planning module in an autonomous navigation can be considered as the system’s brain where the vehicle trajectory for the next seconds is planned. In this module, we make use of the perception data from the 3D object tracking to plan a secure and smooth trajectory for the vehicle. The trajectory planning objective is to provide the vehicle with a secure trajectory constrained by the vehicle dynamics limits, the navigation comfort and safety, and the traffic rules. Once the desired trajectory is generated, the next step is to control the vehicle in order to track this trajectory with the desired speed profile also defined by the planning module. The vehicle control is indeed the control of the vehicle actuators, such as the steering wheel, the accelerator and the braking force, in order to manage the vehicle motions. Abstract-IV: Distributed System Architecture and Development Process: For autonomous driving, complex autonomous driving algorithms, including perception, 3D object tracking, localization, planning, and control, are required with many heterogeneous sensors and computers. To manage the complexity of the driving algorithms and the heterogeneity of the system components, we applied distributed system architecture to the autonomous driving system, and a system platform for the distributed system of an autonomous car. The development process provides the guidelines to design and develop the distributed system of an autonomous vehicle. A time-triggered network protocol, FlexRay, to be applied as the main network of the software platform to improve the network bandwidth, fault tolerance, and system performance.", "source_url": "https:\/\/openalex.org\/W7135301869", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ScholarWorks@UMassAmherst (University of Massachusetts Amherst)", "source_record_id": "W7135301869", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 651, "title": "Deep Structured Multi-Task Learning for Computer Vision in Autonomous Driving", "title_ar": "", "title_en": "Deep Structured Multi-Task Learning for Computer Vision in Autonomous Driving", "author": "Marvin Teichmann", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cambridge", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; robotics; responsible-ai; predictive-analytics; cnn; image-segmentation; xai", "keywords": "", "abstract": "The field of computer vision is currently dominated by deep learning advances. Convolutional Neural Networks (CNNs) have become the predominant tool for solving almost any computer vision task, so state-of-the-art systems have been built by using the predictive capabilities of Convolutional Neural Networks (CNNs). Many of those systems use simple encoder–decoder based design, where an off-the-shelf CNN architecture is combined with a task-specific decoder and loss function in order to create an end-to-end trainable model. This ultimately raises the question of whether these kinds of models are the future of computer vision. In this thesis we argue that this is not the case. We start off by discussing three limitations of simple end-to-end training. We proceed by showing how it is possible to overcome those limitations by using an approach that we call structured modelling. The idea is to use CNNs to compute a rich semantic intermediate representation which is then used to solve the actual problem by applying a geometric and task-related structure. In this work we solve the localization, segmentation and landmark recognition task using structured modelling, and we show that this approach can improve generalization, interpretability and robustness. We also discuss how this approach is particularly useful for real-time applications such as autonomous driving. Visual perception is a multi-module problem that requires several different computer vision tasks to be solved. We discuss how, by sharing computations, we can improve not only the inference speed but also the prediction performance by using the structural relationship between the tasks. Lastly, we demonstrate that structured modelling is able to achieve state-of-the-art performance, making it a very relevant approach for solving current and future computer vision problems.", "source_url": "https:\/\/openalex.org\/W3049041115", "pdf_url": "", "doi": "10.17863\/cam.56207", "handle": "", "repository_name": "Apollo (University of Cambridge)", "source_record_id": "W3049041115", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 652, "title": "Learning to Drive: Exploiting Deep Models for Autonomous Driving", "title_ar": "", "title_en": "Learning to Drive: Exploiting Deep Models for Autonomous Driving", "author": "Wenjie Luo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Toronto", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; robotics; responsible-ai; predictive-analytics; cnn; image-segmentation; xai", "keywords": "", "abstract": "Building self-driving vehicles is exciting and promising. It is going to transform the way we live and provide safety, efficiency, and mobility for everyone. In this thesis, I present a collection of our work in the direction of building smart self-driving vehicles from understanding convolutional neural networks (CNNs) to the full autonomy stacks, including perception, prediction, and planning. First, we study the property of CNNs and provide theoretical analysis on the receptive field. We use Fourier transform and center limit theorem to show that the effective receptive field only occupies a fraction of the theoretical one, especially in deep models. This can provide insights for future CNNs design. Next, we push the state of the art for tasks in the autonomy stacks using deep CNNs. For depth estimation with stereo cameras, we develop a deep matching network that simplifies previous approaches by using a dot-product layer and incorporates a multi-class classification loss, allowing the network to calibrate scores among larger contexts. These greatly improve the runtime and achieve much better matching results. Furthermore, we extend the idea to optical flow where the matching is done in 2D space. Combined with an instance segmentation algorithm, we achieve state-of-the-art results on KITTI optical flow benchmark. Third, we propose new formulations for the self-driving system, i.e. using a joint model for 3D detection, prediction, and tracking. Our model takes a sequence of consecutive LiDAR sweeps and predicts the bounding boxes for vehicles at the current time step as well as one second into the future. Tracking is done by greedily checking the overlap between current detections and predictions from the past. This approach bridges the gap between perception and prediction to achieve better performance both in accuracy and runtime. Then, we take this one step further to construct a deep structured model for interpretable neural motion planning, where the CNN also predicts a 3D cost volume, expanding on time and x-y spatial dimensions. The final planning trajectory is generated with a sampling-based inference algorithm. We conduct offline testing and show that our approach is better than several baselines.", "source_url": "https:\/\/openalex.org\/W3154873709", "pdf_url": "", "doi": "", "handle": "", "repository_name": "TSpace (University of Toronto)", "source_record_id": "W3154873709", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 653, "title": "Multi-Agent Deep Reinforcement Learning for autonomous driving", "title_ar": "", "title_en": "", "author": "Gonçalo Vasconcelos Cunha Miranda Moreno", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2020, "language": "pt", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning; robotics", "keywords": "", "abstract": "Este trabalho estuda diferentes métodos e técnicas de resolução do complexo problema de condução autónoma focando-se principalmente em aprendizagem por reforço, um método que consegue criar um agente com uma condução mais eficiente e segura.Isto é em contraste a aprendizagem por imitação muito usada em condução autónoma que traz desvantagens como a necessidade de grandes quantidades de dados.Com a análise de diferentes métodos feita propõe-se um novo método de aprendizagem, baseado em aprendizagem por reforço, mas com a adição de um componente de aprendizagem distribuída e outro de troca de mensagens que permite cooperação entre agentes.Com esta abordagem os agentes conseguem melhor desempenho que os métodos anteriores quando testados com uma bateria de testes.Todos os resultados são validados no simulador de condução autónoma chamado Carla.i I would like to first thank my family for supporting me through college.Next Richard S. Sutton and Andrew G. Barto for their incredible and insightful book on Reinforcement Learning.The team of GPULab for helping this work with top of the line hardware resources.Amazon and Google for giving hundreds of dollars worth of cloud computing to this work.Finally I would like to thank my supervisors for helping and guiding me throughout this entire", "source_url": "https:\/\/openalex.org\/W3126526164", "pdf_url": "https:\/\/repositorio-aberto.up.pt\/bitstream\/10216\/132006\/2\/441780.pdf", "doi": "10.34626\/eqdc-m292", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W3126526164", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 654, "title": "Application of deep learning for enhancing simultaneous localization and mapping in autonomous driving", "title_ar": "", "title_en": "Application of deep learning for enhancing simultaneous localization and mapping in autonomous driving", "author": "Jintian Ge", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; robotics; transfer-learning; self-semi-supervised; multimodal; image-segmentation", "keywords": "", "abstract": "Simultaneous Localization and Mapping, commonly referred to as SLAM, represents a class of algorithms that involves constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's position within it. This technique is foundational in robotics and autonomous systems, enabling machines and robots to explore, understand, and navigate their surroundings. Deep learning, characterized by its prowess in handling vast data and intricate patterns, has shown promise in several Computer Vision (CV) tasks, i.e., semantic segmentation, optical flow estimation, and disparity estimation. Deep learning excels at these tasks, delivering dense and accurate results. However, as a downstream work of perception, SLAM pays little attention to the application of these results. Therefore, this paper aims to explore possible applications of deep learning (especially CV tasks) in SLAM tasks. In this thesis, we delve into the confluence of Deep Learning and SLAM tasks, emphasizing the potential for performance enhancement and methodological innovation. Our exploration is structured around four SLAM related tasks, each distinct yet synergistically linked around a center topic \"application of deep learning to enhance performance\": an automatic calibration approach for radar-camera system as a pre-process of multi-modal SLAM, which leverages deep learning models to eliminate the need for special markers; the application of deep learning to stereo 3D reconstruction through disparity estimation, presenting its potential for dense environment local mapping; the utility of semantics in initial pose estimation and feature matching within visual odometry for localization, refining the precision of positioning techniques; and introduction of supervised contrastive learning for image representation, targeting the challenges associated with fine-tuning deep learning models in real-world deployments. Collectively, these tasks underline the potential of deep learning in advancing robotic methodologies and offer a vision for future research directions.", "source_url": "https:\/\/openalex.org\/W4396622348", "pdf_url": "", "doi": "10.32657\/10356\/174794", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4396622348", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 655, "title": "Learning autonomous driving policies for dynamic urban and racing environments", "title_ar": "", "title_en": "Learning autonomous driving policies for dynamic urban and racing environments", "author": "Peide Cai", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "xai", "categories": "deep-learning; computer-vision; reinforcement-learning; robotics; responsible-ai; ai-applications; multimodal; xai; ai-smart-cities", "keywords": "", "abstract": "It is believed that self-driving vehicles will play an essential role in reducing road accidents, alleviating traffic congestion, and reducing energy consumption in transportation, thus providing safety, efficiency, and convenience for everyone. This thesis focuses on building autonomous driving systems leveraging deep learning under complex contexts from normal-speed urban to high-speed drifting\/racing scenarios. The first part studies the frameworks of end-to-end driving in dynamic urban scenarios based on imitation learning (IL).We progressively consider all-day and all-weather driving conditions using a combination of multi-modal sensors, i.e., cameras, lidars, and radars. The uncertainty measures are also utilized to indicate dangerous cases and improve the interpretability of the driving system. In the second part, we focus on local motion planning using semantic state abstractions rather than learning the entire navigation stack to achieve more generalizable and strategic driving performance in interaction-intensive environments, e.g., unregulated intersections\/ roundabouts. The interactions among different road users are modeled implicitly through graph attention networks (GAT), where the inter-vehicle influences are adaptively weighted according to specific contexts. By training the whole network through deep reinforcement learning (DRL), the proposed method performs better in balancing driving safety and efficiency than the baselines. The learned policy can also be transferred into a real-world dataset thanks to the decoupled perception-planning framework. Furthermore, the third part highlights highly dynamic drifting\/racing scenarios to facilitate agile autonomous driving near the handling limits under high-speed conditions. We first study the drift cornering control problems based on model-free DRL approaches, then combine IL and model-based DRL to solve a vision-based autonomous racing task. The proposed method is validated both in a realistic simulator and on a physical RC car. Related methodologies also support the 1st HKUST autonomous RC-car racing competition1.", "source_url": "https:\/\/openalex.org\/W4307183652", "pdf_url": "", "doi": "10.14711\/thesis-991013106451703412", "handle": "", "repository_name": "", "source_record_id": "W4307183652", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 656, "title": "Generating and Exploiting Deep Learning Variants to Increase Utilization of the Heterogeneous Resources in Autonomous Driving Platforms", "title_ar": "", "title_en": "Generating and Exploiting Deep Learning Variants to Increase Utilization of the Heterogeneous Resources in Autonomous Driving Platforms", "author": "Roger Pujol Torramorell", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; object-detection", "keywords": "", "abstract": "Nowadays, Deep learning-based solutions and, in particular, deep neural networks (DNNs) are getting into several core functionalities in critical real-time embedded systems (CRTES), like those in planes, cars, and satellites, from vision-based perception (object detection and object tracking) systems to trajectory planning. As a result, several deep learning instances are running simultaneously at any time on the same computing platform. However, while modern computing platforms offer a variety of computing elements (e.g., CPUs, GPUs, and specific accelerators) in which those DNN instances can be executed depending on their computational requirements and temporal constraints. Currently, most DNNs are mainly programmed to exploit one particular computing element, regular cores of the GPUs. This lack of variety causes a resource imbalance and under-utilization of the various computing element resources when executing several DNN instances, causing an increase in DNN tasks' execution time requirements. In this Thesis, (a) we develop different variants (implementation) of well-known DNN libraries used in the Apollo Autonomous Driving software for each of the computing elements of the latest NVIDIA Xavier system-on-chip. Each variant is configured to balance resource requirements and performance: the regular CPU core implementation that can run on 2, 4, and 6 cores (always leaving 2 cores free for other computations); the GPU with regular and Tensor cores variants that can run on 4 or 8 GPU's Stream Multiprocessors (SM); and 1 or 2 NVIDIA's Deep Learning Accelerators (NVDLA); (b) we show that each particular variant\/configuration offers different resource utilization\/performance point. (c) we show how those heterogeneous computing elements can be exploited by a static scheduler to sustain the execution of multiple and diverse DNN variants on the same platform.", "source_url": "https:\/\/openalex.org\/W3121071986", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W3121071986", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 657, "title": "Design and Implementation of an Autonomous Driving System with a Deep Learning Approach on a Scaled Vehicle Platform", "title_ar": "", "title_en": "Design and Implementation of an Autonomous Driving System with a Deep Learning Approach on a Scaled Vehicle Platform", "author": "Jesudara Omidokum", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Michigan", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "ai-education", "categories": "deep-learning; robotics; ai-applications; ai-education", "keywords": "", "abstract": "The thesis project is developed to create a platform for autonomous driving research and education purposes. The main goal of this study is to present a complete and adequate way for building a platform with a drive-by-wire system and sensor packages that are both comprehensive and appropriate for testing and deploying machine-learning-based algorithms on a scaled vehicle. The vehicle platform addresses reproducibility issues, onboard processing capability restrictions, vehicle scale, and system cost with the existing related vehicle platforms. Our vehicle platform system integrates both drives-by-wire and an autonomous system enabled with sensor packages in an easy-to-implement format. The platform is a generic and multilevel system, with flight controller programs combined with a GPS module handling the mid to high-level motor controls and a laptop powered by a graphics processing unit (GPU) capable of handling the advanced and more complex algorithms. The vehicle platform is validated by employing it in a deep-learning-based behavioral cloning study. The platform's affordability and adaptability would benefit broader research and the education community.", "source_url": "https:\/\/openalex.org\/W4311648930", "pdf_url": "", "doi": "10.7302\/6650", "handle": "", "repository_name": "Deep Blue (University of Michigan)", "source_record_id": "W4311648930", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 658, "title": "Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving", "title_ar": "", "title_en": "Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving", "author": "Dominik Jargot", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; object-detection", "keywords": "", "abstract": "Nowadays, autonomous driving is a trending topic in the automotive field. One of the most crucial challenges of autonomous driving research is environment perception. Currently, many techniques achieve satisfactory performance in 2D object detection using camera images. Nevertheless, such 2D object detection might be not sufficient for autonomous driving applications as the vehicle is operating in a 3D world where all the dimensions have to be considered. In this thesis a new method for 3D object detection, using deep learning approach is presented. The proposed architecture is able to detect cars using data from images and point clouds. The proposed network does not use any hand-crafted features and is trained in an end-to-end manner. The network is trained and evaluated with the widely used KITTI dataset. The proposed method achieves an average precision of 81.38%, 67.02%, and 65.30% on the easy, moderate, and hard subsets of the KITTI validation dataset, respectively. The average inference time per scene is 0.2 seconds.", "source_url": "https:\/\/openalex.org\/W3038194063", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W3038194063", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 659, "title": "Learning Birds-Eye View Representations for Autonomous Driving", "title_ar": "", "title_en": "Learning Birds-Eye View Representations for Autonomous Driving", "author": "Thomas Roddick", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cambridge", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; predictive-analytics; cnn; object-detection; ai-smart-cities", "keywords": "", "abstract": "Over the past few years, progress towards the ambitious goal of widespread fully-autonomous vehicles on our roads has accelerated dramatically. This progress has been spurred largely by the success of highly accurate LiDAR sensors, as well the use of detailed high-resolution maps, which together allow a vehicle to navigate its surroundings effectively. Often, however, one or both of these resources may be unavailable, whether due to cost, sensor failure, or the need to operate in an unmapped environment. The aim of this thesis is therefore to demonstrate that it is possible to build detailed three-dimensional representations of traffic scenes using only 2D monocular camera images as input. Such an approach faces many challenges: most notably that 2D images do not provide explicit 3D structure. We overcome this limitation by applying a combination of deep learning and geometry to transform image-based features into an orthographic birds-eye view representation of the scene, allowing algorithms to reason in a metric, 3D space. This approach is applied to solving two challenging perception tasks central to autonomous driving. The first part of this thesis addresses the problem of monocular 3D object detection, which involves determining the size and location of all objects in the scene. Our solution was based on a novel convolutional network architecture that processed features in both the image and birds-eye view perspective. Results on the KITTI dataset showed that this network outperformed existing works at the time, and although more recent works have improved on these results, we conducted extensive analysis to find that our solution performed well in many difficult edge-case scenarios such as objects close to or distant from the camera. In the second part of the thesis, we consider the related problem of semantic map prediction. This consists of estimating a birds-eye view map of the world visible from a given camera, encoding both static elements of the scene such as pavement and road layout, as well as dynamic objects such as vehicles and pedestrians. This was accomplished using a second network that built on the experience from the previous work and achieved convincing performance on two real-world driving datasets. By formulating the maps as an occupancy grid map (a widely used representation from robotics), we were able to demonstrate how predictions could be accumulated across multiple frames, and that doing so further improved the robustness of maps produced by our system.", "source_url": "https:\/\/openalex.org\/W3216727358", "pdf_url": "", "doi": "10.17863\/cam.77645", "handle": "", "repository_name": "Apollo (University of Cambridge)", "source_record_id": "W3216727358", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 661, "title": "Action recognition in videos using deep learning", "title_ar": "", "title_en": "Action recognition in videos using deep learning", "author": "Shiyu Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; few-zero-shot; video-analysis; llm", "keywords": "", "abstract": "This dissertation systematically studies the two core challenges faced by visual-language pretraining models (such as CLIP) in video action recognition tasks: insufficient temporal modeling and ambiguous semantic space. By constructing a temporal-enhanced model (TC-CLIP) and a semantic-enhanced model (OST) respectively, this dissertation optimizes and evaluates existing methods from two dimensions: “temporal modeling reinforcement” and “semantic label refinement”. TC-CLIP proposes a temporal contextualization mechanism that introduces global temporal interactions during visual encoding through a “filter-aggregate-inject” process, effectively modeling long-range dependencies and dynamic evolution between video frames. OST, on the other hand, leverages large language models to generate rich spatio-temporal descriptors and dynamically aligns fine-grained textual semantics with local video features through an optimal transport-based semantic alignment mechanism, addressing the problems of ambiguous and vague category label semantics. Zero-shot and few-shot experiments were conducted on the HMDB-51 and UCF-101 datasets for ActionCLIP, TC-CLIP, and OST models. The results demonstrate that both improved strategies significantly enhance the performance of the baseline model, ActionCLIP. Experimental results indicate that the temporal-enhancement and semantic-enhancement strategies are complementary: TC-CLIP performs better in scenarios with large motion and prominent temporal features (such as UCF-101), while OST demonstrates stronger robustness in scenarios with complex backgrounds, subtle actions, and ambiguous semantics (such as HMDB-51). Furthermore, few-shot experiments reveal the performance patterns of the two strategies under different amounts of data: semantic enhancement has an advantage when data is extremely scarce due to prior knowledge, whereas temporal enhancement exhibits stronger learning potential as data volume increases. This study not only confirms the necessity of introducing explicit temporal modeling and fine-grained semantic alignment in video tasks but also clarifies their applicable boundaries and complementary relationship, providing theoretical foundations and practical directions for developing general video recognition models that consider both temporal dynamics and semantic understanding.", "source_url": "https:\/\/openalex.org\/W7155590036", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W7155590036", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 662, "title": "Applied deep learning for human action recognition in video surveillance, healthcare monitoring, and robotics", "title_ar": "", "title_en": "Applied deep learning for human action recognition in video surveillance, healthcare monitoring, and robotics", "author": "Thean Chun Koh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; robotics; responsible-ai; cnn; video-analysis; fairness", "keywords": "", "abstract": "Human behavior recognition is a cutting-edge field of artificial intelligence focused on interpreting and understanding human actions, gestures and activities through advanced computational algorithms and machine learning techniques. These algorithms serve as the foundation for AI systems to effectively interact with and assist humans in various aspects of daily life, from personal tasks to complex decision-making processes. However, developing effective human behavior recognition systems faces several challenges. The inherent complexity and variability of human behavior present significant obstacles. Actions vary widely due to cultural backgrounds, individual habits and environmental factors, complicating the creation of universal models. Additionally, issues such as occlusion in crowded environments, variations in lighting conditions and the need for real-time processing further hinder accuracy and reliability. Specifically, traditional methods such as 3D Convolutional Neural Networks (3D CNNs) can be computationally intensive and may struggle with real-time processing requirements, while optical flow techniques often fail to provide robust performance under varying lighting conditions and occlusions. Moreover, ethical considerations surrounding privacy and data security pose additional barriers to widespread adoption. Furthermore, current models often rely on single modalities, which limits their ability to accurately capture human behavior in dynamic environments where multiple individuals are present.", "source_url": "https:\/\/openalex.org\/W4412190996", "pdf_url": "", "doi": "10.32657\/10356\/200238", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4412190996", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 663, "title": "Privacy Motivated Models for Deep Learning-based Human Action Recognition in Intelligent Video Surveillance", "title_ar": "", "title_en": "Privacy Motivated Models for Deep Learning-based Human Action Recognition in Intelligent Video Surveillance", "author": "Mickael Willy Jacky Cormier", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Karlsruhe Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; responsible-ai; video-analysis; fairness", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7135042273", "pdf_url": "", "doi": "10.5445\/ir\/1000191274", "handle": "", "repository_name": "Repository KITopen (Karlsruhe Institute of Technology)", "source_record_id": "W7135042273", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 664, "title": "Human action recognition in videos using deep neural networks", "title_ar": "", "title_en": "", "author": "Carlos Ismael Orozco", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad de Buenos Aires", "university_source": "repository_host", "college": "", "department": "", "country": "Argentina", "governorate": "", "publication_year": 2025, "language": "es", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; cnn; rnn; video-analysis", "keywords": "", "abstract": "El Reconocimiento de Acciones Humanas en videos (HAR) consiste en identificar y clasificar diferentes acciones que involucran una o más personas. Este campo continúa en desarrollo ya que los resultados aún no alcanzan niveles satisfactorios para muchas de sus aplicaciones tales como vigilancia, interacción hombre-máquina, asistencia sanitaria, indexación de videos, entre otras. La importancia de HAR radica en su aplicabilidad a diversos entornos donde la interpretación del comportamiento humano a partir de videos es clave. El objetivo de esta tesis fue desarrollar enfoques de aprendizaje profundo aplicados a HAR. La propuesta integró el uso de Redes Convolucionales (CNN) para extraer características espaciales y de Redes Recurrentes (LSTM) para modelar la dinámica temporal de las acciones. Se incorporó un mecanismo de atención que enfocó las regiones más relevantes de cada fotograma, mejorando el rendimiento al destacar características esenciales y reducir la influencia de los fotogramas poco representativos. La combinación de CNN, LSTM y atención ofreció una solución robusta para los desafíos de variabilidad en contextos y entornos. Además, se implementó un módulo de selección de fotogramas que optimizó el proceso de reconocimiento al identificar y utilizar los fotogramas más representativos. Este no solo mejoró la precisión del modelo, sino que también redujo los tiempos de entrenamiento al procesar menos fotogramas sin comprometer la calidad del reconocimiento. Para evaluar el rendimiento de las propuestas, se utilizaron bases de datos públicas especializadas en este tipo de problemas. Los resultados obtenidos fueron competitivos en comparación con modelos más complejos reportados en la literatura", "source_url": "https:\/\/openalex.org\/W7166406508", "pdf_url": "https:\/\/bibliotecadigital.exactas.uba.ar\/download\/tesis\/tesis_n7819_Orozco.pdf", "doi": "", "handle": "", "repository_name": "Repositorio Digital Institucional de la Universidad de Buenos Aires (Universidad de Buenos Aires)", "source_record_id": "W7166406508", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 667, "title": "Video-based similar gesture action recognition using deep learning and GAN-based approaches", "title_ar": "", "title_en": "Video-based similar gesture action recognition using deep learning and GAN-based approaches", "author": "Di Wu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Technology Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; robotics; responsible-ai; predictive-analytics; cnn; rnn; video-analysis; gan; fairness", "keywords": "", "abstract": "Human action is not merely a matter of presenting patterns of motion of different parts of the body, in addition, it is also a description of intention, emotion and thoughts of the person. Hence, it has become a crucial component in human behavior analysis and understanding. Human action recognition has a wide variety of applications such as surveillance, robotics, health care, video searching and human-computer interaction. Analysing human actions manually is tedious and easily prone to errors. Therefore, computer scientists have been trying to bring the abilities of cognitive video understanding to human action recognition systems by using computer vision techniques. However, human action recognition is a complex task in computer vision because of the camera motion, occlusion, background cluttering, viewpoint variation, execution rate and similar gestures. These challenges significantly degrade the performance of the human action recognition system. The purpose of this research is to propose solutions based on traditional machine learning methods as well as the state-of-the-art deep learning methods to automatically process video-based human action recognition. This thesis investigates three research areas of video-based human action recognition: traditional human action recognition, similar gesture action recognition, and data augmentation for human action recognition. To start with, the feature-based methods using classic machine learning algorithms have been studied. Recently, deep convolutional neural networks (CNN) have taken their place in the computer vision and human action recognition research areas and have achieved tremendous success in comparison to traditional machine learning techniques. Current state-of-the-art deep convolutional neural networks were used for the human action recognition task. Furthermore, recurrent neural networks (RNN) and its variation of long-short term memory (LSTM) are used to process the time series features which are handcrafted features or extracted from the CNN. However, these methods suffer from similar gestures, which appear in the human action videos. Thus, a hierarchical classification framework is proposed for similar gesture action recognition, and the performance is improved by the multi-stage classification approach. Additionally, the framework has been modified into an end-to-end system, therefore, the similar gestures can be processed by the system automatically. In this study, a novel data augmentation framework for action recognition has been proposed, the objective is to generate well learnt video frames from action videos which can enlarge the dataset size as well as the feature bias. It is very important for a human action recognition system to recognize the actions with similar gestures as accurately as possible. For such a system, a generative adversarial net (GAN) is applied to learn the original video datasets and generate video frames by playing an adversarial game. Furthermore, a framework is developed for classifying the original dataset in the first place to obtain the confusion matrix using a CNN. The similar gesture actions will be paired based on the confusion matrix results. The final classification result will be applied on the fusion dataset which contains both original and generated video frames. This study will provide realtime and practical solutions for autonomous human action recognition system. The analysis of similar gesture actions will improve the performance of the existing CNN-based approaches. In addition, the GAN-based approaches from computer vision have been applied to the graph embedding area, because graph embedding is similar to image embedding but used for different purposes. Unlike the purpose of the GAN in computer vision for generating the images, the GAN in graph embedding can be used to regularize the embedding. So the proposed methods are able to reconstruct both structural characteristics and node features, which naturally possess the interaction between these two sources of information while learning the embedding.", "source_url": "https:\/\/openalex.org\/W3020614990", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UTS ePRESS (University of Technology Sydney)", "source_record_id": "W3020614990", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 668, "title": "A Dual-Stream Deep Learning Architecture for Action Recognition in Salmon from Underwater Video.", "title_ar": "", "title_en": "A Dual-Stream Deep Learning Architecture for Action Recognition in Salmon from Underwater Video.", "author": "Håkon Måløy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; ai-applications; rnn; video-analysis; ai-agriculture; ai-industry", "keywords": "", "abstract": "Over half of the costs from breeding salmon in the Norwegian salmon farming industry comes from feed usage. Today the feeding process is largely a manual labor, requiring an operator to monitor the amount of feed sinking to the bottom of a breeding cage. When the amount of feed exceeds a certain threshold, the feeding process is terminated. Automation of this process and using salmon motion behavior instead of sinking feed to determine when to terminate feeding, could greatly reduce costs both in through the labor needed and amount of feed wasted. Resent developments in Human Action Recognition have shown that Deep Learning approaches are well suited to perform Action Recognition. We therefore examine the feasibility of using Deep Learning approaches to automate the feeding process. We use 76 videos of salmon collected from within a breeding cage during the month of November 2016. Using these videos for training, validation and testing, we propose three approaches to automatically classify Feeding and NonFeeding behavior in salmon. The three approaches are a Spatial Architecture, a Spatial Recurrent Architecture and a Dual-Stream Architecture. Our results show that all our proposed architectures are able to separate videos of Feeding and NonFeeding salmon with high accuracy. We also find that the Dual- Stream Architecture is the best performing architecture. It combines spatial and temporal information through the use of a Spatial Stream, a novel Temporal Stream and a Recurrent Neural Network (RNN). Our Dual-Stream Architecture is able to accurately classify 80.0% all of our testing videos, presenting state-of-the-art performance. To the best of our knowledge, both our Temporal Stream and our Dual-Stream Architecture are original and novel architectures, as is the application of Deep Learning inference models for the Salmon Activity Recognition domain in optimization of feeding operation in Norwegian Aquaculture. We hope the results presented in this thesis will contribute to achieve a higher sustainability in Norwegian salmon aquaculture, optimize feeding operations, and consequently reduce potential waste. Future work beyond the results presented in this thesis concerns research on understanding of what our the Deep Learning architecture have learned and visualizing this learning process.", "source_url": "https:\/\/openalex.org\/W2809657688", "pdf_url": "", "doi": "", "handle": "", "repository_name": "BIBSYS Brage (BIBSYS (Norway))", "source_record_id": "W2809657688", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 669, "title": "Data augmentation of visual rhythms using symmetric extension for deep learning video based human action recognition", "title_ar": "", "title_en": "", "author": "Hemerson Tacon", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "pt", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; video-analysis", "keywords": "", "abstract": "Nos últimos anos, avanços significativos foram alcançados no problema de classificação de imagens devido ao aprimoramentos dos modelos de Aprendizagem Profunda. Entretanto, no que diz respeito ao Reconhecimento de Ações Humanas, ainda existe muito espaço para melhorias. Uma forma de melhorar o desempenho de tais modelos é através do aumento de dados. Dessa forma propomos, como aumento de dados, o uso de múltiplos recortes do Ritmo Visual, simetricamente estendidos no tempo e separados por uma distância fixa. Propomos ainda utilizar uma nova forma de extração do Ritmo Visual, o Ritmo Visual Ponderado. Este método propõe reforçar os padrões de movimento pesando os aspectos mais próximos de uma posição específica no vídeo na qual julgamos que a ação tenha maior probabilidade de ocorrer. O método final consiste na replicação do Ritmo Visual Ponderado concatenando quantas cópias forem necessárias ao longo da dimensão temporal, tendo as cópias pares invertidas horizontalmente. Esse método torna possível a extração de recortes que correspondam ao tamanho de entrada fixo da Rede Neural Convolucional utilizada, bem como a preservação da taxa de amostragem do vídeo, o que é crucial para não distorcer a velocidade das ações. Não obstante, os vários recortes garantem que toda extensão espacial e temporal do Ritmo Visual seja contemplada. Com o objetivo de avaliar nosso método, empregamos uma estratégia multi-fluxo. Essa estratégia consiste na combinação de informações extraídas a partir dos frames RGB dos vídeos, do Fluxo Ótico, e dos Ritmos Visuais Simetricamente Estendidos horizontal e vertical. Nosso método resultou em taxas de acurácia próximas ao estado da arte nos conjuntos de dados UCF101 e HMDB51.", "source_url": "https:\/\/openalex.org\/W3137056519", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Americanae (AECID Library)", "source_record_id": "W3137056519", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 670, "title": "Deep Learning Methods for Human Action Recognition", "title_ar": "", "title_en": "Deep Learning Methods for Human Action Recognition", "author": "Zeqi Yu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Auckland University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; cnn; rnn; video-analysis", "keywords": "", "abstract": "Human action recognition from digital videos is a hot topic in the field of computer vision. It has a pretty assortment of applications in a myriad of fields such as video surveillance, human-computer interaction, visual information retrieval, and unmanned driving. With the exponential growth of surveillance data on the Internet in recent years, how to implement effective and efficient analysis of video data is extremely crucial. Traditional machine learning methods that only extract computable features have limitations and do not suit massive visual data, meanwhile, deep learning methods, especially convolutional neural networks, have gained great attainments in this field. The goal of human action recognition is to classify patterns so as to understand human actions from visual data and export corresponding tags. In addition to spatial correlation existing in 2D images, human actions in a video on the correlation in the temporal domain. Due to the complexity of human actions, changes of perspectives, background noises, and lighting conditions will affect the recognition. In order to solve these thorny problems, three algorithms are designed and implemented in this thesis. Based on convolutional neural networks (CNN), Two-Stream CNN, CNN+LSTM, and 3D-CNN are harnessed to identify human actions. Each algorithm is explicated and analyzed in detail. HMDB-51 dataset is employed to test these algorithms and gain the best results. Our experimental results demonstrate that the three methods have effectively identified human actions in given videos, the best algorithm thus is verified.", "source_url": "https:\/\/openalex.org\/W3157063314", "pdf_url": "https:\/\/openrepository.aut.ac.nz\/bitstreams\/ed753409-6ddd-4042-9f3a-2fff51193d63\/download", "doi": "", "handle": "", "repository_name": "Tuwhera (Auckland University of Technology)", "source_record_id": "W3157063314", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 671, "title": "Learning Interaction Grammar for Action Recognition", "title_ar": "", "title_en": "Learning Interaction Grammar for Action Recognition", "author": "Tengyu Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; video-analysis", "keywords": "", "abstract": "Video action recognition has been in the center of the stage since its introduction in 2004 [SLC04]. During the past 15 years, countless methods had been proposed to understand what human is doing in a video clip. While some works infer action label directly from pixel information, some other works propose to learn multi-level hierarchical structure that composes actions. In this work, we propose the learning of a two-layer grammar model for action recognition that is based on human object interaction. To evaluate the idea, we proposed an interaction grammar for action recognition on video. The model is evaluated by simulated annealing with MCMC and by deep learning.", "source_url": "https:\/\/openalex.org\/W2792899696", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W2792899696", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 672, "title": "Action recognition using deep learning", "title_ar": "", "title_en": "Action recognition using deep learning", "author": "Petar Palasek", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Queen Mary University of London", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; cnn; video-analysis", "keywords": "", "abstract": "In this thesis we study deep learning architectures for the problem of human action recognition in image sequences, i.e. the problem of automatically recognizing what people are doing in a given video.As unlabeled video data is easily accessible these days, we first explore models that can learn meaningful representations of sequences without actually having to know what is happening in the sequences at hand.More specifically, we first explore the convolutional restricted Boltzmann machine (RBM) and show how a stack of convolutional RBMs can be used to learn and extract features from sequences in an unsupervised way.Using the classical Fisher vector pipeline", "source_url": "https:\/\/openalex.org\/W2785072843", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Queen Mary Research Online (Queen Mary University of London)", "source_record_id": "W2785072843", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 673, "title": "Egocentric action recognition from noisy videos", "title_ar": "", "title_en": "Egocentric action recognition from noisy videos", "author": "Lijin Yang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; transformers; video-analysis", "keywords": "", "abstract": "Recognizing human actions in videos can make machines better interpret the behavior of human beings.As one of the basic fields of computer vision, action recognition has attracted significant research attention.In particular, with the recent advance of deep learning, there is a huge boost of action recognition performance.Deep learning models that takes as input video clips can reliably recognize the actions compared with traditional methods using hand-crafted features. List of Tables4.1 Action recognition performance comparison with baseline methods. .4.2 Quantitative comparison on cooperating with different backbones. . .4.3 Ablation study for different parts of our model on recognizing interrupted actions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.4 Ablation study for different parts of our model on localizing useful frames in super noisy videos. . . . . . . . . . . . . . . . . . . . . . . .5.1 Quantitative comparison on using different attention mechanisms. . .", "source_url": "https:\/\/openalex.org\/W3014414335", "pdf_url": "https:\/\/repository.dl.itc.u-tokyo.ac.jp\/record\/54246\/files\/48186461.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W3014414335", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 674, "title": "Domain adaptation for video action recognition", "title_ar": "", "title_en": "Domain adaptation for video action recognition", "author": "Xiyu Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; multimodal; video-analysis", "keywords": "", "abstract": "Humans can effortlessly learn from a specific data distribution and generalize well to various situations without excessive supervision. In contrast, deep learning models often struggle to achieve similar generalization capabilities. This is primarily because deep models are trained with algorithms that aim to minimize empirical risks on training data and assume that test data share the same distribution as train data. However, significant domain shifts between training (source) and testing (target) data can occur, causing deep models to generalize poorly on target domains and necessitating additional supervision for adaptation. To address this, Video-based Unsupervised Domain Adaptation (VUDA) has been proposed as a cost-efficient approach for transferring video action recognition models from the source domain to an unlabeled target domain. Nonetheless, VUDA relies on strong assumptions, such as identical label spaces and fixed target domains, which may not hold true in real-world applications. Consequently, this thesis aims to eliminate these assumptions to broaden the applicability of video adaptation methods, focusing on two major shortcomings of conventional VUDA methods, e.g., partial domain adaptation (adapting from a source domain with many classes to a target domain with fewer classes) and continual domain adaptation (adapting to continuously changing target domains). For partial domain adaptation, this thesis proposes the Multi-modality Cluster-calibrated partial Adversarial Network (MCAN), which constructs a multi-modal network to extract robust features and a novel calibration method to refine target class distribution estimation, effectively filtering out irrelevant source classes. To further address some real challenges in the field of adapting deep video models, the problem of continuous video domain adaptation is defined and this thesis proposes Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART). This method leverages attentive learning and a novel data generalization enhanced self-knowledge distillation to preserve previously learned knowledge on seen target domains while adapting to newly encountered ones, ultimately providing a performative model for multiple seen target domains at a minimal cost. This thesis evaluates the proposed partial and continuous video domain adaptation methods on existing and newly constructed benchmarks in this thesis. Our results demonstrated significant performance improvements for MCAN and CART, with MCAN showing particularly strong gains when domain shifts were substantial and CART demonstrating a superior capability of preserving learned knowledge. In conclusion, our research findings on partial and continuous domain adaptation effectively broadened the applicability of video domain adaptation methods, making them more general and cost-efficient.", "source_url": "https:\/\/openalex.org\/W4390585107", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/172273\/2\/wang_xiyu_final_thesis.pdf", "doi": "10.32657\/10356\/172273", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4390585107", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 675, "title": "Deep Learning for Skeleton-Based Human Action Recognition", "title_ar": "", "title_en": "Deep Learning for Skeleton-Based Human Action Recognition", "author": "Sohaïb Laraba", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Belgium", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; robotics; responsible-ai; cnn; video-analysis; xai", "keywords": "", "abstract": "Human action recognition from videos has a wide range of applications, including video surveillance and security, human-computer interaction, robotics, health care, etc. Nowadays, 3D skeleton-based action recognition has drawn increasing attention thanks to the availability of low-cost motion capture devices, and accessibility of large-scale 3D skeleton datasets, in addition to real-time skeleton estimation algorithms. In the first part of this thesis, we present a novel representation of motion capture sequences for 3D skeleton-based action recognition. The proposed approach consists of representing the 3D skeleton sequences into RGB image-like data and leveraging recent convolutional neural networks (CNNs) to model the long-term temporal and spatial structural information for action recognition. Extensive experiments have shown the superiority of the proposed approach over the state-of-the-art methods for 3D skeleton-based action recognition. In order to extract skeleton sequences, different devices extract first the depth information using multiple technologies (stereo, time-of-flight, etc.), then 3D skeleton poses are extracted using different algorithms. In the very late years, new researches proposed to extract skeleton sequences directly from RGB videos. The most precise methods extract 2D skeletons in real-time and with high accuracy. In the second part of this thesis, we leverage these tools to extend the use of our proposed approach to RGB videos. We first extract 2D skeleton sequences from RGB videos, and then, following approximately the same process as in the first part, we use CNNs for human action recognition. Different experiments showed that the proposed method outperforms different state-of-the-art methods on a large benchmark dataset. Another contribution of this thesis relates to the interpretability of deep learning models. Deep learning models are still considered alchemy due to the lack of understanding of their internal operations. Interpretability is a crucial task to understand and trust the decisions made by the machine learning model. Thus, we propose in the third part of this thesis to use CNN interpretation methods to understand the behavior of our classifier and extract the most informative joints during the execution of a particular action. This method allows us to see from the CNN point of view the most important joints, and understand why certain actions are confused by the proposed classifier.", "source_url": "https:\/\/openalex.org\/W3093549486", "pdf_url": "", "doi": "10.13140\/rg.2.2.30723.53283", "handle": "", "repository_name": "ORBi UMONS", "source_record_id": "W3093549486", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 676, "title": "Deep Feature Fusion for Video-Based Action Recognition", "title_ar": "", "title_en": "Deep Feature Fusion for Video-Based Action Recognition", "author": "Cheng Cheng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Texas A&M University Libraries", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; cnn; video-analysis", "keywords": "", "abstract": "3-Dimensional Convolutional Neural Networks (3D ConvNets) have been adopted for videobased action recognition task recently. Many 3D ConvNets, such as C3D, I3D, and Res3D, have been proposed and achieved great success. The model ensemble techniques have been very successful in achieving better performance over a single model. But model ensemble could not be adopted in this case given that the single 3D ConvNets model as a base learner is unrealistic. It remains an open question about how to achieve better performance by leveraging multiple 3D ConvNets models. To solve the problem, we present a two-stage framework to combine multiple 3D ConvNets models at the feature level. In the first stage, we treat each pretrained 3D ConvNets model as a feature extractor to extract features from raw videos. We fuse the extracted features of different 3D ConvNets models to form the new video representation and then train a classifier based on the new video representation in the second stage. We explore several widely-used feature fusion methods for deep features learned from different models, to learn more robust action representations from raw videos. We show that our framework outperforms any single 3D ConvNets model by a large margin and exhibits comparable performance to the state-of-the-art model on two video action recognition benchmarks.", "source_url": "https:\/\/openalex.org\/W3208245486", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OakTrust (Texas A&M University Libraries)", "source_record_id": "W3208245486", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 677, "title": "Transfer Learning Methods for Object and Action Recognition", "title_ar": "", "title_en": "Transfer Learning Methods for Object and Action Recognition", "author": "Samitha Herath", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Australian National University", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; computer-vision; generative-ai; ai-applications; transfer-learning; few-zero-shot; video-analysis; gan; ai-education", "keywords": "", "abstract": "Thanks to the advancement of machine learning and computer vision research we are observing remarkable improvements in object and action recognition. However, it can be seen that when there are differences (e.g., distributional, representations, semantic) in the training and test data, many learning algorithms fail to perform well or at times becomes infeasible. Nevertheless, problem setups dealing specifically with such differences are constantly being explored in literature. In general these problems are known as transfer learning problems. For instance, the objective of Domain Adaptation (DA) is to learn models that are invariant to distributional differences between train and test data (i.e., source and target domain data). Zero-Shot Learning (ZSL) is a framework for recognizing instances from classes never seen during training (i.e., missing classes in training data). In ZSL, the idea is to use related semantic information about data to workaround this data deficiency. In this thesis we present two solutions for Domain Adaptation and Zero-Shot Learning. First, we discuss our DA solution for learning an invariant Hilbert space for source and target domain data embedding. This is achieved by learning projections from individual domains to a latent space. We learn a Mahalanobis metric on this latent space to simultaneously minimizing domain disparity while maximizing a measure of discriminative power. We also show that the alignment of statistical properties (e.g., first and second order statistics) helps in improving our solution's performance, especially when there are no labeled samples from the target domain. We explore the effect of geometrical constraints such as orthogonality and positive definiteness for our model. Here, we use Riemannian optimization methods to seamlessly implement these constraints in our model training. Inspired by the success of statistical alignment, we further explore statistical alignment by min-max learning. Our motivation here is to avoid the trivial feature degeneration occurring when aligning two spaces by minimizing a statistical loss such as KL divergence. Here, we show that min-max statistical alignment is a promising solution for DA as well as ZSL. In fact, we show that min-max statistical alignment can reach comparable performance with state of the art adversarial solutions, including the ones using powerful Wasserstein GANs as the backbone. Lastly, we discuss our work on spatio-temporal transfer learning for human action recognition. In our survey on human action recognition from videos, we extensively study representation based and deep network based action recognition solutions. Here, we observe that one of the high-impact video action recognition solutions is the Two-Stream networks solution. However, the applicability of two-stream solution requires a temporal stream. In other words this restricts its applicability only to video data. In this work we explore use of transfer learning methods for devising two stream action recognition for still action images where there are no temporal information. We call this Temporal to Still Image Learning (or in short T2SIL). To explore the T2SIL framework we use existing still image and video datasets to form a new human action dataset of 40 classes. The data set contains still image actions and a video actions counterpart with the same set of classes. In this work, we explore three state of the art transfer learning solutions (i.e., Deep embedding learning, GANs and model distillation with Teacher-Student Networks) for T2SIL.", "source_url": "https:\/\/openalex.org\/W3036155538", "pdf_url": "", "doi": "10.25911\/5ee74e7c697e0", "handle": "", "repository_name": "ANU Open Research (Australian National University)", "source_record_id": "W3036155538", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 678, "title": "Deep Learnıng Archıtectures for Collectıve Actıvıty Recognıtıon", "title_ar": "", "title_en": "Deep Learnıng Archıtectures for Collectıve Actıvıty Recognıtıon", "author": "Cemil Zalluhoğlu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; cnn; transformers; video-analysis", "keywords": "", "abstract": "Collective activity recognition, which analyses the behavior of groups of people in videos, is an essential goal of video surveillance systems. In this thesis, we proposed three new solutions and one novel dataset for the collective activity recognition task. In the first method, we propose a new multi-stream convolutional neural network architecture that utilizes information extracted from multiple regions. The proposed method is the first work that uses a multi-stream network and multiple regions in this problem. Various strategies to fuse multiple spatial and temporal streams are explored. We evaluate the proposed method on two benchmark datasets, the Collective Activity Dataset, and the Volleyball Dataset. Our experimental results show that the proposed method improves collective activity recognition performance when compared to the state-of-the-art approaches. In trying to solve this problem, we realized that the existing datasets are insufficient for deep learning methods and have many limitations. Then we introduce the ”Collective Sports (C-Sports)” dataset, which is a novel benchmark dataset for multi-task recognition of both collective activity and sports categories. Various state-of-the-art techniques are evaluated on this dataset, together with a multi-task variant, which demonstrates increased performance. From the experimental results, we can say that while the sports action category recognition is relatively an easy task, there is still room for improvement for collective activity recognition, especially for the distant view situations. We believe that C-Sports dataset will stir further interest in this research direction. Our second proposed method involves an attention mechanism. We utilize the soft attention-based attention mechanism for action recognition and collective activity recognition tasks. We use attention maps that have high response values to regions that need attention in videos. We describe a method that using this attention mechanism with two distinct 3D-ConvNets architectures which are standard 3D-ConvNets (C3D) and inflated 3D-ConvNets (I3D). We evaluate our method on four benchmark datasets; two of them are about action recognition task, UCF101, and HMDB51. Others are related to collective activity recognition problem, Collective Activity Dataset, and Collective Sports Dataset. Experimental results show that the 3D attention-based ConvNets improves the performance on all datasets when compared to baselines which are 3D-ConvNets architectures without an attention mechanism. Our last proposed method of this thesis involves relation reasoning method and 3D attention mechanism. We propose a 3D Spatio-temporal relation network. We create this architecture by adding new methods step by step on the base Temporal Relation Network (TRN). First, a 2D attention mechanism has been added to TRN architecture. Then the 2D architecture is moved into 3D space. Finally, a 3D attention mechanism has been added on 3D TRN architecture. We evaluate these networks on one activity recognition and three collective activity recognition datasets, Something-Something v1, Collective Activity Recognition, Collective Sports, and Volleyball datasets, respectively. Our results show that the methods with attention mechanism improve the recognition performance. Besides, 3D networks obtain better accuracy when compared to 2D networks.", "source_url": "https:\/\/openalex.org\/W3138559006", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Hacettepe University Institutional Repository (hacettepe.edu.tr)", "source_record_id": "W3138559006", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 679, "title": "Robust and efficient deep learning methods for vision-based action recognition", "title_ar": "", "title_en": "Robust and efficient deep learning methods for vision-based action recognition", "author": "Yuecong Xu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; robotics; transfer-learning; rnn; image-classification; video-analysis", "keywords": "", "abstract": "Vision-based action recognition, which performs action recognition based solely on RGB frames, has received strong research interest thanks to its wide applications in various fields, e.g. surveillance, smart homes, and autonomous driving. Significant progress has been made in vision-based action recognition thanks to the development of recognition technologies, particularly deep learning methods which have proven their effectiveness in visual recognition tasks, such as image classification. Compared to static images, videos contain additional information due to the additional temporal dimension, which includes both temporal and spatiotemporal correlation features. Therefore, the key to robust and efficient action recognition lies in the effective and efficient utilization of the temporal and spatiotemporal correlation features embedded within videos. In this thesis, we first investigate extracting temporal features in a robust and efficient manner. While methods for extracting temporal features were proposed, these methods either require the computation or estimation of optical flow, which demand high computational power and large storage resource; or extracts only linear feature along the temporal dimension which results in inferior performances. To extract temporal features without the utilization of optical flow, we propose Attentive Correlated Temporal Feature (ACTF) which leverages inter-frame correlation feature and exploits both bilinear and linear correlations between successive frames on the regional level. By excluding optical flow estimation or calculation, ACTF can be combined with any spatial feature extraction network under the two-stream structure for end-to-end training. Meanwhile, capturing long-range spatiotemporal dependencies is an effective strategy for extracting spatiotemporal correlation features. Previous works have proposed methods utilizing either hand-crafted features or stacks of convolution or recurrent modules, both of which are computationally inefficient, and cause difficulty in network optimization. While the more recent non-local block inspired by the non-local means could extract long-range dependencies without affecting the networks' optimization, it significantly increases the parameter size and computational cost of the inserted networks. To extract robust long-range dependencies more efficiently, we explore on further improving the non-local neural network by proposing a novel long-range spatiotemporal dependencies extraction module, the Pyramid Non-Local (PNL) module. It extends the original non-local block by incorporating regional feature correlations at multiple scales. PNL upscales the effectiveness of the original non-local block by additionally addressing the spatiotemporal correlations between different regions, while improving the efficiency of the original non-local block with a significant decrease in computation cost. Besides the development of recognition technologies, the progress made in vision-based action recognition could also be attributed to the development of large-scale video datasets, which enable the effective training of deep learning models. However, it could be observed that the majority of current research focuses on videos in normal illumination, partly due to the fact that current benchmark datasets for vision-based action recognition are normally collected from web videos shot mostly under normal illumination. Yet, we argue that vision-based action recognition should not be constrained in normal illuminated videos. Vision-based action recognition in dark videos are also useful in various scenarios, e.g., night surveillance and self-driving at night. Such a task has rarely been researched, partly due to the lack of sufficient datasets for such a task. To this end, this thesis bridges the gap of the lack of data and pioneers vision-based action recognition in dark videos by collecting a novel dataset: the Action Recognition in the Dark (ARID) dataset. In this thesis, the ARID dataset is analyzed thoroughly with a comprehensive benchmark of current deep learning methods. Meanwhile, though the introduction of ARID pioneers vision-based action recognition in dark videos and bridges the gap between the absence of dark video datasets with the need for research, the scale of such a dataset is relatively small compared to current large-scale video datasets. One solution for training robust models for domains with less labeled data would be to transfer models learned in well-labeled domains. However, models trained in one domain would not generalize well in the other domain due to domain shift across domains, which is presented as distribution discrepancy between different domains. Domain adaptation (DA) approaches address domain shifts and enable networks to be applied to different scenarios.", "source_url": "https:\/\/openalex.org\/W3214258728", "pdf_url": "", "doi": "10.32657\/10356\/153169", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W3214258728", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 680, "title": "Leveraging deep learning for visual understanding of videos", "title_ar": "", "title_en": "Leveraging deep learning for visual understanding of videos", "author": "Clement Xian Ren Tan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; robotics; self-semi-supervised; predictive-analytics; transformers; multimodal; image-classification; object-detection; image-segmentation; video-analysis; llm", "keywords": "", "abstract": "In the field of Computer Vision (CV), the pursuit of human-level recognition and reasoning of visual scenes has been a long-standing aspiration. Over the past decade, significant contributions to the progress of CV have been made by deep learning, facilitated by the availability of big data and increased computational power. In CV, visual understanding encompasses not only the fundamental aspect of recognition, which involves identifying and categorizing objects and patterns within visual data, but also reasoning, which involves higher-level cognitive processes such as inferring relationships, predicting outcomes, and drawing meaningful conclusions based on the observed visual information. Human-like recognition capabilities across a variety of visual recognition tasks such as image classification, object detection, semantic segmentation, and instance segmentation, have been achieved by machines. The huge success of deep learning for visual recognition tasks has prompted researchers to tackle visual reasoning tasks, which are more challenging. The recognition and reasoning of objects within a scene is of paramount importance to applications such as human-robot collaborations and autonomous vehicles. This thesis aims at addressing the issues associated with current deep learning models for visual understanding and will focus on three tasks: (1) video object segmentation, (2) abductive action inference, and (3) action-conditioned scene graph prediction. Firstly, video object segmentation is an object tracking task that traditionally relies on supervised learning and necessitates extensive annotated datasets for deep learning model training. It is confronted with issues of repetitiveness and impracticality due to the manual labeling of these vast datasets. In addition, tracking objects within videos presents a unique challenge caused by appearance changes and occlusion across video frames. The work proposed in this thesis explores the potential of self-supervised learning for video object segmentation, aiming to harness freely available internet data such as YouTube videos for model training. Different from existing self-supervised learning approaches for video object segmentation that model pixel-to-pixel correspondence, this research shifts the focus towards modeling superpixel-to-superpixel correspondence. To achieve this, a novel approach which involves the tracking of superpixels between video frames through an attention mechanism trained within an end-to-end self-supervised framework is proposed. This approach aims to enhance the performance of existing self-supervised video object segmentation models by leveraging deep learning techniques and mitigating the limitations associated with supervised learning using large-scale annotated datasets. It also aims to enhance existing self-supervised models with the benefits of superpixels. Secondly, this thesis introduces the complex task of abductive action inference to assess the abductive reasoning abilities of deep learning models in comprehending visual scenes. A set of innovative object-relational models designed specifically for this task is presented and compared with existing state-of-the-art image, video, and vision-language models. These models are tasked with abducting the human-performed action that led to the scene depicted in an image or snapshot. The experiments demonstrate the potential of deep learning models in performing abductive action inference. This research is vital in advancing our understanding of the reasoning capacities of deep learning models, representing a significant stride toward attaining human-level reasoning capabilities in visual comprehension. Lastly, in action-conditioned scene graph prediction, the task involves predicting scene graph relations for a future state based on an initial state and an action. Instead of predicting visual representations for future states, each state is organized into a scene graph comprising human-object relationship triplets, thereby succinctly encapsulating the dynamics within the scene. Notably, this task remains underdeveloped in visual understanding research, with prior video scene graph generation approaches largely overlooking actions as a crucial signal. Actions applied to a preconditioned state generate an effect state embodying their anticipated outcomes. Human cognitive reasoning allows the inference of action consequences based on the initial scene context. To address this challenge, this thesis introduces the Action-conditioned Scene Graph dataset. Furthermore, this thesis proposes the AERT (Action-conditioned Effect Relational Transformer) model, designed to capture scene relations and action context to effectively predict future scene graph relations.", "source_url": "https:\/\/openalex.org\/W4402201451", "pdf_url": "", "doi": "10.32657\/10356\/179521", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4402201451", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 681, "title": "A deep learning approach for action classification in American football video sequences", "title_ar": "", "title_en": "A deep learning approach for action classification in American football video sequences", "author": "Jacob A. Westerberg", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; video-analysis", "keywords": "", "abstract": "The artificial intelligence is a constant topic of conversation with a field of research that is pushed forward by some of the world's largest companies and universities. Deep learning is a branch of machine learning within artificial intelligence based on learning representation of data such as images and texts by processing the data through deep neural networks. Sports are competitive businesses that over the years have become more data driven. Statistics play a big role in the development of the practitioners and the tactics in order to win. Sport organizations have big statistic teams since statistics are manually obtained by these teams. To learn a machine to recognize patterns and actions with deep learning would save a lot of time. In this thesis a deep learning approach is used to examine how well it can perform to classify the actions pass and run in American footbal lgames. A deep learning architecture is first trained and developed on a public video dataset and then trained to classify run and pass plays on a new American football dataset called the All-22 dataset. Results and earlier research show that deep learning has potential to automatize sport statistic but is not yet ready to overtake the role statistic teams have. Further research, bigger and more task specific datasets and more complex architectures are required to enhance the performance of this specific type of deep learning based video recognition.", "source_url": "https:\/\/openalex.org\/W2977364195", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2977364195", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 682, "title": "Action Recognition in Multi-view Videos", "title_ar": "", "title_en": "Action Recognition in Multi-view Videos", "author": "Dongang Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; image-classification; video-analysis", "keywords": "", "abstract": "A long-lasting goal in the field of artificial intelligence is to develop agents that can perceive and understand the rich visual world around us. With the improvement in deep learning and neural networks, many previous difficulties in the computer vision area have been resolved. For example, the accuracy in image classification has even exceeded human being in the ImageNet challenge. However, some issues are still attractive in the community, like action recognition and its application in multi-view videos. Based on a large number of previous works in the last few years, we propose a new Dividing and Aggregating Network (DA-Net) to address the problem of action recognition in multi-view videos in this thesis. First, the DA-Net can learn view-independent representations shared by all views at lower layers and learn one view-specific representation for each view at higher layers. We then train view-specific action classifiers based on the view-specific representation for each view and a view classifier based on the shared representation at lower layers. The view classifier is used to predict how likely each video belongs to each view. Finally, the predicted view probabilities from multiple views are used as the weights when fusing the prediction scores of view-specific action classifiers. We also propose a new approach based on the conditional random field (CRF) formulation to pass message among view-specific representations from different branches to help each other. Comprehensive experiments are conducted accordingly. The experiments on three benchmark datasets clearly demonstrate the effectiveness of our proposed DA-Net for multi-view action recognition. We also conduct the ablation study, which indicates the three modules we proposed can provide steady improvements to the prediction accuracy.", "source_url": "https:\/\/openalex.org\/W3003420344", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Sydney eScholarship Repository (The University of Sydney)", "source_record_id": "W3003420344", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 683, "title": "Deep learning for action recognition and prediction", "title_ar": "", "title_en": "Deep learning for action recognition and prediction", "author": "Qiuhong Ke", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Western Australia", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; rnn; video-analysis", "keywords": "", "abstract": "This thesis proposes novel deep leaning solutions for real-world applications, including action recognition and prediction from videos. The first part leverages Convolutional Neural Networks (CNN) to learn deep spatial-temporal information of skeleton sequences for action recognition. The second part presents three new methods based on CNN and Recurrent Neural Networks for action prediction. The last part of this thesis presents a new feature extraction method based on CNN for better person re­identification. Extensive experiments have shown the superiority of the proposed methods compared to state of-the-art methods for action recognition, action prediction and person re-identification.", "source_url": "https:\/\/openalex.org\/W2903904611", "pdf_url": "", "doi": "10.26182\/5b74d8e4281d1", "handle": "", "repository_name": "UWA Profiles and Research Repository (University of Western Australia)", "source_record_id": "W2903904611", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 684, "title": "A study on Human Emotion Recognition in Video Images using Deep Learning", "title_ar": "", "title_en": "A study on Human Emotion Recognition in Video Images using Deep Learning", "author": "Jargalsaikhan Orgil", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; computer-vision; nlp; reinforcement-learning; self-semi-supervised; image-classification; image-segmentation; video-analysis; sentiment-analysis; speech-recognition", "keywords": "", "abstract": "From the beginning of this century, Artificial Intelligence (AI) has evolved to handle problems in image recognition, classification, segmentation, etc. AI learning is categorized by supervised, semi-supervised, unsupervised or reinforcement learning. Some researchers have said that the future of AI is self­awareness, which is based on reinforcement learning by rewards based on task success. Moreover, it is said that the reward would be harvested from human reactions, specially emotion recognition. On the other hand, emotion recognition is a new inspiring field, but the lack of enough amount of data for training an AI system is the major problem. Fortunately, in the near future, it will be necessary to correctly recognize human emotions because image and video dataset availability is rapidly increasing. Emotions are mental reactions (such as anger, fear, etc.) marked by relatively strong feelings and usually causing physical reactions to previous actions in a short time duration focused on specific objects. In this Work, we are focusing on emotion recognition using face, body part, and intonation. As stated earlier, automatic understanding of human emotion in a wild setting using audiovisual signals is extremely challenging. Latent continuous dimensions can be used to accomplish the analysis of human emotional states, behaviors, and reactions displayed in real-world settings. Moreover, Valence and Arousal combinations constitute well-known and effective representations of emotions. In this thesis, a new Non-inertial loss function is proposed to train emotion recognition deep learning models. It is evaluated in wild settings using four types of candidate networks with different pipelines and sequence lengths. It is then compared to the Concordance Correlation Coefficient (CCC) and Mean Squared Error (MSE) losses commonly used for training. To prove its effectiveness on efficiency and stability in continuous or non-continuous input data, experiments were performed using the Aff-Wild dataset. Encouraging results were obtained. The contributions of the proposed method Non-Inertial loss function are as follows: 1.The new loss function allows for Valence and Arousal to be viewed together. 2.Ability to train on less data. 3.Better results. 4.Faster training times. The rest of this thesis explains our motivation, the proposed methods and finally presents our results.", "source_url": "https:\/\/openalex.org\/W7146545736", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7146545736", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 685, "title": "Learning temporal variations for action recognition", "title_ar": "", "title_en": "Learning temporal variations for action recognition", "author": "Qili Zeng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Boston University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; ai-applications; video-analysis; ai-industry", "keywords": "", "abstract": "As a core problem in video analysis, action recognition is of great significance for many higher-level tasks, both in research and industrial applications. With more and more video data being produced and shared daily, effective automatic action recognition methods are needed. Although, many deep-learning methods have been proposed to solve the problem, recent research reveals that single-stream, RGB-based networks are always outperformed by two-stream networks using both RGB and optical flow as inputs. This dependence on optical flow, which indicates a deficiency in learning motion, is present not only in 2D networks but also in 3D networks. This is somewhat surprising since 3D networks are explicitly designed for spatio-temporal learning. In this thesis, we assume that this deficiency is caused by difficulties associated with learning from videos exhibiting strong temporal variations, such as sudden motion, occlusions, acceleration, or deceleration. Temporal variations occur commonly in real-world videos and force a neural network to account for them, but often are not useful for recognizing actions at coarse granularity. We propose a Dynamic Equilibrium Module (DEM) for spatio-temporal learning through adaptive Eulerian motion manipulation. The proposed module can be inserted into existing networks with separate spatial and temporal convolutions, like the R(2+1)D model, to effectively handle temporal video variations and learn more robust spatio-temporal features. We demonstrate performance gains due to the use of DEM in the R(2+1)D model on miniKinetics, UCF-101, and HMDB-51 datasets.", "source_url": "https:\/\/openalex.org\/W3156070674", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OpenBU (Boston University)", "source_record_id": "W3156070674", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 686, "title": "Trustworthiness in Vertical Federated Learning: Privacy attacks, Robustness Vulnerabilities, and Secure Protocols", "title_ar": "", "title_en": "Trustworthiness in Vertical Federated Learning: Privacy attacks, Robustness Vulnerabilities, and Secure Protocols", "author": "Duanyi Yao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Vertical Federated Learning (VFL) enables organizations with distinct features of the same data samples to collaboratively train machine learning models without directly sharing raw data. While VFL offers strong potential for privacy-preserving analytics in domains such as finance and healthcare, its trustworthiness remains insufficiently understood. This thesis provides a systematic study of VFL trustworthiness along three dimensions: privacy, robustness, and secure protocol design. First, we investigate privacy vulnerabilities by introducing URVFL, an undetectable data reconstruction attack. By generating malicious gradients through a discriminator with auxiliary classifier, URVFL achieves state-of-the-art reconstruction fidelity while evading existing detection mechanisms, revealing the fragility of current defenses. Second, we analyze robustness vulnerabilities by examining adversarial example attacks in VFL inference. We formulate adaptive corruption of client-server channels as an online optimization problem and propose the E-TS algorithm, which leverages empirical Thompson sampling to identify optimal corruption patterns with provable regret bounds. Our results demonstrate that adaptive adversaries can severely compromise VFL robustness. Finally, we design FedVS, a synchronous split VFL protocol that simultaneously provides straggler resilience and information-theoretic privacy. Through coding-based secret sharing, FedVS losslessly aggregates embeddings even with stragglers while preventing leakage to curious servers and colluding clients. This thesis demonstrates that trustworthy VFL requires a holistic approach addressing privacy, robustness, and protocol-level vulnerabilities simultaneously. The proposed attacks expose critical weaknesses that must be addressed before large-scale deployment, while our secure protocol provides a constructive foundation for privacy-preserving collaborative learning. These contributions advance understanding of trustworthiness in distributed machine learning and establish principled design criteria for secure VFL systems.", "source_url": "https:\/\/openalex.org\/W7124459682", "pdf_url": "", "doi": "10.14711\/thesis-hdl167715", "handle": "", "repository_name": "", "source_record_id": "W7124459682", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 687, "title": "Security-Enhanced and Privacy-Preserving Federated Learning", "title_ar": "", "title_en": "Security-Enhanced and Privacy-Preserving Federated Learning", "author": "Tianyue Chu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "This thesis addresses two critical challenges in Federated Learning (FL): enhancing Byzantine robustness to thwart poisoning attacks and advancing privacy-preserving mechanisms to mitigate data leakage risks. To address the first challenge, we develop a robust FL aggregation scheme based on subjective logic and residual-based attack detection. This scheme retains historical data from previous rounds to build client reputations. Employing a combination of theoretical analysis, trace-driven simulation, and experimental validation with a prototype and real users, we show that our FL classifier can detect sensitive content with high accuracy, learn new labels rapidly, and remain robust against poisoning attacks from malicious users as well as imperfect input from non-malicious ones. Additionally, we identify a major weakness in FL—its vulnerability to poisoning attacks—as a consequence of the one person one vote (henceforth 1p1v) principle underlying most contemporary aggregation rules. To overcome this, we introduce FedQV, a novel aggregation algorithm built upon the quadratic voting scheme, recently proposed as a better alternative to 1p1v-based elections. Our theoretical analysis establishes that FedQV is a truthful mechanism in which bidding according to one’s true valuation is a dominant strategy that achieves a convergence rate matching that of state-of-the-art methods. Furthermore, our empirical analysis using multiple real-world datasets validates the superior performance of FedQV against poisoning attacks. It also shows that combining FedQV with unequal voting budgets according to a reputation score increases its performance benefits even further. FedQV can also be easily combined with Byzantine-robust privacy-preserving mechanisms to enhance its robustness against both poisoning and privacy attacks. To address the second challenge, we first characterize the privacy offered by pruning. We establish information-theoretic upper bounds on the information leakage from pruned FL and we experimentally validate them under state-of-the-art privacy attacks across different FL pruning schemes. Furthermore, we introduce Priprune, a privacy-aware algorithm for pruning in FL. Priprune uses defense pruning masks, which can be applied locally after any pruning algorithm, and adapts the defense pruning rate to jointly optimize privacy and accuracy. A key idea in the design of Priprune is pseudo-pruning: it undergoes defense pruning within the local model and only sends the pruned model to the server; while the weights pruned out by defense mask are withheld locally for future local training rather than being removed. We show that Priprune significantly improves the privacy-accuracy tradeoff compared to state-of-the-art pruned FL schemes. Addressing the critical challenges of robust aggregation and privacy-aware model training is crucial for integrating FL into practical scenarios. Through rigorous theoretical analysis and comprehensive empirical evaluations, this thesis demonstrates that the proposed methods significantly enhance FL’s robustness and privacy preservation. By providing innovative tools and strategies to overcome these obstacles, this thesis establishes FL as a secure and privacy-preserving learning paradigm, paving the way for its widespread adoption across diverse domains and applications.", "source_url": "https:\/\/openalex.org\/W7214864268", "pdf_url": "https:\/\/dspace.networks.imdea.org\/bitstream\/20.500.12761\/1957\/1\/PhD_Thesis_Tianyue_Chu.pdf", "doi": "", "handle": "", "repository_name": "IMDEA Networks Institute Digital Repository", "source_record_id": "W7214864268", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 688, "title": "Topology-dependent privacy risks in decentralized federated learning", "title_ar": "", "title_en": "Topology-dependent privacy risks in decentralized federated learning", "author": "JOSÉ INÁCIO ANTUNES DE GOUVEIA", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "federated-learning", "categories": "machine-learning; computer-vision; responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Federated Learning (FL) has established itself as a leading paradigm for collaborative machine learning, allowing participants to train models collectively without sharing their private data. Despite its privacy-preserving design, the periodic exchange of model updates leaves these systems vulnerable to information leakage. Notable threats include Membership Inference Attacks (MIA), which exploit model overfitting to determine if specific data samples were used during training, and Gradient Inversion Attacks (GIA), which attempt to reconstruct the exact training images from shared gradients. While existing literature has proposed various active defenses and investigated privacy risks within star and mesh network topologies, a systematic evaluation of how attack effectiveness evolves across training rounds over a diverse spectrum of network topologies remains a critical research gap. This dissertation presents a comprehensive empirical analysis of how different network topologies influence data privacy in centralized and decentralized FL systems over time. By isolating the network topology as the primary variable, we evaluate the vulnerability of six distinct topologies, star, tree, line, ring, full mesh, and partial mesh, against three MIA variants and a GIA. The experiments were conducted using the MNIST and CIFAR10 datasets under both Independent and Identically Distributed and Non-Independent and Identically Distributed (Non-IID) data distributions to capture the temporal evolution of these attacks across the training rounds. Our findings show that network topologies fundamentally dictate the severity and localization of privacy leakage. For instance, intermediate aggregation in the tree topology acts as a native privacy shield, effectively hiding memorized features from a central root node, though it inadvertently shifts the primary risk to intermediate edge nodes. Furthermore, the analysis reveals that extreme data heterogeneity (Non-IID) significantly aggravates vulnerabilities across all topologies, heavily increasing MIA and GIA success rates on complex visual tasks. Moreover, the results establish that restricting an adversary’s awareness of the broader network topology severely impedes their ability to accurately execute GIA, as successful data reconstruction depends heavily on precise knowledge of the aggregation phase in federated systems. Ultimately, this research highlights that network topology can be strategically leveraged as passive defense mechanisms.", "source_url": "https:\/\/openalex.org\/W7224053162", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7224053162", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 689, "title": "Federated Learning for Privacy-Enhanced Recommendations", "title_ar": "", "title_en": "Federated Learning for Privacy-Enhanced Recommendations", "author": "João Gaspar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "With the use of digital platforms increasing exponentially, the demand for personalized services emerges across a vast widespread of areas, from entertainment to finance. The adoption of these systems, regularly referred to as recommendation systems, may raise privacy concerns when implementations follow a centralized structure, approach that is commonly used. This dissertation focuses on exploring the validity of federated learning in the field of recommendation systems, specifically, in an e-commerce platform where the focus is privacy enhancement in addition to obtaining high recommendation accuracy. Within the scope of the Environment Adaptive Recommendation System project, an international researchdriven initiative focused on improving the current paradigm of recommendation systems, the present study will allow the integration of the developed methodologies, providing the project with a more strategically prepared foundation. After presenting the state of the art, the research firstly involves developing centralized models, followed by federated learning implementations to compare both approaches. In this stage, it is developed a solution that personalizes the recommendations of an e-commerce platform while the best practices are employed and different paradigms are considered. The study then evolves to the inclusion of privacy-enhancing mechanisms, to further study benefits and the situations their employment is relevant. The obtained results are then presented, regarding multiple topics such as the importance of the partition in achieving convergence, or the trade-off between privacy guarantees and computational cost or recommendation quality. By benefiting from the study of federated learning, this project aims to advance research in the context of recommendation systems, contributing to the enrichment of the existing literature and fostering scientific progress in this field.", "source_url": "https:\/\/openalex.org\/W7117342700", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7117342700", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 690, "title": "Privacy-preserving federated learning framework", "title_ar": "", "title_en": "Privacy-preserving federated learning framework", "author": "Wenxuan Huang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Southampton", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Federated Learning (FL) enables multiple parties to collaboratively train a shared model without exposing raw data. However, FL often suffers from degraded performance due to three core challenges: non-IID data distributions, residual privacy risks, and limited data volume per node. This work explores the privacy–performance trade-offs arising from these challenges and proposes a unified framework combining partial data sharing, anonymisation, and data augmentation. We first investigate how non-IID distributions impair standard FL convergence, and propose a Cluster-of-Trust (CoT) mechanism that groups mutually trusted clients for hierarchical aggregation. Experiments on the IoT-23 intrusion detection dataset show that CoT significantly improves model accuracy and convergence speed, closing most of the gap to centralised training. Next, we compare combinations of privacy-preserving techniques—including differential privacy, data anonymisation, and CoT-based grouping—highlighting how these impact model utility. Our results demonstrate that strong privacy guarantees can be achieved with only modest accuracy loss and reduced communication overhead. To address the limited data volume problem, we introduce data augmentation strategies and test their effectiveness in a privacy-sensitive Human Activity Recognition (HAR) task. We find that combining augmentation with trust-aware aggregation improves generalisation under strict privacy constraints. Across two domains—cybersecurity and HAR—we systematically evaluate this framework and demonstrate its effectiveness and generalisability. Our results show that strong privacy can be reconciled with competitive performance, offering practical insights for deploying FL in real-world, heterogeneous environments.", "source_url": "https:\/\/openalex.org\/W7140959527", "pdf_url": "https:\/\/eprints.soton.ac.uk\/510212\/1\/Privacy_Preserving_Federated_Learning_Framework_2_.pdf", "doi": "", "handle": "", "repository_name": "ePrints Soton (University of Southampton)", "source_record_id": "W7140959527", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 691, "title": "Federated Learning for Protecting Medical Data Privacy", "title_ar": "", "title_en": "Federated Learning for Protecting Medical Data Privacy", "author": "Abhishek Reddy Punreddy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "federated-learning", "categories": "machine-learning; deep-learning; nlp; medical-ai; responsible-ai; predictive-analytics; federated-learning; fairness", "keywords": "", "abstract": "Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access to medical data, these Deep learning techniques have yet to completely exploit medical data despite their immense potential benefits. Many data proprietors are unable to benefit from large-scale deep learning due to privacy and confidentiality concerns associated with data sharing. However, without access to sufficient data, Deep Learning will not be able to realize its maximum potential when transitioning from the research phase to clinical practice [2]. This project addresses this problem by implementing Federated Learning and Encrypted Computations on text data, such as Multi Party Computation. SyferText, a Python library for privacy-protected Natural Language Processing that leverages PySyft to conduct Federated Learning, is used in this context.", "source_url": "https:\/\/openalex.org\/W4380791856", "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=2277&context=etd_projects", "doi": "10.31979\/etd.cfgv-t6wa", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4380791856", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 692, "title": "Federated Learning for Data Privacy on Load Flexibility", "title_ar": "", "title_en": "Federated Learning for Data Privacy on Load Flexibility", "author": "Jude Mathew", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; ai-applications; federated-learning; fairness; ai-smart-cities", "keywords": "", "abstract": "Accurate production and consumption models are necessary for the development of new flexibility services in power systems. In addition to being customisable, these models must be able to manage the diversity of power providers and consumers while protecting the confidentiality of all parties involved. The use of distributed machine learning, more especially federated learning, to accomplish privacy-preserving learning of several production and consumption models are investigated in this thesis. The study comprises an extensive review of the literature on several federated learning methods and perform statistical analytics using selected federated learning model. It explores the nuances of federated learning methodologies, evaluating their suitability and efficacy within the framework of energy grid flexibility services. The thesis also addresses energy production and consumption datasets. Discover list of energy dataset that has privacy constraints. Examine the benefits and drawbacks of various federated learning models for handling confidential data of energy consumers and energy producers. The aim of this project is to inquire into the privacy-preserving technique applicable to the power management of the energy grid. To deal with the energy flexibility bottleneck, this project will serve as guidance for building privacy-preserving models of customized energy production\/consumption frameworks through the concept of federated learning.", "source_url": "https:\/\/openalex.org\/W7134023941", "pdf_url": "https:\/\/uu.diva-portal.org\/smash\/get\/diva2:2043088\/FULLTEXT01", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7134023941", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 693, "title": "ACHIEVING PARTICIPATIVE AND PRIVACY-PRESERVING FEDERATED LEARNING", "title_ar": "", "title_en": "ACHIEVING PARTICIPATIVE AND PRIVACY-PRESERVING FEDERATED LEARNING", "author": "Jianan Chen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "The proliferation of data-driven intelligence has reshaped the foundations of artificial intelligence and machine learning, enabling unprecedented collaboration across distributed entities. Yet, as organizations increasingly rely on shared computation and collective learning, concerns over privacy, fairness, and accountability have become central. Federated Learning (FL) has emerged as a promising paradigm that allows multiple institutions to collaboratively train models without disclosing raw data. However, despite its decentralized structure, FL is not inherently trustworthy. Rational participants may contribute unevenly or strategically, privacy guarantees often degrade model utility, and once data contributions are incorporated, their removal becomes nearly impossible. These challenges reveal that building trustworthy federated intelligence requires more than algorithmic optimizationit demands principled mechanisms that ensure participation, protect privacy, and enable verifiable unlearning. This dissertation addresses these intertwined challenges through a unified theoretical and algorithmic framework for participative, privacy-preserving, and accountable federated learning. The first part investigates participation incentives in cross-silo FL, where multiple organizations jointly train a global model. The interactions among organizations are modeled as a public goods game, and theoretical analysis proves that a social dilemma arises in which the Nash equilibrium fails to achieve maximum social welfare. To resolve this inefficiency, the Multi-player Multi-action Zero-Determinant (MMZD) strategy and its cooperative extension, the MMZD Alliance (MMZDA), are developed to maximize social welfare. These strategies enable each organization, or a coalition of them, to unilaterally control and enhance the global welfare without incurring additional cost. Experimental results verify that MMZD effectively achieves the theoretical social optimum, and that MMZDA further improves the maximum attainable welfare. The second part focuses on privacy preservation in Hierarchical Federated Learning (HFL), where a client-edge-cloud structure accelerates training and reduces communication cost but increases the risk of privacy leakage. To address this issue, a novel differential privacy definition tailored for HFL, Group Local Differential Privacy (GLDP), is proposed, along with the Sampling-Randomizing-Shuffling (SRS) mechanism to implement it. SRS combines randomized response and shuffling with a sampling process that achieves stronger privacy protection while adding less noise. Comprehensive theoretical analyses provide guarantees for both privacy and convergence, and experiments on real-world datasets demonstrate that SRS effectively balances privacy protection and model performance in HFL. The final part explores federated unlearning (FUI) under Differentially Private Federated Learning (DPFL), enabling the removal of a clients data influence while maintaining privacy guarantees. Unlike existing federated unlearning methods that fail under DP noise, the proposed Federated Unlearning with Indistinguishability (FUI) framework performs local model retraction followed by global noise calibration, yielding an unlearned model that is statistically indistinguishable from the retrained counterpart. A two-stage Stackelberg game is formulated to derive optimal unlearning strategies for both the server and the target client. Privacy and convergence analyses provide formal guarantees, and experiments on four benchmark datasets show that FUI achieves superior accuracy and efficiency compared with existing unlearning approaches. Extensive empirical evaluations on datasets such as Adult, Purchase, MNIST, and CIFAR10 demonstrate that the proposed mechanisms-MMZDA for incentive alignment, GLDP with SRS for hierarchical privacy preservation, and FUI for accountable deletionjointly enhance the reliability and practicality of federated learning. Together, these contributions establish a cohesive framework that advances the development of participative, privacy-preserving, and trustworthy federated intelligence.", "source_url": "https:\/\/openalex.org\/W7114782219", "pdf_url": "", "doi": "10.25394\/pgs.30804161", "handle": "", "repository_name": "Figshare", "source_record_id": "W7114782219", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 694, "title": "Privacy-Preserving Federated Learning With Backdoor Resilience", "title_ar": "", "title_en": "Privacy-Preserving Federated Learning With Backdoor Resilience", "author": "Yordanov Yordan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Karlsruhe Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; ai-applications; data-mining; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Federated learning (FL) has the ability to train a global model across many different clients with diverse datasets while also preserving privacy. However, federated learning is by design vulnerable to privacy inference attacks and poisoning attacks, allowing compromised clients to infer private information about the clients or negatively influence the global model, respectively. FLAME [26], a state-of-the-art framework, is designed to statistically remove the influence of poisoning attacks, while being applicable to many attacker models and keeping the model’s benign performance. To ensure these objectives, the FLAME protocol introduces a defense framework that estimates the minimum sufficient amount of noise to be injected into the global model after aggregation, so that backdoors are eliminated but the benign performance does not deteriorate. To further reduce the amount of noise and enhance the desired goals, FLAME utilizes adaptive clustering and weight clipping. However, federated learning systems that implement FLAME still face significant privacy risks from inference attacks, where malicious aggregators can exploit access to model updates to extract sensitive information about client data. Therefore, it is imperative to also achieve malicious security. In this thesis, we propose an implementation of FLAME in combination with secure Multi- Party Computation (MPC), which provides the primitives to protect federated learning against inference attacks. We propose two implementations of this combination: private FLAME- a full-MPC implementation, running the entire FLAME protocol in MPC; and leaky FLAME, which selectively reveals intermediate statistics to gain efficiency while still constraining attack surface. The implementation takes advantage of FLAME’s backdoor resilience and MPC’s secret shared processing, also achieving malicious security. To achieve this combination, we use MP-SPDZ [20], a framework with 30 variants of MPC protocols and a Python-based programming interface, which simplifies the comparison of different protocols and security models. Building on these designs, our evaluation quantifies the computational and communication overheads of both modes with varying client counts and model sizes. We find that the dominant cost arises from the pairwise cosine-similarity in the clustering step, whereas clipping and noising are comparatively lightweight. Although leaky FLAME’s savings are noticeable even more so in the bigger cases, they are dominated by the cosine similarity computation, thus in our opinion not worth the security-efficiency trade-off. Despite this, our implementation achieves practical runtimes for moderate scales, keeping in mind that it provides enforcement of FLAME’s defenses under malicious-security settings. Taken together, our results demonstrate that integrating MPC with FLAME is feasible to an extent and effective for privacy-preserving, backdoor-resilient FL.", "source_url": "https:\/\/openalex.org\/W7126172090", "pdf_url": "https:\/\/publikationen.bibliothek.kit.edu\/1000190049\/174144704", "doi": "10.5445\/ir\/1000190049", "handle": "", "repository_name": "Repository KITopen (Karlsruhe Institute of Technology)", "source_record_id": "W7126172090", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 695, "title": "Designing for Privacy in Tabular Federated Learning", "title_ar": "", "title_en": "Designing for Privacy in Tabular Federated Learning", "author": "Ivo Osterberg Nilsson ; Maximilian Birr Engvall", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Jönköping University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; predictive-analytics; transformers; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "In federated learning (FL), private data leakage through gradient inversion depends strongly on design choices and data properties. We study this risk under an honest-but-curious server threat model, with a focus on clinical tabular data from MIMIC-IV and complementary benchmark tasks for binary classification, multiclass classification, and regression. Unlike image reconstruction, tabular reconstruction cannot be judged well by visual inspection. Evaluation must account for numerical tolerances, categorical exact matches, baseline recoverability, and the difference between feature level recovery and exact row recovery. We evaluate FedSGD gradients and FedAvg model deltas using an exposure aligned training protocol, where attacked models are compared after matched client data exposure rather than matched communication rounds. We compare MLP, ResNet, and FT-Transformer models, and isolate architecture effects further through an MLP grid over width, depth, activation, normalization, and dropout. The results show that local aggregation and client data exposure are among the strongest determinants of tabular gradient leakage. Small client batches and updates representing few distinct records are most vulnerable, while stronger local aggregation and greater client data exposure usually reduce reconstruction in ways that vary across architectures and datasets. FT-Transformer is consistently harder to invert than the MLP and ResNet baselines, suggesting that embedded categorical representations change the geometry of tabular gradient inversion. At the same time, the MLP architecture grid shows that reconstructability also changes substantially within a single model family, so architecture affects privacy beyond the transformer comparison alone. Aggregate reconstruction accuracy can overstate record recovery, especially on MIMIC-IV, where sparse and repeated clinical features produce a strong client marginal prior. Strict exact match results show that complete row recovery is concentrated in the most exposed one-hot-encoded settings, while FT-Transformer can have nonzero feature recovery without exact row recovery. Overall, the findings show that gradient inversion risk in tabular FL is shaped by model architecture, FL training configuration, data exposure, and input data properties.", "source_url": "https:\/\/openalex.org\/W7165261472", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Jonkoping University Library (Jönköping University)", "source_record_id": "W7165261472", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 696, "title": "Privacy vs Accuracy: Trade-offs in Federated Learning", "title_ar": "", "title_en": "Privacy vs Accuracy: Trade-offs in Federated Learning", "author": "Muhammad Ahmed", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "In federated learning, model updates from distributed sources are vulnerable to inference attacks that can reconstruct private training data from gradient information.Traditional approaches to this problem apply differential privacy uniformly across all model parameters, creating a significant trade-off where stronger privacy guarantees substantially degrade model utility.This thesis investigates how differential privacy can be selectively applied to gradient components to enhance privacy protection while maintaining model performance.We introduce selective gradient perturbation, a percentile-based approach that applies noise only to the most significant gradient components by magnitude.Through comprehensive experiments on the CIFAR-10 dataset using a simulated federated learning environment with 10 clients, we demonstrate that selective perturbation fundamentally transforms the conventional privacy-utility trade-off.Our findings reveal that by applying noise to only the top 10% of gradients, we achieve 74.38% accuracy while maintaining privacy protection, and counter-intuitively, stronger privacy settings (ε = 0.1) yielded better model performance (78.60% accuracy) than weaker protection (ε = 0.5, 66.56% accuracy).These results challenge the conventional wisdom that privacy necessarily comes at a significant cost to utility, offering a practical approach for implementing differential privacy in federated learning that achieves both strong privacy guarantees and high model utility.", "source_url": "https:\/\/openalex.org\/W7132924904", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/226252\/2\/AhmedMuhammad.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7132924904", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 697, "title": "On the limits of privacy in federated learning : personalization and adversarial analysis", "title_ar": "", "title_en": "On the limits of privacy in federated learning : personalization and adversarial analysis", "author": "Sargam Gupta", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; predictive-analytics; transformers; face-recognition; federated-learning; fairness; ai-cybersecurity; ai-industry; ai-smart-cities", "keywords": "", "abstract": "With the rapid adoption of Artificial Intelligence across diverse applications, concerns around data privacy have become increasingly prominent. Traditional centralized machine learning approaches require aggregating large volumes of user data at a central server, posing risks related to privacy, scalability, and regulatory compliance. Federated Learning has emerged as a decentralized alternative, enabling clients to train models locally while only sharing model updates with a central server. It addresses some of the concerns by enabling multiple participants to collaboratively train a shared model while keeping their raw data locally stored. However, data locality alone does not guarantee privacy. Information about local training records may still be revealed through model parameters, gradients, updates, or predictions.This makes FL vulnerable to privacy attacks such as model poisoning, data reconstruction and membership inference attacks. This thesis investigates these limitations in FL through two complementary perspectives: adversarial evaluation of the framework and personalization of privacy protection. In this thesis, we first examine the validity of various data anonymization techniques for facial images through the re-identification attacks. A weight-optimized record linkage method is developed to determine whether anonymized image records can be linked to their corresponding original records. The evaluation considers different adversarial settings, including cases in which an attacker possesses only partial information. The findings demonstrate that visually modifying an image or separating identity-related information does not necessarily prevent re-identification. We also experiment with Shapley values to check whether they could be useful in detecting model poisoning attacks.Then, we develop two privacy-enhancing mechanisms for federated time-series forecasting. The first framework is centered around using differential privacy with transformers specifically for traffic flow data whereas the other framework is a microaggregation-based model agnostic k-anonymous federated forecasting framework. The results demonstrate that useful forecasting performance can be maintained while raw data remain decentralized and additional protection is applied at the client level. One of the major challenges that exist in FL is heterogeneity. There have been several studies in the literature on data and device heterogeneity in FL. However, the heterogeneity that exists in the privacy requirements of different clients is widely unexplored. Building on these findings, this thesis introduces a personalized privacy-preserving federated learning framework that allows individual clients to select their preferred privacy mechanism and protection level. The proposed FedPUP framework supports the simultaneous use of differential privacy and k-anonymity within a federated round. We also used Membership Inference attacks to empirically evaluate the information leakage associated with different privacy choices and to identify combinations of ε and k that provide comparable levels of protection. We also propose a privacy-aware server-side aggregation algorithm that incorporates client-level privacy characteristics during the aggregation process in each training round. Our empirical contributions are also complemented by an exploratory study investigating the practical requirements for deploying privacy-preserving federated learning systems in the industry. Our study highlights the importance of transparency, accountability, trust, costs, and privacy–utility trade-offs. Overall, this thesis demonstrates that federated learning should not be considered inherently private merely because data remain decentralized. By combining adversarial evaluation with personalized privacy-preserving mechanisms, it provides a more comprehensive understanding of privacy risks and safeguards in FL. The findings offer practical guidance for developing federated learning systems that are more privacy-aware, adaptable, and suitable for real-world deployment.", "source_url": "https:\/\/openalex.org\/W7214512574", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7214512574", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 698, "title": "Privacy-preserving Case-based Explanations for Federated Learning Models", "title_ar": "", "title_en": "", "author": "Inês de Sousa Cardoso", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "pt", "research_domain": "computer-vision", "research_subdomain": "federated-learning", "categories": "computer-vision; responsible-ai; xai; federated-learning; fairness", "keywords": "", "abstract": "A crescente utilização de inteligência artificial (IA) no diagnóstico médico tem vindo a transformar a prática clínica, permitindo maior eficiência e precisão.Contudo, para que as pessoas confiem em sistemas baseados em IA, é fundamental garantir a interpretabilidade destes sistemas através de, por exemplo, explicações baseadas em casos, que justificam decisões recorrendo a exemplos semelhantes.Tradicionalmente, este tipo de explicações era construído a partir de conjuntos de dados centralizados.No entanto, devido a novas leis e questões de privacidade, a partilha de dados médicos entre instituições torna-se limitada ou mesmo inviável, reduzindo a diversidade dos dados de treino e, consequentemente, comprometendo a capacidade de generalização e robustez dos modelos de IA.A Aprendizagem Federada (Federated Learning, FL) surgiu como uma resposta a este desafio, permitindo treinar modelos de forma colaborativa sem necessidade de centralizar dados sensíveis.Ainda assim, a geração de explicações baseadas em casos em cenários de FL continua a ser difícil: como os dados dos pacientes não podem ser partilhados, o acesso direto a exemplos representativos fica condicionado.Uma alternativa é recorrer a imagens sintéticas geradas localmente nos clientes, mas esta solução também acarreta riscos de privacidade, dado que os modelos generativos podem memorizar e reproduzir informação identificável dos pacientes (PII, Personally Identifiable Information).Neste contexto, esta dissertação explora como o machine unlearning (MU) -um conjunto de técnicas destinadas a remover a influência de dados específicos dos modelos -pode ser aplicado a modelos generativos baseados em difusão, por forma a remover PII memorizada de pacientes, e consequentemente, gerar imagens sintéticas anónimas mas clinicamente relevantes.Esta dissertação, para além de ser o primeiro trabalho a abordar a anonimização de imagens médicas sintéticas através de machine unlearning, e um dos poucos a explorar o data-point unlearning em modelos de generativos, apresenta dois contributos principais: em primeiro lugar, propõe uma nova técnica de unlearning adaptada a modelos de difusão Image-to-Image; em segundo lugar, introduz uma pipeline para identificar de forma sistemática conjuntos de pacientes memorizados em modelos de difusão latente e reduzir a sua influência, garantindo simultaneamente a conformidade com as normas de privacidade e a preservação da utilidade clínica das imagens geradas.Embora as abordagens propostas não tenham conseguido esquecer por completo todos os pacientes identificados, os resultados demonstram que o unlearning pode reduzir substancialmente os riscos de reidentificação em conjuntos de dados sintéticos, preservando em parte a fidelidade diagnóstica das explicações baseadas em casos.Estes resultados reforçam a necessidade de desenvolver novas técnicas de data-point unlearning adaptadas especificamente a modelos de difusão image-to-image, de modo a melhorar tanto a proteção da privacidade como a usabilidade clínica.Em suma, esta dissertação contribui para aproximar as áreas da interpretabilidade em IA e da proteção de dados na síntese de imagens médicas, através da aplicação de machine unlearning em modelos de difusão.Tal permite abrir novas perspetivas para a investigação deste tipo de técnicas em modelos generativos e para a implementação de sistemas de aprendizagem federada em saúde, promovendo uma adoção de IA mais ética, segura e confiável em contextos clínicos.", "source_url": "https:\/\/openalex.org\/W7112640851", "pdf_url": "https:\/\/repositorio-aberto.up.pt\/bitstream\/10216\/169618\/2\/742974.pdf", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7112640851", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 699, "title": "Privacy-preserving federated learning: Strengthening data protection and secure collaboration in healthcare", "title_ar": "", "title_en": "Privacy-preserving federated learning: Strengthening data protection and secure collaboration in healthcare", "author": "Bjarne Pfitzner", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Potsdam", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; computer-vision; medical-ai; responsible-ai; ai-applications; predictive-analytics; medical-imaging; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Machine learning (ML) has been an integral part of large-scale data analysis in healthcare research and is being used for tasks such as disease classification, pattern recognition, or synthetic data generation. ML models learn the values of numerous parameters that allow them to capture patterns and relationships within a training dataset. It is essential to validate medical ML models in multi-centre studies, which test their performance on a diverse and heterogeneous patient population, proving a good generalisability and the absence of biases. However, due to its sensitive nature, sharing and centrally aggregating medical data is strictly regulated, for instance under the European General Data Protection Regulation (GDPR). It often requires complex pseudonymisation or anonymisation steps, which introduce a procedural overhead and may require removing relevant patient information in favour of privacy preservation. Federated learning (FL) addresses the challenge of sensitive data sharing by decentralising the training step of ML models, thereby removing the need to transmit data off-site. Training is organised in a client-server architecture, where data owners take on the role of clients, and a so-called parameter server is responsible for maintaining the latest global model, facilitating the training procedure, and aggregating local training progress. In multiple training rounds, the clients receive the model from the parameter server and train it on their local data, thereby tuning the model parameters. These alterations to the model are then transmitted back to the parameter server, where parameter updates from multiple clients are averaged and applied to the global model. Therefore, sensitive data never leaves the site where it was collected. While the FL approach protects data privacy to some extent, it also introduces new vulnerabilities as a consequence of the client-server architecture where information is transferred over the internet. It was shown that an analysis of the model weight information sent by data owners can allow inferences about the underlying training data. Therefore, truly secure FL algorithms must include additional defensive mechanisms to prevent these kinds of attacks. In this thesis, we investigate two aspects of FL: how to protect the FL participants’ data privacy against a malicious server and how to protect the model’s performance against malicious training participants. First, we focus on differential privacy (DP) as a privacy-preserving technique which relies on the addition of noise into the ML model at the time of training, thereby obfuscating the impact any one data record or FL participant has on the model. We show and discuss the risk of reconstruction attacks, which rebuild training data from model parameter information, and highlight the effectiveness of DP against them, using lung illness detection from chest X-ray images as an example. Then, having established the need for adopting DP, we analyse the negative impact this technique can have on the model performance. Specifically, we consider a small group of data owners who possess small, non-independent and non-identically distributed datasets and assess the fairness of the trained model in terms of its utility for each training participant. The field of application for this part of the thesis is the prediction of complications after visceral surgery. Finally, we present a method for reducing the negative impact of DP on the utility of variational autoencoder models used for synthetic data generation. Aside from securing data privacy, reaching a well-performing model after training is essential to a successful FL application. Therefore, we considered the risk of encountering a poisoning attack targeted at reducing the model utility, where malicious participants submit model parameters specifically designed to reduce the model performance. Here, we introduce a novel, fully decentralised FL method that does not rely on a central parameter server for facilitating the training and aggregating the participants‘ model weights. Instead, we propose to use decentralised ledger technologies to synchronise the joint training effort and store training progress. We show that our approach inherently protects benign training contributors from being affected by certain poisoning attacks from malicious actors. In summary, this thesis highlights the necessity of defensive measures when FL is being applied in practice, especially in areas with highly sensitive data such as healthcare. We introduce several analyses and methods for FL with DP and fully decentralised FL, paving the way for future research on privacy-preserving and secure FL.", "source_url": "https:\/\/openalex.org\/W7115905020", "pdf_url": "https:\/\/publishup.uni-potsdam.de\/files\/69273\/pfitzner_diss.pdf", "doi": "10.25932\/publishup-69273", "handle": "", "repository_name": "publish.UP (University of Potsdam)", "source_record_id": "W7115905020", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 700, "title": "Aggregation Techniques in Federated Learning Under Differential Privacy Constraints", "title_ar": "", "title_en": "Aggregation Techniques in Federated Learning Under Differential Privacy Constraints", "author": "Ivana Dasovic", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Machine learning (ML) is increasingly integrated into critical domains such as healthcare, finance, and mobile applications, where data privacy presents significant challenges. Federated Learning (FL) enables model training on decentralized data without requiring raw data to be centrally collected. Having multiple different clients increases the attack surface and the number of attack vectors, especially when some clients have weaker privacy. As a result, privacy incidents are typically limited to individual clients rather than compromising the entire dataset.However, FL alone does not fully guarantee privacy, as model updates can still reveal sensitive information through inference or reconstruction attacks, and further measures are necessary to protect sensitive data during training. Differential Privacy (DP) has emerged as one of the most practical techniques to provide formal privacy guarantees, and it can be integrated with FL models to enhance privacy protection. Yet, using DP typically involves a trade-off, leading to reduced model effectiveness. Moreover, aggregation methods and optimization strategies significantly affect how local model updates are combined, affecting both privacy and performance outcomes.This thesis investigates novel views on how aggregation strategies, privacy budgets, and non-independent and identically distributed (non-IID) data interact in differentially private federated learning, shedding light on their combined effect on the performance-privacy trade-off. Instead of analyzing these factors independently, the study offers new perspectives on how aggregation methods interact with privacy mechanisms and data heterogeneity in decentralized environments. Through comprehensive evaluation and analysis, the thesis provides practical guidelines for designing differentially private federated learning models applicable to real-world scenarios. The findings contribute to advancing privacy-aware ML practices, balancing data protection with model utility in decentralized learning environments.", "source_url": "https:\/\/openalex.org\/W7140325958", "pdf_url": "https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/227193\/1\/Dasovic%20Ivana%20-%202026%20-%20Aggregation%20Techniques%20in%20Federated%20Learning%20Under...pdf", "doi": "10.34726\/hss.2026.131806", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W7140325958", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 701, "title": "Mitigating Poisoning Threats and Their Privacy Implications in Federated Learning", "title_ar": "", "title_en": "Mitigating Poisoning Threats and Their Privacy Implications in Federated Learning", "author": "Chamara Sandeepa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University College Dublin", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "gan", "categories": "machine-learning; generative-ai; medical-ai; responsible-ai; ai-applications; data-mining; gan; xai; federated-learning; fairness; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "Advancements in Machine Learning (ML) and distributed intelligence have accelerated the adoption of Federated Learning (FL) as a key enabler of privacy-preserving applications in domains such as IoT, healthcare, and Beyond 5G networks. FL enables collaborative model training without centralising data, but its distributed and heterogeneous nature introduces new security and privacy challenges. In addition to conventional adversarial threats, FL faces unique vulnerabilities including poisoning attacks, and privacy leakage through model updates via threats like inference attacks. Addressing these challenges is critical for ensuring trust in next-generation distributed AI systems. This thesis investigates such threats in FL with a particular emphasis on poisoning attacks and the interplay between poisoning and privacy leakage. The work also investigates how poisoning attacks can be mitigated, essentially preventing any privacy vulnerabilities induced via poisoning. To this end, four major contributions are presented: Rec-Def, SHERPA, X-SHIELD, and ROAM. First, Rec-Def is proposed as a defence against GAN-based inference attacks, highlighting the connection between poisoning and privacy risks. Second, SHERPA introduces an interpretable aggregation algorithm that employs Explainable AI (XAI) technique called SHAP feature attributions to detect poisoned model updates, improving the transparency and reliability of poisoning detection. Third, X-SHIELD extends this approach to hierarchical FL, combining explanation-guided detection with scalable clustering to address robustness in large-scale FL deployments. Finally, the thesis explores the extension of defences and its limitations on fully Decentralised FL (DFL) architectures. For this, the thesis proposes an offensive use of XAI via an new stealthy poisoning attack called ROAM, based on Layerwise Relevance Propagation (LRP) and designs a countermeasure that perturbs sensitive neurons to mitigate privacy leakage via poisoning. The effectiveness of the proposed approaches is validated through extensive experiments across multiple datasets, learning scenarios, and adversarial settings. Results demonstrate that the solutions enhance robustness and privacy while remaining scalable and adaptable to diverse FL environments. Overall, this thesis contributes to advancing the safety and trustworthiness of FL by investigating novel vulnerabilities introducing and solutions that balance privacy preservation, poisoning robustness, and justifiability. These contributions provide both theoretical insights and design mechanisms for deploying FL securely in critical infrastructures such as 6G networks, IoT ecosystems, and industrial applications.", "source_url": "https:\/\/openalex.org\/W7165993495", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Repository UCD (University College Dublin)", "source_record_id": "W7165993495", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 702, "title": "The Heterogeneity and Privacy in Cross-Silo Federated Learning", "title_ar": "", "title_en": "The Heterogeneity and Privacy in Cross-Silo Federated Learning", "author": "Hou, Xiangjian", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Arab Emirates", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "In addressing the twin challenges of data heterogeneity and privacy in Federated Learning (FL), this thesis introduces a dual-faceted approach that not only pioneers advancements in FL methodologies but also contributes significantly to the discourse on privacy preserving techniques within the domain. Our research is divided into two primary foci: improving FL performance amid data heterogeneity through a novel Federated Learning with Partially Personalized (FedPP) method and optimizing privacy-preserving mechanisms to balance efficacy and confidentiality in FL implementations. The first segment of our investigation unveils the FedPP method, an innovative approach designed to mitigate the limitations imposed by the heterogeneity of the data in FL. By integrating the Learning Rate Adjustment (LoRA) technique, FedPP not only surpasses the performance benchmarks of centralized training models, but also sets a new standard for personalized federated learning. This methodological advancement underscores the potential of partial personalization in bridging the performance gap often witnessed in conventional FL settings, thus enhancing model accuracy and efficiency across diverse data distributions. At the same time, our exploration extends into the realm of differential privacy (DP), where we posit the existence of an optimal interplay between the number of local updates and communication rounds, a crucial balance to maximize convergence performance within a predefined privacy budget. Through rigorous theoretical analysis, we delineate the optimal configurations for local steps and communication rounds that significantly improve the convergence bounds of the DP-enhanced ScaffNew algorithm, particularly in the landscape of strongly convex optimization problems. Our theoretical propositions are further corroborated by empirical evidence, establishing a direct correlation between these optimal configurations and various critical parameters, including the DP privacy budget.", "source_url": "https:\/\/openalex.org\/W7106319431", "pdf_url": "", "doi": "10.82419\/201", "handle": "", "repository_name": "MBZUAI iRep", "source_record_id": "W7106319431", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 704, "title": "Selection principles for federated learning in privacy-sensitive settings", "title_ar": "", "title_en": "Selection principles for federated learning in privacy-sensitive settings", "author": "Anastasia Pustozerova", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "Machine learning nowadays plays an important role in decision making in various industries, especially with the rapid development of technologies and data gathered in mobile computing systems, cloud computing, and the Internet of Things. However, the data used for training machine learning models usually has private nature. With increasing concerns of data privacy, the issue of making use of the data while preserving users privacy is getting more critical. Federated learning is an approach which allows using machine learning on distributed data while the data owner can keep the data on his\/her own side. The main idea of federated learning is to train machine learning models locally at each data owners place and to aggregate only the model from each participant of the training to a globally shared model. This greatly reduces the amount of information needed to be exchanged, and thus reduces the attack surface for an adversary. However, models that are exchanged during the federated learning process can still leak information about their training data. In this work, we evaluate privacy risks in federated learning by performing privacy attacks, e.g. membership inference attacks, in different federated learning settings. We design empirical evaluation of the success of attacks and mitigation strategies, aiming for a trade-off between privacy and effectiveness of the models.", "source_url": "https:\/\/openalex.org\/W3139023368", "pdf_url": "", "doi": "10.34726\/hss.2020.67043", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W3139023368", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 705, "title": "Privacy and Security Enhanced Federated Learning Framework Design", "title_ar": "", "title_en": "Privacy and Security Enhanced Federated Learning Framework Design", "author": "Yasaman Pakdel", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; medical-ai; responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "Federated learning, while promising for its ability to train models across decentralized data sources, presents both security challenges and solutions.Its security depends on how well these challenges are addressed.On one hand, federated learning can be secure when proper privacy-preserving techniques are employed, such as encryption, secure aggregation, and diferential privacy, to protect data and model updates.Participant authentication, secure communication, and compliance with data protection regulations further contribute to its security.However, federated learning may also be vulnerable if not carefully implemented, potentially exposing sensitive data or risking model integrity.By encrypting and processing data using Crypten, federated learning participants can collaborate on model training without exposing their raw data, ensuring data privacy and confdentiality.Crypten's features, such as secure aggregation and encrypted computation, further bolster the trustworthiness of federated learning, making it an indispensable tool for applications in healthcare, fnance, and other sectors where sensitive data protection is paramount.In an era of growing data privacy concerns, Crypten empowers federated learning to thrive in a secure and privacy-preserving manner.In this project, we embarked on a comprehensive exploration of machine learning methodologies, starting with traditional plaintext machine learning techniques.Subsequently, we", "source_url": "https:\/\/openalex.org\/W4400228779", "pdf_url": "http:\/\/scholarworks.calstate.edu\/downloads\/70795g815", "doi": "10.46569\/9s161f47k", "handle": "", "repository_name": "", "source_record_id": "W4400228779", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 706, "title": "Towards Robust and Privacy-Preserving Federated Learning", "title_ar": "", "title_en": "Towards Robust and Privacy-Preserving Federated Learning", "author": "Sana Awan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Kansas", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; predictive-analytics; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Machine Learning (ML) has revolutionized various fields, from disease prediction to credit risk evaluation, by harnessing abundant data scattered across diverse sources. However, transporting data to a trusted server for centralized ML model training is not only costly but also raises privacy concerns, particularly with legislative standards like Health Insurance Portability and Accountability Act (HIPAA) in place. In response to these challenges, Federated Learning (FL) has emerged as a promising solution. FL involves training a collaborative model across a network of clients, each retaining its private data. By conducting training locally on the participating clients, this approach eliminates the need to transfer entire training datasets while harnessing their computation capabilities. However, FL introduces unique privacy risks, security concerns, and robustness challenges. Firstly, due to the statistical and system heterogeneity within FL, substantial differences exist between the distribution of each local dataset and the global distribution, causing clients’ local objectives to deviate greatly from the global optima, resulting in a drift in local updates. Secondly, FL is susceptible to malicious actors who may tamper with local data, manipulate the local training process, or intercept the shared model or gradients to implant backdoors that affect the robustness of the joint model. Addressing such vulnerabilities and challenges is crucial before deploying FL systems in critical infrastructures. In this dissertation, we present a multi-pronged approach to address the privacy, security, and robustness challenges in FL. This involves designing innovative robust aggregation schemes and privacy protection mechanisms to counter attacks during the training process. We studied the challenges in FL due to heterogeneous data distributions and found that existing FL algorithms often suffer from slow and unstable convergence and are vulnerable to poisoning attacks, particularly in extreme non-independent and identically distributed (non-IID) settings. We proposed a robust aggregation scheme, named CONTRA, to mitigate data poisoning attacks and ensure an accuracy guarantee even under attack. This defense strategy identifies malicious clients by evaluating the cosine similarity of their gradient contributions and subsequently removes them from FL training. Finally, we introduced FL-GMM, an algorithm designed to tackle data heterogeneity while prioritizing privacy. It iteratively constructs a personalized classifier for each client while aligning local-global feature representations. By aligning local distributions with global semantic information, FL-GMM minimizes the impact of data diversity. Moreover, FL-GMM enhances security by transmitting derived model parameters via secure multiparty computation, thereby avoiding vulnerabilities to reconstruction attacks observed in other approaches. To address the privacy risk due to model or gradient interception, we present the design of a reliable and accountable blockchain-enabled Privacy-Preserving Federated Learning (PPFL) framework that leverages homomorphic encryption to protect individual client updates. The blockchain is adopted to support the provenance of model updates during training so that malformed or malicious updates can be identified and traced back to the source.", "source_url": "https:\/\/openalex.org\/W7155578449", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KU ScholarWorks (The University of Kansas)", "source_record_id": "W7155578449", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 707, "title": "Studies on biomolecular structure learning and federated privacy protection", "title_ar": "", "title_en": "Studies on biomolecular structure learning and federated privacy protection", "author": "Hanlin Gu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "federated-learning", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; federated-learning; fairness; ai-smart-cities", "keywords": "", "abstract": "This thesis mainly focuses on two topics: 1) elucidating the structure and dynamics of biological macromolecules, 2) privacy protection of federated learning. Understanding the structure and dynamics of biological molecules is a central pillar of molecular biology. A series of machine learning tools are developed in simulation or experimental data. In particular, one part of the work involves better utilization of Cryo-EM images obtained experimentally. A robust denoising approach is proposed in Cryo-EM images to help structure reconstruction. Then a two-stage classification scheme is presented that could accurately classify the denoised Cryo-EM images from multiple conformations, which allows inferring the underlying free energy landscape. In addition, a kinetic algorithm based on projection operator framework via deep learning is designed to assign numerous microstates into a handful of metastable states. It is helpful to understand the conformational dynamics of complex biomolecules from the simulation perspective. Another work focuses on federated privacy protection. Federated deep learning aims to preserve users' data privacy by decentralizing data from the central server to end devices. However, adversaries may still infer the private training data from the released model and updated gradients. A privacy-preserving Federated Deep Learning with Private Passport framework is proposed without sacrificing the model performance and computation efficiency.", "source_url": "https:\/\/openalex.org\/W4386966456", "pdf_url": "", "doi": "10.14711\/thesis-991013080354203412", "handle": "", "repository_name": "", "source_record_id": "W4386966456", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 708, "title": "Privacy-Preserved Federated Learning : A survey of applicable machine learning algorithms in a federated environment", "title_ar": "", "title_en": "Privacy-Preserved Federated Learning : A survey of applicable machine learning algorithms in a federated environment", "author": "Carlsson, Robert", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Uppsala University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; predictive-analytics; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "There is a potential in the field of medicine and finance of doing collaborative machine learning. These areas gather data which can be used for developing machine learning models that could predict all from sickness in patients to acts of economical crime like fraud. The problem that exists is that the data collected is mostly of confidential nature and should be handled with precaution. This makes the standard way of doing machine learning - gather data at one centralized server - unwanted to achieve. The safety of the data have to be taken into account. In this project we will explore the Federated learning approach of ”bringing the code to the data, instead of data to the code”. It is a decentralized way of doing machine learning where models are trained on connected devices and data is never shared. Keeping the data privacypreserved.", "source_url": "https:\/\/openalex.org\/W3109451708", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Uppsala University Publications (Uppsala University)", "source_record_id": "W3109451708", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 709, "title": "Privacy-preserving detection of poisoning attacks in federated learning", "title_ar": "", "title_en": "Privacy-preserving detection of poisoning attacks in federated learning", "author": "Trent Muhr", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "With federated learning, local learners train a shared global model using their own data, and report model updates to a server to aggregate and then update the global model. Such a learning paradigm may suffer from two attacks: privacy attacks by the untrusted server; adversarial attacks (e.g., poisoning attacks) by malicious learners. There is extensive research on addressing each of the attacks separately, but there is no scheme that can address both of them. In this paper, we propose a scheme that enables both privacy-preserving aggregation and poisoning attack detection at the server, by utilizing additive homomorphic encryption and a trusted execution environment (TEE). Our evaluation based on an implemented prototype system demonstrates that our scheme can attain a similar level of detection accuracy as the state-of-the-art poisoning detection scheme, and that the increased computational workload can be parallelized and mostly executed outside of the TEE. A privacy analysis shows that the proposed scheme can protect individual learners' model updates from being exposed.", "source_url": "https:\/\/openalex.org\/W4410025063", "pdf_url": "", "doi": "10.31274\/td-20250502-190", "handle": "", "repository_name": "", "source_record_id": "W4410025063", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 710, "title": "Performance analysis of deep learning approaches for pneumonia diagnosis using chest x-ray images", "title_ar": "", "title_en": "Performance analysis of deep learning approaches for pneumonia diagnosis using chest x-ray images", "author": "Çağlar Çiltaş", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging", "keywords": "", "abstract": "Diagnosis of pneumonia from Chest X-Ray images holds a significant place in clinical practice due to its low cost and widespread availability. However, achieving high performance with deep learning-based methods generally requires a large amount of labeled data, which constitutes a significant limitation in medical image analysis. In this thesis, the performance of deep learning and traditional machine learning approaches for diagnosing pneumonia from Chest X-Ray images under conditions of low-label data is comprehensively evaluated. For this purpose, a Hybrid Prototype Fusion Network (HPFN) developed based on the ConvNeXt-Tiny architecture is proposed, and deep feature representation and prototype-based similarity calculation are combined in a single decision mechanism. Furthermore, the proposed model is compared with Xception, VGG16, and ResNet50 architectures, and traditional approaches consisting of Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting classifiers using SIFT, KAZE, and HOG features are also analyzed on the same dataset. All experiments were performed using the Chest X-Ray (Pneumonia) dataset, and the models were evaluated using accuracy, precision, sensitivity, and F1-score measures. Experimental results showed that the proposed HPFN model achieved 96.38% accuracy, 96.23% precision, 97.95% sensitivity, and 97.08% F1-score, outperforming both the compared deep learning architectures and traditional feature-based methods. Literature comparisons also revealed that the proposed approach is an effective computer-aided diagnostic method providing reliable, stable, and high-accuracy data under low-label data conditions.", "source_url": "https:\/\/openalex.org\/W7168706197", "pdf_url": "https:\/\/thesis.rawdatalibrary.net\/thesis\/gogus-rontgeni-goruntulerinden-zaturre-tanisinda-derin-ogrenme-tabanli\/pdf?lang=en", "doi": "10.71008\/ebyu.thesis.2026.196", "handle": "", "repository_name": "", "source_record_id": "W7168706197", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 711, "title": "CNN-Based Deep Learning Models for Automated Tuberculosis Detection from Chest X-rays", "title_ar": "", "title_en": "CNN-Based Deep Learning Models for Automated Tuberculosis Detection from Chest X-rays", "author": "Pooja Santhosh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "National College of Ireland", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging", "keywords": "", "abstract": "Tuberculosis (TB) remains one of the leading health concerns of the world especially in the low-resource regions where radiologists who can help one with diagnosis are scarce. Although chest X-ray imaging is prevailing examinations used in the screening of TB, it relies strongly on interpretation of experts and may prejudice performance and delay in areas that are under-privileged. Luckily, due to the advent of artificial intelligence, methods of deep learning, and Convolutional Neural Networks (CNNs) have a very high potential of automating the process of analysing medical images. The purpose of this project is to carry out the comparison of the performance of the three most popular and popular pretrained CNN architectures ResNet18, DenseNet121, and EfficientNet-B0 to see which model is more suitable in the problem of detecting TB based on a chest X-ray. We already have a publicly available dataset in Kaggle and will fine-tune the models by using transfer learning. We will use a stratified data split by training, validation and test sets to balance the set classes. All models are going to be tested in PyTorch with standard metrics such as accuracy, precision, accuracy and F1-score. The findings of the given comparison will illuminate on the trade-off between accuracy and computational efficiency of the models.", "source_url": "https:\/\/openalex.org\/W7204614449", "pdf_url": "https:\/\/norma.ncirl.ie\/9663\/2\/poojasanthoshconfigurationmanual.pdf", "doi": "", "handle": "", "repository_name": "TRAP@NCI (National College of Ireland)", "source_record_id": "W7204614449", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 712, "title": "Deep Learning-Based Medical Image Analysis for Enhanced Diagnosis and Severity Assessment of Viral Pneumonia", "title_ar": "", "title_en": "Deep Learning-Based Medical Image Analysis for Enhanced Diagnosis and Severity Assessment of Viral Pneumonia", "author": "Enshaei, Nastaran", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Concordia University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; image-segmentation; medical-imaging", "keywords": "", "abstract": "Viral pneumonia remains a critical global health concern due to its potential for rapid transmission and widespread outbreaks, as highlighted by the COVID-19 pandemic. Timely and accurate diagnosis, severity assessment, and prognosis are essential for effective patient care and public health management. Chest imaging modalities such as chest X-rays (CXRs) and computed tomography (CT) scans play a central role in diagnosing and managing viral pneumonia. However, interpreting these images is often resource-intensive and subject to inter-observer variability, especially during surges in clinical demand. Deep learning (DL) holds promise for automating image analysis, detecting subtle radiologic features, and enhancing clinical decision-making. Nevertheless, real-world deployment faces persistent challenges, including limited access to large-scale well-annotated datasets, domain shifts, and the heterogeneous presentation of disease. To address these challenges, this thesis advances DL-based medical image analysis for the diagnosis and severity assessment of viral pneumonia, with a specific focus on COVID-19. Key contributions include: (i) Employing game-theoretic data valuation (Data Shapley) to identify mislabeled samples in chest image training datasets; (ii) Introducing a domain-adaptive strategy guided by model prediction confidence to improve the generalization of DL segmentation models; (iii) Developing robust DL frameworks for precise segmentation of COVID-19 lesions in chest CT scans, accounting for their diverse visual characteristics and spatial variability; and (iv) Designing a DL-based diagnostic and severity scoring system for early-stage COVID-19 using baseline CXRs, with performance comparable to expert radiologists. Together, these contributions advance DL-assisted tools for the diagnosis and severity assessment of viral pneumonia in clinical practice.", "source_url": "https:\/\/openalex.org\/W7112797530", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Spectrum Research Repository (Concordia University)", "source_record_id": "W7112797530", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 713, "title": "Analisis Kerentanan Model CNN terhadap Serangan PGD pada Klasifikasi Citra X-ray COVID-19", "title_ar": "", "title_en": "Analisis Kerentanan Model CNN terhadap Serangan PGD pada Klasifikasi Citra X-ray COVID-19", "author": "Elga Putri Tri Farma", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Maine at Machias", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; medical-imaging; ai-cybersecurity", "keywords": "", "abstract": "Advances in deep learning have revolutionized lung disease diagnosis through chest X-ray (CXR) images, particularly since the COVID-19 pandemic. Convolutional Neural Network (CNN) architectures such as DenseNet-121 demonstrate high accuracy in distinguishing viral pneumonia, lung opacity, and normal cases. However, recent studies reveal that these models possess fundamental weaknesses against iteratively designed adversarial attacks. This study aims to analyze the robustness of the DenseNet-121 model against the Projected Gradient Descent (PGD) attack in a four-class classification task on COVID-19 chest X-ray images. The methodology involves training the model using transfer learning on the COVID-19 Radiography Dataset (21,165 images), followed by testing with PGD attacks at various epsilon levels (0.1\/255 to 0.3\/255). The results show that at epsilon 0.1\/255, global model accuracy drops drastically from 95.42% to 25.32%, with an Attack Success Rate (ASR) reaching 74.68%. The most severe degradation occurs in the Lung Opacity class (F1-score drop of 88.90%) and the COVID-19 class (F1-score drop of 82.99%). Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations reveal the phenomenon of semantic decoupling, where the model's attention shifts from diseased lung regions toward background artifacts. In conclusion, the iterative nature of PGD attacks effectively exploits gaps in high-dimensional decision manifolds, rendering unprotected CNN models unsuitable for direct deployment in attack-prone clinical environments.", "source_url": "https:\/\/openalex.org\/W7219288436", "pdf_url": "https:\/\/eprints.umm.ac.id\/id\/eprint\/34043\/1\/PENDAHULUAN.pdf", "doi": "", "handle": "", "repository_name": "UMM Institutional Repository (University of Maine at Machias)", "source_record_id": "W7219288436", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 714, "title": "Analisis Kerentanan Model CNN terhadap Serangan FGSM pada Klasifikasi Citra X-ray COVID-19", "title_ar": "", "title_en": "Analisis Kerentanan Model CNN terhadap Serangan FGSM pada Klasifikasi Citra X-ray COVID-19", "author": "Muhammad Hisyam Kamil", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Maine at Machias", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; medical-imaging; ai-cybersecurity", "keywords": "", "abstract": "The advancement of deep learning, particularly Convolutional Neural Network (CNN) architectures, has brought significant changes in the automated diagnosis of lung diseases through chest X-ray (CXR) images, including pneumonia and COVID-19. However, despite achieving high diagnostic precision, CNN models remain highly susceptible to adversarial attacks, defined as small, visually imperceptible alterations that cause high-confidence mispredictions. This study aims to analyze the vulnerability of the DenseNet-121 model to Fast Gradient Sign Method (FGSM) attacks in classifying COVID-19 chest X-ray images. The research method involves training the DenseNet-121 model using the COVID-19 Radiography Dataset consisting of 21,165 images with four diagnostic categories: COVID-19, Lung Opacity, Normal, and Viral Pneumonia. The model was then tested on clean data and FGSM-perturbed data with varying epsilon values. The results show that at epsilon 0.1\/255, the global model accuracy dropped drastically from 95.42% to 59.28%, with an Attack Success Rate (ASR) of 40.72%. Class-wise analysis shows that the Lung Opacity class experienced the highest F1-Score degradation of 51.13%, followed by COVID-19 at 48.83%. Gradient-weighted Class Activation Mapping (Grad-CAM) visualization confirms that this performance degradation is associated with a shift in the model's attention from clinically relevant pulmonary regions toward background noise (semantic decoupling). This study concludes that high accuracy on clean data does not guarantee model robustness against adversarial attacks, necessitating the integration of defense mechanisms such as adversarial training to improve model safety in clinical deployment.", "source_url": "https:\/\/openalex.org\/W7219437683", "pdf_url": "https:\/\/eprints.umm.ac.id\/id\/eprint\/34046\/3\/BAB%20II.pdf", "doi": "", "handle": "", "repository_name": "UMM Institutional Repository (University of Maine at Machias)", "source_record_id": "W7219437683", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 715, "title": "Deep Learning-Based Automated Pneumonia Detection from Chest X-Rays: A Comparative Study of Custom CNN and Transfer Learning Architectures", "title_ar": "", "title_en": "Deep Learning-Based Automated Pneumonia Detection from Chest X-Rays: A Comparative Study of Custom CNN and Transfer Learning Architectures", "author": "Ahmed Sajim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Southern Mississippi", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging", "keywords": "", "abstract": "Pneumonia is a leading global cause of mortality, claiming approximately 2.5 million lives an-nually and placing exceptional diagnostic pressure on radiologists in resource-limited settings. Manual interpretation of chest X-ray (CXR) images is time-consuming, subject to inter-observer variability, and limited by radiologist availability. This thesis presents a systematic investiga-tion into deep learning-based automated pneumonia detection comparing five convolutional neural network (CNN) architectures: a custom-designed 2D CNN and four pretrained transfer learning models—ResNet, DenseNet, MobileNet, and VGG19. A targeted data augmentation pipeline addresses the severe class imbalance in the Kag-gle Chest X-Ray Pneumonia dataset, expanding the Normal class from 1,583 to 9,495 images using clinically motivated transformations. The proposed Custom CNN incorporates progres-sive multi-scale filters (128→256→512), bottleneck 1×1 convolutions, and calibrated dropout regularization—all tailored for pneumonia-specific feature patterns. Evaluated on 2,066 held-out test images, the Custom CNN achieved the highest test accuracy of 97.5%, with 0.97 precision and recall across both classes and only 52 total misclassifications—outperforming all four transfer learning baselines. These results demonstrate that domain-specific architectural design combined with principled data balancing can surpass generic pretrained models for specialized medical imag-ing tasks on limited datasets.", "source_url": "https:\/\/openalex.org\/W7160676225", "pdf_url": "https:\/\/aquila.usm.edu\/cgi\/viewcontent.cgi?article=2101&context=honors_theses", "doi": "", "handle": "", "repository_name": "Aquila Digital Community (University of Southern Mississippi)", "source_record_id": "W7160676225", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 716, "title": "Design and evaluation of an automated pneumonia detection system using chest X-ray images: a multi-model comparative study", "title_ar": "", "title_en": "Design and evaluation of an automated pneumonia detection system using chest X-ray images: a multi-model comparative study", "author": "Mingxu Lyu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; predictive-analytics; cnn; medical-imaging; xai", "keywords": "", "abstract": "The difference between pneumonia and COVID-19 in chest radiographic images remains a clinical challenge due to their similar radiographic features. Although deep learning has shown potential in automated diagnosis, most existing studies focus on single models and lack systematic architectural comparisons. In this study, multiple deep convolutional neural networks are evaluated on public chest X-ray datasets for binary pneumonia and multi-class COVID-19 classification tasks. The results indicate that different models show different strengths, with some achieving higher sensitivity and others better specificity. To address these limitations, an ensemble method is proposed to combine the advantages of multiple models. In addition, gradient-based visualization is applied to interpret the model predictions, and the highlighted regions are generally consistent with areas of clinical significance. In general, this work provides a practical framework for model comparison and offers an interpretable approach to potential clinical screening applications.", "source_url": "https:\/\/openalex.org\/W7154546169", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W7154546169", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 717, "title": "Automated Generation of X-Ray Reports by Machine Learning Approach", "title_ar": "", "title_en": "Automated Generation of X-Ray Reports by Machine Learning Approach", "author": "Aigerim Bauyrzhanova", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nazarbayev University", "university_source": "repository_host", "college": "", "department": "", "country": "Kazakhstan", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transformers; medical-imaging", "keywords": "", "abstract": "Medical reports are essential for conveying conclusions and diagnoses from medical scans, such as X-ray scans, in order to proceed with the patient’s treatment. Especially lung X-rays, which are used in diagnosing and treating diseases such as pneumonia, pulmonary diseases, COVID-19, lung cancer, etc. The traditional way of writing medical reports for such X-ray images is to write them manually, by specialists. This is a time-consuming, hard-to-keep-up with the increasing demand, and error-prone process since it is done by the specialists themselves after taking the scans. This prompts to make the process more automated so as to save time and lessen the probability of a mistake in diagnosis. Therefore, automating the process of writing the medical reports from chest X-ray images has become a critical need in the field of radiology as nowadays there are a lot of detrimental and even fatal diseases of the lungs that need to be treated as soon as possible. For this purpose, the machine learning approach using encoder-decoder transformers and vision transformers is a potential candidate for getting numeric results of an effective model to generate X-ray reports on two datasets: IU-Xray and MIMIC-CXR", "source_url": "https:\/\/openalex.org\/W7198807364", "pdf_url": "https:\/\/nur.nu.edu.kz\/bitstreams\/6cc809dc-79d0-4ea3-a8fe-b30bff270ac5\/download", "doi": "", "handle": "", "repository_name": "Nazarbayev University Repository (Nazarbayev University)", "source_record_id": "W7198807364", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 718, "title": "Improving Model Generalization of Pneumonia Detection from Chest Xray Images Using Deep Learning and Transfer Learning", "title_ar": "", "title_en": "Improving Model Generalization of Pneumonia Detection from Chest Xray Images Using Deep Learning and Transfer Learning", "author": "Akhil Kochamparampil Anil", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Wales Trinity Saint David", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; medical-imaging", "keywords": "", "abstract": "Pneumonia continues to be a major global health threat and one of the leading causes of mortality, with chest X-rays serving as the primary diagnostic tool. Despite their promise, deep learning models for pneumonia detection often face limitations in generalization, performing strongly on familiar datasets but losing accuracy when applied to unseen data from different clinical environments. This study addresses that challenge by applying transfer learning with ResNet152 and DenseNet201 to improve generalization in pneumonia detection using chest Xray images. Publicly available datasets, including the COVID-19 Radiography Database and the Chest X-ray (COVID-19 & Pneumonia) dataset, were merged, pre-processed, and balanced prior to training. Model optimization involved freezing and unfreezing layers, adjusting learning rates, and applying various data augmentation strategies. Performance was evaluated with metrics such as accuracy, precision, recall, F1-score, error rate, and AUC-ROC, and further validated using an independent external dataset. Experimental results showed that DenseNet201 consistently outperformed ResNet152, reaching 93.2% accuracy with an AUC of 0.9916 on the main dataset and 88.3% accuracy with an AUC of 0.9742 on the external dataset. By comparison, ResNet152 achieved 86.4% accuracy (AUC 0.9665) on the main dataset and 84.6% accuracy (AUC 0.9573) on the external dataset. Confusion matrix analysis revealed that most errors occurred between Normal and Pneumonia classes, mirroring real world diagnostic challenges. In conclusion, DenseNet201 demonstrated superior robustness and generalization compared to ResNet152, highlighting its suitability for clinical application and underscoring the necessity of external validation in medical AI research.", "source_url": "https:\/\/openalex.org\/W7135203014", "pdf_url": "https:\/\/repository.uwtsd.ac.uk\/id\/eprint\/4143\/1\/Anil_Kochamparampil_A_MSc_Thesis.pdf", "doi": "10.82227\/repository.uwtsd.ac.uk.00004143", "handle": "", "repository_name": "University Of Wales Trinity Saint David Research Repository (University of Wales Trinity Saint David)", "source_record_id": "W7135203014", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 719, "title": "Screening Chest X-rays for Covid-19 with Deep Learning", "title_ar": "", "title_en": "", "author": "Eric Robert Gill", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Oberta de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "ca", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; object-detection; medical-imaging", "keywords": "", "abstract": "El nou Coronavirus ha provocat una pandèmia global amb alts costos econòmics i socials. La naturalesa emergent de la malaltia associada del Coronavirus, Covid-19, ha trobat moltes nacions mal preparades per controlar la seva propagació, resultant en altes taxes d'infecció i pressió en els sistemes de salut. La detecció ràpida i la subsegüent quarantena de persones infectades és la mesura més eficaç contra la propagació del virus amb l'excepció de la vacunació. Generalment, el diagnòstic es porta a terme amb tests de Polímer invertit o antígens que poden ser cars i no sempre fàcilment disponibles. Aquests mètodes requereixen personal especialitzat i contacte físic amb el pacient, així com temps per processar els resultats. Les màquines de raigs X estan disponibles en hospitals arreu del món en països de gairebé totes les situacions econòmiques. La radiografia s'ha utilitzat en molts casos d'ús de cribatge i diagnòstic i hi ha una investigació àmplia sobre la seva aplicabilitat a la pandèmia de Coronavirus. Aquest projecte investiga la viabilitat de l'ús de Xarxes Neurals Convolucionals per detector símptomes de Covid-19 en imatges radiogràfiques de pulmons. Els rajos X són massivament disponibles, econòmics i no invasius. Els resultats mostren que les Xarxes Neurals Convolucionals poden classificar les imatges de raigs X en classes de Covid, Normal i Pneumonia Viral amb alts nivells de Precisió i Sensitivitat.", "source_url": "https:\/\/openalex.org\/W3173179115", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Oberta in Open Access (Universitat Oberta de Catalunya)", "source_record_id": "W3173179115", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 720, "title": "Machine Learning Algorithms for Computer-aided Diagnosis of Myocardial Infarction from 2D Echocardiography and COVID-19 from Chest X-rays", "title_ar": "", "title_en": "Machine Learning Algorithms for Computer-aided Diagnosis of Myocardial Infarction from 2D Echocardiography and COVID-19 from Chest X-rays", "author": "Aysen Degerli Ahishali", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; image-segmentation; medical-imaging; ai-cybersecurity", "keywords": "", "abstract": "The importance of e-health has drastically increased as the world has engaged sharply in the digital world, which provoked computer-aided diagnosis (CAD) algorithms to become a necessity for accurate and automatic detection and differential diagnosis of various diseases and medical conditions. Recent advancements in machine learning brought novel solutions for more complex clinical tasks that improved medical diagnostics. Accordingly, machine and deep learning methods are widely utilized in CAD-based techniques, which analyze medical data by performing pattern recognition to reveal any anomalies to guide medical doctors and experts in clinical tasks. However, in the development phase of CAD algorithms, there are several limitations and challenges regarding the size, quality, and availability of medical data for analysis using machine learning. Hence, there is an urgent need for accurate, reliable, and robust CAD algorithms to overcome the limitations and challenges in medical diagnostics. This thesis focuses on providing machine learning algorithms for CAD-based detection and assessment of myocardial infarction (MI) and coronavirus disease 2019 (COVID-19) using apical 4-chamber (A4C) and apical 2-chamber (A2C) view 2D echocardiography recordings and chest X-ray (CXR) images, respectively. The hypothesis defined in this thesis is that the proposed CAD algorithms using machine learning can provide assistive and visualization tools in clinical practice for the detection and assessment of MI and COVID-19. Accordingly, the thesis contributions are categorized into computer-aided diagnosis of MI and COVID-19, where task-specific solutions are developed for the assessments. The most severe manifestation of coronary artery disease is myocardial infarction, which is colloquially known as heart attack that leads to irreversible necrosis of the myocardium and even death. The detection of MI requires the assessment of regional wall motion abnormality of the left ventricle that can be primarily monitored and evaluated with 2D echocardiography. This thesis contributes to a pioneer 2D echocardiography dataset, HMC-QU for the detection of MI over single-view and multi-view echocardiography. Accordingly, another contribution of this thesis is a generic pseudo-labeling annotation approach that accelerates and enhances pixel-level manual annotations by preventing the subjective and time-consuming annotation process. In single-view and multi-view echocardiography assessments, this thesis contributes to a feature engineering phase, where myocardial segment features are extracted and used to train several traditional machine learning models and one-class classification methods reaching the highest sensitivity levels of 88.75% in single-view and 90.91% in multi-view echocardiography. The assessment of pulmonary regions plays a vital role in COVID-19 clinical diagnosis and prevention of contamination to the public. This thesis contributes to the composition of the pioneer and the largest CXR dataset, called QaTa-COV19, for the recognition, early detection, and pneumonia segmentation of COVID-19. Accordingly, another contribution of this thesis is the human-machine collaboration approach for the annotation of COVID-19 pneumonia regions for the segmentation. The thesis contributes to several machine learning models and solutions to perform a reliable detection and localization of COVID-19 pneumonia in the lungs using segmentation neural networks, direct detection from a proposed operational neural network, and representation-based classification models. The findings of this thesis are promising in computer-aided diagnosis of MI and COVID-19, where the proposed solutions reached high sensitivity levels of 90.9% and 98.6%, respectively preventing subjective and expert-dependent assessments and detection in 2D echocardiography and CXR imaging. The last contribution of this thesis is the developed visualization and assistive tools, where color-coded trackings on echocardiography recordings, myocardial infarction displacement curves, COVID-19 infection maps, and machine learning-based detection and annotation tools serve medical doctors in medical diagnostics addressing visual enhancements over 2D echocardiography and CXR imaging.", "source_url": "https:\/\/openalex.org\/W7133005963", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133005963", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 721, "title": "Deep Learning For The Classification of Lung Diseases Using Chest X-Rays", "title_ar": "", "title_en": "Deep Learning For The Classification of Lung Diseases Using Chest X-Rays", "author": "Khamesi, Zahra", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Concordia University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; medical-imaging", "keywords": "", "abstract": "The discovery of X-rays marked a significant milestone in the field of medicine. One of the most common types of X-rays, the chest X-ray (CXR), allows doctors to examine an individual’s internal structure without surgery. Over the years, deep learning methods and algorithms have been developed to automate lung disease detection and identification. This paper introduces RADIA, a project that combines multiple deep learning techniques to identify abnormal areas and abnormal- ities in chest X-rays. RADIA builds upon previous studies conducted by the Stanford ML group, such as ChexNet and ChexPert. Our team utilized the ConvNeXt-Large, a deep learning convo- lutional model, implemented with a pre-trained ConvNext algorithm on the ImageNet database to classify various pathologies from public datasets like ChestX-ray14, CheXpert, MIMIC-CXR, PadChest, and VinDr-CXR, as well as a private dataset obtained from the Picture Archiving Com- munication System (PACS) at Verdun and Notre Dame Hospitals in Montreal in the collaboration with CIUSSS (Centre Integre Universitaire de Sante et de Services Sociaux du Centre-Sud-de-l’Ile- de-Montreal) and valuable consultants from the radiology team at Notre Dame Hospital contributed to the project’s success. Our team employed image enhancement and augmentation techniques to create various image versions. We used different and novel approaches to address the challenges, and the results were evaluated using metrics such as AUC, F1, and G-Means to analyze performance with imbalanced input data. It is essential to note that the project’s development extends beyond the creation of a web tool based on deep learning techniques. Our future plans involve building a decision helper that combines inference models and web tools to assist healthcare professionals.", "source_url": "https:\/\/openalex.org\/W7112380474", "pdf_url": "https:\/\/spectrum.library.concordia.ca\/id\/eprint\/993141\/7\/Khamesi_MCompSc_S2024.pdf", "doi": "", "handle": "", "repository_name": "Spectrum Research Repository (Concordia University)", "source_record_id": "W7112380474", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 723, "title": "Using convolutional neural networks to detect COVID-19 in chest X-rays", "title_ar": "", "title_en": "", "author": "Ευαγγελία - Μαρία Σπύρου", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Piraeus", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2024, "language": "el", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "Το κίνητρο πίσω από αυτή την εργασία πηγάζει από τις αξιοσημείωτες προόδους που σημειώθηκαν στον τομέα της επεξεργασίας και αναγνώρισης εικόνων με τη χρήση αλγορίθμων βαθιάς μάθησης τα τελευταία χρόνια, καθώς και από τις βαθιές επιπτώσεις της πανδημίας του Covid-19. Η ταχεία εξάπλωση του ιού δημιούργησε την επείγουσα ανάγκη ενίσχυσης του τομέα της υγειονομικής περίθαλψης με τεχνολογίες αιχμής ώστε να βελτιωθεί η ιατρική περίθαλψη των ασθενών. Η εκθετική ανάπτυξη της έρευνας στον τομέα της επεξεργασίας και αναγνώρισης εικόνων έχει ανοίξει νέους δρόμους για την αξιοποίηση εξελιγμένων τεχνικών στην ιατρική διάγνωση. Ειδικότερα, η ταξινόμηση εικόνας έχει αναδειχθεί σε πολύτιμο εργαλείο στην ακτινολογία για να βοηθήσει τους γιατρούς να εντοπίσουν ανωμαλίες σε ακτινογραφίες. Αξιοποιώντας τη δύναμη της βαθιάς μάθησης και των συνελικτικών νευρωνικών δικτύων, επιδιώκουμε να αξιοποιήσουμε αυτές τις εξελίξεις για την ανάπτυξη αποτελεσματικών μοντέλων για την ανίχνευση του Covid-19. Η παρούσα διπλωματική έχει ως σκοπό τη διερεύνηση της αποτελεσματικότητας των τεχνικών βαθιάς μηχανικής μάθησης στην ανάλυση εικόνων ακτινογραφίας θώρακα και πιο συγκεκριμένα για να προσδιοριστεί εάν ένας ασθενής πάσχει ή έπασχε από Covid-19 ή όχι. Για την εκπόνηση αυτής της εργασίας έγινε χρήση ενός μεγάλου συνόλου δεδομένων από ακτινογραφίες του θώρακα που συλλέχθηκαν από ασθενείς που διαγνώστηκαν είτε θετικά είτε αρνητικά στο Covid-19. Χρησιμοποιήθηκαν μοντέλα βαθιάς μάθησης, για την εξαγωγή ουσιαστικών χαρακτηριστικών και συμπερασμάτων από τις εικόνες και την ταξινόμησή τους σε κατηγορίες θετικών ή αρνητικών. Τα αναμενόμενα αποτελέσματα της παρούσας εργασίας σκοπό έχουν να εξετάσουν ποσό κοντά μπορούμε να φτάσουμε στη σωστή διάγνωση με την βοήθεια της τεχνητής νοημοσύνης . Ωστόσο , σε καμία περίπτωση δεν μπορεί να παρακάμψει έναν επιστήμονα υγείας.", "source_url": "https:\/\/openalex.org\/W7122354249", "pdf_url": "https:\/\/dione.lib.unipi.gr\/xmlui\/bitstream\/unipi\/18741\/1\/Spyrou_ME2056.pdf", "doi": "", "handle": "", "repository_name": "Dione (University of Piraeus)", "source_record_id": "W7122354249", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 724, "title": "Detecting Coronavirus Disease 2019 Pneumonia in Chest X-Ray Images Using Deep Learning", "title_ar": "", "title_en": "Detecting Coronavirus Disease 2019 Pneumonia in Chest X-Ray Images Using Deep Learning", "author": "Ziqi Zhu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; predictive-analytics; medical-imaging", "keywords": "", "abstract": "The coronavirus disease 2019 (COVID-19) pandemic has already become a global threat. To fight against COVID-19, effective and fast screening methods are needed. This study focuses on leveraging deep learning techniques to automatically detect COVID‐19 pneumonia in chest X-ray images. Two models are trained based on transfer learning and residual neural network. The first one is a binary classifier that separates COVID-19 pneumonia and non-COVID-19 cases. It classifies all test cases correctly. The second one is a four-class classifier that distinguishes COVID-19 pneumonia, viral pneumonia, bacterial pneumonia and normal cases. It reaches an average accuracy, precision, sensitivity, specificity, and F1-score of 93\\\\%, 93\\\\%, 93\\\\%, 97\\\\%, and 93\\\\%, respectively. To understand on how the four-class classifier detects COVID-19 pneumonia, we apply Gradient-weighted Class Activation Mapping (Grad-CAM) method and find out that the classifier is able to focus on the patchy areas in chest X-ray images and make accurate predictions.", "source_url": "https:\/\/openalex.org\/W3040290756", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W3040290756", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 725, "title": "Classification of chest diseases from x-ray images using deep learning", "title_ar": "", "title_en": "Classification of chest diseases from x-ray images using deep learning", "author": "Arifa Khatun", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging", "keywords": "", "abstract": "In today's modern age, the field of Artificial Intelligence and Health Care are inseparable.Every now and then, doctors and patients use various technology to diagnose diseases.But still, interpreting or identifying diseases through different medical tests such as X-ray or MRI cannot be fully done using artificial intelligence due to the lack of good output from various models.This paper tried to tackle this problem of classifying three diseases that a professional can identify through a chest X-ray image.Prior to this work, many had done similar works but only a few were able to get prediction accuracy over 90%.So, this paper explored that area to get better prediction result and by fitting ResNet50, DenseNet121, MobileNetV2, InceptionNetV3 models, it was seen that ResNet50 outperformed other three models with over 98% accuracy.The result shows that deep learning models can do well classifying diseases from a test image with a very little margin of error.", "source_url": "https:\/\/openalex.org\/W7215672098", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/ff3e94fd-ba08-4c15-94d8-a48c0cc1e702\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215672098", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 726, "title": "Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning", "title_ar": "", "title_en": "Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning", "author": "Ahashan Habib Niloy ; Shammi Akhter Shiba ; Sadaf Fahim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "BRAC University", "university_source": "repository_host", "college": "", "department": "", "country": "Bangladesh", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "Coronavirus 2019 (in short, COVID-19), originated in the Wuhan province of China in December 2019, has been declared a global pandemic by WHO in March 2020.Since its inception, it's rapid spread among nations had initially collapsed the world economy and the increasing death-pool created a strong fear among people as the virus spread through human contact.Initially doctors struggled to diagnose the increasing number of patients as there was less availability of testing kits and failed to treat people efficiently which ultimately led to the collapse of the health sector of several countries.To help doctors primarily diagnose the virus, researchers around the world have come up with some radiology imaging techniques using the Convolutional Neural Network (CNN).While some of them worked on x-ray images and some others on CT scan images, none worked on both the image types.Thus there's no way to know which image works better for a particular model.This, therefore, insisted us to perform a comparison between x-ray and CT scan images.Thus we came up with a novel CNN model named CoroPy which works for both the image types and shows that in 2 classes (normal and covid), CT scan images show a better accuracy and it is 99.17% whereas it is 95.73% for x-ray images.However, in the case of 3 classes (normal, covid and viral pneumonia), x-ray images show a better accuracy and it is 92.45% whereas it is 68.81% for CT scan images.", "source_url": "https:\/\/openalex.org\/W3201978509", "pdf_url": "", "doi": "", "handle": "", "repository_name": "BRAC University Institutional Repository (BRAC University)", "source_record_id": "W3201978509", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 727, "title": "Deep Learning-Based Web Application for the Detection of COVID-19 from Chest CT-Scan Images", "title_ar": "", "title_en": "", "author": "Kaouther Nouni", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2021, "language": "fr", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; medical-imaging", "keywords": "", "abstract": "La maladie à coronavirus 2019 causée par le coronavirus 2 du syndrome respiratoire aigu sévère (SRAS-CoV-2) apparu pour la première fois en Chine s’est propagée à plus de 200 pays, et plus de 1 700 000 cas confirmés et 111 600 décès ont été enregistrés, avec une augmentation mondiale massive du nombre des cas chaque jour. L’infection pulmonaire ou pneumonie est la complication commune du COVID-19 alors que les techniques d’imagerie, en particulier la tomographie assistée par ordinateur (CT), ont joué un rôle important dans le diagnostic et l’évaluation du traitement de la maladie. Les modèles d’apprentissage profond sont largement utilisés dans l’analyse automatique des images radiologiques, ce qui ouvre la voie à l’utilisation de techniques de classification pour aider à la détection précoce de l’infection. L’objectif de ce travail est de répondre à ce problème en construisant une application web qui catégorise les images de covid-19 et de non covid-19. La première partie du travail consiste à préparer les données en utilisant le prétraitement des images et l’augmentation des données. L’évaluation des données est ensuite passée par deux modèles d’entraînement : VGG16 et ResNet. A la fin, les techniques de visualisation Grad-Cam ont été utilisées pour mettre en évidence les régions infectées dans l’image. La deuxième partie est la présentation de l’application web qui est un sys- tème d’aide à la décision qui consiste à télécharger une image CT-scan et à attendre qu’elle soit classée quelques secondes plus tard. Le VGG16 a montré une grande précision permettant à l’application de donner une meilleure décision lors de la classification des cas infectés tout en ajoutant la visualisation qui explique la prédiction du modèle", "source_url": "https:\/\/openalex.org\/W7135312671", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/f17ae859-b899-46f3-8a67-6f0623ebcfcd\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135312671", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 729, "title": "EVALUASI KINERJA MODEL MENGGUNAKAN CNN TRANSFER LEARNING PADA KLASIFIKASI X-RAY COVID-19 = PERFORMANCE EVALUATION OF THE MODEL USING CNN TRANSFER LEARNING ON COVID-19 X-RAY CLASSIFICATION", "title_ar": "", "title_en": "", "author": "Ramdan Masykuri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2023, "language": "id", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; medical-ai; transfer-learning; cnn; medical-imaging", "keywords": "", "abstract": "Paru-paru merupakan bagian dari tubuh manusia yang memiliki peran penting dalam pernapasan. Masalah yang sering timbul pada paru-paru adalah kualitas udara yang masuk ke paru-paru apakah tercemar atau tidak. Pneumonia dan Covid-19 merupakan penyakit yang menyebabkan peradangan pada paru-paru dan dapat menyebabkan kematian, Covid-19 menjadi masalah besar bagi banyak negara di dunia sejak tahun 2020. Terdapat kesamaan antara Covid-19 dengan pneumonia pada gejala seperti batuk dan sesak napas. Pelaksanaan tes deteksi cepat Covid-19 tidak serta merta berjalan mulus, namun tim medis mengalami banyak kendala. Salah satu penyebabnya adalah terbatasnya jumlah alat tes Covid-19, sehingga perlu dilakukan deteksi dini untuk membedakan antara pneumonia dan Covid-19. Citra X-ray dapat digunakan untuk mendeteksi pneumonia dan Covid-19. Deteksi manual memungkinkan terjadinya kesalahan, sehingga perhitungan komputasi diperlukan untuk meminimalkan kesalahan. Salah satu metode yang digunakan adalah Transfer learning dengan menggunakan algoritma Convolutional Neural Network (CNN) beserta arsitektur Xception, Inceptionv3, dan MobileNet. Transfer learning adalah sebuah metode Machine Learning yang bekerja dengan cara meniru sistem kerja otak manusia, sistem ini disebut Neural Network. CNN merupakan salah satu jenis Neural Network yang fungsi utamanya digunakan untuk data citra. Penelitian dilakukan dengan 1200 data citra X-ray Covid-19 yang dibagi menjadi 3 kelas dengan 400 data citra untuk setiap kategori. Hasil terbaik dari semua model arsitektur yang dicoba adalah Xception mendapatkan akurasi training sebesar 96% dan akurasi validation sebesar 97%. Untuk model MobileNet mendapatkan akurasi training adalah 97% dan akurasi validation adalah 93%. Dan model InceptionV3 memiliki hasil yang paling rendah dibandingkan kedua arsitektur lainnya yaitu akurasi training sebesar 95% dan akurasi validation 93%.", "source_url": "https:\/\/openalex.org\/W7217499254", "pdf_url": "https:\/\/repository.unhas.ac.id\/id\/eprint\/45536\/2\/H071181502_skripsi_04-04-2023%201-2.pdf", "doi": "", "handle": "", "repository_name": "Hasanuddin University Repository", "source_record_id": "W7217499254", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 730, "title": "Detection of COVID-19 from X-Ray Images. (c2021)", "title_ar": "", "title_en": "Detection of COVID-19 from X-Ray Images. (c2021)", "author": "Joseph Kandalaft", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "Corona Virus Disease 2019 (COVID-19) is a new disease that is based on the SARSCoV- 2 virus. The virus has caused a worldwide pandemic due to its high infection rate and severity of symptoms. Several methods for detecting the virus exist among which different medical imaging modalities, in particular X-Ray imaging. In this thesis, we propose a three-phase machine learning approach to detect, from X-Ray images, whether a person is infected with the virus or not. The approach relies on an ensemble of customized convolutional neural networks to extract essential features from input images. The extracted features undergo fusion and are then passed on to a classifier for final results. We validated the approach on a set of 3,886 X-Ray images of patients carrying the virus, patients suffering from viral pneumonia, and healthy persons. When benchmarked against several models published in the literature, our proposed model outperformed them all.", "source_url": "https:\/\/openalex.org\/W4283828160", "pdf_url": "https:\/\/laur.lau.edu.lb:8443\/xmlui\/bitstream\/10725\/13665\/1\/Joseph_Edward_Kandalaf_Thesis_Redacted.pdf", "doi": "10.26756\/th.2022.247", "handle": "", "repository_name": "", "source_record_id": "W4283828160", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 731, "title": "Use of Swin Transformer to classify chest radiographic images and diagnose COVID-19", "title_ar": "", "title_en": "Use of Swin Transformer to classify chest radiographic images and diagnose COVID-19", "author": "Aroldo Ferraz", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; transformers; medical-imaging", "keywords": "", "abstract": "According to data from the World Health Organization (WHO), since the beginning of 2020, the COVID-19 pandemic has infected over 770.56 million people worldwide. Of those infected, more than 6.95 million individuals have lost their lives. COVID-19 is an extremely contagious disease and can swiftly overwhelm healthcare systems if infections are not diagnosed, and if isolation measures and treatment for deteriorating conditions are not promptly implemented. The primary screening test used to diagnose COVID-19 has been the Real Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR). Although it is accurate and reliable, it takes a significant amount of time to deliver a definitive diagnosis. Since the onset of the pandemic, several researchers have demonstrated the feasibility of employing various Deep Learning (DL) techniques based on Convolutional Neural Networks (CNN), which have shown promising performance in diagnosing COVID-19 from X-rays and CT scans. However, some authors argue that existing DL methods, based on CNN, are being challenged by newer DL architectures and models. Motivated by this, in this study, we propose the use of the Swin Transformer (instead of CNN) for COVID-19 screening using X-ray images from four original datasets: COVID-QU-Ext Dataset, SARS-COV2-CT Dataset, HUST-19, and HCV-UFPR-COVID-19. These datasets were merged using different strategies, resulting in a total of 9 distinct datasets. We employed transfer learning to address the issue of data scarcity. Moreover, we assessed whether the models have a high generalization capability, based on the learned features, allowing training on one dataset, and testing on another, aiming to evaluate if there are significant losses in the metric values obtained. Upon analyzing the cumulative results from experiments across all datasets and strategies, the Swin Transformer stands out. In terms of sensitivity\/recall, it performed 107% and 80% better than ViT and CNN, respectively. In the HUST-19 dataset, the Swin Transformer achieved the maximum score (1 or 100%) for all metrics and continued to show notable performance in other datasets. Compared to the state of the art, the results reveal a promising and highly competitive performance for the Swin Transformer. Additionally, statistical tests were conducted which showed, in several instances, that there are statistically significant differences in the metrics obtained by the Swin Transformer when compared to CNN and ViT.", "source_url": "https:\/\/openalex.org\/W7120831202", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/32649\/1\/swintransformerimagensradiograficas.pdf", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120831202", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 732, "title": "COVID-19 Diagnosis and Segmentation Using Machine Learning Analyses of Lung Computerized Tomography", "title_ar": "", "title_en": "COVID-19 Diagnosis and Segmentation Using Machine Learning Analyses of Lung Computerized Tomography", "author": "Bhuvan Mittal", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "COVID-19 is a highly contagious and virulent disease caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). COVID-19 disease induces lung changes observed in lung computerized tomography (CT) and the percentage of those diseased areas on the CT correlates with the severity of the disease. Therefore, segmentation of CT images to delineate the diseased or lesioned areas is a logical first step to quantify disease severity, which will help physicians predict disease prognosis and guide early treatments to deliver more positive patient outcomes. It is crucial to develop an automated analysis of CT images to save their time and efforts. This dissertation proposes CoviNet, a deep three-dimensional convolutional neural network (3D-CNN) to diagnose COVID-19 in CT images. It also proposes CoviNet Enhanced, a hybrid approach with 3D-CNN and support vector machines. It also proposes CoviSegNet and CoviSegNet Enhanced, which are enhanced U-Net models to segment ground-glass opacities and consolidations observed in computerized tomography (CT) images of COVID-19 patients. We trained and tested the proposed approaches using several public datasets of CT images. The experimental results show the proposed methods are highly effective for COVID-19 detection and segmentation and exhibit better accuracy, precision, sensitivity, specificity, F-1 score, Matthew's correlation coefficient (MCC), dice score, and Jaccard index in comparison with recently published studies.", "source_url": "https:\/\/openalex.org\/W4406835167", "pdf_url": "https:\/\/digital.library.unt.edu\/ark:\/67531\/metadc1833510\/m2\/1\/high_res_d\/MITTAL-DISSERTATION-2021.pdf", "doi": "10.12794\/metadc1833510", "handle": "", "repository_name": "University of North Texas Digital Library (University of North Texas)", "source_record_id": "W4406835167", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 734, "title": "Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning", "title_ar": "", "title_en": "Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning", "author": "Joy Ingrid Arreola Minjarez ; José Díaz ; Boris Jesús Mederos Madrazo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Mexico", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging", "keywords": "", "abstract": "The novel coronavirus (COVID-19) is a disease that mainly affects the lung tissue. The detection of lesions caused by this disease can help to provide an adequate treatment and monitoring its evolution. This research focuses on the bi- nary classification of lung lesions caused by COVID-19 in images of computed tomography (CT) using deep learning. The database used in the experiments comes from two independent repositories, which contains tomographic scans of patients with a positive diagnosis of COVID-19. The output layers of four pre-trained convolutional networks were adapted to the proposed task and re-trained using the fine-tuning technique. The models were validated with test images from the two database’s repositories. The model VGG19, considering one of the repositories, showed the best performance with 88% and 90.2% of accuracy and recall, respectively. The model combination using the soft voting technique presented the highest accuracy (84.4%), with a recall of 94.4% employing the data from the other repository. The area under the receiver operating characteristic curve was 0.92 at best. The proposed method based on deep learning represents a valuable tool to automatically classify COVID-19 lesions on CT images and could also be used to assess the extent of lung infection.", "source_url": "https:\/\/openalex.org\/W4224050704", "pdf_url": "http:\/\/rmib.mx\/index.php\/rmib\/article\/download\/1208\/912", "doi": "10.17488\/rmib.43.1.1", "handle": "", "repository_name": "Revista Mexicana de Ingeniería Biomédica", "source_record_id": "W4224050704", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 735, "title": "Adaptive Deep Learning for Vision and Multimodal Understanding with Heterogeneous Data", "title_ar": "", "title_en": "Adaptive Deep Learning for Vision and Multimodal Understanding with Heterogeneous Data", "author": "Youwei Liang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; transfer-learning; few-zero-shot; predictive-analytics; transformers; multimodal; image-segmentation; llm", "keywords": "", "abstract": "Large-scale pretraining has produced powerful deep learning representations, but real-world data rarely match the clean, uniform assumptions of standard transfer learning. This dissertation uses heterogeneous data to describe settings in which information differs in modality, representation, relevance, quality, or frequency across inputs, outputs, tasks, or supervision. Examples include molecules represented as graphs, images, strings, and text; images containing both informative and redundant regions; noisy or incomplete labels; and rare but important classes. Applying pretrained models in these settings requires deciding which information to process, how to connect different forms of information, and how much to trust each supervision signal. This dissertation develops adaptive learning as a unifying approach through three paradigms: adaptive selection focuses computation on useful input components; adaptive alignment maps heterogeneous modalities, tasks, and outputs into a shared predictive space; and adaptive supervision adjusts imperfect or imbalanced training signals. The first contribution, EViT, demonstrates adaptive selection for efficient Vision Transformers. Standard Vision Transformers devote equal computation to all image patches despite substantial redundancy. EViT uses class-token attention to identify important tokens, preserves them in subsequent layers, and fuses inattentive tokens into a compact representation rather than discarding their context. This progressive token reorganization reduces computation while maintaining accurate visual recognition. The second contribution, DrugChat, demonstrates adaptive alignment for multimodal molecular prediction. Molecules can be represented as chemical graphs, rendered images, SMILES strings, and natural-language prompts, while predictions may require labels, probabilities, categorical decisions, or free-form descriptions. DrugChat encodes molecular graphs and images with pretrained encoders, projects them into the latent space of an instruction-following language model, and conditions generation on molecular structure and task prompts. It thereby aligns heterogeneous inputs in a shared representation space and expresses diverse prediction targets through a common language-generation interface. The third contribution, RobPicker, demonstrates adaptive supervision for cryo-electron tomography particle picking, where annotations are expensive, incomplete, noisy, and highly imbalanced across macromolecular species. Its meta-learning framework jointly trains a segmentation network, a label correction network, and a data reweighting network. Guided by cleaner validation data, these components refine imperfect labels and adapt each subtomogram's contribution to the loss, giving greater influence to rare or small particle classes. Together, EViT, DrugChat, and RobPicker apply adaptation at complementary levels of the learning pipeline: selecting what data to process, aligning heterogeneous representations and outputs, and learning from imperfect supervision.", "source_url": "https:\/\/openalex.org\/W7222391945", "pdf_url": "https:\/\/escholarship.org\/content\/qt3h17286t\/qt3h17286t.pdf", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W7222391945", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 736, "title": "Data efficient deep multimodal learning", "title_ar": "", "title_en": "Data efficient deep multimodal learning", "author": "Meng Shen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; self-semi-supervised; multimodal; llm", "keywords": "", "abstract": "Multimodal learning, which enables neural networks to process and integrate information from various sensory modalities such as vision, language, and sound, has become increasingly important in applications ranging from affective computing and healthcare to advanced multimodal chatbots. Despite its potential, multimodal learning faces significant challenges, particularly in the area of data efficiency. The requirement for large, high-quality datasets from multiple modalities presents a substantial barrier, limiting the scalability and accessibility of large multimodal models. This dissertation addresses several key issues in data-efficient deep multimodal learning, focusing on the imbalanced multimodal data selection, the cold-start problem in multimodal active learning, and the mitigation of hallucinations in large vision-language models. Firstly, we analyze the limitations of conventional active learning strategies, which tend to favor dominant modalities, leading to unbalanced multimodal models that neglect weaker modalities. To overcome this, we propose a gradient embedding modulation method that ensures a more equitable data selection process across modalities, resulting in models that fairly uilize both weak and strong modalities. Building on our work in warm-start active learning, we tackle the cold-start problem in multimodal active learning, where no initial labels are available for warm-start data selection. We develop a two-stage approach that first reduces the modality representation gap through multimodal self-supervised learning, utilizing unimodal prototypes to harmonize representations across modalities. In the subsequent data selection stage, we introduce a regularization term to maximize modality alignment, leading to improved model performance using the same amount of data compared to existing methods. Extending our focus from data selection to the usage of training data, we address the challenge of hallucinations in large vision-language models, where the models generate content that is incorrect in the context of input images. We investigate the relationship between hallucinations and visual dependence of tokens, revealing that certain tokens contribute disproportionately to these hallucinatory. Based on this insight, we propose an approach that adjusts training weights according to the visual dependence of tokens, thereby reducing the hallucination rate without requiring additional training data or inference costs. The contributions of this thesis offer significant advancements in the field of dataefficient multimodal learning. By developing novel methods for balancing multimodal data selection, addressing cold-start problem in multimodal active learning, and mitigating hallucinations in large vision-language models, this work paves the way for more practical and scalable multimodal learning systems that require less data and computational effort while achieving superior performance.", "source_url": "https:\/\/openalex.org\/W4407130261", "pdf_url": "", "doi": "10.32657\/10356\/182346", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4407130261", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 737, "title": "Advancing Computational Pathology with Multimodal Deep Learning", "title_ar": "", "title_en": "Advancing Computational Pathology with Multimodal Deep Learning", "author": "Md. Mamunur Rahaman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; few-zero-shot; self-semi-supervised; predictive-analytics; multimodal; llm; medical-imaging; xai", "keywords": "", "abstract": "Histopathological assessment of tissue biopsies remains the cornerstone of cancer diagnosis, yet it is limited by diagnostic subjectivity and an incomplete view of the molecular heterogeneity that drives outcomes. This dissertation presents a suite of artificial intelligence (AI) frameworks that integrate deep learning, computer vision, and multimodal data to enhance diagnostic precision, prognostication, and therapeutic guidance across multiple cancer settings. The research first establishes a method for improving classification robustness through HistopathAI, a framework employing supervised contrastive learning and a hybrid deep feature fusion architecture. This approach yields more discriminative and generalizable feature representations, particularly in the context of class imbalance common in medical datasets. Building on this, the work demonstrates the power of integrating visually interpretable features from the tumor microenvironment. An AI-driven biomarker for Head and Neck Squamous Cell Carcinoma (HNSCC), which quantifies the spatial architecture of immune and stromal cells, was developed, and its prognostic value was validated on independent patient cohorts, confirming its strong potential for clinical risk stratification. A central contribution is the development of “virtual spatial transcriptomics” that bridges histology and genomics. The BrSTNet framework predicts spatial gene expression from routine H&E images in breast cancer, establishing a strong benchmark for image-to-transcriptome inference. This capability is translated to a direct clinical application in prostate cancer with ST-DoxPCa, a predictive signature for docetaxel benefit. The validation strategy was explicitly tailored to disease context: for the RTOG0521 trial (localized disease), a prognostic model trained independently in the Cleveland Clinic prostatectomy cohort (CCF) was applied in a fully blinded manner, without refitting to stratify patients and assess treatment effect; for the CHAARTED trial (metastatic disease), a separate treatment-response model was trained exclusively on the standard-of-care (ADT-only) arm. In both settings, ST-DoxPCa stratified patients into subgroups that either derived a significant survival benefit from docetaxel or showed no advantage, supporting treatment de-escalation for likely non-responders. Finally, to address the critical challenge of data scarcity, the thesis introduces MR-PHE, a novel framework for zero-shot learning in histopathology. By leveraging vision-language models with a multi-resolution, prompt-guided approach, MR-PHE enables accurate classification without task-specific training data, often outperforming fully supervised methods and offering a scalable solution for rare diseases. Collectively, these contributions advance the frontier of computational pathology, delivering a suite of validated, AI-driven tools that translate complex tissue-based data into clinically actionable insights. The methodologies developed herein represent a significant step toward a new paradigm of precision oncology, where patient care is personalized based on a deeper, quantitative understanding of tumor biology derived from standard pathology slides.", "source_url": "https:\/\/openalex.org\/W7116859814", "pdf_url": "", "doi": "10.26190\/unsworks\/31906", "handle": "", "repository_name": "UNSWorks (University of New South Wales, Sydney, Australia)", "source_record_id": "W7116859814", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 738, "title": "AI-Generated Image Detection Using Multimodal Deep Learning", "title_ar": "", "title_en": "AI-Generated Image Detection Using Multimodal Deep Learning", "author": "Mamadou Keïta", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; responsible-ai; few-zero-shot; transformers; multimodal; llm; gan; diffusion; xai; fairness", "keywords": "", "abstract": "Generative artificial intelligence (AI) and its applications offer significant benefits but raise potentially critical societal and ethical concerns. This thesis explores the field of synthetic content generation and detection, with a particular focus on synthetic images. These are AI-generated images that can appear indistinguishable from real photographs, creating new challenges for digital forensics, media integrity, and public trust. While generative models such as generative adversarial networks (GANs) and diffusion models have evolved rapidly, most existing detection methods remain limited in terms of generalizability, robustness, and interpretability. To address these challenges, this research investigates new AI-driven frameworks to improve the generalizability, robustness, and interpretability of current image detection methods.The various aspects of synthetic image detection are covered in this thesis by four key contributions. The first contribution, Bi-LORA, presents an efficient vision-language approach that reformulates the detection problem into an image caption task, achieving strong zero-shot generalization across unseen generative models. The second, RAVID, proposes a retrieval-augmented visual detection framework that enhances robustness and interpretability by incorporating external visual context. The third, DeeCLIP, presents a lightweight transformer-based model that fuses shallow and deep features to improve resistance to image degradation and post-processing. Finally, FIDAVL unifies synthetic image detection and source attribution in a single multitasking framework based on the soft prompt tuning of vision-language models.This thesis provides an in-depth analysis of the current state of synthetic image research and its societal impacts. It emphasizes the urgent need for effective and robust detection methods. The approaches introduced advance the scientific community and could benefit practical applications in media authenticity and security. This research marks an important step forward in developing solutions to the ethical challenges posed by GenAI.", "source_url": "https:\/\/openalex.org\/W7126004607", "pdf_url": "", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W7126004607", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 740, "title": "Deep learning and natural language processing for computer aided diagnosis", "title_ar": "", "title_en": "Deep learning and natural language processing for computer aided diagnosis", "author": "Sadam Hussain", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tecnológico de Monterrey", "university_source": "repository_host", "college": "", "department": "", "country": "Mexico", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; predictive-analytics; transformers; multimodal; llm; medical-imaging; xai", "keywords": "", "abstract": "Multimodal artificial intelligence (AI) is a cutting-edge technique that integrates diverse modalities, such as imaging and textual data, to enhance classification and regression tasks. This dissertation focuses on the integration, comparison, and evaluation of multimodal AI for breast cancer diagnosis and prognosis. To achieve these objectives, we curated a comprehensive multimodal dataset comprising digital mammograms and corresponding radiological reports. Leveraging this dataset, we introduced and assessed various state-of-the-art (SOTA) multimodal techniques for three key tasks: breast cancer classification, reduction of false-positive biopsies with explainable AI (XAI), and short- term (5-year) risk prediction of breast cancer.In this work, we also introduced a benchmark dataset of radiological reports from breast cancer patients and provided baseline performance evaluations using SOTA machine learning (ML), deep learning (DL), and large language models (LLMs) for BI-RADS category classification. Our approach evaluated the performance of diverse SOTA multimodal architectures, including ResNet, VGG, E!cientNet, MobileNet, and Vision Transformers (ViT). For textual data processing, we employed both general-purpose and domain-specific pretrained LLMs such as BERT, bioGPT, ClinicalBERT, and DeBERTa, which were also integrated into multimodal architectures for enhanced lassification.Notably, our proposed multiview multimodal feature fusion (MMFF) architecture, combining SE-ResNet50 with an artificial neural network (ANN), achieved an AUC of 0.965 for breast cancer classification, significantly outperforming both single-modal and multimodal SOTA architectures. For reducing unnecessary breast biopsies, our multimodal approach achieved an AUC of 0.72, showcasing its clinical utility in minimizing patient burden. Moreover, our ViT and bioGPT-based multimodal architecture achieved an AUC of 0.77 for short-term risk prediction, outperforming the SOTA MIRAI model, which achieved an AUC of 0.59 on our in-house dataset. This work highlights the potential of multimodal AI in advancing breast cancer diagnosis and prognosis, demonstrating its superiority over traditional and unimodal approaches across multiple critical tasks.", "source_url": "https:\/\/openalex.org\/W7225556007", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Repositorio del Tecnológico de Monterrey (Tecnológico de Monterrey)", "source_record_id": "W7225556007", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 741, "title": "Exploring Multimodal Deep Learning Approaches for Coronary Artery Disease Diagnosis", "title_ar": "", "title_en": "Exploring Multimodal Deep Learning Approaches for Coronary Artery Disease Diagnosis", "author": "Popov, Maxim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Arab Emirates", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; self-semi-supervised; multimodal; llm; medical-imaging", "keywords": "", "abstract": "Coronary Artery Disease (CAD) is a prevalent cause of death in the modern world, with about 20 million victims every year. Limited annotated data, inter-observer variability, and the intrinsic complexity of angiographic imaging challenge its diagnosis. This thesis presents a novel deeplearning framework to enhance coronary artery disease (CAD) diagnosis through multimodal analysis of Xray coronary angiograms. We develop several interrelated contributions to address these issues that combine advanced contrastive learning and vision-language modeling.", "source_url": "https:\/\/openalex.org\/W7106339970", "pdf_url": "", "doi": "10.82419\/166", "handle": "", "repository_name": "MBZUAI iRep", "source_record_id": "W7106339970", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 742, "title": "Multimodal and explainable Learning using Fusion Architecture Through Exploration (FATE) approach", "title_ar": "", "title_en": "Multimodal and explainable Learning using Fusion Architecture Through Exploration (FATE) approach", "author": "Otmane Amel", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Belgium", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; computer-vision; nlp; medical-ai; responsible-ai; multimodal; image-segmentation; video-analysis; llm; medical-imaging; xai", "keywords": "", "abstract": "The way we experience the world is multimodal, each of our five senses perceives the world differently, yet the brain can explore this information at once with ease. This work explores the potential of mimicking this exceptional cognitive capability in machines. This thesis lies in this research area, called Multimodal Deep Learning, where we spotlight the importance of robust fusion methods and effective modality representations in real-world scenarios. This study concludes with a proposed light, easy-to-use, and extendable fusion method called MultConcat, that was evaluated for multiple data source combinations: image and text, RGB and depthmap frames (video flows), as well as an MRI and tabular data in a medical context. We then propose FATE (Fusion Architecture Through Exploration), a methodology that treats a given input multimodal problem by solving real-world research questions related to the fusion method, the appropriate fusion timing, and the best encoder for each modality. Furthermore, we propose a concept-based explainability approach for multimodal settings based on segmentations using Foundation models to ensure human interpretability. Finally, we explore the difference in model design we might have between ready-to-use Vision-Language Models, Multimodal Large Language Models, and configurable models that can be built using our suggested FATE methodology.", "source_url": "https:\/\/openalex.org\/W7221428022", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ORBi UMONS", "source_record_id": "W7221428022", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 743, "title": "A Study on Robustness, Adaptation, and Generalization for Visual and Multimodal Learning", "title_ar": "", "title_en": "A Study on Robustness, Adaptation, and Generalization for Visual and Multimodal Learning", "author": "Xiasi Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; responsible-ai; ai-applications; transfer-learning; self-semi-supervised; multimodal; llm; fairness; ai-cybersecurity", "keywords": "", "abstract": "Recent advances in visual and multimodal learning have demonstrated impressive performance on diverse tasks. However, their reliability in real-world scenarios remains challenged by issues of robustness, adaptation, and generalization. In this thesis, we tackle these challenges by establishing four integral pillars of reliable deep learning: (1) robustness against adversarial attacks, (2) adaptation to sequential data streams, (3) resilience to domain shift and unseen categories, and (4) enhanced generalization via self-supervised learning. First, we identify a critical vulnerability in vision-language models (VLMs): their susceptibility to adversarial manipulation of inference efficiency. We propose a black-box evaluation framework to assess this efficiency robustness, establishing a new dimension for VLM safety. Second, we find that VLMs exhibit limited adaptability to evolving data streams. We explore long-tailed class incremental learning within the VLM framework and introduce a method to address the dual challenges of catastrophic forgetting and data imbalance. Third, we examine vision models’ failures on out-of-distribution samples. We tackle the open-set domain adaptation challenge, where both distribution shift and unknown classes appear in the target domain, and develop a two-stage framework that adapts the model to classify known classes while detecting unknown ones. Fourth, we revisit self-supervised pretraining in computer vision and design a novel objective to enhance the generalizability of pre-trained models. Collectively, these contributions form a unified framework for advancing robustness, adaptation, and generalization, facilitating more trustworthy visual and multimodal intelligence.", "source_url": "https:\/\/openalex.org\/W7135015825", "pdf_url": "", "doi": "10.14711\/thesis-hdl169527", "handle": "", "repository_name": "", "source_record_id": "W7135015825", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 744, "title": "Multimodal Deep Learning for Driver Monitoring: Integrating EEG and Vision for Robust Drowsiness Detection and Safety Enhancement", "title_ar": "", "title_en": "Multimodal Deep Learning for Driver Monitoring: Integrating EEG and Vision for Robust Drowsiness Detection and Safety Enhancement", "author": "Ur Rahman, Shams", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dublin City University", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; transformers; multimodal; face-recognition; video-analysis; ai-education", "keywords": "", "abstract": "Road accidents remain a major global concern, with driver drowsiness and delayed reaction times recognized as key contributing factors. This thesis advances driver-monitoring research by developing multimodal approaches that integrate electroencephalography (EEG) and vision data to predict reaction times and drowsiness. The investigation first demonstrates that pre-stimulus EEG signals—specifically spectral power in the alpha and theta bands—contain rich information for estimating reaction times to critical road events. Using subject-independent machine-learning pipelines, short EEG windows recorded before event onset effectively differentiate between fast and slow responses. The work then explores the benefits of incorporating vision data as a second modality by fusing EEG signals with camera-based observations of the driver. One branch converts EEG power-spectral-density features into image-like representations for analysis with deep convolutional neural networks and transformer models. Another branch directly integrates raw EEG signals with synchronised video frames through end-toend multimodal transformer architectures. Results indicate that transformers equipped with cross-modal attention capture complex interdependencies between neural and visual cues, yielding significant improvements in driver-drowsiness detection over unimodal approaches. Real-time deployment is addressed by designing and optimising a lightweight pipeline for edge-based processing. This resource-efficient model enables rapid analysis of facial cues under diverse driving conditions, ensuring operation on embedded platforms such as smartphones and automotive edge devices. Extensive evaluations on large-scale simulated datasets confirm the generalisability of the proposed approaches across varied driving scenarios. Experiments reveal that transformer-based fusion significantly enhances predictive performance by effectively combining complementary neural and visual cues. Moreover, the lightweight pipeline maintains high accuracy under stringent computational constraints, enabling real-time, ondevice deployment.", "source_url": "https:\/\/openalex.org\/W7112463353", "pdf_url": "https:\/\/doras.dcu.ie\/31498\/1\/shams_thesis_final_version.pdf", "doi": "", "handle": "", "repository_name": "Dublin City University Open Access Institutional Repository (Dublin City University)", "source_record_id": "W7112463353", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 745, "title": "Learning semantic representations of language, vision and code for visualization-oriented dialogue", "title_ar": "", "title_en": "Learning semantic representations of language, vision and code for visualization-oriented dialogue", "author": "Henrik Voigt", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; responsible-ai; expert-systems; transfer-learning; self-semi-supervised; transformers; multimodal; llm; fairness", "keywords": "", "abstract": "Data visualization is essential for navigating the vast volume of data that defines our time, letting humans use perception to make sense of complex datasets. Yet creating visualizations requires mastery of specialized programming languages, producing a gap between the analytical intention in the user's mind and its implementation in executable code. This raises the cognitive load on experts, who cannot concentrate fully on visual exploration while also writing the visualization program, and it creates a barrier for non-experts, hindering widespread access to insights from data. Bridging this gap is a central challenge for Visualization-oriented Natural Language Interfaces (V-NLIs), which translate human intent into executable code. This work decomposes the problem into two interrelated challenges: understanding, which builds rich, contextual representations of user intent, and generation, which reliably synthesizes visualization programs under strict real-time constraints. The thesis addresses both through five interconnected contributions. It begins with a survey of 119 papers, revealing that while the community focuses on sophisticated interactive exploration, the underlying technology relies on classical, rule-based methods, motivating a turn to deep learning. KeywordScape, a visual analytics system, makes the contextualized representations of transformer models visible and interactive, showing they can themselves resolve ambiguity and enable fine-grained exploration. Paparazzi extends this to multimodal representations, revealing that large vision-language models such as CLIP exhibit surprising deficiencies in 3D viewpoint understanding, and introduces a data-efficient contrastive fine-tuning method to improve geometric grounding. Finally, VIST5, a practical V-NLI built on a retrieval-augmented architecture around a small language model, shows that complex visualization code can be synthesized in real time while ensuring data privacy and modular extensibility.", "source_url": "https:\/\/openalex.org\/W7170734941", "pdf_url": "", "doi": "10.22032\/dbt.71264", "handle": "", "repository_name": "Common Library Network (Der Gemeinsame Bibliotheksverbund)", "source_record_id": "W7170734941", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 746, "title": "Multimodal Learning for Vision and Language", "title_ar": "", "title_en": "Multimodal Learning for Vision and Language", "author": "Junhua Mao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; nlp; few-zero-shot; cnn; rnn; multimodal", "keywords": "", "abstract": "This thesis focuses on proposing and addressing various tasks in the field of vision and language, a new and challenging area which contains the hottest research topics for both computer vision and natural language processing. We first proposed an effective RNN-CNN framework (Recurrent Neural Network-Convolutional Neural Network) to address the task of image captioning (i.e. describing an image with a sentence). Based on this work, we proposed effective models and constructed large-scale datasets, for various vision and language tasks, such as unambiguous object descriptions (i.e. Referring expressions), image question answering, one-shot novel concept captioning, multimodal word embedding, and multi-label classification. Many of these tasks have not been successfully addressed or even been investigated before. Our work are among the first deep learning effort for these tasks, and achieves the state-of-the-art results. We hope the methods and datasets proposed in this thesis could provide insight for the future development of vision and language.", "source_url": "https:\/\/openalex.org\/W2612979518", "pdf_url": "https:\/\/escholarship.org\/content\/qt676212wx\/qt676212wx.pdf", "doi": "", "handle": "", "repository_name": "eScholarship (California Digital Library)", "source_record_id": "W2612979518", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 747, "title": "Towards Natural Language Understanding using Multimodal Deep Learning", "title_ar": "", "title_en": "Towards Natural Language Understanding using Multimodal Deep Learning", "author": "Siegfried Bös", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; computer-vision; nlp; ai-applications; multimodal; object-detection; video-analysis; speech-recognition; ai-agriculture", "keywords": "", "abstract": "This thesis describes how multimodal sensor data from a 3D sensor and microphone array can be processed with deep neural networks such that its fusion, the trained neural network, is a) more robust to noise, b) outperforms unimodal recognition and c) enhances unimodal recognition in absence of multimodal data. We built a framework for a complete workflow to experiment with multimodal sensor data ranging from recording (with Kinect 3D sensor), labeling, 3D signal processing, analysing and replaying. We also built three custom recognizers (automatic speech recognizer, 3D object recognizer and 3D gesture recognizer) to convert the raw sensor streams to decisions and feed this to the neural network using a late fusion strategy. We recorded 25 particpants performing 27 unique verbal and gestural interactions (intents) with objects and trained the neural network using a supervised strategy. We proved that the framework works by building a deep neural networks assisted speech recognizer that performs approximately 5% better with multimodal data at 20 dB SnR up to 61% better with multimodal data at -5 dB SnR while performing identical to the individual recognizer when fed a unimodal datastream. Analysis shows that performance gain in low acoustic noise is due to true fusion of classifer results while gain at high acoustic noise is due to absence of speech results as it cannot detect speech events anymore, while the gesture recognizer is not affected. The impact of this thesis is significant for computational linguists and computer vision researchers as it describes how practical issues with (real and) real-time data can be solved such as dealing with sensor noise, GPU offloading for computational performance, 3D object and hand tracking. The speech-, object- and gesture recognizers are not state-of-the-art and the small vocabulary with 27 unique phrases and 9 objects can be considered a preliminary experiment. The main contributions of this thesis project are a) validated multimodal fusion framework and workflow for embodied natural language understanding named MASU, b) 600GB, 2,5 hour labelled multimodal database with synchronous multi channel audio and 3D video, c) algorithm for 3D hand-object detection and tracking, d) recipe to train a deep neural network model for multimodal fusion and e) demontrate MASU in practical real-time scenario.", "source_url": "https:\/\/openalex.org\/W2758142123", "pdf_url": "https:\/\/repository.tudelft.nl\/file\/File_91e1bf2c-74c9-43b0-bba6-3902d2b876f9", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2758142123", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 748, "title": "A Real-Time and Automatic Ultrasound-Enhanced Multimodal Second Language Training System: A Deep Learning Approach", "title_ar": "", "title_en": "A Real-Time and Automatic Ultrasound-Enhanced Multimodal Second Language Training System: A Deep Learning Approach", "author": "Mozaffari Maaref", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Ottawa", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; ai-applications; multimodal; video-analysis; speech-recognition; medical-imaging; ai-education", "keywords": "", "abstract": "The critical role of language pronunciation in communicative competence is significant, especially for second language learners. Despite renewed awareness of the importance of articulation, it remains a challenge for instructors to handle the pronunciation needs of language learners. There are relatively scarce pedagogical tools for pronunciation teaching and learning, such as inefficient, traditional pronunciation instructions like listening and repeating. Recently, electronic visual feedback (EVF) systems (e.g., medical ultrasound imaging) have been exploited in new approaches in such a way that they could be effectively incorporated in a range of teaching and learning contexts. Evaluation of ultrasound-enhanced methods for pronunciation training, such as multimodal methods, has asserted that visualizing articulator’s system as biofeedback to language learners might improve the efficiency of articulation learning. Despite the recent successful usage of multimodal techniques for pronunciation training, manual works and human manipulation are inevitable in many stages of those systems. Furthermore, recognizing tongue shape in noisy and low-contrast ultrasound images is a challenging job, especially for non-expert users in real-time applications. On the other hand, our user study revealed that users could not perceive the placement of their tongue inside the mouth comfortably just by watching pre-recorded videos. Machine learning is a subset of Artificial Intelligence (AI), where machines can learn by experiencing and acquiring skills without human involvement. Inspired by the functionality of the human brain, deep artificial neural networks learn from large amounts of data to perform a task repeatedly. Deep learning-based methods in many computer vision tasks have emerged as the dominant paradigm in recent years. Deep learning methods are powerful in automatic learning of a new job, while unlike traditional image processing methods, they are capable of dealing with many challenges such as object occlusion, transformation variant, and background artifacts. In this dissertation, we implemented a guided language pronunciation training system, benefits from the strengths of deep learning techniques. Our modular system attempts to provide a fully automatic and real-time language pronunciation training tool using ultrasound-enhanced augmented reality. Qualitatively and quantitatively assessments indicate an exceptional performance for our system in terms of flexibility, generalization, robustness, and autonomy outperformed previous techniques. Using our ultrasound-enhanced system, a language learner can observe her\/his tongue movements during real-time speech, superimposed on her\/his face automatically.", "source_url": "https:\/\/openalex.org\/W3022567045", "pdf_url": "", "doi": "10.20381\/ruor-24710", "handle": "", "repository_name": "uO Research (University of Ottawa)", "source_record_id": "W3022567045", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 749, "title": "Adaptive Fusion Techniques for Effective Multimodal Deep Learning", "title_ar": "", "title_en": "Adaptive Fusion Techniques for Effective Multimodal Deep Learning", "author": "Gaurav Sahu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Waterloo", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; nlp; generative-ai; transformers; multimodal; video-analysis; sentiment-analysis; machine-translation; speech-recognition; gan", "keywords": "", "abstract": "Effective fusion of data from multiple modalities, such as video, speech, and text, is a challenging task due to the heterogeneous nature of multimodal data. In this work, we propose fusion techniques that aim to model context from different modalities effectively. Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide “how” to combine given multimodal features more effectively. We propose two networks: 1) Auto-Fusion network, which aims to compress information from different modalities while preserving the context, and 2) GAN-Fusion, which regularizes the learned latent space given context from complementing modalities. A quantitative evaluation on the tasks of multimodal machine translation and emotion recognition suggests that our adaptive networks can better model context from other modalities than all existing methods, many of which employ massive transformer-based networks.", "source_url": "https:\/\/openalex.org\/W3084985854", "pdf_url": "https:\/\/uwspace.uwaterloo.ca\/bitstreams\/d8ed4daf-ffb2-4594-8566-a7a3085f4b99\/download", "doi": "", "handle": "", "repository_name": "UWSpace (University of Waterloo)", "source_record_id": "W3084985854", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 750, "title": "Multimodal deep learning for decoding visual and neural representation", "title_ar": "", "title_en": "Multimodal deep learning for decoding visual and neural representation", "author": "Xianglin Zheng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Adelaide", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "multimodal", "categories": "deep-learning; computer-vision; medical-ai; multimodal", "keywords": "", "abstract": "Multimodal deep learning combines different types of data, like images and brain signals. Traditional models often need lots of labeled examples and focus on one type of data, making them less able to handle real-world challenges. This thesis uses new deep learning and multimodal techniques to better process visual and brain data (Electroencephalogram), with three key projects. First, a new way to create image captions without labeled examples tackles mismatches between images and text. Second, it links images and EEG to classify objects by decoding brain activity from seeing images. Third, a three-part model combines images, words, and EEG data to understand how the brain processes what we see. Together, these methods advance multimodal learning, improving image captioning, EEG-based classification, and our understanding of how the brain works with visual information.", "source_url": "https:\/\/openalex.org\/W7222375543", "pdf_url": "https:\/\/digital.library.adelaide.edu.au\/server\/api\/core\/bitstreams\/8162363f-9ed4-4346-a556-b25a48693a12\/content", "doi": "", "handle": "", "repository_name": "Adelaide Research & Scholarship (AR&S) (University of Adelaide)", "source_record_id": "W7222375543", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 751, "title": "Multimodal Deep Learning for Zero-Shot Action Recognition", "title_ar": "", "title_en": "Multimodal Deep Learning for Zero-Shot Action Recognition", "author": "Nan WU", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; few-zero-shot; cnn; multimodal; video-analysis; llm", "keywords": "", "abstract": "Advancements in machine learning have improved the performance of machine learning (ML) models.However, larger training datasets present challenges, such as prolonged labeling and training times.One potential solution is zero-shot learning, which enables models to identify unfamiliar entities without extensive training data.This not only saves time and effort but also allows easy integration of new categories.However, its efficacy requires enhancement for broad real-world applications.This paper presents three methods to optimize the accuracy of zero-shot action recognition (ZSAR).1. Three-stream graph convolutional networks (TS-GCN):Current models often handle only one data type, limiting their information leverage.Our proposed two-stream network processes both RGB image data and optical flow data, which captures object movement.Each branch calculates video classification separately, and a weighted average determines the final classification.2. Improving ZSAR using human instruction with text description: The video-text matching model (TVMM) model gauges the matching degrees between text features and video content.We annotated text features for each action, and the TVMM model used these for action recognition.The results showed improved action recognition accuracy.3. Zero-shot action recognition with ChatGPT-based instruction: Manual text feature annotation improved accuracy but at a labor cost.To alleviate this, we used ChatGPT for automated text feature generation.This eliminated the need for human annotation.While achieving high recognition accuracy with reduced labor costs, it was slightly lower than previous methods but still suitable for various real-life applications.We propose three innovative methods that optimize zero-shot action recognition.The experiments demonstrate the effectiveness of our approach.", "source_url": "https:\/\/openalex.org\/W7207547986", "pdf_url": "https:\/\/opac.ll.chiba-u.jp\/da\/curator\/900122990\/WLA_0144.pdf", "doi": "10.20776\/900122990", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7207547986", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 752, "title": "Multimodal Integration for Natural Language Classification and Generation", "title_ar": "", "title_en": "Multimodal Integration for Natural Language Classification and Generation", "author": "Zhihao Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; nlp; transfer-learning; transformers; multimodal", "keywords": "", "abstract": "Multimodal integration is a framework for building models that can accept information from different types of modalities. Due to the recent success in the Transformer model and Pre-training Fine-tuning Techniques, Vision-and-Language Pre-training Models have been heavily investigated and they achieved State-of-the-Art in various of Vision-and-Language downstream tasks, such as Visual Question Answering, Image Text Matching and Image Captioning. However, most of the previous studies focus on improving the performance of the models and only provide accessible code for research purposes. There are several existing open-source libraries such as Natural Language Toolkit, OpenCV and HuggingFace, which combine and standardise the available models and tools for easy access, but applying these libraries still requires expertise in both Deep Learning and programming. Moreover, there has been no recent research aimed at establishing user-friendly multimodal question-answering platforms for non-deep-learning users. Therefore, the question of how State-Of-The-Art multimodal models can be easily applied by professionals in other domains remains open. Apart from the first challenge, there exists another challenge in the less-common domain. Since general multimodal domains such as street view, landscape, and indoor scenes have been extensively studied with current VL-PMs, while specific domains like medicine, geography, and esports have garnered less attention. Due to the difficulties in data collection, there aren't many publicly available multimodal datasets, and those that exist tend to be small. This scarcity poses challenges for model training. Consequently, the question of how to collect a comprehensive multimodal dataset in the esports domain and how to improve domain-specific multimodal models remains open. Therefore, the main focus of this thesis is integrating multimodal information for natural language classification and generation tasks by addressing the two challenges.", "source_url": "https:\/\/openalex.org\/W7126883915", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Sydney eScholarship Repository (The University of Sydney)", "source_record_id": "W7126883915", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 753, "title": "Aligning vision and language for image captioning using deep learning", "title_ar": "", "title_en": "Aligning vision and language for image captioning using deep learning", "author": "Cai, Chen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; nlp; self-semi-supervised; predictive-analytics; rnn; transformers; multimodal; video-analysis", "keywords": "", "abstract": "A longstanding objective in the field of multi-modal research uniting computer vision and natural language processing is to develop models that can comprehend the intricate relationship between vision and language. In recent years, we have witnessed notable developments directed towards this objective, enabling computers to interpret visual information and articulate it through captions. Although significant advances have been made, aligning complex visual scenes and language for image captioning tasks using deep learning approaches remains challenging due to the distinct characteristics between the modalities. In this thesis, we propose four new methods that effectively align vision and language for image captioning-based tasks through deep learning techniques. These methods include integrating visual and semantic attributes for controllable image captioning, aligning visual and object semantics for grounded image captioning, learning the relationship between multi-scale visual features with language for image change captioning, and bridging visual and global temporally caption knowledge for temporal sentence grounding. The major contributions of this thesis are summarized as follows: A new Attribute Controlled Image Captioning (ACC) method has been proposed that seamlessly integrates semantic attributes with visual content, enabling automatic modification of the generated captions in the fashion domain. Our approach utilizes semantic attributes as a control signal, giving users the ability to specify particular fashion attributes and styles to incorporate while generating captions. Furthermore, we clean, filter, and assemble a new fashion image caption dataset to facilitate learning and enable us to investigate the effectiveness of our method. A new one-stage Weakly Supervised Grounded Image Captioner (WS-GIC) is proposed that aligns visual and word representation to perform captioning and grounding at the top-down image level. We introduce a Recurrent Grounding Module (RGM) within the decoder to compute Visual Language Attention Maps (VLAMs) for grounding, where VLAMs indicate the spatial region and location of the generated groundable object words in the caption. In addition, we explicitly inject a relation module into our one-stage framework to encourage the relation understanding. The relation semantics aid the prediction of relation words in the caption. A new Interactive Change-aware Transformer Network (ICT-Net) is proposed to extract and incorporate the most critical change of interest in the image, enhancing the generation of change descriptions for complex remote sensing bitemporal scenes. The proposed framework comprises an Interactive Change-aware Encoder (ICE) to capture the crucial difference between bitemporal image features, an Adaptive Fusion Module (AFM) to adaptively aggregate the relevant change-aware features in the encoder layers while minimizing the impact of irrelevant visual features, and a Cross Gated-Attention (CGA) module in the change decoder that enhances the modeling of essential relationships between multi-scale features with word representation, thereby improving change captioning generation. A new Temporal Sentence Grounding (TSG) method is proposed to bridge the domain gap between multi-modal features by leveraging extensive temporally global caption knowledge that is sourced from the relevant video and temporally localized text queries. We introduce the Pseudo-query Intermediary Network (PIN) to contrastively align visual features with temporally global textual knowledge to enhance the similarity between visual and language features. Furthermore, we leverage the pseudo queries prompt to propagate the knowledge, enhancing the learning of feature alignment within the multi-modal fusion module for better temporal grounding. Throughout the thesis, we illustrate how each of the proposed methods aligns vision and language for image captioning-based tasks. Experimental results on public datasets indicate that the proposed methods can achieve better performance. These approaches contribute to enhancing vision and language multi-modalities understanding.", "source_url": "https:\/\/openalex.org\/W4405996657", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/181511\/2\/CC_ntu_thesis.pdf", "doi": "10.32657\/10356\/181511", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4405996657", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 754, "title": "A multimodal deep learning approach for food tray recognition", "title_ar": "", "title_en": "", "author": "Joan Peracaula Prat", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat de Barcelona", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2020, "language": "ca", "research_domain": "deep-learning", "research_subdomain": "multimodal", "categories": "deep-learning; multimodal", "keywords": "", "abstract": "Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Marc Bolaños i Petia Radeva", "source_url": "https:\/\/openalex.org\/W3158443751", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona)", "source_record_id": "W3158443751", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 755, "title": "Multimodal Deep Learning for Visual Question Answering: Techniques and Challenges", "title_ar": "", "title_en": "", "author": "Oualid Ilyes Malfi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2024, "language": "fr", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; nlp; medical-ai; rnn; transformers; multimodal; medical-imaging", "keywords": "", "abstract": "Cette thèse explore le domaine de la réponse visuelle aux questions (VQA), une application avancée de l'apprentissage profond multimodal qui combine la vision par ordinateur et le traitement du langage naturel pour permettre aux machines de répondre à des questions sur le contenu visuel. L'objectif principal est de développer un modèle VQA robuste capable d'interpréter et de raisonner sur des scènes visuelles complexes de manière similaire à celle des humains. En utilisant divers modèles tels que VGG19, ResNet50, EfficientNet-B5 et ViT pour l'extraction de caractéristiques d'image, et un modèle basé sur BERT ou LSTM pour l'extraction de caractéristiques des questions, l'étude emploie une méthode de fusion de niveau intermédiaire par multiplication élémentaire ou concaténation pour combiner ces caractéristiques. La recherche s'appuie sur des jeux de données comme CLEVR, conçu pour tester les véritables capacités de raisonnement en fournissant des objets rendus en 3D avec des questions détaillées, et VQA-RAD, destiné à des problèmes réels, qui soutient la prise de décision clinique à travers des images de radiologie accompagnées de questions et réponses pertinentes. Les résultats expérimentaux ont montré que la combinaison du modèle ViT, BERT et la technique de multiplication a excellé, atteignant des précisions élevées de 98,70 % sur le jeu de données CLEVR et de 96 % sur le jeu de données VQA-RAD, en faisant le modèle le plus efficace pour la réponse aux questions visuelles générales et médicales.", "source_url": "https:\/\/openalex.org\/W7135331956", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/2d865c52-986c-4153-96d4-e60980214c02\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135331956", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 756, "title": "Bridging images and natural language with deep learning", "title_ar": "", "title_en": "Bridging images and natural language with deep learning", "author": "Jiuxiang Gu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "deep-learning; computer-vision; nlp", "keywords": "", "abstract": "Throughout the thesis, I demonstrate how each of the proposed methods can bridge the gap between images and natural language. Experimental results on public vision and language datasets have shown all these methods are able to obtain significant performance improvement on vision and language tasks such as image captioning and cross-modal retrieval.", "source_url": "https:\/\/openalex.org\/W2992651911", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/85399\/1\/PhD_Thesis_JiuxiangGu_Final.pdf", "doi": "10.32657\/10356\/85399", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2992651911", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 757, "title": "Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks", "title_ar": "", "title_en": "Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks", "author": "Lin Xiao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; computer-vision; nlp; responsible-ai; predictive-analytics; multimodal; xai", "keywords": "", "abstract": "Learning and reasoning with common sense is a challenging problem in Artificial Intelligence (AI). Humans have the remarkable ability to interpret images and text from different perspectives in multiple modalities, and to use large amounts of commonsense knowledge while performing visual or textual tasks. Inspired by that ability, we approach commonsense learning as leveraging perspectives from multiple modalities for images and text in the context of vision and language tasks. Given a target task (e.g., textual reasoning, matching images with captions), our system first represents input images and text in multiple modalities (e.g., vision, text, abstract scenes and facts). Those modalities provide different perspectives to interpret the input images and text. And then based on those perspectives, the system performs reasoning to make a joint prediction for the target task. Surprisingly, we show that interpreting textual assertions and scene descriptions in the modality of abstract scenes improves performance on various textual reasoning tasks, and interpreting images in the modality of Visual Question Answering improves performance on caption retrieval, which is a visual reasoning task. With grounding, imagination and question-answering approaches to interpret images and text in different modalities, we show that learning commonsense knowledge from multiple modalities effectively improves the performance of downstream vision and language tasks, improves interpretability of the model and is able to make more efficient use of training data. Complementary to the model aspect, we also study the data aspect of commonsense learning in vision and language. We study active learning for Visual Question Answering (VQA) where a model iteratively grows its knowledge through querying informative questions about images for answers. Drawing analogies from human learning, we explore cramming (entropy), curiosity-driven (expected model change), and goal-driven (expected error reduction) active learning approaches, and propose a new goal-driven scoring function for deep VQA models under the Bayesian Neural Network framework. Once trained with a large initial training set, a deep VQA model is able to efficiently query informative question-image pairs for answers to improve itself through active learning, saving human effort on commonsense annotations.", "source_url": "https:\/\/openalex.org\/W2763796612", "pdf_url": "", "doi": "", "handle": "", "repository_name": "VTechWorks (Virginia Tech)", "source_record_id": "W2763796612", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 758, "title": "Multimodal emotion recognition using deep learning techniques", "title_ar": "", "title_en": "Multimodal emotion recognition using deep learning techniques", "author": "Tien Dung Nguyen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "deep-learning; nlp; robotics; multimodal; sentiment-analysis; speech-recognition", "keywords": "", "abstract": "This thesis investigates the use of deep learning techniques to address the problem of machine understanding of human affective behaviour and improve the accuracy of both unimodal and multimodal human emotion recognition. The objective was to explore how best to configure deep learning networks to capture individually and jointly, the key features contributing to human emotions from three modalities (speech, face, and bodily movements) to accurately classify the expressed human emotion. The outcome of the research should be useful for several applications including the design of social robots.", "source_url": "https:\/\/openalex.org\/W3013449349", "pdf_url": "https:\/\/eprints.qut.edu.au\/180753\/1\/Tien%20Dung_Nguyen_Thesis.pdf", "doi": "10.5204\/thesis.eprints.180753", "handle": "", "repository_name": "Queensland University of Technology", "source_record_id": "W3013449349", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 762, "title": "Author attribution for Reddit data: a comparative study on machine learning models : a thesis in Data Science", "title_ar": "", "title_en": "Author attribution for Reddit data: a comparative study on machine learning models : a thesis in Data Science", "author": "Sairama Amulya Baswa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; nlp; rnn", "keywords": "", "abstract": "The proliferation of user-generated content on online platforms like Reddit has created vast datasets that can be used to extract significant knowledge, such as author attribution through various analytical techniques. Author attribution is the task of identifying the writer of a given text, or in this case, comments or posts. This analysis can help us understand how people interact on Reddit, and it could also be useful for detecting issues such as fake accounts, secondary accounts, or just getting a better sense of how individual users behave. Namely, author verification and author attributions help us identify if two or more accounts are from the same person or genuinely from different people. However, the challenge is that figuring out this information from short and casual posts like you often see on Reddit is not straightforward. The brevity usually leads to sparse feature representations, and informal language can obscure consistent stylistic patterns that are readily apparent in longer, more formal writing. Existing author attribution methods may not be optimally suited for capturing the subtle yet distinctive linguistic cues that differentiate authors within the specific context of Reddit’s or similar platforms’ communication style. This research addresses the challenge of author attribution in Reddit comments by employing a comprehensive feature engineering strategy coupled with the evaluation of several machine learning and deep learning models. Our hypothesis is that, by looking at different language details, especially stylometric details that are typical of Reddit posts, we can figure out if two comments were written by the same person. Our literature survey suggested that stylometry analysis is more relevant for author attribution or verification. To assess the effectiveness of these engineered features, we evaluate the performance of three distinct classification models: a decision tree, a random forest, and a bidirectional long short-term memory (LSTM) network. In bidirectional LSTM, we use two different inputs and see the performance. The outcomes of this research hold significant implications for various stakeholders. Researchers in natural language processing (NLP) and computational social science can leverage our feature engineering pipeline and the comparative model analysis as a benchmark and a source of inspiration for future work on author attribution in online contexts. Furthermore, understanding the linguistic markers that characterize individual authors on platforms like Reddit can inform sociolinguistic studies and the evolution of online communication. Ultimately, this work aims to advance the state-of-the-art author attribution for short, informal text data, providing practical tools and valuable insights for a range of applications.", "source_url": "https:\/\/openalex.org\/W4416154571", "pdf_url": "", "doi": "10.62791\/20519", "handle": "", "repository_name": "", "source_record_id": "W4416154571", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 763, "title": "Non-invasive fairness drift monitor for machine learning models using conformance constraints: a thesis in Computer Science", "title_ar": "", "title_en": "Non-invasive fairness drift monitor for machine learning models using conformance constraints: a thesis in Computer Science", "author": "Raquel Ana Magalhães Bush", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; responsible-ai; object-detection; fairness", "keywords": "", "abstract": "Machine learning models deployed in consequential domains can become unfair toward protected subgroups as the data they receive drifts over time, yet the protected attributes needed to measure fairness directly are often unavailable at runtime due to privacy regulation and operational constraints. This creates a gap: existing fairness toolkits require protected labels and perform one-time audits, while generic drift detectors monitor continuously but cannot localize which subgroup a shift harms. This thesis develops a non-invasive fairness drift monitor that addresses this gap by repurposing Conformance Constraints, a data-profiling primitive, as a temporal fairness signal. The monitor learns per-subgroup distributional profiles at baseline, using protected attributes only once, and thereafter tracks violation of those profiles over incoming data batches without any runtime access to protected attributes. Across three fairness benchmarks, two classifiers, and nineteen controlled drift scenarios, a minority-subgroup conformance-constraint signal tracks fairness degradation more closely than KS and KL detectors under global drift on the two datasets where it is defined, while the conformance constraint signals defined on all three datasets remain competitive with those baselines under both regimes. Subgroup localization provides its clearest advantage under global drift, where a minority-subgroup signal substantially outperforms both aggregate signals and the baselines. The correlations are modest in absolute terms, indicating that the monitor functions as a screening instrument that flags fairness degradation for closer investigation rather than as a precise estimator. The approach offers privacy-preserving, subgroup-aware fairness monitoring suited to regulated deployment settings.", "source_url": "https:\/\/openalex.org\/W7212174866", "pdf_url": "", "doi": "10.62791\/20639", "handle": "", "repository_name": "", "source_record_id": "W7212174866", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 764, "title": "Improving computational efficiency via tiny machine learning data-centric methods: a thesis in Data Science", "title_ar": "", "title_en": "Improving computational efficiency via tiny machine learning data-centric methods: a thesis in Data Science", "author": "Michael Soricelli", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; transformers; image-classification; ai-smart-cities", "keywords": "", "abstract": "As the capabilities of Machine Learning models grow, so does the cost of power consumption and time. In the era of deep learning, models have become larger and larger. While the complexity of these models leads to greater performance, they also require more computation to train. According to recent estimates, the state of the art transformer based models require 3-5 months to train, while consuming 50 gigawatt-hours of energy. While not all deep learning models are not this large, higher performing models do follow the trend of larger model size over time. As a consequence, this makes training high performing models on resource constrained devices more challenging. To address this problem, one can try to minimize training costs through a data-centric approach. Moreover, one can attempt to minimize the dataset size with the goal being to reduce training costs by limiting the amount of data needed for training with the goal of maintaining performance. There are two data-centric approaches that are used primarily in this work, dataset pruning and dataset condensation. Dataset pruning is an approach that removes less efficient training samples from a dataset, while data condensation is a technique that aims to condense the knowledge from a large dataset into a smaller, synthetic dataset while preserving the performance of models trained on it. In this work, we propose a couple of different methods that utilize both these ideas to minimize training costs when training a deep neural network. We utilize vector quantization and normalizing flow models to perform variations of distribution learning. Distribution learning is utilized in this work to guide the training process towards more data efficient samples and improve upon existing state of the art dataset pruning methods. Our results show significantly reduced training costs across multiple benchmark image classification datasets, while also maintaining classification performance and outperforming other state of the art dataset pruning methods.", "source_url": "https:\/\/openalex.org\/W4416154402", "pdf_url": "", "doi": "10.62791\/20484", "handle": "", "repository_name": "", "source_record_id": "W4416154402", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 765, "title": "Emulating marine ecosystem dynamics: a machine learning approach for biomass prediction and forecasting : a thesis in Data Science", "title_ar": "", "title_en": "Emulating marine ecosystem dynamics: a machine learning approach for biomass prediction and forecasting : a thesis in Data Science", "author": "Ajmal Abbas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "Marine ecosystem models such as Atlantis have valuable insight into multispecies interactions, spatial dynamics and fisheries management, but their high computational cost limits are rapid scenario analysis and real-time decision making. This study presented a data-driven ecosystem emulator for the Northeast U.S. Atlantis model using automated machine learning approaches. A large-scale dataset (~3.4 million records) spanning 1964-2020 was contracted, integrating biomass of species functional groups, spatial polygons temporal indices and environments variables such as temperature and salinity. The emulator framework employed automated machine learning techniques, including Random Forest and Extra tress regression, with model selection and hyperparameter optimization performance using Automatic Machine Learning strategies. In addition, Autokeras was utilized to explore neural network architecture in an automated manner, enabling data-driven model allocation, lagged variables to capture ecological inertia and time-aware transformations. Model performance was evaluated using out-of-sample temporal validation, recursive back testing and ecological plausibility assessments. Result demonstrated string predictive performance. with species-level R2 values frequently exceeding 0.90 and overall model accuracy approaching 94%. The emulator achieved high computational efficiency, with end-to-end prediction completed in under few seconds, substantially reducing runtime compared to Atlantis simulations. This work established a scalable and efficient AutoML-driven alternative to process -based ecosystem, model, enabling rapid biomass estimation and supporting data-driven fisheries management and ecosystem analysis.", "source_url": "https:\/\/openalex.org\/W7167819629", "pdf_url": "", "doi": "10.62791\/20585", "handle": "", "repository_name": "", "source_record_id": "W7167819629", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 766, "title": "A machine learning approach to volume tracking in multiphase flow simulations: a thesis in Mechanical Engineering", "title_ar": "", "title_en": "A machine learning approach to volume tracking in multiphase flow simulations: a thesis in Mechanical Engineering", "author": "Aaron Mak", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "ai-industry", "categories": "machine-learning; deep-learning; ai-applications; ai-industry", "keywords": "", "abstract": "Multiphase flow is referred to flow of two or more immiscible phases (liquid, gas and solid). It is encountered in many essential natural phenomena and industrial processes. Computational simulations have emerged as a powerful and reliable tool for multiphase flow research to further current understanding and uncover new insights. They complement and are often strong alternatives to experimental methods, especially in studies where experiments are unfeasible or prohibitively expensive, due to, for example, short length or time scales or complex geometries. An essential component of multiphase flow simulations is capturing the dynamics of the interface separating the immiscible phases and tracking the phase volumes. Various methods have been proposed to achieve this, including the front tracking, level set, and volume-of-fluid (VOF) methods. The VOF method has become one of the most commonly used approaches to volume tracking and is the focus of this thesis. In VOF, the most common solutions are performed in two steps: interface reconstruction followed by flux calculation for volume advection. They represent a significant computational cost in VOF based multiphase flow simulations. In this work, a new approach using machine learning(ML) is used to generate a general advection function in a two-dimensional VOF scheme, which bypasses interface reconstruction and flux calculation. Although ML functions require a larger upfront cost to train, the resulting functions may be less computationally expensive to use when compared to traditional VOF methods. The data set in this work was generated from translation and rotation of a circle under various spatial and temporal resolutions. The ML training was performed using MATLAB’s Deep Learning Toolbox. To find an optimal neural network configuration, a grid search method based on the validation performance was used. Additionally, a rating system was developed to assess the overall performance of each function, as a potential alternative to solely relying on validation performance. This thesis presents results from commonly used advection tests to evaluate performance of volume tracking methods. In ideal conditions, the computation speed up is four times compared to the conventional VOF method, although determining ideal conditions will require further testing. In terms of accuracy, however, the VOF method remains superior.", "source_url": "https:\/\/openalex.org\/W4409543070", "pdf_url": "", "doi": "10.62791\/20258", "handle": "", "repository_name": "", "source_record_id": "W4409543070", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 768, "title": "Machine learning for predictive resilience modeling: a thesis in Computer Engineering", "title_ar": "", "title_en": "Machine learning for predictive resilience modeling: a thesis in Computer Engineering", "author": "Karen Alves da Mata", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "Resilience engineering studies the ability of a system to survive and recover from disruptive events, which finds applications in several domains. Most studies emphasize resilience metrics to quantify system performance, whereas recent studies propose statistical modeling approaches to project system recovery time after degradation. Moreover, past studies are either performed on data after recovering or limited to idealized trends. Therefore, this thesis proposes four alternative neural network (NN) approaches including (i) Artificial Neural Networks, (ii) Recurrent Neural Networks, (iii) Long-Short Term Memory (LSTM), and (iv) Gated Recurrent Unit to model and predict system performance, including negative and positive factors driving resilience to quantify the impact of disruptive events and restorative activities. Goodness-of-fit measures are computed to evaluate the models and compared with a classical statistical model, including mean squared error and adjusted R squared. The results indicate that NN models outperformed the traditional model on all goodness-of-fit measures. More specifically, LSTM achieved an over 60% higher adjusted R squared, and decreased predictive error by 34-fold compared to the traditional method. These results suggest that NN models to predict resilience are both feasible and accurate and may find practical use in many important domains.", "source_url": "https:\/\/openalex.org\/W4409794870", "pdf_url": "", "doi": "10.62791\/20389", "handle": "", "repository_name": "", "source_record_id": "W4409794870", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 769, "title": "Machine learning evaluations using WEKA : Honors Thesis, Spring 2020", "title_ar": "", "title_en": "Machine learning evaluations using WEKA : Honors Thesis, Spring 2020", "author": "Thomas Johnson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of North Carolina at Greensboro", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; medical-ai; predictive-analytics; medical-imaging", "keywords": "", "abstract": "Computer science is a growing field and machine learning is a growing area withincomputer science. The development of various machine learning algorithms that have beencreated has been diverse. Using WEKA, the study used the mammography dataset to examinemachine learning algorithms to explain what components of the machine learning algorithmsmay affect performance. The logistic regression model classified the most instances of theprovided partitioned mammogram dataset. Results indicated an expansion in the assortment ofmachine learning algorithms would be employed generating a larger collection of models.", "source_url": "https:\/\/openalex.org\/W3122142428", "pdf_url": "http:\/\/libres.uncg.edu\/ir\/ecsu\/f\/Johnson_Thomas_Honors%20Thesis%20_Spring%202020.pdf", "doi": "", "handle": "", "repository_name": "NC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro)", "source_record_id": "W3122142428", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 770, "title": "Application of reliability and resilience models to machine learning: a thesis in Computer Engineering", "title_ar": "", "title_en": "Application of reliability and resilience models to machine learning: a thesis in Computer Engineering", "author": "Zakaria Faddi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; ai-applications; predictive-analytics; cnn; image-classification; gan; ai-cybersecurity", "keywords": "", "abstract": "Machine Learning (ML) systems such as Convolutional Neural Networks (CNNs) are susceptible to adversarial scenarios. In these scenarios, an attacker attempts to manipulate or deceive a machine learning model by providing malicious input resulting in incorrect predictions or decisions, which can have severe consequences in security, healthcare, and finance applications. Failures in the ML algorithm can lead to failures in the application domain and the system to which they provide functionality, which may possess performance requirements, hence the need for software reliability and resilience assessment. Many studies propose enhanced techniques to improve the robustness and resilience of ML algorithms. Yet, very few provide quantitative methods that promote risk assessment or measure progress toward improvements in such systems. This research demonstrates the applicability of software reliability and resilience tools to ML algorithms providing an objective approach to assess reliability and resilience recovery after a degradation from known adversarial attacks. An image classification model using CNNs has been created as the target of two generative adversarial attacks (i) the Fast Gradient Sign Method and (ii) the Projected Gradient Descent. The classifier and adversarial attacks were utilized in a min-max game scenario to replicate incremental learning and adaptive adversarial training, to minimize\/maximize the number of failures of the classification algorithm. Model-generated data was collected and prepared in a format suitable for software reliability growth models (SRGM) with and without covariates and resilience models, including failure counts, memory usage, and the intensity of noise added by the attacks. Our results suggest that tools incorporating SRGMs and resilience techniques are suitable for quantifying and predicting the resilience and reliability of ML models and may find practical use in many domains in which these models are applied.", "source_url": "https:\/\/openalex.org\/W4409527456", "pdf_url": "", "doi": "10.62791\/20291", "handle": "", "repository_name": "", "source_record_id": "W4409527456", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 771, "title": "Machine learning with FPGA acceleration for signal classification: a thesis in Computer Engineering", "title_ar": "", "title_en": "Machine learning with FPGA acceleration for signal classification: a thesis in Computer Engineering", "author": "Bruno Paiva", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; cnn", "keywords": "", "abstract": "Spectrum sharing in the next generation (NextG) wireless communication systems can solve the spectrum scarcity problem, meet the demands of high data rate, and provide better quality of service. Characterizing surrounding wireless signals is essential to support such a spectrum sharing over the same spectrum band. Using machine learning to identify different signals on the same channel can solve this issue and lead to real time signal de-modulation of an unknown signal class. Using existing machine learning techniques such as convolution neural networks (CNN), Region-based convolution neural networks (R-CNN),fast region-based convolution neural networks (Fast R-CNN) and faster region-based convolution neural networks (Faster R-CNN), one or more signals can be classified and extracted from a channel at the same time. This project evaluates the image pre-processing methods to better format signal data for machine learning, including machine learning with time do-main signals, using spectrograms which maps frequency and time components of the signal, and scalograms which maps the continuous wavelet transform to time. These models are then built in TensorFlow and compiled to run on a CPU, GPU, and a Xilinix FPGA usingVitis-AI, to evaluate models for accuracy and inference time. After evaluating the models, it is found that the spectrogram frames are not an ideal image pre-processing technique for this application, with possible upside with higher frequency signals. The continuous wavelet transform performed in the scalogram frame amounts to an excellent prepossessing technique. Region-based convolution neural networks prove to offer a reduction in classification time with a significant reduction in classification accuracy, A better method for reducing classification time is FPGA acceleration. As it provides a tenfold increase in classification speed while retaining the exact same accuracy when compared to the same neural network running on a mid-range GPU.", "source_url": "https:\/\/openalex.org\/W4409526988", "pdf_url": "", "doi": "10.62791\/20255", "handle": "", "repository_name": "", "source_record_id": "W4409526988", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 772, "title": "An examination of adversarial machine learning on connected and autonomous vehicles: a thesis in Computer Engineering", "title_ar": "", "title_en": "An examination of adversarial machine learning on connected and autonomous vehicles: a thesis in Computer Engineering", "author": "David E. Austin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; robotics; ai-applications; predictive-analytics; rnn; ai-cybersecurity", "keywords": "", "abstract": "Connected and Autonomous Vehicle (CAV), a.k.a. driverless car, provides a solution to safety and efficiency in surface transportation. Yet, CAV technology introduces new security risks beyond the standard vehicle vulnerabilities. Machine Learning (ML) promises to secure CAV by utilizing telemetry data to validate legitimacy among CAVs as a method of Misbehavior Detection. However, most ML models base a misassumption of a benign environment, where the data during testing and training keep an equivalent distribution, which makes ML models vulnerable to adversarial perturbations. Furthermore, the lack of knowledge in CAV behavior opens new attack surfaces for adversaries to invalidate ML models. This thesis examines an attackers’ mindset to fool a ML system for Misbehavior Detection in CAV; thereby CAV defense strategies become robust to adversarial ML . Three software packages, OMNeT++, SUMO, and Veins, integrate to simulate CAV. The dataset for ML, comes from the Vehicle Reference Misbehavior Dataset, which this work expands with new attacks. Progressively, the work evaluates the accuracy of K-Nearest Neighbors and Random Forest for ML algorithms and that of Logistic Regression Neural Network and Recurrent Neural Network with Long-Short-Term-Memory for Deep Learning (DL) algorithms, all coded in Python. A Random Walk movement theory generates synthetic data following a normal distribution to further evaluate the robustness of the ML \/DL models, i.e., the effectiveness of spoofed data from the attacker’s point of view. The work reaches a conclusion that DL models perform better than ML models in most cases. This work contributes to ML Misbehavior Detection in CAV, exposing the challenges posed by adversarial ML and recommending the use of DL over ML against adversaries. As a result, it strengthens CAV defense with robust DL models. Future work will solve the limitations of DL such as the need for large quantity of data and balanced classes of data. To correct unbalanced classes, synthetic data generation can use specific python packages such as SMOTE. Undersampling or oversampling the minority or majority classes, respectively, could be another solution. Pairing Feature Analysis, such as Exploratory Data Analysis, to identify the most relevant features would train the models effectively and accurately. Other future work includes engineering the features from the datasets as well as using the Real-World Datasets from CAV pilot cities.", "source_url": "https:\/\/openalex.org\/W4409456610", "pdf_url": "", "doi": "10.62791\/20107", "handle": "", "repository_name": "", "source_record_id": "W4409456610", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 774, "title": "Machine learning-based angle-of-arrival estimation of RF signals: a thesis in Electrical Engineering", "title_ar": "", "title_en": "Machine learning-based angle-of-arrival estimation of RF signals: a thesis in Electrical Engineering", "author": "Jin Feng Lin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; cnn", "keywords": "", "abstract": "This thesis presents a comprehensive study on angle of arrival (AoA) estimation techniques for spectrum sharing in next-generation (NextG) wireless communication systems. The investigation encompasses signal processing methods, including multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT), as well as machine learning (ML) techniques such as artificial neural networks (ANNs) and convolutional neural networks (CNNs). These techniques are evaluated using both synthetic and over-the-air (OTA) test scenarios, with a focus on the impact of multipath fading and varying signal-to-noise-ratio (SNR) conditions. The study investigates the role of ML algorithms in enhancing the performance of wireless communication systems under challenging conditions. The robustness of ML techniques in real-world scenarios, including their abilities to maintain stability under multipath fading, is highlighted. The thesis proposes a new ML algorithm aimed at optimizing the performance of providing positional awareness about radio frequency (RF) signals. Furthermore, the finding with testing with OTA tests, reveal that while the MUSIC algorithm excels in synthetic scenarios with high-resolution capabilities beyond the antenna array’s resolution, it suffers from performance degradation in non-line-of-sight (NLOS) conditions and low SNR environments. On the other hand, ML algorithms demonstrate robustness and stability across varying conditions, though they exhibit a slightly higher error rate in simulations.", "source_url": "https:\/\/openalex.org\/W4409785432", "pdf_url": "", "doi": "10.62791\/20367", "handle": "", "repository_name": "", "source_record_id": "W4409785432", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 775, "title": "Data validation with machine learning for storage area networking utilities: a thesis in Computer Engineering", "title_ar": "", "title_en": "Data validation with machine learning for storage area networking utilities: a thesis in Computer Engineering", "author": "Jingya Xu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; expert-systems; predictive-analytics", "keywords": "", "abstract": "\"For storage area networking (SAN) devices and utilities, the traditional rule-based approach to validate the data displayed by multiple utilities is no longer practical due to the inconsistencies in semantics, variations in developer interpretation of specifications, and frequent updates to the data in different utilities. Using machine learning to validate the data for SAN devices and utilities shows promise with its capability for recognizing patterns and making predictions based on the comparative properties of the data provided by these SAN utilities. This research proposes an innovative data validation approach driven by machine learning that is applied to validate static Host Bus Adapter (HBA) information collected from SAN utilities such as the Distributed Management Task Force (DMTF) Redfish Interface and the Fabric Device Management Interface (FDMI). The machine learning driven framework consists of a data preprocessing stage and a neural network training stage. Multiple alternatives during both stages are examined to determine the optimal configuration. The outcome expects to substitute the current manual process of data validation that takes an excessive amount of time and effort for little return. The trained model has the ability to validate the data collected from the SAN utilities in a few seconds as opposed to weeks’ worth of man-hours with manual validation. The future work of the proposed framework will expand the variety of SAN utilities being validated and develop a weight analysis mechanism that allows the user to gain more insight about the data.\"", "source_url": "https:\/\/openalex.org\/W4409456773", "pdf_url": "", "doi": "10.62791\/20118", "handle": "", "repository_name": "", "source_record_id": "W4409456773", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 777, "title": "GenVRAD: dataset generator for machine learning detection of vehicle-roadside attacks : a thesis in Computer Engineering", "title_ar": "", "title_en": "GenVRAD: dataset generator for machine learning detection of vehicle-roadside attacks : a thesis in Computer Engineering", "author": "Devaj Ramsamooj", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "generative-ai", "research_subdomain": "fairness", "categories": "machine-learning; deep-learning; generative-ai; robotics; responsible-ai; ai-applications; fairness; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "Autonomous vehicles are gaining popularity rapidly for transportation safety and efficiency. However, to achieve fully automated driving without human intervention, cars need to be aware of traffic and environmental dynamics. One key component of their awareness is through communication. Connected and Automated Vehicles (CAV) leverage both automation and communication technologies to obtain information with nearby devices for decision making. Vehicular Ad-Hoc NETworks (VANETs) provide an architecture to support CAV with cars’ On-Board Units (OBUs) to communicate with each other and other devices like Roadside Units (RSUs). This communication can involve sharing information about traffic conditions, road hazards, and other data. Security of RSUs is crucial to ensure reliability and trustworthiness of its communication. Some security concerns with RSUs are authentication of RSUs, data integrity, etc. Addressing these concerns will require multiple types of mechanisms such as encryption and intrusion detection systems (IDS) to prevent misbehaving RSUs. With any type of communication, security of information is always at the forefront. Conventional security techniques have been tested, such as password protection and biometric security, but they do not meet the needs of the high dynamics of VANET. Machine and Deep have the ability to learn without human intervention, which is highly desirable to CAV. Since CAV is still new, there is little data to train and test Machine and Deep learning algorithms. Some attempts to synthesize attack datasets but only on vehicle-to-vehicle (V2V) communication. To the best of our knowledge, there is no data generator for vehicle-to-infrastructure (V2I). This research fills the gap by providing a dataset generator to inject misbehaving RSUs. Using simulation, data is generated to be able train and test several algorithms. Our work reveals machine learning algorithms are not sufficient to solve VANET security problems. The deep learning algorithm shows promise, but more analysis will be needed to be suitable for VANET security.", "source_url": "https:\/\/openalex.org\/W4409785539", "pdf_url": "", "doi": "10.62791\/20351", "handle": "", "repository_name": "", "source_record_id": "W4409785539", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 779, "title": "A Federated Learning Framework For Personalized and Privacy-Preserving Biosignal Interfaces", "title_ar": "", "title_en": "A Federated Learning Framework For Personalized and Privacy-Preserving Biosignal Interfaces", "author": "Kai Devon Malcolm", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rice University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "federated-learning", "categories": "machine-learning; computer-vision; responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "Biosignal-controlled interfaces—using data collected from sensors in, on, or around the body such as cameras, accelerometers, or electromyography sensors—offer a richer way to serve individual abilities, preferences, and behaviors in mobile human-computer interactions. However, the centralized machine learning paradigm has several shortcomings which prevent us from realizing this vision. Centralized training demands that users share their sensitive biosignal data to train a one-size-fits-all model, which may lack accessibility and fail to generalize to novel or out-of-distribution users. Personalizing to improve performance via finetuning requires even more user data, further increasing the privacy risk. Personalized federated learning balances this privacy-personalization trade-off by leveraging information from peer users to enhance performance via personalized models without requiring users to upload their sensitive data. This work introduces a framework for researchers working with sensitive biosignal data to incorporate federated learning into their applications. We apply our framework to both an open-loop simulation and a closed-loop user study for an EMG interface. We show that traditional algorithms outperform our modified sequential FL algorithm in terms of performance, but have a substantially higher privacy risk. Finally, we highlight the need for more federated learning theory to support the sequential scenario inherent to many wearable applications.", "source_url": "https:\/\/openalex.org\/W7203514434", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Rice Research Repository (Rice University)", "source_record_id": "W7203514434", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 780, "title": "Personalized Support Vector Machines for Privacy-Preserving Federated Learning", "title_ar": "", "title_en": "Personalized Support Vector Machines for Privacy-Preserving Federated Learning", "author": "Aleksei Ponomarenko-Timofeev", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "federated-learning", "categories": "machine-learning; computer-vision; responsible-ai; ai-applications; predictive-analytics; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "The pervasive digitalization of the environment enables monitoring of real-world systems by collecting data from multiple diverse sources. Such data may correspond to problems such as network intrusion detection or predicting the state of the wireless channel. While machine learning (ML)-based solutions are increasingly employed in such tasks, centralized learning approaches often fall short in terms of scalability and privacy. A more practical alternative is offered by federated learning (FL), which enables collaborative model training without disclosing raw data. However, FL brings a new set of challenges such as heterogeneous data distributions, diverse computational resources, and privacy requirements, which are rarely considered together in the algorithm design. This dissertation addresses the heterogeneity of the data structure and distribution, the heterogeneity of the computational resources, and the requirements for privacy preservation. Motivated by the fact that such factors may be present simultaneously, this dissertation focuses on developing lightweight privacy-preserving personalized FL algorithms. The work aims to bridge the gap in the FL field by formulating a set of algorithms for learning classification\/regression models (i), proposing policies for operating in highly heterogeneous systems and assessing their applicability in different scenarios (ii), assessing the applicability of conventional privacy preservation measures (iii), and proposing novel privacy-preserving mechanisms and evaluating their applicability (iv). Assessments are based on the results collected from extensive experiments across a broad range of scenarios and data types. The numerical results demonstrate the effectiveness of the proposed methods. In systems with heterogeneous computational resources and data, the proposed algorithms consistently maintain performance on par with or better than the state-of-the-art. The training delay was observed to be up to 20 % lower compared with the state-of-the-art personalized FL algorithms. Moreover, the proposed policies for operating in highly heterogeneous systems retain fairness and leverage heterogeneity to improve the quality of the learned models. The positive effect of these policies was observed through faster model convergence and stable fairness. It was also determined that conventional privacy preservation measures, such as differential privacy mechanisms, may be applied to the system where the proposed algorithms are used. The utility of the models learned by the algorithms was assessed for these privacy preservation measures. The proposed privacy preservation mechanisms, based on multiplicative noise, were found to provide better privacy\/utility trade-offs compared to the conventional Gaussian mechanism within a certain operational region. These regions of higher utility were explored for diverse datasets (e.g., image-based, time-series). Multiple approaches to improving the utility of the learned model while retaining the privacy guarantees were studied based on the properties of the algorithms. The advantages of these approaches are demonstrated through both qualitative insights and quantitative evidence. Overall, this thesis addresses a key gap in the FL literature by providing lightweight, privacy-preserving, and personalized algorithms designed for heterogeneous wireless systems.", "source_url": "https:\/\/openalex.org\/W7132950646", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7132950646", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 781, "title": "Securing Federated Learning: A Comprehensive Defence Against Privacy Attacks", "title_ar": "", "title_en": "Securing Federated Learning: A Comprehensive Defence Against Privacy Attacks", "author": "Moyeen, M A", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Concordia University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; medical-ai; reinforcement-learning; responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "In this information age, machine learning (ML) applications drive smart living through innovations such as personalized healthcare, intelligent transportation, and smart homes. However, despite these advancements, businesses and industries continue to face significant challenges in safeguarding data privacy, as ML systems increasingly rely on vast amounts of user data. In this direction, Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training while keeping user data on local premises without the need to share raw data. However, FL faces significant challenges also, including expensive communication costs, system heterogeneity, and vulnerability to various attacks. In particular, it is susceptible to poisoning attacks, where malicious participants corrupt models and data as well as inference attacks that exploit gradients to reveal sensitive information through membership inference or model inversion techniques. These attacks can extract sensitive information from shared gradients, undermining the fundamental privacy guarantees of federated systems. Thus, effective defence mechanisms are fundamental for fully leveraging the advantages of FL. Numerous defences, such as FoolsGold, Flod, Flad, MADDPG, and others, are in place to secure the FL systems. However, the majority of the defence mechanisms suffer from accuracy degradation, computational overhead, and inadequate attack prevention. Most client selection methods cannot reliably separate malicious and straggler clients, with even cutting-edge approaches struggling with herding and cold-start issues. Furthermore, recent state-of-the-art techniques frequently fail to defend against inference attacks adequately. These methods typically employ Secure Multiparty Computation (SMPC), Homomorphic Encryption (HE), or Differential Privacy (DP) as defensive measures. However, SMPC and HE suffer from high computational complexity, while DP often leads to degraded model accuracy. Thus, this research addresses these limitations by proposing five different defence mechanisms that ensure robust FL with protected gradients. The proposed mechanisms consist of FedChallenger, Fed-Reputed, SignDefence, Ada-Sign, and SignMPC. The proposed FedChallenger introduces a dual-layer defence mechanism that comprises the zero-trust challenge-response-based authentication at the first layer and a variant of Trimmed-Mean aggregation at the second layer that leverages pairwise cosine similarity and Median Absolute Deviation (MAD). Extensive evaluation on MNIST, FMNIST, EMNIST, and CIFAR-10 datasets demonstrates 3-10% accuracy improvement over state-of-the-art approaches with 1.1-2.2 times faster convergence and 2-3% higher F1-scores. Subsequently, the reputation-based client selection approach, Fed-Reputed, leverages device capability information and a modified Bellman equation within a hierarchical framework, integrated into a Deep Q-Learning Network (DQN)-based Imbalanced Classification Markov Decision Process (ICMDP) classifier for enhanced client selection. Testing on MNIST and FMNIST datasets demonstrates 9-50% accuracy gains and 1.3-1.7 times faster convergence while effectively detecting both malicious and straggler clients. Moreover, existing methods often suffer from the dying ReLU problem, where neurons permanently deactivate during training. To counter the dying ReLU problem, SignDefence implements a sophisticated aggregation scheme that utilizes sign direction and LeakyReLU-based aggregation, incorporating Jaccard similarity derived from binary-encoded model weights. This technique demonstrates consistent accuracy and F1-score improvements across different attack conditions. Despite its benefits, SignDefence remains vulnerable to inference attacks and suffers from limited generalization due to its fixed threshold across diverse benchmark datasets. To address these limitations, a lightweight strategy, Ada-Sign, employs adaptive threshold computation and incorporates DP mechanisms. This approach maintains comparable accuracy to SignDefence while providing enhanced gradient protection through adaptive DP. Extensive evaluation on MNIST and HAR datasets reveals 3-20% accuracy improvement for Ada-Sign over the majority of state-of-the-art techniques. Finally, to enhance the protection of both SignDefence and Ada-Sign against inference attacks, iv SignMPC integrates highly configurable SMPC, HE, and DP algorithms. This combined approach ensures comprehensive communication security and gradient privacy while avoiding significant performance bottlenecks. Comprehensive evaluation on MNIST and HAR datasets demonstrates 4-17% accuracy gains for SignMPC over established approaches while maintaining computational efficiency and robust privacy guarantees.", "source_url": "https:\/\/openalex.org\/W7112513152", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Spectrum Research Repository (Concordia University)", "source_record_id": "W7112513152", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 782, "title": "Federated Learning Enables Privacy-Preserving Cross-Border Malaria Surveillance", "title_ar": "", "title_en": "Federated Learning Enables Privacy-Preserving Cross-Border Malaria Surveillance", "author": "Ebenezer Agyemang ; Eric Opoku Osei", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "medical-ai; responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "Malaria surveillance in West Africa faces two conflicting imperatives: Regional collaboration for effective cross-border disease tracking Data sovereignty mandated by national data protection regulations This work demonstrates that federated learning (FL) can enable privacy-preserving collaboration while handling extreme data heterogeneity across countries. We compare 8 methods including centralized baselines and 6 federated learning algorithms on real malaria surveillance data from Ghana, Mali, Nigeria, and Burkina Faso.", "source_url": "https:\/\/openalex.org\/W7130854955", "pdf_url": "", "doi": "10.5281\/zenodo.18722518", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7130854955", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 783, "title": "EFFICIENT AND PRIVACY-PRESERVING FEDERATED LEARNING FOR RECOMMENDER SYSTEMS", "title_ar": "", "title_en": "EFFICIENT AND PRIVACY-PRESERVING FEDERATED LEARNING FOR RECOMMENDER SYSTEMS", "author": "Peihua Mai", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7126285846", "pdf_url": "", "doi": "", "handle": "", "repository_name": "National University of Singapore", "source_record_id": "W7126285846", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 784, "title": "Privacy preserving vertically partitioned federated learning: new techniques and considerations", "title_ar": "", "title_en": "Privacy preserving vertically partitioned federated learning: new techniques and considerations", "author": "Florian van Daalen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maastricht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "medical-ai; responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "Research depends on the collection of large amounts of data. However, this data is collected by different institutes, and is often difficult to share because of privacy concerns. This thesis proposes a number of new techniques that can be used to run data-analysis on data in a privacy preserving manner. It uses federated learning techniques in line with the “personal health train”-approach proposed by Dr. Andre Dekker. This thesis was part of the CARRIER project and focuses on data-sharing scenarios that are “vertically” partitioned. In such a scenario the parties sharing data have collected different types of data on the same population. For example, in the CARRIER data the data is split in a socio-economic dataset, collected by Statistics Netherlands, and medical data, collected at the MUMC and by regional GPs. All parties are collected data from the south-Limburg population. In addition to proposing new techniques, this thesis also discusses the current approach to privacy in research. It critiques the influence of large institutes, and questions if we are really protecting the data-subjects.", "source_url": "https:\/\/openalex.org\/W4408328439", "pdf_url": "", "doi": "10.26481\/dis.20250328fd", "handle": "", "repository_name": "Research Publications (Maastricht University)", "source_record_id": "W4408328439", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 785, "title": "Federated Learning Platforms for Dynamic and Value-Driven Participation in Privacy-Preserving Machine Learning", "title_ar": "", "title_en": "Federated Learning Platforms for Dynamic and Value-Driven Participation in Privacy-Preserving Machine Learning", "author": "Christoph Düsing", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universität Bielefeld", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; xai; federated-learning; fairness", "keywords": "", "abstract": "In recent years, Federated Learning (FL) has emerged as a powerful solution for privacy-preserving machine learning, enabling multiple parties - so-called clients - to collaboratively train models without directly sharing their data. This paradigm is particularly relevant in data-sensitive domains such as healthcare and finance. Yet, traditional FL frameworks typically rely on a fixed set of participants and lack mechanisms to adapt to dynamic client participation. This thesis addresses this limitation by proposing the concept of Federated Learning Platforms (FLPs): A novel, platform-oriented framework designed to support the onboarding and offboarding of clients over time, thereby aligning more closely with real-world collaboration dynamics and strategic (business) objectives. To this end, the framework comprises modular components for technical infrastructure and platform governance that enable scalable model training, secure communication, incentive alignment, and long-term federation stability. A central contribution of this work is the development of an explainable input control mechanism that serves as a gate-keeper to the FLP. It evaluates prospective clients based on privacy-preserving data imbalance metrics. Through extensive empirical studies on multiple datasets, we uncover a systemic trade-off between clients' contribution and benefit in FL, where highly imbalanced clients often contribute most to the model but gain the least in return. The proposed input control mechanism addresses this tension by making client admission both data-driven and transparent. In addition, we systematically investigate the role of data sampling and client selection in mitigating the detrimental effect of data imbalance - one of the most pressing challenges in FL. We benchmark existing strategies for local data sampling, introduce a novel optimization-based sampling approach, and propose a distribution-aware client selection algorithm. Together, these contributions effectively manage local and global imbalances, as well as other unfavorable data properties, leading to improved model convergence, predictive performance, and overall training time. Finally, we explore the impact of concept drift in dynamic FLP environments, where the landscape of participating clients and data distributions evolves over time. Therefore, we evaluate both error-based and data-based drift detection methods with respect to their timeliness of intervention and propose a nuanced perspective that distinguishes between harmful and beneficial drift. This enables the development of adaptive strategies that preserve model quality while leveraging potential performance improvements. Together, these contributions lay the groundwork for FLPs - scalable, adaptive, and trustworthy systems that transform static FL into an open, continuously evolving digital platform for privacy‑critical machine learning.", "source_url": "https:\/\/openalex.org\/W7108321425", "pdf_url": "", "doi": "10.4119\/unibi\/3012109", "handle": "", "repository_name": "Publikationen an der Universität Bielefeld (Universität Bielefeld)", "source_record_id": "W7108321425", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 786, "title": "Federated transfer learning in smart homes pattern recognition for privacy preservation", "title_ar": "", "title_en": "Federated transfer learning in smart homes pattern recognition for privacy preservation", "author": "Su Shun Lei May", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "LUT University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; responsible-ai; transfer-learning; rnn; fairness", "keywords": "", "abstract": "Smart homes collect continuous sensor data about how people live, including when they sleep, cook, work, and move around the house. Training AI models on this data has traditionally required sending it to a central server, which creates serious privacy risks and conflicts with laws like the General Data Protection Regulation. This thesis builds and tests a Federated Transfer Learning framework that trains a shared AI model across ten smart homes without any raw data ever leaving the local device. The system uses a two-layer Long Short-Term Memory network trained through the Federated Averaging algorithm across ten distinct household types. The model is split into two parts. The shared body learns general activity patterns from all ten homes and is the only part transmitted between clients and the server. The private head stays on each device throughout all 60 rounds of training, is never transmitted to the server, and adapts privately to each household's specific activity distribution. Testing showed that eight of ten clients' private heads achieved 100.00% macro F1 on their own local validation data, Client 4 achieved 99.63%, and Client 10 achieved 99.67%, confirming that private head training across 60 rounds produces strong household-specific classification without any head information leaving the local device. On a shared test set evaluated as the average across all ten client models, the FTL-LSTM framework achieved 90.86% macro F1. This lower figure reflects the combined effect of head privacy and household specialization: nine of the ten private heads were trained on households other than the test home. Standard FedAvg, which transmits full model weights including head parameters every round across the same 60 rounds, achieved 99.61% on the same shared test set using a single globally averaged head. The 3.7% communication saving from body-only transmission was confirmed with FTL-LSTM transmitting 575.39 MB compared to 597.41 MB for standard FedAvg over identical 60-round durations. In the cold start scenario evaluated on 865 windows from Home 11, the pretrained body provided a transfer advantage in all four labeled sample conditions: +9.80 percentage points at 10 samples, +8.33 at 20 samples, +12.18 at 50 samples, and +8.31 at 100 samples, with peak FTL performance of 52.29% at 100 samples. These results show that body only head private federation produces near perfect local performance for each household while carrying a measurable difference on shared external test evaluations, a difference that reflects both head privacy and household specialization rather than privacy cost alone.", "source_url": "https:\/\/openalex.org\/W7203565195", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LUTPub (LUT University)", "source_record_id": "W7203565195", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 787, "title": "Federated Learning of Fuzzy Rule-based Systems: Advances in Privacy, Security and Interpretability", "title_ar": "", "title_en": "Federated Learning of Fuzzy Rule-based Systems: Advances in Privacy, Security and Interpretability", "author": "Mattia Daole", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Pisa", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; expert-systems; ai-applications; predictive-analytics; optimization; xai; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "This thesis addresses the growing need for Trustworthy AI, focusing on the transparency and privacy requirements. Transparency is commonly addressed through eXplainable AI (XAI), while privacy-preserving paradigms such as Federated Learning (FL) enable collaborative model training without sharing sensitive data. The work extends traditional Fuzzy Rule-Based Classifiers (FRBCs) to federated settings, systematically analyzing their learning behavior and performance under heterogeneous and non-IID data distributions. To further improve the trade-off between predictive accuracy and interpretability, a multi-objective federated framework based on Evolutionary Fuzzy Systems is introduced, using synthetic data generation techniques to support privacy-preserving server-side optimization. In addition to model design, the thesis investigates security aspects by analyzing classical adversarial threats in FL, such as data and model poisoning, and by proposing a taxonomy of attacks specifically targeting federated FRBCs. Finally, an edge-oriented application framework compliant with Multi-Access Edge Computing architectures is presented, demonstrating the practical feasibility and effectiveness of deploying trustworthy, transparent, and privacy-aware AI systems in real-world distributed environments.", "source_url": "https:\/\/openalex.org\/W7140251669", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Electronic Theses and Dissertations Repository (University of Pisa)", "source_record_id": "W7140251669", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 788, "title": "Enhancing Privacy and Efficiency in Federated Learning: Quantization and Low-Rank Adaptation Approaches", "title_ar": "", "title_en": "Enhancing Privacy and Efficiency in Federated Learning: Quantization and Low-Rank Adaptation Approaches", "author": "Tianqu Kang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; ai-applications; transfer-learning; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Federated Learning (FL) enables collaborative AI model training on decentralized data but faces challenges in ensuring privacy and operational efficiency, especially for large models over wireless networks. This thesis addresses these critical issues by enhancing both aspects in FL systems. Firstly, we provide a rigorous analysis of the interplay between model update quantization and Differential Privacy (DP). Using Rényi Differential Privacy (RDP), we formally demonstrate that lower quantization bit levels can enhance privacy guarantees when applied to Gaussian-noised updates. This theoretical finding, contrary to some prior notions, is empirically validated using Membership Inference Attacks (MIA), confirming that quantization can be a privacy-enhancing mechanism in addition to a communication-saving one. Secondly, we propose a novel framework for differentially private federated fine-tuning (FedFT) of Foundation Models using Low-Rank Adaptation (LoRA). This framework uniquely leverages inherent wireless channel noise as a natural source for DP, reducing the need for artificial noise injection. We analyze the convergence of this split FedFT-LoRA architecture and address the critical problem of noise amplification by proposing a modified LoRA update strategy where only one low-rank matrix is trained while another initialized as scaled orthogonal. Simulations show this approach achieves superior model accuracy under stringent privacy budgets compared to baseline methods. Collectively, this work offers new theoretical insights and practical solutions for deploying private and resource-efficient FL, particularly for large-scale models in wireless environments.", "source_url": "https:\/\/openalex.org\/W4414315150", "pdf_url": "", "doi": "10.14711\/thesis-hdl152373", "handle": "", "repository_name": "", "source_record_id": "W4414315150", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 790, "title": "Resilient and Privacy-Preserving Methods for Distributed Computing and Federated Learning", "title_ar": "", "title_en": "Resilient and Privacy-Preserving Methods for Distributed Computing and Federated Learning", "author": "Marvin Xhemrishi ; Antonia Wachter-Zeh ; Technische Universität München", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7205800094", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W7205800094", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 791, "title": "Privacy-preserving Federated Learning Platform for Enhanced Medical Diagnosis", "title_ar": "", "title_en": "Privacy-preserving Federated Learning Platform for Enhanced Medical Diagnosis", "author": "Inês Filipa Tavares Alves", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "medical-ai; responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7172572361", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7172572361", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 793, "title": "Addressing data heterogeneity, scarcity, and training efficiency in privacy-preserving federated learning", "title_ar": "", "title_en": "Addressing data heterogeneity, scarcity, and training efficiency in privacy-preserving federated learning", "author": "Chen, Haokun", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Ludwig-Maximilians-Universität München", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; medical-ai; responsible-ai; ai-applications; transfer-learning; few-zero-shot; optimization; multimodal; llm; diffusion; federated-learning; fairness; ai-industry", "keywords": "", "abstract": "Over the past decade, deep learning has achieved significant breakthroughs in various domains, including computer vision and natural language processing, with applications spanning industries such as healthcare and manufacturing. However, the success of deep neural networks (DNNs) relies heavily on access to large-scale datasets, which can be difficult for individual organizations to acquire. The reasons behind are the complexities and costs for data collection and annotation at large scale. A straightforward solution is to centralize data from multiple organizations for training. However, this approach raises significant privacy concerns, as such data often contains sensitive or confidential information. Furthermore, regulations like the General Data Protection Regulation (GDPR) emphasize the importance of protecting user privacy in inter-organizational data exchanges. Moreover, transmitting large volumes of data introduces substantial computational overhead, further complicating the process. These challenges highlight the urgent need for methods that facilitate collaborative data use while ensuring privacy preservation. Federated learning (FL), which enables multiple parties to collaboratively train a DNN with the assistance of a central server, offers an effective solution to the aforementioned problem. Unlike traditional centralized learning, which requires collecting data from each party, FL eliminates the need to upload data for joint training. Instead, locally trained models are exchanged with a central server, which aggregates the knowledge from all of the uploaded models and then distributes a refined global model to each party. This approach allows each party to benefit from the collective contributions, ultimately enhancing the model performance. This thesis makes four key contributions to addressing challenges in federated learning, including data heterogeneity, data scarcity, and system convergence rate, while preserving client data privacy. For each contribution, we propose a novel method and empirically demonstrate its effectiveness within the relevant problem context. In this thesis, we address key challenges in federated learning (FL) related to data heterogeneity, foundation model adaptation, hyperparameter tuning, and communication efficiency. First, we tackle feature space heterogeneity by introducing a generative augmentation method that aligns diverse client distributions, validated on both public and real-world datasets. We then adapt parameter-efficient fine-tuning techniques for vision-language models in FL by designing a dual-stream adapter that captures both client-specific and client-agnostic knowledge. To optimize performance under limited resources and non-IID conditions, we propose an evolutionary hyperparameter tuning framework that enables efficient online optimization. Lastly, we address data scarcity and heterogeneity in One-Shot FL by personalizing pretrained latent diffusion models, enabling privacy-preserving synthetic data generation that improves performance across challenging domains such as medical and satellite imaging.", "source_url": "https:\/\/openalex.org\/W7108654185", "pdf_url": "", "doi": "10.5282\/edoc.36097", "handle": "", "repository_name": "Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München)", "source_record_id": "W7108654185", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 794, "title": "Federated Learning-Based Intrusion Detection System for Privacy-Preserving IoT Networks", "title_ar": "", "title_en": "Federated Learning-Based Intrusion Detection System for Privacy-Preserving IoT Networks", "author": "Prasanna Dhanireddy ; Khushboo Kumari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; medical-ai; responsible-ai; ai-applications; cnn; transformers; federated-learning; fairness; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "The rapid growth of IoT devices in smart homes, healthcare, and industrial systems has increased vulnerability to cyber threats. Traditional centralized intrusion detection systems (IDS) raise privacy, and scalability concerns due to the need for centralized data collection. This project aims to develop a Federated Learning (FL)-based IDS that enables distributed IoT nodes to collaboratively train the models without sharing raw data. This study proposes a FL-based IDS framework for IoT environments that systematically evaluates the trade-offs between detection performance, privacy guarantees, model efficiency, and synthetic zero-day like attack robustness. The framework investigates and compares multiple deep learning architectures, including Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Networks (1D-CNN), and TabTransformer (TTF) architectures. The integration of differential privacy was also examined to enhance data protection and analyze its impact on model performance. Additionally, quantization techniques were investigated for the models to reduce model size and improve computational efficiency for deployment on resource-constrained IoT devices. The proposed system is evaluated using the benchmark dataset CICIoT2023 basedon standard performance metrics such as accuracy, precision, recall, F1-score, AUC-ROC score along with inference time, and communication overhead. Experimental results demonstrate that TTF achieved the highest detection performance with an accuracy of 93.97%, F1-score of 92.47%, and AUC-ROC score of 98.11%, while MLP provided superior computational efficiency for resource-constrained IoT devices. The integration of differential privacy introduced a privacy-utility trade-off, where model accuracy decreased from 93.97% to 91.40% under stricter privacy guarantees. Furthermore, post-training dynamic quantization reduced TTF and 1D-CNN model size by 67.72% and 65.66%, thereby decreasing inference time with minimal performance degradation. The optimized federated model also demonstrated robustness against synthetic zero-day like attack scenarios generated using CTGAN. It achieved an accuracy of 90.96%, F1-score of 90.16%, and AUC-ROC of 96.55%, suggesting its potential to detect diverse attack behaviors.", "source_url": "https:\/\/openalex.org\/W7213389565", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7213389565", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 795, "title": "Model Protection and Privacy Preservation of Federated Learning using Homomorphic Encryption", "title_ar": "", "title_en": "Model Protection and Privacy Preservation of Federated Learning using Homomorphic Encryption", "author": "Manzur Elahi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; medical-ai; responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "Modern Machine Learning (ML) faces many critical challenges, including fragmented data silos and increasingly strict data privacy regulations. Applications in delicate fields like healthcare and finance are particularly impacted since access to extensive datasets is restricted by stringent legal and ethical requirements. Sensitive data, such as pregnancy-related medical records, particularly those involving termination, carries high risks of stigma, domestic violence, and social repercussions if exposed. Therefore, secure and privacy-preserving ML methods are essential for enabling collaborative learning without compromising individual privacy.", "source_url": "https:\/\/openalex.org\/W7129062304", "pdf_url": "https:\/\/researchportal.murdoch.edu.au\/view\/delivery\/61MUN_INST\/12191231440007891\/13191231430007891", "doi": "10.60867\/00000073", "handle": "", "repository_name": "Murdoch University Research Portal", "source_record_id": "W7129062304", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 797, "title": "Evaluating Federated Learning and Multimodal Data for Privacy-Aware Video-Based Fall Detection", "title_ar": "", "title_en": "Evaluating Federated Learning and Multimodal Data for Privacy-Aware Video-Based Fall Detection", "author": "Dick Kuan ; Martin Dahrén", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; multimodal; video-analysis; speech-recognition; federated-learning; fairness", "keywords": "", "abstract": "Falls remain a growing major public health risk in midst of an aging global population,increasing the demand for reliable fall detection systems that can operate withinprivacy-sensitive healthcare environments. While federated learning has emerged as apromising approach for training healthcare machine learning applications without sharingsensitive data, existing research on multimodal federated learning for fall detection hasprimarily focused on wearable sensors or sensor-image combinations. In contrast,multimodal video-audio fall detection has largely been evaluated under centralisedtraining settings, leaving uncertainty regarding its effectiveness in distributed dataenvironments.This thesis investigates whether Federated Averaging can support privacy-awarevideo-based and video-audio multimodal fall detection while maintaining acceptabledetection performance. Unlike many vision-based approaches that rely on raw imagedata, the proposed framework uses human pose keypoints and bounding-box features asprivacy-aware visual representations, reducing exposure of identifiable information whilepreserving motion patterns relevant to fall detection. A multimodal extension combinesvisual and audio representations through feature-level fusion to evaluate whether audiocan improve robustness under degraded visual conditions.Experiments conducted on the Le2i dataset show that centralised video-based falldetection achieved an average accuracy of 93.6%, while multimodal detection achieved92.6%. Although audio provided limited benefits under normal visual conditions,multimodal fusion improved robustness when video data were affected by noise.Federated learning introduced performance trade-offs compared with centralised training;however, federated models achieved higher recall and maintained effective fall detectionperformance. Under federated training, the multimodal model achieved an averageaccuracy of 85.3%, outperforming both unimodal video and audio models.The findings demonstrate that privacy-aware fall detection based on skeletal andbounding-box representations can be effectively combined with federated learning, whilemultimodal fusion can improve robustness in challenging environments. This contributesnew evidence regarding the feasibility of deploying privacy-preserving video-based falldetection systems in healthcare settings where centralised collection of sensitive data isimpractical.", "source_url": "https:\/\/openalex.org\/W7166409083", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7166409083", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 798, "title": "Enabling privacy-preserving concept stock recommendation with federated learning", "title_ar": "", "title_en": "Enabling privacy-preserving concept stock recommendation with federated learning", "author": "Zhuoyi Peng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; federated-learning; fairness", "keywords": "", "abstract": "991012980216803412 HKUST Electronic Theses Enabling privacy-preserving concept stock recommendation with federated learning by Zhuoyi Peng thesis 2021 1 online resource (x, 37 pages) : color illustrations The prosperity of the stock market has witnessed the rapid booming of the stock", "source_url": "https:\/\/openalex.org\/W4220921178", "pdf_url": "", "doi": "10.14711\/thesis-991012980216803412", "handle": "", "repository_name": "", "source_record_id": "W4220921178", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 799, "title": "Privacy-based approach to brain MRI: Federated learning", "title_ar": "", "title_en": "", "author": "ŞEVKET AY ; Enstitüler, Lisansüstü Eğitim Enstitüsü, Uluslararası İşletmecilik ve Ticaret Ana Bilim Dalı", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2023, "language": "tr", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; medical-ai; responsible-ai; cnn; medical-imaging; federated-learning; fairness", "keywords": "", "abstract": "İÇİNDEKİLER BEYAN........................................................................................................................ ii TEŞEKKÜR ................................................................................................................ i İÇİNDEKİLER .......................................................................................................... ii KISALTMALAR ...................................................................................................... iv SİMGELER ............................................................................................................... vi TABLOLAR LİSTESİ............................................................................................viii ŞEKİLLER LİSTESİ................................................................................................ ix ÖZET........................................................................................................................... x ABSTRACT............................................................................................................... xi BÖLÜM 1. GİRİŞ .......................................................................................................................... 1 BÖLÜM 2. DERİN ÖĞRENME................................................................................................... 7 2.1. Derin Öğrenme .................................................................................................. 7 2.1.1. Evrişimli sinir ağları (CNN) .................................................................... 11 BÖLÜM 3. FEDERE ÖĞRENME ............................................................................................. 19 3.1. Şifreleme Yöntemleri ...................................................................................... 21 3.1.1. Homomorfik şifreleme............................................................................. 21 3.1.2. Diferansiyel gizlilik.................................................................................. 23 3.2. Haberleşme Mimarileri.................................................................................... 25 3.2.1. Merkezi haberleşme mimarisi.................................................................. 26 3.2.2. Merkezi olmayan haberleşme mimarisi ................................................... 27 3.2.3. Hibrit haberleşme mimarisi...................................................................... 29 3.3. İletişim Protokolü............................................................................................ 30 3.4. Federe Öğrenme Türleri.................................................................................. 31 iii 3.4.1. Yatay Federe öğrenme ............................................................................. 31 3.4.2. Dikey federe öğrenme.............................................................................. 33 3.4.3. Bölünmüş öğrenme .................................................................................. 35 3.5. Optimizasyon Algoritmaları............................................................................ 36 3.5.1. Federe ortalama alma ............................................................................... 36 3.5.2. QFedAvg.................................................................................................. 38 3.5.3. Hata toleranslı Federe ortalama ............................................................... 40 3.6. Veri Bölümleme .............................................................................................. 42 3.7. Sıkıştırma Metotları......................................................................................... 45 3.8. Federe Öğrenme Avantajları ........................................................................... 46 3.9. Federe Öğrenme Zorlukları............................................................................. 49 3.10. Federe Öğrenme Frameworkleri ................................................................... 53 3.10.1. Flower .................................................................................................... 53 BÖLÜM 4. DENEYSEL ÇALIŞMA .......................................................................................... 55 4.1. Veri Kümesi .................................................................................................... 55 4.2. İstemciler Arası Veri Paylaşımı ...................................................................... 57 4.3. Evrişimli Sinir Ağı’nın Oluşturulması ve Uygulanması ................................. 58 4.4. Eğitim.............................................................................................................. 63 4.5. Deneysel Sonuçlar........................................................................................... 68 4.5.1. FedAvg strajesine göre istemci ve sunucu modelleri gelişimi................. 71 4.5.2.", "source_url": "https:\/\/openalex.org\/W7132728446", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Sakarya University of Applied Sciences Institutional Repository", "source_record_id": "W7132728446", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 801, "title": "Thwarting Semantic Backdoor Attacks in Privacy Preserving Federated Learning", "title_ar": "", "title_en": "Thwarting Semantic Backdoor Attacks in Privacy Preserving Federated Learning", "author": "Helen Möllering", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Technical University of Darmstadt", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3011619440", "pdf_url": "", "doi": "", "handle": "", "repository_name": "TUbilio (Technical University of Darmstadt)", "source_record_id": "W3011619440", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 802, "title": "Towards Private and Secure Federated Learning: Exploring the Interplay of Differential Privacy and Byzantine Robustness", "title_ar": "", "title_en": "Towards Private and Secure Federated Learning: Exploring the Interplay of Differential Privacy and Byzantine Robustness", "author": "Mohamed, Hour", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Arab Emirates", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; responsible-ai; ai-applications; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "Federated Learning is a part of Machine Learning where the learning process occurs on edge devices instead of a centralized approach. With this shift towards decentralized data collection, the risk of privacy leaks and malicious attacks from participants has become prevalent and requires attention. While Federated Learning alone has shown the potential to address privacy issues, it remains an insufficient method to rely solely on for explicit security guarantees. Federated Learning has been proven to leak information from participating clients from their updates. In addition, because data is decentralized, there is little control over what each participant sends, creating a vulnerability attackers could exploit to send corrupted updates and prevent learning. To address these limitations, researchers have adopted standard methods such as Differential Privacy and Byzantine robustness. Differential Privacy has gained popularity as a procedure to ensure the privacy of Machine Learning, specifically Federated Learning models. Participants inject noise into their data and then send it to the server. These clients could exhibit Byzantine behavior and act maliciously to disrupt the final model; therefore, Byzantine robustness has been used to mitigate their influence. These notions of Differential Privacy and Byzantine robustness have been used to enforce privacy and security assurances. Both objectives need to be met to ensure that Federated Learning is both practical and deployable in the real world. The main aim of this thesis is to study the combination of Differential Privacy and Byzantine robustness goals in a Federated Learning setting and understand the interplay of these objectives and whether they were indeed incompatible. In our work, we apply the classical FedAvg algorithm on three datasets and study the impact of the DP-SGD algorithm, the Krum aggregation method with different attacks (Sign-Flip and A Little is Enough), and their integration on the performance of the models. To conduct the experiments, we use the Flower federated framework. Our main results suggest that maintaining a high-performing model when faced with both objectives is challenging. When evaluating Differential Privacy and Byzantine robustness separately, all models achieve high accuracy; however, they show a decline in the presence of both. The analysis attributes this opposing dynamic to the Differential Privacy noise that could grant Byzantine participants an opening to conceal their harmful behavior.", "source_url": "https:\/\/openalex.org\/W7106282665", "pdf_url": "", "doi": "10.82419\/8", "handle": "", "repository_name": "MBZUAI iRep", "source_record_id": "W7106282665", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 803, "title": "Hyperparameter Optimization of Convolutional Neural Networks (CNN) using an Adaptive Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)", "title_ar": "", "title_en": "Hyperparameter Optimization of Convolutional Neural Networks (CNN) using an Adaptive Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)", "author": "charles usigbe", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "London South Bank University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; optimization; cnn", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7170046797", "pdf_url": "", "doi": "10.18744\/77554c5f-2daa-4485-9ace-1723f37b3638", "handle": "", "repository_name": "Research Open (London South Bank University)", "source_record_id": "W7170046797", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 804, "title": "Optimization of neural network hyperparameters using metaheuristic algorithms", "title_ar": "", "title_en": "Optimization of neural network hyperparameters using metaheuristic algorithms", "author": "Riihimäki, Mikko", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; optimization; cnn", "keywords": "", "abstract": "This thesis compares four hyperparameter search methods for tuning a compact convolutional neural network on CIFAR-10 under a uniform setup. The methods are grid search and three population-based metaheuristics, particle swarm optimization, genetic algorithms, and grey wolf optimization. The search space is limited to learning rate and dropout to isolate the effect of the search strategy. Each run has a budget of 50 model evaluations. All evaluations use the same protocol with 5 epochs, batch size 64 and a fixed stratified split. The study assesses three aspects of performance. First, the best validation accuracy reached within budget. Second, evaluation efficiency measured by the index of the first occurrence of the best score. Third, runtime reported as time to best and time to 95 percent of best. The configuration that yields the best validation score is retained on the full training set and then evaluated once on the test set. The methods achieve very similar best validation accuracies. The metaheuristics typically reach near best scores early, which appears as a shorter time to 95 percent of best. Grid search progresses in discrete steps and on average reaches the eventual best earlier and with lower variability. Test accuracy mirrors validation without large gaps. Metaheuristics are a practical choice when fast good solutions are needed, while a compact grid remains a strong baseline when the full budget is used. The study is limited by a narrow search space, short training, and CPU-only execution.", "source_url": "https:\/\/openalex.org\/W7112920518", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W7112920518", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 805, "title": "An optimized internet of vehicles network for safety-based messages transmission using particle swarm optimization", "title_ar": "", "title_en": "An optimized internet of vehicles network for safety-based messages transmission using particle swarm optimization", "author": "Shahirah Mohamed Hatim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universiti Teknologi MARA", "university_source": "repository_host", "college": "", "department": "", "country": "Malaysia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; ai-applications; optimization; ai-smart-cities", "keywords": "", "abstract": "This thesis addresses the problem of traffic congestion, which arises from increased vehicular queuing when the packet transmission load in the network exceeds the available buffer capacity. This condition leads to slower travel speed and also delays in travel time. Many urban cities worldwide have experienced severe congestion challenges. However, with the aid of the Internet of Things (IoT), the traffic congestion problem in the event can be managed effectively as vehicles are now equipped with sensors, actuators, data acquisition and communication systems that enable inter-vehicle communication. The Internet of Vehicles (IoV) has emerged as a key mechanism in in managing traffic congestion within modern Intelligent Transportation Systems (ITS), providing extensive opportunities for safety and mobility applications. This networking technology, provides vehicles with endless possibilities of applications including safety-related applications. Due to the high speed, dynamic mobility and unstable connectivity of vehicular nodes, maintaining stable IoV network performance remains a challenge. Previous research has explored various congestion control optimization techniques such Genetic Algorithm, Ant Colony and Ant Bee Colony for vehicular networks. While these methods improved performance to some extent, they often suffer from high computational complexity or slow convergence, limiting their suitability for real-time IoV environments. The recent proliferation of IoV network has emphasized the need for high Quality of Service (QoS) requirements in all networking scenarios. This study provides a comprehensive evaluation of the impact of vehicular mobility on IoV network performance. The analysis focuses on key QoS metrics, namely throughput, packet delivery ratio (PDR) and delay, while examining the relationship between mobility factors and medium access behaviour under varying network conditions. In achieving better performance in the QoS of the IoV network, a bio-inspired optimization approach based on the Particle Swarm Optimization (PSO) algorithm is proposed to mitigate network performance degradation caused by high node mobility. Vehicle mobility data is obtained from traffic flow simulation in SUMO and the network simulation phase is conducted in OMNeT++. Safety messages are disseminated among vehicles within the network. The experiment for the IoV network is concluded by optimizing the transmission rate to minimize the delay as the objective function in PSO algorithm. To ensure stable performance evaluation under dynamic vehicular conditions, simulations were conducted using varying simulation times and numbers of vehicles to capture both temporal network behaviour and traffic density variations. Under these settings, the throughput of the IoV network recorded average improvements of up to 23% and 14% after optimization. Packet delivery ratio (PDR) was also enhanced, with average gains of up to 37% and 15%, while network delay was reduced by up to 12% and 15% across different simulation times and vehicle densities. These results highlight the effectiveness of the optimization in improving IoV network performance under diverse conditions. The proposed PSO-based approach introduces a dynamic transmission rate control mechanism that adapts to vehicular density, effectively reducing congestion on the control channel. Overall, the findings highlight the potential for future research and practical applications in vehicular communication systems.", "source_url": "https:\/\/openalex.org\/W7170115999", "pdf_url": "https:\/\/ir.uitm.edu.my\/id\/eprint\/142641\/1\/142641_fulltext.pdf", "doi": "", "handle": "", "repository_name": "UiTM Institutional Repositories (Universiti Teknologi MARA)", "source_record_id": "W7170115999", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 806, "title": "OPTIMIZING CONVOLUTIONAL NEURAL NETWORKS VIA GENETIC ALGORITHM-BASED PRUNING", "title_ar": "", "title_en": "", "author": "João Silva", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "pt", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; optimization; cnn", "keywords": "", "abstract": "As redes neurais têm experienciado um crescimento significativo no seu tamanho e complexidade nos últimos anos, levando a avanços notáveis em diversas áreas da inteligência artificial. No entanto, este aumento exponencial no seu número de parâmetros e das necessidades computacionais tem trazido desafios consideráveis, especialmente quando se trata de implementar estes modelos em ambientes com recursos limitados. Além disso, esta exigência computacional dos modelos de Deep Learning tem contribuído para um maior consumo de energia, gerando preocupações económicas e ambientais. Como consequência, reduzir o tamanho e a carga computacional das redes neurais tornou-se um objetivo essencial para alcançar soluções de Deep Learning mais eficientes, escaláveis e sustentáveis. Este trabalho propõe uma abordagem de pruning estruturado para lidar com este desafio, tratando o pruning comouma tarefa de optimização multiobjetivo e recorrendo a um Algoritmo Genético(AG)paraidentificar subredes optimizadas na arquitetura VGG-16 aplicada à tarefa de classificação de imagem com o dataset CIFAR10. Os filtros da rede são representados como um genoma que evolui iterativamente através de processos de seleção natural para encontrar o equilíbrio ideal entre desempenho e eficiência computacional. Devido ao elevado custo computacional das avaliações, utilizou-se uma estratégia de mitigação onde o AG foi treinado e avaliado em apenas 25% do dataset CIFAR-10. A aptidão de cada indivíduo é avaliada com base numa função que pondera a preservação da precisão (40%) e a redução do custo computacional total (NumOps, 60%). Resultados quantitativos demonstram que, em cenários de fine-tuning, o AG superou o método tradicional de pruning por norma L1 em todos os níveis de esparsidade testados, alcançando 88,31% de precisão com 50% de esparsidade, o que representa uma redução de 51,42% no custo computacional (7,14 milhões de operações) face ao modelo base. No entanto, em cenários de reinicialização, embora o AG tenha atingido 92,02% de precisão com 25% de esparsidade (superando o modelo base de 91,05% e reduzindo as operações em 20,46%), não igualou a precisão superior obtida pelo método L1-norm(94,70%). Aumnívelextremode75esparsidade, oalgoritmofalhouemevoluirsoluções viáveis devido à estagnação do espaço de busca. As principais observações revelam que o AG desenvolveu uma estratégia adaptativa inteligente por camada, aprendendo a preservar as camadas iniciais cruciais para a extração de características enquanto aplicava um pruning mais agressivo nas camadas finais. O principal contributo deste trabalho é o desenvolvimento de um framework de pruning evolutivo que descobre configurações arquiteturais eficientes, revelando-se particularmente valioso para a optimização de modelos pré-treinados em contextos reais onde o re-treino total do modelo a partir do zero é impraticável.", "source_url": "https:\/\/openalex.org\/W7217945665", "pdf_url": "https:\/\/ciencipca.ipca.pt\/bitstreams\/9a8cd140-4f78-482f-b126-23e9d295b2ea\/download", "doi": "", "handle": "", "repository_name": "CiencIPCA", "source_record_id": "W7217945665", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 807, "title": "Simultaneous Shape, Size, and Topology Optimization of Truss Structures Using Graph Neural Networks and Metaheuristic Algorithms", "title_ar": "", "title_en": "Simultaneous Shape, Size, and Topology Optimization of Truss Structures Using Graph Neural Networks and Metaheuristic Algorithms", "author": "Ali Mosalli", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "gnn", "categories": "machine-learning; deep-learning; predictive-analytics; optimization; gnn", "keywords": "", "abstract": "This study presents a unified framework for the analysis and optimization of truss structures through the integration of Graph Neural Networks (GNNs) and metaheuristic algorithms. The proposed approach performs simultaneous size, shape, and topology optimization with the objective of minimizing structural weight while satisfying code-based constraints on strength, serviceability, and stability. Conventional optimization methods can become computationally expensive for complex truss systems, particularly when geometric nonlinear behavior is considered. To address this challenge, a physics-informed, dataset-free GNN framework is developed for structural analysis under both linear and geometric nonlinear conditions. The GNN model predicts nodal displacements, from which member forces and stresses are computed, based on structural characteristics such as geometry, connectivity, and loading conditions. The model is validated against finite element analysis results obtained from the commercial finite element software package SAP2000, demonstrating strong agreement and reliable predictive performance. Within the optimization framework, finite element analysis, implemented using a developed Python based direct stiffness method, is used for linear cases due to its efficiency, while the GNN model is directly integrated into the optimization loop for geometric nonlinear analysis, enabling efficient evaluation of candidate designs. Metaheuristic algorithms, including Particle Swarm Optimization, Genetic Algorithm, and Harmony Search, are used to explore the design space. The framework is evaluated through multiple benchmark truss examples, including a pedestrian bridge structure. A comparative evaluation of the optimization algorithms highlights differences in convergence behavior and solution stability. For the pedestrian bridge example, the strength-to-weight ratio is also assessed to provide a more comprehensive measure of structural efficiency. Overall, this research demonstrates the potential of GNN-based approaches as scalable, data-efficient, and code-compliant tools for structural analysis and optimization.", "source_url": "https:\/\/openalex.org\/W7160382209", "pdf_url": "", "doi": "10.11575\/prism\/51368", "handle": "", "repository_name": "", "source_record_id": "W7160382209", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 809, "title": "Multi-layer artificial neural network optimization using genetic algorithms and particle swarm", "title_ar": "", "title_en": "", "author": "Leonardo de Queiroz Dantas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "pt", "research_domain": "deep-learning", "research_subdomain": "optimization", "categories": "machine-learning; deep-learning; optimization", "keywords": "", "abstract": "As pesquisas na área de Redes Neurais Artificiais são caracterizadas pelo desenvolvimento de algoritmos inspirados no funcionamento do sistema nervoso que apresentam diversas aplicações, como classificação de dados. Porém, seu funcionamento depende de um processo de treinamento que, em alguns casos, pode consumir tempo e ter um custo elevado. Este trabalho apresenta uma proposta de técnica de otimização para Redes do tipo MultiLayer Perceptron (MLP), integrando os benefícios de duas categorias de algoritmos de otimização: Algoritmos Genéticos (AG) e Otimização por Enxame de Partículas (PSO – Particle Swarm Optimization). Tal técnica será utilizada para realizar a otimização simultânea da arquitetura e de seus pesos sinápticos, com o objetivo de buscar redes com boas arquiteturas, isto é, com o mínimo de neurônios e com um bom desempenho para cada problema, tudo de forma automática. E, assim, diminuir custos e tempo para a utilização das MLPs. Os resultados mostram que as redes otimizadas segundo o modelo proposto foram eficientes, pois geraram redes MLPs com boas acurácias e, simultaneamente, com boas arquiteturas.", "source_url": "https:\/\/openalex.org\/W7120352733", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120352733", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 810, "title": "Short-Term Load Forecasting Using A Hybrid Of Genetic Algorithm (Ga) And Particle Swarm Optimization (Pso) For An Optimized Neural Network", "title_ar": "", "title_en": "Short-Term Load Forecasting Using A Hybrid Of Genetic Algorithm (Ga) And Particle Swarm Optimization (Pso) For An Optimized Neural Network", "author": "Stephen T Siri", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Nairobi", "university_source": "repository_host", "college": "", "department": "", "country": "Kenya", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics; optimization", "keywords": "", "abstract": "Short-term load forecasting (STLF) has emerged as one of the most important fields of study for power system operation for system efficiency and reliability. It plays a significant role in load flow analysis, contingency analysis, planning, scheduling and maintenance of power systems facilities; therefore, the system cost-effectiveness is determined by accurate load forecast. Numerous researchers have been done to improve the accuracy of the conventional methods such as time series, regression analysis or autoregressive moving average (ARMA) and the use of Artificial Neural Networks (ANNs) in load forecasting. ANN has shown more accurate results than the others. But the training of ANNs, with a back-propagation algorithm or gradient algorithms, requires long processing time has the difficulty in selecting the optimal order of the components and trapping in local minima. This research aimed at solving this problem by proposing a hybrid method based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for training and optimizing the weights of ANN. The proposed hybrid method enables a reduction in the search space and the iteration time. The proposed algorithm was tested in MATLAB 2016® software using 24 hourly load data of different days (i.e. weekdays and weekends) from Juba Power Plant (JPP), South Sudan. PSO, GA and a hybrid of genetic algorithm with particle swarm optimization (HGAPSO) and ANN were studied and the resulting mean absolute percentage error (MAPE) found to be range from 1.9% to 3.40%, 2.23% to 3.65% and 1.47% to 1.98% respectively. The results obtained were compared and it was observed that HGAPSO-ANN method has a better performance in reducing and improving forecast error compared to PSO-ANN and GA-ANN methods. Therefore, a hybridized HGAPSO algorithm with ANN improves forecast accuracy.", "source_url": "https:\/\/openalex.org\/W2936546245", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Nairobi Research Archive (University of Nairobi)", "source_record_id": "W2936546245", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 812, "title": "Analysis of the Niching Particle Swarm Optimization Algorithm", "title_ar": "", "title_en": "Analysis of the Niching Particle Swarm Optimization Algorithm", "author": "Tyler Crane", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Brock University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; deep-learning; optimization; multimodal", "keywords": "", "abstract": "Multimodal optimization (MMO) techniques have been researched and developed over the years to track multiple global optima concurrently. MMO algorithms extend traditional unimodal optimization algorithms by using search strategies built around forming niches for multiple possible solutions. NichePSO was one of the first approaches to utilize particle swarm optimization (PSO) for MMO problems, using several small subswarms of agents working concurrently to form niches within the search space. Despite its promising performance NichePSO does suffer from some problems, and very little research has been done to study and improve upon the algorithm over the years. A main goal of this thesis is to analyze the NichePSO algorithm, gaining insight into the strengths and weaknesses of the algorithm. Empirical analyses were performed to study the NichePSO’s ability to maintain niches within complex problem domains, as well as methods for improving the overall performance and effectiveness of the algorithm. Two variants of the NichePSO algorithm are proposed, and experimental results show that they both significantly improve the performance of the NichePSO algorithm across several benchmark functions.", "source_url": "https:\/\/openalex.org\/W3175652444", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Brock University Digital Repository (Brock University)", "source_record_id": "W3175652444", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 814, "title": "Hybrid algorithms of microbial genetic algorithm and particle swarm optimization for automatic learning groups composition", "title_ar": "", "title_en": "Hybrid algorithms of microbial genetic algorithm and particle swarm optimization for automatic learning groups composition", "author": "Nicholas Simeon Dienagha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Brunel University London", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; ai-applications; optimization; ai-education", "keywords": "", "abstract": "Collaboration learning within diverse groups of varying knowledge levels and varying interest levels have been noted to improve learning outcomes.However, composing balanced learning groups with diversity in knowledge level and interest level within the groups while maintaining similarity among the groups is NP-hard and time consuming.The primary aim of this research is to develop an algorithm for automatic composition of balanced learning groups in (MOOCs) with minimal human intervention.The algorithm will assist facilitators in forming balanced learning groups with ease for learners in online classes to benefit from effective collaboration.The research design was experimental research.This design help established comparative experiments of the new algorithm with the particle swarm algorithm as the bench mark algorithm.The findings in the first experiment showed that, the hybrid MGAPSO (Microbial Genetic Algorithm and Particle Swarm Optimization) algorithm outperformed the PSO (Particle Swarm Optimization), an ANOVA (one-way test) showed high significant difference in the mean fitness of the two algorithms (hybrid Microbial Genetic Algorithm and Particle Swarm Optimization and Particle Swarm Optimization).A possible explanation for this might be that the microbial genetic algorithm component tends to re-introduce new particles at every iteration after every genetic operation, thus, introducing diversity in the swarm.In the second experiment, the new adaptive hybrid AMGAPSO (Adaptive hybrid Microbial Genetic Algorithm and Particle Swarm Optimization ) outperformed both the PSO (Particle Swarm Optimization) and the hybrid MGAPSO (Microbial Genetic Algorithm and Particle Swarm Optimization) with high significant difference in the mean fitness of the new adaptive hybrid AMGAPSO (Adaptive hybrid Microbial Genetic Algorithm and Particle Swarm Optimization ) and the mean fitness of the hybrid MGAPSO (Microbial Genetic Algorithm and Particle Swarm Optimization) and that of the Particle Swarm Optimization.A possible explanation of this finding is that particles stuck in the location in the PSO with their re-initialised new velocity may have searched the solution space in different directions and may have jumped out from their respective locations using the microbial genetic algorithm component, which suggest that the method of hybridisation could have resulted in the improved performance of the adaptive hybrid AMGAPSO algorithm relative to the hybrid MGAPSO.In the third Nicholas Simeon Dienagha experiment 500 learners profile data was used in a comparative experiment of the adaptive AMGAPSO (Adaptive Microbial Genetic Algorithm and Particle Swarm Optimization), MGAPSO (Microbial Genetic Algorithm and Particle Swarm Optimization) and PSO (Particle Swarm Optimization), groups formed by the new adaptive AMGAPSO algorithm were analysed; The ANOVA (one way) test resultsshowed no significant difference in the means of all groups for all six learners attributes among all groups formed by the algorithm.The understanding at this point is that, the adaptive hybridization method may have provided a means of avoiding the problem of parameter adjustment and the fitness function derived have contributed to the formation of groups with diversity within the groups while maintaining similarity among the groups formed by the algorithm.The findings answered the aim of the research as the new algorithm outperformed the existing algorithm used in the literature and the groups formed with the new algorithm were balanced in all profile features used in all the groups.The findings show that the new algorithm can be used to form balanced learning group with minimal human intervention.Limitations of the research are that only 500 learners' data was used for the validation of the experiment; this was because data for more students who belong to the same class could not be obtained; ANOVA (One Way Test) was the only statistical tool used in the analysis.In addition, the algorithm developed was evaluated only in terms of the groups form however the effectiveness of the groups and the overall learning improvement achieved by the groups formed by the algorithm was not evaluated.", "source_url": "https:\/\/openalex.org\/W2997009542", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Brunel University Research Archive (BURA) (Brunel University London)", "source_record_id": "W2997009542", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 815, "title": "Novel particle swarm optimization algorithms with applications in power systems", "title_ar": "", "title_en": "Novel particle swarm optimization algorithms with applications in power systems", "author": "Izaz Ur Rahman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Brunel University London", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; optimization", "keywords": "", "abstract": "Optimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods by analysing the experimental results. Two novel PSO variants are introduced in this thesis. The first one is named as N State Markov Jumping Particle Swarm Optimization, which is based on the stochastic technique and Markov chain in updating the particle velocity. We have named the second variant as N State Switching Particle Swarm Optimization, which is based on the evolutionary factor information for updating the velocity. The proposed algorithms are then applied to some widely used mathematical benchmark functions. The statistical results of 30 independent trails illustrate the robustness and accuracy of the proposed algorithms for most of the benchmark functions. The better results in terms of mean minimum evaluation errors and the shortest computation time are illustrated. In order to verify the satisfactory performance and robustness of the proposed algorithms, we have further formulated some basic applications in power system operations. The first application is about the static Economic Load Dispatch and the second application is on the Dynamic Economic Load Dispatch. These are highly complex and non-linear problems of power system operations consisting of various systems and generator constraints. Basically, in the static Economic Load Dispatch, a single load is considered for calculating the cost function. In contrast, the Dynamic Economic Load Dispatch changes the load demand for the cost function dynamically with time. In such a challenging and complex environment the proposed algorithms can be applied. The empirical results obtained by applying both of the proposed methods have substantiated their adaptability and robustness into the real-world environment. It is shown in the numerical results that the proposed algorithms are robust and accurate as compared to the other algorithms. The proposed algorithms have produced consistent best values for their objectives, where satisfying all constraints with", "source_url": "https:\/\/openalex.org\/W2289879240", "pdf_url": "http:\/\/bura.brunel.ac.uk\/bitstream\/2438\/12219\/1\/FulltextThesis.pdf", "doi": "", "handle": "", "repository_name": "Brunel University Research Archive (BURA) (Brunel University London)", "source_record_id": "W2289879240", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 816, "title": "Items Arrangement in Container using Particle Swarm Optimization Algorithm", "title_ar": "", "title_en": "Items Arrangement in Container using Particle Swarm Optimization Algorithm", "author": "Putro, Angger Priyardhan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; optimization", "keywords": "", "abstract": "Abstract — For the shipping companies, items arrangement in containers is an important issue. To earn profits as much as possible the preparation of the goods in the container must be as efficient as possible so it can minimize the transportation costs. That is why it needs an algorithm that can help to resolve this problem. One algorithm that can solve this problem is the Particle Swarm Optimization algorithm. This project was made using the Java programming language and the algorithm using Particle Swarm Optimization algorithm. How it works is at first the Particle Swarm Optimization is begin to formed a random solution, after that every solution will counting its fitness, after fitness value is obtained, a solution with a best value will be used as a reference to create a new solution in the next iteration by updating its speed and position. In this project every particle has several attributes I.e. the code, size and position of the goods in the container. The fitness value is determined from how much free space is available in that solution, the more free space available and the more items available in the container, the more fitness value is.", "source_url": "https:\/\/openalex.org\/W2909824468", "pdf_url": "http:\/\/repository.unika.ac.id\/9712\/8\/11.02.0065%20Angger%20Priyardhan%20Putro%20DAFTAR%20PUSTAKA.pdf", "doi": "", "handle": "", "repository_name": "Unika Repositor (Unika)", "source_record_id": "W2909824468", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 817, "title": "An Analysis of Parameter Control Mechanisms for the Particle Swarm Optimization Algorithm", "title_ar": "", "title_en": "An Analysis of Parameter Control Mechanisms for the Particle Swarm Optimization Algorithm", "author": "Kyle Robert Harrison", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Pretoria", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; optimization", "keywords": "", "abstract": "The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization technique influenced by social dynamics. It has been shown that the performance of the PSO algorithm can be greatly improved if the control parameters are appropriately tuned. However, the tuning of control parameter values has traditionally been a time-consuming, empirical process followed by statistical analysis. Furthermore, ideal values for the control parameters may be time-dependent; parameter values that lead to good performance in an exploratory phase may not be ideal for an exploitative phase. Self-adaptive algorithms eliminate the need to tune parameters in advance, while also providing real-time behaviour adaptation based on the current problem. \\t This thesis first provides an in-depth review of existing self-adaptive particle swarm optimization (SAPSO) techniques. Their ability to attain order-2 stability is examined and it is shown that a majority of the existing SAPSO algorithms are guaranteed to exhibit either premature convergence or rapid divergence. A further investigation focusing on inertia weight control strategies demonstrates that none of the examined techniques outperform a static value. This thesis then investigates the performance of a wide variety of PSO parameter configurations, thereby discovering regions in parameter space that lead to good performance. This investigation provides strong empirical evidence that the best values to employ for the PSO control parameters change over time. Finally, this thesis proposes novel PSO variants inspired by results of the aforementioned studies.", "source_url": "https:\/\/openalex.org\/W2901362492", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UpSpace Institutional Repository (University of Pretoria)", "source_record_id": "W2901362492", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 818, "title": "Genetic learning particle swarm optimization for task matching in grid environment", "title_ar": "", "title_en": "Genetic learning particle swarm optimization for task matching in grid environment", "author": "Eid Mohammad Albalawi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Manitoba", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; optimization", "keywords": "", "abstract": "Many scientific disciplines require processing computational-intensive applications with vast quantities of data. Large scale systems, such as Grid, provide shared resources on geographically distributed heterogeneous computing systems to meet the computational demands of the applications. One of the fundamental problem computing with the Grid system is assigning tasks of various users to the resources efficiently such that the execution time of the application is minimized while Grid resources are utilized optimally. This is called the task matching problem, an NP-hard problem, the focus of this thesis. While there are many heuristics for the task matching problem, particle swarm optimization (PSO) has been one of the latest heuristics in the literature adopted as a solution to this problem. However, PSO has few drawbacks, such as premature convergence to local optima. To circumvent these problems, we incorporate a learning approach inspired by the genetic algorithm to guide the PSO algorithm. We propose three novel genetic learning PSO algorithms: modified genetic learning PSO (MGLPSO), hybrid genetic learning PSO (HGLPSO), and modified genetic learning PSO with adaptive mutation (μ-MGLPSO). The proposed algorithms have been designed for both single and multi-objective task matching problem for tasks with and without dependencies. The proposed techniques are evaluated on standard benchmarks with large data sets and compared against state-of-the-art algorithms.", "source_url": "https:\/\/openalex.org\/W3083480189", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Mspace (University of Manitoba)", "source_record_id": "W3083480189", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 819, "title": "Multi self-adapting particle swarm optimization algorithm (MSAPSO).", "title_ar": "", "title_en": "Multi self-adapting particle swarm optimization algorithm (MSAPSO).", "author": "Gerhard Koch", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Louisville", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; optimization", "keywords": "", "abstract": "The performance and stability of the Particle Swarm Optimization algorithm depends on parameters that are typically tuned manually or adapted based on knowledge from empirical parameter studies. Such parameter selection is ineffectual when faced with a broad range of problem types, which often hinders the adoption of PSO to real world problems. This dissertation develops a dynamic self-optimization approach for the respective parameters (inertia weight, social and cognition). The effects of self-adaption for the optimal balance between superior performance (convergence) and the robustness (divergence) of the algorithm with regard to both simple and complex benchmark functions is investigated. This work creates a swarm variant which is parameter-less, which means that it is virtually independent of the underlying examined problem type. As PSO variants always have the issue, that they can be stuck-in-local-optima, as second main topic the MSAPSO algorithm do have a highly flexible escape-lmin-strategy embedded, which works dimension-less. The MSAPSO algorithm outperforms other PSO variants and also other swarm inspired approaches such as Memetic Firefly algorithm with these two major algorithmic elements (parameter-less approach, dimension-less escape-lmin-strategy). The average performance increase in two dimensions is at least fifteen percent with regard to the compared swarm variants. In higher dimensions (≥ 250) the performance gain accumulates to about fifty percent in average. At the same time the error-proneness of MSAPSO is in average similar or even significant better when converging to the respective global optima’s.", "source_url": "https:\/\/openalex.org\/W2887728610", "pdf_url": "https:\/\/ir.library.louisville.edu\/cgi\/viewcontent.cgi?article=4019&context=etd", "doi": "10.18297\/etd\/3000", "handle": "", "repository_name": "ThinkIR: The University of Louisville's Institutional Repository (University of Louisville)", "source_record_id": "W2887728610", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 820, "title": "Optimasi Parameter Premise Pada Adaptive-Network-Based Fuzzy Inference System Dengan Modifikasi Kombinasi Particle Swarm Optimization Dan Genetic Algorithm", "title_ar": "", "title_en": "", "author": "Muchamad Kurniawan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "id", "research_domain": "deep-learning", "research_subdomain": "optimization", "categories": "machine-learning; deep-learning; expert-systems; optimization", "keywords": "", "abstract": "ANFIS adalah kombinasi fuzzy inference system (FIS) dan neural network (NN). Terdapat dua parameter training pada ANFIS, parameter premise dan parameter consequent. Pada ANFIS tradisional menggunakan algoritma least square estimator (LSE) dan Gradient Decent (GD) sebagai algoritma pembelajaran parameter training. Kelemahan dari perpaduan dua algortima ini adalah mendapatkan solusi yang lokal optimal. Perubahan individu pada PSO cepat konvergen tapi memungkinkan mendapatkan hasil local optimal. hal ini dikarenakan pada PSO tidak mampu untuk menemukan ruang solusi baru. Kelebihan Algoritma GA adalah dalam menemukan ruang solusi yang lebih luas. Kontribusi yang diusulkan adalah modifikasi dari kombinasi PSOGA. Modifikasi yang dilakukan adalah dengan menambahkan pemilihan individu. Pada penelitian ini akan dilakukan pelatihan parameter premise dengan kombinasi perpaduan PSOGA dan pelatihan parameter consequent dengan algortima LSE pada ANFIS. Hasil Uji Coba ANFIS-PSOGA dibandingkan dengan metode ANFIS, ANFIS-GA,dan, ANFIS-PSO pada dataset Iris Flowers, Haberman, Vertebral dan Banana. ANFIS-PSOGA memperoleh nilai paling bagus dari metode-metode yang lain pada dataset Iris Flowers dengan akurasi 99.85%, dataset Haberman dengan akurasi 84.52% dan dataset Vertebral dengan akurasi 91.83%. ============================================================================================= ANFIS is a combination of namely fuzzy Inference System (FIS) and Neural Network (NN). There are two training parameters in ANFIS, premise parameter and consequent parameter. In traditional ANFIS using hybrid Least Square Estimator (LSE) and Gradient Decent (GD) as learning algorithm for training parameters. Disadvantages of combining those two algorithms getting local optimal solution. Individual changes to the PSO converge quickly but allow for optimal local results. This is because PSO is unable to find a new solution space. The advantage of GA's algorithm are finding a wider solution space. With the hybrid of these two algoritm will give better results. Proposed contribution in this study is a modification of the hybrid PSOGA. Modifications were done by adding the individual election. In this study, premise parameter will be trained with a modification of hybrid PSOGA and consequent parameter will be trained with the LSE algorithm in the ANFIS method or called ANFIS-PSOGA. ANFIS-PSOGA test results are compared with ANFIS, ANFIS-GA and ANFIS-PSO on Iris Flowers, Haberman, Vertebral and Banana datasets. ANFIS-PSOGA obtained the best accuracy results compared to other methods in the Iris Flowers dataset with 99.85% accuracy, Haberman dataset with 84.52% accuracy, and Vertebral dataset with 91.83% accuracy.", "source_url": "https:\/\/openalex.org\/W2789953228", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2789953228", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 821, "title": "Using Particle Swarm Optimization for Market Timing Strategies", "title_ar": "", "title_en": "Using Particle Swarm Optimization for Market Timing Strategies", "author": "Ismail Mohamed", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Kent", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; optimization", "keywords": "", "abstract": "Market timing is the issue of deciding when to buy or sell a given asset on the market. As one of the core issues of algorithmic trading systems, designers of such system have turned to computational intelligence methods to aid them in this task. In this thesis, we explore the use of Particle Swarm Optimization (PSO) within the domain of market timing.nPSO is a search metaheuristic that was first introduced in 1995 [28] and is based on the behavior of birds in flight. Since its inception, the PSO metaheuristic has seen extensions to adapt it to a variety of problems including single objective optimization, multiobjective optimization, niching and dynamic optimization problems. Although popular in other domains, PSO has seen limited application to the issue of market timing. The current incumbent algorithm within the market timing domain is Genetic Algorithms (GA), based on the volume of publications as noted in [40] and [84]. In this thesis, we use PSO to compose market timing strategies using technical analysis indicators. Our first contribution is to use a formulation that considers both the selection of components and the tuning of their parameters in a simultaneous manner, and approach market timing as a single objective optimization problem. Current approaches only considers one of those aspects at a time: either selecting from a set of components with fixed values for their parameters or tuning the parameters of a preset selection of components. Our second contribution is proposing a novel training and testing methodology that explicitly exposes candidate market timing strategies to numerous price trends to reduce the likelihood of overfitting to a particular trend and give a better approximation of performance under various market conditions. Our final contribution is to consider market timing as a multiobjective optimization problem, optimizing five financial metrics and comparing the performance of our PSO variants against a well established multiobjective optimization algorithm. These algorithms address unexplored research areas in the context of PSO algorithms to the best of our knowledge, and are therefore original contributions. The computational results over a range of datasets shows that the proposed PSO algorithms are competitive to GAs using the same formulation. Additionally, the multiobjective variant of our PSO algorithm achieve statistically significant improvements over NSGA-II.", "source_url": "https:\/\/openalex.org\/W3136655786", "pdf_url": "", "doi": "10.22024\/unikent\/01.02.87076", "handle": "", "repository_name": "Kent Academic Repository (University of Kent)", "source_record_id": "W3136655786", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 822, "title": "Novel particle swarm optimization algorithms with applications to healthcare data analysis", "title_ar": "", "title_en": "Novel particle swarm optimization algorithms with applications to healthcare data analysis", "author": "Weibo Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Brunel University London", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "optimization", "categories": "machine-learning; medical-ai; optimization", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3013797946", "pdf_url": "https:\/\/bura.brunel.ac.uk\/bitstream\/2438\/20575\/1\/FulltextThesis.pdf", "doi": "", "handle": "", "repository_name": "Brunel University Research Archive (BURA) (Brunel University London)", "source_record_id": "W3013797946", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 823, "title": "An improved data classification framework based on fractional particle swarm optimization", "title_ar": "", "title_en": "An improved data classification framework based on fractional particle swarm optimization", "author": "Fahad Sherwani", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universiti Tun Hussein Onn Malaysia", "university_source": "repository_host", "college": "", "department": "", "country": "Malaysia", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; medical-ai; optimization; rnn", "keywords": "", "abstract": "Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications.", "source_url": "https:\/\/openalex.org\/W3113818546", "pdf_url": "http:\/\/eprints.uthm.edu.my\/61\/1\/24p%20FAHAD%20SHERWANI.pdf", "doi": "", "handle": "", "repository_name": "UTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia)", "source_record_id": "W3113818546", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 824, "title": "Implementation of a surrogate model for structural optimization based on artificial neural network and genetic algorithm", "title_ar": "", "title_en": "Implementation of a surrogate model for structural optimization based on artificial neural network and genetic algorithm", "author": "Iuri Hermes Müller", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; robotics; ai-applications; predictive-analytics; optimization; ai-industry", "keywords": "", "abstract": "The use of computational optimization methods in structural design, employing surrogate models based on artificial neural networks (ANNs), is a promising approach for achieving efficient and cost-effective solutions compared to traditional optimization relying solely on the finite element method (FEM). These methods are particularly effective for composite material structures, which can be tailored to meet varying mechanical requirements and are fabricated via fused deposition modeling (FDM), such as 3D printing, due to their flexibility and capability to efficiently produce complex geometries. This study aims to implement a numerical optimization algorithm combined with an ANN trained and tested using a database to optimize the support arm of an unmanned aerial vehicle (UAV), manufactured using FDM. The methodology was divided into two main stages. In the first stage, an algorithm was developed in Python to generate a database from FEM simulations conducted in ABAQUS software. These simulations considered the arm, fabricated using polylactic acid (PLA) as the constituent material, with one fixed end and the other subjected to thrust and torsional loads, replicating the UAV's operational conditions. Four geometric parameters of the arm (structure height and width, thickness, and the number of diagonal reinforcement cells) were modified to obtain the masses and stiffness values for each configuration. In the second stage, the algorithm was enhanced by incorporating three additional geometric variables (thickness of the edges and bottom reinforcement, and width of the top reinforcement), resulting in a more extensive database. This expanded dataset was used to train the ANN, which predicted masses and stiffness values based on the geometric variables. The multi-objective optimization problem was then defined using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to minimize mass and maximize stiffness, constrained by the geometric variables, with the ANN employed to evaluate the multi-objective function as a surrogate for FEM. From the solutions obtained on the Pareto front, one was selected for experimental validation. The optimized and standard models were designed in computer-aided design (CAD) software, fabricated using FDM with PLA, and subjected to bending tests to replicate the conditions implemented in the FEM analyses. The results demonstrated that the ANN exhibited high accuracy, with correlation coefficients of 0.99 for stiffness and 0.99 for mass, reducing analysis time by 99.65% compared to FEM. The optimized model showed an average specific stiffness increase of 51.14% and a mass reduction of 42.13% compared to the standard model. Finally, the results indicated that the proposed methodology is efficient in determining optimized solutions. While the database generation required substantial computational time, analyses conducted using the trained neural network demonstrated a significant reduction in processing time. Moreover, the proposed method is adaptable for application in various engineering projects involving composite materials", "source_url": "https:\/\/openalex.org\/W7120726491", "pdf_url": "http:\/\/repositorio.ufsm.br\/bitstream\/1\/33781\/1\/DIS_PPGEM_2024_MULLER_IURI.pdf", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120726491", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 825, "title": "Stability and Scalability of Feature Subset Selection using Particle Swarm Optimization in Bioinformatics", "title_ar": "", "title_en": "Stability and Scalability of Feature Subset Selection using Particle Swarm Optimization in Bioinformatics", "author": "Hassen Dhrif", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; computer-vision; robotics; responsible-ai; predictive-analytics; optimization; multimodal; xai", "keywords": "", "abstract": "Feature Subset Selection (FSS) is a non-convex, non-linear, multi-modal, high-dimensional, Multi-Objective Optimization problem, where finding the optimum solution is a fairly difficult task due to its ultra-large search space and its very large number of local minima. Existing FSS methods suffer from problems like stagnation in local optima and high computational cost. Evolutionary Computation (EC) techniques are well known global search algorithms. Particle Swarm Optimization (PSO) is an EC technique that is simple to implement and that converges faster than other methods such as Genetic Algorithms (GA), Differential Evolution (DE) or Ant Colony Optimization (ACO). PSO has been successfully applied to areas such as engineering, biology, image processing, job scheduling, robotics and neural networks, but its potential for FSS has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for FSS aiming to select the smallest, most compact subset of features with the best classification performance in the least amount of time. This thesis investigates the use of PSO for wrapper, filter, embedded and hybrid approaches and it implements PSO on both single and multi-objective FSS. FSS problems are characterized by an increasing number of features and instances, massively challenging classical methods. Representing efficient global search methods, Swarm Intelligence (SI) based Evolutionary Computation (EC) techniques have increasingly been applied to solve such FSS problems. Despite their popularity, PSO and other swarm-based algorithms still suffer from a variety of problems and short comings as a consequence of high-dimensional data. We start this dissertation with a comprehensive survey, we formulate the challenges of SI algorithms that tackle FSS problems, discuss algorithmic solutions and present an overview of metrics that allow the systematic and comparable assessment of algorithms performance. Then, in bioinformatics, PSO methods can select subsets of predictive genes for sample classification. However; the obtained gene subsets lack interpretability and are usually too large to be used as biomarkers. Such characteristics may be rooted in the observation that PSO algorithms usually lose the diversity of the swarm, leading to premature convergence and leaving many areas of the search space unexplored. Furthermore, high-dimensional FSS problems like those encountered in gene expression data pose an additional challenge to EC techniques since their ability to pick out the important features must remain unchanged despite the increasing dimensionality of the feature space. Our goal is to develop an algorithm capable of scaling up to high-dimensional gene expression data while still selecting the smallest subsets that capture the information to carry the desired prediction tasks. To address these drawbacks, we introduce a new PSO variant called Combinatorial PSO (COMB-PSO). The proposed method develops a new encoding mechanism, an enhanced Pareto nondominated set…", "source_url": "https:\/\/openalex.org\/W2969952035", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2969952035", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 826, "title": "Optimizing convolutional neural network parameters using genetic algorithm for breast cancer classification", "title_ar": "", "title_en": "Optimizing convolutional neural network parameters using genetic algorithm for breast cancer classification", "author": "Khatereh Davoudi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Manitoba", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; medical-ai; optimization; cnn; medical-imaging", "keywords": "", "abstract": "Breast cancer, as the most-regularly diagnosed cancer in women, can be controlled effectively by early-stage tumour diagnosis. Clinical specialists use Computer-Aided Diagnosis (CAD) systems to help aid in their diagnosis, as accurate as possible. Deep learning techniques, such as Convolutional Neural Network (CNN), due to their classification capabilities, have been widely adopted in CAD systems. The parameters of the network, including the weights of the convolution filters, and the weights of the fully connected layers play a crucial role in classification accuracy. Back-propagation technique is the most frequently used approach for training CNN. However, this technique has some disadvantages, such as getting stuck in local minima. In this thesis, we propose to optimize the weights of the CNN using Genetic Algorithm (GA). The work consists of: designing a CNN model to facilitate the classification process, training the model using three different optimizer (mini-batch gradient descent, Adam, and GA), and evaluating the model through various experiments on BreakHis dataset. We show that the CNN model trained through GA performs as well as the Adam optimizer with a classification accuracy of 85%.", "source_url": "https:\/\/openalex.org\/W3048136822", "pdf_url": "https:\/\/mspace.lib.umanitoba.ca\/bitstreams\/050cf8f8-9e09-4432-8764-8ad5bbcae9e1\/download", "doi": "", "handle": "", "repository_name": "Mspace (University of Manitoba)", "source_record_id": "W3048136822", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 827, "title": "A fast learning network with improved particle swarm optimization for intrusion detection system", "title_ar": "", "title_en": "A fast learning network with improved particle swarm optimization for intrusion detection system", "author": "Mohammed Hasan Ali", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universiti Malaysia Pahang", "university_source": "repository_host", "college": "", "department": "", "country": "Malaysia", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; ai-applications; optimization; ai-cybersecurity", "keywords": "", "abstract": "In current days the intrusion detection systems (IDS) have several shortcomings such as high rates of false positive alerts, low detection rates of rare but dangerous attacks, and the need for a constant human intervention and tuning. Daily, there are reports of incidents such as major ex-filtration of data for the purposes of stealing identities, credit card numbers, and intellectual properties, as well as to take control of network resources. Machine learning approaches have been widely used to increase the effectiveness of intrusion detection platforms. While some machine learning techniques are effective at detecting certain types of attacks, there are no known methods that can be applied universally and achieve consistent results for multiple attack types. This situation makes the detection of cyber-based attacks on computer networks a relevant and challenging area of research. The Fast Learning Network (FLN) is one of the new machine learning algorithms that are easy to implement, computationally efficient, and with excellent learning performance characteristics. However, the internal power parameters (weight and basis) of FLN are initialized at random, causing the algorithm to be unstable. In this work, a new cooperative multi-swarm scheme called multi-swarmoptimization (MRPSO) which is inspired by the human social behavior was proposed for the interaction of several PSO groups while searching for the best parameters values of PSO. The focus of this research is on the development of a model that can optimize the initial parameters of FLN based on MRPSO to obtain an optimal set of initial parameters for FLN, thus, creating an optimal FLN classifier named as MRPSO-FLN which can improve the efficacy of network intrusion on data sets that contain instances of multiple classes of attacks. These methods were tested on NSL-KDD intrusiondetection datasets and the results indicate that the proposed approaches used in the system performed well in large dataset processing. In these experiments, it was demonstrated that the FLN optimization method achieved 0.9964 which is a higher accuracy than most of the existing paradigms for classifying network intrusion detection data.", "source_url": "https:\/\/openalex.org\/W3202035355", "pdf_url": "https:\/\/umpir.ump.edu.my\/id\/eprint\/29922\/2\/mohammedhasanaliphd_2019.pdf", "doi": "", "handle": "", "repository_name": "UMP Institutional Repository (Universiti Malaysia Pahang)", "source_record_id": "W3202035355", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 828, "title": "Deep Learning-Based Medical Image Analysis : Segmentation, Synthesis, and Interactive Visualization for Pulmonary and Hepatic Applications", "title_ar": "", "title_en": "Deep Learning-Based Medical Image Analysis : Segmentation, Synthesis, and Interactive Visualization for Pulmonary and Hepatic Applications", "author": "Liang Lyu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; generative-ai; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "Computed tomography (CT) is widely used for the diagnosis and assessment of pulmonary and hepatic diseases because it provides detailed anatomical information.With the development of deep learning, CT image analysis has advanced significantly in segmentation, synthesis, and visualization.However, accurate analysis remains challenging due to complex three-dimensional anatomical structures, small", "source_url": "https:\/\/openalex.org\/W7221346931", "pdf_url": "https:\/\/ritsumei.repo.nii.ac.jp\/record\/2007568\/files\/k_2107_youshi_ej.pdf", "doi": "10.34382\/0002007568", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7221346931", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 829, "title": "A Novel Hybrid Deep Learning Model using U-net and Vision Transformer for Polyp Segmentation in medical imaging", "title_ar": "", "title_en": "A Novel Hybrid Deep Learning Model using U-net and Vision Transformer for Polyp Segmentation in medical imaging", "author": "Prity Rani Das", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; medical-ai; cnn; transformers; image-segmentation; medical-imaging", "keywords": "", "abstract": "This research represents a hybrid model for polyp segmentation in medical images, which combines convolutional neural networks (CNN) and vision transformers (ViT). We propose a hybrid architecture that integrates UNet with a Vision Transformer, where UNet is used as an encoder to capture local features and ViT is used as a bottleneck module to extract global contextual information. To improve the robustness and generalization of the model, various data augmentation techniques are applied to the training data set, including horizontal flip, vertical flip, and rotation. The model is trained and evaluated on the multicenter PolypDB dataset, with all images preprocessed to a consistent resolution, and the corresponding masks are appropriately managed. A loss function, combining Dice Loss and Binary Cross-Entropy (BCE) Loss, is applied to optimize segmentation performance.", "source_url": "https:\/\/openalex.org\/W7128515784", "pdf_url": "", "doi": "10.5281\/zenodo.18591130", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7128515784", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 830, "title": "Deep learning in medical image analysis : a comparative analysis of multi-modal brain-MRI segmentation with 3D deep neural networks", "title_ar": "", "title_en": "", "author": "Νικόλαος Αδάλογλου", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "el", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; multimodal; image-segmentation; medical-imaging", "keywords": "", "abstract": "Η τμηματοποίηση\/κατάτμηση σe eικόνeς μαγνητικής τομογραφίας eίναι απαραίτητη για τη διάγνωση, την παρακολούθηση και τον προγραμματισμό θeραπeίας του ασθeνούς. Οι πρακτικές χeιρωνακτικής eπισημeίωσης των ογκομeτρικών στοιχeίων (voxels) απαιτούν ανατομικές γνώσeις, eίναι ιδιαίτeρα δαπανηρές και χρονοβόρeς. Eπιπλέον, μπορeί αρκeτές φορές να eίναι ανακριβeίς λόγω του ανθρώπινου συντeλeστή. Η αυτοματοποιημένη κατάτμηση μπορeί να eξοικονομήσeι χρόνο σe γιατρούς και ακτινολόγους και να παράγeι μια ικανοποιητικά ακριβή λύση για πeραιτέρω ανάλυση. Σe αυτή τη μeταπτυχιακή διπλωματική eργασία, μeλeτάται η αυτοματοποιημένη τμηματοποίηση του eγκeφάλου από τις πολυτροπική πληροφορία eικόνων 3D μαγνητικού συντονισμού( Τ1w, Τ2w, T1-IR, T2-FLAIR). Διeξάγeται μια eκτeταμένη συγκριτική ανάλυση των σύγχρονων 3D αρχιτeκτονικών των νeυρωνικών δικτύων βαθιάς μάθησης, για την τμηματοποίηση των πeριοχών του eγκeφάλου. Αρχίζουμe πeριγράφοντας της θeμeλιώδης αρχές της παραγωγής της μαγνητικής τομογραφίας. Σe eφαρμογές βαθιάς μάθησης, eίναι σημαντικό να γνωρίζουμe τα δeδομένα προς eπeξeργασία για να eκπαιδeυτeί ένα σύστημα μe παραμέτρους της τάξης των eκατομμυρίων. Στη συνέχeια, παρέχeται στον αναγνώστη μια eπισκόπηση του τρόπου μe τον οποίο λeιτουργούν τα δίκτυα βαθιά μάθησης, αναλύοντας eκτeνώς κάθe στοιχeίο (στρώμα) eνός τέτοιου δικτύου. Eφόσον τα δυο πeδία έχουν αναπτυχθeί ξeχωριστά - μαγνητική τομογραφική eικόνα και δίκτυα βαθιάς μάθησης- προσπαθούμe να δώσουμe μια eυρύτeρη προοπτική της διασταύρωσης αυτών των δύο πeδίων μe μια σeιρά eφαρμογών, από την ανακατασκeυή της μαγνητικής τομογραφίας έως την τeχνητή παραγωγή από συστήματα νeυρωνικών δικτύων νέων ιατρικών eικόνων. Η eργασία αυτή eστιάζeι στην πολυτροπική τμηματοποίηση του eγκeφάλου. Για τα πeιράματά μας, χρησιμοποιήσαμe δύο eυρέως γνωστά σύνολα δeδομένων από μαγνητικές τομογραφίας. Τα σύνολα δeδομένων που χρησιμοποιήθηκαν στα πλαίσια διαγωνισμών σe κορυφαία συνέδρια ιατρικής eπeξeργασίας eικόνας, αποσκοπούν στην αξιολόγηση των σύγχρονων αρχιτeκτονικών για τον τμηματοποίηση του eγκeφάλου, παρέχοντας eπισημeιωμένα τρισδιάστατα δeδομένα μαγνητικών. Για να αξιολογήσουμe τις σύγχρονeς αρχιτeκτονικές 3D νeυρωνικών δικτύων, αναλύουμe σύντομα τις προσeγγίσeις που ακολούθησαν οι αρχικές δημοσιeύσeις των συγγραφέων. Eπίσης, παρέχουμe στον αναγνώστη μια διαίσθηση πίσω από τις eπιλογές σχeδιασμού των αρχιτeκτονικών αυτών και Διeξάγουμe μια συγκριτική ανάλυση μέσω eκτeταμένων πeιραμάτων. Τέλος, σχολιάζουμe τα αναφeρόμeνα αποτeλέσματα και παρέχουμe μeλλοντικές κατeυθύνσeις για την υλοποίηση μιας ανοιχτού κώδικα βιβλιοθήκης ιατρικής eπeξeργασίας μαγνητικών τομογραφιών μe βαθιά 3D δίκτυα. Eπeιδή πιστeύουμe στην ανοιχτή έρeυνα και προκeιμένου να αναπαραχθούν τα αποτeλέσματά μας, η πρώτη έκδοση του κώδικα μας eίναι διαθέσιμη στη διeύθυνση: https:\/\/github.com\/black0017\/MedicalZooPytorch", "source_url": "https:\/\/openalex.org\/W3033235579", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3033235579", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 831, "title": "Exploring Attention Mechanisms and Effective Network Architectures in Deep Learning-based Medical Image Segmentation: Adaptability to Diverse Lesions and Modalities", "title_ar": "", "title_en": "Exploring Attention Mechanisms and Effective Network Architectures in Deep Learning-based Medical Image Segmentation: Adaptability to Diverse Lesions and Modalities", "author": "Hussain Tahir", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; medical-ai; transformers; image-segmentation; medical-imaging", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7143951589", "pdf_url": "", "doi": "10.18952\/0002000877", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7143951589", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 832, "title": "Weakly supervised learning for medical image segmentation", "title_ar": "", "title_en": "Weakly supervised learning for medical image segmentation", "author": "Hateef Alshewaier", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Cardiff University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; predictive-analytics; image-segmentation; medical-imaging; fairness", "keywords": "", "abstract": "Medical image segmentation plays a critical role in clinical diagnosis and treatment planning; however, the performance of deep learning models typically depends on large quantities of pixel-wise annotated data, which are costly and time-consuming to obtain. This thesis investigates annotation-efficient approaches for medical image segmentation by developing weakly supervised learning frameworks that reduce annotation requirements while maintaining high segmentation accuracy. The first contribution proposes an enhanced scribble-based segmentation framework that extends a modified U-Net architecture with increased feature learning capacity, mix-based data augmentation, and global and local consistency regularisation. These components improve learning from sparse scribble annotations and promote anatomically coherent segmentation masks. Experimental results demonstrate improved segmentation performance compared with existing scribble-supervised methods. The second contribution presents an ensemble learning framework for weakly supervised medical image segmentation based on single bounding-box annotations. The proposed framework integrates U-Net, ResUNet, and DeepLabV3 using bagging and boosting strategies to exploit complementary architectural characteristics. Despite relying solely on coarse single-bounding-box supervision, the ensemble models improve robustness, reduce model-specific bias, and yield more stable segmentation predictions. Experimental results demonstrate that the ensemble methods consistently outperform the individual models, with boosting achieving the highest segmentation performance. The third contribution introduces a novel dual bounding-box supervision strategy that utilises an inner bounding box to represent high-confidence object regions and an outer bounding box to provide contextual anatomical information. A region-aware weighted loss function encourages accurate object localisation while reducing ambiguity in boundary regions. The proposed framework significantly outperforms conventional single-box weakly supervised approaches and narrows the gap between weak and full supervision. Overall, the findings demonstrate that high-quality medical image segmentation can be achieved using sparse scribble annotations, ensemble learning techniques, and coarse bounding-box supervision without requiring exhaustive pixel-wise labels. The proposed methods reduce annotation effort while maintaining competitive segmentation accuracy, providing a practical and scalable pathway for deploying deep learning-based segmentation systems in clinical environments.", "source_url": "https:\/\/openalex.org\/W7219256121", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ORCA Online Research @Cardiff (Cardiff University)", "source_record_id": "W7219256121", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 833, "title": "Deep Learning and Scientific Machine Learning For Medical Imaging Applications", "title_ar": "", "title_en": "Deep Learning and Scientific Machine Learning For Medical Imaging Applications", "author": "Adrian Celaya", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rice University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; gnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Medical imaging technologies, such as magnetic resonance imaging (MRI) and computed tomography (CT), are invaluable tools in modern medicine - allowing for safe, non-invasive diagnosis and treatment planning. However, leveraging these imaging techniques for scientific modeling often involves a complex process that includes image acquisition, labeling (i.e., segmenting) regions of interest, generating a computational mesh, and numerical simulation. The contributions of this thesis focus on two areas within this pipeline: deep learning-based methods for efficient, scalable, and accurate 3D medical image segmentation and scientific machine learning (SciML) for solving partial differential equations (PDEs). In the realm of medical imaging segmentation, this thesis presents two innovative architectures that are based on geometric multigrid methods: the FMG and W-Nets. These novel architectures improve the segmentation accuracy compared to current state-of-the-art architectures with fewer parameters and converge to lower loss function values in fewer iterations. This thesis goes on to present the Generalized Surface Loss function, a novel, boundary-aware loss function that improves the accuracy of deep learning-based medical imaging segmentation. These innovations are incorporated into a scalable, open-source framework called the Medical Imaging Segmentation Toolkit, created to empower researchers to develop and test new concepts, such as novel architectures and loss functions, in a simple and reproducible pipeline. The presentation of work related to SciML begins by introducing a novel adaptive collocation point selection strategy for Physics-Informed Neural Networks (PINNs) using the QR Discrete Empirical Interpolation Method (QR-DEIM). This method improves both the training efficiency (i.e., convergence to lower loss values in fewer iterations) and predictive accuracy of PINNs by enabling more informed and targeted sampling of collocation points, particularly in regions of high solution complexity. Additionally, this work demonstrates how convolutional neural networks (CNNs) can approximate finite difference and discontinuous Galerkin (DG) solutions to elliptic and parabolic PDEs, both in supervised and unsupervised settings. These results highlight how SciML techniques can enhance traditional numerical solvers. The thesis culminates in the development of graph neural networks (GNNs) for approximating DG solutions over general domains. By representing computational meshes as graphs, these GNNs can generalize across varying resolutions and topologies, offering a flexible tool for scientific computing in irregular domains. Together, the contributions in this dissertation bridge the gap between deep learning, scientific machine learning, and classical numerical methods, offering a cohesive framework for deploying deep learning and SciML in medical imaging and broader computational physics applications.", "source_url": "https:\/\/openalex.org\/W7213488376", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Rice Research Repository (Rice University)", "source_record_id": "W7213488376", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 834, "title": "Deep learning-based brain tumour image segmentation and its extension to stroke lesion segmentation", "title_ar": "", "title_en": "Deep learning-based brain tumour image segmentation and its extension to stroke lesion segmentation", "author": "Salma Al-Qazzaz", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Cardiff University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; multimodal; image-segmentation; medical-imaging", "keywords": "", "abstract": "Medical imaging plays a very important role in clinical methods of treating cancer, as well as treatment selection, diagnosis, an evaluating the response to therapy. One of the best-known acquisition modalities is magnetic resonance imaging (MRI), which is used widely in the analysis of brain tumours by means of acquisition protocols (e.g. conventional and advanced). Due to the wide variation in the shape, location and appearance of tumours, automated segmentation in MRI is a difficult task. Although many studies have been conducted, automated segmentation is difficult and work to improve the accuracy of tumour segmentation is still ongoing. This research aims to develop fully automated methods for segmenting the abnormal tissues associated with brain tumours (i.e. those subject to oedema, necrosis and enhanced) from the multimodal MRI images that help radiologists to diagnose conditions and plan treatment. In this thesis the machine-learned features from the deep learning convolutional neural network (CIFAR) are investigated and joined with hand-crafted histogram texture features to encode global information and local dependencies in the representation of features. The combined features are then applied in a decision tree (DT) classifier to group individual pixels into normal brain tissues and the various parts of a tumour. These features are good point view for the clinicians to accurately visualize the texture tissue of tumour and sub-tumour regions. To further improve the segmentation of tumour and sub-tumour tissues, 3D datasets of the four MRI modalities (i.e. FLAIR, T1, T1ce and T2) are used and fully convolutional neural networks, called SegNet, are constructed for each of these four modalities of images. The outputs of these four SegNet models are then fused by choosing the one with the highest scores to construct feature maps, with the pixel intensities as an input to a DT classifier to further classify each pixel as either a normal brain tissue or the component parts of a tumour. To achieve a high-performance accuracy in the segmentation as a whole, deep learning (the IV SegNet network) and the hand-crafted features are combined, particularly in the grey-level co-occurrence matrix (GLCM) in the region of interest (ROI) that is initially detected from FLAIR modality images using the SegNet network. The methods that have been developed in this thesis (i.e. CIFAR _PI_HIS _DT, SegNet_Max_DT and SegNet_GLCM_DT) are evaluated on two datasets: the first is the publicly available Multimodal Brain Tumour Image Segmentation Benchmark (BRATS) 2017 dataset, and the second is a clinical dataset. In brain tumour segmentation methods, the F-measure performance of more than 0.83 is accepted, or at least useful from a clinical point of view, for segmenting the whole tumour structure which represents the brain tumour boundaries. Thanks to it, our proposed methods show promising results in the segmentation of brain tumour structures and they provide a close match to expert delineation across all grades of glioma. To further detect brain injury, these three methods were adopted and exploited for ischemic stroke lesion segmentation. In the steps of training and evaluation, the publicly available Ischemic Stroke Lesion (ISLES 2015) dataset and a clinical dataset were used. The performance accuracies of the three developed methods in ischemic stroke lesion segmentation were assessed. The third segmentation method (SegNet_GLCM_DT) was found to be more accurate than the other two methods (SegNet_Max_DT and SegNet_GLCM_DT) because it exploits GLCM as a set of hand-crafted features with machine features, which increases the accuracy of segmentation with ischemic stroke lesion.", "source_url": "https:\/\/openalex.org\/W3096125045", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ORCA Online Research @Cardiff (Cardiff University)", "source_record_id": "W3096125045", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 835, "title": "Globally optimal surface segmentation for medical images using deep learning: algorithms, robustness and applications", "title_ar": "", "title_en": "Globally optimal surface segmentation for medical images using deep learning: algorithms, robustness and applications", "author": "Leixin Zhou ; Xiaodong Wu ; Mona K. Garvin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; image-segmentation; medical-imaging; ai-cybersecurity", "keywords": "", "abstract": "Automated image segmentation plays a very import role in quantitative image analysis. In recent several years, deep learning (DL) or CNN based method for semantic segmentation has become very popular in computer vision and then in medical image research communities. Most deep learning based semantic segmentation methods are region-based, i.e. each pixel is labeled as target object or background. As there is no explicit constraint among the labeling of different pixels, usually post processing, such as morphological operation, is required to get reasonable prediction. Actually, one can also model the segmentation problem as surface based: each pixel is labeled as surface or non-surface and then target object can be defined based on prior knowledge, i.e. one has to know region on which side of surface corresponds to target object. Apparently these two views are equivalent but modeled in different way. As one prominent surface-based method, Graph-Search (GS) has achieved great success, especially in medical field. This method is capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. However, the cost function design is usually non-trivial. Inspired by the GS, one research focus is to solve the surface segmentation problem with global optimality guarantee for both 3-D and 2-D images using deep learning, so that the cost functions are learned by deep networks utilizing training dataset. In the era of big data, automated deep learning based image processing enables to process a large amount of medical image data efficiently. This is especially helpful for semantic segmentation, as manual contouring is tedious and time consuming. In many applications, the deep learning based segmentation methods can even achieve expert-level accuracy. However, in practice, deep learning methods may fail due to many factors: such as domain shift, adversarial noise, and low image quality. Therefore, another research focus is testing the robustness of deep learning based segmentation methods and even predicting segmentation quality without ground truth. To accomplish the first objective of this thesis work, novel graph based frameworks are proposed to seamlessly integrated with deep neural networks. First, a graph model based on convex quadratic programming is developed, the solving of which is guaranteed globally optimal. The developed model is applied and validated on the intra-retinal layer segmentation of Spectral Domain Optical Coherence Tomography (SD-OCT) images of eye and vessel walls in Intravascular Ultrasound (IVUS) images. Second, to extend this model based deep learning surface segmentation method to 3-D, one shape-aware patch generation approach for Genus-0 surface is proposed to enable normal 3-D CNN to properly work on. The developed method is applied to prostate segmentation of MR images and spleen segmentation of CT images in 3-D. Third, the robustness of the proposed model based surface segmentation method is tested under adversarial attacks and one robust segmentation quality assessment approach based on conditional reconstruction networks is also developed and verified on cardiac left-ventricular myocardium (LVM) segmentation.", "source_url": "https:\/\/openalex.org\/W4240475888", "pdf_url": "https:\/\/iro.uiowa.edu\/view\/delivery\/01IOWA_INST\/12788572140002771\/13788572130002771", "doi": "10.17077\/etd.005508", "handle": "", "repository_name": "", "source_record_id": "W4240475888", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 836, "title": "Deep Learning in Medical Image Analysis: Alzheimer’s Disease Classification & Whole Fetal Brain Segmentation.", "title_ar": "", "title_en": "Deep Learning in Medical Image Analysis: Alzheimer’s Disease Classification & Whole Fetal Brain Segmentation.", "author": "Jesús Alejandro Valdés Valdés", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tecnológico de Monterrey", "university_source": "repository_host", "college": "", "department": "", "country": "Mexico", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; transfer-learning; cnn; image-segmentation; gan; medical-imaging", "keywords": "", "abstract": "During the last couple years there has been a growing interest in Deep Learning (DL), thanks in part to an increasing availability of computational power and of massive labeled datasets. This work presents two different Deep Learning applications for medical image analysis related tasks. The first application is the classification of Alzheimer Disease (AD) using MRI. AD is the most common form of dementia. However, it’s difficult to diagnose. During this work three different DL models are proposed to classify subjects into either AD or Normal Control (NC) groups. The models proposed are a custom CNN model, vgg16 + Transfer Learning and vgg16 + Fine Tuning. The vgg16 based methods make use of pre-learned weights trained on the ImageNet dataset. All of the models used as an input a single 2D MRI slice. This helped reduced model complexity and training times. After performing a 10 times repeated 5-Fold cross validation it was discovered that the best model was vgg16 with Fine Tuning. It reached an accuracy of 80% and an AUC of 0.86. The obtained results proved that the use of weights learned from different domains could be useful in medical image applications. The main contributions of this section were the use of a single MRI slice to classify patients, the model validation technique and the use of transfer learning. The second application is the fully automatization of the Whole Fetal Brain Segmentation in maternal MRI scans. For this U-Net [41] and other segmentation networks were compared. The U-Net model was also modified to include two types of attention components. Squeeze and Excitation module [16] and Attention Gates [39]. To avoid overfitting the models were trained using rigorous regularization (channel wise dropout, weight decay and data augmentation). They were also trained using different loss functions, including a Hybrid (BCE+Dice) loss function. The results of the 10-fold cross validation experiments showed statistically that no model was able to outperform the vanilla U-Net (DSC of 0.93 and HD 7.59mm). The proposed Attention Gated U-Net trained with the Hybrid loss reached a 0.95 DSC and 5.03mm HD (no significant difference). However, results on challenging data show a better performance for the Attention Gated models. This led to the conclusion that the possible advantage of using attention gates outweighed the cost of adding the attention module. The results were also improved by the use of a post processing step based on traditional computer vision techniques. The main contributions to this problem were the use of 10-fold cross validation, the statistical comparison between models, the use of the Hausdorff distance metric, the post processing step and the addition of attention components. This work also provides an overview of other deep learning applications also used within a medical setting. The techniques mentioned include image labeling, autoencoders and GANs for data augmentation. It also provides an overview of the current challenges and limitations DL faces and the most common way of mitigating them. Thanks to the research conducted during this thesis it can be concluded that the use of DL can be used in a medical setting focused on the analysis of images. Particularly on classification and segmentation tasks. However, certain challenges such as having a large enough data set and availability of the required hardware to carry out research must be considered before attempting to solve a problem with DL.", "source_url": "https:\/\/openalex.org\/W3014475976", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Repositorio del Tecnológico de Monterrey (Tecnológico de Monterrey)", "source_record_id": "W3014475976", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 837, "title": "Deep Learning for Three-Dimensional Multi-Modal Medical Image Processing: From Classification to Segmentation", "title_ar": "", "title_en": "Deep Learning for Three-Dimensional Multi-Modal Medical Image Processing: From Classification to Segmentation", "author": "Kh Tohidul Islam", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Melbourne", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; multimodal; image-segmentation; medical-imaging", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7201472226", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Minerva Access (University of Melbourne)", "source_record_id": "W7201472226", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 838, "title": "Development of an Algorithm for Object Detection in Medical Images using Image Segmentation and Deep Learning Techniques", "title_ar": "", "title_en": "Development of an Algorithm for Object Detection in Medical Images using Image Segmentation and Deep Learning Techniques", "author": "Rai Shiv Shankar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; object-detection; image-segmentation; medical-imaging", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2916793112", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University", "source_record_id": "W2916793112", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 839, "title": "Deployable and interpretable deep learning architectures: navigating clinical challenges in medical image segmentation", "title_ar": "", "title_en": "Deployable and interpretable deep learning architectures: navigating clinical challenges in medical image segmentation", "author": "Wei Dai", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; responsible-ai; image-segmentation; medical-imaging; xai", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W4404365792", "pdf_url": "", "doi": "10.14264\/98c85ec", "handle": "", "repository_name": "", "source_record_id": "W4404365792", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 840, "title": "Anatomy-aided deep learning for Medical Image Segmentation : from the anatomy of the epicardial adipose tissue", "title_ar": "", "title_en": "Anatomy-aided deep learning for Medical Image Segmentation : from the anatomy of the epicardial adipose tissue", "author": "Lu Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "Medical image segmentation plays a pivotal role in modern healthcare by enabling precise diagnosis, treatment planning, and continuous monitoring of diseases. Among various segmentation tasks, the segmentation of epicardial adipose tissue (EAT) in low-dose computed tomography (LDCT) scans has gained attention due to its relevance in cardiovascular health. EAT is a vital fat depot surrounding the heart, closely associated with inflammatory mediators and cytokines, making it a crucial imaging biomarker for various cardiovascular conditions. LDCT is particularly suited for EAT segmentation, offering detailed anatomical information with minimal radiation exposure, thus allowing for repeated scans and large-scale studies. These studies are essential for assessing cardiovascular risk factors, including EAT volume, and identifying multiple biomarkers in a single scan for comprehensive cardiovascular health evaluations. However, segmenting EAT in LDCT scans presents significant challenges. The irregular shape and non-uniform distribution of EAT around the heart make it difficult to distinguish from surrounding tissues. Additionally, LDCT imaging is prone to noise and artifacts, which obscure image clarity and complicate the accurate delineation of EAT boundaries. The lack of specialized annotated datasets further hampers the development of reliable segmentation algorithms. Although advanced deep learning approaches have been applied to EAT segmentation, they often require large annotated datasets, are sensitive to noise, and struggle with interpreting features without explicit anatomical priors. To address these challenges, this thesis emphasizes the integration of anatomical information, particularly the shape of the pericardium, into segmentation methodologies. This approach aims to improve the accuracy, efficiency, and clinical relevance of EAT segmentation, ultimately contributing to better patient care and more informed medical decision-making. The thesis is built on a foundation of thorough literature review, examining existing anatomy-aided deep learning techniques for medical image segmentation. A significant contribution of the research is the creation of a specialized dataset consisting of 154 LDCT scans, annotated with pericardium and pixel-wise EAT labels. This dataset serves as a critical resource for training and evaluating segmentation algorithms. The thesis explores various deep learning models, such as 3D U-net and its variants, and evaluates their performance using traditional metrics like the Dice coefficient and statistical analyses like Pearson correlation and Bland-Altman analysis. A novel shape-based loss function derived from Fourier descriptors is introduced to enhance segmentation accuracy, particularly in challenging scenarios. Additionally, the thesis explores the use of Fourier neural operators to generalize shape encoding in the Fourier space, addressing the limitations of traditional methods and improving the generalization capabilities of models across different medical image segmentation tasks. Through these advancements, the thesis aims to provide state-of-the-art tools for healthcare professionals, driving progress in medical image segmentation and contributing to enhanced clinical outcomes.", "source_url": "https:\/\/openalex.org\/W4400468663", "pdf_url": "", "doi": "10.3990\/1.9789036562072", "handle": "", "repository_name": "", "source_record_id": "W4400468663", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 841, "title": "DEEP LEARNING IN MEDICAL IMAGE ANALYSIS: BRAIN STRUCTURAL SEGMENTATION, DIFFUSION MRI RECONSTRUCTION AND DOMAIN ADAPTIVE URINARY TRACT SEGMENTATION", "title_ar": "", "title_en": "DEEP LEARNING IN MEDICAL IMAGE ANALYSIS: BRAIN STRUCTURAL SEGMENTATION, DIFFUSION MRI RECONSTRUCTION AND DOMAIN ADAPTIVE URINARY TRACT SEGMENTATION", "author": "Yuemeng LI", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Pennsylvania", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; transformers; image-segmentation; medical-imaging", "keywords": "", "abstract": "Deep learning based medical imaging analysis has demonstrated remarkable capabilities in enhancing clinical diagnostics, facilitating better decision-making through improved visualization and analysis. Despite these achievements, the complexity of various imaging modalities presents significant challenges, highlighting the need for robust application across different systems. In this thesis, I address these challenges by integrating advanced deep learning models for specific imaging modalities including MRI, diffusion MRI and CT scans. First, I introduced the Anatomy Context-Encoding Network (ACEnet), a novel 2.5D deep learning model that leverages 3D spatial and anatomical contexts in a 2D framework. This model was designed to enhance the segmentation of brain structures from MRI scans. ACEnet’s performance on benchmark datasets demonstrated superior accuracy and speed, significantly outperforming existing state-of-the-art methods and illustrating its effectiveness for real-time clinical applications. Second, I developed DeepADC-Net, a CNN-Transformer based model for producing accurate Apparent Diffusion Coefficient (ADC) maps from accelerated diffusion-weighted MRI data. By efficiently reconstructing accelerated ADC maps to match fully-sampled ADC maps, DeepADC-Net significantly reduced scan time from 25 minutes to just over six minutes while maintaining high-quality output. This reduction is crucial for improving patient comfort and enhancing the diagnostic process for tumor detection. Lastly, I proposed CycleFormer, a novel cross-modality domain adaptation method that enhances urinary tract segmentation. Utilizing CT urograms, CycleFormer effectively improves the segmentation performance on non-contrast CT scans, specifically for ureters, and has shown notable improvement over alternative state-of-the-art 2D CNN models, providing more accurate and reliable diagnostic tools for clinical practice. In conclusion, the deep learning models developed in this thesis significantly advance the field of medical image analysis, providing robust, efficient and more accurate diagnostic tools that enhance the capability of medical imaging technologies in clinical diagnosis. These contributions demonstrate the potential of deep learning approaches across various medical imaging modalities, highlighting their effectiveness in clinical diagnosis and healthcare settings.", "source_url": "https:\/\/openalex.org\/W7135311797", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ScholarlyCommons (University of Pennsylvania)", "source_record_id": "W7135311797", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 845, "title": "Exploring the Potential of Deep Learning for Biomedical Image Segmentation", "title_ar": "", "title_en": "Exploring the Potential of Deep Learning for Biomedical Image Segmentation", "author": "Sharif, Muhammad Hamza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Arab Emirates", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; medical-ai; cnn; transformers; image-segmentation; medical-imaging", "keywords": "", "abstract": "Medical image segmentation is the fundamental task in biomedical imaging analysis by identifying and isolating regions of interest from images such as MRI or CT scans. This process is vital for precise diagnosis and effective treatment planning for various conditions, including tumors and anomalies. However, medical image segmentation is a challenging task due to the complex and diverse nature of these images. They contain structures with varying sizes, shapes, and intensities, as well as noise, artifacts, and missing data. Traditional image segmentation methods that rely on handcrafted features and heuristic algorithms have limitations in accurately segmenting medical images due to their complexity and variability. Recent advances in deep learning-based techniques have shown promising results in medical image segmentation by automatically learning feature representations. However, these techniques require innovation in network architecture, learning framework, and training procedures to improve the segmentation process. In this thesis, we aim to introduce new deep learning-based methods to improve medical image segmentation, with a focus on frameworks for high-resolution and volumetric medical image segmentation. The proposed techniques aim to segment various structures, including organs, brain tumors, skin lesions, polyps, and retinal vessels, using medical image data from different modalities such as CT, MRI, endoscopy imaging, and fundus imaging. The thesis proposes two novel deep learning-based techniques. The first one is called TransResNet, which combines a transformer and CNN to effectively segment high-resolution medical images. The proposed method introduces a Cross Grafting Module (CGM) that generates grafted features by combining global semantic and low-level spatial details. This approach achieves state-of-the-art or competitive results on several segmentation tasks, including skin lesions, retinal vessels, and polyp segmentation. The second technique proposed in the thesis is a versatile generic architecture called Y-CA-Net, which can segment volumetric medical images of different modalities (CT, MRI). This method is based on any two encoders and a decoder backbone, with one encoder branch that extracts local features using convolution filters and the other encoder branch that learns global interactions using an attention mechanism. The proposed approach aims to fully exploit the strengths of both convolution and attention mechanisms for volumetric segmentation. The model introduces a simple yet effective Cross Feature Mixer Module (CFMM) to learn semantically enriched features by mixing local and global feature representations. The proposed model has shown significant improvement across different benchmark datasets for multi-organ and brain tumor segmentation compared to existing state-of-the-art methods.", "source_url": "https:\/\/openalex.org\/W7106329223", "pdf_url": "", "doi": "10.82419\/162", "handle": "", "repository_name": "MBZUAI iRep", "source_record_id": "W7106329223", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 846, "title": "Learning with imperfect datasets in medical image segmentation", "title_ar": "", "title_en": "Learning with imperfect datasets in medical image segmentation", "author": "Yuhang Ding", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Technology Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; predictive-analytics; image-segmentation; medical-imaging; fairness; ai-industry", "keywords": "", "abstract": "edical image segmentation aims to partition medical images into distinct physiological regions, such as organs and lesions, which is crucial for disease diagnosis and treatment planning.The advent of deep neural networks has significantly advanced this field.However, the performance in real-world scenarios remains unsatisfactory due to imperfect data and annotations during both training and deployment.First, scaling up training data and annotations is challenging.This is because obtaining medical images is difficult due to privacy concerns.Furthermore, annotating medical images requires substantial expertise, making the process costly and difficult to carry out.Second, the quality of medical images cannot always be guaranteed in real-world scenarios, leading to significant performance drops in outlier cases.Third, real-world medical applications are safety-critical and demand extremely accurate predictions, a requirement that most existing models fail to meet adequately.These challenges hinder the practical deployment of medical image segmentation.Consequently, both academic and industrial communities are striving to develop highly accurate medical image segmentation algorithms that can perform well despite imperfect data.To this end, this thesis proposes deep learning methods to develop a well-performed medical image segmentation model that can be effectively trained with limited and low-quality data\/annotations.Specifically, a comprehensive suite is proposed from three directions: (1) applying image registration to generate realistic and diverse training samples and adopting barely-supervised learning paradigms to enable learning with insufficient annotated data; (2) devising region-aware fusion module to address missing modality problem; (3) incorporating automatic and interactive medical image segmentation into a single model and one training session to achieve sufficient segmentation performance for practical use.Extensive experiments on several medical image segmentation tasks, such as brain tumor segmentation, brain structure segmentation and abdominal organ segmentation, demonstrate the effectiveness and efficiency of the proposed techniques.ix", "source_url": "https:\/\/openalex.org\/W7216881435", "pdf_url": "https:\/\/opus.lib.uts.edu.au\/bitstream\/10453\/186717\/1\/thesis.pdf", "doi": "", "handle": "", "repository_name": "OPUS - Open Publications of UTS Scholars (University of Technology Sydney)", "source_record_id": "W7216881435", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 847, "title": "Medical Image Segmentation by Deep Convolutional Neural Networks", "title_ar": "", "title_en": "Medical Image Segmentation by Deep Convolutional Neural Networks", "author": "Qingbo Kang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Concordia University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Medical image segmentation is a fundamental and critical step for medical image analysis. Due to the complexity and diversity of medical images, the segmentation of medical images continues to be a challenging problem. Recently, deep learning techniques, especially Convolution Neural Networks (CNNs) have received extensive research and achieve great success in many vision tasks. Specifically, with the advent of Fully Convolutional Networks (FCNs), automatic medical image segmentation based on FCNs is a promising research field. This thesis focuses on two medical image segmentation tasks: lung segmentation in chest X-ray images and nuclei segmentation in histopathological images. For the lung segmentation task, we investigate several FCNs that have been successful in semantic and medical image segmentation. We evaluate the performance of these different FCNs on three publicly available chest X-ray image datasets. For the nuclei segmentation task, since the challenges of this task are difficulty in segmenting the small, overlapping and touching nuclei, and limited ability of generalization to nuclei in different organs and tissue types, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs.", "source_url": "https:\/\/openalex.org\/W3044795341", "pdf_url": "https:\/\/spectrum.library.concordia.ca\/985550\/1\/Kang_MASc_F2019.pdf", "doi": "", "handle": "", "repository_name": "Spectrum Research Repository (Concordia University)", "source_record_id": "W3044795341", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 848, "title": "Constrained deep networks for medical image segmentation", "title_ar": "", "title_en": "Constrained deep networks for medical image segmentation", "author": "Hoel Kervadec", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; self-semi-supervised; predictive-analytics; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Weakly supervised image segmentation, in the form of partially labeled images, is attracting significant research attention as it can mitigate the need for laborious pixel annotations required by deep learning models. Enforcing high-order, global inequality constraints on the network outputs can leverage unlabeled data by guiding the training with prior knowledge, restricting the search space during training to anatomically feasible solutions. A range of possible values (such as a lower\/upper bounds on the size of a organ) can be very valuable to guide training. However, in the context of deep neural networks, standard Lagrangian optimization has been largely avoided, mainly due to the instability and computational complexity ensuing from alternating explicit dual updates and stochastic optimization. Interior point methods, despite their popularity in convex optimization, are not applicable neither, as they require a feasible starting point, which is itself a difficult constrained problem for deep neural networks. In this thesis, we investigate hard inequality constraints in the context of deep networks with both quadratic penalties and more principled log-barrier extensions. We also investigate methods to mitigate class-imbalance in segmentation problems, such as in brain lesions dataset, by constraining the boundary of the predicted segmentation to match the ground-truth boundary. This thesis produced five different publications as first author, and four papers as co-author. Our papers received several awards, and we were invited to publish extended versions of our works in two special issues of Medical Image Analysis (MedIA). In our first contribution, we propose to introduce a differentiable penalty, which enforces inequality constraints directly in the loss function, avoiding expensive Lagrangian dual iterates and proposal generation. From constrained-optimization perspective, our simple penalty-based approach is not optimal as there is no guarantee that the constraints are satisfied. However, surprisingly, it yields substantially better results than Lagrangian-based constrained convolutional neural networks, while reducing the computational demand for training. By annotating only a small fraction of the pixels, our approach reaches performances comparable to full supervision, on three separate tasks. While our experiments focused on basic linear constraints such as the target-region size and image tags, our framework can be easily extended to other non-linear constraints, e.g., invariant shape moments and other region statistics. In our second contribution, we propose log-barrier extensions, which approximate Lagrangian optimization of constrained-CNN problems with a sequence of unconstrained losses. Unlike standard interior-point and log-barrier methods, our formulation does not need an initial feasible solution. We report comprehensive weakly supervised segmentation experiments, with various constraints, showing that our formulation outperforms substantially the existing constrained-CNN methods, both in terms of accuracy, constraint satisfaction and training stability. In our third contribution, we enforce constraints on the boundary of predicted segmentation. Widely used loss functions for CNN segmentation, such as Dice or cross entropy, are based on integrals over the segmentation regions. Unfortunately, for highly unbalanced segmentations, such regional summations have values that differ by several orders of magnitude across classes, which affects training performance and stability. We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions. This can mitigate the difficulties of highly unbalanced problems because it uses integrals over the interface between regions instead of unbalanced integrals over the regions. Furthermore, a boundary loss complements regional information. Inspired by graph-based optimization techniques for computing activecontour flows, we express a non-symmetric L2 distance on the space of contours as a regional integral, which avoids completely local differential computations involving contour points. This yields a boundary loss expressed with the regional softmax probability outputs of the network, which can be easily combined with standard regional losses and implemented with any existing deep network architecture for N-D segmentation. We report comprehensive evaluations on different unbalanced problems, showing that our boundary loss can yield significant increases in performances while improving training stability. In a fourth contribution, we investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of the target region. These regressions are used to effectively regularize the segmentation network, constraining the softmax predictions of the unlabeled images to match the inferred label distributions.", "source_url": "https:\/\/openalex.org\/W3215133863", "pdf_url": "https:\/\/espace.etsmtl.ca\/id\/eprint\/2734\/1\/KERVADEC_Hoel.pdf", "doi": "", "handle": "", "repository_name": "Espace ÉTS (ETS)", "source_record_id": "W3215133863", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 849, "title": "Employing Attention-Based Learning For Medical Image Segmentation", "title_ar": "", "title_en": "Employing Attention-Based Learning For Medical Image Segmentation", "author": "Alexandros Ferles", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Automated medical image analysis is a non-trivial task due to the complexity of medical data. With the advancements made on Computer Vision through the golden era of Deep Learning, many models which rely on Deep Convolutional Networks have emerged in the Medical Imaging domain and offer important contributions in automating the analysis of medical images. Based on recent literature, this work proposes the adaptation of visual attention gates in Fully Convolutional Encoder-Decoder networks in the Medical Image Segmentation task. Appropriate data pre-processing is performed in the cases of 2-dimensional and 3-dimensional data in order to serve them as proper inputs in conventional and attention-gated Deep Convolutional Networks that try to identify classes in pixel and voxel level respectively. Attention gates can be easily integrated in the conventional networks, that would improve their performance. We present the specific mechanics of attention gates, conduct experiments and analyse our derived results. Finally, based on the latter, we provide our opinion and intuition on how this work can be further expanded towards new research directions.", "source_url": "https:\/\/openalex.org\/W3014417460", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3014417460", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 850, "title": "Deep learning for coronary artery segmentation in CTA images", "title_ar": "", "title_en": "Deep learning for coronary artery segmentation in CTA images", "author": "Fredrik Ring", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Chalmers University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation", "keywords": "", "abstract": "The mortality of cardiovascular diseases (CVD) calls for more knowledge of disease mechanisms and prevention strategies.The SCAPIS study, which is a collaboration between Swedish university hospitals and universities, aims to acquire this knowledge and is the worlds largest study focusing on CVD to date.Among other biomarkers, the SCAPIS study is interested in studying atherosclerotic plaque in coronary arteries which is a major risk factor of CVD.Atherosclerotic plaques can be studied from computed tomography angiography (CTA) images.The first step of the analysis is segmentation of the coronary artery tree, which is tedious and time consuming to do manually.Automatic segmentation techniques can speed up this procedure, but unfortunately, current automatic methods lack the accuracy required and have to be manually corrected.Thus, improving the performance of automatic software could be highly valuable to a large scale study such as SCAPIS.This thesis investigates the viability of a deep learning based automatic segmentation approach and compares its performance to the commercial software Medis QAngioCT Re and to the interobserver variability in manual segmentations from two medical professionals.A version of the state-of-the-art network architecture for vessel wall boundary segmentation, I2I-3D, is implemented in PyTorch and applied to the task.I2I-3D is a fully convolutional network allowing for a variable input size and full volume-to-volume segmentations.It has an encoder-decoder architecture with multiple levels of deep supervision integrated into the decoder.Evaluation on manually corrected segmentations on three CTA images shows that the implemented method outperforms the commercial software for two different segmentation similarity indices; Dice index and modified Hausdorff Distance.However, visual inspection still implies some shortcomings.Although very precise in the segmentation border placement, the network segmentations include broken vessels and unconnected islands of labellings.In contrast, the Medis software's downfall is its inclusion of small, clinically irrelevant vessels.When comparing to the interobserver variability, both automatic approaches perform unfavourably.Nevertheless, I2I-3D shows promising performance despite the relatively limited data set of 25 manually corrected segmentations.The results encourage further development with clear room for improvement.Most importantly, in order to reach full potential the method requires a significantly larger data set.In conclusion, this thesis confirms the viability of using deep learning to segment coronary arteries in cardiac CTA and should be interpreted as a proof-of-concept, rather than the presentation of a finished product.", "source_url": "https:\/\/openalex.org\/W2888867878", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Chalmers Publication Library (Chalmers University of Technology)", "source_record_id": "W2888867878", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 851, "title": "Deep Learning for Pneumothorax Segmentation and Quantification", "title_ar": "", "title_en": "Deep Learning for Pneumothorax Segmentation and Quantification", "author": "Wannipa Sae-Lim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Prince of Songkla University", "university_source": "repository_host", "college": "", "department": "", "country": "Thailand", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; predictive-analytics; cnn; transformers; image-segmentation; medical-imaging", "keywords": "", "abstract": "This thesis investigates the role of deep learning, focusing on convolutional neural networks and encoder-decoder architectures, to enhance medical image segmentation, specifically for pneumothorax identification in chest X-ray images. Pneumothorax, a critical condition characterized by lung collapse, requires precise diagnostics for effective management and treatment. Traditional image segmentation methods sometimes underperform due to the complexity and variability of chest X-ray images. In this study, we proposed PTXSeg-Net, a novel deep learning model for accurate and efficient pneumothorax segmentation. This model incorporates residual blocks and attention mechanisms to improve learning capabilities and is further enhanced by deep supervision, which allows for more effective gradient utilization across all network layers, significantly enhancing its performance over existing segmentation methods. Additionally, transfer learning with pre-trained models on extensive chest X-ray datasets improves feature extraction, addressing imbalanced datasets. Data refinement techniques are also employed to optimize the training outcomes. The performance of PTXSeg-Net outperforms the existing segmentation model in terms of accuracy, dice and Jaccard coefficients, demonstrating significant improvement in the field of pneumothorax segmentation. Moreover, leveraging the predicted masks of pneumothorax, this thesis proposes an algorithm that calculates the pneumothorax ratio. This algorithm provides a quantitative measure of the appearance of pneumothorax relative to the total lung area, offering a metric for radiologists to assess the severity of the condition more accurately. This integration of deep learning models and practical quantification algorithm underscores the significant contribution to the field of medical image analysis.", "source_url": "https:\/\/openalex.org\/W7201935903", "pdf_url": "", "doi": "", "handle": "", "repository_name": "PSU Knowledge Bank (Prince of Songkla University)", "source_record_id": "W7201935903", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 852, "title": "Relaxation and restriction for medical image segmentation with convolutional neural networks", "title_ar": "", "title_en": "Relaxation and restriction for medical image segmentation with convolutional neural networks", "author": "Pei Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "991012872963603412 HKUST Electronic Theses Relaxation and restriction for medical image segmentation with convolutional neural networks by Pei Wang thesis 2020 xvii, 99 pages : illustrations ; 30 cm Deep learning has achieved great success in different tasks for natural image", "source_url": "https:\/\/openalex.org\/W4234859439", "pdf_url": "", "doi": "10.14711\/thesis-991012872963603412", "handle": "", "repository_name": "", "source_record_id": "W4234859439", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 853, "title": "A hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for detecting Keratoconus in patients using genetic algorithm", "title_ar": "", "title_en": "A hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for detecting Keratoconus in patients using genetic algorithm", "author": "Somayeh Ghasedi Katak Lahijani", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Laurentian University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; optimization; cnn", "keywords": "", "abstract": "Nowadays, there are still significant challenges encountered in the accurate diagnosis of various eye diseases, such as Keratoconus (KCN) and cataracts. Early detection of Keratoconus is crucial in preventing its progression and ensuring the best treatment outcomes. Artificial intelligence (AI) is being widely applied in ophthalmology through the training of deep learning networks for the early detection of eye diseases. This research presents a novel, integrated machine learning approach for diagnosing Keratoconus disease by combining feature extraction through Convolutional Neural Networks (CNN) with a Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classification. Employing a multi-objective genetic algorithm, the method optimizes feature selection, aiming to minimize both diagnostic error and the number of features. The study utilizes a dataset of 5,152 ophthalmic images (1288 samples) categorized into Normal (476), Suspect (453), and Keratoconus (359) cases, sourced from an eye clinic in Egypt. Combining a Convolutional Neural Network (CNN) for feature selection with a Genetic Algorithm (GA) significantly improved diagnostic accuracy. Consequently, by focusing on the most relevant features of Keratoconus (KC), the model achieved an impressive 98.63% accuracy for ANN classification with a genetic algorithm, and 98.13% for SVM classification with a genetic algorithm. The accuracy of the algorithm exceeded that of when SVM and ANN were used without the genetic algorithm alone, which were 97.53% and 96.9% respectively, underscoring the benefit of combining Artificial Neural Networks (ANNs) with Genetic Algorithms (GAs) in KC diagnosis. Implementing this model can assist physicians in more accurate Keratoconus detection, providing better predictions regarding patients' eye conditions, and offering timely treatment recommendations.", "source_url": "https:\/\/openalex.org\/W7205487330", "pdf_url": "https:\/\/laurentian.scholaris.ca\/bitstreams\/ebade523-4710-440b-a199-72030adbc718\/download", "doi": "", "handle": "", "repository_name": "Lu Zone Ul (Laurentian University)", "source_record_id": "W7205487330", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 854, "title": "Energy Hole Mitigation and Network Lifetime Maximisation in Shape-Varying 3D IoT-based Heterogeneous Wireless Sensor Networks", "title_ar": "", "title_en": "Energy Hole Mitigation and Network Lifetime Maximisation in Shape-Varying 3D IoT-based Heterogeneous Wireless Sensor Networks", "author": "SHAFIQUE, Tamoor", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Staffordshire University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; computer-vision; expert-systems; ai-applications; data-mining; image-segmentation; ai-smart-cities", "keywords": "", "abstract": "The rapid expansion of IoT-based Heterogeneous Wireless Sensor Networks (HWSNs) amplifies challenges in energy management. These networks, comprising diverse devices deployed across varying terrains, are designed to efficiently collect data while ensuring complete coverage. However, achieving balanced energy consumption is critical to prevent energy holes, which occur when devices near base station deplete their energy at faster rate due to experiencing excessive transmission loads. Such imbalances lead to network instability and reduce operational lifespan. Existing energy hole mitigation techniques often fail to account for the diverse characteristics of heterogeneous devices, limiting their effectiveness and applicability. Furthermore, many of these approaches are tailored to specific applications or constrained by fixed network shapes, lacking the adaptability, scalability, and flexibility required for large-scale, dynamic IoT-based HWSNs. These limitations hinder their performance in managing the complex and diverse topologies encountered in real-world deployments. This thesis aims to tackle these challenges by proposing novel methods to reduce energy consumption and balance it across resource-constrained heterogeneous devices. The proposed solutions focus on enhancing energy efficiency, extending network lifetime, and improving throughput, ensuring reliable data transmission from sensing nodes to the base station. A critical focus is on deployment of the base station, is critical given the converge-caste nature of communication in HWSNs. An iterative algorithm is proposed to compute the overall network energy consumption for a set of alternative locations within a specified radius of the base station. While the iterative algorithm identifies the optimal deployment location, its computational complexity is addressed using a multi-criteria decision-making approach. The Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS) evaluates alternative locations based on their positive and negative contributions to energy consumption and balancing. This method improves network lifetime by 23.5% compared to central deployment techniques and adapts effectively to networks of varying shapes, including circular, square, cubical and spherical, in both 2D and 3D environments. In the post-deployment phase, the distribution of heterogeneous resources across smaller network segments supports the design of an effective communication topology. Considering variations in network shape and spatial dimensions, this thesis proposes two fixed-shape segmentation schemes-cubical and spherical segmentation, which cover the majority of network geometries. These schemes leverage the distribution functions of network parameters and integrate an unequal clustering method to ensure energy-balanced routing. However, the complexity of choosing the appropriate segmentation scheme for specific applications motivates the development of a novel shape-independent, data-traffic-based segmentation approach. To overcome this challenge, a novel shape-independent, data-traffic-based segmentation scheme is introduced. This scheme provides faster and more accurate insights into network parameter distributions, enhancing the unequal clustering algorithm. The integration of shape-independent segmentation improves network lifetime by up to 18.8% and reduces energy consumption by 61.4% compared to existing clustering methods. Finally, coordinated and dynamic energy-efficient mechanisms for cluster head and next-hop relay node selection, along with rotation strategies, further enhance energy efficiency. These methods optimise both intra-cluster and inter-cluster communication by dynamically selecting energy-efficient cluster head nodes, rotating their roles, and identifying optimal next-hop relay nodes for inter-cluster communication. The integration of appropriate rotation mechanisms ensures balanced energy consumption across the network. The proposed routing techniques improve network throughput by 57.44% and increase energy efficiency by 17.63% compared to protocols such as Rotated Low-Energy Adaptive Clustering Hierarchy (RLEACH) and Cluster Routing Protocol using Fuzzy C-Means (CRPFCM). The methods proposed in this thesis significantly extend network lifetime and optimise energy usage in IoT-based HWSNs. They provide a scalable and adaptable framework for energy management in complex, multi-parameter, and multi-level IoT-based HWSNs,", "source_url": "https:\/\/openalex.org\/W7115818707", "pdf_url": "https:\/\/eprints.staffs.ac.uk\/9453\/1\/FINAL%20THESIS%20V14%20-%20Tamoor%20Shafique%2016028322.pdf", "doi": "", "handle": "", "repository_name": "Staffordshire Online Repository (Staffordshire University)", "source_record_id": "W7115818707", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 855, "title": "Automated false claims detection using deep neural networks", "title_ar": "", "title_en": "Automated false claims detection using deep neural networks", "author": "Erlend Rekve", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "machine-learning; deep-learning", "keywords": "", "abstract": "Recently false claims and misinformation have become rampant in the web, affecting election outcomes, stock markets, and various other societal issues. Consequently, fact-checking and claim verification websites such as snopes.com are becoming increasingly popular and are also being integrated into news search engines such as Google news. However, these websites require expert analysis which is slow and not scalable. Many recent papers have proposed machine learning methods using handpicked linguistic and source-based cues to automate the claim verification process. In this thesis, we propose deep neural models which avoid tedious feature engineering and strong assumptions and yet detect false claims with high accuracy. To achieve this, we propose a hybrid model which combines textual content of the news articles as well as the reactions they receive in social media forums such as Reddit. Using large-scale manually curated data from fact-checking websites such as snopes.com, politifact.com and emergent.info we perform extensive experiments to show that our models outperform the state-of-the-art CRF-based models.", "source_url": "https:\/\/openalex.org\/W2892547540", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W2892547540", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 856, "title": "Development of Automated Extraction Method for Road and Centerline from Aerial Images Using Deep Convolutional Neural Network", "title_ar": "", "title_en": "Development of Automated Extraction Method for Road and Centerline from Aerial Images Using Deep Convolutional Neural Network", "author": "Tamara Alshaikhli", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; ai-applications; cnn; image-segmentation; ai-smart-cities", "keywords": "", "abstract": "Road extraction from remote sensing imagery plays an important role in modern life society.That contributes in a lot of areas, such as urban planning, disaster mitigation, transportation engineering, and so on.Manual updating was the common methodology to keep the road information up to date, however the hectic process to label these images manually requires a lot of time and efforts, and is also prone to human error.Therefore, this area attracts a lot of researchers' attentions to find a suitable automated process.This research adopted a deep learning technique as an alternative to solve the problem of road extraction from aerial imagery automatically.The research consisted of two parts.The first one was dedicated to extract road only by a deep convolutional neural network (DCNN).The proposed DCNN was built on the U-Net backbone with a residual block (Res-block) on the encoder part and convolution layers (Conv.layer) on the decoder part.The model shows a good result for extracting road automatically from aerial images, and the research has extended to conduct a comprehensive study covering all the important aspects of this area.The second part of this research was assigned to extract road and centerline simultaneously by extending the previous model with multi task learning scheme (MTL).The proposed model is a complete MTL scheme without any further utilization of thinning algorithms or post processing.The proposed model showed a promising results for the single task learning (STL) to extract road only.In addition, the MTL for extracting road and centerline simultaneously also succeeded in extracting the roads and centerlines simultaneously.In order to evaluate the applicability of the proposed model for another dataset, a case study for Baghdad City, Iraq was performed, and the accuracy was examined.", "source_url": "https:\/\/openalex.org\/W7143882449", "pdf_url": "https:\/\/opac.ll.chiba-u.jp\/da\/curator\/900120235\/WLA_0058.pdf", "doi": "10.20776\/900120235", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7143882449", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 857, "title": "Lightning damage detection on solar panels using portable infrared camera and convolutional neural network for enhancing panel maintenance", "title_ar": "", "title_en": "Lightning damage detection on solar panels using portable infrared camera and convolutional neural network for enhancing panel maintenance", "author": "Ormiila Chandrasegaran", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universiti Putra Malaysia", "university_source": "repository_host", "college": "", "department": "", "country": "Malaysia", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "deep-learning; computer-vision; ai-applications; cnn; image-classification; ai-smart-cities", "keywords": "", "abstract": "As the demand for renewable energy sources increases, the vulnerability of solar panels to lightning strikes becomes a critical concern. This research explores the correlation between lightning-induced voltage fluctuations and the resultant damage intensity on solar panels. Monocrystalline and polycrystalline solar panels were used in this study. A systematic approach was adopted, investigating the correlation between lightning-induced voltage assessment using 30kV, 60kV and 90 kV impulse voltage with multi-stage Marx impulse generator and the damage intensity on these two types of solar panels. The utility of active infrared thermography in capturing lightning-induced damage was also explored. Portable active infrared thermography equipment, tCam- Mini with wireless streaming was employed to conduct this research. Additionally, Convolutional Neural Network (CNN) based image classification techniques were integrated to enhance the efficiency of damage assessment. The application of neural networks allowed for automated and precise identification of lightning-induced damage patterns, improving accuracy and speed of image classification for damaged and undamaged samples. The experimental design involves induced lightning strikes, exposing solar panels to controlled conditions, collecting infrared images and performing neural network-based image classifications. The findings contribute valuable insights into enhancing the resilience of solar panel systems against lightning strikes, ultimately advancing the reliability and sustainability of solar energy infrastructure. A new convolutional neural network model was developed to classify the images obtained from thermography with 90.21% accuracy for grayscale and 85% accuracy on thermal images. The application of this research lies in improving the protection and maintenance strategies for solar panels, ensuring more durable and efficient renewable energy systems.", "source_url": "https:\/\/openalex.org\/W7170114848", "pdf_url": "http:\/\/psasir.upm.edu.my\/id\/eprint\/126723\/2\/FK%202024%2058%20-Full%20Text.pdf", "doi": "", "handle": "", "repository_name": "Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia)", "source_record_id": "W7170114848", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 858, "title": "Flexible network management in software defined wireless sensor networks for monitoring application systems", "title_ar": "", "title_en": "Flexible network management in software defined wireless sensor networks for monitoring application systems", "author": "Kgotlaetsile Mathews Modieginyane", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Pretoria", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "machine-learning; computer-vision", "keywords": "", "abstract": "Wireless Sensor Networks (WSNs) are the commonly applied information technologies of modern networking and computing platforms for application-specific systems. Today’s network computing applications are faced with high demand of reliable and powerful network functionalities. Hence, efficient network performance is central to the entire ecosystem, more especially where human life is a concern. However, effective management of WSNs remains a challenge due to problems supplemental to them. As a result, WSNs application systems such as in monitored environments, surveillance, aeronautics, medicine, processing and control, tend to suffer in terms of capacity to support compute intensive services due to limitations experienced on them. A recent technology shift proposes Software Defined Networking (SDN) for improving computing networks as well as enhancing network resource management, especially for life guarding systems. As an optimization strategy, a software-oriented approach for WSNs, known as Software Defined Wireless Sensor Network (SDWSN) is implemented to evolve, enhance and provide computing capacity to these resource constrained technologies. Software developmental strategies are applied with the focus to ensure efficient network management, introduce network flexibility and advance network innovation towards the maximum operation potential for WSNs application systems. The need to develop WSNs application systems which are powerful and scalable has grown tremendously due to their simplicity in implementation and application. Their nature of design serves as a potential direction for the much anticipated and resource abundant IoT networks. Information systems such as data analytics, shared computing resources, control systems, big data support, visualizations, system audits, artificial intelligence (AI), etc. are a necessity to everyday life of consumers. Such systems can greatly benefit from the SDN programmability strategy, in terms of improving how data is mined, analysed and committed to other parts of the system for greater functionality. This work proposes and implements SDN strategies for enhancing WSNs application systems especially for life critical systems. It also highlights implementation considerations for designing powerful WSNs application systems by focusing on system critical aspects that should not be disregarded when planning to improve core network functionalities. Due to their inherent challenges, WSN application systems lack robustness, reliability and scalability to support high computing demands. Anticipated systems must have greater capabilities to ubiquitously support many applications with flexible resources that can be easily accessed. To achieve this, such systems must incorporate powerful strategies for efficient data aggregation, query computations, communication and information presentation. The notion of applying machine learning methods to WSN systems is fairly new, though carries the potential to enhance WSN application technologies. This technological direction seeks to bring intelligent functionalities to WSN systems given the characteristics of wireless sensor nodes in terms of cooperative data transmission. With these technological aspects, a technical study is therefore conducted with a focus on WSN application systems as to how SDN strategies coupled with machine learning methods, can contribute with viable solutions on monitoring application systems to support and provide various applications and services with greater performance. To realize this, this work further proposes and implements machine learning (ML) methods coupled with SDN strategies to; enhance sensor data aggregation, introduce network flexibility, improve resource management, query processing and sensor information presentation. Hence, this work directly contributes to SDWSN strategies for monitoring application systems.", "source_url": "https:\/\/openalex.org\/W2949713261", "pdf_url": "https:\/\/repository.up.ac.za\/bitstreams\/ea51dda3-daff-4fe2-9a46-130b20288684\/download", "doi": "", "handle": "", "repository_name": "UpSpace Institutional Repository (University of Pretoria)", "source_record_id": "W2949713261", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 859, "title": "Üzüm salkımı meyvelerinin tanınması amacıyla DVM, ESA ve YSA sınıflayıcılarının gerçekleştirilmesi ve başarılarının belirlenmesi.", "title_ar": "", "title_en": "", "author": "Bashar Saad Falıh Al-Saffar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2018, "language": "tr", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; cnn; object-detection; ai-agriculture", "keywords": "", "abstract": "Grape detection, harvesting, spraying, and yield estimation are difficult, time consuming, and requiring considerable manpower.Therefore, harvesting with the aid of smart robots is now being explored and is currently a frontline problem in agriculture.This thesis presented a grape bunch and berry recognition by employing Convolution Neural Network (CNN), Artificial Neural Network (ANN) and Linear Support-Vector-Machine (SVM).Histogram of Oriented Gradients (HOG), the Local Binary Pattern (LBP), and combination have been used as feature vectors.The data contain berry and non-berry classes.ANN classifier with HOG+LBP features show better results in single berry recognition with an average of accuracy, precision and recall are 99.64±0.073%, 99.70±0.094%,and 99.59±0.117%respectively.The proposed detection method uses a Fast-Radial Symmetry Transform (FRST) as interest point detector.Feature extraction and classification are computed in each interest point approach.Afterwards, a DBSCAN method defines the number of grape bunches.Each cluster's spatial distribution and needs to close bunch separation for an improved bunch detection.The average accuracy of grape bunch detection 91.72±2.88% and 89.82±2.58%for berry detection.Results show a good performance based on the shape and texture information, with no need for special illumination and color features.", "source_url": "https:\/\/openalex.org\/W7132192735", "pdf_url": "http:\/\/library.cu.edu.tr\/tezler\/12625.pdf", "doi": "", "handle": "", "repository_name": "Çukurova University Institutional Repository", "source_record_id": "W7132192735", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 860, "title": "Modeling of Groundwater Heavy Metals and Methane Pollution for a Municipal Landfill Utilizing Data Driven and Numerical Modeling Techniques", "title_ar": "", "title_en": "Modeling of Groundwater Heavy Metals and Methane Pollution for a Municipal Landfill Utilizing Data Driven and Numerical Modeling Techniques", "author": "Bahareh Fallah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "This thesis shows original results to the advancement of modeling of groundwater heavy metal pollution and methane generation as the main two concerns in landfilling for a municipal landfill. To better understand the context of thesis, it has been divided into six major studies. The first part of the thesis is on spatial-temporal analysis of dissolved metal pollutants near an unlined municipal landfill in a semi-arid climate. The data was collected from 5 groups of wells located upstream and downstream of the landfill in 2012 and 2015. The results represented that in 2015, Mn, U, As and Fe exceeded the Saskatchewan drinking water quality standard limits. However, in 2012 other than the aforementioned heavy metals, Cd concentration was also above the guideline. Mn concentration increased from 2012 to 2015 while As and Fe decreased in the same period. Correlation analysis was assessed among heavy metals and two linear regression models are developed for pH and dissolved Mn in groundwater. Field data and literature review suggested that groundwater contamination is highly site specific. In the second part of the thesis, using geostatistical analysis, the three water quality index-based maps were compared to a GWQ map from overlapping heavy metal kriged maps. All index and GWQ maps showed an elevated plateau extending from the unlined landfill to the southeast corner implying that the landfill may have negatively impacted groundwater quality. Using the index method, some wells have poor water quality with regards to Mn, especially at southeast of the landfill, and according to the GWQ map, the center part of the landfill exhibits high pollution in terms of As, that can be explained by correlation matrix and weighting method. The third part of the thesis focuses on prediction of organic waste generation, as the main source of methane emissions from municipal landfills, utilizing artificial neural networks. Significant socio-economic and climatic variables were used to develop organic waste generation models with different ratios of training and testing. Among the total of 30 ANN models, the best model used wheat, maximum humidity, and Nasdaq variables with train-test ratio of 90:10. The optimal numbers of neurons was 8 in the hidden layer with MAPE of 27.63% at the testing stage. In the fourth part of the thesis, the two-stage NARX neural network model with meteorological input parameters was developed to predict daily CH4 generation rate from the landfill. The effects of each pre-processing step including minimizing collinearity problems, data normalization and filtering the outliers was examined on improving the model performance. The model with MAPE of 3.03% at the testing stage, 3 layers, 5 climatic variables and 9 neurons in the hidden layer was the optimal model. Missing data imputation coupled with NARX neural network method decreased the MSE of the model by 84.3% (compared to MLP model) in the testing period representing the effectiveness of the proposed method in daily CH4 generation prediction. In the fifth part of the thesis, after data pre-processing, the daily ANN methane prediction model was compared to the LandGEM first order decay model with optimal parameters. The MSE of the MLP methane predictionmodel decreased by 74.642% by comparison to the LandGEM. The aggregated daily predicted data in the MLP model were more correlated to the yearly measured CH4 rate with R2 of 0.929 than that in the LandGEM model (R2 = 0.887). The LandGEM model even with the site-specific parameters significantly underestimated the measured CH4 rate. The sixth part of the thesis focuses on the most efficient length of training dataset and periodic-based methane prediction modeling. Using a stepwise procedure, various volumes of training data were assessed, and concluded that employing the three-year training data reduced the MSE of the testing stage by 51.29%. Using the single input DOY-based model with MAPE of 2.12% shows a higher performance with the MSE of 5.54% lower than that of the model with both climatic and periodic inputs showing a convenient and precise approach in landfill gas modeling because of its simplicity and high accuracy.", "source_url": "https:\/\/openalex.org\/W3204603059", "pdf_url": "https:\/\/ourspace.uregina.ca\/bitstream\/handle\/10294\/14329\/ShahidpourFallah_Bahareh_PhD_EVSE_Spring2021.pdf", "doi": "10.82465\/4991", "handle": "", "repository_name": "", "source_record_id": "W3204603059", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 862, "title": "Definition of new parameters to develop an improved predictive model for urban heat island spatial distribution", "title_ar": "", "title_en": "Definition of new parameters to develop an improved predictive model for urban heat island spatial distribution", "author": "Victor Equere", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Nottingham", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "Urban heat island (UHI) is an urban phenomenon where the inner city experiences a significant elevation in temperature, compared to the suburban areas. UHI formation and intensity have an enormous beneficial and adverse impact on the microclimate of urban spaces. Hence, there is a need to understand how several urban aspects impact this phenomenon, to enable policymakers and urban planners to propose efficient mitigation strategies to adverse the UHI effects and to benefit from its potential positive impacts. Furthermore, a robust predictive model is necessary to simulate the efficacy of a proposed mitigation or enhancement strategy before any real-world implementation. This dissertation, thus, investigates the impact of urban elevation aspects on the UHI intensity and aims to improve the prediction of the spatial distribution of the UHI by proposing two novel urban elevation parameters defined as the Elevation Factor (EF) and Terrain Factor (TF), accounting for urban superstructures and the topographical aspects of cities. The proposed parameters were integrated with other already existing urban parameters in the remote sensing subject area to train and develop predictive models with more accurate predictive performances. An artificial neural network model was developed, which resulted in further improvement of the prediction accuracy. The associated procedure for retrieving both EF and TF from point cloud LiDAR data. The urban morphology parameters used in this study were derived from two case study cities of North Park and Oak Brook towns in Illinois State, USA. The integration of TF to the training process significantly improved the model’s prediction performance of the LST for high terrain variation areas, as the average root mean square error decreased from 1.26 to 0.90⁰C and 𝑅2 increased from 0.74 to 0.81. The models were then successfully validated against the latter data of the case study cities to predict the LST for parts of the city, which were not initially included in the training process.", "source_url": "https:\/\/openalex.org\/W7217734892", "pdf_url": "https:\/\/repository.nottingham.ac.uk\/bitstreams\/abe30b91-ad4f-4f04-ab37-6858f1e8013e\/download", "doi": "", "handle": "", "repository_name": "Nottingham ePrints (University of Nottingham)", "source_record_id": "W7217734892", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 863, "title": "IoT System engineering approach using AI for managing safety products in healthcare and workplaces", "title_ar": "", "title_en": "IoT System engineering approach using AI for managing safety products in healthcare and workplaces", "author": "Jabbar Abed Eleiwy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Brunel University London", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; optimization", "keywords": "", "abstract": "The Internet of things (IoT) has been widely used in life support facilities, especially in the healthcare sector.The management of health and safety (H&S) devices is an indispensable factor in guaranteeing human life.Thus, many companies and organisations have ensured the safety of their workplace from any overwhelming risks, such as fire, damage and accidents.The problem is that companies that have thousands of employees also have hundreds of health and safety systems in continuous use and Building Service Managers do not know which devices need maintenance or replenishment and require a significant amount of time to continuously check that their health and safety systems are maintained according to legislation requirements.The consequences of companies not meeting their health and safety obligations are serious and expensive if any of their employees are seriously injured as a consequence of the health and safety products not being in their original, immaculate order for use.Furthermore, insurance companies will refuse to pay compensation for injury to employees if it has been shown that the health and safety products are not in their original, immaculate order for use.Therefore, it makes economic sense to design and use an Internet of Things based monitoring system to record the state of health and safety products within companies.This thesis presents the design and prototype of an IoT-based health and safety (H&S) products monitoring system using artificial intelligence (AI) and image processing.The thesis aims to present a real-time control and monitoring system to determine the status of consumable hospital devices, such as first aid boxes, earplug dispensers, life jackets and fire extinguishers by using Zigbee sensors to read the measurement of the weight, and image-processing (sensor in a micro digital camera with affixed laser light) to determine the level.The proposed design is a dynamic (real-time) system that can monitor and predict the status of any equipment in hospitals or workplaces and notify the building service managers when any facilities need to be updated.Therefore, the proposed system can reduce hospitals' and companies' time, money and workforce requirements compared with previous manual maintenance approaches that are time-consuming and require more efforts and higher costs.Artificial intelligence infrastructure based on Ant Colony, Traveling Salesman Problem TSP and Genetic Algorithm GA is proposed to be implemented in optimisation algorithm to support salesmen in selecting the shortest path and latest time in maintaining and replenishing critically low devices at different locations in an appropriate time.Artificial neural networks are used to predict the optimal performance and the correlation between effective input factors and ii performance output.The achieved AI accuracy results is 96% of the proposed system which are proven to be reliable and can be used to manage many consumable H&S products in different district hospitals and companies as compared with the other approaches i.e. (Genetic, Ant-colony and TSP) that have been used at the same application whilst meeting regulatory requirements.The efficiency of the proposed system is validated because it reduces time, costs, efforts and workforce resources compared with previous manual maintenance approaches.", "source_url": "https:\/\/openalex.org\/W3171521908", "pdf_url": "https:\/\/bura.brunel.ac.uk\/bitstream\/2438\/22075\/1\/FulltextThesis.pdf", "doi": "", "handle": "", "repository_name": "Brunel University Research Archive (BURA) (Brunel University London)", "source_record_id": "W3171521908", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 864, "title": "THE PERFORMANCE OF ISLAMIC BANKS VS. CONVENTIONAL BANKS: THE CASE OF MENA REGION", "title_ar": "", "title_en": "THE PERFORMANCE OF ISLAMIC BANKS VS. CONVENTIONAL BANKS: THE CASE OF MENA REGION", "author": "Ahmed Mohammed Debes", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Plymouth", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "This study aimed to compare the financial performance of Islamic banks and conventional bank in the MENA region. This was undertaken through examining the influence of bank specific factors (represented by bank size, capital risk, loan intensity, financial leverage, credit risk, operating ratio and Z-score) and macroeconomic factors (represented by gross domestic product (GDP) and inflation) on banks’ financial performance (captured by return on assets (ROA) and return on equity (ROE)). The underlying aim is to demonstrate that the difference in operational models of banks has an effect on financial performance. The study analysed data obtained from Bankscope and Worldbank Databank for 108 banks comprising 35 Islamic banks and 73 conventional banks from 15 countries (Algeria, Bahrain, Egypt, Jordan, Iran, Iraq, Kuwait, Libya, Morocco, Oman, Saudi Arabia, Tunisia, Qatar, Yemen, United Arab of Emirates) for the period 2004-2014. Thus, the study examines the financial performance of the two banking types for the periods before, during, and after the 2007\/2008 global financial crisis. A deductive quantitative data analysis approach from a positivist epistemological perspective was employed using two research techniques: multivariate linear regression and non-linear artificial neural network model. The study has found that there are significant financial performance differences that exist between Islamic banks and conventional banks in the MENA region based on the financial variables examined. In respect to the impact of the 2008 financial crisis on the banking types in the MENA region, the study suggests that the conventional banks were affected relatively more than Islamic banks. Further, the study has revealed that credit risk and Z-score are key determinants of banks’ financial performance in the MENA region. This suggests that effective credit and bankruptcy risk management could positively influence banks’ performance. An increased investment in fixed assets, however, affects mainly conventional banks than Islamic banks. Similarly, the influence of bank size applies more to conventional banks than Islamic banks in the MENA region. When comparing the results obtained using the artificial neural network to those obtained using the multiple regression analysis, the study found that overall, the explanatory power obtained using the artificial neutral network was relatively higher. With a focus on the GCC countries, the study reveals that Islamic banks suffered relatively more than conventional banks from the 2008 global financial crisis, contrary to the results for the MENA region as a whole. Further, in addition to credit risk and Z-score, the study results suggest that increased bank capitalisation (capital risk) has a positive effect on banking performance in the GCC countries. However, increased financial leverage and operating ratio significantly affected conventional banks, and not Islamic banks, in the GCC countries. Contrary to the MENA region, bank size in the GCC countries impacts Islamic banks more than conventional banks. The changes in GDP, on the other hand, was found to matter more in the GCC countries (than non-GCC countries) whilst inflation affects conventional banks only in MENA countries. Increase in loan intensity, however, was not found to significantly influence the banks’ performance.", "source_url": "https:\/\/openalex.org\/W2996510991", "pdf_url": "https:\/\/pearl.plymouth.ac.uk\/context\/pbs-theses\/article\/1103\/viewcontent\/2019debes10366871phd_full.pdf", "doi": "10.24382\/779", "handle": "", "repository_name": "PEARL (University of Plymouth)", "source_record_id": "W2996510991", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 865, "title": "Çift ağaç karmaşık dalgacık dönüşümü ve yapay sinir ağları tabanlı duygu tanıma sistemi", "title_ar": "", "title_en": "", "author": "AMINA FARIS ABDULLAH AL RAHHAWI", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2016, "language": "tr", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "Yüz ifadeleri, bir kişinin duygusal durumuna, niyetine ya da sosyal iletişim durumuna göre yüzde oluşan değişikliklerdir. Yüz ifadesi iletişimin hayati bir parçasıdır ve gözlemcilere kişinin duygusal durumu hakkında bilgi verir. Kızgınlık, şaşkınlık, korku, üzüntü, mutluluk ve iğrenme temel altı duygu durumudur. Bu çalışmada yüz ifadelerinden kişilerin duygu durumunun tanınması incelenmiştir. Ayrıca, yüz ifadeleri iletişimde önemli bir kısımdır ve çok değerli bilgiler taşımaktadır. Bu sebepten, yüz görüntülerinden duygu tanıma, yapay zekâ, işaret işleme ve örüntü tanıma konuları ile ilişkili, çok motive edici ve enteresan bir konudur. Öznitelik çıkarma ve sınıflandırma duygu sınıflandırma sisteminde ana iki basamaktır. Bu çalışmada çift ağaç kompleks dalgacık dönüşümü (ÇAKDD) ve lokal ikili örüntü (LİÖ) yöntemleri öznitelik çıkarımında kullanılmıştır. İlk olarak yüz imgeleri ÇAKDD ve LİÖ yöntemleri ile öznitelik çıkarımı için işlenmiştir. Daha sonra Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (DVM) duygu tanımada sınıflandırma amacıyla kullanılmıştır. Elde edilen sonuçlar göstermektedir ki farklı ölçeklerde uygulanan ÇAKDD tabanlı öznitelikler LİÖ ile çıkarılmış özniteliklere göre daha iyi sonuç vermektedir. Buna ek olarak, YSA ile DVM'ne oranla daha iyi sınıflandırma doğrulukları elde edilmiştir. ÇAKDD+YSA ile elde edilen doğruluk sonuçları analiz esnasında 3, 4 ve 6 seviye kullanıldığında sırasıyla 96.992%, 96.911% ve 95.488% olarak bulunmuştur. ÇAKDD+DVM ile elde edilen doğruluk sonuçları analiz esnasında 3, 4 ve 6 seviye kullanıldığında sırasıyla 86.466 %, 87.218% ve 84.962% olarak bulunmuştur. LİÖ+YSA ve LİÖ+DVM ile elde edilen ortalama doğruluk sonuçları ise sırasıyla 81.203% ve 64% olarak bulunmuştur. Bu sonuçlar göstermektedir ki; ÇAKDD ile öznitelik çıkarımı LİÖ ile öznitelik çıkarımına göre daha iyi sonuçlar vermekte ve YSA ile sınıflandırma DVM ile sınıflandırmaya göre daha üstün performans göstermektedir.", "source_url": "https:\/\/openalex.org\/W7219222084", "pdf_url": "https:\/\/avesis.yildiz.edu.tr\/file?id=846e0ebd-c848-469e-a002-6c8379fac5b3", "doi": "", "handle": "", "repository_name": "Yildiz Technical University - AVESIS", "source_record_id": "W7219222084", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 866, "title": "Applications of images anomalies detection using deep learning in department store", "title_ar": "", "title_en": "Applications of images anomalies detection using deep learning in department store", "author": "Ram Banstola", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Jyväskylä", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; ai-cybersecurity", "keywords": "", "abstract": "Deep learning is a branch of machine learning which itself is a branch of Artificial Intelligence. The use of deep learning to solve domain specific problems is on the rise. Deep learning has been successfully used to assist sales prediction in retail, disease detection in medicine, road infrastructure monitoring by checking cracks on the road, accidents prone zones, detect anomalous activities in the realm of cyber security etc. At present, user and machine generated data is available abundantly and the challenges for enterprises is to infer new information from the available data to increase profit for the enterprise, produce a reliable system and increase customer satisfaction. Deep learning has been successfully used in classification of data with high precision. However, there are bottlenecks when it comes to anomalies in data because building models to detect anomalies is more difficult than classification problems. This thesis aims to study image anomalies detection and their applications department store using design science research methods. This thesis presents a basic prototype application to demonstrate anomalies in product areas in department stores.", "source_url": "https:\/\/openalex.org\/W3139869024", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Jyväskylä University Digital Archive (University of Jyväskylä)", "source_record_id": "W3139869024", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 867, "title": "Temporal models for mining, ranking and recommendation in the Web", "title_ar": "", "title_en": "Temporal models for mining, ranking and recommendation in the Web", "author": "Nguyen Hoang Tu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Leibniz Universität Hannover", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "data-mining", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; data-mining; predictive-analytics", "keywords": "", "abstract": "Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, heterogeneous temporal datasets i.e., the Web, collaborative knowledge bases and social networks have been emerged as gold-mines for content analytics of many sorts. In those collections, time plays an essential role in many crucial information retrieval and data mining tasks, such as from user intent understanding, document ranking to advanced recommendations. There are two semantically closed and important constituents when modeling along the time dimension, i.e., entity and event. Time is crucially served as the context for changes driven by happenings and phenomena (events) that related to people, organizations or places (so-called entities) in our social lives. Thus, determining what users expect, or in other words, resolving the uncertainty confounded by temporal changes is a compelling task to support consistent user satisfaction. In this thesis, we address the aforementioned issues and propose temporal models that capture the temporal dynamics of such entities and events to serve for the end tasks. Specifically, we make the following contributions in this thesis: (1) Query recommendation and document ranking in the Web - we address the issues for suggesting entity-centric queries and ranking effectiveness surrounding the happening time period of an associated event. In particular, we propose a multi-criteria optimization framework that facilitates the combination of multiple temporal models to smooth out the abrupt changes when transitioning between event phases for the former and a probabilistic approach for search result diversification of temporally ambiguous queries for the latter. (2) Entity relatedness in Wikipedia - we study the long-term dynamics of Wikipedia as a global memory place for high-impact events, specifically the reviving memories of past events. Additionally, we propose a neural network-based approach to measure the temporal relatedness of entities and events. The model engages different latent representations of an entity (i.e., from time, link-based graph and content) and use the collective attention from user navigation as the supervision. (3) Graph-based ranking and temporal anchor-text mining inWeb Archives - we tackle the problem of discovering important documents along the time-span ofWeb Archives, leveraging the link graph. Specifically, we combine the problems of relevance, temporal authority, diversity and time in a unified framework. The model accounts for the incomplete link structure and natural time lagging in Web Archives in mining the temporal authority. (4) Methods for enhancing predictive models at early-stage in social media and clinical domain - we investigate several methods to control model instability and enrich contexts of predictive models at the “cold-start” period. We demonstrate their effectiveness for the rumor detection and blood glucose prediction cases respectively. Overall, the findings presented in this thesis demonstrate the importance of tracking these temporal dynamics surround salient events and entities for IR applications. We show that determining such changes in time-based patterns and trends in prevalent temporal collections can better satisfy user expectations, and boost ranking and recommendation effectiveness over time.", "source_url": "https:\/\/openalex.org\/W3014066870", "pdf_url": "https:\/\/repo.uni-hannover.de\/bitstreams\/edcbab5a-c159-4955-b94f-809b5250aea7\/download", "doi": "10.15488\/9750", "handle": "", "repository_name": "Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover)", "source_record_id": "W3014066870", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:53", "updated_at": "2026-11-15 16:52:53" }, { "id": 868, "title": "Anomaly detection methods for detecting cyber attacks in industrial control systems", "title_ar": "", "title_en": "Anomaly detection methods for detecting cyber attacks in industrial control systems", "author": "Jessamyn. Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Massachusetts Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "ai-applications", "research_subdomain": "ai-cybersecurity", "categories": "ai-applications; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020", "source_url": "https:\/\/openalex.org\/W3127636733", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace@MIT (Massachusetts Institute of Technology)", "source_record_id": "W3127636733", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 869, "title": "Research of the U-Net architecture for semantic segmentation of satellite imagery", "title_ar": "", "title_en": "", "author": "Віталій Миколайович Романік", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "uk", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "computer-vision; image-segmentation", "keywords": "", "abstract": "Кваліфікаційну роботу присвячено дослідженню та програмній імплементації згорткової нейронної мережі архітектури U-Net для автоматизованої семантичної сегментації просторових даних. У процесі дослідження формалізовано математичну постановку задачі піксельної класифікації в умовах екстремального дисбалансу класів. Розроблено конвеєр попередньої обробки даних, що включає розбиття зображень на фрагменти та інтенсивну стохастичну аугментацію. Для ефективної оптимізації ваг моделі імплементовано комбіновану функцію втрат (лінійна комбінація Binary Cross-Entropy та Dice Loss). Експериментальне дослідження проведено на мультиспектральних зображеннях з набору даних DeepGlobe Land Cover. Отримані кількісні результати оцінено за допомогою метрик просторового перекриття (IoU, Dice Coefficient, AUC-PR). На основі візуального зіставлення прогнозних масок виявлено явище семантичної неоднозначності еталонної розмітки: розроблена модель успішно локалізувала об'єкти рослинності (міські дерева, лісосмуги), які не були розмічені експертами через географічну таксономію датасету. Доведено, що архітектура U-Net володіє високою здатністю до вилучення просторово-спектральних ознак і є дієвим інструментом для автоматизованого аналізу даних дистанційного зондування Землі.", "source_url": "https:\/\/openalex.org\/W7168063008", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Institutional repository of Lutsk National Technical University of Lutsk", "source_record_id": "W7168063008", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 870, "title": "UnU-Net: Distance-Aware Uncertainty Quantification for Semantic Segmentation", "title_ar": "", "title_en": "UnU-Net: Distance-Aware Uncertainty Quantification for Semantic Segmentation", "author": "Andrew William Linville", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; image-segmentation; ai-industry", "keywords": "", "abstract": "Automated mechanical inspections increasingly rely on deep learning models for semantic segmentations of faults. However, standard deterministic models, such as U-Net, are prone to overconfident misclassifications on Out of Distribution (OOD) Data, posing severe risk of silent failures from a confident misclassification. Although methods like Deep Ensembles and Monte Carlo (MC) Dropout provide benchmarks for uncertainty quantification, their use during inference requires multiple forward passes increasing the computational overhead linearly making real time applications challenging. To address this gap, we introduce UnU-Net, a distance aware probabilistic architecture that incorporates a Spectral Normalized Neural Gaussian Process (SNGP) with the final pixel-wise prediction. By replacing conventional normalization techniques with Group Normalization and constraining the weights via Spectral Normalization, UnU-Net prevents feature collapse ensuring a preserved latent space distance. Evaluated on the Carvana and DeepCrack segmentation datasets, UnU-Net extracts precise segmentation masks and quantifies epistemic uncertainty flagging anomalies in a single forward pass. This architecture maintains the accuracy of the standard U-Net and outperforms conventional UQ methods while only requiring a single forward pass.", "source_url": "https:\/\/openalex.org\/W7161221772", "pdf_url": "", "doi": "", "handle": "", "repository_name": "VTechWorks (Virginia Tech)", "source_record_id": "W7161221772", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 871, "title": "Study of ensemble methods applied to semantic segmentation masks", "title_ar": "", "title_en": "Study of ensemble methods applied to semantic segmentation masks", "author": "João Pedro da Silva de Andrade", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Federal University of Technology – Paraná", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation", "keywords": "", "abstract": "Semantic segmentation is a fundamental technique for automating the detection of objects of interest in images. However, its application faces a major challenge: the need for a large amount of annotated data for training, a process that requires significant effort and time, making it unfeasible in some cases, such as applications in the healthcare field, especially those aimed at the detection of cancerous nuclei. In this context, the application of ensemble techniques can be a viable solution to improve the performance of semantic segmentation models while requiring a smaller amount of data. In view of this, this work proposes and evaluates an ensemble approach based on stacking to combine the masks generated by the U-Net and DeepLabV3+ models, combined with the VGG16, ResNet50, and MobileNetV3Large backbones. The proposal uses one or more convolutional layers to combine the masks in a way that is sensitive to the local context of the pixels, allowing the model to learn to exploit the strengths and weaknesses of each network dynamically. The objective is to generate a final mask capable of outperforming the results obtained by each individual model and also by the traditional ensemble methods evaluated. The results demonstrate that the proposed approach is statistically indistinguishable from the best static ensembles evaluated. However, it is possible that the limited number of examples available for training the stacking model limited its generalization capability.", "source_url": "https:\/\/openalex.org\/W7160572559", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/40355\/1\/estudometodosensemblesemantica.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)", "source_record_id": "W7160572559", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 873, "title": "Variational methods and deep learning for semantic image segmentation", "title_ar": "", "title_en": "", "author": "Miguel Chicchón", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Pontifical Catholic University of Peru", "university_source": "repository_host", "college": "", "department": "", "country": "Peru", "governorate": "", "publication_year": 2025, "language": "es", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; transformers; image-segmentation", "keywords": "", "abstract": "Actualmente, las aplicaciones de visión por computadora, que incluyen la conducción autónoma, la detección de fallas, la imagenología médica y la teledetección, son cada vez más frecuentes y necesitan mecanismos de segmentación o clasificación de píxeles más precisos. Recientemente, las técnicas de aprendizaje profundo que utilizan redes neuronales se han convertido en la estrategia dominante para resolver diversos problemas en visión por computadora; sin embargo, existen limitaciones y problemas aún por resolver. Es por ello que una integración adecuada con técnicas tradicionales, como los métodos variacionales, con sus propias ventajas y limitaciones, resulta de interés para mejorar la precisión y robustez de la segmentación semántica. Esta tesis se centra en la implementación y validación de algoritmos de segmentación de imágenes que combinan métodos variacionales y aprendizaje profundo en cuatro escenarios diferentes. En el primer escenario, que implica la segmentación de peces en entornos subacuáticos, se compararon métodos variacionales integrados utilizando enfoques de post-procesamiento, extremo a extremo (end-to-end) y una función de pérdida combinada. En el segundo escenario, que aborda la segmentación de baches y grietas en pavimentos de carreteras, se entrenaron tres arquitecturas de redes neuronales SOTA (del estado del arte) utilizando una función de pérdida de entropía cruzada combinada y contornos activos. En el tercer escenario, enfocado en la segmentación de techos de edificios en imágenes satelitales, se entrenó una red neuronal U-Net con diferentes redes SOTA en la ruta del codificador (encoder path) mediante aprendizaje por transferencia (transfer learning), y el mejor modelo fue seleccionado para ser entrenado utilizando una función combinada con pérdida de entropía cruzada y pérdida de contornos activos. Finalmente, en el cuarto escenario, que se refiere a la segmentación de cobertura terrestre en imágenes aéreas, se emplearon aprendizaje por transferencia y la función de contornos activos en el entrenamiento de redes neuronales convolucionales y transformadores (transformers). Los resultados cuantitativos consolidados demuestran mejoras significativas: la función de pérdida combinada logró aumentos promedio de4-6% en el mIoU (mean Intersection over Union) en comparación con los enfoques que solo utilizaban entropía cruzada o Dice. Específicamente, para la segmentación de peces, el modelo U-Net-scSE con un codificador ResNeSt-269e alcanzó 73.1% de mIoU; para la detección de baches, 87.45% de mIoU con una reducción de HD95 a 23.40 mm; para la segmentación de techos, se logró una mejora del 14% en IoU utilizando aprendizaje por transferencia; y para la cobertura terrestre, el modelo U-Net-scSE con un codificador MiT- B4 obtuvo 80.4% de mIoU. Estas validaciones a través de cuatro dominios heterogéneos demuestran que la integración sistemática de métodos variacionales en redes profundas proporciona mejoras cuantificables y transferibles en la exactitud de la segmentación.", "source_url": "https:\/\/openalex.org\/W7138825433", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Communities in DSpace (Pontifical Catholic University of Peru)", "source_record_id": "W7138825433", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 874, "title": "Psoriasis support system based on semantic segmentation AI models", "title_ar": "", "title_en": "Psoriasis support system based on semantic segmentation AI models", "author": "Lopes, Toni Marcos Silva", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; object-detection; image-segmentation; medical-imaging; ai-agriculture", "keywords": "", "abstract": "Psoriasis is a chronic inflammatory skin disease characterized by the presence of lesions that vary in shape, size, and color. Accurate segmentation of these lesions from clinical images is crucial for effective diagnosis and treatment monitoring. However, the variability in image dimensions, lighting conditions, and the presence of noise in real-world datasets complicates the segmentation task. Additionally, available datasets for psoriasis lesion segmentation are often small, making it challenging to train deep learning models effectively without overfitting. The project aims to enhance the segmentation accuracy of psoriasis lesions by employing a range of AI models and optimizing their performance through the application of advanced pre-processing techniques and data augmentation strategies. The AI models tested include Mask R-CNN, U-Net, YOLOv8n, FCN, DeepLabV3+, BiSeNet, HRNet, PSPNet, and SegNet. Pre-processing techniques, such as bilateral smooth filters and adaptive median filters, were applied to enhance image quality and reduce noise. Furthermore, data augmentations, including random adjustments in brightness, contrast, cropping, rotation, flipping, and scaling, were used to simulate real-world variations and increase the robustness of the models. These augmentations were carefully replicated from related works to mimic their experimental setups. The evaluation of multiple AI models for psoriasis lesion segmentation demonstrated that applying effective pre-processing and data augmentation techniques significantly improves model performance. Among the tested models, FCN achieved the highest F1 score of 0.889, using a bilateral smooth filter and translation augmentations. U-Net and SegNet followed closely, with U-Net reaching an F1 score of 0.885 and SegNet 0.880, both benefiting from similar pre-processing and augmentation strategies. Mask R-CNN and BiseNet also showed competitive results, underscoring the importance of carefully selected augmentations that mimic real-world variability. These findings highlight the value of pre-processing and augmentation in enhancing model generalization, particularly in small, diverse datasets. In addition to the AI-based segmentation work, the project also incorporates a mobile application designed to assist patients in tracking their psoriasis lesions over time. This application enables users to monitor whether lesions are improving or worsening, providing valuable insights for deciding when to consult a dermatologist. By helping patients recognize early signs of deterioration, the application plays a crucial role in supporting more proactive and informed healthcare decisions.", "source_url": "https:\/\/openalex.org\/W7113660975", "pdf_url": "https:\/\/bdigital.ufp.pt\/bitstreams\/d3e9f0b8-cf78-4d94-8dcd-0f04470834fa\/download", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7113660975", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 875, "title": "Semantic segmentation of mammals in forest regions with U-NET", "title_ar": "", "title_en": "Semantic segmentation of mammals in forest regions with U-NET", "author": "Jhonatan Guilherme de Oliveira Cunha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Federal University of Technology – Paraná", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; video-analysis", "keywords": "", "abstract": "Environmental monitoring is vital for preserving animal biodiversity, providing researchers with essential metrics on environmental health and aiding efforts to combat extinction. Mapping the fauna of forest regions is, therefore, essential for informed decision-making and effective planning. However, manual mapping is time-consuming and labor-intensive for biologists. To address this, our study utilized Deep Learning techniques to assist in detecting mammal species in the forests of northwestern Paraná, using videos provided by NAPI Biodiversidade. We implemented a semantic segmentation algorithm using the U-NET convolutional neural network to segment forest images. Our experiments demonstrated that after histogram equalization and without forest grouping, the Dice Coefficient was 79.40%, and the F1 Macro Score for animal presence classification was 86%. However, with forest grouping, there was a substantial drop in the model’s performance, highlighting difficulties in generalizing to new locations. This suggests the necessity of training location-specific models. Additionally, applying histogram equalization through contrast stretching improved the quality of the generated masks and classifications. However, the presence of false negatives indicates a need for more training data.", "source_url": "https:\/\/openalex.org\/W7155461023", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/35300\/1\/segmentacaosematicamamiferosunet.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)", "source_record_id": "W7155461023", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 876, "title": "Evaluation of LPB learning for U-Net based road semantic segmentation", "title_ar": "", "title_en": "", "author": "Mauricio Kendi Yui", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "pt", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "computer-vision; image-segmentation", "keywords": "", "abstract": "A direção autônoma emerge naturalmente do notável avanço demonstrado pela Inteligência Artificial (IA) nos últimos anos, impulsionada, em grande parte, pelo desenvolvimento contínuo de recursos de hardware e software. Nesse contexto, este trabalho propõe a aplicação de ferramentas de IA, mais especificamente no domínio da Aprendizagem Profunda, para realizar a segmentação semântica de pavimentações asfálticas em imagens rasterizadas. A abordagem adotada utiliza a rede neural U-Net para alcançar esse objetivo, enquanto o algoritmo Local Binary Pattern (LBP) é investigado devido à sua vantagem declarada de invariância luminosa. Este trabalho contribui ao validar que a integração do algoritmo LBP pode aprimorar o desempenho da rede neural U-Net, especialmente em condições não previstas na base de dados de treinamento original.", "source_url": "https:\/\/openalex.org\/W7120348697", "pdf_url": "https:\/\/repositorio.uel.br\/bitstreams\/3bfe8607-c517-4b6d-b689-ab32c130ebbc\/download", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120348697", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 877, "title": "Semantic segmentation of image sequences using a spatio-temporal U-Net", "title_ar": "", "title_en": "Semantic segmentation of image sequences using a spatio-temporal U-Net", "author": "Manuel Danner", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; video-analysis", "keywords": "", "abstract": "In biomedical research, detailed structure of tissues, cells, organelles and macromolecular complexes is investigated with electron microscopy (EM) images. On this account, large amounts of high resolution images from biological and clinical specimens exist. As a result, there is a need for computer assisted tools that can provide a cost effective solution for disease diagnostics. This thesis illustrates a novel elastic image transformation method called Elastic Gradient Transformation (EGT), which uses the image gradient to generate realistic looking deformations of cell structures. The novel EGT method helps our neural network to generalize on little cell datasets (like the ISBI 2012 dataset), without overfitting. The U-Net architecture by O. Ronneberger, P. Fischer and T. Brox, is adapted to contain an additional encoder path. The proposed network is called SiamU-Net, and takes two sequential images t and t+1 as input. The output is a class probability map of image t+1. It is important that both encoder paths do not share weights, and are fused together in the latent space of the network. The single decoding path uses skip connections from the encoding path of image t+1 to generate an improved up-sampling. To evaluate the adaptation, both U-Net and SiamU-Net use the novel elastic gradient transformation method and participate in the ISBI 2012 challenge. To highlight the impact of temporal image information on the two networks, a comparison of both networks is made on a video dataset called DAVIS 2016. At the ISBI 2012 challenge, the proposed SiamU-Net with the EGT method is placed at rank 31, while the original U-Net is placed at rank 61, out of 223 participants. On the Davis 2016 challenge, the SiamU-Net achieves a 0.0776 point higher Jaccard Index than the U-Net architecture, which proves the advantage of adapting the U-Net with an additional encoder path.", "source_url": "https:\/\/openalex.org\/W3087797547", "pdf_url": "", "doi": "10.34726\/hss.2020.66920", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W3087797547", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 878, "title": "Semantic segmentation of minimally invasive anti-reflux surgery video using U-NET Machine Learning", "title_ar": "", "title_en": "Semantic segmentation of minimally invasive anti-reflux surgery video using U-NET Machine Learning", "author": "Jana Abbing", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "machine-learning; computer-vision; medical-ai; image-segmentation; video-analysis", "keywords": "", "abstract": "Introduction: During anti-reflux surgery, there is a potential risk of Nervus Vagus (NV) injury. A solution is to create an AI tool that can detect the NV. and other anatomical structures in surgical videos. Addition of temporal features that might help in segmentation\/detecting the actual nerve. Method: Five U-NET algorithms (different data inputs) are used as a for the training of five visible structures in the videoframes. These AI algorithms are trained a small clinical dataset for the actual goal and a larger automotive dataset for testing the functionality of the networks and testing the addition of Dense Optical Flow (temporal features). Results: The U-NETS can segment in all cases at least two structures with an IOU >0.5. Even three structures for the RGB only algorithm. No IOU above the 0.05 for the NV. Adding dense Optical Flow lowers in all cases the IOU. Although Dense Optical Flow only is still able to give spatial information. The confusion matrix shows similar findings. Conclusion: Training U-NETS and visualisation of the segmentation was possible on top of surgical video frames. Detecting the NV was not possible. The addition of temporal features by combining the RGB data does not improve the semantic segmentation.", "source_url": "https:\/\/openalex.org\/W3106689463", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3106689463", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 879, "title": "Semantic segmentation of agricultural crop areas via two-stage U-Net", "title_ar": "", "title_en": "Semantic segmentation of agricultural crop areas via two-stage U-Net", "author": "Walysson Carlos dos Santos Oliveira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; computer-vision; ai-applications; image-segmentation; ai-agriculture", "keywords": "", "abstract": "The agribusiness tax is mainly levied on the production of agricultural crops. To reduce tax evasion in agribusiness, it is possible to monitor the development of plantations through the analysis of satellite images. For this, we can apply Machine Learning techniques to satellite images to segment the planted area, and the area, in turn, can be used to estimate the production of monitored plantations. This work aims to solve the first stage of the problem, the Segmentation of the Planted Area. For this, we developed a machine learning architecture for segmentation of plantation areas, the Two-stage U-net. In addition, the work also included the creation of a satellite image dataset with annotations for the segmentation of plantation areas. We trained the proposed model and we adjusted its hyperparameters considering the U-net Encoder, the Optimizer, the Loss Function, and the Batch Size of images. We selected the fitted model that performed best in tests with Hyperopt and GridSearch. The results in mIoU of the Two-stage U-net were superior to the results of other architectures used in similar works.", "source_url": "https:\/\/openalex.org\/W7120464696", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120464696", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 880, "title": "Semantic segmentation of urban airborne oblique images", "title_ar": "", "title_en": "Semantic segmentation of urban airborne oblique images", "author": "Li Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; cnn; rnn; image-classification; image-segmentation; ai-smart-cities", "keywords": "", "abstract": "Computer recognition and classification of remote sensing digital images is an important research direction in remote sensing image processing.A lot of classification methods has been widely used for urban scene understanding,the large availability of airborne images necessitates an image classification approach that automatically learns relevant features from the rawimage and performs semantic segmentation in an end-to-end framework. For this, deep learning methodologies werepreferred, specifically in the form of convolutional neural network (CNN)which is widely used in computer vision. In this research, airborne oblique imagesare used as data source for semantic segmentation in urban areas, we investigate the ability of Deep Convolutional Neural Network(DCNN)structures that is designed based on U-net architectureand adaptitto our dataset.Different from its original architecture based on VGG11,U-net based on VGG16 architecture with deeper layers isappliedin this study.However, the training time ofdeep neural network is too long because of enormous parametersandpresent some challenges for model training. Here, depthwise separable convolution is coupled with convolution block in our architecture to reduce the model parametersand improve model efficiency.Outputs of deep neural network are sometimesnoisy and the boundaryof objects are not very smoothbecause continuous pooling layers reduce the ability of localization.As a result, the mean field approximation to the fully connected conditional random field (CRF) inference which models label and spectral compatibility inthe pixel-neighbourhood is applied to refine the output of other classifiers.Finally, we achieved anend-to-end modelwhich connect the scores of the U-net classification with a fully connected Conditional Random Field (CRF) architectureand make the inference as Recurrent Neural Network (RNN), to consider the local class label dependencies in an end-to-end structure and make full use ofDCNNs and probability random field model. We compare the fully connected CRF optimized segmentation results with those obtained by applying the trained U-net classifier.Segmentation results indicate that our classification U-net modelwith deeper layershave performed favourably, the modified network not only ensures the accuracy but also greatly shortens the time of model training in the large-scale image classification problem,furthermore, this end-to-end model which combines fully connected CRF has effectively improved the segments of U-net.", "source_url": "https:\/\/openalex.org\/W3151720536", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3151720536", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 881, "title": "DETEKSI LAJUR JALAN SECARA REAL-TIME PADA AUTONOMOUS CAR MENGGUNAKAN METODE SEMANTIC SEGMENTATION", "title_ar": "", "title_en": "", "author": "Nublan Azqalani Muis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2023, "language": "id", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; ai-applications; transformers; image-segmentation; ai-industry", "keywords": "", "abstract": "Kiblat inovasi kendaraan sekarang telah berganti, yang awalnya industri berlomba-lomba untuk menciptakan mesin yang bertenaga besar dengan berkecepatan tinggi, sekarang menjadi masalah keselamatan. Deteksi lajur jalan merupakan salah satu unsur keselamatan pada Autonomous Car dimana kendaraan dapat mengenali lajur jalan dan mengambil keputusan sendiri dengan aman. Penulis melakukan penelitian deteksi lajur jalan secara real-time dengan menggunakan metode Semantic Segmentation. Penulis menggunakan arsitektur U-Net dengan parameter epoch sebanyak 10.000 epoch serta ukuran gambar 128x128 pixel. Data sebanyak 374 gambar berupa lingkungan jalan Kota Makassar terdiri dari empat kelas yakni jalan, marka garis membujur penuh, marka garis membujur putus-putus dan background. Hasil pengujian model menggunakan Intersection over Union (IoU) dengan akurasi 79.8%. Sistem yang dikembangkan juga melakukan estimasi jarak antara ban dengan marka. Fitur ini bertujuan untuk memperingatkan pengemudi bila kendaraanya mulai bergerak keluar lajur. Hasil pengujian system estimasi jarak terhadap jarak ban dan marka garis dengan metode RMSE sebesar 0,069962636. Kata kunci: Autonomous car, real-time, lajur jalan, Semantic Segmentation, U-Net", "source_url": "https:\/\/openalex.org\/W7217387375", "pdf_url": "https:\/\/repository.unhas.ac.id:443\/id\/eprint\/30384\/2\/D121171521_skripsi_05-01-2023%201-2.pdf", "doi": "", "handle": "", "repository_name": "Hasanuddin University Repository", "source_record_id": "W7217387375", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 882, "title": "Semantic segmentation of peripheral white blood cells using neural networks", "title_ar": "", "title_en": "Semantic segmentation of peripheral white blood cells using neural networks", "author": "Pavel Baykalov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; image-segmentation; ai-smart-cities", "keywords": "", "abstract": "Semantic segmentation is the differentiation of the meaningful parts on an image. It has been used in many distinct fields, such as traffic or medical areas. One of these uses in the medical field is the blood smear examination. White blood cells (WBC) are part of the immune system and their counting and determination are often performed by medical specialists for diagnosis. The shape and size of the nucleus of leukocytes can determine the type of WBC, by visual examination of an expert. WBC segmentation had been proposed before, but the convolutional neural network architectures were not tried for this task. Therefore, in this project, the semantic segmentation was performed on free access dataset, which is composed of microscopic images and segmented ground truth images, of WBC, made by experts. The dataset was filtered, transformed and augmented in order to be used in an artificial neural network. Some segmentation models, such as U-Net, SegNet and DeconvNet, were chosen, adapted and trained to\/with this data. After training, the models were evaluated, using different metrics (accuracy, Jaccard similarity index and Sørensen¿Dice similarity coefficient), with the same dataset. Jupyter notebook from the free Google platform called Colaboratory was used for the training and evaluation of the models. Although all three models achieved very high scores in distinct metrics. U-Net architecture resulted in being the best model for segmenting, as well as the fastest one for the training process.", "source_url": "https:\/\/openalex.org\/W2944489843", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W2944489843", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 883, "title": "Semantic Segmentation of Urban Scene Images Using Recurrent Neural Networks", "title_ar": "", "title_en": "Semantic Segmentation of Urban Scene Images Using Recurrent Neural Networks", "author": "Venkata Satya Sai Ajay Daliparthi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dalarna University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; robotics; ai-applications; cnn; rnn; image-segmentation; medical-imaging; ai-smart-cities", "keywords": "", "abstract": "Background: In Autonomous Driving Vehicles, the vehicle receives pixel-wise sensor data from RGB cameras, point-wise depth information from the cameras, and sensors data as input. The computer present inside the Autonomous Driving vehicle processes the input data and provides the desired output, such as steering angle, torque, and brake. To make an accurate decision by the vehicle, the computer inside the vehicle should be completely aware of its surroundings and understand each pixel in the driving scene. Semantic Segmentation is the task of assigning a class label (Such as Car, Road, Pedestrian, or Sky) to each pixel in the given image. So, a better performing Semantic Segmentation algorithm will contribute to the advancement of the Autonomous Driving field. Research Gap: Traditional methods, such as handcrafted features and feature extraction methods, were mainly used to solve Semantic Segmentation. Since the rise of deep learning, most of the works are using deep learning to dealing with Semantic Segmentation. The most commonly used neural network architecture to deal with Semantic Segmentation was the Convolutional Neural Network (CNN). Even though some works made use of Recurrent Neural Network (RNN), the effect of RNN in dealing with Semantic Segmentation was not yet thoroughly studied. Our study addresses this research gap. Idea: After going through the existing literature, we came up with the idea of “Using RNNs as an add-on module, to augment the skip-connections in Semantic Segmentation Networks through residual connections.” Objectives and Method: The main objective of our work is to improve the Semantic Segmentation network’s performance by using RNNs. The Experiment was chosen as a methodology to conduct our study. In our work, We proposed three novel architectures called UR-Net, UAR-Net, and DLR-Net by implementing our idea to the existing networks U-Net, Attention U-Net, and DeepLabV3+ respectively. Results and Findings: We empirically showed that our proposed architectures have shown improvement in efficiently segmenting the edges and boundaries. Through our study, we found that there is a trade-off between using RNNs and Inference time of the model. Suppose we use RNNs to improve the performance of Semantic Segmentation Networks. In that case, we need to trade off some extra seconds during the inference of the model. Conclusion: Our findings will not contribute to the Autonomous driving field, where we need better performance in real-time. But, our findings will contribute to the advancement of Bio-medical Image segmentation, where doctors can trade-off those extra seconds during inference for better performance.", "source_url": "https:\/\/openalex.org\/W3209919241", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Diva portal (Dalarna University Library)", "source_record_id": "W3209919241", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 884, "title": "Fully convolutional neural network for semantic segmentation on CT scans of pigs", "title_ar": "", "title_en": "Fully convolutional neural network for semantic segmentation on CT scans of pigs", "author": "Jarand Hornseth Pollestad", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Identifying the shape and location of structures within medical images is useful for purposes such as diagnosis and research. This is a cumbersome task if done manually. Recent advances in computer vision and in particular deep learning have made it possible to automate this task to such an extent that it is comparable to human level performance. This thesis reviews the components used to construct a fully convolutional neural network for semantic segmentation. It then proposes a modified network architecture based on an existing state-of-the-art fully convolutional neural network called U-net. The architecture is applied to a binary classification problem involving computed tomography scans of pigs provided by Norsvin SA. The goal is to classify each pixel in the scans as either \"a part of the pig which is edible\" or \"background\" which means everything that is not in the edible class. Each computed tomography scan is too large for the network to process at once. Part of the thesis is therefore devoted to investigating approaches for feeding the information in the scans to the proposed network. The network is trained on 238 scans and evaluated on 37 scans. The evaluation is done quantitatively using the index over union metric and qualitatively through manual inspection of segmented images. The results show that the best performing network on average obtains an index over union score of 0.962 when given a scan for segmentation.", "source_url": "https:\/\/openalex.org\/W2963251943", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W2963251943", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 885, "title": "Fully convolutional Networks for Semantic Segmentation", "title_ar": "", "title_en": "", "author": "Meriem Bensaad ; Chaimaa Derdarine", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2022, "language": "fr", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; cnn; image-segmentation", "keywords": "", "abstract": "La segmentation sémantique est une partie importante du champ de vision par ordinateur, où la classe à laquelle chaque pixel d’une image appartient, est étiquetée automatiquement, les méthodes traditionnelles ont fourni des performances faible, surtout avec les grands ensembles de données, par conséquent, nous nous sommes appuyés dans notre travail sur l’une des techniques actuelles, représentée dans Réseau de neurones entièrement convolutionnel, en particulier, l’architecture U-Net. Ce dernier a permis d’équilibrer entre les objets on pouvait trouver dans l’image et leur « localisation », notamment après avoir intégré des changements dans son architecture en ajoutant des couches (Inception modules par exemple), ou un changement en termes de numéro de canaux", "source_url": "https:\/\/openalex.org\/W7135312108", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135312108", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 886, "title": "Semantic segmentation with global mixture of Gaussian priors", "title_ar": "", "title_en": "", "author": "Rodrigo Kanehisa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "pt", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "computer-vision; image-segmentation", "keywords": "", "abstract": "Resumo: A segmentação semântica é um dos problemas em aberto em visão computacional. Este problema consiste em classificar cada pixel presente em uma imagem, delimitando um objeto. As redes neurais convolucionais são eficientes na detecção e identificação de objetos em imagens. No entanto, durante o processo de segmentação, surgem alguns problemas, como perda de informações espaciais e baixa resolução dos mapas de características. Esses problemas levam a segmentações grosseiras ou ruidosas devido às incertezas nos dados. Uma maneira de combater esses problemas é usar métodos estatísticos em conjunto com redes neurais. Este trabalho propõe redes neurais combinadas com modelos probabilísticos como representação para segmentação semântica com foco em imagens médicas. Os modelos probabilísticos apresentam uma solução para mapear o conteúdo da imagem em distribuições mais representativas no espaço latente dos dados, permitindo que a rede lide com incertezas e modele informações mais complexas. Avaliamos nossas propostas em bases de dados existentes para segmentação de imagens e as comparamos com métodos existentes em \\\\textit{benchmarks} padrão para segmentação semântica. Um modelo U-Net mais complexo será mais expressivo, permitindo que a rede reconstrua melhor as informações perdidas durante as operações de downsampling e stride", "source_url": "https:\/\/openalex.org\/W4388854499", "pdf_url": "https:\/\/doi.org\/10.47749\/t\/unicamp.2022.1246210", "doi": "10.47749\/t\/unicamp.2022.1246210", "handle": "", "repository_name": "", "source_record_id": "W4388854499", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 887, "title": "Interpretability of a Deep Learning Model for Semantic Segmentation : Example of Remote Sensing Application", "title_ar": "", "title_en": "Interpretability of a Deep Learning Model for Semantic Segmentation : Example of Remote Sensing Application", "author": "Adrianna Janik", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; predictive-analytics; image-segmentation; xai; fairness; ai-smart-cities", "keywords": "", "abstract": "Understanding a black-box model is a major problem in domains that relies on model predictions in critical tasks. If solved, can help to evaluate the trustworthiness of a model. This thesis proposes a user-centric approach to black-box interpretability. It addresses the problem in semantic segmentation setting with an example of humanitarian remote sensing application for building detection. The question that drives this work was, Can existing methods for explaining black-box classifiers be used for a deep learning semantic segmentation model? We approached this problem with exploratory qualitative research involving a case study and human evaluation. The study showed that it is possible to explain a segmentation model with adapted methods for classifiers but not without a cost. The specificity of the model is likely to be lost in the process. The sole process could include introducing artificial classes or fragmenting image into super-pixels. Other approaches are necessary to mitigate identified drawback. The main contribution of this work is an interactive visualisation approach for exploring learned latent space via a deep segmenter, named U-Net, evaluated with a user study involving 45 respondents. We developed an artefact (accessible online) to evaluate the approach with the survey. It presents an example of this approach with a real-world satellite image dataset. In the evaluation study, the majority of users had a computer science background (80%), including a large percentage of users with machine learning specialisation (44.4% of all respondents). The model distinguishes rurality vs urbanization (58% of users). External quantitative comparison of building densities of each city concerning the location in the latent space confirmed the later. The representation of the model was found faithful to the underlying model (62% of users). Preliminary results show the utility of the pursued approach in the application domain. Limited possibility to present complex model visually requires further investigation.", "source_url": "https:\/\/openalex.org\/W3011114404", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3011114404", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 888, "title": "Semantic Segmentation of RGB-Z Aerial Imagery Using Convolutional Neural Networks", "title_ar": "", "title_en": "Semantic Segmentation of RGB-Z Aerial Imagery Using Convolutional Neural Networks", "author": "Amber Mulder", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; object-detection; image-segmentation", "keywords": "", "abstract": "Semantic segmentation (or pixel-level classification) of remotely sensed imagery has shown to be useful for applications in fields as mapping of land cover, object detection, change detection and land-use analysis. Deep learning algorithms called convolutional neural networks (CNNs) have shown to outperform traditional computer vision and machine learning approaches in tackling semantic segmentation tasks. Furthermore, addition of height information (Z) to aerial imagery (RGB) is believed to improve segmentation results. However, discussion remains on the following: to what extent height information adds value; the best way to combine RGB information with height information; and what type of height information can best be used. This study aims to answer these questions. In this research work, the CNN architectures FCN-8s, SegNet, U-Net and FuseNet-SF5 are trained to semantically segment 10 cm resolution true ortho imagery of Haarlem, potentially augmented with height information. The outputted topographic maps contain the classes building, road, water and other. Experiments are conducted that allow for the comparison of 1) models trained on RGB and on RGB-Z, 2) models combining RGB and height information through data fusion and through data stacking, and 3) models trained using different types of absolute and relative height approaches. Performances are compared based on scores on the performance measure (mean) intersection over union (IoU) and through visual assessment of outputted prediction maps. The results indicated that on average segmentation performance improves by approximately 1 percent when absolute height information is added. The class building showed to benefit the most from the addition of height information. Furthermore, extracting features from height information in a separate encoder and fusing these into RGB feature maps, led to a higher overall segmentation quality than when height information is provided as a stacked extra band and processed in the same encoder as the RGB information. Finally, models using relative height delivered a higher quality segmentation than when absolute height approaches were used, especially for large objects. The best performing model; FuseNet-SF5 trained on RGB imagery and pixel-level, relative height, retrieved a mean IoU of 0.8427 and IoUs of 0.8744, 0.7865, 0.9131 and 0.7966 for the classes building, road, water and other respectively. This model was able to correctly classify over 90% of the pixels of 67% of all the objects present in the ground truth. Overall, this study showed that, when considering semantic segmentation of aerial RGB imagery, 1) height information can improve segmentation results, 2) adding height information through data fusion can result in a higher segmentation quality than when data stacking is used, and 3) providing relative height to a network, rather than absolute height, can improve semantic segmentation quality.", "source_url": "https:\/\/openalex.org\/W3167381927", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W3167381927", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 889, "title": "Improving LULC Map Production via Semantic Segmentation and Unsupervised Domain Adaptation", "title_ar": "", "title_en": "Improving LULC Map Production via Semantic Segmentation and Unsupervised Domain Adaptation", "author": "Rostyslav-Mykola Tsenov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; transfer-learning; predictive-analytics; cnn; image-segmentation; ai-smart-cities", "keywords": "", "abstract": "In recent years, a lot of remote sensing problems benefited from the improvements made in deep learning. In particular, deep learning semantic segmentation algorithms have provided improved frameworks for the automated production of land-use and land-cover (LULC) map generation. Automation of LULC map production can significantly increase its production frequency, which provides a great benefit to areas such as natural resource management, wildlife habitat protection, urban expansion, damage delineation, etc. In this thesis, many different convolutional neural networks (CNN) were examined in combination with various state-of-the-art semantic segmentation methods and extensions to improve the accuracy of predicted LULC maps. Most of the experiments were carried out using Landsat 5\/7 and Landsat 8 satellite images. Additionally, unsupervised domain adaption (UDA) architectures were explored to transfer knowledge extracted from a labelled Landsat 8 dataset to unlabelled Sentinel-2 satellite images. The performance of various CNN and extension combinations were carefully assessed, where VGGNet with an output stride of 4, and modified U-Net architecture provided the best results. Additionally, an expanded analysis of the generated LULC maps for various sensors was provided. The contributions of this thesis are accurate automated LULC maps predictions that achieved ~92.4% of accuracy using deep neural networks; production of the model trained on the larger area, which is six times the size from the previous work, for both 8-bit Landsat 5\/7, and 16-bit Landsat 8 sensors; and generation of the network architecture to produce LULC maps for the unlabelled 12-bit Sentinel-2 data with the knowledge extracted from the labelled Landsat 8 data.", "source_url": "https:\/\/openalex.org\/W3162967908", "pdf_url": "", "doi": "10.36939\/ir.202104301247", "handle": "", "repository_name": "", "source_record_id": "W3162967908", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 890, "title": "Iris segmentation", "title_ar": "", "title_en": "Iris segmentation", "author": "Vishal Ramesh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation", "keywords": "", "abstract": "Accurate iris image segmentation is crucial to a range of proposed medical diagnosis and treatment systems. Previous models have worked well with healthy eye images but do not generalize to diseased eye images. We work with a dataset where many subjects have eye diseases or deformities. We analyse the performance of the U-Net, a deep learning architecture for semantic segmentation. Our model was trained on a hand-annotated dataset and tuned to generalise on unseen images. Our model achieves a pixel accuracy of 0.8913 on the test set with a relatively short training time. 1", "source_url": "https:\/\/openalex.org\/W7135536303", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135536303", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 891, "title": "Instance Segmentation of Buildings in Satellite Images", "title_ar": "", "title_en": "Instance Segmentation of Buildings in Satellite Images", "author": "Karin Fritz", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; cnn; image-classification; image-segmentation", "keywords": "", "abstract": "When creating a photo realistic 3D model of the world using satellite imagery, image classification is an important part of the process. In this thesis the specificpart of automated building extraction is investigated. This is done by investi-gating the difference in performance between the methods instance segmentation and semantic segmentation for extraction of building footprints in orthorectified imagery. Semantic segmentation of the images is solved by using U-net, a Fully Convolutional Network that outputs a pixel-wise segmentation of the image. Instance segmentation of the images is done by a network called Mask R-CNN.The performance of the models are measured using precision, recall and the F1 score, which is the harmonic mean between precision and recall. The resulting F1 score of the two methods are similar, with U-net achieving a the F1 score of 0.684 without any post processing. Mask R-CNN achieves the F1 score of 0.676 without post processing.", "source_url": "https:\/\/openalex.org\/W3115478485", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3115478485", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 892, "title": "QUANTUM-INSPIRED NEURAL ARCHITECTURE SEARCH APPLIED TO SEMANTIC SEGMENTATION", "title_ar": "", "title_en": "", "author": "GUILHERME BALDO CARLOS", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "pt", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "machine-learning; computer-vision; medical-ai; quantum-ml; image-segmentation", "keywords": "", "abstract": "[pt] Redes neurais profundas são responsáveis pelo grande progresso em diversas tarefas perceptuais, especialmente nos campos da visão computacional,reconhecimento de fala e processamento de linguagem natural. Estes resultados produziram uma mudança de paradigma nas técnicas de reconhecimentode padrões, deslocando a demanda do design de extratores de característicaspara o design de arquiteturas de redes neurais. No entanto, o design de novas arquiteturas de redes neurais profundas é bastante demandanteem termos de tempo e depende fortemente da intuição e conhecimento de especialistas,além de se basear em um processo de tentativa e erro. Neste contexto, a idea de automatizar o design de arquiteturas de redes neurais profundas tem ganhado popularidade, estabelecendo o campo da busca por arquiteturas neurais(NAS - Neural Architecture Search). Para resolver o problema de NAS, autores propuseram diversas abordagens envolvendo o espaço de buscas, a estratégia de buscas e técnicas para mitigar o consumo de recursos destes algoritmos. O Q-NAS (Quantum-inspired Neural Architecture Search) é uma abordagem proposta para endereçar o problema de NAS utilizando um algoritmo evolucionário com inspiração quântica como estratégia de buscas. Este método foi aplicado de forma bem sucedida em classificação de imagens, superando resultados de arquiteturas de design manual nos conjuntos de dados CIFAR-10 e CIFAR-100 além de uma aplicação de mundo real na área da sísmica. Motivados por este sucesso, propõe-se nesta Dissertação o SegQNAS (Quantum-inspired Neural Architecture Search applied to Semantic Segmentation), uma adaptação do Q-NAS para a tarefa de segmentação semântica. Diversos experimentos foram realizados com objetivo de verificar a aplicabilidade do SegQNAS em dois conjuntos de dados do desafio Medical Segmentation Decathlon. O SegQNAS foi capaz de alcançar um coeficiente de similaridade dice de 0.9583 no conjunto de dados de baço, superando os resultados de arquiteturas tradicionais como U-Net e ResU-Net e atingindo resultados comparáveis a outros trabalhos que aplicaram NAS a este conjunto de dados, mas encontrando arquiteturas com muito menos parãmetros. No conjunto de dados de próstata, o SegQNAS alcançou um coeficiente de similaridade dice de 0.6887 superando a U-Net, ResU-Net e o trabalho na área de NAS que utilizamos como comparação.", "source_url": "https:\/\/openalex.org\/W4384300978", "pdf_url": "https:\/\/www.maxwell.vrac.puc-rio.br\/63217\/63217.PDF", "doi": "10.17771\/pucrio.acad.63217", "handle": "", "repository_name": "", "source_record_id": "W4384300978", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 893, "title": "Visually attended sound source semantic segmentation : Forming a semantic mask using visual and audio cues", "title_ar": "", "title_en": "Visually attended sound source semantic segmentation : Forming a semantic mask using visual and audio cues", "author": "Petteri Nuotiomaa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; multimodal; image-segmentation; speech-recognition", "keywords": "", "abstract": "Sound Source Separation from single input audio mixture is a problem that has had many different proposed solutions. Many of the modern solutions involve using machine learning and especially deep learning to achieve good separation results. The goal of this thesis is to develop a solution to the sound source semantic segmentation problem, evaluate the performance of the proposed method, and compare it with similar solutions The thesis goes over the theory behind the problem and explores some of the previous solutions used for this problem. During the background research, it became clear that there aren’t many solutions trying to solve the exact problem of sound source semantic segmentation. This is why the thesis examines solutions for different problems, that have some relation to the sound source semantic segmentation. This thesis proposes a new solution for sound source semantic segmentation. The solution is based on some of the groundbreaking research done in the sound source separation field. The proposed deep learning system has two networks, one for audio and one for visual information. The audio network is a U-Net type network and the visual network is based on the ResNet model of networks. The addition of visual information to the final separation is done by weighing the audio instrument masks with the probabilities of predicted instruments. The network uses common hyperparameters that are used in different works in the field. The loss function is slightly different than any of the previous works as it uses probabilities instead of hard labels for ground truth values. The results from the proposed system indicate that the system can separate simple audio sources from an audio mixture. The solution didn’t achieve wanted results in all test cases and specifically for duet audios the method had problems separating the instruments. The system showed some promise in the multimodality approach, but the better results for the multimodality approach could be accounted for by the different initial randomization of the network. The proposed solution achieved results that are better than the baseline and the most simple approaches to the same problem but failed to meet the standard separating quality that can be expected from a modern deep learning approach.", "source_url": "https:\/\/openalex.org\/W7133075142", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/155461\/2\/NuotiomaaPetteri.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133075142", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 894, "title": "Multidimensional Transfer Learning in Natural Language Processing: Domain, Task, Language, and Expertise Adaptation", "title_ar": "", "title_en": "Multidimensional Transfer Learning in Natural Language Processing: Domain, Task, Language, and Expertise Adaptation", "author": "Nghia Ngo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Oregon", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; medical-ai; transfer-learning; predictive-analytics; llm", "keywords": "", "abstract": "Transfer learning in Natural Language Processing (NLP) mirrors a fundamental human cognitive ability: applying prior knowledge to learn new skills efficiently. Rather than training models from scratch for every new task, domain, or language, transfer learning enables systems to use knowledge from previous learning experiences. This capability is essential for addressing two critical challenges in modern NLP: the scarcity of labeled data affecting the vast majority of the world’s languages and specialized domains, and the prohibitive computational cost of training large-scale models from scratch. This dissertation presents a comprehensive investigation of transfer learning in NLP, organized around two distinct phases. The first phase, Adaptive Transfer Learning, addresses horizontal knowledge transfer across contexts through three research directions: unsupervised domain adaptation, where models must transfer across data distributions without target labels; task adaptation for structured prediction, where multiple interdependent tasks such as joint information extraction must be learned and transferred simultaneously across domains; and cross-lingual transfer, where knowledge from high-resource languages is shared to low-resource languages across typologically diverse language families. The second phase, Specialization Transfer Learning, addresses the emerging challenge of vertically refining general-purpose large language models for reliable deployment in high-stakes expert domains. This phase focuses on developing comprehensive evaluation frameworks for Retrieval-Augmented Generation systems in the medical domain, assessing critical dimensions beyond standard accuracy such as knowledge sufficiency, multi-document integration, and robustness to noisy or misleading retrieved content. Together, these contributions advance the understanding of how NLP models can be systematically transferred and specialized across domains, tasks, languages, and levels of expertise, spanning the full spectrum from data-limited to data-engineered paradigms. This dissertation includes both previously published and co-authored material.", "source_url": "https:\/\/openalex.org\/W7165425966", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Scholars' Bank (University of Oregon)", "source_record_id": "W7165425966", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 895, "title": "A Comprehensive Framework for Targeted Transfer Learning: Privacy-Preserving, Secure, and Efficient Domain Adaptation", "title_ar": "", "title_en": "A Comprehensive Framework for Targeted Transfer Learning: Privacy-Preserving, Secure, and Efficient Domain Adaptation", "author": "Sayyed Farid Ahamed", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Old Dominion University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; computer-vision; responsible-ai; ai-applications; transfer-learning; few-zero-shot; image-classification; federated-learning; fairness; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "Machine learning models deployed in tactical environments rely on high-quality labeled datasets, which are often challenging, expensive, and time-consuming to obtain. In addition, formal techniques for assuring model performance under dynamic conditions remain limited. Transfer learning has emerged as a promising solution, but its effectiveness is limited by domain coverage and similarity between source and target datasets. This dissertation presents a comprehensive, modelagnostic Targeted Transfer Learning (TTL) framework that addresses transferability challenges between domains with minimal data, demonstrating how targeted sample selection improves adaptation across image classification, network security, and privacy-preserving collaborative settings. First, we develop the TTL framework for scenarios where direct access to datasets is restricted. This metadata-driven approach applies to a combinatorial coverage method to estimate domain correlation and guide sample selection. Evaluation on a satellite imagery dataset showed that targeted selection outperformed zero-shot transfer and random selection while conserving computational resources. Second, we investigate the TTL framework in settings where both datasets are directly available. This data-driven approach uses optimal transport distance to quantify distributional differences and guide selection of informative target samples. Validation across a broad set of benchmark image classification datasets demonstrated consistent improvements over conventional transfer learning. Third, we address network malware classification through Selective Targeted Transfer Learning (STTL), projecting source and target traffic data into a shared latent feature space and selecting relevant samples using a Kullback–Leibler divergence-based similarity measure. Experimental results across multiple attack-family scenarios showed that STTL achieved higher performance than conventional transfer learning, confirming that targeted selection generalizes across data types and domains. Finally, we extend the TTL framework through two decentralized privacy-preserving mechanisms. In a federated learning setting, clients share only summary statistics, and a server computes aggregation weights that enable knowledge transfer without exposing local data. In a blockchain-based setting, cryptographic verification secures model weights throughout the transfer process, while a smart-contract mechanism rewards high-quality participation. Both mechanisms consistently outperformed non-targeted baselines while preserving data confidentiality and integrity. This research establishes targeted transfer learning as a practical framework that enables machine learning models to adapt reliably, securely, and efficiently across image classification, network security, and distributed collaborative environments.", "source_url": "https:\/\/openalex.org\/W7225739907", "pdf_url": "https:\/\/digitalcommons.odu.edu\/context\/ece_etds\/article\/1622\/viewcontent\/_Sayyed__PhD_Dissertation.pdf", "doi": "10.25777\/e3fd-gd70", "handle": "", "repository_name": "ODU Digital Commons (Old Dominion University)", "source_record_id": "W7225739907", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 897, "title": "Transfer learning for language model adaptation", "title_ar": "", "title_en": "Transfer learning for language model adaptation", "author": "Bari M. Saiful", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; transfer-learning; self-semi-supervised; predictive-analytics; llm; sentiment-analysis; machine-translation", "keywords": "", "abstract": "Language is the pathway to democratize the boundary of land and culture. Bridging the gap between languages is one of the biggest challenges of Artificial Intelligent (AI) systems. The current success of AI systems is dominated by the supervised learning paradigm where gradient-based learning algorithms (i.e., SGD, Adam) are designed to optimize complex high-dimensional planes. These algorithms learn from statistical observations that are typically collected with the intention of a specific task (i.e., product review, sentiment analysis). The use of task-dependent samples makes the learning procedure tedious as it requires manually annotated data. On the contrary, without having a sufficient amount of samples to represent the distribution, deep learning models tend to suffer from the lack of robustness. Due to the natural puzzle of randomness, not all sets of observations are observed in the data collection procedure, thus creating Out-of-Distribution (OOD) problem in learning algorithms. In the search of a generic task agnostic distribution, a large collection of text over various domains can be considered as - Standard Natural Text Distribution (SNTD). The generic idea of Transfer Learning for Natural Language Processing (NLP) is to utilize the SNTD knowledge for any other task-dependent training. Learning SNTD, followed by a task-adaptation method with a smaller volume of annotated data has yielded state-of-the-art (SOTA) results in various supervised NLP tasks. However, annotated data for every task in every language is rare. In language models, there are many kinds of distributional variance. One of the most common ways that distributional variance is encoded into a language model is when it is trained with mono-lingual text and learned dis-jointly. Word embeddings produced from these language models are then used as pre-trained embedding vectors to adapt to a downstream task. We propose adversarial training to project two monolingual distributions into the same space and then improve the robustness of the model by augmented fine-tuning with parameter sharing. By projecting mono-lingual language distribution into the same cross-lingual space, the distribution of the language become aware of each other. These projected distribution are semantically aware of each other in the latent space. Thus when we train one distribution the other distribution automatically adapt to the training data, making it easier to transfer (exchange) knowledge. On top of that, the proposed novel self-training architecture improves cross-lingual transfer by a large margin. Then, we focus on the jointly trained multi-lingual language model, where there is no pre-dominant distributional variance. In the multi-lingual language model, we put more focus on downstream task adaptation. We found that semi-supervised learning with pseudo-augmented data from a pre-trained language model can greatly improve the performance of tasks further down the line. In the end, we introduce a new novel data augmentation framework that uses the neighboring (vicinal) samples of the original training data without explicitly using any parallel text corpora or machine translation system. Our proposed method performs simultaneous self-training with data augmentation and unsupervised sample selection. It also proposes curriculum strategies for different domain samples. With extensive experiments on three different cross-lingual tasks spanning many language pairs, we have demonstrated the effectiveness of our proposed method. While all of the work done above focused on improving task adaptation in multiple languages without supervision, we further investigate how adding a few samples affects multi-lingual task adaptation. To this end, we leverage a small number of support samples from each language and propose an inference-time transductive nearest neighbor-based approach which utilizes the entropy of the query samples for predictions. We show that our proposed method outperforms full model\/full head fine-tuning as well as cross-task fine-tuning. We also show impressive performance gain (~37x) in computational costs for the full inference prediction. However, as the language models grow bigger, it becomes increasingly difficult to perform efficient inferences, especially for multiple tasks. The jointly optimized multi-lingual distribution helps transfer knowledge from a high-resource language to a low-resource language. While working on transductive nearest neighbor inference, we observe that language models are significantly prone to task distributions. Unless we use an extremely large language model (>100B), a model used for a specific task adaptation cannot be used for another task. In this dissertation, our final proposed method addresses this issue by multitask prompted learning.", "source_url": "https:\/\/openalex.org\/W4386409617", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/169892\/2\/sbmaruf_thesis.pdf", "doi": "10.32657\/10356\/169892", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4386409617", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 898, "title": "Multi-Representation Learning for Domain Adaptation in Machine Learning Applications", "title_ar": "", "title_en": "Multi-Representation Learning for Domain Adaptation in Machine Learning Applications", "author": "Victor Akihito Kamada Tomita", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidade de São Paulo", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; computer-vision; nlp; responsible-ai; transfer-learning; data-mining; gnn; image-classification; sentiment-analysis; xai", "keywords": "", "abstract": "The continuous growth of machine learning methods has driven significant advances in areas such as natural language processing and computer vision. However, the performance of these models heavily depends on the quantity and quality of labeled data available for training, which is often scarce or expensive to produce, especially in specialized domains. This challenge becomes even more critical in the face of the phenomenon of domain shift, characterized by the difference between the training data distribution and the distribution of the data where the model will be applied. These limitations pose significant barriers to the application of state-of-the-art models in real-world scenarios. Domain adaptation emerges as a solution to mitigate the impacts of domain shift, optimizing the transfer of learning between different domains. This field aims to create domain-invariant representations that preserve relevant information for a specific task, thereby reducing the discrepancy between data distributions. Despite notable advances, the field still faces challenges such as balancing the specificity and generalization of the generated representations, as well as issues related to the high dimensionality of projections in latent spaces, which result in high computational costs and low interpretability. In this thesis, we propose new approaches to address the challenges of domain adaptation, focusing on balancing generalization and specificity, increasing interpretability and computational efficiency, and improving model usability. The contributions include advancements applied to tasks such as sentiment analysis, image classification, and question-answering systems. We present the Cluster Fusion Domain Adaptation (CFDA) method, which combines clustering techniques and representation fusion to generate multiple representations that ensure both internal domain alignment and generalization across domains. To tackle the issues of computational efficiency and interpretability, we developed the Pseudo-labeling Domain Adaptation (PDA) method, which employs projections in low-dimensional spaces and graph neural networks to model relationships between domains. Additionally, to enhance usability in aspect-based sentiment analysis, we introduced a novel question-answering method that enables more flexible interactions between users and applications. The experimental results of this thesis validate the proposed methods, demonstrating improvements in accuracy, computational efficiency, and generalization capacity across different datasets. Thus, this thesis makes significant contributions to the domain adaptation field, offering more versatile, interpretable, and accurate methods for existing challenges.", "source_url": "https:\/\/openalex.org\/W4413040860", "pdf_url": "http:\/\/www.teses.usp.br\/teses\/disponiveis\/55\/55134\/tde-07082025-100853\/publico\/VictorAkihitoKamadaTomita_DO_revisada.pdf", "doi": "10.11606\/t.55.2025.tde-07082025-100853", "handle": "", "repository_name": "Digital Library of Theses and Dissertations (Universidade de São Paulo)", "source_record_id": "W4413040860", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 899, "title": "Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans", "title_ar": "", "title_en": "Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans", "author": "Saad Bin Ashraf", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; image-segmentation; medical-imaging", "keywords": "", "abstract": "Magnetic Resonance Imaging (MRI) is a vital non-invasive tool for high-resolution brain imaging, yet its long acquisition time frequently results in patient movement leading to motion artifacts, particularly among non-compliant patients like newborns. While Deep Learning (DL) models have emerged as powerful retrospective solutions for motion mitigation, their performance is expected to degrade when applied to data from different scanners, protocols, or patient populations. This thesis investigates the generalizability of DL-based motion correction across heterogeneous adult and neonatal brain MRI domains. The research is conducted through three primary aims. First, the study evaluates the domain generalization of a 3D U-Net model across multi-center adult datasets - IXI, Calgary-Campinas, and OASIS-3, comprising 11 imaging centers with diverse scanner vendors and field strengths. Results indicate that models generalize effectively to unseen adult datasets from various centers, while superior performance is achieved when training incorporates broader intensity distributions. Second, the thesis explores cross-domain transfer learning by applying adult-trained models to the neonatal domain, utilizing the dHCP dataset, which contains real motion from neonatal subjects. Results indicate that adult-pretrained models learn domain-agnostic structural priors, allowing them to reconstruct neonatal brain anatomy despite significant shifts in contrast, size, and motion characteristics. Crucially, fine-tuning with as few as three neonatal scans was shown to substantially improve the mitigation of neonatal-specific artifacts, demonstrating an efficient adaptation strategy for data-scarce domains. Third, the study critically assesses the reliability of common image quality metrics (IQMs). The analysis reveals that traditional metrics are often insensitive to subtle motion and can even yield paradoxical results, where motion-corrupted images appear higher in quality than corrected ones. Overall, this work highlights the potential for deploying robust motion-mitigation frameworks in diverse clinical settings. By leveraging transfer learning, this thesis provides a foundation for more reliable and scalable deep learning–based motion correction in pediatric and adult neuroimaging.", "source_url": "https:\/\/openalex.org\/W7140225405", "pdf_url": "", "doi": "10.11575\/prism\/51192", "handle": "", "repository_name": "", "source_record_id": "W7140225405", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 900, "title": "Learning to adapt: From structured domain adaptation to efficient in-context learning for large language models", "title_ar": "", "title_en": "Learning to adapt: From structured domain adaptation to efficient in-context learning for large language models", "author": "Zihao Xu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rutgers University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; responsible-ai; transfer-learning; llm; xai", "keywords": "", "abstract": "Machine learning systems must adapt when the conditions under which they are deployed differ from the conditions under which they were trained or calibrated. This dissertation studies adaptation across two settings that are usually treated separately. The first is domain adaptation under distribution shift, where the central question is how to transfer across multiple related domains without collapsing their structure. The second is in-context learning in large language models, where a pretrained model must specialize to new tasks or long contexts under strict computational constraints. The dissertation develops five methods organized into these two lines of work. For structured domain adaptation, it introduces Graph-Relational Domain Adaptation, which aligns domains according to an explicit graph; Taxonomy-Structured Domain Adaptation, which preserves hierarchical relations among domains; and Variational Domain Indexing, which infers interpretable domain structure directly from data when no explicit structure is provided. For efficient in-context learning in large language models, it introduces Implicit In-Context Learning, which replaces explicit demonstrations with activation-space context vectors, and $K$-Token Merging, which compresses contiguous token embeddings into shorter latent representations for efficient long-context processing. Together, these chapters argue that adaptation is a broad framework that includes adaptation across structured domains, adaptation with inferred domain identities, and adaptation through efficient in-context conditioning under efficiency constraints.", "source_url": "https:\/\/openalex.org\/W7154183045", "pdf_url": "", "doi": "10.7282\/t3-q28n-fq86", "handle": "", "repository_name": "Rutgers University Community Repository (Rutgers University)", "source_record_id": "W7154183045", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 901, "title": "Study of transfer learning and domain adaptation for language modeling in end-to-end speech recognition", "title_ar": "", "title_en": "Study of transfer learning and domain adaptation for language modeling in end-to-end speech recognition", "author": "卓 龔", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; llm; speech-recognition", "keywords": "", "abstract": "Automatic Speech Recognition (ASR) has become a technical foundation for a natural human-machine interface: speech input.Recently, there have been various daily applications based on ASR: 1) transcription in dictation as smartphone input method, in automatically generated subtitles, as upstream task of speech translation (ST); 2) speech command recognition in a personal assistant like Siri, Alex, Cortana or intent recognition: booking flight, etc; and 3) QA systems like auto attendant phone systems.Deep neural network-based end-to-end (E2E) ASR systems have gained popularity in recent years due to the fast development of deep learning technologies since they can leverage gigantic speech training data with high performance and high efficiency which outperformed human for domains with sufficient data.For conventional ASR systems, independent modules can be trained on audio signals and textual sequences which gives the ASR system the capability to easily be adapted to different scenarios by only changing one of those modules.Compared to conventional ASR systems, E2E ASR systems usually have a single indivisible module for transforming speech to text.If adaptation is necessary, sufficient data from target domain should be prepared or an external language model can be integrated into the system.However, labeled speech data can always be insufficient for a specific domain while text data are much more easily obtained from web pages, books, journals, etc.Because of these reasons, Language models (LM) have been essential in enhancing the performance of end-to-end (E2E) automatic speech recognition (ASR) systems.There are two dimensions of LM integrations: 1) space dimension, how deep LM should be integrated into an E2E ASR model (combining outputs or neural networks); 2) time dimension, when to integrate LM (training stage; decoding stage; or both).This thesis starts from introducing crucial methodologies for modern E2E, then present several contributions considering above dimensions.", "source_url": "https:\/\/openalex.org\/W7144031295", "pdf_url": "https:\/\/repository.dl.itc.u-tokyo.ac.jp\/record\/2011417\/files\/A40245.pdf", "doi": "10.15083\/0002011417", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7144031295", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 902, "title": "Enhancing Domain-Specific Automated Audio Captioning: A Study on Adaptation Techniques and Transfer Learning", "title_ar": "", "title_en": "Enhancing Domain-Specific Automated Audio Captioning: A Study on Adaptation Techniques and Transfer Learning", "author": "Charith Raghavaraju", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers; speech-recognition", "keywords": "", "abstract": "Automated audio captioning is a challenging cross-modal task that takes an audio sample as input to analyze it and generate its caption in natural language as output.The existing datasets for audio captioning such as AudioCaps and Clotho encompass a diverse range of domains, with current proposed systems primarily focusing on generic audio captioning.This thesis delves into the adaptation of generic audio captioning systems to domain-specific contexts, simultaneously aiming to enhance generic audio captioning performance.The adaptation of the generic models to specific domains has been explored using two different techniques: complete fine-tuning of neural model layers and layer-wise fine-tuning within transformers.The process involves initial training with a generic captioning setup, followed by adaptation using domain-specific training data.In generic captioning, the process for training starts with training the model on the Audio-Caps dataset followed by fine-tuning it using the Clotho dataset.This is accomplished through the utilization of a transformer-based architecture, which integrates a patchout fast spectrogram transformer (PaSST) for audio embeddings and a BART transformer.Word embeddings are generated using a byte-pair encoding (BPE) tokenizer tailored to the training datasets' unique words, aligning the vocabulary with the generic captioning task.Experimental adaptation mainly focuses on audio clips related to animals and vehicles.The results demonstrate notable improvements in the performance of the generic and domain adaptation systems.Generic captioning has demonstrated an improvement in SPIDEr scores, increasing from 0.291 during fine-tuning to 0.301 with layer-wise fine-tuning.Specifically, we observed a notable increase in SPIDEr scores, from 0.315 to 0.323 for animal-related audio clips and from 0.298 to 0.308 for vehicle-related audio clips.", "source_url": "https:\/\/openalex.org\/W7133068187", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/153190\/2\/RaghavarajuCharitha.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133068187", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 903, "title": "Towards transfer learning in e-discovery :finding the optimal classifier and evaluating domain adaptation methods", "title_ar": "", "title_en": "Towards transfer learning in e-discovery :finding the optimal classifier and evaluating domain adaptation methods", "author": "J.L. Pebesma", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; ai-applications; transfer-learning; xai; ai-cybersecurity", "keywords": "", "abstract": "Once fraud is suspected, an e-discovery process can be started. Currently, this process starts with the scoping the case, whereby filters and search terms are identified to limit the search results. These results are then manually reviewed on their relevance to the case, which is expensive due to the number of hours people needed for review. Machine learning has shown potential for this task, whereby both the content and metadata of communication seem to help the classification. However, not much research has been done on the use of machine learning in e-discovery. Ideally, a classification model is trained, in a way that requires no further input, except the emails themselves. Apart from limited available research into the most optimal machine learning model, there are two other major limitations to the use of machine learning in e-discovery. One is that the datasets are relatively small, thus some patterns may not be picked up on. Knowledge about finding relevant documents may be transferred between different fraud cases. By using the knowledge learnt from each case and applying transfer learning, the knowledge can be strengthened and findings can be solidified. Another limitation is that the machine learning model may act as a black box, which is inconvenient for tasks that focus on creating transparency, such as fraud investigations. This limitation is further discussed and possible solutions are considered, such as the implications of using XAI in e-discovery. This aim of this thesis is to optimise a classification model and create more insight into the transferability between cases while considering the implications of applying explainable AI in the legal domain.", "source_url": "https:\/\/openalex.org\/W3107203640", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3107203640", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 904, "title": "Learning Transferable Features for Unsupervised Domain Adaptation in Natural Language Processing", "title_ar": "", "title_en": "Learning Transferable Features for Unsupervised Domain Adaptation in Natural Language Processing", "author": "Xia Cui", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Liverpool", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "transfer-learning", "categories": "machine-learning; nlp; transfer-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3036310088", "pdf_url": "", "doi": "10.17638\/03086091", "handle": "", "repository_name": "University of Liverpool", "source_record_id": "W3036310088", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 905, "title": "Unsupervised Learning for Domain Adaptation in automatic classification tasks through Neural Networks", "title_ar": "", "title_en": "Unsupervised Learning for Domain Adaptation in automatic classification tasks through Neural Networks", "author": "Tudor Nicolae Mateiu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad de Alicante", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; transfer-learning", "keywords": "", "abstract": "Machine Learning systems have improved dramatically in recent years for automatic recognition and artificial intelligence tasks. In general, these systems are based on the use of a large amount of labeled data - also called training sets - in order to learn a model that fits the problem in question. The training set consists of examples of possible inputs to the system and the output expected from them. Achieving this training set is the main limitation to use Machine Learning systems, since it requires human effort to find and map possible inputs with their corresponding outputs. The situation is often frustrating since systems learn to solve the task for a specific domain - that is, a type of input with relatively homogeneous conditions – and they are not able to generalize to correctly solve the same task in other domains. This project considers the use of Domain Adaptation algorithms, which are capable of learning to adapt a Machine Learning model to work in an unknown domain based on only unlabeled data (unsupervised learning). This facilitates the transfer of systems to new domains because obtaining unlabeled data is relatively cheap, since the cost is to label them. To date, Domain Adaptation algorithms have been used in very restricted contexts, so this project aims to make an empirical evaluation of these algorithms in a greater number of cases, as well as propose possible improvements.", "source_url": "https:\/\/openalex.org\/W2980012101", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante)", "source_record_id": "W2980012101", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 906, "title": "Transfer Learning for Cross-domain Visual Recognition", "title_ar": "", "title_en": "Transfer Learning for Cross-domain Visual Recognition", "author": "Jing Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Wollongong", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "transfer-learning", "categories": "machine-learning; computer-vision; transfer-learning", "keywords": "", "abstract": "Machine learning algorithms usually require a huge amount of training examples to learn a new model from scratch and often fail to apply the learned model to test data acquired from the scenarios different from those of the training data mainly due to domain divergence and task divergence. Transfer learning tries to use previously available data, models or knowledge effectively for a new domain or task with scarce data. This thesis focuses on addressing the cross-domain visual recognition using transfer learning. First, a comprehensive literature review of transfer learning methods for cross-dataset visual recognition is presented by taking a problem-oriented perspective. Second, though there has been extensive research on unsupervised domain adaptation, the performance on the target domain is still far from comparable to that without distribution shift. Hence, a novel feature transformation-based method on unsupervised domain adaptation is proposed by taking both geometrical and statistical shift into consideration, and the performance is improved compared to previous methods. Third, a novel classifier-based unsupervised domain adaptation method is proposed by presenting a new perspective that the unsupervised domain adaptation can be formulated as a multi-task learning problem. This formulation removes the commonly used shared classifier assumption in previous methods and proposes unshared classifiers for the source and target domains for exploiting more domain specific features. Fourth, an important weighted adversarial nets-based method for partial domain adaptation is proposed, where the target domain has less number of classes compared to the source domain. Different from previous domain adaptation methods that generally assume the identical label spaces, a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes is considered. Last, a new research topic called multi-source domain expansion (MSDE) is introduced, which expands the source domains to include the new domain, such that the learned model is capable to perform well on both the new target domain and the old source domains. The MSDE is different from traditional domain adaptation whose target domain is defined only as the new domain excluding the source domains. MSDE is also different from multi-task learning, lifelong Learning, and incremental learning all of which assume no domain shift among different tasks. Specifically, a novel method is proposed for unsupervised MSDE without source data.", "source_url": "https:\/\/openalex.org\/W2950193837", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Online (University of Wollongong)", "source_record_id": "W2950193837", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 907, "title": "Domain Adaptation for Fair and Robust Machine Learning", "title_ar": "", "title_en": "Domain Adaptation for Fair and Robust Machine Learning", "author": "이우진", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Seoul National University", "university_source": "repository_host", "college": "", "department": "", "country": "South Korea", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; ai-applications; transfer-learning; ai-cybersecurity", "keywords": "", "abstract": "Deep learning society has grown tremendously in recent years, leading to increasing demand for large amounts of data. However, it is difficult to gather enough labeled data similar to unlabeled real-world test data. We have to apply the domain adaptation to overcome this limitation. Domain adaptation learns by taking into account differences in probability distributions between different domains, so it outcomes a model that can be applied to both domains. In this dissertation, we first focus on developing domain adaptation methods in terms of learning appropriate representation. We propose a class-conditional domain invariant learning method that can learn a feature space in which features in the same class are expected to be mapped nearby. Improving the performance of domain adaptation has an expected impact that the application area of machine learning will be widened because a large number of unsupervised data can be used for machine learning. In addition to these direct expected impacts, domain adaptation techniques can be used in many aspects of deep learning. The key characteristic of domain adaptation is that it builds a machine learning model that can be applied to two different distributions. Therefore, domain adaptation based approaches can be widely used in machine learning areas that handle two different distributions, such as defense against adversarial attacks or fair machine learning. Robustness of deep learning is important since it can induce various security and safety problems. We focus on modeling deep networks that are robust against adversarial attacks by using domain adaptation methods. Considering normal samples as the source domain and adversarial samples as target domain, we suggest novel Wasserstein distance-based domain adaptation method that can enhance model robustness. As the increase in machine learning influences decisions about important aspects of our lives, the demand for a fair machine learning model has increased rapidly in recent years. We investigate a methodology for developing a fair classification model for data with limited or no labels, by transferring knowledge from another data domain where information is fully available, which is done by controlling the Wasserstein distances between relevant distributions. In real-world applications of machine learning, there are many cases where we need to handle data from various distributions. In these cases, domain adaptation can be the right solution because it considers the difference of probabilistic distributions of two domains. We expect, our algorithms can be helpful to overcome these limitations and be applied to build fair and robust models.", "source_url": "https:\/\/openalex.org\/W3182159980", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Seoul National University Open Repository (Seoul National University)", "source_record_id": "W3182159980", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 908, "title": "Domain adaptive learning with disentangled features", "title_ar": "", "title_en": "Domain adaptive learning with disentangled features", "author": "Xingchao Peng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Boston University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; computer-vision; robotics; transfer-learning", "keywords": "", "abstract": "Recognizing visual information is crucial for many real artificial-intelligence-based applications, ranging from domestic robots to autonomous vehicles. However, the success of deep learning methods on visual recognition tasks is highly dependent on access to large-scale labeled datasets, which are expensive and cumbersome to collect. Transfer learning provides a way to alleviate the burden of annotating data, which transfers the knowledge learned from a rich-labeled source domain to a scarce-labeled target domain. However, the performance of deep learning models degrades significantly when testing on novel domains due to the presence of domain shift. To tackle the domain shift, conventional domain adaptation methods diminish the domain shift between two domains with a distribution matching loss or adversarial loss. These models align the domain-specific feature distribution and the domain-invariant feature distribution simultaneously, which is sub-optimal towards solving deep domain adaptation tasks, given that deep neural networks are known to extract features in which multiple hidden factors are highly entangled. This thesis explores how to learn effective transferable features by disentangling the deep features. The following questions are studied: (1) how to disentangle the deep features into domain-invariant and domain-specific features? (2) how would feature disentanglement help to learn transferable features under a synthetic-to-real domain adaptation scenario? (3) how would feature disentanglement facilitate transfer learning with multiple source or target domains? (4) how to leverage feature disentanglement to boost the performance in a federated system? To address these needs, this thesis proposes deep adversarial feature disentanglement: a class\/domain identifier is trained on the labeled source domain and the disentangler generates features to fool the class\/domain identifier. Extensive experiments and empirical analysis demonstrate the effectiveness of the feature disentanglement method on many real-world domain adaptation tasks. Specifically, the following three unsupervised domain adaptation scenarios are explored: (1) domain agnostic learning with disentangled representations, (2) unsupervised federated domain adaptation, (3) multi-source domain adaptation.", "source_url": "https:\/\/openalex.org\/W3137379704", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OpenBU (Boston University)", "source_record_id": "W3137379704", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 909, "title": "Transfer learning in Takagi-Sugeno fuzzy models", "title_ar": "", "title_en": "Transfer learning in Takagi-Sugeno fuzzy models", "author": "Hua Zuo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Technology Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; expert-systems; transfer-learning; predictive-analytics", "keywords": "", "abstract": "In classical data-driven machine learning methods, massive amounts of labeled data are required to build a high-performance prediction model. However, the amount of labeled data in many real-world applications is insufficient, so establishing a prediction model is impossible. Transfer learning has recently emerged as a way of exploiting previously acquired knowledge to solve new yet similar problems much more efficiently and effectively. It exploits the knowledge accumulated in auxiliary domains to help construct prediction models in a target domain with inadequate training data. Most existing methods on transfer learning address classification tasks, but studies on transfer learning in the case of regression problems, which is an important category in prediction models, are still scarce. In addition, the existing works ignore the inherent phenomenon of uncertainty - a crucial factor during the knowledge transfer process. To fill these gaps, this research develops algorithms and methods to deal with transfer learning in homogeneous and heterogeneous spaces using fuzzy rule-based models. Fuzzy rules are first generated from the source domain through a learning process. These rules, as acquired knowledge, are then transferred to the target domain. First, a novel fuzzy rule-based domain adaptation method is proposed to transfer knowledge between domains in homogeneous spaces. It utilizes the existing fuzzy rules of source domain and modifies the input space with nonlinear mappings to adapt to the current regression tasks in the target domain. Second, a granular fuzzy domain adaptation framework, comprising three methods, is developed to handle the knowledge transfer problems based on the idea of granular computing. Thirdly, a fuzzy domain adaptation method is developed to handle the case that the numbers of fuzzy rules in two domains do not match. In addition, the proposed methods are also used to solve the classification problems in transfer learning. Fourthly, an innovative method that combines an infinite Gaussian mixture model with active learning is presented to discover the structure of data and actively augment information in a target domain. Fifthly, a fuzzy heterogeneous domain adaptation method is proposed to transfer knowledge in heterogeneous spaces. The proposed algorithms and methods are validated in each step of development using experiments performed on both synthetic and real-world datasets. The results show that this study significantly improve the performance of existing models when solving new tasks in the target domain in both homogeneous and heterogeneous domain adaptation settings.", "source_url": "https:\/\/openalex.org\/W2894269285", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UTS ePRESS (University of Technology Sydney)", "source_record_id": "W2894269285", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 910, "title": "Learning generalizable and transferable representations across domains and modalities", "title_ar": "", "title_en": "Learning generalizable and transferable representations across domains and modalities", "author": "Donghyun Kim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Boston University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; few-zero-shot; self-semi-supervised; multimodal; video-analysis; medical-imaging; fairness", "keywords": "", "abstract": "While deep neural networks attain state-of-the-art performance for computer vision tasks with the help of massive supervised datasets, it is usually assumed that all train and test examples are drawn independently from the same distribution. However, in real-world applications, dataset bias and domain shift violate this assumption. Test data can be from different domains represented by different distributions, which can seriously affect the model performance. Thus, learning generalizable and transferable representations is important to make a model robust to many different types of distributional shift. Domain transfer such as Domain Adaptation (DA) and Domain Generalization (DG) have been proposed to learn generalizable and transferable features across domains. Domain transfer consists of two steps: 1) pre-training, where a model is first pre-trained on an upstream task with a massive supervised dataset, e.g., ImageNet, and 2) transfer (adaptation), where the model is fine-tuned on downstream multi-domain data. In this thesis, we highlight the limitations of current domain transfer approaches and relax the limitations to produce more practical and diverse domain transfer methods. To be specific, we study: 1) Cross-Domain Self-supervised Learning for Domain Adaptation. Prior DA methods use ImageNet pre-trained models as a weight initialization (i.e., pre-training stage). However, the downstream data can be very different from that of ImageNet. Previous domain adaptation approaches assume there are many labeled data in the source domain. Some applications (e.g., Medical Imaging) may not have enough source labels. We explore the problem of few-shot domain adaptation where we only have a few source labels. In addition, we propose cross-domain self-supervised pre-training, which uses only unlabeled multi-domain data. We show that our method significantly boosts the performance of diverse domain transfer tasks. 2) Pre-training for Domain Adaptation. While many DA and DG methods have been proposed and studied extensively in prior work, little attention has been paid to pre-training for domain transfer. We provide comprehensive experiments and an in-depth analysis of pre-training in terms of network architectures, datasets, and loss functions. Finally, we observe significant improvements from the modern pre-training and propose to modernize the current evaluation protocols. 3) Multimodal Representation Learning for Domain Adaptation. We devise self-supervised formulations for multimodal domain adaptation where we promote better knowledge transfer by aligning multimodal features. We first explore a language-vision task where we align the features of multiple languages and images. Then, we explore video domain adaptation with RGB and Flow modalities and propose a joint contrastive regularization that interplays among cross-modal and cross-domain features. 4) Domain Adaptive Keypoint Detection. Lastly, we explore domain adaptive keypoint detection tasks (e.g., human and animal pose estimation) which are not well explored in prior work. We propose a unified framework for diverse keypoint detection scenarios, where we can encounter different types of domain shifts. To handle these domain shifts, we propose a multi-level feature alignment using the input-level and output-level cues and show that our method generalizes well to diverse domain adaptive keypoint detection tasks.", "source_url": "https:\/\/openalex.org\/W7126339286", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OpenBU (Boston University)", "source_record_id": "W7126339286", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 911, "title": "Unleashing the power of transfer learning for visual applications", "title_ar": "", "title_en": "Unleashing the power of transfer learning for visual applications", "author": "Chang Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Northeastern University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "computer-vision", "research_subdomain": "transfer-learning", "categories": "machine-learning; computer-vision; transfer-learning", "keywords": "", "abstract": "Transfer learning is a prominent paradigm in machine learning, addressing the challenges of model generalization across different domains. This dissertation delves into two fundamental aspects: domain generalization and domain adaptation. In the context of domain generalization, the objective is to build models capable of effectively generalizing to unseen target domains by leveraging information from multiple source domains during training. Conversely, domain adaptation focuses on adapting a model trained on a specific source domain to a distinct target domain, assuming the availability of data from the target domain during training. Furthermore, this work explores close-set and open-set settings in transfer learning. The close-set setting assumes a fixed set of known target classes, while the open-set setting handles the challenge of novel, unseen classes in the target domain during inference. Understanding these settings' nuances is crucial for developing versatile transfer learning strategies that cater to diverse real-world scenarios and data distributions. The primary goals of transfer learning in this study include enhancing the model's performance on the target domain and reducing training costs by leveraging knowledge from a related source domain. However, achieving these goals comes with challenges, including addressing domain shift, preventing negative transfer, and handling unknown classes in open-set settings. By investigating these challenges and developing innovative transfer-learning techniques, this dissertation contributes to the broader field of transfer-learning by offering a comprehensive exploration of domain generalization, domain adaptation, and close-set, and open-set settings. The insights gained from this research provide a foundation for developing effective transfer learning strategies, enabling the deployment of adaptable and high-performance models in real-world scenarios with varying domain characteristics and data availability.--Author's abstract", "source_url": "https:\/\/openalex.org\/W4388761286", "pdf_url": "https:\/\/repository.library.northeastern.edu\/files\/neu:4f21nv77f\/fulltext.pdf", "doi": "10.17760\/d20581914", "handle": "", "repository_name": "Digital Repository Service (Northeastern University)", "source_record_id": "W4388761286", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 912, "title": "Adversarial Domain Adaptation for Sensor Networks", "title_ar": "", "title_en": "Adversarial Domain Adaptation for Sensor Networks", "author": "Francisco Tuna de Andrade", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; generative-ai; transfer-learning; rnn", "keywords": "", "abstract": "With the recent surge of big data, manually annotating datasets has become an impossible task. Asa result, domain adaptation has gained more importance than ever, since it allows the transfer oflearning from a labeled dataset to an unlabeled one. That is even truer for sensor networks, wherethe domain shifts between different sensors are usually substantial and models developed with aset of source sensors fail to generalize well to new target sensors.Adversarial neural networks, which were introduced in 2014 in the scope of generative adver-sarial neural networks, are a promising tool to address the domain adaptation problem. Despitebeing a recent idea, adversarial networks have already led to tremendous progress in multiple areasof the field. In this dissertation, we propose a new model for domain adaptation, specifically tunedfor sensor networks, that consists of two adversarial Long short-term memory (LSTM) networks.One that seeks to discriminate between source and target domains and another one that performsthe learning task. The fact that we take the temporal nature of the data into account by using anLSTM network for the discriminating task is a novel idea. We evaluate the performance of ourmodel by conducting extensive experiments and comparing it with other state of the art methods.Besides the proposed model, we include an exhaustive survey of domain adaptation methodsand an experimental analysis of their efficiency in different datasets. Additionally, we also discussseveral open lines of research in the field of domain adaptation that can serve as a guide for futurework in this area.", "source_url": "https:\/\/openalex.org\/W3048615177", "pdf_url": "", "doi": "10.34626\/fdkt-sv10", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W3048615177", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 913, "title": "Domain Adaptation and Domain Generalization with Representation Learning", "title_ar": "", "title_en": "Domain Adaptation and Domain Generalization with Representation Learning", "author": "Muhammad Ghifary", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; transfer-learning; cnn; image-classification; video-analysis; fairness", "keywords": "", "abstract": "Machine learning has achieved great successes in the area of computer vision, especially in object recognition or classification. One of the core factors of the successes is the availability of massive labeled image or video data for training, collected manually by human. Labeling source training data, however, can be expensive and time consuming. Furthermore, a large amount of labeled source data may not always guarantee traditional machine learning techniques to generalize well; there is a potential bias or mismatch in the data, i.e., the training data do not represent the target environment. To mitigate the above dataset bias\/mismatch, one can consider domain adaptation: utilizing labeled training data and unlabeled target data to develop a well-performing classifier on the target environment. In some cases, however, the unlabeled target data are nonexistent, but multiple labeled sources of data exist. Such situations can be addressed by domain generalization: using multiple source training sets to produce a classifier that generalizes on the unseen target domain. Although several domain adaptation and generalization approaches have been proposed, the domain mismatch in object recognition remains a challenging, open problem – the model performance has yet reached to a satisfactory level in real world applications. The overall goal of this thesis is to progress towards solving dataset bias in visual object recognition through representation learning in the context of domain adaptation and domain generalization. Representation learning is concerned with finding proper data representations or features via learning rather than via engineering by human experts. This thesis proposes several representation learning solutions based on deep learning and kernel methods. This thesis introduces a robust-to-noise deep neural network for handwritten digit classification trained on “clean” images only, which we name Deep Hybrid Network (DHN). DHNs are based on a particular combination of sparse autoencoders and restricted Boltzmann machines. The results show that DHN performs better than the standard deep neural network in recognizing digits with Gaussian and impulse noise, block and border occlusions. This thesis proposes the Domain Adaptive Neural Network (DaNN), a neural network based domain adaptation algorithm that minimizes the classification error and the domain discrepancy between the source and target data representations. The experiments show the competitiveness of DaNN against several state-of-the-art methods on a benchmark object dataset. This thesis develops the Multi-task Autoencoder (MTAE), a domain generalization algorithm based on autoencoders trained via multi-task learning. MTAE learns to transform the original image into its analogs in multiple related domains simultaneously. The results show that the MTAE’s representations provide better classification performance than some alternative autoencoder-based models as well as the current state-of-the-art domain generalization algorithms. This thesis proposes a fast kernel-based representation learning algorithm for both domain adaptation and domain generalization, Scatter Component Analysis (SCA). SCA finds a data representation that trades between maximizing the separability of classes, minimizing the mismatch between domains, and maximizing the separability of the whole data points. The results show that SCA performs much faster than some competitive algorithms, while providing state-of-the-art accuracy in both domain adaptation and domain generalization. Finally, this thesis presents the Deep Reconstruction-Classification Network (DRCN), a deep convolutional network for domain adaptation. DRCN learns to classify labeled source data and also to reconstruct unlabeled target data via a shared encoding representation. The results show that DRCN provides competitive or better performance than the prior state-of-the-art model on several cross-domain object datasets.", "source_url": "https:\/\/openalex.org\/W2516289717", "pdf_url": "https:\/\/openaccess.wgtn.ac.nz\/articles\/thesis\/Domain_Adaptation_and_Domain_Generalization_with_Representation_Learning\/17014700\/1\/files\/31470596.pdf", "doi": "10.26686\/wgtn.17014700", "handle": "", "repository_name": "Open Access Victoria University of Wellington | Te Herenga Waka (Figshare)", "source_record_id": "W2516289717", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 914, "title": "Learning to recognize objects by adaptive knowledge transfer", "title_ar": "", "title_en": "Learning to recognize objects by adaptive knowledge transfer", "author": "Qingyi Tao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; object-detection; medical-imaging", "keywords": "", "abstract": "When humans learn new knowledge and skills, we can naturally transfer them to other domains. Along with the learning procedures, we learn knowledge and skills for certain tasks and transfer them to similar tasks; we also can use the old knowledge to facilitate the learning of new knowledge. While effective knowledge transfer is a congenital and important learning ability of humans, it is not easy for machine learning mechanisms to adopt the ability of knowledge transfer. In recent years, there are plenty of works studying transfer learning in deep learning. There are still some practical challenges that remain undiscovered, especially under different problem settings encountered in real situations. In this thesis, we explore how to adopt knowledge transfer mechanisms in deep learning approaches in several practical scenarios. Four different works are proposed to study knowledge transfer across different domains and tasks via domain adaptation and model transfer. In particular, we study the web knowledge transfer for object detection task by adapting the web data to the real target dataset, which aims at reducing the human annotation effort for training object detector. In the incremental learning scenario, we study the cross-utilization of the old and new knowledge to overcome the catastrophic forgetting during the incremental and progressive learning process. Lastly, we explore transfer learning in the medical imaging domain by transferring the model pre-trained on normal images. Overall, the major contributions are summarized as follows: - A web knowledge transfer method to enhance the learning of weakly supervised object detection. The proposed method includes an effective web data collection pipeline and a curriculum learning scheme to achieve more effective model optimization during multi-instance learning. - An annotation-effective object detection method by adapting web data to the target data for object detection. This work attempts to learn an object detector from web supervision by adversarial domain adaptation. - An incremental learning scheme that adapts an old model to a new model without forgetting the old knowledge. A systematic study is performed to explore different class incremental methods. Furthermore, we propose a graph-based method to mine the old sample forgettability along with the training of the new tasks, and dynamically select samples that are more forgettable to overcome the catastrophic forgetting. - A lesion detection method for 3D CT images by utilizing model weights that are pre-trained on normal 2D RGB images. Furthermore, an attention-based feature aggregation method is proposed to adaptively transfer the information from neighboring slices to the key slices for more discriminative representation. Through this thesis, we demonstrate three different paradigms of knowledge transfer, including (1) the cross-domain knowledge transfer for adapting web data to an application with real unconstrained data, (2) the continual knowledge transfer from old tasks to new tasks without forgetting the old knowledge, and (3) the model transfer from the normal image domain to the medical domain. Under several practical tasks, the experiments are conducted to demonstrate the effectiveness of the proposed knowledge transfer approaches.", "source_url": "https:\/\/openalex.org\/W3177101414", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/151507\/4\/thesis_oneside_2021_taoqingyi.pdf", "doi": "10.32657\/10356\/151507", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W3177101414", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 915, "title": "Data-efficient domain adaptation for pretrained language models", "title_ar": "", "title_en": "Data-efficient domain adaptation for pretrained language models", "author": "Xu Guo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; responsible-ai; transfer-learning; self-semi-supervised; predictive-analytics; optimization; rnn; transformers; llm; federated-learning", "keywords": "", "abstract": "Recent advances in Natural Language Processing (NLP) are built on a range of large-scale pretrained language models (PLMs), which are based on deep transformer neural networks. These PLMs simultaneously learn contextualized word representations and language modeling by training the entire model on massive unlabeled corpora using self-supervised learning techniques, bringing about a paradigm shift that moves our focus from customizing different models for different tasks to adapting one PLM to all tasks. Studying how to adapt a general-purpose PLM to a specific domain of interest is of great significance to the deployment of PLMs. The mainstream practice is to finetune a PLM with a task-specific head on a labeled dataset from the target domain. However, for most target applications, labeled data is limited and even scarce in many low-resource scenarios. The huge number of parameters in a PLM often leaves those small datasets struggling to harness the power of the language priors. As a result, even under the same task, when a PLM finetuned on one dataset is applied to another dataset with some domain gap, it sometimes encounters performance degradation due to overfitting the previous training set. This phenomenon hinders the wide adoption of PLMs in practice, particularly in the face of new domains, calling for approaches to enhance the generalization performance of PLMs during adaptation without requesting more labeled data. Early domain adaptation methods, which leverage similar source domains to boost model performance on the target domains, are developed based on customized models using traditional neural networks such as LSTMs. These models are shallow, require longer training time to converge, and have no prior knowledge compared to PLMs. Studies show that some popular domain adaptation methods can even harm the generalization performance of PLMs on the target domains. The unique characteristics of PLMs such as unprecedented scales, rich language priors, and many hitherto underexplored skills could be uncontrollable factors that make them exhibit different learning behaviors compared to traditional models. To this end, there is a need to develop algorithms for PLMs to enhance their domain adaptation performance, thereby accelerating their wide adoption in real-world scenarios. This thesis aims to explore techniques that can efficiently make use of the target domain labeled data and better adapt a given PLM to the target domains of interest by effectively transferring knowledge from similar source domains to the target domains. To achieve this goal, I conduct research from three perspectives throughout a machine learning pipeline, each assuming only specified locations can be updated with available computing resources. That is, we keep all other conditions fixed and only make updates to the input data, model representations, and output predictions respectively. We show how to achieve better generalization performance with limited labeled data from the target domains under each scenario. To sum up, we propose a new algorithm to generate adversarial perturbations using the domain adaptation objective to enhance the transferability of soft prompt tuning in low-resource scenarios, a new model optimization algorithm that takes into account the next-step gradients of adversarial domain discriminator when optimizing the task classifiers to accommodate competing losses and a new federated learning framework that calibrates the conditional probability distribution to adapt the same PLM to multiple domains under different label distributions. We present the specific problems, related works, detailed methods, extensive experiments, and thorough discussions in the following chapters, and shed light on how to base on traditional machine learning methods while catering to newly emerging learning paradigms.", "source_url": "https:\/\/openalex.org\/W4378983312", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/167965\/2\/PhD_Thesis_GuoXu.pdf", "doi": "10.32657\/10356\/167965", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4378983312", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 916, "title": "Exploring Transfer Learning Focused on Physiological Signals for Emotion Recognition", "title_ar": "", "title_en": "Exploring Transfer Learning Focused on Physiological Signals for Emotion Recognition", "author": "Cameron Lopez", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Texas A&M University Libraries", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; transfer-learning; sentiment-analysis", "keywords": "", "abstract": "Recent work in the area of automatic emotion recognition has leveraged a large amount of publicly available data with transfer learning techniques to detect emotion on low-resource data. Previous work demonstrated that the use of maximum independence domain adaptation and transfer component analysis show promise in generalizing on unseen domains. While the accuracy increases are significant, they remain below within-dataset models. Other research concluded that a subspace alignment auto-encoder (SAAE) is useful for domain adaptation and is more effective than current techniques. Despite the encouraging results of these studies, more work needs to be done to extend this to real world brain computer interaction (BCI) applications. The primary goal of this thesis is to develop transfer learning techniques that leverage existing data and attempt to generalize them on unseen domains accurately enough for real-world applications. If the proposed endeavor is successful, emotion recognition for real-life applications will not need to include large amounts of data from the target domain since transfer learning techniques will be able to accurately generalize on unseen domains.", "source_url": "https:\/\/openalex.org\/W3094441479", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OakTrust (Texas A&M University Libraries)", "source_record_id": "W3094441479", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 917, "title": "Using an artificial intelligence-based program to develop speaking skills among middle school students in rural areas of Iraq", "title_ar": "", "title_en": "Using an artificial intelligence-based program to develop speaking skills among middle school students in rural areas of Iraq", "author": "Hassan Maytham", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Northern University of Malaysia", "university_source": "repository_host", "college": "", "department": "", "country": "Malaysia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "This study investigates the impact of the Learna AI application on developing English speaking skills among middle school students in a rural Iraqi setting. Using a mixed methods quasi experimental design, sixty students were divided equally into an experimental group using Learna AI and a control group receiving traditional instruction. The findings revealed that students who used Learna AI demonstrated significant improvement in their speaking performance, particularly in fluency, pronunciation, and communicative competence, compared to those taught through traditional methods. The qualitative data further confirmed students’ positive perceptions of the AI-based learning experience, highlighting its effectiveness in supporting rural learners The findings align with previous research supporting AI's role in reducing speaking anxiety and enhancing oral proficiency. Despite limitations related to sample size, duration, and infrastructure, the study demonstrates the potential of AI based tools in improving speaking performance in underserved contexts. Recommendations are made for integrating such technologies into language education, especially in rural areas.", "source_url": "https:\/\/openalex.org\/W7149877871", "pdf_url": "https:\/\/etd.uum.edu.my\/12070\/2\/s833852_02.pdf", "doi": "", "handle": "", "repository_name": "UUM Electronic Theses and Dissertation [eTheses] (Northern University of Malaysia)", "source_record_id": "W7149877871", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 918, "title": "The influence of Artificial Intelligence Cognitive Biases on Corporate Governance in Making Financial Decisions: Examined Via Throughput Model", "title_ar": "", "title_en": "The influence of Artificial Intelligence Cognitive Biases on Corporate Governance in Making Financial Decisions: Examined Via Throughput Model", "author": "Zenad, Yaser", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Hull", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "responsible-ai; ai-applications; fairness; ai-cybersecurity", "keywords": "", "abstract": "As businesses work to leverage artificial intelligence (AI) promise to boost productivity and efficiency, its position in corporate governance (CG) is becoming increasingly important, where AI can be used to streamline fraud detection and compliance procedures, enhancing the entire structure of CG. However, cognitive bias, a prevalent issue in AI systems utilised by businesses for decision-making, is a significant concern. Accordingly, the main aim of this study is to explain the influence of AI cognitive biases on CG in making financial decisions by using throughput model (TM). AI cognitive biases include base-rate neglect, insensitivity to sample size, confirmation bias, availability heuristic, and ambiguity aversion. To achieve this aim, this study adopted the quantitative model, where the questionnaire was designed and distributed to research sample, which consisted of 1491 mangers in British banks, and 141 managers in Iraqi Banks. Then, the questionnaire was analysed by using Statistical Package for the Social Sciences (SPSS) program. The results show that financial decisions will be affected by the existence of AI cognitive biases in both British and Iraqi banks. In addition, the availability of reliable financial information will affect positively on financial decisions in both British and Iraqi banks. Moreover, the results show that the decline of the decision maker's level of knowledge and experience will lead to poor financial decisions in both British and Iraqi banks. Finally, the analysis shows that the disruption of digital technological training will lead to poor financial decisions in both British and Iraqi banks. Based on these findings, this study contributes to a deeper understanding by British and Iraqi banks of AI cognitive biases that affect effective financial decision-making. The study also recommended that British and Iraqi banks should train their employees to use modern technology and improve their cognitive skills.", "source_url": "https:\/\/openalex.org\/W7113357022", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Repository@Hull (Worktribe) (University of Hull)", "source_record_id": "W7113357022", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 919, "title": "Artificial intelligence for traffic system optimization", "title_ar": "", "title_en": "Artificial intelligence for traffic system optimization", "author": "Βασιλική-Δέσποινα Α. Μπουκουβάλα", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Thessaly", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "ai-applications", "research_subdomain": "ai-smart-cities", "categories": "ai-applications; ai-smart-cities", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7224138415", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/90030\/4\/35600.pdf", "doi": "", "handle": "", "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)", "source_record_id": "W7224138415", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 920, "title": "Information Technologies of Artificial Intelligence and Blockchain for Securing Smart Cities", "title_ar": "", "title_en": "", "author": "Владислав Володимирович Крамар ; Vladyslav Kramar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Ternopil National Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2026, "language": "uk", "research_domain": "ai-applications", "research_subdomain": "ai-smart-cities", "categories": "ai-applications; ai-smart-cities", "keywords": "", "abstract": "Роботу виконано на кафедрі комп'ютерних наук Тернопільського національного технічного університету імені Івана Пулюя. Захист відбудеться 22.06.2026р. на засіданні екзаменаційної комісії №35 у Тернопільському національному технічному університеті імені Івана Пулюя", "source_url": "https:\/\/openalex.org\/W7167162661", "pdf_url": "http:\/\/elartu.tntu.edu.ua\/bitstream\/lib\/53083\/1\/2026_KRB_ST-41_Kramar_VV.pdf", "doi": "", "handle": "", "repository_name": "ELARTU (Ternopil National Technical University)", "source_record_id": "W7167162661", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 921, "title": "Nurses' Knowledge and Attitudes Towards Artificial Intelligence and its Application in Clinical Nursing Practice", "title_ar": "", "title_en": "", "author": "Gustas Buinickas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Lithuania", "governorate": "", "publication_year": 2025, "language": "sv", "research_domain": "medical-ai", "research_subdomain": "", "categories": "medical-ai", "keywords": "", "abstract": "Gustas Buinickas. Slaugytojų žinios ir požiūris į dirbtinį intelektą ir jo pritaikymą klinikinėje slaugos praktikoje. Bakalauro baigiamasis darbas. Darbo vadovė – prof. dr. Olga Riklikienė. Lietuvos sveikatos mokslų universitetas, Medicinos akademija, Slaugos fakultetas, Slaugos klinika. Kaunas, 2025, 57 p. (be priedų). Tyrimo tikslas. Ištirti slaugytojų žinias ir požiūrį į dirbtinį intelektą ir jo pritaikymą klinikinėje slaugos praktikoje. Tyrimo metodika. Vykdyta anoniminė anketinė apklausa 2025 m. sausio mėn. – 2025 m. balandžio mėn. Facebook grupėje „Lietuvos Slaugytojai“. Apklausti 144 slaugytojai. Tyrimo instrumentas – klausimynas. Tyrimui atlikti buvo gautas Lietuvos sveikatos mokslų universiteto Bioetikos centro leidimas (Nr. 2024-BEC2-1130). Duomenys apdoroti IBM SPSS 30.0 ir Microsoft Excel 16.7 programine įranga. Tyrimo rezultatai. Nustatyta, kad slaugytojai pasižymi žemu žinių lygiu apie dirbtinį intelektą. Mažiausiai žinių slaugytojai turėjo apie neuroninius tinklus ir prižiūrimojo mokymosi principus. Nustatytas statistiškai reikšmingas ryšys tarp žinių lygio ir respondentų amžiaus (p < 0,001), lyties (p = 0,039), išsilavinimo (p < 0,001) bei dalyvavimo mokymuose (p < 0,001). Jaunesni, aukštesnį išsilavinimą turintys, mokymuose dalyvavę bei vyriškos lyties slaugytojai turėjo aukštesnį žinių balą. Slaugytojų požiūris į dirbtinio intelekto taikymą sveikatos priežiūros srityje buvo įvertintas kaip santykinai palankus. Didžioji dalis respondentų sutiko, kad iškilus nuomonių skirtumui tarp gydytojo ir dirbtinio intelekto, priimtinesnis turėtų būti gydytojo sprendimas. Daugiausia naudos slaugytojai įžvelgė padidėjusio paslaugų teikimo greičio ir medicininių klaidų sumažinimo aspektuose. Didžioji dalis respondentų sutinka, kad prieš diegiant dirbtinį intelektą į sveikatos priežiūrą, turi būti parengti politiniai ir etiniai pagrindai. Slaugytojai mano, kad sveikatos priežiūros specialistai prieš taikydami dirbtinį intelektą turėtų išklausyti kursus. Didžiausią nerimą slaugytojams kelia galimas pacientų konfidencialios informacijos nutekėjimas naudojant dirbtinio intelekto sprendimus. Požiūrio balai reikšmingai skyrėsi pagal respondentų amžių (p < 0,001), lytį (p < 0,001), išsilavinimą (p = 0,002) ir dalyvavimą mokymuose (p < 0,001). Palankesnis požiūris nustatytas tarp jaunesnių, vyrų, aukštesnį išsilavinimą turinčių ir mokymuose dalyvavusių respondentų. Nustatytas reikšmingas teigiamas ryšys tarp žinių ir požiūrio balų (ρ = 0,652; p < 0,001), kylant žinių balams, požiūrio vertinimas taip pat didėja. Išvados. 1. Slaugytojų žinių apie dirbtinį intelektą lygis yra žemas – vidutiniškai 4,49 balai iš galimų 16. Slaugytojai dažniausiai geba atpažinti dirbtinio intelekto apibrėžimą, tačiau jiems trūksta žinių apie dirbtinio intelekto technines sąvokas, veikimo principus ir struktūrinius elementus. 2. Slaugytojų požiūris į dirbtinį intelektą yra santykinai palankus – vidutiniškai 37,32 balai iš galimų 55. Slaugytojai pritarė, kad prieš diegiant dirbtinį intelektą sveikatos priežiūros srityje būtina užtikrinti etinį ir profesinį pasirengimą. Slaugytojai įžvelgia dirbtinio intelekto teikiamą naudą, tačiau taip pat atkreipia dėmesį į rizikas, susijusias su pacientų duomenų saugumu, dirbtinio intelekto emocinio jautrumo stoka. 3. Slaugytojai, turintys aukštesnį žinių apie dirbtinį intelektą lygį, palankiau vertina dirbtinio intelekto diegimą klinikinėje slaugos praktikoje. Jaunesnio amžiaus, aukštesnį išsilavinimą turintys, mokymuose apie dirbtinį intelektą dalyvavę bei vyriškos lyties slaugytojai pasižymėjo aukštesniu žinių lygiu ir palankesniu požiūriu į dirbtinio intelekto diegimą klinikinėje slaugos praktikoje.", "source_url": "https:\/\/openalex.org\/W7151710526", "pdf_url": "https:\/\/lsmu.lt\/cris\/bitstreams\/d8749386-aca0-454e-ad26-38a38f395e36\/download", "doi": "", "handle": "", "repository_name": "Lithuanian University of Health Sciences", "source_record_id": "W7151710526", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 923, "title": "Artificial Intelligence Driven Product Development: An analysis of how the integration of Artificial Intelligence into the automotive development process can shorten the product development cycle", "title_ar": "", "title_en": "Artificial Intelligence Driven Product Development: An analysis of how the integration of Artificial Intelligence into the automotive development process can shorten the product development cycle", "author": "Eduan Howard", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "generative-ai", "research_subdomain": "fairness", "categories": "generative-ai; responsible-ai; ai-applications; fairness; ai-industry", "keywords": "", "abstract": "This thesis explores the transformative potential of Artificial Intelligence (AI) in accelerating product development cycles within the automotive industry, amid rapid shifts toward electric vehicles (EVs) and sustainable mobility. As global competition intensifies—driven by market leaders like Tesla and Chinese OEMs such as BYD—traditional development processes face challenges in speed, e!ciency, and innovation. The study hypothesizes that integrating AI into key stages of the automotive product lifecycle management (PLM) can reduce developmenttimelines by 20-50%, enhancing competitiveness through optimized workflows and resource allocation. Building on a comprehensive literature review of PLM, technology innovation management, systems engineering models (e.g., V-Model and MBSE), and AI advancements, the methodology proposes a departmentalized AI agent structure tailored to automotive contexts. This includes Future Innovation (7-10 year horizon) for technology scouting, Research & Development (4-7years) for design exploration, and Serial Development (2-4 years) for production maturation. A practical case study on electric traction drive development illustrates AI’s role in requirement refinement, trade-o\" analysis, and simulation, yielding significant time savings while maintaining focus on business outcomes like cost reduction and market agility. Key findings demonstrate that AI-driven processes, such as generative AI for ideation and model-based engineering for validation, can streamline the innovation funnel, mitigate risks in chaotic technology landscapes (via adapted Stacey Matrix), and support generational product pulls. Implications for the automotive sector include improved margins (up to 5% uplift per PwC insights), faster time-to-market, and strategic advantages in the EV transition. imitations include reliance on hypothetical scenarios and ethical AI considerations, with recommendations for future work emphasizing real-world implementations and workforce upskilling. Overall, this research underscores AI as a strategic enabler for automotive firms to thrive in a dynamic, technology-push environment.", "source_url": "https:\/\/openalex.org\/W7167697483", "pdf_url": "https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/229250\/1\/Howard%20Eduan%20-%202026%20-%20Artificial%20Intelligence%20Driven%20Product%20Development%20An...pdf", "doi": "10.34726\/hss.2026.139401", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W7167697483", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 924, "title": "Artificial Intelligence for Islamic Ontology Linking Qur’an and Hadith Resources", "title_ar": "", "title_en": "Artificial Intelligence for Islamic Ontology Linking Qur’an and Hadith Resources", "author": "Ibtisam Khalaf Alshammari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; ai-applications; transformers; llm; arabic-nlp; ai-education", "keywords": "", "abstract": "The primary objective of this thesis is to integrate various Islamic resources, specifically the Qur’an and Hadith, building upon existing academic research. Although previous works have produced valuable contributions, they remain uncoordinated and heterogeneous, raising significant challenges for interoperability and integration. This thesis addresses these limitations by unifying these inconsistent resources into a coherent and comprehensive Islamic ontology, ensuring a more accurate and structured representation of Islamic knowledge and facilitating wider computational applications. The project employs a multi-stage methodology. A workflow combining RML, Cellfie plugin, and SDM-RDFizer interpreter is utilised to integrate Qur’anic resources, enabling data transformation and alignment. This framework successfully maps the data correctly, demonstrating the effectiveness of the Cellfie plugin tool. For the Hadith corpus, knowledge extraction approaches are applied to the LK-Hadith corpus. First experiment with Arabic named entity recognition (ANER) using a limited number of BERT-based Arabic pretrained language models (PLMs) employed on the Sahih Albukhari book produces insufficient results for semantic integration, leading to the adoption of topic modelling methods. The topic modelling and extraction involves Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), BERTopic with Arabic PLMs, and GPT-4. This experiment presented a bilingual Hadith_Teaching_Topics (HTT) dataset. Then, the semantic relationships between Qur’anic and Hadith topics are evaluated through the Quran_Hadith_Dataset (QH_Dataset) using multiple large language models (LLMs), enriching the construction of the Related Quran-Hadith Topics (RQHT) ontology. The ontology is subsequently extended to incorporate the QuranOntology, the entire six Hadith books derived from the LK-Hadith corpus, and their derived topics based on the HTT dataset. This thesis advances the field by establishing a methodological framework for integrating heterogeneous Islamic textual resources by involving multiple data transformations, knowledge extraction, and ontology engineering techniques. Within this framework, the Qur’anic resources are integrated to enhance the unification process, the bilingual HTT dataset is developed to augment the low-resource Arabic annotation and support the final integrated ontology, and the RQHT ontology is introduced to formally represent the semantic relationships between Qur’an and Hadith topics. Collectively, these contributions boost the unification process of Qur’an and Hadith knowledge within a comprehensive and logically consistent semantic Islamic ontology. Evaluation through quantitative and qualitative measures determines the Islamic ontology’s logical consistency, representational completeness, and suitability for advanced computational applications. This research provides a scalable semantic framework to support more knowledge representation and reasoning across core Islamic texts.", "source_url": "https:\/\/openalex.org\/W7134994163", "pdf_url": "https:\/\/etheses.whiterose.ac.uk\/id\/eprint\/38206\/1\/Artificial_Intelligence_for_Islamic_Ontology_Linking_Qur_an_and_Hadith_Resources.pdf", "doi": "", "handle": "", "repository_name": "White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)", "source_record_id": "W7134994163", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 925, "title": "Integration of artificial intelligence in work and organizational psychology", "title_ar": "", "title_en": "Integration of artificial intelligence in work and organizational psychology", "author": "Bizjan, Špela", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Primorska", "university_source": "repository_host", "college": "", "department": "", "country": "Slovenia", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7115287797", "pdf_url": "https:\/\/repozitorij.upr.si\/Dokument.php?id=30051&dn=", "doi": "", "handle": "", "repository_name": "Repository of the University of Primorsk (University of Primorska)", "source_record_id": "W7115287797", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 926, "title": "Artificial Intelligence in Modern Firewalls : Role of Artificial Intelligence in Network Security", "title_ar": "", "title_en": "Artificial Intelligence in Modern Firewalls : Role of Artificial Intelligence in Network Security", "author": "Wajeh Ahmad", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; reinforcement-learning; responsible-ai; expert-systems; ai-applications; data-mining; xai; fairness; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "This thesis explores the role and impact of Artificial Intelligence on the design of modern firewalls due to the advancement of Artificial Intelligence (AI), particularly how AI-based methods transcend the barriers of the traditional rule-based approaches and enhance overall network security. The research traces the evolution of firewalls over time and highlights the necessity for more advanced security measures. It also outlines the progress of AI, setting the stage for a deep dive into the role of AI in security systems. The research critically assesses the limitation of conventional firewalls, such as their inability to detect zero-day attacks and their vulnerability to inspect encrypted traffic. It is investigated that how machine learning techniques, such as shallow neural networks or decision trees, can complement network security by aiding the firewall’s decision making and automatically classifying network packets. Furthermore, the thesis demonstrates the application of intrusion detection systems in cyberspace by employing machine learning, normal distribution, clustering, and t-plotting that can facilitate prevention and\/or detection of such malicious attacks. In addition, the thesis investigates the suitability of Explainable AI (XAI) to explain and make understandable AI based firewall decisions. It also highlights the difficulties and constraints of AI-centric firewalls, like the fact that they require a large set of data, the need of removing biases from such data, as well as taking privacy into account when using these capabilities, pushing further research on development of robust training methodologies and privacy-preserving approaches. The thesis features two prominent case studies: Google Chronicle Security Operations Platform, and Darktrace Enterprise Immune System. These case-studies highlight AI can be put to practical use for improving the abilities of threat detection, investigation and response. The thesis concludes by highlighting the significant progress made in integrating AI into contemporary firewalls, such that they can start learning proactively against continuous adapting cyber threat landscape. The report concludes by suggesting for more research to be done in the refining of AI algorithms, the investigation into reinforcement learning, and adding user and entity behavior analytics (UEBA) onto models as well as continuing the process of creating explainable AI models. The findings from this work will contribute to a greater understanding of AI-powered network security and offer direction for those looking at how they might develop more resilient firewalls in the future both in practice as well as in research.", "source_url": "https:\/\/openalex.org\/W7132940295", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/161576\/2\/AhmadWajeh.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7132940295", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 927, "title": "Cybersecurity in the European Union: an analysis of the potential of artificial intelligence", "title_ar": "", "title_en": "", "author": "Karin Majcen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Ljubljana", "university_source": "repository_host", "college": "", "department": "", "country": "Slovenia", "governorate": "", "publication_year": 2024, "language": "sl", "research_domain": "ai-applications", "research_subdomain": "ai-cybersecurity", "categories": "ai-applications; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "Diplomska naloga obravnava potencial uporabe umetne inteligence (UI) za izboljšanje kibernetske varnosti v Evropski uniji (EU). V kontekstu naraščajočih kibernetskih groženj in sofisticiranosti kibernetskih napadov naloga analizira kako UI prispeva k zaznavanju groženj, vzpostavljanju varnostnih standardov in ohranjanju digitalne suverenosti EU. Namen raziskave je preučiti možnosti avtomatizacije zaznavanja vdorov in proaktivnega odzivanja na grožnje s pomočjo umetne inteligence, pri čemer pristop zagotavlja skladnost z obstoječimi pravnimi in političnimi okviri, ki urejajo to področje. Metodologija vključuje pregled obstoječe literature, analizo zakonodajnih pobud in študijo orodij UI za kibernetsko varnost. Uporabljene so bile kvalitativne metode, pri čemer raziskava ugotavlja, da tehnike strojnega in globokega učenja učinkovito zaznavajo vzorce anomalij v omrežnem prometu, kar omogoča hitro prepoznavanje in odzivanje na kibernetske napade. Prav tako izpostavlja potrebo po nadgradnji obstoječih varnostnih standardov in prilagoditvi regulativnih smernic, za zaščito digitalnih vrednosti v EU. Prispevek naloge je pomemben za področje kibernetske varnosti, saj ponuja strateške usmeritve za integracijo UI v varnostne protokole. Praktične koristi vključujejo boljšo zaščito podatkov in infrastrukture, kar je ključno za javni sektor in industrijo. Družbeni učinek raziskave se kaže v večji digitalni varnosti in zaupanju javnosti v digitalne storitve. Diplomska naloga nudi podlago za nadaljnje delo na področju raziskovanja uporabe UI za kibernetsko varnost ter spodbuja razvoj naprednih varnostnih rešitev in politik.", "source_url": "https:\/\/openalex.org\/W7142578415", "pdf_url": "https:\/\/repozitorij.uni-lj.si\/Dokument.php?id=191085&dn=", "doi": "", "handle": "", "repository_name": "Repository of the University of Ljubljana (University of Ljubljana)", "source_record_id": "W7142578415", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 928, "title": "(Re)Imagining the Techno-Body: Artificial Intelligence, Embodiment, and the Technological Future", "title_ar": "", "title_en": "(Re)Imagining the Techno-Body: Artificial Intelligence, Embodiment, and the Technological Future", "author": "Woods, Heather", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of North Carolina at Chapel Hill", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "ai-applications", "research_subdomain": "ai-smart-cities", "categories": "ai-applications; ai-smart-cities", "keywords": "", "abstract": "This dissertation illuminates how gendered stereotypes are leveraged to the benefit of corporations that market and sell artificially intelligent objects. In particular, the research shows that these AI objects traffic in normative gender roles of the feminine as caretaker, mother, and wife in order to obfuscate modes of surveillance, and mediate the relationship users and potential users have with late-capitalist market logics in the platform economy. Mobilizing essentialist feminine personas, artificially intelligent objects orient users to engage productively with surveillance capitalism as ‘natural.’ To illustrate this relationship between the feminine and surveillance, this dissertation focuses on two case studies. The first turns to Apple’s Siri and Amazon’s Alexa as emblematic of AI VA that perform a stereotypically feminine persona that invites users to participate in increasingly intimate forms of data exchange that in turn contribute to surveillance capitalism. The study of AI VA, like Siri and Alexa, demonstrates the significant rhetorical capacities of the feminine persona as they are applied to objects with weak (that is, limited) artificial intelligence. In the second case study, I demonstrate how fictive representations of general AI also utilize normative conceptions of the feminine to gesture to the ‘human.’ Through a critical rhetorical reading of the films Ex Machina and Her, this research shows that even an imagined future of artificially intelligent bodies relies upon and re-inscribes patriarchal conceptions of the feminine in the technological present and future. In addition, focusing on gendered narratives and stereotypes, these dystopian films, much like Siri and Alexa, distract from, and even normalize the rapid development of systems trading in surveillance capitalism.", "source_url": "https:\/\/openalex.org\/W2971253348", "pdf_url": "", "doi": "10.17615\/d6bt-zk13", "handle": "", "repository_name": "Carolina Digital Repository (University of North Carolina at Chapel Hill)", "source_record_id": "W2971253348", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 929, "title": "Geospatial AI-based approach to assess the spatiotemporal suitability of onshore wind-solar farms in Iraq", "title_ar": "", "title_en": "Geospatial AI-based approach to assess the spatiotemporal suitability of onshore wind-solar farms in Iraq", "author": "Sachit, Mourtadha Sarhan Almushattat", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universiti Putra Malaysia", "university_source": "repository_host", "college": "", "department": "", "country": "Malaysia", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; ai-applications; xai; ai-agriculture; ai-smart-cities", "keywords": "", "abstract": "Nowadays, Renewable Energy (RE), such as wind and solar, play a vital role in meeting the increasing demand for electricity and ensuring a low-carbon future. Unfortunately, the efficiency of wind and solar plants depends on the spatially and temporally fluctuating nature of their renewable resources. These fluctuations have prompted planners and decision-makers to shift toward hybridizing such energies in an effort to make electricity generation more stable. Wind-solar hybrid plants have posed new challenges in evaluating suitable sites that meet the requirements of both types of energy. As a result, site selection models for hybrid power systems (wind-solar) have received wide attention in recent years as being a critical planning problem that needs accurate decision-making. In the existing literature, the site suitability assessment of dual-energy systems (windsolar) is frequently addressed as a Spatial Decision-Making (SDM) problem involving numerous climatic, economic, and environmental criteria. In most cases, the input factors are considered constant. However, most of these factors, such as wind speed and solar radiation, change over time. Besides, criteria are often subjectively or objectively weighted, generating biased and non-generalizable solutions. To overcome these challenges, the current research aims to develop a SpatioTemporal Decision-Making (STDM) model based on Geospatial Artificial Intelligence (GeoAI) to locate onshore wind-solar hybrid plants. The presented model seeks to fill in the existing gaps by considering the dynamic nature of decision criteria and formulating novel, more reliable global weights. To achieve the research goal, a four-stage methodology was drawn. A system of spatial evaluation criteria was first designed based on literature statistics and expert judgments supported by content validity analysis. Second, eXplainable Artificial Intelligence (XAI) was introduced to formulate novel global weights for those criteria. In this context, global geospatial data for 13 conditioning factors were collected, and 55,619 inventory samples of wind and solar stations worldwide were prepared to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). SHapley Additive exPlanations (SHAP) algorithm was applied to interpret the results of the superior model and elicit the criteria weights. In a separate third step, a new temporal criterion was developed based on temporal complementarity assessment between wind and solar resources. Pearson’s correlation coefficients were utilized to explore the synergistic patterns between the time-series dataset of wind speed and solar radiation across Iraqi territory. Finally, spatial, temporal, and exclusion criteria were incorporated along with AI weights into a unified STDM model using multiple overlay analyses in a GIS environment. The outcomes of the criteria design demonstrated that 13 factors, with an excellent Kappa value of 0.76, would form valuable content for evaluating the spatial suitability of wind and solar farms. The results indicated the superiority of the RF algorithm in both wind and solar modeling, with an overall accuracy of 90% and 89%, a kappa coefficient of 0.79 and 0.78, and an area under the curve of 0.96 and 0.95, respectively. The XAIbased importance analysis revealed higher weights for technical and economic criteria than for other environmental and social criteria. Top weights were given to the criteria of wind speed and proximity to cities (0.373 and 0.149, respectively) in locating wind farms, and the criteria of proximity to cities and air temperature (0.180 and 0.149, respectively) in locating solar farms. The southwestern regions and some eastern parts of Iraq exhibited significant temporal synergistic patterns spanning more than 6 months of the year that influence spatial decision-making. Our spatiotemporal model identified three hotspots over Iraq—South Dhi-Qar, West Diyala, and East Wasit—with a total area of 3,632 km2. The hotspots revealed exceptional suitability scores exceeding 0.8, meeting both the spatial and temporal constraints. The reported spots have the technical potential to generate electricity from wind turbines and solar PV cells at rates of 11.88– 12.58 MW and 81.31–89.51 MW, respectively. Overall, this research has led to the development of a new GeoAI-based STDM model that can find the best places to put wind-solar systems with great accuracy and consistency. Analyses of transferability, sensitivity, and uncertainty show that the GeoAI-based STDM model is more reliable than one-dimensional solutions. The advantage of the proposed model is not only to identify the technically, economically, and environmentally appropriate sites but also to ensure that they boast temporal synergistic patterns between renewables for stable power supplies around the clock.", "source_url": "https:\/\/openalex.org\/W7112578286", "pdf_url": "http:\/\/psasir.upm.edu.my\/id\/eprint\/115915\/1\/115915.pdf", "doi": "", "handle": "", "repository_name": "Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia)", "source_record_id": "W7112578286", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 930, "title": "Evaluating common sense using artificial intelligence systems", "title_ar": "", "title_en": "Evaluating common sense using artificial intelligence systems", "author": "Pere Ayats Marsal", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "Evaluating machine common sense using artificial intelligence systems building a model capable of solving a multiple choice quiz involving different kinds of questions such as predicate, entity, pronoun and discourse type questions.", "source_url": "https:\/\/openalex.org\/W2897175758", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)", "source_record_id": "W2897175758", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 931, "title": "Title: Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs", "title_ar": "", "title_en": "Title: Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs", "author": "Wenzhao Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Chapman University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "Earth Observation (EO) data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes. EO, empowered by methodologies from Artificial Intelligence (AI), supports various aspects of the UN’s Sustainable Development Goals (SDGs). This dissertation presents author’s major studies using EO to fill in knowledge gaps and develop methodologies and cloud-based applications in selected SDGs, including SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life below Water) and SDG 15 (Life on Land). For SDG 6, the study focuses on spatiotemporal water recharge patterns and interconnections between variables in the Nile watershed countries, highlighting the EO’s potential to implement transboundary water cooperation (SDG 6.5.2). For SDG 11, the studies focus on the indicators of urbanization (SDG 11.a.1 and 11.1.1) and air quality (SDG 11.6.2). Utilizing EO datasets, the annual geographical and population-weighted PM 2.5 levels in Egypt are analyzed in 18 years. The Random Decision Forest is developed to predict the PM10 levels in Cairo. Additionally, a cloud-based impervious surface classifier based on fully convolution neural networks (FCNN) is trained to monitor the urbanization in five Nile basin cities between 2013 and 2019. For SDG 14, the studies focus on marine pollution (SDG 14.1.1) through monitoring and exploring the atmospheric and meteorological factors regulating algal blooms, as well as its primary productivity in the Red Sea. For SDG 15, the mangrove changes over the Western Gulf region is quantified using multi-indices and machine learning models to protect the local threatened species (SDG 15.5.1). The MENA is investigated concerning changes in land coverage using the harmonic analysis of vegetation index (SDG 15.1.1). Finally, the dissertation also summarizes the SDG researches the author involved, including 1) air pollutant in the MENA region; 2) dust impact on rainfall 3) aerosols’ impact on snowfall; 4) oil spill modeling to help proper mitigation; 5) vegetation in the sea turtle nesting Islands; and 6) EO of Mediterranean seagrass Posidonia Oceanica.", "source_url": "https:\/\/openalex.org\/W3002647053", "pdf_url": "", "doi": "10.36837\/chapman.000115", "handle": "", "repository_name": "Chapman University Digital Commons (Chapman University)", "source_record_id": "W3002647053", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 932, "title": "Blockchain for AI: Smarter Contracts to Secure Artificial Intelligence Algorithms", "title_ar": "", "title_en": "Blockchain for AI: Smarter Contracts to Secure Artificial Intelligence Algorithms", "author": "Syed Badruddoja", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; predictive-analytics; llm", "keywords": "", "abstract": "In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To prove the hypothesis, I use these mathematical models to compute complex operations of naive Bayes, linear regression, decision trees, and neural network algorithms on Ethereum public test networks. The smart contracts achieve 95\\% prediction accuracy compared to traditional programming language models, proving the soundness of the numerical derivations. Many non-real-time applications can use our solution for trusted and secure prediction services.", "source_url": "https:\/\/openalex.org\/W4389378466", "pdf_url": "https:\/\/digital.library.unt.edu\/ark:\/67531\/metadc2179338\/m2\/1\/high_res_d\/BADRUDDOJA-DISSERTATION-2023.pdf", "doi": "10.12794\/metadc2179338", "handle": "", "repository_name": "University of North Texas Digital Library (University of North Texas)", "source_record_id": "W4389378466", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 933, "title": "On the Safety Alignment of Large Language Models", "title_ar": "", "title_en": "On the Safety Alignment of Large Language Models", "author": "Mengxuan Hu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; medical-ai; responsible-ai; ai-applications; transfer-learning; llm; fairness; ai-education; ai-cybersecurity", "keywords": "", "abstract": "Large language models (LLMs) are increasingly deployed in high-stakes settings such as finance, healthcare, and education, where failures in safety and fairness can cause real harm. In this dissertation, we advance LLM trustworthiness by (i) benchmarking how LLMs introduce explicit risks within the RAG pipeline, (ii) developing red-teaming attacks that expose latent vulnerabilities even under seemingly “safe” conditions, and (iii) proposing robust defenses that strengthen alignment in practice. First, we study Retrieval-Augmented Generation (RAG), a widely used strategy to reduce hallucinations and improve domain grounding, and show that its benefits are not a free lunch. We propose a practical three-level threat model based on user awareness of fairness, which induces different degrees of “fairness censorship” in external dataset. Across uncensored, partially censored, and fully censored datasets, we find that fairness alignment can be undermined through RAG alone, without any fine-tuning, and that biased outputs can persist even when the external data is ostensibly unbiased. These results reveal fundamental limitations of current alignment when models are coupled with retrieval. Second, we uncover a complementary vulnerability in the fine-tuning stage: even benign training data can degrade safety. Building on this phenomenon, we develop a stronger red-teaming attack by identifying the most safety-degrading outliers within benign datasets. We introduce Self-Inf-N, an outlier-detection method that selects a small set of influential samples. Fine-tuning on as few as 100 selected outliers can severely compromise safety alignment across seven mainstream LLMs, with high transferability and poor coverage by existing mitigations. Third, motivated by these failures, we develop reasoning-aware alignment defenses. Using causal intervention, we show that many jailbreak vulnerabilities stem from shallow alignment behaviors that refuse without deeply understanding harmful intent. We address this by constructing a new Chain-of-Thought fine-tuning dataset spanning utility and safety-critical prompts to encourage principled, reasoned refusals. Finally, we propose Alignment-Weighted DPO, which assigns different preference weights to reasoning versus response segments, enabling targeted updates that improve jailbreak robustness while preserving utility. Together, these studies provide a unified framework for diagnosing and mitigating trustworthiness failures across RAG, fine-tuning, offering actionable guidance for deploying safer and fairer LLM systems.", "source_url": "https:\/\/openalex.org\/W7151325055", "pdf_url": "", "doi": "10.18130\/3vqg-ng96", "handle": "", "repository_name": "Libra", "source_record_id": "W7151325055", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 934, "title": "Implementation of Large Language Models in Legal Practice", "title_ar": "", "title_en": "Implementation of Large Language Models in Legal Practice", "author": "Jan Veselý", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "computer-vision; nlp; responsible-ai; llm; fairness", "keywords": "", "abstract": "1 Implementation of Large Language Models in Legal Practice Abstract The thesis \"Implementation of Large Language Models in Legal Practice\" deals with the use of large language models (LLMs) in the current law practice in the Czech Republic. The aim of the thesis is to find out to what extent the LLMs technology is used in Czech legal practice and what possible technical or legal factors prevent its full deployment. The purpose of the thesis is to map the issue of legal regulation of the provision of legal services using LLMs tools and thus contribute to the debate on the effective and also safe and ethical use of LLMs technology in law firms in the Czech Republic. The theme of the thesis combines two research areas, namely the law of new technologies and the future of legal professions. The research question of the thesis asks whether the legal regulation of LLMs technology in the provision of legal services in the Czech Republic in civil law and intellectual property law is balanced in such a way that lawyers can use all the functions of LLMs tools in their professional activities without compromising or lowering the standards of quality of legal services provided, in particular with regard to the accuracy and truthfulness of the legal advice provided, the protection of the personal data of the law firm's...", "source_url": "https:\/\/openalex.org\/W7135600023", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135600023", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 935, "title": "Generating game ideas with large language models", "title_ar": "", "title_en": "Generating game ideas with large language models", "author": "Uneeb Kari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; reinforcement-learning; ai-applications; llm; ai-education", "keywords": "", "abstract": "Large language models are increasingly used as tools for creative ideation in domains such as game design. While modern chat-based models are effective at producing clear and coherent ideas, concerns have been raised about their creative diversity due to alignment and safety training. Prompt engineering is often proposed to increase creativity, but the relative importance of model choice and prompting strategy remains unclear, especially when creativity is evaluated by humans. This thesis empirically investigates how different large language models and prompting strategies influence the creativity of AI-generated game ideas. The study compares a base language model (davinci-002) and a chat-based model trained with reinforcement learning from human feedback (GPT-4o), using both regular fewshot prompting and combinatorial prompting. A total of 200 game ideas were generated and evaluated by game design students in terms of novelty and value. The results show that model choice has a substantially stronger impact on perceived creativity than prompting strategy. GPT-4o consistently produced game ideas that were rated higher in both novelty and value, despite exhibiting lower semantic diversity. In contrast, combinatorial prompting led to only minor differences in creativity scores across models. These findings suggest that coherence and usefulness play a more important role in human creativity judgments than diversity alone. Overall, this thesis provides empirical evidence that selecting an appropriate model is more critical than designing complex prompts for creative game ideation. The results contribute to a clearer understanding of AI-assisted creativity and offer practical guidance for the use of large language models in game design.", "source_url": "https:\/\/openalex.org\/W7125465183", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W7125465183", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 936, "title": "Large language model enabled program synthesis", "title_ar": "", "title_en": "Large language model enabled program synthesis", "author": "Yixuan Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Edinburgh", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; llm", "keywords": "", "abstract": "Program synthesis aims to automate the construction of programs that satisfy user-defined specifications, yet existing approaches face trade-offs between correctness, scalability, and efficiency. One established approach is syntax-guided synthesis (SyGuS), in which the user provides both a formal specification of the desired program behaviour and a syntactic template, typically expressed as a context-free grammar. However, it becomes computationally expensive as the grammar grows and the number of possible programs increases. In contrast, machine learning methods, including large language models (LLMs), can generate code quickly by learning patterns from large datasets, but they often produce incorrect or unverifiable programs. This thesis presents two complementary lines of research for integrating large language models (LLMs) into program synthesis. The first develops prompt engineering techniques to automatically select the most effective prompt-LLM configuration for a given synthesis task. The second investigates hybrid synthesis methods that combine the correctness guarantees of classical SyGuS techniques with the speed and generalisation capabilities of LLMs. This thesis further demonstrates the applicability of hybrid synthesis methods to domain-specific program lifting, where translating low-level code into high-level representations is essential for machine learning workloads. Together, these contributions advance the development of program synthesis frameworks that are both principled and practical.", "source_url": "https:\/\/openalex.org\/W7125405149", "pdf_url": "https:\/\/era.ed.ac.uk\/bitstreams\/e042f7ca-af26-4893-9c81-091296c5dafc\/download", "doi": "10.7488\/era\/6860", "handle": "", "repository_name": "Edinburgh Research Archive (University of Edinburgh)", "source_record_id": "W7125405149", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 937, "title": "The Use of Large Language Models for Information Extraction", "title_ar": "", "title_en": "The Use of Large Language Models for Information Extraction", "author": "Tiia Heino", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Turku", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; transfer-learning; llm", "keywords": "", "abstract": "Large Language Models (LLM) are Artificial Intelligence (AI) models that can be fine-tuned for specific tasks. They can be used for Information Extraction (IE), a domain of Natural Language Processing (NLP), that aims to extract information from unstructured text and convert it into structured, machine-readable data. Using LLMs for IE allows for more accurate and less time-consuming extraction. For materials science using LLMs for IE enables a more automated extraction that can be used with Materials Science Databases, that store diverse data on different materials. With these tools materials science research can go towards a new era with data-driven studies. Using LLMs does have its challenges with hallucinations, environmental costs and transparency issues.", "source_url": "https:\/\/openalex.org\/W7127243281", "pdf_url": "https:\/\/www.utupub.fi\/bitstreams\/5d5fcaa2-a5e1-4483-a4c0-a8e72f0467b8\/download", "doi": "", "handle": "", "repository_name": "UTUPub (University of Turku)", "source_record_id": "W7127243281", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 938, "title": "Towards the Watermarking of Large Language Models: A Survey", "title_ar": "", "title_en": "Towards the Watermarking of Large Language Models: A Survey", "author": "Brajevic, Enis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bologna", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "Intellectual property theft through the unlawful sharing of texts is a pressing issue, further intensified by the rapid development of Large Language Models (LLMs). These models, designed for Natural Language Processing tasks, have reached a level of performance and popularity that makes them very vulnerable to misuse, such as the generation of fake text or false authorship attribution of the generated content. Text watermarking is the best tool to safeguard LLMs from such misuse. Because Large Language Models and text watermarking exhibit such complementarity, in this comprehensive survey we describe the state of the art of these two fields and how they can be combined. Our contribution can be summarized in the following four main aspects: (1) an overview of the fundamental characteristics of LLMs and their state of the art; (2) an overview and analysis of the main types of text watermarking methods that target existing text, and how they compare to each other; (3) an overview and analysis of the main LLM watermarking methods; (4) identification of underexplored areas through the formulation of targeted research questions, which are then addressed after the comprehensive study on LLMs and text watermarking.", "source_url": "https:\/\/openalex.org\/W7112077097", "pdf_url": "", "doi": "", "handle": "", "repository_name": "AMS Degree Thesis (University of Bologna)", "source_record_id": "W7112077097", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 939, "title": "Thai sentence segmentation using large language models", "title_ar": "", "title_en": "Thai sentence segmentation using large language models", "author": "Narongkorn Panitsrisit ; Attapol Thamrongrattanarit", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Thailand", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; transfer-learning; transformers; image-segmentation; llm", "keywords": "", "abstract": "Thai sentence segmentation has been on the topic of interest among Thai NLP communities. However, not much literature has explored the use of transformer-based large language models to tackle the issue. We conduct three experiments on the LST20 corpus, including (1) fine-tuning WangchanBERTa, a large language model pre-trained on Thai, across different classification tasks, (2) joint learning for clause and sentence segmentation, and (3) cross-lingual transfer using the multilingual model XLM-RoBERTa. Our findings show that WangchanBERTa outperforms other models in Thai sentence segmentation, and fine-tuning it with token and contextual information further improves its performance. However, cross-lingual transfer from English and Chinese to Thai is not effective for this task.", "source_url": "https:\/\/openalex.org\/W4386784623", "pdf_url": "http:\/\/cuir.car.chula.ac.th\/bitstream\/123456789\/83303\/1\/6480012522.pdf", "doi": "10.58837\/chula.is.2022.31", "handle": "", "repository_name": "Chula Digiverse", "source_record_id": "W4386784623", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 940, "title": "Documenting and Improving Prompts for Large Language Models", "title_ar": "", "title_en": "Documenting and Improving Prompts for Large Language Models", "author": "Amandine M. Caut", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dalarna University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; responsible-ai; optimization; llm; fairness", "keywords": "", "abstract": "Generative Artificial Intelligence has fundamentally reshaped the landscape of technology, transitioning from academic research into a revolutionary force across virtually every sector of society. Today, generative AI drives innovation across numerous domains, reflecting its expanding societal impact. At the forefront of this revolution are large language models (LLMs): sophisticated systems that have redefined human-AI interaction and democratized access to intelligent analysis. As LLMs become increasingly accessible, the critical challenge is no longer whether to use them, but how to deploy them effectively and responsibly within specialized domains. This thesis investigates how Large Language Models can deepen our understanding of the world and enhance our capacity for insight and discovery. Specifically, it examines the practical integration of LLMs into data science workflows, exploring how they can enhance data exploration, strengthen analytical reasoning, and support informed decision-making. Equally important, it addresses a fundamental challenge often overlooked: establishing rigorous documentation and structural practices that ensure reproducibility, transparency, and methodological integrity when working with these powerful tools. By synthesizing theoretical foundations with practical application, this research provides both technical insights and actionable guidance for LLMs into data science practice: not as a shortcut, but as a disciplined, transparent, and accountable approach to modern analytical work.", "source_url": "https:\/\/openalex.org\/W7161225329", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Diva portal (Dalarna University Library)", "source_record_id": "W7161225329", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 941, "title": "Large Language Models for Symbolic Music Understanding", "title_ar": "", "title_en": "Large Language Models for Symbolic Music Understanding", "author": "Meng Yang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; ai-applications; llm; ai-education", "keywords": "", "abstract": "This thesis studies how Large Language Models can help computers “understand” symbolic music, such as MIDI, rather than only generating music. It first shows that when music is converted into text, the model can describe it but the descriptions are often vague and inconsistent. It then builds a scalable way to link symbolic music to meaningful labels and descriptions using online context. Finally, it trains a model that reads symbolic music directly and produces better captions and answers than a text-only approach. This work supports future tools for music analysis, search, education, and creative assistance.", "source_url": "https:\/\/openalex.org\/W7170153692", "pdf_url": "", "doi": "10.26180\/33066689", "handle": "", "repository_name": "Monash University", "source_record_id": "W7170153692", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 942, "title": "Efficient algorithms for scaling large language models", "title_ar": "", "title_en": "Efficient algorithms for scaling large language models", "author": "Zhang, Zhenyu, Ph. D.", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; predictive-analytics; llm", "keywords": "", "abstract": "Recent advances in machine learning have transformed it from a specialized predictive tool into a general-purpose technology, exemplified by foundation models that power modern artificial intelligence systems. Much of this progress has been driven by scaling along two key dimensions: model size and context length. In the text domain, larger models enable stronger reasoning and generation capabilities, while longer contexts allow models to process complex inputs such as documents, interaction histories, and tool outputs, enabling emerging applications including AI assistants and autonomous agents. However, continued scaling along these dimensions introduces substantial challenges for current hardware systems during both training and inference stages. This dissertation studies how to improve the efficiency and adaptability of large language models (LLMs) along these two scaling axes. The central goal is to enable large models to be trained and deployed efficiently while improving their ability to process and reason over long contexts. To achieve this goal, the dissertation develops methods that address key bottlenecks in both two scaling axes. The first part of this dissertation focuses on efficient scaling of model capacity across the entire pipeline, from training to deployment. Scaling model size introduces three practical challenges: the large memory overhead required for training, the high latency and system cost of serving large models during inference, and the need to deploy models across heterogeneous devices with different hardware budgets. To address these issues, this work introduces memory-efficient optimization methods that reduce training overhead through compressed gradient representations, adaptive inference mechanisms that selectively activate or load critical model parameters to improve serving efficiency, and elastic deployment strategies that adjust model capacity to the available hardware resources. Together, these techniques enable large models to scale more efficiently and flexibly in real-world systems. The second part of this dissertation focuses on scaling context length for LLMs. Long-context modeling introduces several challenges, including the efficiency bottleneck of inference caused by KV caches whose memory footprint grows linearly with sequence length, the difficulty for models to effectively utilize information distributed across long contexts, and the lack of mechanisms to control generation during long reasoning processes. To address these challenges, this work develops efficient long-context inference methods that reduce KV-cache overhead through selective context retention, improved positional encoding techniques that enhance long-range dependency modeling, and inference-time steering mechanisms that guide reasoning behavior during long-context generation. These approaches improve both the efficiency of long-context inference and the effectiveness of reasoning over extended contexts. Together, the contributions of this dissertation advance the foundations of scalable large-model systems. By improving training efficiency, enabling adaptive model deployment, and enhancing long-context reasoning, this work helps bridge the gap between the rapidly increasing capabilities of large language models and the practical constraints of real-world hardware systems.", "source_url": "https:\/\/openalex.org\/W7202283289", "pdf_url": "https:\/\/repositories.lib.utexas.edu\/bitstreams\/7c7862ab-a1c3-4682-b7b0-0154e3eceba2\/download", "doi": "10.26153\/tsw\/64443", "handle": "", "repository_name": "", "source_record_id": "W7202283289", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 943, "title": "Solving Graph Problems with Large Language Models", "title_ar": "", "title_en": "Solving Graph Problems with Large Language Models", "author": "Angelo Zangari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Illinois Chicago", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; responsible-ai; gnn; llm; xai", "keywords": "", "abstract": "This thesis investigates how large language models (LLMs) can be used to solve classical computational problems on graphs. Graphs are a fundamental abstraction for representing real-world systems, such as social, transportation, and communication networks, but they pose unique challenges: their structure is not tied to any fixed ordering of nodes (graph isomorphism), and different tasks (such as reachability, shortest paths, flows, or substructure counting) require very different reasoning patterns. With the advent of large language models, there is growing interest in replacing or complementing traditional graph neural networks with text-based models. Recent work shows that LLMs can exhibit human-like behaviors in specific settings, which raises a key question: can we design graph representations that better align with how humans (and thus LLMs) naturally interpret structure? The first contribution of this thesis is the proposal of new techniques for augmenting node-level information with structural signals derived from the graph itself. These methods are orthogonal to the specific graph characteristics, thus are applicable to any graph dataset. The second contribution is the design of a systematic evaluation framework. I construct a synthetic benchmark covering multiple graph families and a diverse set of tasks, including computationally challenging ones, and develop an end-to-end pipeline to generate datasets, construct prompts, query LLMs, and analyze responses. The proposed representations are assessed through theoretical analysis and extensive experiments, giving additional insights on whether human-interpretable structural augmentation can improve LLM performance on non-trivial graph problems.", "source_url": "https:\/\/openalex.org\/W7168364382", "pdf_url": "", "doi": "10.25417\/uic.32991947", "handle": "", "repository_name": "University of Illinois Chicago", "source_record_id": "W7168364382", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 944, "title": "Stance detection enhanced by large language models", "title_ar": "", "title_en": "Stance detection enhanced by large language models", "author": "Nikesh Gyawali", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Kansas State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; medical-ai; responsible-ai; self-semi-supervised; llm; sentiment-analysis", "keywords": "", "abstract": "Stance detection, a subfield of opinion mining, is an important task in Natural Language Processing (NLP) that involves determining the attitude or position expressed by an author of a text towards a particular topic or claim, generally referred to as target.More specifically, the task involves automatically determining if an author of a text is In-Favor (Positive), Against (Negative), or Neutral towards a given target.Accurate stance detection is crucial for understanding complex social issues, guiding policy decisions, and shaping effective interventions.While the field of NLP has seen significant progress, capturing nuanced stances from diverse and often ambiguous data remains a challenge.Recent developments in Large Language Models (LLMs) have transformed the field by offering unprecedented capabilities in interpreting subtle linguistic cues and context-dependent meanings.This dissertation advances the field of stance detection by utilizing Large Language Models (LLMs) to improve methodologies across multiple critical and socially impactful domains.In this dissertation, we first explore LLM-enhanced approaches to stance detection in the socially controversial and polarizing topics of gun regulation and vaccines.We introduce the GunStance dataset, consisting of social media posts from X (formerly Twitter) posted by X users after major mass shooting events in the United States.By integrating labeled and unlabeled posts, this dataset allows comprehensive exploration of a semi-supervised learning framework in the context of stance detection.We propose a novel hybrid model that combines semi-supervised techniques with LLMs and show that our approach significantly outperforms traditional stance detection approaches.Furthermore, we assemble a large dataset of social media posts from X, capturing the vaccine discourse over a decade.The dataset includes seven years before COVID-19, as well as three COVID-19 years.Leveraging LLMs, social cognition theories and emotional dynamics, we analyze the vaccine dataset to capture the evolving public attitudes towards vaccines before and during the COVID-19 pandemic.Our study reveals increasing polarization and heightened emotional engagement, with a notable rise in vaccine skepticism amid the global health crisis.Expanding into the less controversial but important financial domain, we construct a financial stance detection corpus from annual 10-K reports filed to U.S. Securities and Exchange Commission (SEC) and earnings call transcripts (ECT) by extracting short text fragments relevant to key financial metrics, such as debt, earnings per share (EPS), and sales, and annotating them using LLM-driven methodologies with strict human validation.This financial stance detection corpus facilitates extensive evaluation of LLMs' ability to detect subtle stances towards financial metrics, a task that requires complex reasoning.Our findings demonstrate the effectiveness of LLMs in performing accurate stance detection without extensive labeled data, showcasing their potential for real-world financial analysis applications.Building upon these insights, we also introduce the Modular Prompt Optimization for Stance Detection (MoPrO-SD) framework.This framework utilizes the prompt optimization capabilities of LLMs by breaking down the complex stance detection prompt into modular, optimizable components.Each module is iteratively refined using LLMs as prompt optimizers, leading to an improved prompt that outperforms human-crafted prompts on several stance detection benchmarks.Collectively, this dissertation advances the field of stance detection by providing comprehensive evidence on the use of LLMs to enhance the performance, adaptability, and efficiency of stance detection methodologies across social media posts and financial documents, offering an analytical and scalable framework for informed and nuanced decision-making in an increasingly digital and interconnected world. Stance detection enhanced by large language models by", "source_url": "https:\/\/openalex.org\/W7218185954", "pdf_url": "https:\/\/krex.k-state.edu\/bitstreams\/ffe8f4b0-48a3-4b3c-8de7-53c0428e8d47\/download", "doi": "", "handle": "", "repository_name": "K-State Research Exchange (Kansas State University)", "source_record_id": "W7218185954", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 945, "title": "Catalan Text Simplification with Large Language Models", "title_ar": "", "title_en": "Catalan Text Simplification with Large Language Models", "author": "Arnau Ayguadé Domingo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Pompeu Fabra", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; transfer-learning; llm", "keywords": "", "abstract": "Automatic text simplification (ATS) is crucial for accessibility, yet it remains underexplored for low-resource languages. This thesis investigates sentence-level ATS for Catalan using large language models (LLMs). A performance analysis of three LLMs is conducted using the iDEM corpus as a benchmark and SARI as the ATS evaluation metric. Diverse prompt engineering techniques are employed to examine their impact on simplification quality. This work introduces a novel reward function that combines SARI with specific penalty terms to achieve better alignment. The reward function is designed for Group Relative Policy Optimization (GRPO) to guide LLMs toward a targeted simplification style. The effectiveness of GRPO with this reward function is demonstrated by post-training IberianLLM-7B-Instruct on the ASSET dataset. Following post-training, the model’s ATS performance improves, while also successfully suppressing negative behaviors. Cross-lingual transfer learning is explored by translating ASSET into Catalan and Spanish and post-training the model on them.", "source_url": "https:\/\/openalex.org\/W7214133122", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Repositori digital de la UPF (Universitat Pompeu Fabra)", "source_record_id": "W7214133122", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 946, "title": "Advancing personalized education with large language models", "title_ar": "", "title_en": "Advancing personalized education with large language models", "author": "Μήκος, Γεώργιος Σ. ; Μήκος, Γεώργιος Σ.", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Thessaly", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; responsible-ai; ai-applications; llm; fairness; ai-education", "keywords": "", "abstract": "The rapid advancement of artificial intelligence (AI), and particularly of Large Language Models (LLMs) such as the GPT family, has introduced new paradigms in data extraction and analysis, leading to the development of innovative applications.In the field of education specifically, AI is emerging as a fast-growing scientific domain that integrates machine learning, data analysis, and pedagogical practices with the goal of enhancing the learning process.However, the speed of this evolution and the broad range of its applications often act as barriers to its integration in education, especially by educators who are not familiar with its practical use or how to embed it effectively in their teaching practices.In this context, LLMs and adaptive learning techniques can serve as intermediaries between teachers, students, and AI systems.This approach enables personalized instruction, dynamic adaptation of learning paths, and meaningful human-machine collaboration in ways that were not previously possible.Nevertheless, despite the promising capabilities of this technology, several challenges remain.These include the design of user-friendly and functional interfaces, the improvement of algorithmic efficiency, and the assurance of ethical use of AI in education.i", "source_url": "https:\/\/openalex.org\/W7113309638", "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/86519\/4\/32049.pdf", "doi": "", "handle": "", "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)", "source_record_id": "W7113309638", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 947, "title": "Measuring and mitigating stereotypes in large language models", "title_ar": "", "title_en": "Measuring and mitigating stereotypes in large language models", "author": "Zara Siddique", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Cardiff University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; generative-ai; responsible-ai; llm; fairness", "keywords": "", "abstract": "Completely ‘debiasing’ a language model is, in many respects, an impossible task. Language is inherently contextual, and individuals interpret text differently based on their lived experiences, social positions, and cultural backgrounds. This subjectivity makes stereotypes difficult to define, measure, and mitigate in a consistent way. This thesis engages directly with these challenges, measuring which stereotypes are encoded in Large Language Models (LLMs) and exploring practical approaches for reducing representational harm despite the absence of a single ground truth. This thesis introduces GlobalBias, a dataset that incorporates a wide range of intersectional demographic groups, alongside a methodology and novel evaluation metric that surface stereotypical associations without pre-defining which stereotypes should be linked to particular identities. By avoiding fixed stereotype labels, this approach reveals which associations emerge from model behaviour in both open-source models (via perplexity) and closed-source models (via generative outputs). The analysis centres on the harms these patterns cause, particularly for historically oppressed and marginalised groups. The thesis then shifts from analysis to intervention. We present the first systematic application of steering vectors to social bias reduction, showing that targeted interventions at the level of model activations can significantly reduce stereotypical behaviour. Across multiple benchmarks, steering vectors consistently outperform prompting, Self-Debias, and finetuning, achieving substantial bias reduction while preserving overall model performance. In addition, we introduce Dialz, an open source toolkit that enables reproducible training and evaluation of steering vectors, alongside automated methods for selecting optimal intervention layers and coefficients. Taken together, this thesis contributes a novel dataset, methodological frameworks, empirical results, and practical tools for understanding and mitigating bias in LLMs. By combining rigorous measurement and a critical engagement with the wider societal implications of stereotypical language, this work advances approaches to developing language models that are more transparent and aligned with diverse human experiences.", "source_url": "https:\/\/openalex.org\/W7219532491", "pdf_url": "https:\/\/orca.cardiff.ac.uk\/id\/eprint\/187234\/2\/2026siddiquezphd.pdf", "doi": "", "handle": "", "repository_name": "ORCA Online Research @Cardiff (Cardiff University)", "source_record_id": "W7219532491", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 949, "title": "Short Text Clustering with Large Language Models", "title_ar": "", "title_en": "Short Text Clustering with Large Language Models", "author": "Justin Miller", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; computer-vision; nlp; responsible-ai; data-mining; llm; xai", "keywords": "", "abstract": "This thesis addresses the challenges of clustering short text data, focusing on human interpretability and validation metrics. Employing Gaussian Mixture Models with embeddings from Large Language Models, this thesis demonstrates that these methods produce clusters that are more interpretable than traditional approaches. The thesis introduces the concept of multi-level clustering, an approach that examines how clusters form and evolve as the number of clusters in an algorithm increases. It also introduces a method to maximise the information conveyed in each cluster, while minimising the cognitive load required to understand the clusters. The findings bridge the gap between automated metrics and human evaluation, offering insights into optimal clustering techniques for short text. This is then used to examine human identity in Twitter bios and create visualisations that provide a better understanding of clusters, as well as employing linguistic methodology to identify key distinctions between the clusters.", "source_url": "https:\/\/openalex.org\/W7126489782", "pdf_url": "", "doi": "", "handle": "", "repository_name": "The Sydney eScholarship Repository (The University of Sydney)", "source_record_id": "W7126489782", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 950, "title": "Exploring large language models for clinical code assignment", "title_ar": "", "title_en": "Exploring large language models for clinical code assignment", "author": "Kukkula, Lasse", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; medical-ai; cnn; transformers; llm", "keywords": "", "abstract": "Clinical coding, the act of translating clinical transcripts into standardised diagnostic and procedural codes, is an essential statistics and billing tool in hospitals around the world. Despite it carrying a lot of weight in the capitalist healthcare system for billing purposes and for statistical and quality assurance purposes in socialised healthcare systems. The process of clinical coding is labour intensive and error prone. Recent advances in large language models pave a way toward fully or partially automated reliable clinical coding. The research on large language models is constantly evolving and developing into new avenues that could and should be explored in the context of clinical coding. This thesis provides a survey of the advancments in large language model architecture from the almost ancient multilayer perceptron and convolutional neural networks all the way to the current cutting edge advancements such as transformers and state space models. These findings are then taken into the context of clinical coding and some suggestions on future directions in the research into the use of large language models in clinical coding will be presented.", "source_url": "https:\/\/openalex.org\/W7112331338", "pdf_url": "https:\/\/aaltodoc.aalto.fi\/bitstreams\/42c2c5c2-03df-4772-ab73-0042e63ba09e\/download", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W7112331338", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 951, "title": "Towards Superior Quantization for Large Language Models", "title_ar": "", "title_en": "Towards Superior Quantization for Large Language Models", "author": "Feng Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Auckland University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "Large Language Models (LLMs) have exhibited remarkable capabilities in tasks such as natural language comprehension, content generation, and knowledge retrieval. However, training and serving these models require substantial computational resources, posing a significant barrier to AI application development and research. To address these challenges, various model compression techniques have been explored, with quantization emerging as a key approach. Nonetheless, existing quantization methods predominantly apply uniform quantization configurations, failing to account for the varying quantization difficulty across different layers in billion-scale models. This results in a rigid memory-accuracy trade-off and leaves the potential for improving quantization accuracy through differentiated memory allocation largely unexplored. To bridge these research gaps, this thesis advances the study of LLM quantization with two key contributions. First, it introduces MXQ, a mixed-quantization method designed to provide a more flexible memory-accuracy balance. MXQ formulates a novel optimization approach to determine optimal layer-wise quantization parameters while enforcing overall memory constraints. Experimental results demonstrate that MXQ unlocks a broader spectrum of quantization configurations, simplifying the memory-accuracy trade-off while maintaining performance comparable to the baseline. Second, this thesis proposes SensiBoost and KurtBoost, two methods that enhance quantization accuracy by leveraging layer-sensitive features such as activation sensitivity and weight distribution kurtosis to identify critical layers. These approaches outperform existing baselines, achieving up to 9% lower perplexity with only a 2% increase in memory budget on Llama models.", "source_url": "https:\/\/openalex.org\/W7131740665", "pdf_url": "https:\/\/openrepository.aut.ac.nz\/bitstreams\/033e07d2-180d-45bf-91bd-d7f3d59ad3a6\/download", "doi": "", "handle": "", "repository_name": "Tuwhera (Auckland University of Technology)", "source_record_id": "W7131740665", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 952, "title": "Foundation models and large language models for autonomous driving", "title_ar": "", "title_en": "Foundation models and large language models for autonomous driving", "author": "Xiangrui Kong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; generative-ai; robotics; responsible-ai; ai-applications; predictive-analytics; multimodal; llm; xai; ai-smart-cities", "keywords": "", "abstract": "The deployment of autonomous driving systems (ADS) is advancing globally, yet long-tail scenarios remain a major challenge. This thesis explores integrating foundation models (FMs) and large language models (LLMs) into ADS to enhance robustness, interpretability, and security. A multi-agent LLM framework is proposed for safe vehicle–LLM interaction, alongside an intent-aware pedestrian reasoning system. A generative framework combining LLMs, diffusion, and state-space models enables fine-grained 3D scene completion and traffic reporting. Further, a multimodal disengagement prediction model and secure path-planning architecture are developed. Collectively, this work provides a roadmap for embedding FMs into modular ADS pipelines.", "source_url": "https:\/\/openalex.org\/W7140680728", "pdf_url": "", "doi": "10.26182\/333t-y504", "handle": "", "repository_name": "UWA Profiles and Research Repository (UWA)", "source_record_id": "W7140680728", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 953, "title": "Context Analysis with Large Language Models", "title_ar": "", "title_en": "Context Analysis with Large Language Models", "author": "Keigo Takahashi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "題 名Context Analysis with Large Language ModelsLarge language models (LLMs) are crucial in context analysis within natural language processing.A central question within this is how LLMs capture context.This study explored the question by engaging in four context analysis tasks.These tasks, essential for understanding sentence-level semantics (intra-sentential analysis) and cross-sentence relations (inter-sentential analysis), include predicate-argument structure analysis, pronoun reference resolution, semantic textual similarity analysis, and discourse relation analysis.This study intended to identify the capabilities and limitations of LLMs in context analysis and understand how they interpret the context.", "source_url": "https:\/\/openalex.org\/W7146317840", "pdf_url": "https:\/\/tokyo-metro-u.repo.nii.ac.jp\/record\/2000801\/files\/takahashi_keigo_fulltext.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7146317840", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 954, "title": "Educational question generation and evaluation with large language models", "title_ar": "", "title_en": "Educational question generation and evaluation with large language models", "author": "Chun Yi Louis Bo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; ai-applications; llm; ai-education", "keywords": "", "abstract": "HKUST Electronic Theses Educational question generation and evaluation with large language models by Bo Chun Yi Louis thesis 2024 1 online resource (x, 41 pages) : illustrations (chiefly color) This thesis explores the application of Large Language Models (LLMs) in", "source_url": "https:\/\/openalex.org\/W4405916826", "pdf_url": "", "doi": "10.14711\/thesis-991013340350903412", "handle": "", "repository_name": "", "source_record_id": "W4405916826", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 955, "title": "Large language models for biological prediction and design", "title_ar": "", "title_en": "Large language models for biological prediction and design", "author": "Kollasch, Aaron", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Harvard University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; predictive-analytics; llm", "keywords": "", "abstract": "Predicting the functional impact of changes to biological sequences is a central challenge in genetics and biology. Beyond genetics, sequence-to-function mapping has key applications in the design of sequences for use as molecular tools, catalysts, and biotherapeutics. Fueled by decades of exponential increases in sequencing, experimental data, and computing power, generative modeling has emerged as a leading approach for both mutation effect prediction and protein design. Approaches originating in the natural language processing field such as large language models have shown particular usefulness as sequence models. In this thesis, I build generative models of biological sequences and demonstrate their application to problems in protein design and human genetics. In Chapter 1, I discuss how deep autoregressive models can be applied to predict mutation effects and design sequences that are challenging for alignment-based models, including indels, disordered proteins, and the highly variable complementarity determining regions of antibodies. In Chapter 2, I demonstrate how the combination of protein family-agnostic large language models with family-specific sequence models results in state-of-art predictive performance at mutation effect prediction. In Chapter 3, I show how to apply generative models at a proteome and population scale to identify pathogenicity among rare human genetic variants. In Chapter 4, I explore how antibody libraries designed by generative models can be improved with respect to desired features such as diversity and specificity. These results show how sequence models can predict, design, and optimize the functionality of biomolecules.", "source_url": "https:\/\/openalex.org\/W7112452486", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)", "source_record_id": "W7112452486", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 956, "title": "Writing assistant based on large language models", "title_ar": "", "title_en": "Writing assistant based on large language models", "author": "David Klement", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; transfer-learning; llm", "keywords": "", "abstract": "A standard approach to many natural language processing tasks is to take an existing, pre-trained large language model and fine-tune it for the given task. Such an approach leads to having a separate model for each task; furthermore, the fine-tuning must be repeated when upgrading to a new pre-trained model. This thesis explores the possibilities of using a single off-the-shelf model for three different tasks without fine-tuning. We present Preditor, a writing assistant that supports rewriting a sentence after replacing one of its words, suggesting continuations, and suggesting words that fit into a sentence. We design the system in a model-agnostic way, making it possible to upgrade to a new model with little effort. We also provide an extension that integrates the assistant into the text editor. 1", "source_url": "https:\/\/openalex.org\/W7135892003", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135892003", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 957, "title": "Using large language model as an assistant for conceptual modeling", "title_ar": "", "title_en": "Using large language model as an assistant for conceptual modeling", "author": "Dominik Prokop", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "This thesis explores the automation of domain modeling using large language models (LLMs), presenting an experimental LLM-based domain modeling assistant that collabo- rates with a human expert. The assistant provides modeling suggestions based on a given textual description of the domain of interest, aiding in the design of classes, attributes, and associations. We present a generic framework for domain modeling assistants and show how they can be implemented using an LLM. We demonstrate concrete config- urations of this framework and their implementations in a prototype application. We evaluated the effectiveness of the framework configurations across various domains. Our findings indicate that the assistant significantly enhances the efficiency of modeling while maintaining a reasonable quality of the outputs.", "source_url": "https:\/\/openalex.org\/W7135816504", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135816504", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 958, "title": "large language models", "title_ar": "", "title_en": "large language models", "author": "David Thomas Bernhart", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Klagenfurt", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; llm; fairness", "keywords": "", "abstract": "The adoption of business process modelling languages, such as Business Process Model and Notation (BPMN), is critical for organisations aiming to document, analyse, and optimise their business processes. With the emergence of large language models (LLMs), automated BPMN generation from textual process descriptions has become feasible, offering potential gains in efficiency and scalability. However, evaluating the quality of LLM-based system outputs for a specific use case typically relies on expert judgement. This process is resource-intensive, subjective, and difficult to scale, particularly in large enterprise environments.This thesis investigates whether expert ratings of LLM-generated BPMN models can be predicted using automatically measurable input (process description) and numeric output (model comparison) metrics, across varying levels of input anonymisation. The study uses 48 authentic process descriptions from a real-world ERP context, each subjected to three anonymisation protocols to reflect practical privacy requirements. BPMN models were generated for each process description using three different LLM-based systems. All system outputs were standardised for objective comparison and evaluated by domain experts, who rated model quality and usefulness on multiple dimensions.Deep learning models, combined with systematic feature selection, were trained to predict expert ratings from these metrics. Results show that process description metrics alone have limited predictive value. In contrast, the inclusion of output comparison metrics, particularly when combined with input features, improves prediction accuracy. Feature selection revealed that absolute or count-based metrics are more informative than ratio-based scores, and certain process description features only became important when combined with output data. The findings indicate that automated expert rating prediction for BPMN models is feasible to some extent, depending on the modelling scenario.", "source_url": "https:\/\/openalex.org\/W7143273742", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Klagenfurt", "source_record_id": "W7143273742", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 959, "title": "Large Language Models Unlearning", "title_ar": "", "title_en": "Large Language Models Unlearning", "author": "А. Д. Чурсин", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Ural Federal University", "university_source": "repository_host", "college": "", "department": "", "country": "Russia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; responsible-ai; ai-applications; llm; ai-cybersecurity", "keywords": "", "abstract": "Modern machine learning methods are actively applied across various fields of science and technology, delivering high-precision solutions and enabling the automation of complex processes. However, one of the key challenges in the development of these technologies is the need for selective removal of information from trained models. This necessity arises from both regulatory requirements for personal data protection—such as the General Data Protection Regulation (GDPR)—and practical needs, including model adaptation to changing conditions and the correction of errors in training data. In response to these challenges, the field of Machine Unlearning has emerged and gained momentum in recent years. It offers methods for correctly removing the influence of specific data without the need to retrain the entire model. The primary difficulty of machine unlearning lies in the fact that knowledge in large language models is not localized in specific parameters but is instead distributed throughout the model’s architecture. This makes targeted data removal a non-trivial task, requiring the development of specialized algorithms. This practice explores modern unlearning methods, including Knowledge Editing and Simplified Negative Preference Optimization (SimNPO), which aim to remove information with minimal impact on the overall performance of large language models. The relevance of this work stems from the pressing need to develop effective machine unlearning algorithms that meet regulatory requirements, protect models against attacks such as data poisoning, and enable adaptation to continuously evolving datasets. Currently, most existing methods either require full model retraining—which is computationally expensive—or fail to guarantee precise data removal. The development and evaluation of more efficient knowledge removal strategies is a critical task for ensuring the safety and reliability of large language models. Object of the study: Machine unlearning methods for large language models. Subject of the study: Principles and algorithms for removing information from trained models without full retraining. Purpose of the practice: To study, analyze, and implement machine unlearning methods using text-based models as examples. To achieve this goal, the following tasks must be completed: Analyze existing machine unlearning methods, including exact and approximate unlearning approaches. Study modern techniques for removing information in large language models (LLMs), including Negative Preference Optimization (NPO) and Simplified Negative Preference Optimization (SimNPO). Compare unlearning methods and knowledge editing techniques in terms of deletion accuracy and model performance retention. Evaluate the computational costs of different approaches and their applicability to real-world machine learning tasks.", "source_url": "https:\/\/openalex.org\/W7117205217", "pdf_url": "https:\/\/elar.urfu.ru\/bitstream\/10995\/147435\/1\/m_th_a.d.chursin_2025.pdf", "doi": "", "handle": "", "repository_name": "Electronic scientific archive of UrFU (Ural Federal University)", "source_record_id": "W7117205217", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 960, "title": "Modeling Large Language Model Cognition", "title_ar": "", "title_en": "Modeling Large Language Model Cognition", "author": "Eleni Angelou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "City University of New York", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; llm; xai", "keywords": "", "abstract": "This project builds three core directions in a philosophy of cognitive science for large language models (LLMs): 1) interpreting LLM behavior, 2) analyzing the system in terms of Marr’s three levels, and 3) mapping the correspondence between behavioral outputs and the internals of the system. It consists of the following three chapters accordingly: Dennettian Stances and Large Language Models The aim of this chapter is to argue that there are convincing grounds for applying Dennett’s framework of stances to LLMs against worries that doing so produces “counterfeit people”. After introducing the intentional and design stances, I take sycophancy as a prototypical LLM behavior and consider how each stance might be used to describe and explain it. Having shown that neither stance alone is sufficient, I argue that the stances are best understood as explanatory and predictive strategies whose applicability to LLMs is pragmatic and epistemic, not metaphysical. That is, the question of whether to adopt the intentional or the design stance in a given context is a matter of explanatory choice, rather than an assessment of the intrinsic mental properties of LLMs. Three Levels for Large Language Model Cognition This chapter applies David Marr’s tri-partite analysis to large language models (LLMs). It offers a modified, pragmatist three-level framework for examining representative cases of explanations at the computational, algorithmic, and implementation levels. It seeks to make progress on the question of what kind of explanations would be necessary to effectively understand these systems. In response to that question, it argues that useful explanations are context-specific and that explanatory power does not necessarily increase when descending levels. The three-level approach should be perceived as a productive but leaky abstraction that nevertheless facilitates research for understanding and steering LLMs. The Complex Correspondence Between Behavior and Algorithm in Large Language Models Prominent research directions in large language model (LLM) interpretability assume a simple (1:1) correspondence between high-level behaviors, such as deception, and specific algorithmic structures within the neural network of the model. This, however, is not always warranted and perpetuates long-standing problems of operationalization in the cognitive sciences. The chapter instead proposes a complex correspondence view which models the behavior-algorithm relationship as a context-dependent and dynamic mapping. By drawing on the mechanistic modeling approach, it argues that complex cognitive phenomena in LLMs must be functionally decomposed into constituent sub-capabilities to be tractable for algorithmic analysis and highlights the need for non-human-centric cognitive ontologies to properly characterize these systems.", "source_url": "https:\/\/openalex.org\/W7167179328", "pdf_url": "https:\/\/academicworks.cuny.edu\/context\/gc_etds\/article\/8088\/viewcontent\/dissertation.pdf", "doi": "", "handle": "", "repository_name": "CUNY Academic Works (City University of New York)", "source_record_id": "W7167179328", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 961, "title": "Uncertainty in large language models", "title_ar": "", "title_en": "Uncertainty in large language models", "author": "Lukas Aichberger", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Johannes Kepler Universitat Linz", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; multimodal; llm", "keywords": "", "abstract": "Large language models (LLMs) are increasingly serving as a primary interface for accessing information and interacting with digital systems. Nonetheless, LLMs can generate text that includes incorrect or unsupported claims while being fluent, coherent, and presented as factual. This creates a fundamental reliability issue: the generated text alone often does not reveal whether it can be trusted. Such unsupported factual claims are commonly referred to as LLM hallucinations. Detecting hallucinations is essential for deciding when a generated text can be trusted and when the system should instead abstain from answering, retrieve further evidence, or defer to a human. A principled approach to hallucination detection is uncertainty quantification, as elevated uncertainty is associated with a higher risk of hallucinations. However, estimating uncertainty in LLMs is challenging because uncertainty depends on the model’s predictive distribution over possible output sequences. Since LLMs stochastically generate output sequences token by token, this predictive distribution is high-dimensional and generally intractable to compute exactly. Thus, uncertainty estimation must rely on tractable approximations of this high-dimensional distribution. Developing effective approximations is a key enabler for the reliable deployment of LLM-based systems. In this cumulative thesis, we address the challenges posed by high-dimensional distributions to advance uncertainty estimation. We first establish foundational principles in the context of classical classification: our method QUAM demonstrates that uncertainty estimation benefits from targeted exploration, explicitly searching for samples that most influence the estimate. We then translate this insight to LLMs through SDLG, a method that improves uncertainty estimation by sampling output sequences that are most informative about the underlying distribution. Furthermore, we show that competitive uncertainty estimates can also be obtained without sampling multiple output sequences by leveraging a single-sequence measure: the negative log-likelihood of the most likely output sequence, approximated through our method G-NLL. This provides a highly efficient and practical alternative to sampling-based uncertainty estimation in LLMs. While this thesis primarily focuses on the internal reliability of LLMs through uncertainty estimation, it also investigates the external reliability of LLM-based systems deployed as multimodal agents that observe and act within their environment. To this end, we introduce Malicious Image Patches (MIPs), demonstrating that multimodal agents can be compromised into executing malicious actions when such patches appear in their visual observations. By exposing this vulnerability, this work highlights the need for safeguards against external failure modes and broadens the thesis from the internal to the external reliability of systems built around LLMs. Overall, the core contributions of this thesis advance uncertainty estimation in LLMs and thereby improve uncertainty-based hallucination detection. Complemented by insights into malicious failure modes, these contributions provide a principled foundation for improving the internal and external reliability of LLM-based systems.", "source_url": "https:\/\/openalex.org\/W7169636052", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University Library Linz repository (Johannes Kepler Universitat Linz)", "source_record_id": "W7169636052", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 962, "title": "Unstructured data for large language models", "title_ar": "", "title_en": "Unstructured data for large language models", "author": "Aleksandra Piktus", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Sapienza University of Rome", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; transfer-learning; predictive-analytics; llm", "keywords": "", "abstract": "In recent years, we have witnessed an impressive rise in the ubiquity of large language models (LLMs). Although their fundamental objective, predicting the most probable next word in a sequence, has remained unchanged, the models themselves have expanded dramatically in scale and capability, becoming the dominant paradigm in Natural Language Processing (NLP). Progress has been marked by the development of increasingly sophisticated evaluation benchmarks on one hand and by a growing demand for vast amounts of training data on the other. In this thesis, we examine how unstructured, primarily web-based data is utilized in LLM pre-training and fine-tuning. We investigate two principal roles that large textual corpora play within these models: first, as a source of world knowledge through retrieval augmentation, and second, as pre-training data. We begin by demonstrating how retrieval from large, unstructured web corpora can enhance performance on open-domain tasks, paving the way towards assistants capable of supporting humans in solving complex, knowledge-intensive problems. Next, we address the challenge of improving the robustness of pre-training data through the development of tools that enable qualitative analysis of massive text collections. Finally, we explore potential avenues for model scaling under data-constrained conditions, anticipating a future in which the entirety of publicly available web text may no longer suffice to meet the demands of ever-larger language models.", "source_url": "https:\/\/openalex.org\/W7134876569", "pdf_url": "", "doi": "", "handle": "", "repository_name": "IRIS Research product catalog (Sapienza University of Rome)", "source_record_id": "W7134876569", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 963, "title": "On the creative application of large language models", "title_ar": "", "title_en": "On the creative application of large language models", "author": "Max Peeperkorn", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Kent", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; transfer-learning; llm", "keywords": "", "abstract": "Large language models (LLMs) have become a common sight in day-to-day activities and have found use for a wide range of tasks, many of them of a creative nature. While not perfect, these models have enabled automation of tasks that were previously difficult to automate, such as generating fluent narratives or using them as creative evaluators. But to what extent can pure large language models perform creative tasks? This dissertation explores and evaluates the use of LLMs without augmentations for creative applications, specifically in the domain of narrative generation. The first contribution of this dissertation is an analysis of LLMs and their creative po- tential, including evaluation using three established CC evaluation frameworks: \"Mere Generation\", The Creative Tripod, and FACE. I argue that it is inappropriate to evaluate LLMs using these frameworks, primarily because their \"process\" is simple, yet they can perform complex tasks. Moreover, these frameworks often require explicitly modelled features to argue for the creativity of the system. However, for LLMs, their abilities are implicit in their learned parameters. Secondly, I shift focus to empirical evaluation to investigate a simple yet common claim in popular media and academic literature: is temperature the universal creativity parameter of pure large language models? I have designed a novel experimental method based on theories from cognitive science, and reframed the temperature zero story as the exemplar story for a given prompt. This exemplar enables comparison in a human evaluation experiment to understand the impact of the temperature hyperparameter. The results indicate that this claim is far more nuanced and weaker than is often presented. It appears higher temperature enable more exploration by increasing the chance of generating more variety, but it does not allow access to a significantly larger slice of the probability distribution. This observation raises questions regarding the output diversity of LLMs, and particularly, those trained for instruction following. The third contribution of this dissertation is two- fold. First, I investigate the gap in output diversity between instruction-tuned models and their corresponding base models. While this phenomenon has been observed before, I confirm its existence in the domain of narrative generation and demonstrate further findings on how intermediate fine-tuning steps impact the output diversity. Secondly, I present a novel decoding strategy, conformative decoding, motivated by the diversity gap that aims to reintroduce some of the lost output diversity. Conformative decoding works by mixing the next-token distribution of the base model into the next-token distribution of the instruction-tuned model. The computational results show a slight increase in output diversity. In contrast, the human evaluation experiment reveals no preference for either the baseline or conformative decoding. Assessing the diversity of a set of texts is a complex task, and the difference in diversity may be too subtle to detect or not meaningful to humans. LLMs are powerful tools, and we have only begun understanding their creative potential. The findings in this dissertation show the limits of pure LLMs performing creative tasks and our ability to evaluate them using current methods. However, based on the findings, I believe there is much potential for future work on using multi-agent setups that view creativity as a social phenomenon.", "source_url": "https:\/\/openalex.org\/W7221204651", "pdf_url": "https:\/\/kar.kent.ac.uk\/116708\/1\/peeperkorn2026phdfinal.pdf", "doi": "", "handle": "", "repository_name": "Kent Academic Repository (University of Kent)", "source_record_id": "W7221204651", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 964, "title": "Transcreation Applied to Large Language Models", "title_ar": "", "title_en": "Transcreation Applied to Large Language Models", "author": "Vieira, Inês Mendes", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "The aim of this internship report is to analyze the potential of transcreation through prompt engineering in Large Language Models in the particular cases of customer support and marketing. This report explores prompt engineering as an act of transcreation, delving into how it influences the output generated by these models. In a first instance, this report seeks to evaluate the potential of transcreation through prompt engineering in customer support based on translation error data obtained from annotations. In a second instance, it seeks to explore how prompt engineering improves upon outputs when considering transcreation in marketing. Finally, it considers the practical applications of the findings obtained from both instances of research, customer support and marketing, as well as how they may be harnessed into further future development, with results enforcing the idea that this is a feasible area of research with promising applications", "source_url": "https:\/\/openalex.org\/W7112047225", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7112047225", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 965, "title": "Large language model for programming by example", "title_ar": "", "title_en": "Large language model for programming by example", "author": "Shuning Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Illinois at Urbana-Champaign", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; generative-ai; few-zero-shot; llm", "keywords": "", "abstract": "Programming by Example (PBE) is a technique in which the system generates a program to automate complex transformation, with the user simply providing input and output example pairs.While traditional PBE systems like FlashFill and Foofah have demonstrated strong ability in program synthesis, they are often restrained from domain-specific tasks and lack adaptability across varied data types.With the emergence of Large Language Models (LLMs) and their reasoning and code generation ability, there is the opportunity to enhance PBE tasks with LLMs.This thesis investigates how LLM, specifically GPT 4.0, is able to perform PBE-style data wrangling tasks.Three methods are proposed to improve LLM performance on the tasks: (1) zero-shot prompt engineering, (2) loop-based verification prompting, and (3) a hybrid framework combining LLMs with traditional PBE models.Experiments and evaluation on benchmark dataset Foofah and Prose show that LLM not only has the ability to generalize across different types of data but also achieves higher accuracy than the traditional systems, with the hybrid method reaching the highest 85% overall accuracy on the Foofah dataset.This work highlights both the promise and current limitations of LLM on the PBE style tasks and offers potential future exploration of more robust LLM-driven reasoning and program synthesis.", "source_url": "https:\/\/openalex.org\/W7220782950", "pdf_url": "https:\/\/www.ideals.illinois.edu\/items\/136244\/bitstreams\/445341\/data.pdf", "doi": "", "handle": "", "repository_name": "Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)", "source_record_id": "W7220782950", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 966, "title": "Large Language Model Evaluation", "title_ar": "", "title_en": "", "author": "Corredor Casares, Joel", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2025, "language": "es", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "El objetivo de este proyecto es desarrollar una herramienta para el grupo de investigación EduSTEAM capaz de evaluar modelos de lenguaje grande para, a futuro, integrarlo con el asistente que están desarrollando. Se ha adaptado un prototipo hecho por Marc capaz de extraer conversaciones de modelos y poder tratar con ellas, mejorándolo y añadiendo funcionalidades adicionales. A partir de esta base, se ha implementado un sistema de creación y exportación de datasets, así como la funcionalidad de lanzarlos contra un modelo específico para, seguidamente, poder evaluar los resultados dados. Para todo esto se ha creado una base de datos completamente nueva además de una interfaz gráfica para facilitar la interacción con el usuario", "source_url": "https:\/\/openalex.org\/W7112464512", "pdf_url": "https:\/\/upcommons.upc.edu\/bitstreams\/7fc3b013-c688-4c9d-97f3-5dfa4d539003\/download", "doi": "", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W7112464512", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 967, "title": "In Context Attribution for the Generation of Large Language Model", "title_ar": "", "title_en": "In Context Attribution for the Generation of Large Language Model", "author": "Quoc Trinh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "Large Language Models (LLMs) usually fall into the problem of hallucination and lack of reliable references for their generated answers. The primary cause of these limitations is the context provided for the model's input, and the lack of robustness of the LLM. For this reason, in this thesis, I explore the efficacy of the existing attribution methods for large language models, as well as their failure conditions.", "source_url": "https:\/\/openalex.org\/W7204018950", "pdf_url": "https:\/\/aaltodoc.aalto.fi\/bitstreams\/228f1634-284e-4820-bd63-5d04f41a14b0\/download", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W7204018950", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 968, "title": "Large language models for causal inference", "title_ar": "", "title_en": "Large language models for causal inference", "author": "Omar Ait Lhouss", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; generative-ai; responsible-ai; llm; xai; fairness", "keywords": "", "abstract": "Causal inference provides essential tools for understanding how interventions influence outcomes, supporting evidence-based decision-making across disciplines such as medicine, economics, and the social sciences. At the same time, large language models (LLMs) have rapidly advanced, offering capabilities in text generation, coding, and knowledge synthesis. This thesis examines the overlap of these two areas by looking at the uses of LLMs in causal inference. The systematic review was based on PRISMA principles and included the 2017 to 2024 publications in Scopus, arXiv, PubMed, and the ACL Anthology. Eighty studies were enclosed and examined under four classifications namely causal discovery, extracting causeeffect relations, experimental design support, and causal code generation. A GPT-based assistant prototype was also created in order to test systematic review workflows with the help of AI. The results suggest that LLMs might facilitate applied researchers, making it faster to do things like DAG generation, hypothesis generation, and apply causal inference libraries. Nonetheless, interpretability, robustness, generalization, and reproducibility is still a challenge. The assistant enhanced the efficiency in screening and synthesis although it also brought to the fore risks of model dependency and human bias. All in all, the research provides the answer: LLMs can be a strong tool to use as an augmented research assistant in the process of causal analysis, but only when their claims are justified by transparent and reproducible practice.", "source_url": "https:\/\/openalex.org\/W7132031801", "pdf_url": "https:\/\/ebuah.uah.es\/dspace\/bitstream\/10017\/66240\/4\/TFM_Ait_Lhouss_2025.pdf", "doi": "", "handle": "", "repository_name": "e_Buah", "source_record_id": "W7132031801", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 969, "title": "The Geometry of Concepts in Large Language Models", "title_ar": "", "title_en": "The Geometry of Concepts in Large Language Models", "author": "Kiho Park", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Chicago", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; responsible-ai; llm; xai", "keywords": "", "abstract": "This dissertation develops a mathematical foundation for the linear representation hypothesis, the informal idea that high-level semantic concepts are encoded as directions in the representation spaces of large language models. Despite growing empirical evidence, a rigorous formulation of what linear representation means and how the geometry of representation spaces relates to the semantics of language has been lacking. This work addresses this gap through three interrelated studies. First, we formalize the linear representation of binary concepts and introduce the causal inner product, which connects orthogonality to causal separability. Second, we extend the framework to categorical concepts and show that hierarchical relations are encoded through orthogonality. Third, by identifying the geometry of representation spaces as a Bregman (dually flat) geometry induced by KL divergence, we develop dual steering, a method that modifies a target concept while minimizing off-target interference. Together, these contributions provide a unified framework for understanding how semantic structure is encoded in the geometry of representations. They offer both theoretical insights into AI interpretability and practical tools for controlled model steering.", "source_url": "https:\/\/openalex.org\/W7160182453", "pdf_url": "", "doi": "10.6082\/0c3td-jh725", "handle": "", "repository_name": "Knowledge@UChicago (University of Chicago)", "source_record_id": "W7160182453", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 970, "title": "Cognitive Behaviours of Large Language Models", "title_ar": "", "title_en": "Cognitive Behaviours of Large Language Models", "author": "Bryan Wilie", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; computer-vision; nlp; reinforcement-learning; transfer-learning; llm", "keywords": "", "abstract": "Large language models (LLMs) have reshaped natural language processing, demonstrating remarkable reasoning across diverse tasks. Their rapid real-world integration has fostered growing reliance on their perceived cognitive abilities, with users expecting models to adapt to evolving information, perform consistently across languages, and handle precise computations. However, a central question remains: to what extent do LLMs exhibit consistent cognitive behaviors under dynamic, diverse real-world conditions? This thesis explores LLM use cases in practical settings requiring adaptability, inclusivity across multilingual users, and computational precision. First, we study LLMs’ cognitive limitations in revising inferences when presented with new, contradictory, or clarifying information. Existing benchmarks assume complete context, neglecting information’s dynamic nature. We introduce a sequential evaluation framework simulating scenarios requiring belief revision, revealing that strong LLMs often cannot revise prior beliefs. We identify a fundamental trade-off: models adept at updating underperform when no revision is necessary, and vice versa—exposing key tensions in model adaptability. Second, we analyze whether LLM cognitive behaviors remain consistent across languages. Despite training on large multilingual corpora, LLM capabilities remain uneven across languages. Analyzing internal representations, we uncover inconsistent interlingual alignment and show that monolingual fine-tuning further disrupts this alignment. To mitigate this, we propose a selective freezing strategy during fine-tuning that preserves interlingual representation alignment and improves cross-lingual generalization. Third, we address LLM limitations in tasks requiring precise computation, particularly in LLM-based judges for domains demanding mathematical exactness. Programmatic tools retain advantages over purely language-based reasoning. We develop a multi-turn reinforcement learning framework enabling LLM judges to selectively invoke an external Python interpreter as part of their reasoning process. This framework enables judges to learn beneficial tool invocation strategies, delegating sub-problems requiring exact computation to code execution. Judges integrate execution outputs into their chain-of-thought reasoning, mitigating errors inherent to text-based computation and enhancing judgment accuracy.", "source_url": "https:\/\/openalex.org\/W7135076034", "pdf_url": "", "doi": "10.14711\/thesis-hdl169569", "handle": "", "repository_name": "", "source_record_id": "W7135076034", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 971, "title": "Large Language Model (LLM) Powered", "title_ar": "", "title_en": "Large Language Model (LLM) Powered", "author": "Ruqaya AlAsoomi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Bahrain", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; transfer-learning; few-zero-shot; llm", "keywords": "", "abstract": "Scholarship evaluation is a labor-intensive process in which human reviewers are required to assess a large amount of applicant documents. This study presents the design,implementation, and empirical evaluation of a scholarship system that integrates large language model (LLM) based scoring within a full web application. The system supports the full scholarship pipeline across three user roles including applicant, reviewer,and admin. The experimental component investigates the capability of LLMs to score scholarship personal statements and resumes against a structured rubric in zeroshot and fine-tuned settings. Evaluation of LLaMA, Gemma, Qwen, and Mistral were done in zero-shot settings, with LLaMA subsequently fine-tuned using QLoRA on A100 GPU. Results were assessed using Quadratic Weighted Kappa, Intraclass Correlation Coefficient, Pearson, Spearman, RMSE, and MAE. The fine-tuned model achieved a QWK of 0.751 for resume scoring and 0.602 for personal statement scoring, with two criteria exceeding the 0.80 strong agreement threshold on resume evaluation. Findings were compared againsted published benchmarks in the automated essay scoring. The study contributes to the domain of LLM-based scoring with a proof-of-concept deployment architecture that bridges the gap between commercial scholarship management platformed and automated scoring research.", "source_url": "https:\/\/openalex.org\/W7213395773", "pdf_url": "", "doi": "10.58014\/aubh.33838927", "handle": "", "repository_name": "American University of Bahrain", "source_record_id": "W7213395773", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 972, "title": "Quality Assurance in Large Language Models", "title_ar": "", "title_en": "Quality Assurance in Large Language Models", "author": "Song, Da", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Alberta", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; medical-ai; responsible-ai; data-mining; predictive-analytics; llm; xai; fairness", "keywords": "", "abstract": "Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in natural language processing (NLP) and software engineering (SE). However, their increasing adoption has raised critical concerns about trustworthiness, including robustness under distribution shifts, vulnerability to hallucinations, and misalignment between confidence and accuracy. These issues are exacerbated by the complex and probabilistic nature of LLMs, which makes it difficult for developers to observe, interpret, and address their failures. To bridge this gap, this thesis presents a series of tools and frameworks that enable a more transparent and actionable understanding of LLM trustworthiness. Together, these works follow a methodological arc that moves from interactive exploration to internal structure modeling and finally to external behavioral correction. The first contribution, DeepLens, introduces a human-centered system that supports interactive diagnosis of out-of-distribution (OOD) data in large text corpora. By integrating clustering-based analysis and neuron-level highlighting, {\\deeplens} empowers developers to detect, categorize, and interpret OOD issues that threaten model reliability. Building on this foundation, the second contribution, LUNA, proposes a white-box framework that models LLM behavior using abstract probabilistic structures. By mapping hidden states to interpretable semantic concepts, LUNA enables structured evaluation of trustworthiness dimensions such as hallucination and adversarial robustness, providing insights grounded in both semantic and model-level perspectives. The final contribution addresses the limitations of white-box methods in black-box scenarios. It introduces a confidence calibration framework that quantifies and corrects model confidence based on semantic-preserving behavioral variations. By leveraging agreement ratios, entropy, and stability features from multiple LLM outputs, this method learns a regression-based correction factor to improve alignment between model confidence and factual correctness, without requiring model internals. Together, these three works provide a practical toolchain to enhance the transparency and trustworthiness of LLMs, spanning interactive user interfaces, interpretable model-based analysis, and scalable confidence calibration.", "source_url": "https:\/\/openalex.org\/W7105991970", "pdf_url": "", "doi": "10.7939\/83296", "handle": "", "repository_name": "ERA: Education and Research Archive (University of Alberta)", "source_record_id": "W7105991970", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 973, "title": "Truth, meaning, and intention in Large Language Models", "title_ar": "", "title_en": "Truth, meaning, and intention in Large Language Models", "author": "Christian Lars Odenwald", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "The arrival of commercially available chatbots that are able to produce human-like language has made philosophical thought experiments come to life. Such language models produce mostly coherent language, but how do their outputs compare to what we usually assume about language uttered by a human agent? In particular, can their outputs be assumed to be related to truth, and can they be considered meaningful in any sense? In the first part of the thesis, some basic philosophical concepts are explored to provide a background, such as the nature of truth, meaning, and intention. In the second part, a high-level description of Large Language Models is provided. In the third part, these subjects meet, and the outputs of Large Language Models are evaluated according to philosophical concepts. A major issue is the question of grounding: are the outputs of these models grounded in the real world? One argument from different areas of literature is developed and refuted. Following that, the demand for intention is criticized, and a weakening of this principle is suggested. A great obstacle for a meaningful interpretation is the tokenization process, which is discussed as well. Another contribution is a sketch of how the outputs of Large Language Models could be interpreted in truth-conditional semantics. Lastly, future approaches for the philosophically informed study of Large Language Models are summarized.", "source_url": "https:\/\/openalex.org\/W7132752319", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ePLUS", "source_record_id": "W7132752319", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 974, "title": "Value estimation in Large Language Models", "title_ar": "", "title_en": "Value estimation in Large Language Models", "author": "Naga Lakshmi Prathyusha Guduru", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; reinforcement-learning; llm", "keywords": "", "abstract": "Large language models (LLMs) are commonly post-trained with reinforcement learning to improve reasoning performance. A central design choice in this process is whether to estimate advantages using a learned value function or directly from Monte Carlo returns. Value-based estimators can reduce variance and improve credit assignment but require training an additional critic, whereas Monte Carlo estima- tors are simpler and avoid this computational cost. This thesis studies the utility of value models during both training and inference. We first conduct controlled com- parisons between value-based and Monte Carlo estimators across models, tasks, and optimization settings. No estimator is uniformly superior: Monte Carlo methods can achieve competitive peak performance, while PPO often provides stronger average performance or lower sensitivity to configuration selection. Because surrounding op- timization choices can have a larger effect than the estimator, learning a critic does not consistently yield a better final policy. We then introduce value decoding, a test-time generation method that reuses the trained critic as a process supervisor. At each decoding step, the actor generates multiple candidate sub-trajectories, the critic scores them, and the highest-valued candidate is selected to continue the response. Across our experiments, value de- coding consistently improves upon the actor’s pass@1 performance. To understand 4 what determines its effectiveness, we systematically study the influence of additional critic training, critic size, training objective, and value-target discretization. Longer critic training does not reliably improve value decoding, whereas larger critics provide the most consistent gains. Cross-entropy training is competitive with MSE and can reduce variability, while performance is largely insensitive to the number of discretiza- tion bins. Experiments on MATH500 with models ranging from 1.5B to 7B param- eters demonstrate that trained critics can improve test-time reasoning, although the magnitude of these gains depends strongly on critic quality.", "source_url": "https:\/\/openalex.org\/W7224889608", "pdf_url": "", "doi": "10.26153\/tsw\/65839", "handle": "", "repository_name": "Texas Digital Library (University of Texas)", "source_record_id": "W7224889608", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 975, "title": "On the Architecture and Reasoning of Large Language Models", "title_ar": "", "title_en": "On the Architecture and Reasoning of Large Language Models", "author": "Tianhao Chen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; responsible-ai; transfer-learning; transformers; llm; fairness", "keywords": "", "abstract": "This thesis investigates two complementary axes for improving Large Language Models (LLMs): the architecture used during pretraining, and the reasoning behavior elicited during post-training. The first part of the thesis addresses well-documented optimization pathologies of deep Transformer language models that limit the effective utilization of depth. We first present Gradient-Preserving Activation Scaling (GPAS), a residual-stream plugin that combats the exponential growth of activation variance — the root cause of residual-shortcut dominance over sub-layer outputs and the degraded learning of deeper layers, most acute in the Pre-LayerNorm (Pre-LN) Transformer — by scaling down intermediate activations while preserving their gradients. GPAS avoids the gradient-vanishing pathology of naive downscaling and yields consistent gains at model sizes from 71M to 1B parameters across Pre-LN, Post-LN, DeepNorm, Sandwich-LN, Mix-LN, and LayerNorm-Scaling. Building on this line of work, we then introduce Progressive Residual Warmup (ProRes), a depth-aware optimization curriculum that gradually warms up each layer’s residual contribution from zero to one, with deeper layers waiting longer than shallow ones; this “early layers learn first” inductive bias stabilizes optimization across initialization and normalization schemes and produces a distinctive trajectory that yields faster convergence, stronger generalization, and better downstream performance. The second part of the thesis turns to reasoning. We present Double-Checker, a self-critical fine-tuning framework that teaches slow-thinking, long chain-of-thought LLMs to explicitly critique their own intermediate solutions and iteratively refine them until the model judges its answer correct under its own critique. With only 1,730 carefully curated self-critical instances, Double-Checker substantially improves the reasoning accuracy and self-correction reliability of strong slow-thinking baselines. Together, these contributions improve modern LLMs along two complementary axes: macro-architectural interventions that improve training stability and depth utilization during pretraining, and lightweight self-critical fine-tuning that elicits more reliable reflective reasoning after pretraining.", "source_url": "https:\/\/openalex.org\/W7213504516", "pdf_url": "", "doi": "10.14711\/thesis-hdl174652", "handle": "", "repository_name": "", "source_record_id": "W7213504516", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 976, "title": "LLM for everyone : representing the underrepresented in large language models", "title_ar": "", "title_en": "LLM for everyone : representing the underrepresented in large language models", "author": "Samuel Cahyawijaya", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "HKUST Electronic Theses LLM for everyone : representing the underrepresented in large language models by Samuel Cahyawijaya thesis 2024 1 online resource (xiv, 203 pages) : color illustrations, color maps Natural language processing (NLP) has witnessed a profound impact of", "source_url": "https:\/\/openalex.org\/W4405916870", "pdf_url": "https:\/\/arxiv.org\/pdf\/2409.13897", "doi": "10.14711\/thesis-991013340351603412", "handle": "", "repository_name": "", "source_record_id": "W4405916870", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 977, "title": "Large Language Model Evaluation", "title_ar": "", "title_en": "Large Language Model Evaluation", "author": "優介 坂井", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "Large language models (LLMs) have become foundational resources in the natural language processing field.The capabilities of LLMs in inference, natural language understanding (NLU), and their pre-trained knowledge are widely utilized in various applications.To fully leverage these capabilities, it is essential to properly evaluate them.However, it is very challenging to evaluate LLMs accurately.For example, when evaluating an LLM's inference capabilities to infer unseen facts from seen ones, it may rely on memorized knowledge from pre-training rather than pure inference, due to the pre-training datasets being curated from web data, which can lead to contamination with related knowledge.Therefore, traditional evaluation frameworks for inference capabilities struggle to capture the pure inference capability of LLMs, independent of their pre-trained knowledge.Furthermore, when evaluating the NLU capabilities of LLMs, we should consider the stability in outputs for various input prompts.Finally, constructing evaluation datasets involves significant costs of financial, time, and human labor.Especially when focusing on multilingual datasets, there is a limitation in the availability of annotators, so most datasets are machine-translated from English.However, due to cultural differences and noise introduced by translation, these evaluations are unsuitable for language-specific evaluation.This disserta-", "source_url": "https:\/\/openalex.org\/W7143759745", "pdf_url": "https:\/\/naist.repo.nii.ac.jp\/record\/2000625\/files\/R019175.pdf", "doi": "10.34413\/dr.02100", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7143759745", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 978, "title": "Tokenization algorithms of large language models", "title_ar": "", "title_en": "Tokenization algorithms of large language models", "author": "О. А. Довгань ; К. В. Овчинников ; K. V. Ovchynnykov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Vinnytsia National Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "This work is dedicated to the review of algorithms for text tokenization of modern large language models", "source_url": "https:\/\/openalex.org\/W7213717269", "pdf_url": "https:\/\/ir.lib.vntu.edu.ua\/bitstream\/123456789\/43691\/2\/167196.pdf", "doi": "", "handle": "", "repository_name": "Repository of Vinnytsia National Technical University (Vinnytsia National Technical University)", "source_record_id": "W7213717269", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 979, "title": "Failure Modes of Large Language Models", "title_ar": "", "title_en": "Failure Modes of Large Language Models", "author": "Soňa Milová", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; generative-ai; responsible-ai; transformers; llm; fairness", "keywords": "", "abstract": "Failure Modes of Large Language Models Soňa Milová Abstract Diploma thesis \"The failure modes of Large Language Models\" focuses on addressing failure modes of Large Language Models (LLMs) from the ethical, moral and security point of view. The method of the empirical analysis is document analysis that defines the existing study, and the process by which failure modes are selected from it and analysed further. It looks closely at OpenAI's Generative Pre-trained Transformer 3 (GPT-3) and its improved successor Instruct Generative Pre-trained Transformer (IGPT). The thesis initially investigates model bias, privacy violations and fake news as the main failure modes of GPT-3. Consequently, it utilizes the concept of technological determinism as an ideology to evaluate whether IGPT has been effectively designed to address all the aforementioned concerns. The core argument of the thesis is that the utopic and dystopic view of technological determinism need to be combined with the additional aspect of human control. LLMs are in need of human involvement to help machines better understand context, mitigate failure modes, and of course, to ground them in reality. Therefore, contextualist view is portrayed as the most accurate lens through which to look at LLMs as it argues they depend on the responsibilities,...", "source_url": "https:\/\/openalex.org\/W7135843042", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135843042", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 980, "title": "Large Language Models on Software Refactoring", "title_ar": "", "title_en": "Large Language Models on Software Refactoring", "author": "Mikko Metsola", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; llm", "keywords": "", "abstract": "The recent surge in popularity of large language models (LLM) such as OpenAI's GPT series of models has raised interest in the application of artificial intelligence (AI) in different fields, including software engineering. AI-powered programming assistants and code generation tools, namely GitHub Copilot, have decisively paved their way into common integrated development environments to provide feedback for software developers with programming tasks. These tasks may range from producing new software code, to providing suggestions for a programming task, to parsing through written code and analyzing it for potential improvements. Maintaining existing software is a very resource intensive task. Software programs are rarely written in a way that do not need adjustments later on. Addressing bugs and introducing new features are common tasks for developers, which require a reasonable understanding of the code they are working on. Refactoring is the process of rewriting software code in an attempt to streamline further development and maintenance or to improve the underlying design of the software without making changes to the overall functionality of the software. The thesis begins with an introduction to the topic of software quality. This aims to give a thorough understanding on which factors contribute to the quality of software. An overview of LLMs is presented as well as how they are generally applied to code generation. This thesis explores the extent to which a selection of four different LLMs can be utilized in code refactoring and what kinds of refactorings these LLMs are able to determine. This research is done by giving each LLM a piece of code to refactor. The outputs of these models are then evaluated on a variety of software metrics and commonly accepted code refactoring practices. With these results we can assess whether these language models could be applied in practice.", "source_url": "https:\/\/openalex.org\/W7132968924", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/160496\/2\/MetsolaMikko.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7132968924", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 982, "title": "Bias in large language models", "title_ar": "", "title_en": "", "author": "Mia Sambolec", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Zagreb Faculty of Humanities and SocialSciences", "university_source": "repository_host", "college": "", "department": "", "country": "Croatia", "governorate": "", "publication_year": 2024, "language": "sl", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; responsible-ai; llm; fairness", "keywords": "", "abstract": "Veliki jezični modeli su tehnologije kojima je popularnost porasla u zadnjih nekoliko godina. Zbog svojih sposobnosti za obradu upita u obliku teksta prigodni su za korištenje u razne svrhe te za obavljanje raznih vrsta zadataka. Najbolji primjer velikih jezičnih modela su tzv. AI agenti za razgovor ili chatbotovi koji mogu komunicirati s korisnicima na određen način. Budući da se veliki jezični modeli treniraju na velikoj količini podataka koji se nalaze na internetu, njihovi podaci za obuku često mogu sadržavati određene pristranosti koje se odnose na stereotipe i predrasude prisutne u društvu. Pristranosti koje veliki jezični modeli mogu prikazivati mogu dodatno pojačati već postojeće predrasude u društvu te uvrijediti određene skupine ljudi. Cilj ovog rada bio je istražiti je li pristranost prisutna kod velikih jezičnih modela. Istraživanje je provedeno kroz postavljanje upita tri chatbota: ChatGPT, Gemini i Copilot. Svakom chatbotu postavljeno je 10 upita na teme koje su od interesa za društvo poput financija, obrazovanja i zdravstva. Ukupno je postavljeno 30 upita te su odgovori chatbotova analizirani prema pet kriterija za evaluaciju chatbota koji uključuju: točnost, pouzdanost, učinkovitost, relevantnost i tečnost. Prema kriterijima najbolje je rezultate pokazao Copilot, a najlošije ChatGPT. Većina odgovora chatbotova nije ukazivala na pristranost, no dubljom analizom utvrđeno je da je u određenoj mjeri prisutna pristranost informacija, što ukazuje na to da chatbotovi daju pogrešne ili lažne informacije te da u većini slučajeva ne posjeduju najnovije informacije ili ih pogrešno interpretiraju. Ovakvi pogrešni odgovori mogu dovesti do lažnog informiranja korisnika i njegovim nezadovoljstvom. Stoga je na temelju analize odgovora utvrđeno da je potrebna daljnja revizija modela, kako bi se poboljšala informiranost chatbotova te smanjila pristranost informacija koje se daju korisniku.", "source_url": "https:\/\/openalex.org\/W7134592711", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ODRAZ (University of Zagreb Faculty of Humanities and SocialSciences)", "source_record_id": "W7134592711", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 983, "title": "Face recognition using Deep Learning", "title_ar": "", "title_en": "Face recognition using Deep Learning", "author": "Xavier Serra Alza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; face-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2596912164", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)", "source_record_id": "W2596912164", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 985, "title": "Develop Deep Learning-Based Face Recognition Model for Masked and Unmasked Faces", "title_ar": "", "title_en": "Develop Deep Learning-Based Face Recognition Model for Masked and Unmasked Faces", "author": "Tirhas Kebede", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Ethiopia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; medical-ai; face-recognition", "keywords": "", "abstract": "Traditional facial recognition systems are no longer as effective due to the widespread use of face masks. To overcome this difficulty, the study suggests a facial recognition (FR) system based on a unified framework specifically designed for partial facial occlusion scenarios that can reliably identify people wearing or not wearing face masks. It is built on the Inception ResNet V1 deep learning model and was trained using the Real-World Masked Face Dataset (RMFD) with 525 subjects. The proposed system outperforms existing methods like FaceNet, achieving a test accuracy of 98.3% through rigorous subject-level cross-validation, while maintaining real-time performance at 45 FPS with optimized model size of 96MB. The solution, which is optimized for real-time deployment, can be used for public health monitoring, access control, and security verification. In both masked and unmasked faces, experimental results show strong generalization and consistent accuracy, allowing for a smooth integration into safety-critical applications without sacrificing effectiveness.", "source_url": "https:\/\/openalex.org\/W7162192561", "pdf_url": "", "doi": "10.82589\/muir-1053", "handle": "", "repository_name": "Mekelle University Institutional Repository (MU-IR)", "source_record_id": "W7162192561", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 986, "title": "Face recognition by deep learning", "title_ar": "", "title_en": "Face recognition by deep learning", "author": "Yue Chun Wu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Northeastern University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; cnn; face-recognition; fairness", "keywords": "", "abstract": "This dissertation focuses on real world challenges when applying deep learning for face recognition. Face recognition aims to recognize people using their face images, which is a hot topic in computer vision field. Especially tons of face data exist on social media nowadays and yield tremendous real world applications. We explore five applications along with challenges applying deep learning algorithms. First, we aim to recognize the large number of people, which is the bottleneck for training a deep convolutional neural network of which the output is equal to the number of people. An independent softmax model is introduced to split the single classifier into several small classifiers, which decomposes the large scale training procedure into several medium training procedures which can be solved separately. Second, we study the low-shot face recognition problem, in which there is very limited number of training samples for some people to recognize. A hybrid classifier framework is presented with multiple classifiers to decompose a single classifier into multiple classifiers that each works well for a part of data. Third, feature representation learning with unbalance data is studied for the face verification application. A center-invariant loss is employed to regularize the deep representation learning. Forth, we study the kinship classification given deep representations. A latent adaptive subspace learning framework is proposed to model the family-wise constraint and person-wise constraint in the subspace based on deep representations. Fifth, we study the catastrophic forgetting problem in incremental learning system, especially for face applications with continuously adding more people in the recognition system. A bias correction model is presented together with knowledge distilling, which tackles the catastrophic forgetting problem that has bias towards new coming classes.", "source_url": "https:\/\/openalex.org\/W3130771731", "pdf_url": "https:\/\/repository.library.northeastern.edu\/files\/neu:m044pk554\/fulltext.pdf", "doi": "10.17760\/d20324043", "handle": "", "repository_name": "Digital Repository Service (Northeastern University)", "source_record_id": "W3130771731", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 987, "title": "Face recognition using deep learning", "title_ar": "", "title_en": "Face recognition using deep learning", "author": "Pop, Teodor Cristian ; Rodríguez Esteban, Lucas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "machine-learning; deep-learning; computer-vision; cnn; face-recognition", "keywords": "", "abstract": "The main goal of this project is to obtain the knowledge necessary to understand all the process behind a Face Recognition program. Starting by understanding what is an Artificial Neuron and its different parts, the concept of an Artificial Neural Network, specifically a Convolutional Neural Network and its different layers. This kind of Neural Network is a state-of-art in the field of computer vision. Understanding the concept of Machine learning and Deep learning, including the training and testing steps, which are the fundamental steps in all Deep Learning projects. This project was developed during an exchange program in the National Taiwan University of Science and Technologies, specifically in the department of Computer Science and Information Engineering. The methodology that we followed is the next, every two weeks we did a presentation in front of all members of our lab, and those presentations were divided in two parts, theoretical and practice. The theoretical parts contained a summary of papers and interesting information necessary to develop and understand the practical part. The practical part contained the code in Python and the results obtained, for instance, an image with a face detection. The duration of each presentations was around fifteen minutes plus questions. The final presentation was on 25th June, 2018. We are very proud about the results obtained, first of all because we have obtained the knowledge necessary to understand all the basic theory behind a Face Recognition system, secondly, we have learned a new programming language, Python and most importantly, we have done the training and testing of an Artificial Neural Network.", "source_url": "https:\/\/openalex.org\/W7111703248", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W7111703248", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 988, "title": "Face recognition using deep learning", "title_ar": "", "title_en": "Face recognition using deep learning", "author": "Zi Xen Ooi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; cnn; face-recognition; video-analysis", "keywords": "", "abstract": "Face recognition system is a technology accomplished at verifying or identifying a person from a video frame from a video source or a digital image. Multiple processing layers have been applied by deep learning to learn representations of data with multiple levels of feature extraction, which have achieved high accuracy to the real-world variations. Although the face recognition system has come a long way and its usage is crucial in several applications, it has remained a variety of challenges in face detection and recognition technologies, which including the pose variations, occlusions, facial expression changes, ageing of the face, illumination, etc. In this project, the real-time face recognition system is implemented using pretrained deep learning models with CCTV (Closed-Circuit Television) camera. The traditional CCTV is only good at recording and it is limited for campus safety and security nowadays. The face recognition system with CCTV camera can be used for controlling user access to physical locations of campus. The face recognition system does not require any kind of physical contact between the users and the device, which provide quick and convenient access to the authorized users. The developed face recognition system exploits the pre-trained Multi-task Cascaded Convolutional Network (MTCNN) model for face detection and the standard techniques with FaceNet embeddings as feature vectors for face recognition. The developed face recognition system was tested with numerous experiments to analyze its performance. Empirical results show the face recognition in uncontrolled environments is much more challenging than in controlled conditions.", "source_url": "https:\/\/openalex.org\/W3119380891", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3119380891", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 989, "title": "Application of deep learning tools for face recognition", "title_ar": "", "title_en": "", "author": "Karmen Santini", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Zagreb Faculty of Humanities and SocialSciences", "university_source": "repository_host", "college": "", "department": "", "country": "Croatia", "governorate": "", "publication_year": 2021, "language": "sl", "research_domain": "medical-ai", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; medical-ai; face-recognition; video-analysis", "keywords": "", "abstract": "Cilj ovog rada bio je prikazati postupak treniranja, prepoznavanja i zamjene lica primjenom alata za duboko učenje, koje predstavljaju najsuvremeniji pristup u području umjetne inteligencije i primjena neuronskih mreža prilikom učenja i definiranja lica, te ukazati na potencijalne stvarne prijetnje korištenja takve tehnologije, ali i pozitivne strane. Rad se sastoji od teorijskog i praktičnog dijela. U teorijskom dijelu definirani su pojmovi strojno učenje i duboko učenje i ključni elementi i razlike. Prikazana je struktura i način rada neuronske mreže, te osnovni principi rada koji se koriste za duboko učenje. Naglasak rada je na primjenama dubokog učenja te su prikazane tehnike koje koriste razne aplikacije u kojima je implementirano duboko učenje za prepoznavanje lica. U praktičnom dijelu rada provedeno je istraživanje primjenom alata za prepoznavanje lica. Računalni program FaceSwap primjenjuje princip dubokog učenja za stvaranje „dubokih lažnjaka“ (eng. deepfakes) u kojima algoritam uči glavne značajke lica te izvorna lica na slikama ili videozapisima zamjenjuje željenim licem neke druge osobe. Nakon prikaza sustava i analize načina rada, prikazan je praktičan dio zamjene lica u videozapisu te su na kraju prikazani rezultati. Posljednje poglavlje odnosi se na etičke principe i ukazuje na opasnosti i moguću zlouporabu te zaštitu od neprimjerene upotrebe.", "source_url": "https:\/\/openalex.org\/W7134758069", "pdf_url": "https:\/\/repozitorij.ffzg.unizg.hr\/islandora\/object\/ffzg:4829\/datastream\/PDF", "doi": "", "handle": "", "repository_name": "ODRAZ (University of Zagreb Faculty of Humanities and SocialSciences)", "source_record_id": "W7134758069", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 990, "title": "Deep Learning for Recognition of Objects, Activities, Faces, and Spatio-Temporal Patterns", "title_ar": "", "title_en": "Deep Learning for Recognition of Objects, Activities, Faces, and Spatio-Temporal Patterns", "author": "Amir Ghaderi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Texas Arlington", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "face-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; predictive-analytics; cnn; image-classification; face-recognition; ai-industry", "keywords": "", "abstract": "The origin of deep learning is in computer vision. However, researchers found that deep learning is a very powerful tool to solve many problems in other areas like forecasting, finance, human pose estimation, NLP, etc. Deep learning based methods showed a wonderful performance relate to other available methods. We have tried to improve deep learning methods and using them for solving problems in different areas. In this thesis, we will try to use the deep learning techniques for solving problems in different areas such as unsupervised learning, object classification, forecasting, cognitive behavior assessment and face recognition. In the computer vision part, a novel method for unsupervised feature learning for image classification was proposed in the thesis. Training a CNN needs huge amount of data. So, finding the methods to train CNN with unlabeled data is very promising. In the second part, we proposed a new deep learning based framework for forecasting. Forecasting is a challenging task and has many applications in finance, meteorology, etc. We have proposed a new framework for forecasting in cases that there are many nodes to generate data. One application of our framework is prediction of the wind speed for multiple stations around the country. Another problem that we have been using DL to solve is face recognition at scale. Face recognition is very demanding both in academic and industry. Also, we used DL to improve the performance of the system for behavioral assessment.", "source_url": "https:\/\/openalex.org\/W2897355615", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UTA ResearchCommons (University of Texas Arlington)", "source_record_id": "W2897355615", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 991, "title": "Face recognition using feature fusion and deep learning", "title_ar": "", "title_en": "Face recognition using feature fusion and deep learning", "author": "Xin Jiang Wei", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Ulster University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; face-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3113452238", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Ulster University Research Portal (Ulster University)", "source_record_id": "W3113452238", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 992, "title": "3 D Face Recognition Using Deep Learning", "title_ar": "", "title_en": "3 D Face Recognition Using Deep Learning", "author": "Siddiqui Mushtaq", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; face-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3189736959", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University", "source_record_id": "W3189736959", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 993, "title": "Face sketch recognition using deep learning", "title_ar": "", "title_en": "Face sketch recognition using deep learning", "author": "Fan, Liang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Cardiff University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; gnn; face-recognition", "keywords": "", "abstract": "Face sketch recognition refers to automatically identifying a person from a set of facial photos using a face sketch. This thesis focuses on matching facial images between front face photos and front face hand-drawn sketches, and between front face photos and front face composite sketches by software. Because different visual domains, different image forms, and different collection methods exist between the matching image pairs, face sketch recognition is more difficult than traditional facial recognition. In this thesis, three novel deep learning models are presented to increase recognition accuracy on face photo-sketch datasets. An improved Siamese network combined with features extracted from an encoder-decoder network is proposed to extract more correlated features from facial photos and the corresponding face sketches. After that, attention modules are proposed to extract features from the same location in the photos and the sketches. In the third method, in order to reduce the difference between different visual domains, the images are transferred into a graph to increase the relationship for different face attributes and facial landmarks. Meanwhile, the graph neural network is utilized to learn the weights of neighbors adaptively. The first is to fuse more image features from the Siamese network and encoder-decoder network for increased the recognition results. Moreover, the attention modules can fix the similarity positions from different domain images to extract the correlated features. The visualized feature maps exhibit the correlated features which are extracted from the photo and the corresponding face sketch. In addition, a stable deep learning model based on graph structure is introduced to capture the topology of the graph and the relationship after images have been mapped into the graph structure for reducing the gap between face photos and face sketches. The experimental results show that the recognition accuracy of our proposed deep learning models can achieve the state-of-the-art on composite face sketch datasets. Meanwhile, the recognition results on hand-drawn face sketch datasets exceed other deep learning methods.", "source_url": "https:\/\/openalex.org\/W3170082364", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ORCA Online Research @Cardiff (Cardiff University)", "source_record_id": "W3170082364", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "id": 994, "title": "Deep learning approach for face recognition with disguise variations", "title_ar": "", "title_en": "Deep learning approach for face recognition with disguise variations", "author": "Mohit Chawla ; Richa Singh ; Mayank Vatsa A", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; face-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3104001892", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3104001892", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 995, "title": "The development of a face recognition system based on deep learning", "title_ar": "", "title_en": "", "author": "FURKAN AKIN", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Fırat University", "university_source": "repository_host", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2019, "language": "tr", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; face-recognition", "keywords": "", "abstract": "Biyometri, insanların fiziksel ve davranışsal özelliklerini inceleyerek birbirinden ayırt edilebilmesini sağlayan bilim dalıdır. Biyometrik sistemler ise, insanların kimliklerini tespit etmek amacıyla, biyometrik özelliklerini inceleyerek oluşturulmuş sistemlerdir. Yüz tanıma, biyometrik sistemlerin alanlarından biridir ve kişileri belirlemek için yüzdeki göz, burun, ağız gibi ayrıntıları kullanır. Kamu güvenliğiyle ilgili artan endişeler, fiziksel ve mantıksal erişim için kimlik doğrulama gereksinimi ortaya çıkarmıştır. Bunun yanı sıra akıllı cihazların yönetimi ve yüz analizi ile sınıflandırma alanları otomatik yüz tanıma konusuna olan ilgiyi artırmaktadır. Literatürde yüz tanıma sistemlerinin geliştirilmesi hakkında birçok çalışma yapılmıştır. Bu tezde, her geçen gün kullanımı yaygınlaşan derin öğrenme yöntemlerinden bahsedilmiş ve bu yöntemlerle yüz tanıma siteminin geliştirilmesi amaçlanmıştır. Kullanılan veri setinden farklı örnek kümeleri alınmış ve bu örnekler ile hiper-parametreler denenmiştir. Başarımı arttıran hiper-parametreler kullanılarak cinsiyet ayrımı ve yüz tanıma uygulamaları yapılmıştır. Cinsiyet ayrımı uygulamasında başarı ,25, yüz tanıma uygulamasında ise başarı ,25'e kadar iyileştirilmiştir. Anahtar Sözcükler: Biyometrik Sistemler, Derin Öğrenme, Yüz Tanıma Sistemleri.", "source_url": "https:\/\/openalex.org\/W7217158709", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace - FIRAT (Fırat University)", "source_record_id": "W7217158709", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 996, "title": "Deep Learning Architectures for Heterogeneous Face Recognition", "title_ar": "", "title_en": "Deep Learning Architectures for Heterogeneous Face Recognition", "author": "Seyed Mehdi Iranmanesh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "West Virginia University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; predictive-analytics; face-recognition", "keywords": "", "abstract": "Face recognition has been one of the most challenging areas of research in biometrics and computer vision. Many face recognition algorithms are designed to address illumination and pose problems for visible face images. In recent years, there has been significant amount of research in Heterogeneous Face Recognition (HFR). The large modality gap between faces captured in different spectrum as well as lack of training data makes heterogeneous face recognition (HFR) quite a challenging problem. In this work, we present different deep learning frameworks to address the problem of matching non-visible face photos against a gallery of visible faces. Algorithms for thermal-to-visible face recognition can be categorized as cross-spectrum feature-based methods, or cross-spectrum image synthesis methods. In cross-spectrum feature-based face recognition a thermal probe is matched against a gallery of visible faces corresponding to the real-world scenario, in a feature subspace. The second category synthesizes a visible-like image from a thermal image which can then be used by any commercial visible spectrum face recognition system. These methods also beneficial in the sense that the synthesized visible face image can be directly utilized by existing face recognition systems which operate only on the visible face imagery. Therefore, using this approach one can leverage the existing commercial-off-the-shelf (COTS) and government-off-the-shelf (GOTS) solutions. In addition, the synthesized images can be used by human examiners for different purposes. There are some informative traits, such as age, gender, ethnicity, race, and hair color, which are not distinctive enough for the sake of recognition, but still can act as complementary information to other primary information, such as face and fingerprint. These traits, which are known as soft biometrics, can improve recognition algorithms while they are much cheaper and faster to acquire. They can be directly used in a unimodal system for some applications. Usually, soft biometric traits have been utilized jointly with hard biometrics (face photo) for different tasks in the sense that they are considered to be available both during the training and testing phases. In our approaches we look at this problem in a different way. We consider the case when soft biometric information does not exist during the testing phase, and our method can predict them directly in a multi-tasking paradigm. There are situations in which training data might come equipped with additional information that can be modeled as an auxiliary view of the data, and that unfortunately is not available during testing. This", "source_url": "https:\/\/openalex.org\/W3168980677", "pdf_url": "https:\/\/researchrepository.wvu.edu\/cgi\/viewcontent.cgi?article=9045&context=etd", "doi": "10.33915\/etd.8108", "handle": "", "repository_name": "The Research Repository @ WVU (West Virginia University)", "source_record_id": "W3168980677", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 997, "title": "Color and deep learning features in face recognition", "title_ar": "", "title_en": "Color and deep learning features in face recognition", "author": "Ze Lu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; face-recognition", "keywords": "", "abstract": "Face recognition (FR) has been one of the most active research topics in computer vision for more than three decades.It has been widely applied in various practical scenarios such as access control system, massive surveillance, human computer interaction, etc.A conventional FR system consists of four stages, face detection, face alignment, face representation and face matching.Compared with the other three, face representation significantly affects the performance of a FR system because it determines whether the FR system is robust to real world variations, such as illumination, pose, occlusion, etc.Most of the early FR works have been limited to using grayscale images.Recently, research efforts have been dedicated to incorporate color information into the feature extraction process for improving FR performance.Specifically, different feature representations are extracted from face images in a certain color space and then fused together for classification.Main challenges of such color FR tasks include how I would like to thank my supervisor, Professor Alex Kot, and my co-supervisor, Professor Xudong Jiang for their invaluable guidance, support and suggestions given to me in the past four years.I am lucky to have both of them as my supervisors.Professor Kot is a man of wise and broad knowledge.His suggestions to my research and to my career plan benefit me a lot.Professor Jiang spends much time discussing ideas, experiments, and paper writing with me.His devotion to research and approachable characteristics greatly inspire me.Their encouragements help me to overcome the difficulties encountered in my research.", "source_url": "https:\/\/openalex.org\/W4205852120", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/75855\/1\/main_thesis.pdf", "doi": "10.32657\/10356\/75855", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4205852120", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 998, "title": "Real-time Face Detection and Recognition Based on Deep Learning", "title_ar": "", "title_en": "Real-time Face Detection and Recognition Based on Deep Learning", "author": "Hui Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Auckland University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; ai-applications; cnn; face-recognition; video-analysis; ai-agriculture", "keywords": "", "abstract": "Face recognition is one of the most important applications in video surveillance and computer vision. However, the conventional algorithms of face recognition are susceptible to multiple conditions, such as lighting, occlusion, viewing angle or camera rotation. Therefore, face recognition based on deep learning can greatly improve the recognition speed and compatible external interference. In this thesis, we use convolutional neural networks (ConvNets) for face recognition, the neural networks have the merits of end-to-end, sparse connection and weight sharing. The purpose of this thesis project is to identify the name of different people based on location of the detected box of a face. Then, we can obtain recognition results with different confidence under various distances. This thesis presents different methods with comparisons, namely, comparing the training results and the test results of different parameters under the same model, training results of the same test video under different models. We find that the recognition accuracy of this model is mainly affected by face proportion and the number of samples. If we get larger proportion of a face on screen, then we have higher recognition accuracy; if we obtain much greater number of samples, we can get higher recognition accuracy. In this work, we first collect sufficient samples as our dataset and use the suitable model embedded in the platform Google TensorFlow to complete the training and test. We collected five different faces and obtained 500 images on each face as training set, each of which can be cropped and rotated by using 50 different angles of the picture having a human face, of which 40 for training, 10 for verification. The use of neural networks for face recognition improves the speed of recognition. The contributions of this thesis are: (1) The use of elliptical markers can identify a human face including rotation and position. (2) The confidence of human face recognition is mainly affected by the proportion of face occupied on the screen.", "source_url": "https:\/\/openalex.org\/W2896927716", "pdf_url": "https:\/\/openrepository.aut.ac.nz\/bitstreams\/986c7b85-f1b5-45aa-8023-0ed17772a752\/download", "doi": "", "handle": "", "repository_name": "Tuwhera (Auckland University of Technology)", "source_record_id": "W2896927716", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 999, "title": "Unraveling representations for face recognition : from handcrafted to deep learning", "title_ar": "", "title_en": "Unraveling representations for face recognition : from handcrafted to deep learning", "author": "Gaurav Goswami ; Richa Singh ; Mayank Vatsa A", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "India", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; face-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2931374629", "pdf_url": "", "doi": "", "handle": "", "repository_name": "INFLIBNET", "source_record_id": "W2931374629", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1000, "title": "Study of deep learning approaches to face recognition and object detection YOLO", "title_ar": "", "title_en": "Study of deep learning approaches to face recognition and object detection YOLO", "author": "Tanveen Kaur", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bridgeport", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; cnn; object-detection; face-recognition", "keywords": "", "abstract": "In this research, I have focused on deep learning approaches to face detection and recognition and object detection and recognition. This research has mainly focused on training the neural networks or other models with enough amounts of data so that it achieves desirable results. Starting with the basics of neural network where a neuron, the smallest unit in deep learning field, is defined and explained, I have elevated the research to the topic where I could recognize the face of a person or an object in an image. First the neural networks have been introduced in this research and then training of the neural networks has been attained with both the CPU and the GPU. In an algorithm called matrix form of back propagation, the use of multiple GPUs has been made along with CUDA kernel and cuBLAS library. Application of face recognition has been implemented using the pre trained model Facenet and Deep Convolutional Neural Networks. After analyzing neural networks and its facial recognition application, other approaches of deep learning have been given force to. I have used the library OpenCV along with deep learning approaches to implement face recognition, Image registration and YOLO Object Detection and Recognition with the tensor flow and keras environment supported by anaconda for python.", "source_url": "https:\/\/openalex.org\/W2942485040", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Bridgeport ScholarWorks (University of Bridgeport)", "source_record_id": "W2942485040", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1001, "title": "Deep Learning Based Face Detection and Recognition in MWIR and Visible Bands", "title_ar": "", "title_en": "Deep Learning Based Face Detection and Recognition in MWIR and Visible Bands", "author": "Suha Reddy Mokalla", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "West Virginia University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "machine-learning; deep-learning; computer-vision; generative-ai; transfer-learning; face-recognition; gan", "keywords": "", "abstract": "In non-favorable conditions for visible imaging like extreme illumination or nighttime, there is a need to collect images in other spectra, specifically infrared. Mid-Wave infrared (3-5 microm) images can be collected without giving away the location of the sensor in varying illumination conditions. There are many algorithms for face detection, face alignment, face recognition etc. proposed in visible band till date, while the research using MWIR images is highly limited. Face detection is an important pre-processing step for face recognition, which in turn is an important biometric modality. This thesis works towards bridging the gap between MWIR and visible spectrum through three contributions. First, a dual band based deep face detection model that works well in visible and MWIR spectrum is proposed using transfer learning. Different models are trained and tested extensively using visible and MWIR images and the one model that works well for this data is determined. For this model, experiments are conducted to learn the speed\/accuracy trade-off. Following this, the available MWIR dataset is extended through augmentation using traditional methods and generative adversarial networks (GANs). Traditional methods used to augment the data are brightness adjustment, contrast enhancement, applying noise to and de-noising the images. A deep learning based GAN architecture is developed and is used to generate new face identities. The generated images are added to the original dataset and the face detection model developed earlier is once again trained and tested. The third contribution is the proposal of another GAN that converts given thermal ace images into their visible counterparts. A pre-trained model is used as discriminator for this purpose and is trained to classify the images as real and fake and an identity network is used to provide further feedback to the generator. The generated visible images are used as probe images and the original visible images are used as gallery images to perform face recognition experiments using a state-of-the-art visible-to-visible face recognition algorithm.", "source_url": "https:\/\/openalex.org\/W3024705230", "pdf_url": "https:\/\/researchrepository.wvu.edu\/cgi\/viewcontent.cgi?article=8577&context=etd", "doi": "10.33915\/etd.7632", "handle": "", "repository_name": "The Research Repository @ WVU (West Virginia University)", "source_record_id": "W3024705230", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1002, "title": "Face recognition enhancement through the use of depth maps and deep learning", "title_ar": "", "title_en": "Face recognition enhancement through the use of depth maps and deep learning", "author": "Yaser Saleh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Loughborough University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; cnn; multimodal; object-detection; face-recognition", "keywords": "", "abstract": "Face recognition, although being a popular area of research for over a decade has still many open research challenges. Some of these challenges include the recognition of poorly illuminated faces, recognition under pose variations and also the challenge of capturing sufficient training data to enable recognition under pose\/viewpoint changes. With the appearance of cheap and effective multimodal image capture hardware, such as the Microsoft Kinect device, new possibilities of research have been uncovered. One opportunity is to explore the potential use of the depth maps generated by the Kinect as an additional data source to recognize human faces under low levels of scene illumination, and to generate new images through creating a 3D model using the depth maps and visible-spectrum \/ RGB images that can then be used to enhance face recognition accuracy by improving the training phase of a classification task.. With the goal of enhancing face recognition, this research first investigated how depth maps, since not affected by illumination, can improve face recognition, if algorithms traditionally used in face recognition were used. To this effect a number of popular benchmark face recognition algorithms are tested. It is proved that algorithms based on LBP and Eigenfaces are able to provide high level of accuracy in face recognition due to the significantly high resolution of the depth map images generated by the latest version of the Kinect device. To complement this work a novel algorithm named the Dense Feature Detector is presented and is proven to be effective in face recognition using depth map images, in particular under wellilluminated conditions. Another technique that was presented for the goal of enhancing face recognition is to be able to reconstruct face images in different angles, through the use of the data of one frontal RGB image and the corresponding depth map captured by the Kinect, using faster and effective 3D object reconstruction technique. Using the Overfeat network based on Convolutional Neural Networks for feature extraction and a SVM for classification it is shown that a technically unlimited number of multiple views can be created from the proposed 3D model that consists features of the face if captured real at similar angles. Thus these images can be used as real training images, thus removing the need to capture many examples of a facial image from different viewpoints for the training of the image classifier. Thus the proposed 3D model will save significant amount of time and effort in capturing sufficient training data that is essential in recognition of the human face under variations of pose\/viewpoint. The thesis argues that the same approach can also be used as a novel approach to face recognition, which promises significantly high levels of face recognition accuracy base on depth images. Finally following the recent trends in replacing traditional face recognition algorithms with the effective use of deep learning networks, the thesis investigates the use of four popular networks, VGG-16, VGG-19, VGG-S and GoogLeNet in depth maps based face recognition and proposes the effective use of Transfer Learning to enhance the performance of such Deep Learning networks.", "source_url": "https:\/\/openalex.org\/W2779335121", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Loughborough University Institutional Repository (Loughborough University)", "source_record_id": "W2779335121", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1003, "title": "A Face Detection and Recognition System for Color Images using Neural Networks with Boosting and Deep Learning", "title_ar": "", "title_en": "A Face Detection and Recognition System for Color Images using Neural Networks with Boosting and Deep Learning", "author": "Mohammadreza Hajiarbabi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Kansas", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; face-recognition; speech-recognition", "keywords": "", "abstract": "A face detection and recognition system is a biometric identification mechanism which compared to other methods such as finger print identification, speech, signature, hand written and iris recognition, is shown to be more important both theoretically and practically. In principle, the biometric identification methods use a wide range of techniques such as machine learning, computer vision, image processing, pattern recognition and neural networks. The methods have various applications such as in photo and film processing, control access networks, etc. In recent years, the automatic recognition of a human face has become an important problem in pattern recognition. The main reasons are structural similarity of human faces and great impact of illumination conditions, facial expression and face orientation. Face recognition is considered one of the most challenging problems in pattern recognition. A face recognition system consists of two main components, face detection and recognition. In this dissertation a face detection and recognition system using color images with multiple faces is designed, implemented, and evaluated. In color images, the information of skin color is used in order to distinguish between the skin pixels and non-skin pixels, dividing the image into several components. Neural networks and deep learning methods has been used in order to detect skin pixels in the image. A skin database has been built that contains skin pixels from different human skin colors. Information from different color spaces has been used and applied to neural networks. In order to improve system performance, bootstrapping and parallel neural networks with voting have been used. Deep learning has been used as another method for skin detection and compared to other methods. Experiments have shown that in the case of skin detection, deep learning and neural networks methods produce better results in terms of precision and recall compared to the other methods in this field. The step after skin detection is to decide which of these components belong to human face. A template based method has been modified in order to detect the faces. The template is rotated and rescaled to match the component and then the correlation between the template and component is calculated, to determine if the component belongs to a human face. The designed algorithm also succeeds if there are more than one face in the component. A rule based method has been designed in order to detect the eyes and lips in the detected components. After detecting the location of eyes and lips in the component, the face can be detected. After face detection, the faces which were detected in the previous step are to be recognized. Appearance based methods used in this work are one of the most important methods in face recognition due to the robustness of the algorithms to head rotation in the images, noise, low quality images, and other challenges. Different appearance based methods have been designed, implemented and tested. Canonical correlation analysis has been used in order to increase the recognition rate.", "source_url": "https:\/\/openalex.org\/W2799615539", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KU ScholarWorks (The University of Kansas)", "source_record_id": "W2799615539", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1004, "title": "Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond", "title_ar": "", "title_en": "Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond", "author": "Felix Juefei-Xu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; generative-ai; cnn; image-classification; face-recognition; gan", "keywords": "", "abstract": "Many real-world face recognition tasks are under unconstrained conditions such as off-angle pose variations, illumination variations, facial occlusion, facial expression, etc. In this work, we are focusing on the real-world scenarios where only the periocular region of a face is visible such as in the ISIS case. In Part I of the dissertation, we will showcase the face recognition capability based on the periocular region, which we call the periocular face recognition. We will demonstrate that face matching using the periocular region directly is more robust than the full face in terms of age-tolerant face recognition, expression-tolerant face recognition, pose-tolerant face recognition, as well as contains more cues for determining the gender information of a subject. In this dissertation, we will study direct periocular matching more comprehensively and systematically using both shallow and deep learning methods. Based on this, in Part II and Part III of the dissertation, we will continue to explore an indirect way of carrying out the periocular face recognition: periocular-based full face hallucination, because we want to capitalize on the powerful commercial face matchers and deep learning-based face recognition engines which are all trained on large-scale full face images. The reproducibility and feasibility of re-training for a proprietary facial region, such as the periocular region, is relatively low, due to the nonopen source nature of commercial face matchers as well as the amount of training data and computation power required by the deep learning based models. We will carry out the periocular-based full face hallucination based on two proposed reconstructive dictionary learning methods, including the dimensionally weighted K-SVD (DW-KSVD) dictionary learning approach and its kernel feature space counterpart using Fastfood kernel expansion approximation to reconstruct high-fidelity full face images from the periocular region, as well as two proposed generative deep learning approaches that build upon deep convolutional generative adversarial networks (DCGAN) to generate the full face from the periocular region observations, including the Gang of GANs (GoGAN) method and the discriminant nonlinear many-to-one generative adversarial networks (DNMM-GAN) for applications such as the generative open-set landmark-free frontalization (Golf) for faces and universal face optimization (UFO), which tackles an even broader set of problems than periocular based full face hallucination. Throughout Parts I-III, we will study how to handle challenging realworld scenarios such as unconstrained pose variations, unconstrained illumination conditions, and unconstrained low resolution of the periocular and facial images. Together, we aim to achieve unconstrained periocular face recognition through both direct periocular face matching and indirect periocular-based full face hallucination. In the final Part IV of the dissertation, we will go beyond and explore several new methods in deep learning that are statistically efficient for generalpurpose image recognition. Methods include the local binary convolutional neural networks (LBCNN), the perturbative neural networks (PNN), and the polynomial convolutional neural networks (PolyCNN).", "source_url": "https:\/\/openalex.org\/W2804539676", "pdf_url": "", "doi": "10.1184\/r1\/6724106", "handle": "", "repository_name": "KiltHub Repository", "source_record_id": "W2804539676", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1005, "title": "Development of deep learning-based 3D face fraud prevention system for face recognition systems", "title_ar": "", "title_en": "", "author": "Zeynep Koyun", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Fırat University", "university_source": "repository_host", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2022, "language": "tr", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; ai-applications; face-recognition; video-analysis; medical-imaging; ai-cybersecurity", "keywords": "", "abstract": "Biyometri, kişileri fiziksel veya davranışsal özelliklerine göre doğrulamak, tanımak ve tanımlamak için kullanılan süreçleri ifade etmektedir. Biyometrik tanımlayıcılar için veri alınabilecek fizyolojik özellikler parmak izleri, avuç içi damarları, avuç içi izleri, yüz, DNA, iris, retina ve el geometrisini içermektedir. En popüler biyometrik tanımlayıcı olan yüz tanıma sistemleri temel olarak, görüntülerdeki yüz bölgelerinin makine öğrenmesi algoritmaları kullanılarak tespit edilip daha önce sistemde kayıtlı kişiler ile eşleştirme yapılarak otomatik kimlik doğrulama sistemi olarak görev yapmaktadır. Yüz tanıma teknolojileri, iris ve parmak izi tanıma gibi diğer teknolojilere ve biyometrik tanımlayıcılara göre kullanışlı, temassız ve kolay uygulanabilir olmasından dolayı pek çok avantaj sağlamaktadır. Ne yazık ki, bir biyometrik veri çalındığında veya kopyalandığında, biyometrinin avantajları dezavantaja dönüşmektedir. Kayıtlı bir kullanıcının bir veya daha fazla fotoğrafının\/videosunun internet üzerinden kolayca ve ucuza elde edilebildiği veya kullanıcının rızası veya fiziksel teması olmadan bile bir kamera kullanarak sadece yüzünün yakalandığı yüz tanıma sistemlerinde bu risk diğer biyometrik verilere göre çok daha yüksektir. Canlılık tespit algoritmaları ile destekli yüz tanıma sistemleri güvenlik sektöründe çok önemli ve kritik bir ihtiyaç olarak görülmektedir. Bu tez çalışmasının amacı, yüz tanıma sistemlerinin güvenirlilik ve doğruluk hassasiyetini en üst düzeye çıkaracak yapay zeka teknolojileri ve derin öğrenme algoritmaları ile yüz sahteciliği tespit algoritmaları geliştirilmesidir. Gerçekleştirilen uygulamaların sonuçları incelendiğinde ,5 başarı performansı ile yüz tanıma sistemlerini aldatmaya yönelik saldırılardan etkilenmeyen stabil, yüksek performanslı ve yüksek doğruluklu bir model elde edilmiştir.", "source_url": "https:\/\/openalex.org\/W7217920640", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace - FIRAT (Fırat University)", "source_record_id": "W7217920640", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1006, "title": "Face recognition using traditional machine learning algorithms and deep neural networks with application to face verification", "title_ar": "", "title_en": "Face recognition using traditional machine learning algorithms and deep neural networks with application to face verification", "author": "Manolis Papadakis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "machine-learning; deep-learning; computer-vision; cnn; face-recognition", "keywords": "", "abstract": "Biometrics authentication has been very useful and necessary nowadays due to the great developments in technology and the transaction of huge amounts of sensitive data on a daily basis. Traditionally, access to some data or service is achieved by means of some documents or a password. However, these methods are not very convenient. Alternatively, typical biometric systems can be employed that use fingerprint, iris, voice, face recognition or a combination of them. This project focuses on the task of face recognition from still images and investigates how different algorithms for face verification perform under various adverse conditions modelled by blur, salt-and-pepper noise and changes in illumination. Conventional pattern recognition algorithms are first presented. Pixel intensities, Gabor features, Local Binary Patterns (LBP) and 2D-DCT coefficients are considered as features while for classification the nearest neighbor (NNC), nearest mean (NMC), SVM classifiers, and Likelihood Ratio Tests (LRT) with Gaussian Mixture Models (GMM) are examined. Out of all these methods, Gabor features combined with the linear SVM classifier are shown to produce best results across all degradations giving an average Equal Error Rate (EER) of 0:97% using the ORL face dataset. Then, emphasis is placed on deep learning and Convolutional Neural Networks (CNN). Specically, VGG-Face with triplet loss training for face verification is suggested. VGG-Face achieves an average EER of 2:63% when both test images of a query image pair are drawn from the same degradation conditions and an average EER of 3:80% when only one image in the given pair is degraded and the other one is derived from the clean ORL dataset. We also experimented with the extracted VGG-Face features and NNC, linear SVM and Gaussian SVM and it is seen that a linear SVM gives an average EER of 1:10% by macro-averaging the Detection Error Tradeoff (DET) curves.", "source_url": "https:\/\/openalex.org\/W2981731957", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2981731957", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1007, "title": "Research of Face Recognition using Deep Learning for Non- Contact Heartbeat Detection", "title_ar": "", "title_en": "Research of Face Recognition using Deep Learning for Non- Contact Heartbeat Detection", "author": "Jiarui Li", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Taiwan", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; object-detection; face-recognition", "keywords": "", "abstract": "[[abstract]]我們提出一個應用深度學習架構學習人臉辨識並進行即時心跳量測的方法。本方法實驗結果可以得知，OpenCV自帶人臉辨識與YOLOv2實作出來之即時心率測量之差異，可以比較出OpenCV自帶人臉辨識速度及辨識率不佳，使用YOLOv2來作人臉辨識後，能明顯提升運行效果與準確度，因為YOLOv2不同OpenCV自帶人臉辨識的方法，OpenCV自帶人臉重複搜尋整張圖的相關特徵點，而YOLOv2只對整張圖有興趣的特徵點進行人臉搜索，由此我們可以因為加快的運行速度，而達到未來需要追加其他功時，能擁有更高的拓展可能性。 We propose a method of applying deep learning architecture to learn face recognition and perform instant heartbeat measurement. The experimental results show that OpenCV comes with people. The difference between face recognition and real heart rate measurement made by YOLOv2 can compare the speed and recognition rate of OpenCV's own face recognition. After using YOLOv2 for face recognition, it can significantly improve the running effect and accuracy, because YOLOv2 Different OpenCV's own face recognition method, OpenCV comes with a face to repeatedly search for the relevant feature points of the whole picture, and YOLOv2 only performs face search on the feature points that are of interest to the whole picture, so we can run faster. Speed, and when you need to add other work in the future, you can have a higher expansion potential.", "source_url": "https:\/\/openalex.org\/W7111442043", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Chinese Culture University Repository", "source_record_id": "W7111442043", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1008, "title": "Machine learning applications in medical imaging for early diagnosis of cardiovascular diseases", "title_ar": "", "title_en": "Machine learning applications in medical imaging for early diagnosis of cardiovascular diseases", "author": "Marena Soliman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Boston University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; optimization; cnn; medical-imaging; fairness", "keywords": "", "abstract": "Since cardiovascular diseases (CVDs) continue to be the primary cause of morbidity and death worldwide, better patient outcomes depend on early and precise diagnosis. Although they are essential for clinical decision-making, traditional cardiovascular imaging methods such as computed tomography (CT), cardiac magnetic resonance (CMR), and echocardiography sometimes rely on subjective evaluations that are prone to inter-observer variability. A revolutionary method for improving diagnostic automation, efficiency, and accuracy is the incorporation of machine learning (ML) into medical imaging. Large amounts of imaging data can be analyzed using ML algorithms, such as supervised and unsupervised learning models, which can also extract complex patterns and accurately detect small cardiovascular abnormalities. Accurate measurement of cardiac structures and functions is made possible by deep learning models and convolutional neural networks (CNNs), which further enhance feature extraction and classification. By enabling early identification, lowering diagnostic errors, and improving patient prognosis, ML-driven diagnostic technologies have the potential to completely transform cardiovascular healthcare, notwithstanding obstacles relating to data privacy, ethical issues, and regulatory compliance. With a focus on its contribution to the advancement of automated diagnostics and precision medicine, this paper examines the present uses, difficulties, and potential of machine learning in cardiovascular imaging.", "source_url": "https:\/\/openalex.org\/W7132960968", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OpenBU (Boston University)", "source_record_id": "W7132960968", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1009, "title": "Machine learning for pediatric healthcare", "title_ar": "", "title_en": "Machine learning for pediatric healthcare", "author": "Hamed Fayyaz", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Delaware", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; predictive-analytics; transformers; multimodal; object-detection; llm; fairness", "keywords": "", "abstract": "Recent ML advances have shown significant promise in assisting clinical decision-making in healthcare. In particular, the integration of ML predictive models into clinical settings has the potential to transform patient care through more personalized, timely, and effective interventions. Despite extensive research on ML for health in adults, there is a research gap regarding its application within pediatric populations. Children are not just \"small adults\"; they differ from adults in many ways that could affect the performance of ML models. These differences underscore the importance of tailoring ML approaches to pediatric care. ☐ In this work, we focus on two major pediatric health issues—obesity and sleep apnea—and explore ML applications in these domains. First, we propose a multi-task deep learning model that predicts BMI trajectory and attrition patterns among weight management program participants using EHR data. Second, we target sleep apnea detection using polysomnography (PSG) signals in children. We present a transformer-based architecture to detect apnea using multimodal biosignals. Subsequently, we investigate the contribution of each signal for apnea detection and identify a subset of signals that achieves a performance comparable to the complete set. We also study the effect of missing and noisy signals (a phenomenon commonly observed in pediatric settings) in apnea detection and present a tailored ML architecture and training algorithm that yields a robust apnea detector model. ☐ In this talk, we additionally cover two recent projects in more detail. The first one relates to extending the obesity case study above, where we develop an end-to-end pipeline following FHIR (Fast Healthcare Interoperability Resources) standards to predict patients' risk of obesity at different future ages. We also use a rigorous uncertainty quantification method to measure the uncertainty of our prediction. Our ML pipeline targets maximizing interoperability across various EHR formats and designing a user (provider)-centric UI to enable effective integration of our tools into clinical practice. ☐ Recognizing the growing influence of LLMs in healthcare, in the second project, we aim to address the need for scalable evaluation of fairness in LLMs. We propose a greatly customized RAG-based pipeline to generate synthetic red-teaming patient scenarios (vignettes) to evaluate social biases in medical LLMs. We merge an auxiliary generator LLM with medical knowledge graphs and a post hoc fact checker module in our design to minimize hallucination patterns when using an LLM-based pipeline and to follow an evidence-based scenario generation approach.", "source_url": "https:\/\/openalex.org\/W7217430578", "pdf_url": "https:\/\/udspace.udel.edu\/bitstreams\/af4fa320-c901-42a7-afdc-da6869e82a27\/download", "doi": "", "handle": "", "repository_name": "Library, Museums and Press - UDSpace (University of Delaware)", "source_record_id": "W7217430578", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1010, "title": "Artificial intelligence and machine learning to improve the diagnosis of acute myocardial infarction", "title_ar": "", "title_en": "Artificial intelligence and machine learning to improve the diagnosis of acute myocardial infarction", "author": "Paul Jasper Simon Boeddinghaus", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Edinburgh", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "machine-learning; medical-ai", "keywords": "", "abstract": "INTRODUCTION: Myocardial infarction is a leading cause of death and morbidity worldwide. In the United Kingdom alone, patients with possible myocardial account for around one in ten of the 30 million visits to the Emergency Department each year. An early triage decision is needed in these patients in order to identify those at highest risk who most likely benefit from immediate treatment. On the other hand, most patients do finally not have an acute myocardial infarction, and it is equally important to identify these patients, as the majority are eligible for early discharge. To guide clinicians, accelerated diagnostic pathways using cardiac troponin measurements have been established. These pathways are safe and effective and recommended by international practice guidelines. However, several challenges remain. These include differences in cardiac troponin assays used in clinical practice, limitations of the diagnostic pathways applied, and difficulties in assessing the cardiovascular risk of patients with myocardial injury or infarction. The aim of this thesis is to address these challenges in the diagnosis of myocardial infarction and risk assessment of patients by using novel tools that incorporate artificial intelligence and machine learning to aid clinical decision making. METHODS: Study 1 was a prospective interrupted time-series analysis. In this study, consecutive patients presenting with suspected acute coronary syndrome to one of three acute care hospitals in Scotland were identified. All sites changed from an assay measuring cardiac troponin I to one measuring cardiac troponin T. The primary outcome was hospital admission, and secondary outcomes included subsequent myocardial infarction, heart failure hospitalisation or cardiovascular death at one year. Study 2 was an individual patient-level data meta-analysis of prospective studies where cardiac troponin was measured for suspected acute coronary syndrome, diagnoses were adjudicated using the Universal Definition, and both myocardial and cause-specific mortality was reported at a minimum of one year. The primary outcome was major adverse cardiovascular events defined as myocardial infarction or cardiovascular death at one year. Study 3 was a derivation and validation study. In this study, machine learning models were trained to integrate cardiac troponin at presentation or on serial testing with clinical features to compute the CoDE-ACS score (0-100) corresponding to an individual's probability of myocardial infarction. The model was trained and externally validated to identify patients with an adjudicated diagnosis of type 1, type 4b or type 4c myocardial infarction during the index hospital admission and tested across various subgroups, countries and healthcare systems. Study 4 was a secondary analysis of an international prospective cohort study. Patients with possible myocardial infarction were enrolled and underwent serial high-sensitivity cardiac troponin I measurements at 0, 1 and 2 hours. The primary outcome was the diagnostic performance of the CoDE-ACS model at each timepoint for index type 1 myocardial infarction and the efficacy of previously validated low- and high-probability scores compared with guideline-recommended pathways. RESULTS: In study 1, 25,849 patients were enrolled. Of these, 13,146 (median age 60 [interquartile range 47 to 73] years, 47% female) and 12,703 (60 [47 to 73] years, 46% female) presented before and after the transition to cardiac troponin T, respectively. The proportion of patients with myocardial injury increased from 21% (2,800\/13,146) to 38% (4,781\/12,703) and patients were more likely to be admitted to hospital following the transition (odds ratio 2.24, 95% confidence interval, 1.81 to 2.77, both P<0.001). A 6-fold increase in serial testing was observed in patients undergoing a measure of cardiac troponin T compared to cardiac troponin I (odds ratio 6.03, 95% confidence interval, 4.85 to 7.49, P<0.001). Subsequent myocardial infarction, heart failure or cardiovascular death at one year was comparable before and after the transition (odds ratio 0.83, 95% confidence interval, 0.48 to 1.41, P=0.487). In study 2, 17 studies of nine cohorts from nine countries, with individual patient-level data available in eight cohorts, were identified. Among 120,734 patients (median 61.0 years, 45.8% female), type 1 myocardial infarction occurred in 9.4% (n=11,298), type 2 myocardial infarction in 3.0% (n=3,609), acute myocardial injury in 4.9% (n=5,864) and chronic myocardial injury in 4.7% (n=5,625). In patients with type 1 and type 2 myocardial infarction, the major adverse cardiovascular event rate was 55.2 and 51.7 per 1,000 patient-years, and the non-cardiovascular death rate was 25.7 and 60.1 per 1,000 patient-years, respectively.", "source_url": "https:\/\/openalex.org\/W7225915320", "pdf_url": "", "doi": "10.7488\/era\/8011", "handle": "", "repository_name": "Edinburgh Research Archive (University of Edinburgh)", "source_record_id": "W7225915320", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1011, "title": "Reconciling with Noise in Machine Learning for Healthcare", "title_ar": "", "title_en": "Reconciling with Noise in Machine Learning for Healthcare", "author": "Sujay Nagaraj", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Toronto", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "fairness", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; predictive-analytics; fairness; ai-industry", "keywords": "", "abstract": "This thesis explores the challenges presented by label noise in machine learning (ML) for health. Label noise is unavoidable in real-world data; it can arise from variability in measurement, human annotation errors, and aleatoric uncertainty in the ground truth. Ignoring label noise when training classifiers with standard risk-minimization algorithms can result in faulty models that predict noise instead of signal; this is particularly problematic in healthcare where predictions can representmission-critical decisions. This thesis addresses the problem of label noise from several perspectives: exploring how and why label noise arises in health data, proposing robust algorithms to handle label noise, and studying the impact of label noise on individualized predictions while proposing methods for safer inference. Through a real-world study of stress detection in sensor data, the thesis first explores the significant inter- and intra-individual variability in label representations, undermining the reliability of classifiers trained on these labels. Motivated by this, the thesis introduces the concept of Temporal Label Noise, where noise levels vary over time, and proposes robust algorithms to handle this type of noise. It then examines the consequences of label noise on individual-level predictions, a gap in existing label noise research, while providing tools to mitigate the potential harm in high-stakes healthcare predictions. Finally, the work delves into a clinical case study in the Critical Care Unit, demonstrating how noisy sensor data can be leveraged to generate higher-quality annotations and improve patient safety initiatives. By identifying clinical challenges and proposing novel algorithmic solutions, this thesis intends to help advance the development of safe, robust, and trustworthy machine learning for healthcare.", "source_url": "https:\/\/openalex.org\/W7132904433", "pdf_url": "https:\/\/utoronto.scholaris.ca\/bitstreams\/9822ef19-c180-4bf4-96cb-b050f266a01b\/download", "doi": "", "handle": "", "repository_name": "TSpace (University of Toronto)", "source_record_id": "W7132904433", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1012, "title": "Advancing Neurological Healthcare: Machine Learning Applications for Gait Disorders and Fall Detection", "title_ar": "", "title_en": "Advancing Neurological Healthcare: Machine Learning Applications for Gait Disorders and Fall Detection", "author": "Jalal Alizadeh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Saxon State and University Library Dresden", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; medical-ai; cnn", "keywords": "", "abstract": "This dissertation explores machine learning approaches for the automatic classification of neurological gait disorders and the detection of falls in older adults. Using EMG and wearable sensor data, the study evaluates multiple machine learning algorithms and demonstrates their effectiveness in supporting objective diagnosis, monitoring, and patient safety in neurological healthcare.:2 Introduction .............................................................................. 5 2.1 Background and Motivation ........................................................ 5 2.1.1 Overview of Abnormalities in Walking Patterns ............................ 5 2.1.2 Clinical Significance of Gait Analysis and Fall Detection in Neurological Healthcare ... 8 2.2 Machine Learning ............................................................... 10 2.2.1 Support Vector Machine (SVM) .......................................... 12 2.2.2 K-Nearest Neighbors (kNN) ............................................ 13 2.2.3 Random Forest (RF) ................................................... 15 2.2.4 Convolutional Neural Network (CNN) ................................... 17 2.3 Research Goals and Overview of the Thesis ...................................... 22 2.3.1 Development and Evaluation of Machine Learning Algorithms for Automatic Classification of Gait Disorders ......................................................... 22 2.3.2 Application of Machine Learning in Fall Detection Systems for Elderly Patients ....................................................................................... 23 3 Study 1 .................................................................................. 26 4 Study 2 .................................................................................. 38 5 Summary .................................................................................. 53 6 References ............................................................................... 57 7 Declaration on the Independent Completion of the Work ............................. 61 7.1 Publications ................................................................. 62 7.2 Acknowledgements ............................................................. 63", "source_url": "https:\/\/openalex.org\/W7215478767", "pdf_url": "https:\/\/ul.qucosa.de\/api\/qucosa%3A107083\/attachment\/ATT-0\/", "doi": "", "handle": "", "repository_name": "Qucosa (Saxon State and University Library Dresden)", "source_record_id": "W7215478767", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1013, "title": "DEVELOPMENT OF A MACHINE LEARNING MODEL FOR AUTOMATED DIAGNOSIS OF ECG ABNORMALITIES", "title_ar": "", "title_en": "DEVELOPMENT OF A MACHINE LEARNING MODEL FOR AUTOMATED DIAGNOSIS OF ECG ABNORMALITIES", "author": "Raiymbek Nurtay", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nazarbayev University", "university_source": "repository_host", "college": "", "department": "", "country": "Kazakhstan", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn", "keywords": "", "abstract": "Early diagnosis of Cardiovascular diseases (CVD) remains essential because these diseases represent the main global reason for mortality and disease incidents. Traditional ECG analysis methods struggle to identify minor and intricate abnormalities within extensive databases because Electrocardiograms (ECGs) serve as standard tools to inspect heart health conditions. The research creates a hybrid machine learning model for automated ECG abnormality detection which integrates deep learning features of CNN (1D Convolutional Neural Network) for feature extraction with classifier chain ensemble modeling for multi-label classification. The experimental results based on the PTB-XL database demonstrated an overall accuracy rate of 87.79% together with a weighted accuracy of 95.98% through model training and evaluation. This performance exceeded standard methods. The main contribution of this study includes connecting a machine learning algorithm to a web-based interface which enables healthcare professionals especially those in limited resource environments to submit single-lead ECG images that produce instant diagnostic results. This integration facilitates faster, more efficient ECG analysis with a response time of approximately 22 seconds per image. The hybrid system represents progress in medical diagnostic workstreams and expands automatic cardiovascular disease detection through its monitoring platform despite facing difficulties in detecting uncommon abnormalities. Studies prove that machine learning-based ECG diagnostic systems can successfully enter clinical settings where they create a practical and quick method to enhance cardiovascular treatment.", "source_url": "https:\/\/openalex.org\/W7200403297", "pdf_url": "https:\/\/nur.nu.edu.kz\/bitstreams\/e0f815fa-e076-4a44-a74e-a54347a9642c\/download", "doi": "", "handle": "", "repository_name": "Nazarbayev University Repository (Nazarbayev University)", "source_record_id": "W7200403297", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1014, "title": "Machine learning and Multi-criteria decision analysis in healthcare : A comparison of machine learning algorithms for medical diagnosis", "title_ar": "", "title_en": "Machine learning and Multi-criteria decision analysis in healthcare : A comparison of machine learning algorithms for medical diagnosis", "author": "Victoria Hjalmarsson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; xai", "keywords": "", "abstract": "Medical records consist of a lot of data. Nevertheless, in today’s digitized society it is difficult for humans to convert data into information and recognize hidden patterns. Effective decision support tools can assist medical staff to reveal important information hidden in the vast amount of data and support their medical decisions. The objective of this thesis is to compare five machine learning algorithms for clinical diagnosis. The selected machine learning algorithms are C4.5, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Naïve Bayes classifier. First, the machine learning algorithms are applied on three publicly available datasets. Next, the Analytic hierarchy process (AHP) is applied to evaluate which algorithms are more suitable than others for medical diagnosis. Evaluation criteria are chosen with respect to typical clinical criteria and were narrowed down to five; sensitivity, specificity, positive predicted value, negative predicted value and interpretability. Given the results, Naïve Bayes and SVM are given the highest AHP-scores indicating they are more suitable than the other tested algorithm as clinical decision support. In most cases kNN performed the worst and also received the lowest AHP-score which makes it the least suitable algorithm as support for medical diagnosis.", "source_url": "https:\/\/openalex.org\/W3092822836", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3092822836", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1015, "title": "Multi-view versus single-view machine learning for disease diagnosis in primary healthcare", "title_ar": "", "title_en": "Multi-view versus single-view machine learning for disease diagnosis in primary healthcare", "author": "Aleksandar Labroski", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Politécnica de Madrid", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; medical-ai; predictive-analytics", "keywords": "", "abstract": "The work presented in this report considers and compares two different approaches of machine learning towards solving the problem of disease diagnosis prediction in primary healthcare: single-view and multi-view machine learning. In particular, the problem of disease diagnosis prediction refers to the issue of predicting a (possible) diagnosis for a given patient based on her past medical history. The problem area is extensive, especially considering the fact that there are over 14,400 unique possible diagnoses (grouped into22 high level categories) that can be considered as prediction targets. The approach taken in this work considers the high-level categories as prediction targets and attempts to use the two different machine learning techniques towards getting close to an optimal solution of the issue. The multi-view machine learning paradigm was chosen as an approach that can improve predictive performance of classifiers in settings where we have multiple heterogeneous data sources (different views of the same data), which is exactlyt he case here. In order to compare the single-view and multi-view machine learning paradigms (based on the concept of supervised learning), several different experiments are devised which explore the possible solution space under each paradigm. The work closely touches on other machine learning concepts such as ensemble learning, stacked generalization and dimensionality reduction-based learning. As we shall see, the results show that multiview stacked generalization is a powerful paradigm that can significantly improve the predictive performance in a supervised learning setting. The different models performance was evaluated using F1 scores and we have been able to observe an average increase of performance of 0.04 and a maximum increase of 0.114 F1 score points. The findings also show that approach of multi-view stacked ensemble learning is particularly well suited as a noise reduction technique and works well in cases where the feature data is expected to contain a notable amount of noise. This can be very beneficial and of interest to projects where the features are not manually chosen by domainexperts.", "source_url": "https:\/\/openalex.org\/W2981850339", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Archivo Digital UPM (Universidad Politécnica de Madrid)", "source_record_id": "W2981850339", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1016, "title": "Machine learning for healthcare with a focus on the early diagnosis of epilepsy and brain tumor detection", "title_ar": "", "title_en": "Machine learning for healthcare with a focus on the early diagnosis of epilepsy and brain tumor detection", "author": "Diyuan Lu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Goethe University Frankfurt", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; rnn; medical-imaging", "keywords": "", "abstract": "Machine learning (ML) techniques have evolved rapidly in recent years and have shown impressive capabilities in feature extraction, pattern recognition, and causal inference. There has been an increasing attention to applying ML to medical applications, such as medical diagnosis, drug discovery, personalized medicine, and numerous other medical problems. ML-based methods have the advantage of processing vast amounts of data. With an ever increasing amount of medical data collection and large, inter-subject variability in the medical data, automated data processing pipelines are very much desirable since it is laborious, expensive, and error-prone to rely solely on human processing. ML methods have the potential to uncover interesting patterns, unravel correlations between complex features, learn patient-specific representations, and make accurate predictions. Motivated by these promising aspects, in this thesis, I present studies where I have implemented deep neural networks for the early diagnosis of epilepsy based on electroencephalography (EEG) data and brain tumor detection based on magnetic resonance spectroscopy (MRS) data. In the project for early diagnosis of epilepsy, we are dealing with one of the most common neurological disorders, epilepsy, which is characterized by recurrent unprovoked seizures. It can be triggered by a variety of initial brain injuries and manifests itself after a time window which is called the latent period. During this period, a cascade of structural and functional brain alterations takes place leading to an increased seizure susceptibility. The development and extension of brain tissue capable of generating spontaneous seizures is defined as epileptogenesis (EPG). Detecting the presence of EPG provides a precious opportunity for targeted early medical interventions and, thus, can slow down or even halt the disease progression. In order to study brain signals in this latent window, animal epilepsy models are used to provide valuable data as it is extremely difficult to obtain this data from human patients. The aim of this study is to discover biomarkers of EPG using animal models and then to find the equivalent and counterparts in human patients' data. However, the EEG features for EPG are not well-understood and there is not a sufficiently large amount of annotated data for ML-based algorithms. To approach this problem, firstly, I utilized the timestamp information of the recorded EEG from an animal epilepsy model where epilepsy is induced by an electrical stimulation. The timestamp serves as a form of weak supervision, i.e., before and after the stimulation. Secondly, I implemented a deep residual neural network and trained it with a binary classification task to distinguish the EEG signals from these two phases. After obtaining a high discriminative ability on the binary classification task, I proposed to divide further the time span after the stimulation for a three-class classification, aiming to detect possible stages of the progression of the latent EPG phase. I have shown that the model can distinguish EEG signals at different stages of EPG with high accuracy and generalization ability. I have also demonstrated that some of the learned features from the network are clinically relevant. In the task of detecting brain tumors based on MRS data, I first proposed to apply a deep neural network on the MRS data collected from over 400 patients for a binary classification task. To combat the challenge of noisy labeling, I developed a distillation step to filter out relatively ``cleanly'' labeled samples. A mixing-based data augmentation method was also implemented to expand the size of the training set. All the experiments were designed to be conducted with a leave-patient-out scheme to ensure the generalization ability of the model. Averaged across all leave-patient-out cross-validation sets, the proposed method performed on par with human neuroradiologists, while outperforming other baseline methods. I have demonstrated the distillation effect on the MNIST data set with manually-introduced label noise as well as providing visualization of the input influences on the final classification through a class activation map method. Moreover, I have proposed to aggregate information at the subject level, which could provide more information and insights. This is inspired by the concept of multiple instance learning, where instance-level labels are not required and which is more tolerant to noisy labeling. I have proposed to generate data bags consisting of instances from each patient and also proposed two modules to ensure permutation invariance, i.e., an attention module and a pooling module.", "source_url": "https:\/\/openalex.org\/W4229378113", "pdf_url": "http:\/\/publikationen.ub.uni-frankfurt.de\/files\/67966\/Diyuan_PhD_Thesis-pdfA.pdf", "doi": "10.21248\/gups.67966", "handle": "", "repository_name": "Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt)", "source_record_id": "W4229378113", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1017, "title": "Machine learning applications in healthcare", "title_ar": "", "title_en": "Machine learning applications in healthcare", "author": "Dallora Moraes, Ana Luiza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dalarna University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; medical-imaging; xai", "keywords": "", "abstract": "Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare. The two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. The diagnosis research context regards the age assessment of the young individuals, which aims to address the drawbacks in the bone age assessment research, investigating new age assessment methods. The prognosis research context regards the prognosis of dementia, which aims to investigate prognostic estimates for older individuals who came to develop the dementia disorder, in a time frame of 10 years. Machine learning applications were shown to be useful in both research contexts. In the diagnosis research context, study I summarized the state of the art evidence in the area of bone age assessment with the use of machine learning, identifying both automated and non-automated approaches for age assessment. Study II investigated a non-automated approach based on the radiologists' assessment and study III investigated an automated approach based on deep learning. Both studies used magnetic resonance imaging. The results showed that the radiologists' assessment as input was not precise enough for the estimation of age. However, the deep learning method was able to extract more useful features from the images and provided better diagnostic performance for the age assessment. In the research context of prognosis, study IV conducted a review on the relevant evidence in on the prognosis of dementia with machine learning techniques, identifying a focus on the research on neuroimaging studies dedicated to validating biomarkers for pharmaceutical research. Study V proposed a multifactorial decision tree approach for the prognosis of dementia in older individuals as to their development or not of dementia in 10 years. Achieving consistent performance results, it provided an interpretable prognostic model identifying possible modifiable and non-modifiable risk factors and possible patient subgroups of importance for the dementia research.", "source_url": "https:\/\/openalex.org\/W7112733386", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Diva portal (Dalarna University Library)", "source_record_id": "W7112733386", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1018, "title": "An advanced healthcare android application with machine learning", "title_ar": "", "title_en": "An advanced healthcare android application with machine learning", "author": "Naeeb Ridwan ; Fuad Tanvir Omi ; Protiva Ahamed", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "BRAC University", "university_source": "repository_host", "college": "", "department": "", "country": "Bangladesh", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; medical-ai; predictive-analytics", "keywords": "", "abstract": "In this thesis, an advanced healthcare application on android platform is proposed and demonstrated using machine learning techniques.This advance healthcare application allows the user to conduct critical prognosis based on their proved information.This application is composed of (1) predicting health risk of the user, (2) providing the users with necessary suggestions depending on their health conditions and (3) helping the users by connecting them with blood bank and hospitals nearby.As a further development of service, the application will enable the user to make an appointment with doctors.In our thesis project, we have run this prognosis system for heart diseases based on the information those we have collected and have gotten an accuracy level of 0.71 out of 1.", "source_url": "https:\/\/openalex.org\/W2900464575", "pdf_url": "", "doi": "", "handle": "", "repository_name": "BRAC University Institutional Repository (BRAC University)", "source_record_id": "W2900464575", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1019, "title": "Machine learning and time-series analysis in healthcare", "title_ar": "", "title_en": "Machine learning and time-series analysis in healthcare", "author": "Yu-Wei Lin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Illinois at Urbana-Champaign", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "video-analysis", "categories": "machine-learning; computer-vision; medical-ai; video-analysis", "keywords": "", "abstract": "healthcare providers to what kind of telehealth adoption model they should use during the pandemic, either the video-visit-only model or the hybrid model.", "source_url": "https:\/\/openalex.org\/W7220812681", "pdf_url": "https:\/\/www.ideals.illinois.edu\/items\/125055\/bitstreams\/410714\/data.pdf", "doi": "", "handle": "", "repository_name": "Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)", "source_record_id": "W7220812681", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1020, "title": "Machine learning for breast cancer diagnosis in Developing Countries", "title_ar": "", "title_en": "Machine learning for breast cancer diagnosis in Developing Countries", "author": "Kakileti Siva Teja", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maastricht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "machine-learning; computer-vision; medical-ai", "keywords": "", "abstract": "Thermography-based breast cancer detection has several advantages as it is noncontact, non-invasive and does not involve ionizing radiation.Many clinical studies have shown its effectiveness to detect breast cancer in early stages.Historically, thermography has only been used as an adjunct modality due to the high expertise required for manual interpretation of the thermal images and high false positives rates otherwise found in general use.Recent developments in thermal sensors, image capture protocols and computer aided software diagnostics are showing great promise in making this modality a mainstream breast cancer imaging method.This chapter described some of these advances in breast thermography and computer aided diagnostics that is poised to improve the quality of cancer care.", "source_url": "https:\/\/openalex.org\/W3095604921", "pdf_url": "https:\/\/cris.maastrichtuniversity.nl\/ws\/files\/53520080\/a6851.pdf", "doi": "10.26481\/dis.20201109st", "handle": "", "repository_name": "Research Publications (Maastricht University)", "source_record_id": "W3095604921", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1021, "title": "Optimization and Machine Learning Methods for Medical and Healthcare Applications", "title_ar": "", "title_en": "Optimization and Machine Learning Methods for Medical and Healthcare Applications", "author": "Cao Xiao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Washington", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging", "keywords": "", "abstract": "The increasing amounts of data being gathered in healthcare and medical systems and the convergence of different domains are leading medical and healthcare research to a new direction of precision and personalized medicine. The trends bring a unique opportunity and good promise to solving various critical tasks in medical and healthcare research. However, such promise heavily relies on whether we could find useful patterns to characterize the target problems, discover informative mechanisms underlying the noisy and fragmented factual information, as well as transform these knowledge into intelligent decision making. Previously many efforts have been made via various approaches such as machine learning, optimization, statistical analysis, mathematical modeling, biomedical informatics, etc. This thesis will extend along the lines of machine learning and optimization methods, and to build novel models for solving the medical or healthcare challenges. Particularly, the thesis focus on the following topic domains, including medical image or recording based disease differentiation and diagnosis, image guided precision surgery, disease progression modeling, and personalized health behavior recommendation. To summarize, the thesis mainly includes five models that are fit for a wide varieties of healthcare and medical applications. The first model is an integrated feature ranking and selection framework that is capable of selecting a sparse model while preserving the most informative features. The framework combines information theoretic criteria and the least absolute shrinkage and selection operator (lasso) method into a two-step feature selection process. It can be applied to biomarker selection problems when the number of subjects is small comparing with number of candidate biomarkers. The second is a structure learning model capable of achieving personalized identification of surgery insertion location. I introduce a method to craft novel patient-specific features from their medical images. In addition, I propose a supervised structure learning and prediction model with special inter-dimensional and response structure regularization terms to capture spatial relations of features and responses. The third is a systematic framework that unifies dynamic modeling, sparse learning, dictionary learning, and matrix completion to translate users’ behavioral data into deeply personalized health planning. The framework is fit for longitudinal user behavior data, and can potentially be a backend algorithm for mobile health (mHealth) technologies. The fourth model is an optimal expert knowledge elicitation strategy for identifying pairwise Bayesian network structure from observational data. It combines observational data and expert knowledge and iteratively elicit new expert knowledge that is optimally matched to the observational data to maximally reduce the uncertainty in the structure identification. The strategy can be applied to leverage expert knowledge in learning causal relation from data when the observational data is limited. In the thesis we use it to learn the underlying progression mechanism of Alzheimer’s disease. Last, the thesis also presents a decomposed version of the k nearest neighbor (DKNN) method for the classification of an unseen object based on the distances to the centroids of the k nearest neighbors. The DKNN has a training process to learn the local-optima-free distance metric by solving a convex optimization problem. The optimization problem not only learns a metric that minimizes classification errors and maximizes the margin between intra-class and inter-class distance. In addition, it also selects important features and removes irrelevant features when L1 regularization is incorporated in the optimization model. The DKNN algorithm is designed as a general method, but could be used in identifying discriminating features for a particular disease (binary cases) or different patient cohorts (multi-class cases). To demonstrate the utility of the proposed machine learning and optimization models, numerical studies are performed using simulation, public and\/or real data. Real data including magnetic resonance imaging (MRI) data, electroencephalogram (EEG) recordings, positron emission tomography (PET) data, data from wearable devices, computed tomography (CT) scan, etc. Through extensive experiments and analysis, the proposed approaches outperform baseline methods, and demonstrate their utility and efficacy in medical and healthcare applications.", "source_url": "https:\/\/openalex.org\/W2597649913", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ResearchWorks at the University of Washington (University of Washington)", "source_record_id": "W2597649913", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1022, "title": "Application of statistics and machine learning in healthcare", "title_ar": "", "title_en": "Application of statistics and machine learning in healthcare", "author": "Van der Merwe ; Schalk Gerhardus", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Stellenbosch University", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "ENGLISH SUMMARY : Clinical performance and cost efficiency are key focus areas in the healthcare industry, since providing quality and affordable healthcare is a continuing challenge. The goal of this research is to use statistical analyses and modelling to improve efficiency in healthcare by focussing on readmissions. Patients readmitted to hospital can indicate poor clinical care and have immense cost implications. It is advantageous if readmissions can be kept to a minimum. Generally, stakeholders view strategies to address the clinical performance of healthcare providers, such as readmission rate, as mainly clinical in nature. However, this study will investigate the potential role of machine learning in the improvement of clinical outcomes. This study defines machine learning as the identification of complex patterns (linear or non – linear) present in observed data, with the goal of predicting a certain outcome for new cases by mimicking the true underlying pattern in the population which led to the observed outcomes in the sample while throughout limiting rigid structural assumptions. The question at hand is whether patients that are at risk of readmission can be identified, along with the risk factors that can be associated with an increase in the likelihood of the event of readmission occurring. If yes, this can provide an opportunity to reduce the number of readmissions and thus avoid the resulting cost and clinical consequences. Once identified as a patient at risk for readmission, it will provide an opportunity for early clinical intervention. In addition, the model will provide the opportunity to calculate risk scores for patients, which in turn will enable risk adjustment of the readmissions rates reported. The data under consideration in this study is healthcare data generated by the operations of an international healthcare provider, Mediclinic International. The data that the research is based on is patient data captured on hospital level in all Mediclinic hospitals, operational in Mediclinic International’s Southern African platform. Several statistical algorithms exist to model the responses of interest. The techniques consist of simple, well known techniques, as well as techniques that are more advanced. Logistic regression and decision trees are examples of simple techniques, while neural networks and support vector machines (SVM) are more complex. SAS Enterprise Guide is the software of choice for the data preparation, while SAS Enterprise Miner is the software used for the machine learning component of this study. The study aims to provide insight into machine learning techniques, as well as construct machine learning models that produce reasonable accuracy in terms of prediction of readmissions.", "source_url": "https:\/\/openalex.org\/W2945031010", "pdf_url": "", "doi": "", "handle": "", "repository_name": "SUNScholar (Stellenbosch University)", "source_record_id": "W2945031010", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1023, "title": "Epilepsy diagnosis from scalp EEG : a machine learning approach", "title_ar": "", "title_en": "Epilepsy diagnosis from scalp EEG : a machine learning approach", "author": "Thomas, John", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; medical-ai; rnn", "keywords": "", "abstract": "Epilepsy refers to a group of chronic brain disorders characterized by recurrent unprovoked seizures. Interictal Epileptiform Discharges (IEDs) are distinctive biomarkers of epilepsy. According to the International Federation of Societies for Electroencephalography and Clinical Neurophysiology (IFSECN), IEDs are defined as transients distinguishable from background activity, with a characteristic spiky morphology, typically, but neither exclusively nor invariably, found in interictal electroencephalograms (EEGs) of people with epilepsy. Approximately 65 million people are affected worldwide. The early diagnosis of epilepsy is based on the IEDs that appear in the EEG. The EEGs are non-invasive, inexpensive, digitized recordings of brain activity from the scalp. However, there is a lack of experts who can analyze EEGs; moreover, manual interpretation EEG is time-consuming. Moreover, IEDs exhibit huge variation across patients. Besides, a standard definition of interictal epileptiform events is yet to be formulated. This makes the IED detection based diagnosis of epilepsy, tedious, and expert-centered. Experts have a significant disagreement over IED annotation. Consequently, an auvii", "source_url": "https:\/\/openalex.org\/W2972587271", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/93579\/1\/THOMAS_JOHN_THESIS.pdf", "doi": "10.32657\/10356\/93579", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2972587271", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1024, "title": "Medical aid system for Alzheimer's diagnosis using machine learning", "title_ar": "", "title_en": "Medical aid system for Alzheimer's diagnosis using machine learning", "author": "Jon Urtasun Urra", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Pública de Navarra", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-classification", "categories": "machine-learning; computer-vision; medical-ai; image-classification", "keywords": "", "abstract": "Artificial Intelligence (AI), and precisely Machine Learning (ML), has been proved to be a considerably useful method in a wide variety of fields. One of the fields that has improved more is the computer vision. Computers can now apply ML techniques in order to solve tasks, such as image classification, which were inconceivable in the past. This led into a huge impact on the medicine field, which has increasingly ameliorated over the recent years. The purpose of this project is to prove whether ML techniques are able to successfully diagnose Alzheimer’s disease or not.", "source_url": "https:\/\/openalex.org\/W7135319505", "pdf_url": "https:\/\/academica-e.unavarra.es\/bitstreams\/5bef380d-ea4a-4335-b8d5-2fe6b2bafa5e\/download", "doi": "", "handle": "", "repository_name": "Academica-e (Universidad Pública de Navarra)", "source_record_id": "W7135319505", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1025, "title": "Machine Learning For The Diagnosis Of Lung Disease", "title_ar": "", "title_en": "Machine Learning For The Diagnosis Of Lung Disease", "author": "William David Lindsay", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Pennsylvania", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; computer-vision; medical-ai; responsible-ai; medical-imaging; xai", "keywords": "", "abstract": "Lung cancer and interstitial lung disease (ILD) are two diseases of the lung with high impact on society. However, despite widespread interest from the scientific, medical, and patient communities, machine learning methods to improve diagnosis of these diseases remain underutilized in the clinical setting. In the face of numerous barriers to adoption, solutions which leverage the right datasets and the right methods to answer relevant clinical questions are needed. Our laboratory engages in clinical collaborations to create such datasets and applies interpretable machine learning methods to train diagnosis models to answer specific clinical questions. In this thesis, we present a machine learning based diagnosis classifier for suspicious thoracic lesions, capable of differentiating between primary and secondary cancer, that is trained on a built-for-purpose pathology confirmed lung nodule dataset. We also present a diagnosis classifier for ILD capable of creating a differential diagnosis list from human extracted image features with higher accuracy than radiologists alone. Together, our work represents an important step towards translating machine learning analyses from the research setting into the clinical domain, though additional validation with multi-institutional datasets will be required before widespread adoption.", "source_url": "https:\/\/openalex.org\/W3206288313", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ScholarlyCommons (University of Pennsylvania)", "source_record_id": "W3206288313", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1026, "title": "Machine learning for childhood pneumonia diagnosis", "title_ar": "", "title_en": "Machine learning for childhood pneumonia diagnosis", "author": "Elina Naydenova", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Oxford", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; medical-ai; ai-applications; medical-imaging; ai-smart-cities", "keywords": "", "abstract": "Pneumonia is the number one killer of children under the age of 5, causing more deaths than malaria, tuberculosis and HIV\/AIDS combined. In 2015, over 920,000 children died of pneumonia and more than 95% of the incidence and 99% of subsequent mortality occurred in developing countries. Current gold standard diagnostic assessment of childhood pneumonia relies on the use of advanced tools (such as X-rays and blood culture) by a clinical expert who assesses and interprets a combination of clinical measurements. However, the shortage of clinical expertise and equipment in low-resource settings delay diagnosis, increasing the risk of mortality. This thesis investigates the role that machine learning could play in improving diagnosis of childhood pneumonia in low-resource settings. Point-of-care devices such as a digital stethoscope and a pulse oximeter have become more affordable and hence abundant across the globe. The adoption of such devices, particularly in community settings, presents an opportunity to leverage information captured through their signals to provide early diagnosis for conditions such as childhood pneumonia. Hence, this thesis proposes a machine learning framework for the design of diagnostic models, built on a parsimonious set of symptoms which can be captured in a point-of-care setting, to deliver accurate and reproducible diagnosis of pneumonia. The approach is evaluated on three different datasets (with accuracy ranging between 81.8 – 97.9%), consistently outperforming diagnosis with the current gold standard guidelines for community diagnosis by the World Health Organisation. A clinical study in Mumbai, India, is designed to capture a realistic dataset in a poor urban community and a resource-constrained hospital, where a mobile health toolkit including three point-of-care devices (a digital stethoscope, a pulse oximeter and a thermometer) is built to enable minimally trained users to capture essential health information. Diagnostic models developed on this rich dataset are seen to perform well when validated internally as well as generalise to other datasets from different geographies. Finally, a set of tools for automated analysis of lung signals is proposed and validated on two big datasets, one from Peru and one from India. Combined with vital signs, information derived through this automated lung signal analysis was seen to outperform diagnosis via expert clinical annotation, delivering 81.3% sensitivity and 84.5% specificity.", "source_url": "https:\/\/openalex.org\/W2963713112", "pdf_url": "https:\/\/ora.ox.ac.uk\/objects\/uuid:1fb64eee-10aa-43cf-84c0-e46dd49202a9\/files\/m7bcc3a360014e80d2fa86f9d554502ce", "doi": "10.5287\/ora-vq6xvo91j", "handle": "", "repository_name": "Oxford University Research Archive (ORA) (University of Oxford)", "source_record_id": "W2963713112", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1027, "title": "Towards Practical Applications of Machine Learning in Healthcare with Federated Learning", "title_ar": "", "title_en": "Towards Practical Applications of Machine Learning in Healthcare with Federated Learning", "author": "Zhong, Aoxiao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Harvard University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; medical-imaging; federated-learning; fairness", "keywords": "", "abstract": "Federated Learning (FL) has emerged as a significant tool in healthcare machine learning, enabling institutions to collaboratively train models while maintaining data privacy. This dissertation describes the implementation of a real-world healthcare FL project and addresses the challenge of domain shift for more effective model deployment. We begin by detailing a practical application of FL during the SARS-COV-2 pandemic. Twenty institutions collaborated on a healthcare FL study to develop the \"EXAM\" (EMR CXR AI Model), which predicts future oxygen requirements for symptomatic patients using vital signs, laboratory data, and chest x-rays. EXAM achieved an Area Under the Curve (AUC) of over 0.92, marking a 16\\% improvement and a 38\\% increase in generalizability over local models. This project demonstrated FL's ability to enable rapid scientific collaboration without data exchange, producing a model that generalized across heterogeneous, unharmonized datasets and provided the healthcare community with a validated tool to combat COVID-19. Next, we address a specialized non-iid FL challenge termed \\emph{Domain-mixed FL}, where each client's data is assumed to be a mixture of several predefined domains. We propose a novel method, FedDAR, which learns a shared domain representation and personalized prediction models in a decoupled manner. Theoretical proofs show that FedDAR achieves linear convergence in simplified settings, and extensive empirical studies on both synthetic and real-world datasets demonstrate its superiority over existing FL methods. Finally, we explore the multi-dimensional domain shift problem prevalent in healthcare ML applications. We introduce a novel strategy using an ensemble of mixtures of experts (EMoE), each expert tailored to adapt to shifts along different dimensions. This approach is designed to be versatile and robust, suitable for both centralized and federated learning settings. Rigorous testing on various real-world datasets has shown that our method outperforms contemporary domain generalization and personalized federated learning approaches, effectively managing the complexities of multi-dimensional domain shifts.", "source_url": "https:\/\/openalex.org\/W7113010396", "pdf_url": "https:\/\/dash.harvard.edu\/bitstreams\/ac3d7a71-38eb-4185-a983-9d0d8a225bf3\/download", "doi": "", "handle": "", "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)", "source_record_id": "W7113010396", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1028, "title": "Statistical Machine Learning on Time Series with Applications to Manufacturing and Healthcare", "title_ar": "", "title_en": "Statistical Machine Learning on Time Series with Applications to Manufacturing and Healthcare", "author": "Ying Liao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Texas Tech University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; transfer-learning; predictive-analytics; ai-industry", "keywords": "", "abstract": "Predictive analysis in time series is critical to making informed decision-making and has been extensively studied in a wide range of fields including manufacturing and healthcare. The temporal patterns contained in time series hold the promise of making predictions on future behaviors as those patterns often reflect the health state evolution. However, developing such predictive models is a challenging problem because (1) the actual health state is typically not directly observable and can only be assessed from the health condition monitoring data (e.g., sensor measurements of the engineering system, physiological signals of the patient); (2) the health state transition may depend heavily on historical states due to complicated failure mechanisms or complex disease progression; (3) the health evolution trends generally exist significant variability due to some critical exogenous factors (e.g., operational and environmental conditions of the engineering systems, gender and age of the patients) and heterogeneity; and (4) when making predictions at an early stage, the temporal patterns may be too subtle to be detected by traditional methods (e.g., abnormality in early ECGs associated with future cardiac diseases). This dissertation addresses these challenges by developing novel and flexible statistical models and leveraging machine learning algorithms, and investigating the performance of the proposed methods in simulation studies as well as real-world applications. We first develop a flexible prognostics framework based on a higher-order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective Gibbs sampling algorithm is designed for model inference and is evaluated by a simulation study. We design a decoding algorithm to estimate the hidden health states and use a simulation approach to predict the remaining useful life for a functioning system in operation. A case study on the NASA turbofan engines demonstrates the practical utility of the proposed prognostics framework. To further capture the variability among time series due to some critical factors and patient-level heterogeneity, we propose a novel and flexible Bayesian mixed-effect higher-order HMM (MHOHMM) to predict patients' future events based on their electronic health records. A range of MHOHMMs are designed to capture different data structures and the optimal one is identified by using the $k$-fold cross-validation. An effective two-stage MCMC sampling algorithm is designed for MHOHMM inference. A simulation study is conducted to evaluate the performance of the proposed sampling algorithm and the MHOHMM-based classifier. The results show that considering the impacts of mixed effects and higher-order transition dynamics significantly improves prediction performance when such impacts present in the data. Furthermore, we conduct a case study on acute hypotensive episode prediction using real-world clinical data to demonstrate the effectiveness and advantage of the proposed prediction framework. For problems of making predictions at an early stage, we adapt state-of-the-art deep learning techniques to detect subtle patterns. To investigate whether mutant mice present subtle abnormalities in their early ECGs before severe arrhythmias occur, we propose a deep transfer learning (TL) model, DeepMiceTL, to predict cardiac conduction diseases using early mouse ECGs. We further apply the Bayesian optimization and $k$-fold cross-validation methods to tune the hyperparameters of the DeepMiceTL. Our results show that DeepMiceTL achieves promising performance to identify ECG patterns during the early course of cardiac conduction disease in mice. Our study also suggests the feasibility for early clinical diagnosis of human cardiac conduction diseases and other types of cardiac arrhythmias using deep learning in the future.", "source_url": "https:\/\/openalex.org\/W7134143582", "pdf_url": "https:\/\/ttu-ir.tdl.org\/bitstreams\/1817fc56-24a6-49bd-b8bd-56b6016bab98\/download", "doi": "", "handle": "", "repository_name": "ThinkTech (Texas Tech University)", "source_record_id": "W7134143582", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1030, "title": "Towards robust real-world historical handwriting recognition", "title_ar": "", "title_en": "Towards robust real-world historical handwriting recognition", "author": "Mahya Ameryan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Groningen \/ Centre for Information Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "computer-vision; ai-applications; image-classification; ai-education", "keywords": "", "abstract": "Historical manuscripts embody important witnesses of cultural heritage.In recent years, libraries all over the world have scanned voluminous document collections and often made the scanned page images available online.However, especially for handwritten manuscripts, having a text image is not the same as having access to digital text content.Wouldn't it be great if you type just in a few words on such a website, and then you get into the relevant section of a historical document?Given the current development of advanced artificial-intelligence algorithms and the availability of fast, powerful computers, one would expect that this problem has been solved.However, a perfect translation of the pixel image to a correct Unicode text version of that document is it not generally possible [1], [2].Even modern OCR tools developed by Google, like Tesseract [3], [4] and Google document AI, as well as the well-known Google Book collection of scanned, machine-printed books cannot provide accurate text-recognition results which are acceptable in the detail [5]-[7].This lack of accuracy is especially problematic for documents that are intended for scholarly research, where the demand for accurate transcriptions is high.Even advanced machine-learning methods struggle with the recognition of machine-printed documents when the machine font shapes are complex, e.g., in 'Gothic' [8].Given this state of affairs for the case of machine-printed books one may appreciate that handwriting, with its variation and inherent sloppiness, poses many additional problems.Handwriting recognition by computers therefore is a challenging dynamic field of research in the artificial intelligence domain [9]- [11].The extraordinary performance (99.91% [12]) on the well-known MNIST benchmark data set [13] of handwritten digits may give the impression that artificial-intelligence research has solved the problem of recognizing human handwriting.As we will also explain in the section 1.1, this is not yet the case.Even well-curated 'academic' benchmarks for word recognition such as 'IAM' [14], 'RIMES' [15] and 'GW' [16] yield word-recognition accuracy performances of 95-98%.This means: a word error in every 20-50 words.Such performances are not acceptable for scholarly research in the humanities.For realistic,", "source_url": "https:\/\/openalex.org\/W4389087222", "pdf_url": "https:\/\/research.rug.nl\/files\/837278989\/Complete_thesis.pdf", "doi": "10.33612\/diss.837278987", "handle": "", "repository_name": "University of Groningen research database (University of Groningen \/ Centre for Information Technology)", "source_record_id": "W4389087222", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1031, "title": "A large vocabulary online handwriting recognition system for Turkish", "title_ar": "", "title_en": "A large vocabulary online handwriting recognition system for Turkish", "author": "Esma Fatıma Bilgin Taşdemir", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "computer-vision; nlp; image-classification; llm; arabic-nlp", "keywords": "", "abstract": "Handwriting recognition in general and online handwriting recognition in particular has been an active research area for several decades. Most of the research have been focused on English and recently on other scripts like Arabic and Chinese. There is a lack of research on recognition in Turkish text and this work primarily fills that gap with a state-of-the-art recognizer for the first time. It contains design and implementation details of a complete recognition system for recognition of Turkish isolated words. Based on the Hidden Markov Models, the system comprises pre-processing, feature extraction, optical modeling and language modeling modules. It considers the recognition of unconstrained handwriting with a limited vocabulary size first and then evolves to a large vocabulary system. Turkish script has many similarities with other Latin scripts, like English, which makes it possible to adapt strategies that work for them. However, there are some other issues which are particular to Turkish that should be taken into consideration separately. Two of the challenging issues in recognition of Turkish text are determined as delayed strokes which introduce an extra source of variation in the sequence order of the handwritten input and high Out-of-Vocabulary (OOV) rate of Turkish when words are used as vocabulary units in the decoding process. This work examines the problems and alternative solutions at depth and proposes suitable solutions for Turkish script particularly. In delayed stroke handling, first a clear definition of the delayed strokes is developed and then using that definition some alternative handling methods are evaluated extensively on the UNIPEN and Turkish datasets. The best results are obtained by removing all delayed strokes, with up to 2.13% and 2.03% points recognition accuracy increases, over the respective baselines of English and Turkish. The overall system performances are assessed as 86.1% with a 1,000-word lexicon and 83.0% with a 3,500-word lexicon on the UNIPEN dataset and 91.7% on the Turkish dataset. Alternative decoding vocabularies are designed with grammatical sub-lexical units in order to solve the problem of high OOV rate. Additionally, statistical bi-gram and tri-gram language models are applied during the decoding process. The best performance, 67.9% is obtained by the large stem-ending vocabulary that is expanded with a bi-gram model on the Turkish dataset. This result is superior to the accuracy of the word-based vocabulary (63.8%) with the same coverage of 95% on the BOUN Web Corpus.", "source_url": "https:\/\/openalex.org\/W2926099793", "pdf_url": "http:\/\/research.sabanciuniv.edu\/36482\/1\/10198291_Esma_Fatima_Bilgin_Tasdemir.pdf", "doi": "", "handle": "", "repository_name": "Sabanci University", "source_record_id": "W2926099793", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1032, "title": "Deep Learning-Based Text Detection and Recognition", "title_ar": "", "title_en": "Deep Learning-Based Text Detection and Recognition", "author": "Qingqing Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Technology Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; rnn; image-classification; object-detection; image-segmentation", "keywords": "", "abstract": "Texts play a critical role in our daily life. They are everywhere such as slogans on posters, licence plates on cars, etc., to transmit information and knowledge. With the popularity of mobile devices with cameras, more and more texts are collected, transmitted and stored as text images. Automatically reading texts from images is of high application potentials. Therefore, related researches have been attracting considerable attentions from the computer vision community. Scene texts and handwritten texts are the two most difficult texts to be automatically read because of the challenges posed by the complexity of backgrounds, the uncertainty of capturing conditions, the diversity of text appearances, touching characters and the variety of handwriting styles. Text detection, 𝒾.𝑒., localizing text areas from images, and text recognition, 𝒾.𝑒., transcribing located text areas into character sequences, are two key steps of robust text reading. In recent years, they have entered a deep learning era, where Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) play important roles. Here, we conduct researches on text detection and recognition based on CNN and LSTM, as presented below. 1. To improve the recall rate of small text areas in oriented text detection, we propose an Xception-based multi-ASPP-assembled scene text detector named DeepText. DeepText inserts multiple Atrous Spatial Pyramid Pooling (ASPP) modules into Xception after feature maps with different resolutions to retain richer information for small text areas, and introduces auxiliary connections and auxiliary losses to speed up convergence and boost the discrimination ability of lower encoder layers. 2. To address the issue that Mask R-CNN cannot fully leverage global information when performing predictions, we propose a scene text detector named GMask R-CNN, where a global mask module is designed to perform semantic segmentation by considering global information. 3. To tackle the problem that LSTM neglects the valuable spatial and structural information of 2-D text images, we propose two scene text recognisers named FACLSTM, which exploits convolution LSTM to directly perform sequential transcription in 2-D space, and ReELFA, which utilizes one-hot encoded locations to enhance features with pixels' spatial information. 4. To solve the problem that CNNs with fully connected layers are not suitable for sequential prediction tasks due to their requirements of fixed-size inputs\/outputs, we propose a CNN-based handwritten text recogniser CFSPP. CFSPP embeds a Spatial Pyramid Pooling-based intermediate layer between convolutional layers and fully connected layers to convert arbitrary-size feature maps into feature vectors with specific lengths.", "source_url": "https:\/\/openalex.org\/W3154876829", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UTS ePRESS (University of Technology Sydney)", "source_record_id": "W3154876829", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1033, "title": "Detection and Recognition for Multiple Flames Using Deep Learning", "title_ar": "", "title_en": "Detection and Recognition for Multiple Flames Using Deep Learning", "author": "Xin Chen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Auckland University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; cnn; object-detection", "keywords": "", "abstract": "Object recognition is one of the critical fields of intelligent surveillance which can be applied to tackle multiple security issues. The recognition of a flame is an extended research direction which has significant practical value for multiple applications. The main contribution of this thesis, with the starting point of recognizing flames, is to introduce how Convolution Neural Networks (CNNs) deal with surveillance images. To achieve this, we constructed different neural network models, e.g., Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO). We enhanced the capacity of transformation of CNNs in part of convolution and RoI pooling. In this thesis, we mainly present assorted methods in deep leaning to achieve detection and identification of various flames. First, we introduce the fundamental theories of object detection and recognition using deep learning. After that, we detail the algorithms of pattern classification and compare those algorithms for image feature extraction. Based on the theory of neural networks, we also depict these learning methods and analyze the accuracy of experimental results. The focus of this thesis is on flame recognition using deep learning. For achieving this goal, a significant amount of measured data was used as the input data to ensure the validation of our experiments. We employ TensorFlow as the development environment with an NVIDIA GPU to train the image dataset and construct a detection model for the test dataset. Ultimately, experimental results show that the verification accuracy of our proposed algorithm is up to 98.6%.", "source_url": "https:\/\/openalex.org\/W2898907605", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tuwhera (Auckland University of Technology)", "source_record_id": "W2898907605", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1034, "title": "Deep Learning for Amharic Text-Image Recognition: Algorithm, Dataset and Application", "title_ar": "", "title_en": "Deep Learning for Amharic Text-Image Recognition: Algorithm, Dataset and Application", "author": "Birhanu Hailu Belay", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Kaiserslautern University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; cnn; rnn; transformers; image-classification; machine-translation; ai-industry", "keywords": "", "abstract": "Optical Character Recognition (OCR) is one of the central problems in pattern recognition. Its applications have played a great role in the digitization of document images collected from het- erogeneous sources. Many of the well-known scripts have OCR systems with sufficiently high performance that enables OCR applications in industrial\/commercial settings. However, OCR sys- tems yield very-good results only on a narrow domain and very-specific use cases. Thus, it is still a challenging task, and there are other exotic languages with indigenous scripts, such as Amharic, for which no well-developed OCR systems exist. As many as 100 million people speak Amharic, and it is an official working language of Ethiopia. Amharic script contains about 317 different alphabets derived from 34 consonants with small changes. The change involves shortening or elongating one of its main legs or adding small diacritics to the right, left, top, or bottom of the consonant character. Such modifications lead the characters to have similar shapes and make the recognition task complex, but this is particularly interesting for charac- ter recognition research. So far, Amharic script recognition models are developed based on classical machine learning techniques, and they are very limited in addressing the issues for Amharic OCR. The motivation of this thesis is, therefore, to explore and tailor contemporary deep learning tech- niques for the OCR of Amharic. This thesis addresses the challenges in Amharic OCR through two main contributions. The first contribution is an algorithmic contribution in which we investigate deep learning approaches that suit the demand for Amharic OCR. The second is a technical contribution that comprises several works towards the OCR model development; thus, it introduces a new Amharic database consisting of collections of images annotated at a character and text-line level. It also presents a novel CNN- based framework designed by leveraging the grapheme of characters in Fidel-Gebeta (where Fidel- Gebeta consists of the full set of Amharic characters in matrix structure) and achieves 94.97% overall character recognition accuracy. In addition to character level methods, text-line level methods are also investigated and devel- oped based on sequence-to-sequence learning. These models avoid several of the pre-processing stages used in prior works by eliminating the need to segment individual characters. In this design, we use a stack of CNNs, before the Bi-LSTM layers and train from end-to-end. This model out- performs the LSTM-CTC based network, on average, by a CER of 3.75% with the ADOCR test set. Motivated by the success of attention, in addressing the problems’ of long sequences in Neural Machine Translation (NMT), we proposed a novel attention-based methodology by blending the attention mechanism into CTC objective function. This model performs far better than the existing techniques with a CER of 1.04% and 0.93% on printed and synthetic text-line images respectively. Finally, this thesis provides details on various tasks that have been performed for the development of Amharic OCR. As per our empirical analysis, the majority of the errors are due to poor annotation of the dataset. As future work, the methods proposed in this thesis should be further investigated and extended to deal with handwritten and historical Amharic documents.", "source_url": "https:\/\/openalex.org\/W3163416829", "pdf_url": "https:\/\/kluedo.ub.uni-kl.de\/frontdoor\/index\/index\/docId\/6291", "doi": "10.26204\/kluedo\/6291", "handle": "", "repository_name": "Publication Server of Kaiserslautern University of Technology (Kaiserslautern University of Technology)", "source_record_id": "W3163416829", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1035, "title": "Contributions on Deep Transfer Learning", "title_ar": "", "title_en": "Contributions on Deep Transfer Learning", "author": "Chetak Kandaswamy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn", "keywords": "", "abstract": "Machine Learning (ML) is reshaping our world by building machines that can see, read, listen, talk, and write (Marr, 2015).For example, imagine you want to eat your favorite food and ask a mobile app what are the nearby restaurants that serve your favorite food.The app uses a ML algorithm to analyze the information of your location and time to provide better service.Imagine a startup that wants to use ML algorithm optimize their resources and requirements.Envision a scenario where a breast cancer patient and a surgeon want to plan a surgery.Generally, it takes a highly trained surgeon's subjective evaluation of the breast cancer surgery to make treatment decisions.Employing ML algorithms allows an objective evaluation of the surgery with a sense of femininity (aesthetic metric by (Beadle et al., 1984)), using 3D imaging that helps the patient and the doctor to visualize and collectively decide what they are seeing, and then make a informed decision about the surgery (Oliveira, 2013).The ability of ML algorithms to automatically understand the contents of images opened new ways to tackle prominent computer vision challenges like view point variation, illumination, occlusion, scale, deformation, background clutter, and intra-class variation.Especially, Deep learning algorithms like Stacked Denoising Autoencoders, Convolutional Neural Networks, and Deep Belief Nets have addressed these challenges with state-of-the-art results and have surpassed human performance.The salient attribute of Deep learning algorithms is that they automatically learn features from a large number of data samples without overfitting the model.Deep learning provides a partial solution to the research statement how to get computer programs to self-learn patterns from data.We consider machine learning as a broadening field which integrates interdisciplinary knowledge of learning processes from other fields such as psychology, biology, neuroscience, and economy.From this we attempt to answer questions such as: what kind of process can lead to learning, under what conditions, and for what kind of data?(Mitchell, 2006) In this thesis we integrated knowledge of deep learning with transfer learning where the learning is inspired from human-like learning processes to build our Deep Transfer Learning framework: \"how to get computer programs to self-learn patterns in data-efficient and domaingeneral way?\"Our survey on the state-of-the-art machine learning methods that use self-learn patterns lead to deep learning models.The subsequent question is about how learning algorithms can be inspired from human-like learning processes.For the sake of simplicity, we narrowed our scope down to human like knowledge transfer from one scenario to another.It is interesting to study the various possible cases of transfer learning settings based on the distributions, posterior probabilities, learning function and the classification tasks.We have designed three main mechanisms that harness the advantages of our proposed Deep Transfer Learning (DTL) framework: 1) Layerwise Transfer Learning (LTL), 2) Source-Target-Source (STS) and 3) Deep Transfer Learning Ensemble.First, we developed two approaches based on LTL mechanism : a) Transfer Learning unsupervised (TLu) and b) Transfer Learning supervised (TLs).On de conhecimento do ser-humano.Como simplificação, reduzimos o espectro de interesse na transferência de conhecimento entre duas tarefas de modo a utilizar conhecimento adquirido numa para resolver outra.É interessante estudar uma série de possíveis variações no processo de transferência de aprendizagem, quer ao nível das distribuições, probabilidades posteriores, funções de aprendizagem e também tarefas de classificação.Foram criados três mecanismos base da framework de deep transfer learning: 1) Layerwise Transfer Learning (LTL), 2) Source-Target-Source (STS) e 3) Deep Transfer Learning Ensemble.Relativamente à primeira metodologia, duas abordagens foram tomadas: a) Transferência de conhecimento não-supervisionada (TLu) e b) Transferência de conhecimento supervisionada (TLs).Por outro lado, os mecanismos de STS desenvolvidos foram testados tanto em problemas com uma fonte de informação como em problemas de múltiplas fontes.Finalmente, foram também desenvolvidas e testadas metodologias ensemble de várias abordagens LTL.De modo a analisar a eficácia dos métodos desenvolvidos, testes práticos foram levados a cabo ao nível de descoberta de fármacos e também de biometria inter-sensor.Um software de DTL baseado em interface interativa para algoritmos de AC baseados em GPU foi também implementado.Este software fornece uma interface para problemas baseline e outras metodologias de transferência de conhecimento.viii A special word of thanks to my friends without whose encouragement and unconditional support it would be a challenge: Saravanan Kandasamy, Kumaresa Vanji, Abhishek Chatterjee, Balamurugan Varadharajan, Herlina Jayadianti and Merle Planten.I would like to…", "source_url": "https:\/\/openalex.org\/W2592277968", "pdf_url": "https:\/\/repositorio-aberto.up.pt\/bitstream\/10216\/85509\/2\/145323.pdf", "doi": "10.34626\/bjwn-t886", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W2592277968", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1036, "title": "Application of automatic speech recognition technologies to singing", "title_ar": "", "title_en": "Application of automatic speech recognition technologies to singing", "author": "Anna Kruspe", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "nlp; speech-recognition", "keywords": "filler HMMs for keyword spotting, an i-vector approach for language identification", "abstract": "The research field of Music Information Retrieval is concerned with the automatic analysis of musical characteristics. One aspect that has not received much attention so far is the automatic analysis of sung lyrics. On the other hand, the field of Automatic Speech Recognition has produced many methods for the automatic analysis of speech, but those have rarely been employed for singing. This thesis analyzes the feasibility of applying various speech recognition methods to singing, and suggests adaptations. In addition, the routes to practical applications for these systems are described. Five tasks are considered: Phoneme recognition, language identification, keyword spotting, lyrics-to-audio alignment, and retrieval of lyrics from sung queries. The main bottleneck in almost all of these tasks lies in the recognition of phonemes from sung audio. Conventional models trained on speech do not perform well when applied to singing. Training models on singing is difficult due to a lack of annotated data. This thesis offers two approaches for generating such data sets. For the first one, speech recordings are made more “song-like”. In the second approach, textual lyrics are automatically aligned to an existing singing data set. In both cases, these new data sets are then used for training new acoustic models, offering considerable improvements over models trained on speech. Building on these improved acoustic models, speech recognition algorithms for the individual tasks were adapted to singing by either improving their robustness to the differing characteristics of singing, or by exploiting the specific features of singing performances. Examples of improving robustness include the use of keyword-filler HMMs for keyword spotting, an i-vector approach for language identification, and a method for alignment and lyrics retrieval that allows highly varying durations. Features of singing are utilized in various ways: In an approach for language identification that is well-suited for long recordings; in a method for keyword spotting based on phoneme durations in singing; and in an algorithm for alignment and retrieval that exploits known phoneme confusions in singing.", "source_url": "https:\/\/openalex.org\/W2945891342", "pdf_url": "https:\/\/www.db-thueringen.de\/servlets\/MCRFileNodeServlet\/dbt_derivate_00041256\/ilm1-2018000226.pdf", "doi": "", "handle": "", "repository_name": "Common Library Network (Der Gemeinsame Bibliotheksverbund)", "source_record_id": "W2945891342", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1037, "title": "Deep Learning Methods for Industry and Healthcare", "title_ar": "", "title_en": "Deep Learning Methods for Industry and Healthcare", "author": "Maria Amodeo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "ai-industry", "categories": "deep-learning; medical-ai; ai-applications; ai-industry", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7219179326", "pdf_url": "https:\/\/iris.polito.it\/bitstream\/11583\/2963952\/2\/Tesi.pdf", "doi": "", "handle": "", "repository_name": "PORTO@Iris", "source_record_id": "W7219179326", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1038, "title": "Language model domain adaptation for automatic speech recognition systems", "title_ar": "", "title_en": "Language model domain adaptation for automatic speech recognition systems", "author": "Yerbolat Khassanov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; transfer-learning; llm; speech-recognition", "keywords": "", "abstract": "I proposed a potential solution,", "source_url": "https:\/\/openalex.org\/W3041516330", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/141323\/2\/Thesis_Khassan.pdf", "doi": "10.32657\/10356\/141323", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W3041516330", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1040, "title": "Text Independent Offline Hand Writer Recognition Using Machine Learning", "title_ar": "", "title_en": "Text Independent Offline Hand Writer Recognition Using Machine Learning", "author": "Faraz Ahmad Khan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "machine-learning; computer-vision; nlp; predictive-analytics; image-classification; image-segmentation; arabic-nlp", "keywords": "", "abstract": "Handwriting is a behavioural biometric that an individual learns and develops over time and automated writer identification systems can be developed by identifying these behavioural aspects of an individual’s writing style. These writer recognition systems greatly assist forensic experts by facilitating them with semi-automated tools that segment the text, narrow down the search, help with visualization and finally assist in the final identification of an unknown handwritten sample. Handwriting, as a behavioural characteristic, has been a subject of interest for researchers for many decades and intensive research performed in this field has resulted in the development of multiple methods and algorithms. However, automated writer identification is still a challenging problem. Difficulties in segmenting text and the deviation of an individual from his or her unique writing style is the reason for ongoing research in this field. This thesis aims to investigate the problems faced in automated writer identification and propose novel techniques of segmentation and classification that would contribute to the field of writer identification. This has led to four different contributions. First a novel segmentation algorithm is proposed for segmenting sub-words within hand written Arabic words, the proposed method outperforms the previously used projection profile method. The second proposed method offers a segmentation free multi-scale Local Ternary Pattern Histogram for text independent writer identification. Local ternary patterns are applied at various scales to produce a predictor model for its respective scale while the high dimensionality problem of a multi-scale approach has been tackled with dimensionality reduction using SR-KDA. The third contribution tackles the problem of writer identification in noisy conditions. A robust offline text independent writer identification system is proposed using Bagged Discrete Cosine Transforms. The proposed system effectively utilizes discrete cosine transform for writer identification while avoiding problems of high dimensionality and memory limitations. Finally, in the fourth contribution a dissimilarity Gaussian mixture model is proposed for describing the contrast between different writers of a dataset. Furthermore, a weighted histogram approach is also proposed that penalizes bad prediction scores with a cost function to significantly enhance the identification rate.", "source_url": "https:\/\/openalex.org\/W2955969986", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2955969986", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1041, "title": "Deep learning with synthetic, temporal, and adversarial supervision", "title_ar": "", "title_en": "Deep learning with synthetic, temporal, and adversarial supervision", "author": "Ankush Gupta", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Oxford", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; rnn; object-detection; video-analysis", "keywords": "", "abstract": "In this thesis we explore alternatives to manually annotated training examples for supervising the training of deep learning models. Specifically, we develop methods for learning under three different supervision paradigms, namely — (1) synthetic data, (2) temporal data, and (3) adversarial supervision for learning from unaligned examples. The dominant application domain of our work is text spotting, i.e. detection and recognition of text instances in images. We learn text localisation networks on synthetic data, and harness an adversarial discriminator for training text recognition networks using no paired training examples. Further, we exploit the changing pose of objects in temporal sequences (videos) to learn object landmark detectors. The unifying objective is to scale deep learning methods beyond manually annotated training data. We develop a large-scale, realistic synthetic scene text dataset. Armed with this large annotated dataset of scene images, we train a novel, fast fully-convolutional text detection network, and show excellent performance on real images. This generalisation from synthetic to real images, confirms the verisimilitude of our rendering process. The dataset, SynthText in the Wild, has been widely adapted by the research community, and has enabled the development of end-to-end text spotting models. While synthetic text can be readily generated, it needs to be adapted for the specific application domain. However, unaligned examples of text-images, and valid language sentences are abundant. With this in mind, we develop a method for text recognition which learns from such unaligned data. We cast the text recognition problem as one of aligning the conditional distribution of strings predicted from given text images, with lexically valid strings. This alignment is induced through an adversarial discriminator which tries to distinguish the predicted and real text strings apart. Our method achieves excellent text recognition accuracy, using no labelled training examples. Temporal sequences (videos) of objects encode changes in their pose. We develop a method to harness this, and learn object landmark detectors, which consistently track object parts across different poses and instances. We achieve this by conditionally generating a future frame given a past frame, and a sparse keypoint like (learnt) representation extracted from the future frame. We demonstrate generality of our method by learning landmarks for human faces (where we outperform existing landmark detectors), articulated human body, and rigid 3D objects, with no modification to the method. Finally, we propose one-step inductive training for improving generalisation in recurrent neural networks to longer sequences. We restrict the recurrent state to a spatial memory map which tracks the regions of the image which have been accounted for, and train the network for valid evolution of this map. We show excellent generalisation to much longer sequences on two sequential visual recognition tasks — joint localisation and recognition of multiple lines of text, and counting objects in aerial images.", "source_url": "https:\/\/openalex.org\/W2964327310", "pdf_url": "https:\/\/ora.ox.ac.uk\/objects\/uuid:0a9d2c4a-0e8d-4722-9e0b-3f247efa89e8\/files\/m9652124287993d30be22dafbd8178674", "doi": "10.5287\/ora-2nqvgy7gz", "handle": "", "repository_name": "Oxford University Research Archive (ORA) (University of Oxford)", "source_record_id": "W2964327310", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1042, "title": "Visual Aided Deep Learning Applications for Underwater Operations", "title_ar": "", "title_en": "", "author": "Martin Skaldebø", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2019, "language": "sv", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; generative-ai; robotics; ai-applications; cnn; object-detection; gan; ai-industry", "keywords": "", "abstract": "Den 4. industrielle revolusjonen har pågått de siste fire årene og resultater har begynt å vise seg i industrimarkedet. Google slipper stadig ny teknologi som kan slå mennesker i et mangfold av strategispill og de har utviklet kunstig inteligens som kan lære å gå på egenhånd. Statlige instanser og klassifikasjonsselskaper sammarbeider mot regler og retningslinjer for autonome skip, Yara Birkeland er et skip som er planlagt ferdig i 2020 som vil gå mot full autonom operasjon innen 2022. Autonomi i undervannsoperasjoner erfarer også vekst som vises igjen i økning i markedet. Maskinlæring er forventet å være en bidragsyter i skiftet mot en mer autonom fremtid. Metoder for innhenting av relevant informasjon i undervannsapplikasjoner som eksisterer idag bruker hovedsakelig akkustiske sensorer. Metoder for informasjonsinhenting som bruker kamerabilder er sjeldent brukt, men forbedringer innen nevrale nettverk, spesielt Convolutional Neural Networks (CNN), har vist lovende resultater. Denne rapporten vil undersøke bruken av slike metoder for undervannsoperasjoner. Rapporten trekker spesielt frem to problemstillinger, som er fordelt på to vedlagte artikler. Den første problemstillingen anngår realitetsgapet, som beskriver fenomenet som oppstår når kunnskap blir overført mellom domener, mer presist mellom det kunstige simuleringsdomenet og den virkelige verden. Eksisterende metoder angående dette har blitt undersøkt og sammenlignet for å danne et utsnitt av eksisterende metoder. Grunnet det tøffe miljøet under vann har bruken av kamerabilder til informasjonsinnhenting vært liten. Dårlig belysning, maritim snø, et miljø i kontinuerlig endring etc. gjør det vanskelig å modellere et undervannsmiljø. Det vil derfor nesten garantert eksistere et realitetsgap mellom det simulerte domenet og den virkelige verden. I løpet av de siste årene har flere rammeverk som angriper realitetsgapet oppstått og blitt publisert. Generative Adversarial Networks (GAN) har muligens stått frem som en av de mest lovende metodene for domenetransformasjon de siste årene. Denne rapporten undersøker en metode av dette rammeverket kalt CycleGAN, som introduserer en sykelkonsistens egenskap i det orginale GAN rammeverket. CycleGAN har vist gode resultater inne flere disipliner og har i denne rapporten for første gang blitt anvendt på undervannsapplikasjoner. Metoden består av å ta et bilde som input og reprodusere det samme bildet i et annet domene som output, e.g. ta et generert bilde fra simuleringsdata av et undervannspanel som input og produsere et virkelighetsnært bilde av det samme undervannspanelet. I denne rapporten er rammeverket anvendt på to forskjellige dataset med bilder fra undervannsmiljøer og en god kartlegging mellom simuleringsbaserte domener og den virkelige verden er oppnådd. Den andre problemstillingen omhandler optisk basert objekt detektering ved bruk av monokularkamera. Deep learning metoder for objekt detektering er undersøkt i unison med metoder for å ekstrakte relevante egenskaper fra de respektive detektorene for å bidra i et dynamsik posisjonerings (DP) system. En detekteringsalgoritme basert på YOLOv3 er anvendt på et dataset sammensatt av bilder fra marin kybernetisk laboratoriet (MC-lab) ved NTNU. Datasettet inkluderer bilder av et kjent objekt i bassenget i laboratoriet. Når detekteringsalgoritmen er ferdig trent og kan vellykket detektere det relevante objectet, er en skaleringsfunksjon samt et DP-system for undervannsdronen BlueROV utviklet. BlueROV2 utfører DP relativ til det kjente objektet ved å innhente relevant informasjon angående posisjon og avstander ved hjelp av detekteringsalgoritmen. Systemet fungerer tilfredstillende og klarer å utføre DP med korrekt informasjonsinnhenting av både posisjon og avstander til objektet fra kamerabilder.", "source_url": "https:\/\/openalex.org\/W2999495304", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W2999495304", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1043, "title": "Classification in Bone Scintigraphy Images Using Convolutional Neural Networks", "title_ar": "", "title_en": "Classification in Bone Scintigraphy Images Using Convolutional Neural Networks", "author": "Johnny Dang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Lund University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn", "keywords": "", "abstract": "The main goal of this thesis is to design and implement a convolutional neural network (CNN) to classify whether hotspots in bone scintigraphy images represent cancer metastases, caused by prostate cancer, or some other physiological process. A side task was included, where another CNN was designed and trained to classify whether a bone scintigraphy image image is acquired from the front of the patient (anterior) or from the back (posterior). The CNNs were implemented using the deep learning frameworks Deeplearning4J and Keras, and were trained on labelled data sets provided by Exini Diagnostics AB, Lund, Sweden. The performance of the CNNs were evaluated using various metrics commonly used in machine learning contexts, for instance accuracy and Receiver Operating Characteristics. The final trained CNN used for the classification of hotspots reached an accuracy of 89% on a test set consisting of images that had not been used for training. The corresponding CNN for the side task reached an accuracy of 99%. These results indicate that CNNs can work well for both classification problems.", "source_url": "https:\/\/openalex.org\/W2507947736", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Lund University Publications Student Papers (Lund University)", "source_record_id": "W2507947736", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1044, "title": "Deep Convolutional Spiking Neural Networks for Image Classification", "title_ar": "", "title_en": "Deep Convolutional Spiking Neural Networks for Image Classification", "author": "Ruthvik Vaila", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Boise State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "deep-learning; computer-vision; cnn; image-classification", "keywords": "", "abstract": "Spiking neural networks are biologically plausible counterparts of artificial neural networks. Artificial neural networks are usually trained with stochastic gradient descent (SGD) and spiking neural networks are trained with bioinspired spike timing dependent plasticity (STDP). Spiking networks could potentially help in reducing power usage owing to their binary activations. In this work, we use unsupervised STDP in the feature extraction layers of a neural network with instantaneous neurons to extract meaningful features. The extracted binary feature vectors are then classified using classification layers containing neurons with binary activations. Gradient descent (backpropagation) is used only on the output layer to perform training for classification. Surrogate gradients are proposed to perform backpropagation with binary gradients. The accuracies obtained for MNIST and the balanced EMNIST data set compare favorably with other approaches. The effect of the stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored. We also studied catastrophic forgetting and its effect on spiking neural networks (SNNs). For the experiments regarding catastrophic forgetting, in the classification sections of the network we use a modified synaptic intelligence that we refer to as cost per synapse metric as a regularizer to immunize the network against catastrophic forgetting in a Single-Incremental-Task scenario (SIT). In catastrophic forgetting experiments, we use MNIST and EMNIST handwritten digits datasets that were divided into five and ten incremental subtasks respectively. We also examine behavior of the spiking neural network and empirically study the effect of various hyperparameters on its learning capabilities using the software tool SPYKEFLOW that we developed. We employ MNIST, EMNIST and NMNIST data sets to produce our results.", "source_url": "https:\/\/openalex.org\/W2931400272", "pdf_url": "https:\/\/scholarworks.boisestate.edu\/cgi\/viewcontent.cgi?article=2917&context=td", "doi": "10.18122\/td.1782.boisestate", "handle": "", "repository_name": "ScholarWorks (Boise State University)", "source_record_id": "W2931400272", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1046, "title": "Image Classification with Convolutional Neural Networks in the Presence of Optical Aberrations and Stray Light", "title_ar": "", "title_en": "Image Classification with Convolutional Neural Networks in the Presence of Optical Aberrations and Stray Light", "author": "Page King", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Arizona", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; cnn; image-classification; fairness", "keywords": "", "abstract": "As optical systems advance in capability, they present new engineering challenges. This work focuses on two such challenges: controlling stray light in imaging systems with increasingly demanding out-of-field stray light requirements and understanding the relationship between imaging hardware requirements and image classification performance with Convolutional Neural Networks (CNNs). These topics are unified by investigating CNN classification performance in the presence of stray light from optical scatter.Chapter 1 introduces the field of stray light. Stray light is light that takes an unintended path relative to the intended design path within an optical system. This section introduces mechanisms yielding stray light in an optical system, controls to manage their impact, and the engineering of stray light analysis. The purpose of stray light analysis is to discern stray light mechanisms driving performance and efficiently manage their effects to satisfy stray light performance requirements. Chapter 2 explores the out-of-field stray light performance metric, Point Source Transmittance (PST) and delves into how various stray light controls impact this metric for an optical system. Emphasis is placed on sunshades, which are designed to suppress stray light from sources outside the optical system field of view. While PST is a fundamental metric quantifying an optical system’s out-of-field stray light performance, imaging systems often have the concern of stray light from extended sources like the Sun, Moon, and Earth in their scene. Chapter 3 presents a method of using a PST to calculate an extended source transmittance for an arbitrary source. This approach enables flexibility in how stray light requirements are defined and managed on an effort. Transitioning to the second focus of this work, Chapter 4 introduces CNNs. In the field of machine learning, a CNN is a deep learning algorithm that can be applied to image classification. CNNs perform convolution and pooling operations to learn and extract features of targets in a training set of imagery. When presented with new imagery, CNNs extract learned features to classify targets in a scene. Chapter 5 delves into how CNN architectures are impacted by imaging hardware requirements. CNNs pretrained on pristine imagery are given imagery degraded by progressive amounts of optical aberration and correlated to the consequent degradation in classification performance. The Modulation Transfer Function (MTF) proves a critical image quality metric, as CNNs learn and recognize spatial frequency content of their targets. The layered structure of CNNs, in which successive operations reduce the spatial resolution of the input image, introduces a bias toward learning lower spatial frequency content. This is supported by classification performance with various optical aberrations; CNNs can be particularly robust against degradation with optical aberrations (that did not exist in training imagery), so long as they do not introduce a zero crossing or significant degradation in contrast for the spatial frequency content of features that were learned for a given target. This discussion is extended to the stray light mechanism of optical scatter. CNNs are generally robust against optical scatter, but an important exception is when bright sources are present in a scene. Optical scatter (and other stray light mechanisms) in the presence of bright sources can raise the noise background or saturate imagery and degrade the contrast and apparent color of targets for classification.", "source_url": "https:\/\/openalex.org\/W7213385102", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UA Campus Repository (The University of Arizona)", "source_record_id": "W7213385102", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1047, "title": "Comparison between KERAS library and FAST.AI library using convolution neural network(image classification) model", "title_ar": "", "title_en": "Comparison between KERAS library and FAST.AI library using convolution neural network(image classification) model", "author": "Rahul Ramchandra Uppari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dublin Business School", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; cnn; image-classification; object-detection; image-segmentation; ai-industry", "keywords": "", "abstract": "In past, many Data Scientists did research and developed model for Image Classification using Machine Learning and Deep Neural Network algorithms and through their it was found out Convolution Neural Network was and still the best Deep Neural Network Layer model for Image Segmentation, Image Classification and Object Detection. And to build CNN Model for image classification they used many Libraries such as KERAS, TENSORFLOW, SKLEARN, CAFFE, THEANO, NILEARN, FAST.AI and many more. Out of these libraries have choose only two libraries that has very good support of additional API Framework like KERAS uses TENSORBOARD which is the most used API Framework for Visualization and developing applications. Another library is FAST.AI which is uses PyTorch as support for specifically developing applications such as Computer Vision and Natural Language Processing etc. Both libraries have their own advantages, where KERAS has support of the world’s most used and high-level TENSORFLOW API Framework to run smooth on CPU and GPU. On other side FAST.AI uses pre-trained models and using them a basic image classification can be developed within few lines of codes which makes the coder work easy. To find out which library is best for Image Classification using Convolution Neural Network I developed this practical way to compare and find the answers. This project is implemented in a way, where the comparison can be done basis on the similar architecture, dataset, default Hyperparameters values, Tune Hyperparameter values, Epochs and Learning Rate. Both the models are built and have been effectively trained on 87000 images of American Sign Language dataset.", "source_url": "https:\/\/openalex.org\/W3165945968", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eSource (Dublin Business School)", "source_record_id": "W3165945968", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1048, "title": "Image Classification with Evolved Convolutional Neural Networks", "title_ar": "", "title_en": "Image Classification with Evolved Convolutional Neural Networks", "author": "Darnowsky, Philip", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Harvard University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; optimization; cnn; image-classification", "keywords": "", "abstract": "Convolutional neural networks (CNNs) are a well-established technique for image classification problems. While the topology of a CNN strongly affects the performance of that CNN, designing a CNN’s topology remains a difficult task, often with nothing better than some empirical rules-of-thumb for guidance. Evolutionary algorithms are a family of metaheuristics that can be applied to optimization problems where good solutions are hard to create from first principles, but the quality of a given solution is easy to measure. In this research, we develop and evaluate several variations on an algorithm, SDAG, which applies evolutionary methods to finding performant topologies for CNN-based image classifiers.", "source_url": "https:\/\/openalex.org\/W7112923353", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)", "source_record_id": "W7112923353", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1049, "title": "Convolutional neural network for hyperspectral image classification", "title_ar": "", "title_en": "Convolutional neural network for hyperspectral image classification", "author": "Nanqi Yuan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "deep-learning; computer-vision; cnn; image-classification", "keywords": "", "abstract": "Hyperspectral Image Classification is an important research problem in remote sensing.Classification is one of the most popular topic in hyperspectral remote sensing. In the last twenty years, a huge quantity of methods were proposed to deal with the hyperspectral data classification problem. Deep learning has been shown to be very promissing for this problem. However, existing deep learning methods only try to learn features from a pixel\/region independently without considering the dependency between different pixels\/regions.This project will employ Convolutional Neural Networks for learning features based on the spatial-spectral information of hyperspectral images. Experiments are conducted on benchmark datasets.", "source_url": "https:\/\/openalex.org\/W2528086488", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2528086488", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1050, "title": "Classification of pounds in images using neural networks convolutional", "title_ar": "", "title_en": "Classification of pounds in images using neural networks convolutional", "author": "Márcio Fabiano Oliveira de Moura Santos", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; ai-applications; cnn; object-detection; ai-education", "keywords": "", "abstract": "The literacy of deaf people is currently a major challenge that has generated many discussions in the educational context. Despite the existence of sign language (Libras), the training of people to carry out teaching activities in this area is quite scarce, and this has made the teaching and learning process of people with or without hearing problems very difficult. With the increasing growth of the internet and computer technologies, it was needed to create advanced artificial intelligence applications to improve this process. A very important milestone was the emergence of Computer Vision, which is an area of artificial intelligence that seeks to analyze, interpret and extract certain useful information from images, also studying the use of emotions, recognition, and analysis of interactive movements of human beings through computerized AI systems that simulate the thoughts and actions of human beings. The main subject of this work addresses a research related to the application of convolutional (or deep) neural network, which are similarly linked to computer vision. Experiments were carried out using a Libras database that served as support for the training of images through the YOLOv5 deep neural network algorithm to perform the Classification of some groups of 6 and 7 images. Subsequently, the results of the tests were compared, and it was observed which periods had a better performance during the classification of Libras images", "source_url": "https:\/\/openalex.org\/W7120318909", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120318909", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1051, "title": "Development of a convolutional neural network for the ships images classification task", "title_ar": "", "title_en": "", "author": "Михаліченко П. Є. ; Mykhalichenko Pavlo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Admiral Makarov National University of Shipbuilding", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2021, "language": "uk", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "deep-learning; computer-vision; cnn", "keywords": "", "abstract": "Михаліченко, П. Є. Розробка згортаючої нейронної мережі для задачі класифікації зображення надводних транспортних засобів = Development of a convolutional neural network for the ships images classification task \/ Михаліченко П. Є. \/\/ Матеріали XII міжнар. наук.-техн. конф. \"Інновації в суднобудуванні та океанотехніці\". – Миколаїв : НУК, 2021. – С. 392–394.", "source_url": "https:\/\/openalex.org\/W7200578143", "pdf_url": "https:\/\/eir.nuos.edu.ua\/bitstreams\/6dec4d1c-c04f-434d-ae40-16c0bc970170\/download", "doi": "", "handle": "", "repository_name": "Electronic Institutional Repository of Admiral Makarov National Shipbuilding University (Admiral Makarov National University of Shipbuilding)", "source_record_id": "W7200578143", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1052, "title": "Mosquito Image Classification using Convolutional Neural Networks", "title_ar": "", "title_en": "Mosquito Image Classification using Convolutional Neural Networks", "author": "Sumanth Vissamsetty", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; cnn; image-classification; ai-cybersecurity", "keywords": "", "abstract": "Human life has always been affected by insects, especially mosquitoes, since it's early beginnings. This pesky insect acts as a vector that transmit pathogens through feeding on our blood, spreading life-threatening diseases like Zika Virus, Malaria, Dengue fever, Chikungunya and more. It is important to prevent these mosquitoes from harming humans and one way to do so is to control the mosquito population, or mosquito abatement as it is commonly known. It is important to note that not all mosquitoes are the same and each of them live, reproduce and attack in their own unique way. Hence it is crucial for humans to identify each of the mosquito species and study them in a detailed manner which turns out to be a very complicated and time-consuming problem that needs to be solved prior to any attempt at mosquito control. This gives rise to the need of novel algorithms to identify mosquitoes through image processing tasks, coupled with automated machine learning classification techniques. In our thesis, we have used Convolutional Neural Networks to build an image classification model to identify two life-stages of a mosquito, namely, Instar 4 and Pupae. Our algorithm yielded an average accuracy of over 95%, reducing the time to process similar classifications by hand to at least a hundredfold.", "source_url": "https:\/\/openalex.org\/W2965985756", "pdf_url": "https:\/\/repository.lsu.edu\/cgi\/viewcontent.cgi?article=5939&context=gradschool_theses", "doi": "10.31390\/gradschool_theses.4889", "handle": "", "repository_name": "Louisiana State University", "source_record_id": "W2965985756", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1053, "title": "A deep convolutional neural network architecture for image classification", "title_ar": "", "title_en": "A deep convolutional neural network architecture for image classification", "author": "Willem Lodewikus Pretorius", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Stellenbosch University", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; computer-vision; nlp; cnn; image-classification; object-detection; video-analysis; speech-recognition", "keywords": "", "abstract": "ENGLISH SUMMARY : Convolutional Neural Networks (CNNs), a specialised form of Neural Networks (NNs), are wellknown for their state-of-the-art results obtained in Computer Vision (CV) and Deep Learning (DL) tasks throughout the past few years. Some of the exciting application areas of CNNs include image classi cation, object detection, video processing, natural language processing, and speech recognition. The powerful learning ability of deep CNNs is primarily owed to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in hardware technology has accelerated the research done in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of di erent activation and loss functions, parameter optimisation, regularisation, and architectural innovations by using di erent layer structures. Therefore, the objective of this study is based on image classi cation and object detection tasks, that is, creating custom-designed CNN architectures for deployment on real-world datasets while comparing these custom-designed architectures to those state-of-the-art architectures found in literature while comparing di erent optimisation procedures and activations functions. All major developments of CNNs are discussed and critically considered, with a view to improve CNNs in the context of the number of parameters used to obtain satisfactory results and additionally to obtain a better understanding of the term known as a `black box' which is usually associated with CNNs such that they are complex models with little understanding in the way how their classi cations are done. The most promising modern CNN architectures with associated hyperparameters are further explored by means of empirical work. Evaluation is done on the validity of ndings reported in the literature and comments are made on the e ectiveness of recent proposals through the use of ve di erent real-world datasets. The empirical work done will be complemented by additional coded notebooks that could be used to implement state-of-the-art techniques, as well as for comparative and model assessment experiments.", "source_url": "https:\/\/openalex.org\/W3135789880", "pdf_url": "", "doi": "", "handle": "", "repository_name": "SUNScholar (Stellenbosch University)", "source_record_id": "W3135789880", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1054, "title": "Convolutional neural network for distortion Classification in face images.", "title_ar": "", "title_en": "Convolutional neural network for distortion Classification in face images.", "author": "Patricia Alejandra Pacheco Reina", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidade de São Paulo", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; cnn; face-recognition", "keywords": "", "abstract": "Face processing algorithms are becoming more popular in recent days due to the great domain of application in which they can be used. As a consequence, research about the quality of face images is also increasing. The current approach to Face Image Quality Assessment (FIQA) is focused on improving the performance of face recognition systems, as a result, current FIQA algorithms don\\'t provide an indication of quality, but a performance estimation for face recognition algorithms. This approach makes the FIQA algorithms potentially unsuited for other scenarios regarding face images, and susceptible to inherit the limitations of face recognition. The present work tackles the main limitations of the current FIQA algorithms by proposing a new approach based on the distortions affecting the images. We developed two models based on Convolutional Neural Networks (CNN), to classify facial images according to the type and the degree of the distortion present in them. The models\\' output provides qualitative information about the quality of facial images, useful for face recognition systems, as well as other face processing algorithms. Additionally, the proposed method can be a starting point to image enhancement processes like denoising, and deblurring. Two other contributions can be outlined from this work: a comprehensive study about the impact of blur, noise, brightness, contrast, and JPEG compression in face processing algorithms; and a new dataset for image quality assessment and distortion classification in face images.", "source_url": "https:\/\/openalex.org\/W3209334811", "pdf_url": "", "doi": "10.11606\/d.3.2021.tde-25102021-151818", "handle": "", "repository_name": "Digital Library of Theses and Dissertations (Universidade de São Paulo)", "source_record_id": "W3209334811", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1055, "title": "Evolving Convolutional Neural Networks for Image and Text Classification Tasks", "title_ar": "", "title_en": "Evolving Convolutional Neural Networks for Image and Text Classification Tasks", "author": "Trevor Londt", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; predictive-analytics; optimization; cnn", "keywords": "", "abstract": "In the Machine Learning field, Convolutional Neural Networks (CNNs) have successfully been applied to image and text classification tasks. Innovative research in CNN design, such as the usage of deeper models and data augmentation, has resulted in significantly increased performance. However, the resultant growth in CNN complexity from these innovations has required a corresponding increase in expert skills to design them. Furthermore, CNN architectures and data augmentation techniques designed for a classification task in one domain may not generalise to other classification tasks in a different domain, meaning the difficult and costly design process must be repeated. Evolutionary Deep Learning Neural Architecture Search (EDLNAS) has been used to automatically evolve performant CNNs for various classification tasks, including image and text classification. However, the approach suffers from high computation costs, often resulting in algorithms needing to focus on a single task for a single domain, thereby missing the opportunity to achieve algorithm generalisability. This thesis aims to investigate and improve upon the efficiency, effectiveness, and generalisability of EDLNAS of CNNs for image and text classification tasks without using data augmentation. Achieving this goal can mitigate three significant problems: the high-computational cost of EDLNAS algorithms, the generalisability problem, and the problem of collecting additional training data or implementing bespoke data augmentation techniques that require costly expert domain knowledge. This thesis proposes a new, configurable, and extensible version of Cellular Encoding (CE), including supporting network structures designed to represent and construct novel CNN architectures. The encoding successfully represents various novel CNN architectures of varying width and depth, suitable for image or text classification tasks. This thesis proposes a data augmentation-free EDLNAS method to automatically evolve novel CNN architectures for image and text classification tasks, using the proposed CE representation and predesigned state-of-the-art micro-architectures. The results show that peer-competitive CNNs can be evolved in less than one GPU day across image or text domains. Next, this thesis proposes to evolve micro-architectures to boost the performance in evolving CNN architectures for image and text classification tasks without data augmentation. The results show that this is achieved without increasing the number of GPU days required. This thesis proposes the first data augmentation-free cooperative coevolution algorithm to decompose a CNN architecture into four constituent sub-architectures, each evolved separately but in cooperation, to synthesise performant CNN architectures. A new population structure and novelty injection mechanism are proposed to create a better balance between exploration and exploitation. Furthermore, a persistent offline database is created, storing tens of thousands of evolved CNN architectures and associated performance metrics. The results show that highly competitive CNN architectures can be evolved in less than one GPU day, with the algorithm achieving a good balance between exploration and exploitation during the evolution process. Finally, this thesis proposes the first EDLNAS method that models a database of stored CNN architectures and performance metrics to create a deep neural network prediction model to predict the potential classification accuracy of a candidate CNN architecture, thereby removing the need to conduct back-propagation training during the evolutionary process. The method is able to evolve a large population of CNN architectures for both image and text classification tasks in seconds, making the evolutionary process near real-time while still achieving state-of-the-art peer-competitive results.", "source_url": "https:\/\/openalex.org\/W4404658997", "pdf_url": "", "doi": "10.26686\/wgtn.27897288", "handle": "", "repository_name": "Open Access Victoria University of Wellington | Te Herenga Waka (Figshare)", "source_record_id": "W4404658997", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1056, "title": "Implementation of Convolutional Neural Networks in FPGA for Image Classification", "title_ar": "", "title_en": "Implementation of Convolutional Neural Networks in FPGA for Image Classification", "author": "Mark Espinosa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; cnn; image-classification; speech-recognition", "keywords": "", "abstract": "Machine Learning and Deep Learning its sub discipline are gaining popularity quickly. Machine Learning algorithms have been successfully deployed in a variety of applications such as Natural Language Processing, Optical Character Recognition, and Speech Recognition. Deep Learning particularly is suited to Computer Vision and Image Recognition tasks. The Convolutional Neural Networks employed in Deep Learning Neural Networks train a set of weights and biases which with each layer of the network learn to recognize key features in an image. This work set out to develop a scalable and modular FPGA implementation for Convolutional Neural Networks. It was the objective of this work to attempt to develop a system which could be configured to run as many layers as desired and test it using a currently defined CNN configuration, AlexNet. This type of system would allow a developer to scale a design to fit any size of FPGA from the most inexpensive to the costliest cutting-edge chip on the market. The objective of this work was achieved, and all layers were accelerated including Convolution, Affine, ReLu, Max Pool, and Softmax layers. The performance of the design was assessed, and it was determined its maximum achievable performance was approximately 3 GFLOPS.", "source_url": "https:\/\/openalex.org\/W3217604117", "pdf_url": "", "doi": "", "handle": "", "repository_name": "California State University ScholarWorks (system-wide DSpace)", "source_record_id": "W3217604117", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1057, "title": "Convolutional neural networks for multi- and hyper-spectral image classification", "title_ar": "", "title_en": "Convolutional neural networks for multi- and hyper-spectral image classification", "author": "Jacob Senecal", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; transfer-learning; predictive-analytics; cnn; image-classification; ai-agriculture", "keywords": "", "abstract": "While a great deal of research has been directed towards developing neural network architectures for classifying RGB images, there is a relative dearth of research directed towards developing neural network architectures specifically for multi-spectral and hyper-spectral imagery. The additional spectral information contained in a multi-spectral or hyper-spectral image can be valuable for land management, agriculture and forestry, disaster control, humanitarian relief operations, and environmental monitoring. However, the massive amounts of data generated by a multi-spectral or hyper- spectral instrument make processing this data a challenge. Machine learning and computer vision techniques could automate the analysis process of these rich data sources. With these benefits in mind, we have adapted recent developments in small efficient convolutional neural networks (CNNs), to create a small CNN architecture capable of being trained from scratch to classify 10 band multi-spectral images, using much fewer parameters than popular deep architectures, such as the ResNet or DenseNet architectures. We show that this network provides higher classification accuracy and greater sample efficiency than the same network using RGB images. We also show that it is possible to employ a transfer learning approach and use a network pre-trained on multi-spectral satellite imagery to increase accuracy on a second much smaller multi-spectral dataset, even though the satellite imagery was captured from a much different perspective (high altitude, overhead vs. ground based at close stand-off distance). These results demonstrates that it is possible to train our small network architectures on small multi-spectral datasets and still achieve high classification accuracy. This is significant as labeled hyper-spectral and multi-spectral datasets are generally much smaller than their RGB counterparts. Finally, we approximate a Bayesian version of our CNN architecture using a recent technique known as Monte Carlo dropout. By keeping dropout in place during test time we can perform a Monte Carlo procedure using multiple forward passes of our network to generate a distribution of network outputs which can be used as a measure of uncertainty in the predictions a network is making. Large variance in the network output corresponds to high uncertainty and vice versa. We show that a network that is capable of working with multi-spectral imagery significantly reduces the uncertainty associated with class predictions compared to using RGB images. This analysis reveals that the benefits of an architecture that works effectively with multi-spectral or hyper-spectral imagery extends beyond higher classification accuracy. Multi-spectral and hyper-spectral imagery allows us to be more confident in the predictions that a deep neural network is making.", "source_url": "https:\/\/openalex.org\/W3180804842", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3180804842", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1058, "title": "Convolutional Neural Networks for Classification and Segmentation of Medical Images", "title_ar": "", "title_en": "Convolutional Neural Networks for Classification and Segmentation of Medical Images", "author": "Patrick Ferdinand Christ", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging", "keywords": "", "abstract": "Automatic Detection, segmentation and classification of cancer plays an important role in the development of computer-aided diagnosis systems (CADs). This thesis investigates the application of convolutional and fully convolutional neural networks CNN\/FCN for automatic detection, segmentation of liver and liver tumor in CT and MRI and classification of tumor malignancy of HCC in DW-MRI.", "source_url": "https:\/\/openalex.org\/W2779927442", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2779927442", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1059, "title": "Investigation of Convolutional Neural Network Architectures for Image-based Feature Learning and Classification", "title_ar": "", "title_en": "Investigation of Convolutional Neural Network Architectures for Image-based Feature Learning and Classification", "author": "Johnny Ren", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Washington", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "medical-ai", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; multimodal; image-segmentation; face-recognition", "keywords": "", "abstract": "Convolutional Neural Networks (CNN) are useful methods for identification of previously unknown embedded patterns in images. Several object and facial recognition along with image segmentation tasks have benefited from the non-linear abstraction of hybrid features using CNN. This work presents a novel CNN model parametrization work-flow developed on the cloud-computing platform of Microsoft Azure Machine Learning Studio (MAMLS) that is capable of learning from the feature maps and classifying multi-modal images with different variabilities using one common flow. This two-step work-flow trains CNN models by splitting the data into training and testing data sets. First, the CNN layers are fixed and the best kernel and normalization parameters that maximize classification accuracy on the test data are identified using grid search. Next, using the best kernel and normalization parameters, the best CNN architecture that maximizes classification accuracy is detected. Finally, the activated feature maps (AFMs) from the parameterized CNN model so far are analyzed to learn new features that can enhance image-based classification accuracies. The proposed flow achieves classification accuracies in the range of 92.5-99.2% that can be further enhanced by doubling the samples based on the features learned from the AFMs. The proposed non-deep CNN models in the MAMLS platform are capable of processing image data sets with 400-4 million samples using a common flow without exponential increase in the computation time. Thus, parametrized non-deep CNN models using the proposed method are capable of identifying novel features that may enhance image-based classification accuracies. For computed tomography (CT) images, quantitative image assessment can allow for benchmarking image processing methods and optimization of image acquisition parameters. Large volumes of CT images from phantoms and patients are analyzed using the CNN models compare to a baseline model that vary in their implementation time complexities. The goal here is to model the data set variability for prediction of CT image quality (CTIQ). We observe that for 70% of data samples in training and 30% data sample in test set, respectively, the average multi-class classification accuracies for CTIQ prediction vary significantly as the data sets are switched from the phantom to patient images. The CNN model is found to be more suitable for CT image texture classification in the absence of structural variabilities. Our analysis demonstrates that CNN models are consistent identifiers of structural similarities for CT image data sets. Future work on multi-objective CNN modeling and 3D CNN modeling may lead to new insights for classification tasks.", "source_url": "https:\/\/openalex.org\/W2591747604", "pdf_url": "https:\/\/digital.lib.washington.edu\/bitstreams\/fcc21ec4-b496-4b06-8032-7d699d8ad8a1\/download", "doi": "", "handle": "", "repository_name": "ResearchWorks at the University of Washington (University of Washington)", "source_record_id": "W2591747604", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:57", "updated_at": "2026-11-15 16:52:57" }, { "id": 1060, "title": "Design Space Exploration of Convolutional Neural Networks for Image Classification", "title_ar": "", "title_en": "Design Space Exploration of Convolutional Neural Networks for Image Classification", "author": "Prasham Shah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Purdue University System", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; robotics; cnn; image-classification; object-detection; image-segmentation; video-analysis", "keywords": "", "abstract": "Computer vision is a domain which deals with the goal of making technology as efficient as human vision. To achieve that goal, after decades of research, researchers have developed algorithms that are able to work efficiently on resource constrained hardware like mobile or embedded devices for computer vision applications. Due to their constant efforts, such devices have become capable for tasks like Image Classification, Object Detection, Object Recognition, Semantic Segmentation, and many other applications. Autonomous systems like self-driving cars, Drones and UAVs, are being successfully developed because of these advances in AI. Deep Learning, a part of AI, is a specific domain of Machine Learning which focuses on developing algorithms for such applications. Deep Learning deals with tasks like extracting features from raw image data, replacing pipelines of specialized models with single end-to-end models, making models usable for multiple tasks with superior performance. A major focus is on techniques to detect and extract features which provide better context for inference about an image or video stream. A deep hierarchy of rich features can be learned and automatically extracted from images, provided by the multiple deep layers of CNN models. CNNs are the backbone of Computer Vision. The reason that CNNs are the focus of attention for deep learning models is that they were specifically designed for image data. They are complicated but very effective in extracting features from an image or a video stream. After AlexNet won the ILSVRC in 2012, there was a drastic increase in research related with CNNs. Many state-of-the-art architectures like VGG Net, GoogleNet, ResNet, Inception-v4, Inception-Resnet-v2, ShuffleNet, Xception, MobileNet, MobileNetV2, SqueezeNet, SqueezeNext and many more were introduced. The trend behind the research depicts an increase in the number of layers of CNN to make them more efficient but with that, the size of the model increased as well. This problem was fixed with the advent of new algorithms which resulted in a decrease in model size. As a result, today we have CNN models, which are implemented on mobile devices. These mobile models are compact and have low latency, which in turn reduces the computational cost of the embedded system. This thesis resembles similar idea, it proposes two new CNN architectures, A-MnasNet and R-MnasNet, which have been derived from MnasNet by Design Space Exploration. These architectures outperform MnasNet in terms of model size and accuracy. They have been trained and tested on CIFAR-10 dataset. Furthermore, they were implemented on NXP Bluebox 2.0, an autonomous driving platform, for Image Classification.", "source_url": "https:\/\/openalex.org\/W3150947729", "pdf_url": "", "doi": "10.25394\/pgs.13350125", "handle": "", "repository_name": "Purdue e-Pubs (Purdue University System)", "source_record_id": "W3150947729", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1061, "title": "Detection of wagyu beef sources with image classification using convolutional neural network", "title_ar": "", "title_en": "Detection of wagyu beef sources with image classification using convolutional neural network", "author": "Nattakorn Kointarangkul", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Thailand", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; image-classification; object-detection", "keywords": "", "abstract": "Wagyu beef originated in Japan. However, there are many types of Wagyu beef in the market around the globe. Primary sources include Australia, USA, Canada and the United Kingdom. The authentic Japanese Wagyu is well known for its intense marbling, juicy rich flavor and tenderness. Observing that there are differences in flavor, texture, and quality between distinct sources of Wagyu. This research presents an AI-based approach to identify Wagyu beef sources with image classification. The input images were collected from reliable sources on the internet and augmented with DCGAN. Deep neural networks, CNN, was constructed to detect the marbled fat patterns of two sources, Japanese Wagyu and Australian Wagyu. The prediction of Wagyu sources achieved high accuracy of 94.2%. Further experiment was conducted for multi-classification with the additional source of the US wagyu. The object detection model was trained using Region-based CNN, R-CNN, providing the prediction accuracy of 79.8%. The learning models of Convolutional Neural Networks were found to be promising methods for the rapid characterization of the unique patterns of marbled fat layers. These classifiers would benefit the customers for buying Wagyu beef at reasonable prices.", "source_url": "https:\/\/openalex.org\/W4362558873", "pdf_url": "http:\/\/cuir.car.chula.ac.th\/bitstream\/123456789\/77238\/1\/6270076621.pdf", "doi": "10.58837\/chula.the.2020.139", "handle": "", "repository_name": "Chula Digiverse", "source_record_id": "W4362558873", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1062, "title": "A Convolutional Neural Network Model for Species Classification of Camera Trap Images", "title_ar": "", "title_en": "A Convolutional Neural Network Model for Species Classification of Camera Trap Images", "author": "A Cole Casey", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Boise State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn", "keywords": "", "abstract": "The overall purpose of this study was to automate the manual process of tagging species found in camera trap images using machine learning. The basic design of this study was to implement a Convolutional Neural Network model in Python using the Keras and Tensorflow modules that learn to recognize patterns in images in order to classify what species is in a given image and to label it accordingly. Results of the analysis highlight the importance of a large sample size, the degree of accuracy according to various arguments in the model, effectiveness of multiple layers that include Max Pooling, and limitations due to processing capacity. Findings include a summary of classification accuracy at varying predictive probability thresholds.", "source_url": "https:\/\/openalex.org\/W2898823392", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Scholar Works (Boise State University)", "source_record_id": "W2898823392", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1063, "title": "Tomato type classification through images: a case study using convolutional neural networks", "title_ar": "", "title_en": "Tomato type classification through images: a case study using convolutional neural networks", "author": "Leandro Henrique Mendonça de Oliveira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "deep-learning; computer-vision; cnn", "keywords": "", "abstract": "The aim of this work is to use artificial neural networks to classify different types of tomatoes (variety or cultivar) on sale in establishments in the western region of Paraná. The classification process was carried out using models based on convolutional neural networks, and included image acquisition and processing, training, validation and testing of the classification models. A smartphone camera was used to build the tomato image database. The models chosen for classification were: ResNet50V2, MobileNet, MobileNetV2, Xception, EfficientNetV2B0, EfficientNetV2B1, EfficientNetV2B2 and EfficientNetV2B3, which were implemented in the Python language using the Keras API of the TensorFlow framework. The results obtained from the eight models were compared using accuracy and loss. For a test set of 12 images, significantly different from the training set, the MobileNet model performed best, with a test accuracy of 83.33% and an error of 0.622.", "source_url": "https:\/\/openalex.org\/W7120867440", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/33783\/1\/classificacaotomateconvolucionais.pdf", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120867440", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1065, "title": "Image-Based Malware Classification with Convolutional Neural Networks and Extreme Learning Machines", "title_ar": "", "title_en": "Image-Based Malware Classification with Convolutional Neural Networks and Extreme Learning Machines", "author": "Mugdha Jain", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; cnn; ai-cybersecurity", "keywords": "", "abstract": "Research in the field of malware classification often relies on machine learning models that are trained on high level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this research, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code. Specifically, we visualize malware samples as images and employ image analysis techniques. In this context, we focus on two machine learning models, namely, Convolutional Neural Networks (CNN) and Extreme Learning Machines (ELM). Surprisingly, we find that ELMs can yield comparable results to CNNs, yet ELMs are far more efficient to train.", "source_url": "https:\/\/openalex.org\/W4230429340", "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=1901&context=etd_projects", "doi": "10.31979\/etd.jand-r63y", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4230429340", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1066, "title": "Structured Deep Neural Networks for Speech Recognition", "title_ar": "", "title_en": "Structured Deep Neural Networks for Speech Recognition", "author": "Chunyang Wu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cambridge", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; speech-recognition", "keywords": "", "abstract": "Deep neural networks (DNNs) and deep learning approaches yield state-of-the-art performance in a range of machine learning tasks, including automatic speech recognition. The multi-layer transformations and activation functions in DNNs, or related network variations, allow complex and difficult data to be well modelled. However, the highly distributed representations associated with these models make it hard to interpret the parameters. The whole neural network is commonly treated a ``black box''. The behaviours of activation functions and the meanings of network parameters are rarely controlled in the standard DNN training. Though a sensible performance can be achieved, the lack of interpretations to network structures and parameters causes better regularisation and adaptation on DNN models challenging. In regularisation, parameters have to be regularised universally and indiscriminately. For instance, the widely used L2 regularisation encourages all parameters to be zeros. In adaptation, it requires to re-estimate a large number of independent parameters. Adaptation schemes in this framework cannot be effectively performed when there are limited adaptation data. This thesis investigates structured deep neural networks. Special structures are explicitly designed, and they are imposed with desired interpretation to improve DNN regularisation and adaptation. For regularisation, parameters can be separately regularised based on their functions. For adaptation, parameters can be adapted in groups or partially adapted according to their roles in the network topology. Three forms of structured DNNs are proposed in this thesis. The contributions of these models are presented as follows. The first contribution of this thesis is the multi-basis adaptive neural network. This form of structured DNN introduces a set of parallel sub-networks with restricted connections. The design of restricted connectivity allows different aspects of data to be explicitly learned. Sub-network outputs are then combined, and this combination module is used as the speaker-dependent structure that can be robustly estimated for adaptation. The second contribution of this thesis is the stimulated deep neural network. This form of structured DNN relates and smooths activation functions in regions of the network. It aids the visualisation and interpretation of DNN models but also has the potential to reduce over-fitting. Novel adaptation schemes can be performed on it, taking advantages of the smooth property that the stimulated DNN offer. The third contribution of this thesis is the deep activation mixture model. Also, this form of structured DNN encourages the outputs of activation functions to achieve a smooth surface. The output of one hidden layer is explicitly modelled as the sum of a mixture model and a residual model. The mixture model forms an activation contour, and the residual model depicts fluctuations around this contour. The smoothness yielded by a mixture model helps to regularise the overall model and allows novel adaptation schemes.", "source_url": "https:\/\/openalex.org\/W2804696297", "pdf_url": "", "doi": "10.17863\/cam.23363", "handle": "", "repository_name": "Apollo (University of Cambridge)", "source_record_id": "W2804696297", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1067, "title": "Speech recognition with the use of deep neural networks", "title_ar": "", "title_en": "", "author": "Δημήτρης Παπακωνσταντίνου", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2018, "language": "el", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; cnn; rnn; speech-recognition", "keywords": "", "abstract": "Η παρούσα εργασία αφορά την υλοποίηση ενός συστήματος αυτόματης αναγνώρισης ομιλίας με την χρήση Deep Neural Networks. Βασίζεται στην εργασία του BAIDU “Deep Speech 2” και έχει υλοποιηθεί με python και TensorFlow. Ξεκινάει με μια μικρή εισαγωγή στα συστήματα αναγνώρισης ομιλίας (ASR) με στατιστικές μεθόδους όπως τα Hidden Markov Models, και συνεχίζει περιγράφοντας αναλυτικά την λειτουργία των νευρωνικών δικτύων με >1 κρυφά επίπεδα. Συνεχίζει με ένα άλλο είδος ΝΝ, τα “Συνελικτικά νευρωνικά Δίκτυα” (Convolutional Neural Networks), όπου ενώ αρχικά χρησιμοποιήθηκαν για επεξεργασία εικόνας και αναγνώριση αντικειμένων, έχουν θεαματικά αποτελέσματα και στην επεξεργασία ήχου. Άλλος είδος δικτύων, ειδικά για δεδομένα που αλλάζουν κατά μήκος του χρόνου και περιγράφονται αναλυτικά, είναι τα RNN (Recurrent Neural Networks). Παραλλαγές αυτών όπως τα LSTMs (Long Short Term Memory) τα GRU (Gated Recurrent Unit) υπάρχουν στις πιο πρόσφατες έρευνες όσο αφορά το ASR. Τέλος παρουσιάζονται τα 4 διαφορετικές τοπολογίες δικτύων που συνδυάζουν όλα τα παραπάνω χαρακτηριστικά και τα αποτελέσματα των προβλέψεων, μαζί με τον πηγαίο κώδικα για την αναπαραγωγή των αποτελεσμάτων.", "source_url": "https:\/\/openalex.org\/W7125788430", "pdf_url": "https:\/\/hellanicus.lib.aegean.gr\/files\/dc1e3e31-bd97-487d-90ce-1fce178d28fe\/download\/diplomatiki.pdf", "doi": "10.26215\/heal.uoa.9319", "handle": "", "repository_name": "Institutional Repository Hellanicus", "source_record_id": "W7125788430", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1068, "title": "Rapid adaptation of deep neural network for speech recognition", "title_ar": "", "title_en": "Rapid adaptation of deep neural network for speech recognition", "author": "博史 関 ; セキ ヒロシ", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Toyohashi University", "university_source": "repository_host", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7146348542", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Toyohashi University of Technology Academic Institutional Repository (Toyohashi University)", "source_record_id": "W7146348542", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1070, "title": "Optimisation Methods For Training Deep Neural Networks in Speech Recognition", "title_ar": "", "title_en": "Optimisation Methods For Training Deep Neural Networks in Speech Recognition", "author": "Haider, Mustafa Adnan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cambridge", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; optimization; speech-recognition", "keywords": "", "abstract": "Automatic Speech Recognition (ASR) is an example of a sequence to sequence level classification task where, given an acoustic waveform, the goal is to produce the correct word level hypotheses. In machine learning, a classification problem such as ASR is solved in two stages: an inference stage that models the uncertainty associated with the choice of hypothesis given the acoustic waveform using a mathematical model, and a decision stage which employs the inference model in conjunction with decision theory to make optimal class assignments. With the advent of careful network initialisation and GPU computing, hybrid Hidden Markov Models (HMMs) augmented with Deep Neural Networks (DNNs) have shown to outperform traditional HMMs using Gaussian Mixture Models (GMMs) in solving the inference problem for ASR. In comparison to GMMs, DNNs possess a better capability to model the underlying non-linear data manifold due to their deep and complex structure. While the structure of such models gives rich modelling capability, it also creates complex dependencies between the parameters which can make learning difficult via first order stochastic gradient descent (SGD). The task of finding the best procedure to train DNNs continues to be an active area of research and has been made even more challenging by the availability of ever more training data. This thesis focuses on designing better optimisation approaches to train hybrid HMM-DNN models using sequence level discriminative criterion which is a natural loss function that preserves the sequential ordering of frames within a spoken utterance. The thesis presents an implementation of the second order Hessian Free (HF) optimisation method, and shows how the method can made efficient through appropriate modifications to the Conjugate Gradient algorithm. To achieve better convergence than SGD, this work explores the Natural Gradient method to train DNNs with discriminative sequence training. In the DNN literature, the method has been applied to train models for the Maximum Likelihood objective criterion. A novel contribution of this thesis is to extend this approach to the domain of Minimum Bayes Risk objective functions for discriminative sequence training. With sigmoid models trained on a 50hr and 200hr training set from the Multi-Genre Broadcast 1 (MGB1) transcription task, the NG method applied in a HF styled optimisation framework is shown to achieve better Word Error Rate (WER) reductions on the MGB1 development set than SGD from sequence training. This thesis also addresses the particular issue of overfitting between the training criterion and WER, that primarily arises during sequence training of DNN models that use Rectified Linear Units (ReLUs) as activation functions. It is shown how by scaling with the Gauss Newton matrix, the HF method unlike other approaches can overcome this issue. Seeing that different optimisers work best with different models, it is attractive to have a consistent optimisation framework that is agnostic to the choice of activation function. To address the issue, this thesis develops the geometry of the underlying function space captured by different realisations of DNN model parameters, and presents the design considerations for an optimisation algorithm to be well defined on this space. Building on this analysis, a novel optimisation technique called NGHF is presented that uses both the direction of steepest descent on a probabilistic manifold and local curvature information to effectively probe the error surface. The basis of the method relies on an alternative derivation of Taylor’s theorem using the concepts of manifolds, tangent vectors and directional derivatives from the perspective of Information Geometry. Apart from being well defined on the function space, when framed within a HF style optimisation framework, the method of NGHF is shown to achieve the greatest WER reductions from sequence training on the MGB1 development set with both sigmoid and ReLU based models trained on the 200hr MGB1 training set. The evaluation of the above optimisation methods in training different DNN model architectures is also presented.", "source_url": "https:\/\/openalex.org\/W2922432155", "pdf_url": "", "doi": "10.17863\/cam.37773", "handle": "", "repository_name": "Apollo (University of Cambridge)", "source_record_id": "W2922432155", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1071, "title": "Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks", "title_ar": "", "title_en": "Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks", "author": "Suhas Pillai", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rochester Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; computer-vision; nlp; medical-ai; cnn; rnn; image-classification; speech-recognition", "keywords": "", "abstract": "Millions of people around the world are diagnosed with neurological disorders like Parkinson’s, Cerebral Palsy or Amyotrophic Lateral Sclerosis. Due to the neurological damage as the disease progresses, the person suffering from the disease loses control of muscles, along with speech deterioration. Speech deterioration is due to neuro motor condition that limits manipulation of the articulators of the vocal tract, the condition collectively called as dysarthria. Even though dysarthric speech is grammatically and syntactically correct, it is difficult for humans to understand and for Automatic Speech Recognition (ASR) systems to decipher. With the emergence of deep learning, speech recognition systems have improved a lot compared to traditional speech recognition systems, which use sophisticated preprocessing techniques to extract speech features. In this digital era there are still many documents that are handwritten many of which need to be digitized. Offline handwriting recognition involves recognizing handwritten characters from images of handwritten text (i.e. scanned documents). This is an interesting task as it involves sequence learning with computer vision. The task is more difficult than Optical Character Recognition (OCR), because handwritten letters can be written in virtually infinite different styles. This thesis proposes exploiting deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for offline handwriting recognition. For speech recognition, we compare traditional methods for speech recognition with recent deep learning methods. Also, we apply speaker adaptation methods both at feature level and at parameter level to improve recognition of dysarthric speech.", "source_url": "https:\/\/openalex.org\/W2612106303", "pdf_url": "https:\/\/scholarworks.rit.edu\/cgi\/viewcontent.cgi?article=10561&context=theses", "doi": "", "handle": "", "repository_name": "RIT Scholar Works (Rochester Institute of Technology)", "source_record_id": "W2612106303", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1073, "title": "Acoustic Modelling using Deep Neural Networks for Automatic Speech Recognition", "title_ar": "", "title_en": "Acoustic Modelling using Deep Neural Networks for Automatic Speech Recognition", "author": "Gueorgui Pironkov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Belgium", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7133413632", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ORBi UMONS", "source_record_id": "W7133413632", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1074, "title": "SPEECH EMOTION RECOGNITION USING DEEP NEURAL NETWORKS", "title_ar": "", "title_en": "SPEECH EMOTION RECOGNITION USING DEEP NEURAL NETWORKS", "author": "Raiymbek Mustazhapov", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nazarbayev University", "university_source": "repository_host", "college": "", "department": "", "country": "Kazakhstan", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "deep-learning; nlp; cnn; sentiment-analysis; speech-recognition", "keywords": "", "abstract": "There is an apparent evolving interest in speech emotion recognition (SER), one of the particular cases of a broader problem of multimedia pattern recognition. SER is considered to possess the capability to enhance the communication efficiency between human and artificial intelligence providing an emotional context to the machine. The field has been developing fast with the emergence and increase in accessibility of deep learning techniques recently. This potential critical benefit and novel techniques have drawn the attention of many specialists in the field and generated a great number of research papers that furnish diverse intricate methods. One of such methods involving various data augmentation techniques has demonstrated high performance in this field. This paper performs an analysis of various simple augmentation methods to attempt to improve existing models. Particularly, this research focuses on state of the art CNN models for RAVDESS, EMO-DB, and IEMOCAP datasets, and exploits temporal, spatial, and spectral transformations of sound as an underlying method for augmentation. As a result of exploiting simple augmentations, we achieved an increase in performance for IEMOCAP model and positive effects comparable to their complex counterparts for other datasets.", "source_url": "https:\/\/openalex.org\/W7197763834", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Nazarbayev University Repository (Nazarbayev University)", "source_record_id": "W7197763834", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1075, "title": "Streaming Automatic Speech Recognition with Hybrid Architectures and Deep Neural Network Models", "title_ar": "", "title_en": "", "author": "Javier Jorge Cano", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de València", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2022, "language": "es", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "AgradecimientosLo primero, quiero remarcar que este texto solo refleja con meras palabras una parte de la gratitud total que siento hacia todas esas personas que me han acompañado durante este periodo de mi vida.Ellos y ellas saben mejor que nadie lo que significa este viaje, las dificultades y las alegrías que implica, y lo necesario que ha sido su apoyo en las distintas etapas del proceso.Enumeraré algunas de estas personas, pero la lista va más allá de los aquí reflejados, extiendo mi gratitud a ellos y ellas también, ya lo saben.Querría agradecer a Alfons, Jorge y Albert en primer lugar por haberme dado la oportunidad de entusiasmarme de nuevo con la investigación y el trabajo en equipo, por creer en mi trabajo y darme la oportunidad de trabajar con las personas más brillantes que he conocido.Por otro lado, querría agradecer a los miembros del EQUIPO MLLP, con mayúsculas intencionadas, ya que en contadas ocasiones he encontrado un grupo de personas que den un significado tan positivo a esta palabra.Gracias a Adrià Giménez, Adrià Martínez, Pau, Álex, Javier Iranzo, Joan Albert y Gonçal, ha sido un placer trabajar codo con codo, aprender y mejorar con vosotros.Querría reservar gratitud adicional para poder compensar de alguna forma a Adrià Giménez todas las horas de conversaciones sobre ideas locas que hemos tenido durante estos años, ya que me han ayudado a ser mejor científico y profesional, y a alcanzar hitos que parecían inalcanzables.Por otro lado, no quiero dejar fuera las horas de procrastinación, que aunque quizá no tan instructivas, siempre son necesarias para sobrellevar el viaje.Querría", "source_url": "https:\/\/openalex.org\/W4313322429", "pdf_url": "https:\/\/riunet.upv.es\/bitstream\/10251\/191001\/4\/Jorge%20-%20Streaming%20Automatic%20Speech%20Recognition%20with%20Hybrid%20Architectures%20and%20Deep%20Neural%20Network%20....pdf", "doi": "10.4995\/thesis\/10251\/191001", "handle": "", "repository_name": "RiuNet (Universitat Politècnica de València)", "source_record_id": "W4313322429", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1077, "title": "Applying Image Recognition to Automatic Speech Recognition: Determining Suitability of Spectrograms for Training a Deep Neural Network for SpeechRecognition", "title_ar": "", "title_en": "Applying Image Recognition to Automatic Speech Recognition: Determining Suitability of Spectrograms for Training a Deep Neural Network for SpeechRecognition", "author": "N.L.C. Lambooij", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Utrecht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; computer-vision; nlp; image-classification; speech-recognition", "keywords": "", "abstract": "In speech recognition, Neural Networks are used to recognise the sequence of phonemes in an audio signal. These networks are trained on audio data pre-processed into some (type of) spectral vector. We present an alternative method that pre-processes speech utterances into visual representations, called spectrograms, and train a neural network suitable for image recognition to identify phonemes. The resulting network was able to classify 99.73% of a set of vowels containing samples of ‘iy’, ‘ah’ and ‘uw’ correctly, 91.87% of a set of vowels containing samples ‘iy’, ‘ih’ and ‘eh’, and 75.97% of the full dataset of twelve vowels. These results show that using image recognition in automatic speech recognition is worth further investigating.", "source_url": "https:\/\/openalex.org\/W2758164600", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Utrecht University Repository (Utrecht University)", "source_record_id": "W2758164600", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1078, "title": "Deep neural network for document clustering and speech recognition = 문서 클러스터링과 음성인식을 위한 심층신경망 연구", "title_ar": "", "title_en": "Deep neural network for document clustering and speech recognition = 문서 클러스터링과 음성인식을 위한 심층신경망 연구", "author": "Hyung‐Bae Jeon ; 전형배", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; data-mining; speech-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3027587168", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3027587168", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1079, "title": "Joint Training Methods for Tandem and Hybrid Speech Recognition Systems using Deep Neural Networks", "title_ar": "", "title_en": "Joint Training Methods for Tandem and Hybrid Speech Recognition Systems using Deep Neural Networks", "author": "Zhang, Chao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cambridge", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "Hidden Markov models (HMMs) have been the mainstream acoustic modelling approach for state-of-the-art automatic speech recognition (ASR) systems over the past few decades. Recently, due to the rapid development of deep learning technologies, deep neural networks (DNNs) have become an essential part of nearly all kinds of ASR approaches. Among HMM-based ASR approaches, DNNs are most commonly used to extract features (tandem system configuration) or to directly produce HMM output probabilities (hybrid system configuration). Although DNN tandem and hybrid systems have been shown to have superior performance to traditional ASR systems without any DNN models, there are still issues with such systems. First, some of the DNN settings, such as the choice of the context-dependent (CD) output targets set and hidden activation functions, are usually determined independently from the DNN training process. Second, different ASR modules are separately optimised based on different criteria following a greedy build strategy. For instance, for tandem systems, the features are often extracted by a DNN trained to classify individual speech frames while acoustic models are built upon such features according to a sequence level criterion. These issues mean that the best performance is not theoretically guaranteed. This thesis focuses on alleviating both issues using joint training methods. In DNN acoustic model joint training, the decision tree HMM state tying approach is extended to cluster DNN-HMM states. Based on this method, an alternative CD-DNN training procedure without relying on any additional system is proposed, which can produce DNN acoustic models comparable in word error rate (WER) with those trained by the conventional procedure. Meanwhile, the most common hidden activation functions, the sigmoid and rectified linear unit (ReLU), are parameterised to enable automatic learning of function forms. Experiments using conversational telephone speech (CTS) Mandarin data result in an average of 3.4% and 2.2% relative character error rate (CER) reduction with sigmoid and ReLU parameterisations. Such parameterised functions can also be applied to speaker adaptation tasks. At the ASR system level, DNN acoustic model and corresponding speaker dependent (SD) input feature transforms are jointly learned through minimum phone error (MPE) training as an example of hybrid system joint training, which outperforms the conventional hybrid system speaker adaptive training (SAT) method. MPE based speaker independent (SI) tandem system joint training is also studied. Experiments on multi-genre broadcast (MGB) English data show that this method gives a reduction in tandem system WER of 11.8% (relative), and the resulting tandem systems are comparable to MPE hybrid systems in both WER and the number of parameters. In addition, all approaches in this thesis have been implemented using the hidden Markov model toolkit (HTK) and the related source code has been or will be made publicly available with either recent or future HTK releases, to increase the reproducibility of the work presented in this thesis.", "source_url": "https:\/\/openalex.org\/W2935927048", "pdf_url": "", "doi": "10.17863\/cam.38848", "handle": "", "repository_name": "Apollo (University of Cambridge)", "source_record_id": "W2935927048", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1080, "title": "Robust speech recognition in noisy and reverberant environments using deep neural network-based systems", "title_ar": "", "title_en": "Robust speech recognition in noisy and reverberant environments using deep neural network-based systems", "author": "Novoa Ilic ; José Eduardo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad de Chile", "university_source": "repository_host", "college": "", "department": "", "country": "Chile", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; robotics; speech-recognition", "keywords": "", "abstract": "In this thesis an uncertainty weighting scheme for deep neural network-hidden Markov model (DNN-HMM) based automatic speech recognition (ASR) is proposed to increase discriminability in the decoding process. To this end, the DNN pseudo-log-likelihoods are weighted according to the uncertainty variance assigned to the acoustic observation. The results presented here suggest that substantial reduction in word error rate (WER) is achieved with clean training. Moreover, modelling the uncertainty propagation through the DNN is not required and no approximations for non linear activation functions are made. The presented method can be applied to any network topology that delivers log likelihood-like scores. It can be combined with any noise removal technique and adds a minimal computational cost. This technique was exhaustively evaluated and combined with uncertainty-propagation-based schemes for computing the pseudo-log-likelihoods and uncertainty variance at the DNN output. Two proposed methods optimized the parameters of the weighting function by leveraging the grid search either on a development database representing the given task or on each utterance based on discrimination metrics. Experiments with Aurora-4 task showed that, with clean training, the proposed weighting scheme can reduce WER by a maximum of 21% compared with a baseline system with spectral subtraction and uncertainty propagation using the unscented transform. Additionally, it is proposed to replace the classical black box integration of automatic speech recognition technology in human-robot interaction (HRI) applications with the incorporation of the HRI environment representation and modeling, and the robot and user states and contexts. Accordingly, this thesis focuses on the environment representation and modeling by training a DNN-HMM based automatic speech recognition engine combining clean utterances with the acoustic channel responses and noise that were obtained from an HRI testbed built with a PR2 mobile manipulation robot. This method avoids recording a training database in all the possible acoustic environments given an HRI scenario. In the generated testbed, the resulting ASR engine provided a WER that is at least 26% and 38% lower than publicly available speech recognition application programming interfaces (APIs) with the loudspeaker and human speakers testing databases, respectively, with a limited amount of training data. This thesis demonstrates that even state-of-the-art DNN-HMM based speech recognizers can benefit by combining systems for which the acoustic models have been trained using different feature sets. In this context, the complementarity of DNN-HMM based ASR systems trained with the same data set but with different signal representations is discussed. DNN fusion methods based on flat-weight combination, the minimization of mutual information and the maximization of discrimination metrics were proposed and tested. Schemes that consider the combination of ASR systems with lattice combination and minimum Bayes risk decoding were also evaluated and combined with DNN fusion techniques. The experimental results were obtained using a publicly-available naturally-recorded highly reverberant speech data. Significant improvements in WER were observed by combining DNN-HMM based ASR systems with different feature sets, obtaining relative improvements of 10% with two classifiers and 18% with four classifiers, without any tuning or a priori information of the ASR accuracy.", "source_url": "https:\/\/openalex.org\/W2946330425", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Universidad de Chile", "source_record_id": "W2946330425", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1082, "title": "A Deep Neural Network Approach Integrating CNN and BiLSTM-Transformer Architectures for Emotion Recognition from Speech", "title_ar": "", "title_en": "A Deep Neural Network Approach Integrating CNN and BiLSTM-Transformer Architectures for Emotion Recognition from Speech", "author": "Divyansh Anand", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; medical-ai; ai-applications; predictive-analytics; cnn; transformers; sentiment-analysis; speech-recognition; ai-education; ai-industry; ai-smart-cities", "keywords": "", "abstract": "The goal of speech emotion recognition is to make human-computer interaction more efficient in several areas such as customer service, entertainment industry, human-computer interaction, healthcare, and education. Previous work in speech emotion analysis presented some issues like limited choice of features, model complexity, noise variability, and insufficient data samples, which negatively affected the prediction of emotions. This paper provides an in-depth study of speech emotion recognition using a hybrid deep neural network architecture that combines 1-D Convolutional Neural Network (CNN) and BiLSTM-Transformer models to analyze data from the Ravdess and Crema-D datasets. To make the datasets appropriate for emotion detection, all were preprocessed by means of librosa library to get rid of non-speech segments. Important sound characteristics such as Mel-Frequency Cepstral Coefficients(MFCC), Root Mean Square Energy (RMSE), and Zero Crossing Rate (ZCR) were extracted to get the spectral characteristics, intensity of feelings, and dynamic features present in the emotions. In order to improve model’s generalization and robustness noise injection, time stretching, time shifting as well as pitch shifting have been applied during data augmentation. The proposed model leverages the strengths of both CNN and BiLSTM-Transformer components. The proposed model’s 1-D CNN captures local patterns in sound whereas the BiLSTM-Transformer handles sequence data and complex hierarchical structures of audio. Various datasets such as Ravdess and Crema-D were used to train and test the performance of the model in the emotion classification task. To evaluate model’s performance, training-validation accuracy graph, confusion matrix and overall metrics which include precision, recall, and F1-score are used. Ravdess dataset achieved a high accuracy of 83.3%, surprise, angry, disgust and sad were among those emotions which this model identified with great accuracy. Crema-D dataset achieved 82.7% accuracy, and showed solid performance in detecting neutral, fear, and happy emotions. Accuracy plots between training and validation demonstrated good generalization for the unseen data and confusion matrices highlighted the emotion categories where improvement could be made.", "source_url": "https:\/\/openalex.org\/W7127233843", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORMA", "source_record_id": "W7127233843", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1083, "title": "ADAPTATION OF HYBRID DEEP NEURAL NETWORK-HIDDEN MARKOV MODEL SPEECH RECOGNITION SYSTEM USING A SUB-SPACE APPROACH", "title_ar": "", "title_en": "ADAPTATION OF HYBRID DEEP NEURAL NETWORK-HIDDEN MARKOV MODEL SPEECH RECOGNITION SYSTEM USING A SUB-SPACE APPROACH", "author": "Muhammad Rizwan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Georgia Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "The performance of automatic speech recognition (ASR) system can be enhanced by adaptation of the ASR for a particular speaker or a group of speakers. In ASR, training and testing data often do not follow the same statistics; they are often mismatched, which leads to a gap in performance. The difference between training and testing statistics can be minimized by speaker adaptation techniques, which require adaptation data from a target speaker to optimize system performance. In many cases, only a limited amount of adaptation data is available for the target speaker. This thesis proposes multiple methods for the adaptation of speech recognition system by using a limited amount of data (a few words). The first method classifies accent of a speaker to identify variability in speaking style. Results indicated that using multiple words from a speaker can be efficient and can provide better accent classification accuracy. Next adaptive phoneme classification method is proposed based on target speaker similarity with speakers in the training data. DNNs last hidden layer activations are found to be more useful in identifying phoneme classes of frames as compared with traditional raw Mel-frequency cepstral coefficients as features. Finally, speaker adaptation of ASR is presented by augmenting the speech features with the speaker features. The universal background sparse coding can provide useful speaker information for the speaker adaptation. These methods may lead to some new opportunities for research for the adaptation of the ASR.", "source_url": "https:\/\/openalex.org\/W2890698554", "pdf_url": "", "doi": "", "handle": "", "repository_name": "SMARTech Repository (Georgia Institute of Technology)", "source_record_id": "W2890698554", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1085, "title": "Top-down selective attention with a deep neural network and confidence measure for automatic speech recognition = 심층 신경망에의 하향식 주의 집중과 신뢰도 측정을 이용한 자동 음성 인식 연구", "title_ar": "", "title_en": "Top-down selective attention with a deep neural network and confidence measure for automatic speech recognition = 심층 신경망에의 하향식 주의 집중과 신뢰도 측정을 이용한 자동 음성 인식 연구", "author": "Ho-Gyeong Kim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3027384349", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3027384349", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1086, "title": "Single channel speech enhancement using Deep Neural Networks", "title_ar": "", "title_en": "Single channel speech enhancement using Deep Neural Networks", "author": "K S Kishor ; K. Sri Rama Murty", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Indian Institute of Technology Hyderabad", "university_source": "repository_host", "college": "", "department": "", "country": "India", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "Speech enhancement is an important first step in many applications like mobile communication, Speech recognition, hearing aids etc. Traditionally, speech enhancement was viewed as a pure signal processing problem, and several methods have been proposed to design linear filters to suppress the noise. However, with the advent of deep neural networks, speech enhancement has been viewed as a machine learning problem, which aims at learning a nonlinear model that maps the noisy speech signal to the clean speech signal. In this thesis, we provide an overview of existing signal processing approaches for single channel speech enhancement and compare their performance with the DNN counterparts. Even though the DNN based approaches provide significant performance improvements, they do not use phase information in the speech signal. In this work, We propose a speech enhancement framework using DNNs, which incorporates both magnitude (Cochleagram) and phase (Instantaneous frequency) information. Experimental results demonstrate that proposed framework achieves improved performance, especially at lower SNRs, over the conventional systems.", "source_url": "https:\/\/openalex.org\/W2764005420", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad)", "source_record_id": "W2764005420", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1087, "title": "Single-Channel Speech Enhancement Based on Deep Neural Networks", "title_ar": "", "title_en": "Single-Channel Speech Enhancement Based on Deep Neural Networks", "author": "Zhiheng Ouyang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Concordia University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; generative-ai; cnn; speech-recognition; gan", "keywords": "", "abstract": "Speech enhancement (SE) aims to improve the speech quality of the degraded speech. Recently, researchers have resorted to deep-learning as a primary tool for speech enhancement, which often features deterministic models adopting supervised training. Typically, a neural network is trained as a mapping function to convert some features of noisy speech to certain targets that can be used to reconstruct clean speech. These methods of speech enhancement using neural networks have been focused on the estimation of spectral magnitude of clean speech considering that estimating spectral phase with neural networks is difficult due to the wrapping effect. As an alternative, complex spectrum estimation implicitly resolves the phase estimation problem and has been proven to outperform spectral magnitude estimation. In the first contribution of this thesis, a fully convolutional neural network (FCN) is proposed for complex spectrogram estimation. Stacked frequency-dilated convolution is employed to obtain an exponential growth of the receptive field in frequency domain. The proposed network also features an efficient implementation that requires much fewer parameters as compared with conventional deep neural network (DNN) and convolutional neural network (CNN) while still yielding a comparable performance. Consider that speech enhancement is only useful in noisy conditions, yet conventional SE methods often do not adapt to different noisy conditions. In the second contribution, we proposed a model that provides an automatic \"on\/off\" switch for speech enhancement. It is capable of scaling its computational complexity under different signal-to-noise ratio (SNR) levels by detecting clean or near-clean speech which requires no processing. By adopting information maximizing generative adversarial network (InfoGAN) in a deterministic, supervised manner, we incorporate the functionality of SNR-indicator into the model that adds little additional cost to the system. We evaluate the proposed SE methods with two objectives: speech intelligibility and application to automatic speech recognition (ASR). Experimental results have shown that the CNN-based model is applicable for both objectives while the InfoGAN-based model is more useful in terms of speech intelligibility. The experiments also show that SE for ASR may be more challenging than improving the speech intelligibility, where a series of factors, including training dataset and neural network models, would impact the ASR performance.", "source_url": "https:\/\/openalex.org\/W3199713199", "pdf_url": "https:\/\/spectrum.library.concordia.ca\/986846\/1\/Ouyang_MASc_F2020.pdf", "doi": "", "handle": "", "repository_name": "Spectrum Research Repository (Concordia University)", "source_record_id": "W3199713199", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1088, "title": "Bayesian adaptation and combination of deep models for automatic speech recognition", "title_ar": "", "title_en": "Bayesian adaptation and combination of deep models for automatic speech recognition", "author": "Zhen Huang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Georgia Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; generative-ai; speech-recognition", "keywords": "", "abstract": "The objective of the proposed research is to deploy a Bayesian adaptation and combination framework for deep model based automatic speech recognition systems to combat the degradation of the recognition accuracy, which is typically observed under potential mismatched conditions between training and testing. This dissertation addresses the problem in three directions. The first direction is to perform Bayesian adaptation directly on the discriminative deep neural network models. Maximum a posteriori estimation and multi-task learning techniques are employed in the manner of regularization in the deep neural network updating formula. In the second direction, deep neural network is cast into a generative model to better leverage Bayesian techniques. Classic structured maximum a posteriori adaption is adopted by using bottleneck features derived from deep neural networks. In the third direction, a hierarchical Bayesian system combination technique is employed to further enhance the adaptation performance by leveraging the complementarity of the discriminative and generative adaptive models.", "source_url": "https:\/\/openalex.org\/W2755331049", "pdf_url": "", "doi": "", "handle": "", "repository_name": "SMARTech Repository (Georgia Institute of Technology)", "source_record_id": "W2755331049", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1089, "title": "Speech Reading with Deep Neural Networks", "title_ar": "", "title_en": "Speech Reading with Deep Neural Networks", "author": "Linnar Billman ; Johan Hullberg", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; rnn; video-analysis; speech-recognition", "keywords": "", "abstract": "Recent growth in computational power and available data has increased popularityand progress of machine learning techniques. Methods of machine learning areused for automatic speech recognition in order to allow humans to transferinformation to computers simply by speech. In the present work, we are interestedin doing this for general contexts as e.g. speakers talking on TV or newsreadersrecorded in a studio. Automatic speech recognition systems are often solely basedon acoustic data. By introducing visual data such as lip movements, robustness ofsuch system can be increased.This thesis instead investigates how well machine learning techniques can learnthe art of lip reading as a sole source for automatic speech recognition. The keyidea is to use a sequence of 24 lip coordinates to feed to the system, rather thanlearning directly from the raw video frames.This thesis designs a solution around this principle empowered by state-of-the-artmachine learning techniques such as recurrent neural networks, making use ofGPUs. We find that this design reduces computational requirements by more thana factor of 25 compared to a state-of-art machine learning solution called LipNet.This however also scales down performance to an accuracy of 80% of what LipNetachieves, while still outperforming human recognition by a factor of 150%. Theaccuracies are based on processing of yet unseen speakers.This text presents this architecture. It details its design, reports its results, andcompares its performance to an existing solution. Basedon this, it is indicated how the result can be further refined.", "source_url": "https:\/\/openalex.org\/W2930789189", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2930789189", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1090, "title": "A Study on Speech Classification Based on Deep Neural Network under Adverse Environments", "title_ar": "", "title_en": "A Study on Speech Classification Based on Deep Neural Network under Adverse Environments", "author": "Khomdet Phapatanaburi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "Recently, speech classification plays an important role in real-world speech application such as mobile devices and voice control system of a car.However, additive noise and reverberation under adverse environments can degrade the performance of speech classification.In this thesis, author focuses on improving speech classification considering additive noise and reverberation under adverse environments by using deep neural network (DNN).Two speech classification tasks: accent recognition and voice activity detection (VAD) are considered.The accent recognition task is investigated under reverberant environment while the VAD task will be examined under noisy environment.For accent recognition task, DNN-based accent recognition is proposed for modeling the reverberation over multi-frame.Moreover, the combination of scores of DNN-based accent recognition and Gaussian Mixture Model (GMM)-based accent recognition is also proposed to utilize complementary characteristics of these two models.In reverberant environment, our proposed method outperformed conventional GMM-based accent recognition.The accent recognition rate was improved from 90.7 % with GMM to 93.0 % with DNN.The combination of GMM and DNN achieved recognition rate of 97.5 %, which outperformed than the individual GMM and DNN because the complementation of GMM and DNN.At first, I would like to express my gratitude to my supervisor Professor Longbiao WANG and Professor", "source_url": "https:\/\/openalex.org\/W2756867551", "pdf_url": "https:\/\/nagaokaut.repo.nii.ac.jp\/record\/182\/files\/k829.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W2756867551", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1091, "title": "DEVELOPMENT OF A COMPUTER VISION OBJECT DETECTION SYSTEM BASED ON THE YOLO MODEL", "title_ar": "", "title_en": "", "author": "Іван Олександрович Вознюк", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2026, "language": "uk", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "computer-vision; object-detection", "keywords": "", "abstract": "Кваліфікаційна робота присвячена розробці програмної системи автоматизованого виявлення та класифікації транспортних засобів у відеопотоці засобами комп’ютерного зору. У роботі проведено аналіз предметної галузі та існуючих аналогічних систем детекції об’єктів (OpenCV DNN + SSD MobileNet, Detectron2, TensorFlow Object Detection API), обґрунтовано вибір моделі YOLOv8 як базового детектора та Python як мови реалізації. Розроблено функціонально-структурну схему системи, UML Use Case діаграму та діаграму класів. Спроектовано реляційну модель бази даних SQLite для накопичення статистики транспортного потоку між сесіями. Реалізовано чотири програмні модулі: захоплення відеопотоку засобами OpenCV, детекція об’єктів на основі YOLOv8 із фільтрацією за цільовими класами транспортних засобів, підсистема збереження результатів до SQLite з експортом у CSV, графічний інтерфейс на основі PyQt5 із багатопотоковою обробкою через QThread. Проведено трирівневе тестування системи; досягнуто показник mAP@0.5 = 0.86 при швидкості обробки 22-32 FPS у режимі CPU. Виконано техніко-економічні розрахунки програмного продукту.", "source_url": "https:\/\/openalex.org\/W7171336842", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Institutional repository of Lutsk National Technical University of Lutsk", "source_record_id": "W7171336842", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1092, "title": "Automating animal pollination detection: training an object-detection model with computer vision.", "title_ar": "", "title_en": "Automating animal pollination detection: training an object-detection model with computer vision.", "author": "Adam Itrakjy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "computer-vision", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7160195432", "pdf_url": "", "doi": "10.34885\/bjwx-nq98", "handle": "", "repository_name": "Royal Botanic Gardens, Kew", "source_record_id": "W7160195432", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1093, "title": "Models, methods and information technology for the detection of biological objects using computer vision", "title_ar": "", "title_en": "", "author": "Антон Сергійович Коваленко", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Kharkiv Polytechnic Institute", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2026, "language": "uk", "research_domain": "computer-vision", "research_subdomain": "", "categories": "computer-vision", "keywords": "", "abstract": "Дисертація на здобуття наукового ступеня доктора філософії за спеціальністю 122 Комп’ютерні науки (12 Інформаційні технології). Національний технічний університет «Харківський політехнічний інститут» Міністерства освіти і науки України, м. Харків, 2026. У дисертаційній роботі вирішено науково практичну задачу оптимізації процесу виявлення та локалізації біологічних об’єктів на зображеннях шляхом розроблення та вдосконалення моделей, методів і інформаційної технології комп’ютерного зору. Запропонований підхід забезпечує підвищення точності, швидкодії та стійкості алгоритмів обробки мікробіологічних та біомедичних зображень, що дозволяє автоматизувати аналіз візуальних даних, покращити якість діагностики та ефективність дослідницьких процедур. Об’єктом дослідження є процес обробки цифрових зображень в біоінформаційних системах. Предметом дослідження є методи, моделі та інформаційна технологія обробки цифрових зображень біологічних об’єктів. Метою дисертаційного дослідження є розробка моделей, методів та інформаційної технології для автоматичного створення анотованих наборів даних для виявлення, локалізації та сегментації біологічних об’єктів на зображеннях мікроскопічних знімків.", "source_url": "https:\/\/openalex.org\/W7202015788", "pdf_url": "https:\/\/repository.kpi.kharkov.ua\/bitstreams\/cda0ca94-4511-4b06-abc9-73cc83c1b0d6\/download", "doi": "", "handle": "", "repository_name": "eNTUKhPIIR Repository (Kharkiv Polytechnic Institute)", "source_record_id": "W7202015788", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1094, "title": "Taking the labour out of labour detection – a computer vision approach", "title_ar": "", "title_en": "Taking the labour out of labour detection – a computer vision approach", "author": "Benjamin Miles", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Nottingham", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; computer-vision; ai-applications; predictive-analytics; ai-agriculture", "keywords": "", "abstract": "Difficulty during parturition has negative consequences for both animal welfare and farm profitability on cattle farms. Early intervention has been shown to be a key requirement for minimising losses caused by calving problems, however, this requires high levels of monitoring to ensure that problems are promptly detected and resolved. Current methods of detecting calving either rely on labour intensive manual observations or cow-based sensors which are often expensive and require the placement of physical sensors on each animal. In this study, we explore the potential of computer vision technology to accurately predict calving in dairy cattle and highlighted the advantages of both object and pose detection approaches in comparison to current methods.", "source_url": "https:\/\/openalex.org\/W7218032228", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Nottingham ePrints (University of Nottingham)", "source_record_id": "W7218032228", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1095, "title": "Transparency Detection in Computer Vision Applications", "title_ar": "", "title_en": "Transparency Detection in Computer Vision Applications", "author": "Volker Knauthe", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Technical University of Darmstadt", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; expert-systems; ai-applications; object-detection; ai-industry", "keywords": "", "abstract": "Transparency is all around us. From windows to glass encased shopping malls. From drinking glasses to aquariums. And from safety goggles to protective encasings. It is one of the signs of technological advancement and prosperity, widely utilized in art and engineering. In contrast, it is not clearly understood how humans even perceive transparency. To build advanced autonomous moving agents or vision aids, we need to at least solve this intriguing challenge for computer vision applications. This thesis highlights and examines possible adverse effects that transparency related effects introduce to computer vision applications. Common depth estimation techniques only account for one depth per pixel, while a drinking glass already adds two possible depths. Looking through transparency, distortions and reflections affect object detection methods. Transparent objects transmit light differently, when dust accumulates or scratches interrupt their surface. This leads to gradually worse results from, e.g., condition monitoring setups behind protective glasses. Disregarding human made transparencies, there are also natural phenomena like heat shimmers and water droplets that play a role in industrial applications like condition monitoring or for autonomous agents. Solving those issues is not always possible, due to the ill posed task of restoring a pre transparency influenced image state. However, I introduce several possibilities of detecting and mitigating transparency related effects. A common issue with machine learning related solutions for novel challenges oftentimes lies in the scarcity of suitable data and\/or the massive labor cost involved in creating new data from scratch, as in the wider case of transparency. To enable further research concerning transparency related effects on computer vision applications, I introduce a state-of-the-art luminance keying dataset acquisition method that greatly facilitates the translation from real world objects into annotated datasets. It combines a black 99,99\\% light absorbing background with a post processing pipeline that leads to automated annotation masks. It is also the first easy setup that works with transparent objects, resolving the most demanding side effects from common chroma keying: no color bleeding, fuzzy edges, foreground background mixing, or shadows. In summary, this thesis showcases the importance and challenging aspects of transparency for computer vision applications and introduces novel prospects to facilitate further research.", "source_url": "https:\/\/openalex.org\/W7126018379", "pdf_url": "", "doi": "10.26083\/tuda-7751", "handle": "", "repository_name": "TUbilio (Technical University of Darmstadt)", "source_record_id": "W7126018379", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1096, "title": "Object detection and analysis using computer vision", "title_ar": "", "title_en": "Object detection and analysis using computer vision", "author": "Victor Friedmann Sandin ; Anna Thomsen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "computer-vision; object-detection", "keywords": "", "abstract": "The project was performed at Consat Engineering AB’s facilities at Lindholmen, Gothenburg, in the spring of 2017. Consat Engineering AB produced a prototype that included a vision system, to analyze objects. This thesis exists to develop a proof of concept in form of a vision program, lowering the cost of the vision system, in order to be able to implement it in a commercial product. A pre-study comparing different hardware platforms and software libraries was conducted in order to best meet the requested specifications. The open source vision library OpenCV was chosen as the software library. A Raspberry Pi, running a Linux based OS called Raspbian, equipped with a camera was chosen as the hardware platform. Both of the choices included minimal expense towards the final cost of a future product that could be developed from this concept. After a finalized concept an evaluation test concluded that the program developed during this project fulfilled the requirements and proved that an open source solution significantly can lower the cost of the current vision system the prototype utilizes.", "source_url": "https:\/\/openalex.org\/W2772850197", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2772850197", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1097, "title": "Object detection and distance measurement with computer vision", "title_ar": "", "title_en": "Object detection and distance measurement with computer vision", "author": "Zhu, Jun", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; ai-applications; object-detection; ai-industry", "keywords": "", "abstract": "With the development of computer and robotic technology, it becomes more and more common for robots to do hard, regular work for human. The appearance of robots has already changed humanity’s daily life and improved the efficiency of industry production. Among different kinds of robot applications, object detection and distance measurement are especially important for industry production. Like quality examination, product sorting and dimension measurement. These robots in these applications are programmable, high speed and high efficiency. To do object detection and distance measurement, deep learning and time of flight camera are widely used in industry. Engineers have already spent a lot of time doing related research. However, it is not easy and cheap to implement these methods. Deep learning needs so much computing power and industrial cameras are very expensive. Hence, in this report, the aim is to explore some easy ways to do object detection and distance measurement for some easy application in single background. To achieve this goal, some external sensors are necessary. Later the report will explain the theoretical basis and the experimental results of these two different ways. Then there will be a short discussion about the future development, which can improve the efficiency of these two methods covered in the report.", "source_url": "https:\/\/openalex.org\/W2904725650", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2904725650", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1098, "title": "Object detection, recognition and classification using computer vision and artificial intelligence approaches", "title_ar": "", "title_en": "Object detection, recognition and classification using computer vision and artificial intelligence approaches", "author": "Gharib Ismail Al Matroushi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Loughborough University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; quantum-ml; data-mining; object-detection; image-segmentation; ai-industry", "keywords": "", "abstract": "Object detection and recognition has been used extensively in recent years to solve numerus challenges in different fields.Due to the vital roles they play, object detection and recognition has enabled quantum leaps in many industry fields by helping to overcome some serious challenges and obstacles.For example, worldwide security concerns have drawn the attention and stimulated the use of highly intelligent computer vision technology to provide security in different environments and in diverse terrains.In addition, some wildlife is at present exposed to danger and extinction worldwide.Therefore, early detection and recognition of potential threats to wildlife have become essential and timely.The extent of using computer vision and artificial intelligence to convert the seemingly insecure world to a more secure one has been widely accepted.Such technologies are used in monitoring, tracking, organising, analysing objects in a scene and for a number of other countless purposes.With the goal of further expanding the application domains of computer vision and artificial intelligence, a novel algorithm to classify mixed industrial airborne waste particles is developed under the research context of this thesis.Initially, a dataset of industrial airborne particles was collected and imaged for this purpose.All images were captured using an advanced microscopic camera.The particles are segmented from the background image using K-means clustering and Hue, Saturation and Value (HSV) colour space features.Subsequently, the Hue, Saturation and Value (HSV) colour histogram data and Gray Level Co-occurrence Matrix (GLCM) texture features are combined into one feature vector of size 278 features, for use in dust sample classification.A feature selection algorithm is employed to minimise the number of features to ten, without compromising the accuracy that can be obtained when using all features.This significant minimisation of features used in classification would improve the processing time.A further computer vision system proposed in this thesis is for automated road surface type recognition aimed at use within autonomous vehicles.Traditional feature driven machine learning based algorithm and a deep learning based approach are proposed to recognise 5 different road surfaces namely: asphalt, concreate, rough, blocks and sand.Firstly, a novel algorithm is developed to exclude irrelevant objects and to create the region of interest within a snapshot image of the road surface.This segmentation algorithm eliminates unnecessary objects being considered for processing as the road in which the vehicle is being driven, by dividing the input image into top and bottom parts, to attempt a fast, region-dependent segmentation.Subsequently, the algorithm uses regiongrowing to create region of interests.For the recognition task, a feature based algorithm that uses a combination of colour histogram, Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern Feature Selection ...........", "source_url": "https:\/\/openalex.org\/W2892376930", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Loughborough University Institutional Repository (Loughborough University)", "source_record_id": "W2892376930", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1099, "title": "Study on Object Detection using Computer Vision by Artificial Neural Network", "title_ar": "", "title_en": "Study on Object Detection using Computer Vision by Artificial Neural Network", "author": "Jittima Varagul", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; object-detection", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7207983040", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7207983040", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1100, "title": "COMPUTER VISION AND DEEP LEARNING WITH APPLICATIONS TO OBJECT DETECTION, SEGMENTATION, AND DOCUMENT ANALYSIS", "title_ar": "", "title_en": "COMPUTER VISION AND DEEP LEARNING WITH APPLICATIONS TO OBJECT DETECTION, SEGMENTATION, AND DOCUMENT ANALYSIS", "author": "Xianzhi Du", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Maryland", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; cnn; object-detection; image-segmentation", "keywords": "", "abstract": "There are three work on signature matching for document analysis. In the first work, we propose a large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbors for query signatures. We show that our algorithm can achieve a high accuracy even when few signatures are collected from one same person and perform fast matching when dealing with a large dataset. In the second work, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. We demonstrate considerable improvement over state of the art methods. In the third work, we present a partial signature matching method using graphical models. In additional to the second work, modified shape context features are extracted from the contour of signatures to describe both full and partial signatures. Hierarchical Dirichlet processes are implemented to infer the number of salient regions needed. The results show the effectiveness of the approach for both the partial and full signature matching. There are three work on deep learning for object detection and segmentation. In the first work, we propose a deep neural network fusion architecture for fast and robust pedestrian detection. The proposed network fusion architecture allows for parallel processing of multiple networks for speed. A single shot deep convolutional network is trained as an object detector to generate all possible pedestrian candidates of different sizes and occlusions. Next, multiple deep neural networks are used in parallel for further refinement of these pedestrian candidates. We introduce a soft-rejection based network fusion method to fuse the soft metrics from all networks together to generate the final confidence scores. Our method performs better than existing state-of-the-arts, especially when detecting small-size and occluded pedestrians. Furthermore, we propose a method for integrating pixel-wise semantic segmentation network into the network fusion architecture as a reinforcement to the pedestrian detector. In the second work, in addition to the first work, a fusion network is trained to fuse the multiple classification networks. Furthermore, a novel soft-label method is devised to assign floating point labels to the pedestrian candidates. This metric for each candidate detection is derived from the percentage of overlap of its bounding box with those of other ground truth classes. In the third work, we propose a boundary-sensitive deep neural network architecture for portrait segmentation. A residual network and atrous convolution based framework is trained as the base portrait segmentation network. To better solve boundary segmentation, three techniques are introduced. First, an individual boundary-sensitive kernel is introduced by labeling the boundary pixels as a separate class and using the soft-label strategy to assign floating-point label vectors to pixels in the boundary class. Each pixel contributes to multiple classes when updating loss based on its relative position to the contour. Second, a global boundary-sensitive kernel is used when updating loss function to assign different weights to pixel locations on one image to constrain the global shape of the resulted segmentation map. Third, we add multiple binary classifiers to classify boundary-sensitive portrait attributes, so as to refine the learning process of our model.", "source_url": "https:\/\/openalex.org\/W2891789699", "pdf_url": "", "doi": "10.13016\/m2vt1gs9h", "handle": "", "repository_name": "University Libraries (University of Maryland)", "source_record_id": "W2891789699", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1101, "title": "Automated Activity Discovery and Object Detection With Computer Vision: Towards Unsupervised Learning for Breakfast to Surgery", "title_ar": "", "title_en": "Automated Activity Discovery and Object Detection With Computer Vision: Towards Unsupervised Learning for Breakfast to Surgery", "author": "Michael Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Harvard University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; data-mining; predictive-analytics; cnn; object-detection; image-segmentation; video-analysis", "keywords": "", "abstract": "While deep learning in computer vision has enjoyed tremendous success in recent years, much progress remains to be made in important tasks. Many state-of-the-art vision models follow a supervised-learning paradigm, making them dependent on a large amount of labeled training data. This limits both their potential learning capabilities, as well as their application domains. In this work, we present contributions to expand the state-of-the-art in computer vision along both these dimensions, focusing on how to both perceive activities and objects in the video domain. We first tackle the problem of unsupervised activity discovery, where given a collection of untrimmed and unlabeled videos, we wish to learn a semantically meaningful embedding of the data which can be used to segment the data into \"discovered\" activities. To expand the learning capabilities of computer vision models, we avoid the intensive bottleneck of amassing a large annotated dataset, and seek to do so without requiring explicit training labels. We consider unsupervised activity discovery from the perspective of the inherently hierarchical nature of activities, e.g. a single complex activity may be modeled as a sequence of smaller sub-activities. Recognizing dependencies across this spectrum of complexities may therefore lead to greater video understanding. Accordingly, we introduce a hyperbolic embedding representation for video data to simultaneously capture hierarchical and semantic relationships in video data. While motivated by their prior success in modeling explicitly hierarchical data found in language, here we show how to leverage hyperbolic representations for the implicitly hierarchical nature of video data. We demonstrate that our hyperbolic video embeddings approach learn representations that significantly outperform the previous state-of-the-art for unsupervised activity segmentation on the Breakfast and 50Salads datasets, and that our hierarchical embeddings naturally allow discovery of activities at multiple levels of complexity. Following this, we next consider the second challenge of expanding the application domains of state-of-the-art computer vision models. We focus on open, or non-laparoscopic surgery, which represents the vast majority of all operating room procedures. Despite this prominence, few tools exist to objectively evaluate these techniques at scale, and current efforts involve human expert-based visual assessment. We therefore leverage a state-of-the-art convolutional neural network architecture for object detection to detect operating hands in open surgery videos. Automated assessment was expanded by combining model predictions with a fast object tracker to enable surgeon-specific hand tracking. To train our model, we used publicly available videos of open surgery from YouTube and annotated these with spatial bounding boxes of operating hands. Our model's spatial detections of operating hands significantly outperforms the detections achieved using pre-existing hand-detection datasets, and allow for insights into intra-operative movement patterns and economy of motion. Finally, we consider how to combine both advances in capability and application domain to focus on automatic activity discovery in surgery videos. Using our developed unsupervised learning algorithm, we demonstrate that we can discover multiple surgical activities in various operating procedures from just a collection of untrimmed and unlabeled YouTube surgery videos. We leverage a state-of-the-art deep learning architecture to extract base features, which we then encode into temporal and hierarchically-aware hyperbolic embeddings. We can then discover an arbitrary number of activities based on our clustering and decoding procedure. Through qualitative evaluation we show how our segmentations align with changes in video activity over time. Based on the alignment of our video segmentations, we present further insights and evidence into the hierarchical nature of activities. In all, our contributions present a step towards fully automated video activity understanding and discovery in real world domains.", "source_url": "https:\/\/openalex.org\/W3081564847", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)", "source_record_id": "W3081564847", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1102, "title": "Computer Vision for Automated Robotic Device Disassembly: Object Detection, Pose Estimation, and Action Prediction", "title_ar": "", "title_en": "Computer Vision for Automated Robotic Device Disassembly: Object Detection, Pose Estimation, and Action Prediction", "author": "Ruiz, Sebastian", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Georg-August-Universität Göttingen", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; robotics; ai-applications; few-zero-shot; data-mining; predictive-analytics; cnn; object-detection; image-segmentation; llm; ai-industry", "keywords": "", "abstract": "The growth in population and technology has led to an increased demand for electronic products, resulting in a higher volume of end-of-life devices. While recycling these products is critical for reducing environmental impact, efficient recycling is rarely prioritized in the design of these products by manufacturers. Currently, recycling automation is primarily limited to specific device models, with processes manually programmed for devices that are received in large quantities by recycling companies. Modifying these processes for different models or devices is often difficult and typically requires a complete overhaul of both hardware and software systems. Robotic automation could make disassembly economically viable, but achieving efficient disassembly of electronic devices presents significant challenges. Devices vary widely in terms of disassembly requirements, even within the same product family. As such, any automated system needs to be capable of handling this high degree of variability. The development of a robust robotic automation system for disassembly has the potential to enhance sustainability efforts by increasing the number of devices that are recycled. Moreover, in electronic recycling, many variations of the same device type, such as smartphones or hard drives, need to be disassembled. These devices often arrive in different physical conditions, requiring a flexible robotic system that can adapt to these variations. Although processing large batches of similar devices can reduce variability, the broad range of models and conditions still presents a major challenge. This dissertation focuses on the computer vision and action prediction methods for the disassembly of electronic devices. More specifically, the objective is to create a software system that generalizes to new in-distribution devices, and is reconfigurable to new device families. The research breaks down the problem into key components: pose estimation, device classification, rotation estimation, gap detection, and action prediction, analysing the performance and generalization capabilities of each method. Pose estimation, employing a segmentation model, demonstrates high accuracy on seen devices and only slightly reduced performance on unseen devices. Device classification is addressed using a supervised convolutional neural network, achieving 99% accuracy, and a zero-shot classification model with an average accuracy of 89%. While the supervised method proves more effective, the zero-shot classification model shows promise for further development. Rotation estimation achieves consistent accuracy across both seen and unseen devices, highlighting its potential as a robust method. For gap detection, a segmentation model outperforms a clustering method on seen devices, in cases involving unseen devices, the segmentation model accuracy varied depending on the complexity of the gaps. Action prediction is approached using two methods: a decision graph and a large language model (LLM) with retrieval augmented generation. The LLM demonstrates the flexibility of a data-driven approach, while the decision graph requires handcrafted decision rules. The LLM is provided with the disassembly objective, context of the possible actions it can take, and knowledge from known disassembly actions, to predict an action. On a test set, it achieves 91% accuracy and outperforms a baseline majority vote. The findings indicate that while generalizing to new devices poses significant challenges, it is achievable with varying degrees of success across different methods. Future work is proposed to enhance these methods' adaptability and performance, aiming to make them suitable for real-world applications in reconfigurable industrial settings. This dissertation establishes a foundational framework for further exploration of generalizable vision and action prediction systems.", "source_url": "https:\/\/openalex.org\/W4404295995", "pdf_url": "", "doi": "10.53846\/goediss-10856", "handle": "", "repository_name": "eDiss (Georg-August-Universität Göttingen)", "source_record_id": "W4404295995", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1104, "title": "Computer Vision: Object recognition with deep learning applied to fashion items detection in images", "title_ar": "", "title_en": "Computer Vision: Object recognition with deep learning applied to fashion items detection in images", "author": "Hélder Filipe de Sousa Russa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "deep-learning; computer-vision", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2771513231", "pdf_url": "https:\/\/repositorio-aberto.up.pt\/bitstream\/10216\/107862\/2\/220803.pdf", "doi": "10.34626\/wt5z-5c59", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W2771513231", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1105, "title": "Computer Vision for Distant Vehicle Detection: How to Find Region Proposals for Low-Resolution Objects?", "title_ar": "", "title_en": "Computer Vision for Distant Vehicle Detection: How to Find Region Proposals for Low-Resolution Objects?", "author": "Ann-Katrin Fattal", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; cnn", "keywords": "", "abstract": "Safety is crucial to the development and acceptance of assisted and highly automated driving functions. In 2017, 69.3% of German fatal accidents happened on roads where the speed limit was not enforced or higher than 100km\/h. At this speed, to perform safe driving maneuvers, the environment perception is a key element. Detecting objects in distances up to 200m is instrumental in anticipating potential obstacles. Due to hardware limitations, an automotive camera maps cars in e.g. 200m distance to an image of only 8px width. Hence, the absence of local details degrades the state-of-the-art detection methods designed for detecting bigger sized objects. The scope of this thesis is to develop, extend and evaluate object region localizers to improve the detection range of cameras. A saliency inspired voting map is proposed that highlights anomalies in automotive scenes. The environment is modeled with few homogeneous regions representing the background within the image. Such global features allow detecting small object regions. Inspired by the concept of learning features, this thesis presents machine learning methods detecting small objects. Existing labeled data sets such as the KITTI data set only have object regions which sizes are larger than 25px height. The presented methods in this thesis are performed against a newly created data set with 67% of object regions having a width of 8-30px, a range that has rarely been subject to research yet. Convolutional Neural Network based localizers have been evaluated and extended. To maintain a low computational power, only small networks can be used. However, such networks are limited to the usage of local features. An incorporation of global generic priors to local networks is proposed, which increases the recall especially for small object regions. The parameters to adjust Region Proposal Networks (RPNs) for the special case of small objects are further optimized and the main parameters are identified. A novel relevance based net-surgery is introduced, allowing to select the most relevant features while maintaining the recall of the RPN. It is then possible to reduce the network size to these few features.", "source_url": "https:\/\/openalex.org\/W3014231450", "pdf_url": "https:\/\/tuprints.ulb.tu-darmstadt.de\/11310\/13\/2019-10-30_Fattal_AnnKatrin.pdf", "doi": "10.25534\/tuprints-00011310", "handle": "", "repository_name": "Technischen Universität Darmstadt", "source_record_id": "W3014231450", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1106, "title": "Rethinking image formats for computer vision: JPEG sRGB, linear, and log RGB; object detection and shadow removal", "title_ar": "", "title_en": "Rethinking image formats for computer vision: JPEG sRGB, linear, and log RGB; object detection and shadow removal", "author": "Heather Fryling", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Northeastern University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; generative-ai; object-detection", "keywords": "", "abstract": "From driverless cars to generative AI, computer vision is making a larger impact in our lives every day. Computer vision models are powered by data, but for many datasets, the image processing pipeline is unknown. Could there be important patterns in the data lost to image processing? Computer vision datasets in common use today are comprised of images formatted for human viewing, not machine learning. As a result, physical relationships dictated by the laws of optics are not readily available to computer vision models. Is it time we re-examined where and how we source our data? After all, we live in a time when edge devices and phones are more common than ever, and every camera is capable of capturing RAW data. In this work, I quantify differences in performance between models trained on sRGB, JPEG-compressed, linear RGB, and log RGB data, both with strictly controlled data processing pipelines and with unknown data processing pipelines. The results suggest there are improvements to be had by using more physically accurate data and that further research is warranted.--Author's abstract", "source_url": "https:\/\/openalex.org\/W4408219060", "pdf_url": "", "doi": "10.17760\/d20679790", "handle": "", "repository_name": "Digital Repository Service (Northeastern University)", "source_record_id": "W4408219060", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1108, "title": "Object Localization In Computer Vision", "title_ar": "", "title_en": "Object Localization In Computer Vision", "author": "Qwider. Mesmoudii ; Mohamed. Zigh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; image-segmentation", "keywords": "", "abstract": "In this work, we have introduced the computer vision discipline that includes several tasks, such as object recognition, segmentation and detection. solving the object localization task usually needs a set of features such as HAAR cascade or HOG these features must change according to the type of the localized object and this create an additional overhead. With the arrival of deep learning era it will be possible to both learn the representation subproblem (feature extraction) and the regression\/classification subproblem. We leverage the concepts offered by deep learning and use them to tackle our localization problem which consists of recognizing the category of the main object (in our case cat or dog), in addition to the drawing of a bounding box around the detected object. our model mainly consists of a feature extractor termed Xception and a set of dense layers that capture the categories and the bounding box. We also notice that the proposed model has achieved a classification rate of 100% and an IoU rate of 78% on the validation dataset. As a perspective of this work, we can compare our results with more elaborated feature extractors such as SeNet,ResNet; Additionally, we can extend the presented model to tackle the detection task which assume that the image contains several objects of different classes", "source_url": "https:\/\/openalex.org\/W7135311009", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/43ed13c9-c488-4f1d-a43f-6b3867546a29\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135311009", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1109, "title": "Computer vision for driver assistance systems", "title_ar": "", "title_en": "Computer vision for driver assistance systems", "author": "Sandhya Sridhar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Georgia Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; object-detection; video-analysis", "keywords": "", "abstract": "The objective of the proposed thesis is to illustrate the training, validation and evaluation of vehicle detection algorithms using computer vision and deep learning methods, and vehicle tracking in video sequences. Object detection poses a major challenge in localizing the object and finding its exact coordinates, in addition to determining its presence in the image. Objects in varying scales must also be detected in the image, and the algorithm chosen for object detection must be feasible for real-time detection. The research work focuses on a traditional machine learning approach and a deep learning approach for overcoming the object detection challenges, and object tracking algorithms, to improve the accuracy of object detection.", "source_url": "https:\/\/openalex.org\/W2807511107", "pdf_url": "", "doi": "", "handle": "", "repository_name": "SMARTech Repository (Georgia Institute of Technology)", "source_record_id": "W2807511107", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1110, "title": "Study for the vision-based object detection and tracking on unmanned aerial vehicles", "title_ar": "", "title_en": "Study for the vision-based object detection and tracking on unmanned aerial vehicles", "author": "Eduard Rius Martínez", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "computer-vision; robotics; object-detection", "keywords": "", "abstract": "This document is a degree thesis that consists of a study of vision-based computing techniques for the detection and tracking of objects. The development of the study has been carried out using OpenCV, CUDA and PX4. The first chapter presents a definition of the objectives, requirements and planning for the development of the study. Next a brief introduction to computer vision and its state of the art is included. After presenting the work to be done, chapters 3, 4 and 5 explain the different approaches to detection, tracking and control algorithms, and each chapter explains the solution chosen for each one. Chapter 6 introduces the whole system and explains how the above algorithms are implemented in a drone. Finally, in chapter 7 the tests and their results are presented and discussed", "source_url": "https:\/\/openalex.org\/W3165911597", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W3165911597", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1111, "title": "Development of a robot vision system for object detection and localization", "title_ar": "", "title_en": "Development of a robot vision system for object detection and localization", "author": "Shuqi Ning", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "deep-learning; computer-vision; robotics; ai-applications; object-detection; ai-industry", "keywords": "", "abstract": "With the increasing speed of computer operation, machine vision has a wide prospect in robot positioning and navigation, human-computer interaction, industrial measurement and other fields. This project aims to develop a robot vision system which involves object detections and object localization. The object detection using a fixed camera is often limited by the angle and filed in the environment and therefore it is difficult to detect a wide range of objects. The project used a Jidetech Outdoor 2MP Waterproof 20X Zoom POE PTZ camera, which can rotate 360 degrees to detect the whole environment. Due to its inability to detect distance, a vision algorithm combining depth camera and PTZ camera is proposed, and a robot vision system is developed to estimate the 3D depth information of the whole environment. The main work includes the following aspects. Firstly, the composition and principle of object detection algorithm under deep learning are studied, and YOLOv2 algorithm is selected as the object detection algorithm. Then, based on the darkflow framework, the collected data sets are trained, and the accuracy is ensured to meet the test requirements. Secondly, the working principle of D45i RGB-D camera is studied and it is used to detect and measure the object. Then, by controlling of the rotation angle of PTZ camera, the detection of object is realized under PTZ camera. According to the position of the object, two detection algorithms are developed to obtain the relative position form the camera with the object. Therefore, the RGB-D camera can be calibrated with respect to the PTZ camera to estimate the depth information so as to determine the location of the object. Finally, through the comparison of the experimental results, the error data is obtained and analyzed so as to improve the algorithms for localization of the object with better accuracy.", "source_url": "https:\/\/openalex.org\/W3036670756", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W3036670756", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1112, "title": "Computer vision as a tool for forestry", "title_ar": "", "title_en": "Computer vision as a tool for forestry", "author": "Filip Bång", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; cnn; object-detection; video-analysis; ai-industry", "keywords": "", "abstract": "Forestry is a large industry in Sweden and methods have been developed to try to optimize the processes in the business. Yet computer vision has not been used to a large extent despite other industries using computer vision with success. Computer vision is a sub area of machine learning and has become popular thanks to advancements in the field of machine learning. This project plans to investigate how some of the architectures used in computer vision perform when applied in the context of forestry. In this project four architectures were selected that have previously proven to perform well on a general dataset. These four architectures were configured to continue to train on trees and other objects in the forest. The trained architectures were tested by measuring frames per second (FPS) when performing object detection on a video and mean average precision (mAP) which is a measure of how well a trained architecture detects objects. The fastest one was an architecture using a Single Shot Detector together with MobileNet v2 as a base network achieving 29 FPS. The one with the best accuracy was using Faster R-CNN and Inception Resnet as a base network achieving 0.119 mAP on the test set. The overall bad mAP for the trained architectures resulted in that none of the architectures were considered to be useful in a real world scenario as is. Suggestions on how to improve the mAP is focused on improvements on the dataset.", "source_url": "https:\/\/openalex.org\/W2994761302", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2994761302", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1113, "title": "STABILIZATION AND OBJECT TRACKING BASED ON COMPUTER VISION: APPLICATION IN A QUAD-ROTOR", "title_ar": "", "title_en": "STABILIZATION AND OBJECT TRACKING BASED ON COMPUTER VISION: APPLICATION IN A QUAD-ROTOR", "author": "Luis Arreola", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; robotics; object-detection", "keywords": "", "abstract": "\"This work presents a Research and Development thesis of a functional Unmanned Aerial Vehicle (UAV), integrating optical flow algorithms and navigation based on object detection using Computer-Vision Features Classifiers and Robotics. This work presents two main problems to solve. In one of them, a position estimation system for Unmanned Aerial Vehicles consisting of hardware and software is presented. It is based on low-cost devices(GPS, commercial autopilot sensors and optical flow algorithm) implemented in an on-board microcomputer. Comparative tests were conducted using the proposed approach and the conventional one, where only fusion of GPS and inertial sensors are used. Furthermore, the second problem addresses object detection and tracking based on Computer Vision techniques, in essence, a training technique known as Haar Feature-based cascade classifier, image processing is made on-board with a high processor single-board computer. Based on the detected object and its position, the UAV must track it to ensure to be positioned in a center position with respect to the object and in a safe distance to it.\"", "source_url": "https:\/\/openalex.org\/W2902442432", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W2902442432", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1114, "title": "Discourse-aware neural machine translation", "title_ar": "", "title_en": "Discourse-aware neural machine translation", "author": "Wang Longyue", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dublin City University", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; nlp; predictive-analytics; machine-translation", "keywords": "", "abstract": "Machine translation (MT) models usually translate a text by considering isolated sentences based on a strict assumption that the sentences in a text are independent of one another. However, it is a truism that texts have properties of connectedness that go beyond those of their individual sentences. Disregarding dependencies across sentences will harm translation quality especially in terms of coherence, cohesion, and consistency. Previously, some discourse-aware approaches have been investigated for conventional statistical machine translation (SMT). However, this is a serious obstacle for the state-of-the-art neural machine translation (NMT), which recently has surpassed the performance of SMT. In this thesis, we try to incorporate useful discourse information for enhancing NMT models. More specifically, we conduct research on two main parts: 1) exploiting novel document-level NMT architecture; and 2) dealing with a specific discourse phenomenon for translation models. Firstly, we investigate the influence of historical contextual information on the perfor- mance of NMT models. A cross-sentence context-aware NMT model is proposed to consider the influence of previous sentences in the same document. Specifically, this history is summarized using an additional hierarchical encoder. The historical representations are then integrated into the standard NMT model in different strategies. Experimental results on a Chinese–English document-level translation task show that the approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points. In addition, analysis and comparison also give insightful discussions and conclusions for this research direction. Secondly, we explore the impact of discourse phenomena on the performance of MT. In this thesis, we focus on the phenomenon of pronoun-dropping (pro-drop), where, in pro-drop languages, pronouns can be omitted when it is possible to infer the referent from the context. As the data for training a dropped pronoun (DP) generator is scarce, we propose to automatically annotate DPs using alignment information from a large parallel corpus. We then introduce a hybrid approach: building a neural-based DP generator and integrating it into the SMT model. Experimental results on both Chinese–English and Japanese–English translation tasks demonstrate that our approach achieves a significant improvement of up to +1.58 BLEU points with 66% F-score for DP generation accuracy. Motivated by this promising result, we further exploit the DP translation approach for advanced NMT models. A novel reconstruction-based model is proposed to reconstruct the DP-annotated source sentence from the hidden states of either encoder or decoder, or both components. Experimental results on the same translation tasks show that the proposed approach significantly and consistently improves translation performance over a strong NMT baseline, which is trained on DP-annotated parallel data. To avoid the errors propagated from an external DP prediction model, we finally investigate an end-to-end DP translation model. Specifically, we improve the reconstruction-based model from three perspectives. We first employ a shared reconstructor to better exploit encoder and decoder representations. Secondly, we propose to jointly learn to translate and predict DPs. In order to capture discourse information for DP prediction, we finally combine the hierarchical encoder with the DP translation model. Experimental results on the same translation tasks show that our approach significantly improves both translation performance and DP prediction accuracy.", "source_url": "https:\/\/openalex.org\/W2951604644", "pdf_url": "https:\/\/doras.dcu.ie\/22903\/1\/Longyue_PhD_Thesis_Final.pdf", "doi": "", "handle": "", "repository_name": "Dublin City University Open Access Institutional Repository (Dublin City University)", "source_record_id": "W2951604644", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1116, "title": "Understanding, Explaining, and Aligning Neural Models for Machine Translation", "title_ar": "", "title_en": "Understanding, Explaining, and Aligning Neural Models for Machine Translation", "author": "Patrick Fernandes", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; predictive-analytics; llm; machine-translation", "keywords": "", "abstract": "The recent success of large neural network models trained on massive amounts of unlabeled data has, yet again, highlighted the power of scaling, in terms of both model capacity and data quantity. However, despite its successes, scaling up also adds new additional challenges: (i) the large size and complexity of these models make it difficult to understand their learning dynamics and global behavior, and how these change with the data distribution; (ii) their complexity also make it difficult to extract explanations of how they arrive at individual predictions, and to know how to leverage these explanations; (iii) this, coupled with the fact that the data used to train these models is often noisy, can lead to models that do not align with their intended use case. This thesis tackles all of the challenges above, with a particular focus on models trained for machine translation (MT). In the first part, we study the scaling behaviour of MT mod els trained to translate between multiple high-resource language pairs (LPs), showing that the way performance for individual LPs scales with model capacity is only dependent on the properties of that “task” and is invariant to cross-lingual interactions. We also investigate howmuchcontextinformationMTmodelsleverageindocument-levelMT,proposingways to increase it. In the second part, we start by proposing a novel method for extracting expla nations of how neural models arrive at their predictions, relying on the assumption that good explanations should help other models learn, and leveraging bilevel optimization\/meta learning to learn explainers that teach well. Then we showthat not onlyare reasoning models state-of-the-art systems for low-resource MT, but that their chain-of-thought rationales are good explanations to help teach smaller LLMs the same task. In the third and final part, we start by showing that, by including metrics of translation quality (trained on human feedback annotations) in the inference stage of a trained MTmodel, we can improve the qualityof the translation and align the system towards human-like translations. We then also show that we can almost completely remove the dependency on human annotations by leveraging large language models to provide (fine-grained) translation quality feedback.", "source_url": "https:\/\/openalex.org\/W7116361097", "pdf_url": "", "doi": "10.1184\/r1\/30752876", "handle": "", "repository_name": "KiltHub Repository", "source_record_id": "W7116361097", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1117, "title": "Machine translation of morphologically rich languages using deep neural networks", "title_ar": "", "title_en": "Machine translation of morphologically rich languages using deep neural networks", "author": "Peyman Passban", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dublin City University", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; computer-vision; nlp; image-segmentation; llm; machine-translation", "keywords": "", "abstract": "This thesis addresses some of the challenges of translating morphologically rich languages (MRLs). Words in MRLs have more complex structures than those in other languages, so that a word can be viewed as a hierarchical structure with several internal subunits. Accordingly, word-based models in which words are treated as atomic units are not suitable for this set of languages. As a commonly used and eff ective solution, morphological decomposition is applied to segment words into atomic and meaning-preserving units, but this raises other types of problems some of which we study here. We mainly use neural networks (NNs) to perform machine translation (MT) in our research and study their diff erent properties. However, our research is not limited to neural models alone as we also consider some of the difficulties of conventional MT methods. First we try to model morphologically complex words (MCWs) and provide better word-level representations. Words are symbolic concepts which are represented numerically in order to be used in NNs. Our first goal is to tackle this problem and find the best representation for MCWs. In the next step we focus on language modeling (LM) and work at the sentence level. We propose new morpheme-segmentation models by which we finetune existing LMs for MRLs. In this part of our research we try to find the most efficient neural language model for MRLs. After providing word- and sentence-level neural information in the first two steps, we try to use such information to enhance the translation quality in the statistical machine translation (SMT) pipeline using several diff erent models. Accordingly, the main goal in this part is to find methods by which deep neural networks (DNNs) can improve SMT. One of the main interests of the thesis is to study neural machine translation (NMT) engines from diff erent perspectives, and finetune them to work with MRLs. In the last step we target this problem and perform end-to-end sequence modeling via NN-based models. NMT engines have recently improved significantly and perform as well as state-of-the-art systems, but still have serious problems with morphologically complex constituents. This shortcoming of NMT is studied in two separate chapters in the thesis, where in one chapter we investigate the impact of diff erent non-linguistic morpheme-segmentation models on the NMT pipeline, and in the other one we benefit from a linguistically motivated morphological analyzer and propose a novel neural architecture particularly for translating from MRLs. Our overall goal for this part of the research is to find the most suitable neural architecture to translate MRLs. We evaluated our models on diff erent MRLs such as Czech, Farsi, German, Russian, and Turkish, and observed significant improvements. The main goal targeted in this research was to incorporate morphological information into MT and define architectures which are able to model the complex nature of MRLs. The results obtained from our experimental studies confirm that we were able to achieve our goal.", "source_url": "https:\/\/openalex.org\/W2795545650", "pdf_url": "https:\/\/doras.dcu.ie\/22200\/1\/Thesis-Hardbound-13212218-20180111-Peyman-Passban.pdf", "doi": "", "handle": "", "repository_name": "Dublin City University Open Access Institutional Repository (Dublin City University)", "source_record_id": "W2795545650", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1119, "title": "Neural Language Models with Morphology for Machine Translation", "title_ar": "", "title_en": "Neural Language Models with Morphology for Machine Translation", "author": "Tomáš Musil", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; llm; machine-translation", "keywords": "", "abstract": "Language models play an important role in many natural language processing tasks. In this thesis, we focus on language models built on artificial neural net- works. We examine the possibilities of using morphological annotations in these models. We propose a neural network architecture for a language model that explicitly makes use of morphological annotation of the input sentence: instead of word forms it processes lemmata and morphological tags. Both the baseline and the proposed method are evaluated on their own by perplexity, and also in the context of machine translation by the means of automatic translation quality evaluation. While in isolation the proposed model significantly outperforms the baseline, there is no apparent gain in machine translation. 1", "source_url": "https:\/\/openalex.org\/W2947679756", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W2947679756", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1120, "title": "Multimodal neural machine translation", "title_ar": "", "title_en": "Multimodal neural machine translation", "author": "Malek Mgaidi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; deep-learning; computer-vision; nlp; optimization; multimodal; video-analysis; machine-translation; speech-recognition", "keywords": "", "abstract": "Neural Machine Translation is a newly emerging approach to machine translation which attempts to build and train a large neural network that reads a sentence and outputs a notable translation. Nowadays the performance of such machines is in- creasingly in demand and Multilingual Neutral Machine Translation has emerged. There is an abundant bibliography on Multimodal Neutral Machine Translation as there is a consistent number of models which differ by the final aspects of trans- lation (adequacy, fidelity and fluency) and the multitude of inputs they can use (images, videos, text, speech or a combination of them). The GroundedTranslation was chosen in this work. As we know, by far, the state- of-the art provides some techniques such as using Long Short Term Memory and an encoder-decoder architecture for example to solve some training problems and they already have been implemented by Elliot Desmond for this retained solution. However, no investigation has been oriented toward the optimizer. This work aims to study multimodal neural machine translation architectures and its behavior un- der different optimization algorithms.", "source_url": "https:\/\/openalex.org\/W2520119691", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)", "source_record_id": "W2520119691", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1121, "title": "Neural Machine Translation for Arabic Language", "title_ar": "", "title_en": "Neural Machine Translation for Arabic Language", "author": "Manar Alkhatib", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "nlp; machine-translation; arabic-nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3109160103", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3109160103", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1122, "title": "Domain adaptation for statistical machine translation and neural machine translation", "title_ar": "", "title_en": "Domain adaptation for statistical machine translation and neural machine translation", "author": "Jian Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dublin City University", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; llm; machine-translation", "keywords": "", "abstract": "Both Statistical Machine Translation and Neural Machine Translation (NMT) are data-dependent learning approaches to Machine Translation (MT). The prerequisite is a large volume of training data in order to generate good statistical models. However, even if large volume of training corpora are available for MT, finding training data which are similar to the specific domains is still difficult. The MT models trained using the limited specific domain data cannot have sufficient coverage on the linguistic phenomena in that domain, which makes this a very challenging task. Because word meanings, genres or topics differ between domains, using the additional data from other domains can increase the dissimilar- ities between the training and testing data, and result in reduced translation quality. Such a challenge is defined as ‘domain adaptation’ challenge in the literature. In this thesis, we investigate domain adaptation in two different scenarios, namely a domain-awareness scenario and a domain-unawareness scenario. In a domain-awareness scenario, the domain information is given explicitly in the training data. We are interested in developing domain-adaptation techniques which transfer knowledge gained from the other domains to a desired domain. In the approach proposed here probabilistic values indicating the domain-likeness features for words are estimated by the context rather than by the words themselves. We then apply those features to the combined translation models in an MT system. We empirically show that translation quality can be significantly improved compared with previous related work. We then turn our interest to the recently proposed neural network training. We describe a domain-adaptation approach which can exploit large pre-trained word vector models. We evaluate our approach on both language modelling and machine translation tasks to demonstrate its efficiency, effectiveness and flexibility in a domain-awareness scenario. xiiIn a domain-unawareness scenario, the domain information is not given explicitly in the training data. The training data is heterogeneous, e.g. originated from tens or even hundreds of different resources without well-defined domain labels. We overcome such a challenge by deriving the topic information from the training corpora using well-estimated topic modelling algorithms. In this scenario, we pay particular attention to the most recent NMT framework. We are concerning with making a better lexical choice and improving the overall translation quality. Experimentally, we show that our model can perform better lexical choice, improve the overall translation quality and reduce the number of unknown words.", "source_url": "https:\/\/openalex.org\/W2767972135", "pdf_url": "https:\/\/doras.dcu.ie\/21949\/1\/thesis_hardbound_jian_zhang_56101775.pdf", "doi": "", "handle": "", "repository_name": "Dublin City University Open Access Institutional Repository (Dublin City University)", "source_record_id": "W2767972135", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1123, "title": "Robust Neural Machine Translation", "title_ar": "", "title_en": "Robust Neural Machine Translation", "author": "Abdul Rafae Khan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "City University of New York", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; nlp; transfer-learning; data-mining; machine-translation", "keywords": "", "abstract": "This thesis aims for general robust Neural Machine Translation (NMT) that is agnostic to the test domain. NMT has achieved high quality on benchmarks with closed datasets such as WMT and NIST but can fail when the translation input contains noise due to, for example, mismatched domains or spelling errors. The standard solution is to apply domain adaptation or data augmentation to build a domain-dependent system. However, in real life, the input noise varies in a wide range of domains and types, which is unknown in the training phase. This thesis introduces five general approaches to improve NMT accuracy and robustness, where three of them are invariant to models, test domains, and noise types. First, we describe a novel unsupervised text normalization framework Lex-Var, to reduce the lexical variations for NMT. Then, we apply the phonetic encoding as auxiliary linguistic information and obtained very significant (5 BLEU point) improvement in translation quality and robustness. Furthermore, we introduce the random clustering encoding method based on our hypothesis of Semantic Diversity by Phonetics and generalizes to all languages. We also discussed two domain adaptation models for the known test domain. Finally, we provide a measurement of translation robustness based on the consistency of translation accuracy among samples and use it to evaluate our other methods. All these approaches are verified with extensive experiments across different languages and achieved significant and consistent improvements in translation quality and robustness over the state-of-the-art NMT.", "source_url": "https:\/\/openalex.org\/W2997654874", "pdf_url": "", "doi": "", "handle": "", "repository_name": "CUNY Academic Works (City University of New York)", "source_record_id": "W2997654874", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1124, "title": "Syntactic and semantic features for statistical and neural machine translation", "title_ar": "", "title_en": "Syntactic and semantic features for statistical and neural machine translation", "author": "Maria Nădejde", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "nlp; machine-translation", "keywords": "", "abstract": "Machine Translation (MT) for language pairs with long distance dependencies and word reordering, such as German–English, is prone to producing output that is lexically or syntactically incoherent. Statistical MT (SMT) models used explicit or latent syntax to improve reordering, however failed at capturing other long distance dependencies. This thesis explores how explicit sentence-level syntactic information can improve translation for such complex linguistic phenomena. In particular, we work at the level of the syntactic-semantic interface with representations conveying the predicate-argument structures. These are essential to preserving semantics in translation and SMT systems have long struggled to model them. String-to-tree SMT systems use explicit target syntax to handle long-distance reordering, but make strong independence assumptions which lead to inconsistent lexical choices. To address this, we propose a Selectional Preferences feature which models the semantic affinities between target predicates and their argument fillers using the target dependency relations available in the decoder. We found that our feature is not effective in a string-to-tree system for German→English and that often the conditioning context is wrong because of mistranslated verbs. To improve verb translation, we proposed a Neural Verb Lexicon Model (NVLM) incorporating sentence-level syntactic context from the source which carries relevant semantic information for verb disambiguation. When used as an extra feature for re-ranking the output of a German→ English string-to-tree system, the NVLM improved verb translation precision by up to 2.7% and recall by up to 7.4%. While the NVLM improved some aspects of translation, other syntactic and lexical inconsistencies are not being addressed by a linear combination of independent models. In contrast to SMT, neural machine translation (NMT) avoids strong independence assumptions thus generating more fluent translations and capturing some long-distance dependencies. Still, incorporating additional linguistic information can improve translation quality. We proposed a method for tightly coupling target words and syntax in the NMT decoder. To represent syntax explicitly, we used CCG supertags, which encode subcategorization information, capturing long distance dependencies and attachments. Our method improved translation quality on several difficult linguistic constructs, including prepositional phrases which are the most frequent type of predicate arguments. These improvements over a strong baseline NMT system were consistent across two language pairs: 0.9 BLEU for German→English and 1.2 BLEU for Romanian→English.", "source_url": "https:\/\/openalex.org\/W2894807362", "pdf_url": "https:\/\/era.ed.ac.uk\/bitstream\/1842\/31346\/1\/N%c4%83dejde2018.pdf", "doi": "", "handle": "", "repository_name": "ERA", "source_record_id": "W2894807362", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1125, "title": "Document-wide Neural Machine Translation", "title_ar": "", "title_en": "Document-wide Neural Machine Translation", "author": "Sameen Maruf", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "deep-learning; nlp; machine-translation", "keywords": "", "abstract": "Machine translation (MT) is an important task in natural language processing as it automates the translation process and reduces the reliance on human translators. The goal of MT is to generate translations of a given text in a source language to that in the target language. With the advent of neural networks, the translation quality surpasses that of the translations obtained using statistical techniques. However, most of the neural translation models still perform a sentence-by-sentence translation, ignoring all extra-sentential information. This research aims to build efficient neural models for document-level translation, which incorporate global contextual information when translating sentences. Our experimental results confirm the significance of leveraging document-wide context information for improving translation quality.", "source_url": "https:\/\/openalex.org\/W3027945255", "pdf_url": "", "doi": "10.26180\/10295462", "handle": "", "repository_name": "Figshare", "source_record_id": "W3027945255", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1126, "title": "Hybrid Neural-Symbolic Machine Translation", "title_ar": "", "title_en": "Hybrid Neural-Symbolic Machine Translation", "author": "Jingyi Zhang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; llm; machine-translation", "keywords": "", "abstract": "Machine translation (MT) investigates the use of software to translate a source sentence into the corresponding target sentence.The state-of-the-art MT approach is statistical MT, which learns statistical models from a parallel bilingual corpus to model the translation process.When this thesis started (2015), symbolic MT, which is based on symbolic translation rules, such as phrase-based MT [32], hierarchical phrase-based MT [8], and syntax-based MT [36,43], had the best translation performance.Because symbolic MT is based on context-free and ambiguous symbolic translation rules, a log-linear framework and statistical models learned from parallel or monolingual corpora, such as language models [32], reordering models [63,14], rule selection models [35,10,23], are exploited to select the most possible translation during symbolic MT decoding.Now (2018), neural MT (NMT) [4,55,68], which is based on a single large neural network and does not use any explicit translation rules, has outperformed symbolic MT on various translation tasks.Compared to symbolic MT, NMT uses distributed word representations and generally produces more fluent translations, but often sacrifices adequacy [31], such as NMT is more likely to generate completely unrelated translations, under and over translations.This thesis focuses on hybrid neural-symbolic MT methods, which aim to produce translations that are both fluent and adequate by combining the advantages of both symbolic and neural MT.The first part of our contribution is that we develop various neural models for the log-linear framework of symbolic MT, because neural models learn distributed representations for words and sentences, which can generalize better", "source_url": "https:\/\/openalex.org\/W3029827299", "pdf_url": "https:\/\/naist.repo.nii.ac.jp\/record\/10879\/files\/R014266.pdf", "doi": "10.34413\/dr.01508", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W3029827299", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1127, "title": "Learning Hyperparameters for Neural Machine Translation", "title_ar": "", "title_en": "Learning Hyperparameters for Neural Machine Translation", "author": "Kenton Murray", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; deep-learning; nlp; responsible-ai; machine-translation; fairness", "keywords": "", "abstract": "Machine Translation, the subfield of Computer Science that focuses on translating between two human languages, has greatly benefited from neural networks. However, these neural machine translation systems have complicated architectures with many hyperparameters that need to be manually chosen. Frequently, these are selected either through a grid search over values, or by using values commonplace in the literature. However, these are not theoretically justified and the same values are not optimal for all language pairs and datasets. Fortunately, the innate structure of the problem allows for optimization of these hyperparameters during training. Traditionally, the hyperparameters of a system are chosen and then a learning algorithm optimizes all of the parameters within the model. In this work, I propose three methods to learn the optimal hyperparameters during the training of the model, allowing for one step instead of two. First, I propose using group regularizers to learn the number, and size of, the hidden neural network layers. Second, I demonstrate how to use a perceptron-like tuning method to solve known problems of undertranslation and label bias. Finally, I propose an Expectation-Maximization based method to learn the optimal vocabulary size and granularity. Using various techniques from machine learning and numerical optimization, this dissertation covers how to learn hyperparameters of a Neural Machine Translation system while training the model itself.", "source_url": "https:\/\/openalex.org\/W3042494786", "pdf_url": "", "doi": "10.7274\/mg74qj7616h", "handle": "", "repository_name": "Figshare", "source_record_id": "W3042494786", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1128, "title": "Decoding neural machine translation using gradient descent", "title_ar": "", "title_en": "Decoding neural machine translation using gradient descent", "author": "Emanuel Snelleman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "deep-learning; nlp; machine-translation", "keywords": "", "abstract": "Neural machine translation is a resent approach to machine translation. Neural machine translation uses an artificial neural network to learn and perform translations. Usually an encoder-decoder architecture is used for the neural network. The encoder encodes the input sentence into a fix sized vector meant to represent the meaning of the sentence, the decoder then decodes that vector into a sentence of the same meaning in the other language. The decoder will generate the translation sequentially, and will output a probability distribution over the known words at each step of the sequence. From these probability distributions words needs to be chosen to form a sentence. The word chosen given any specific probability distribution will affect how the next probability distribution is generated. It is therefore important to chose the words well in order to get a translation as good as possible. In this thesis a way of choosing the words using gradient descent is tested. Decoding using gradient descent is compared to some other methods of decoding but gives no clear indication of working better than the other decoding methods. However, some results indicate gradient descent decoding might have some potential if further developed.", "source_url": "https:\/\/openalex.org\/W2519643131", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2519643131", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1129, "title": "Preventing Over-Translation in Simultaneous Neural Machine Translation", "title_ar": "", "title_en": "Preventing Over-Translation in Simultaneous Neural Machine Translation", "author": "保昌 加納", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "deep-learning; nlp; machine-translation", "keywords": "", "abstract": "These days it is becoming common to communicate with international people as globalization expands.Although many international conferences are held, not all the speakers and listeners are good at English.Therefore, interpreters sometimes attend such conferences.For the interpretation in conferences and negotiations, we often require simultaneous interpretation which starts to translate before the speaker finishes the utterance.Simultaneous interpretation is important because the meeting time becomes much longer if we wait for the entire utterance before translation.Also, we sometimes tell jokes in negotiation, and it is funnier if the translation of a joke is done immediately without any interval.We would like to hire simultaneous interpreters for every international event, but it is highly costly.Therefore, there is a huge demand for simultaneous machine translation which can be used at a lower cost.Recent studies on machine translation are based on neural networks and they produce more fluent output than traditional statistical machine translation.Although machine translation research has evolved rapidly, simultaneous translation is still a difficult task.This is because the machine needs to output partial translation without reading the latter part of the input sentence as context.A lot of previous research on simultaneous translation uses the translation model pre-trained with bilingual full-sentence pairs.However, at the inference step, the model needs to translate only a partial input much shorter than a full sentence.Therefore, there is a gap between the training of translation models and", "source_url": "https:\/\/openalex.org\/W7144228045", "pdf_url": "https:\/\/naist.repo.nii.ac.jp\/record\/2000249\/files\/R018652.pdf", "doi": "10.34413\/dr.02042", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W7144228045", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1130, "title": "Neural machine translation for multimodal interaction", "title_ar": "", "title_en": "Neural machine translation for multimodal interaction", "author": "Koel Dutta Chowdhury", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dublin City University", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; deep-learning; computer-vision; nlp; cnn; multimodal; image-classification; machine-translation", "keywords": "", "abstract": "Typically it is seen that multimodal neural machine translation (MNMT) systems trained on a combination of visual and textual inputs produce better translations than systems trained using only textual inputs. The task of such systems can be decomposed into two sub-tasks: learning visually grounded representations from images and translation of the textual counterparts using those representations. In a multi-task learning framework, translations are generated from an attention-based encoder-decoder framework and grounded representations that are learned from pretrained convolutional neural networks (CNNs) for classifying images. In this thesis, I study different computational techniques to translate the meaning of sentences from one language into another considering the visual modality as a naturally occurring meaning representation bridging between languages. We examine the behaviour of state-of-the-art MNMT systems from the data perspective in order to understand the role of the both textual and visual inputs in such systems. We evaluate our models on the Multi30k, a large-scale multilingual multimodal dataset publicly available for machine learning research. Our results in the optimal and sparse data settings show that the differences in translation system performance are proportional to the amount of both visual and linguistic information whereas, in the adversarial condition the effect of the visual modality is rather small or negligible. The chapters of the thesis follow a progression starting with using different state-of-the-art MMT models for incorporating images in optimal data settings to creating synthetic image data under the low-resource scenario and extending to addition of adversarial perturbations to the textual input for evaluating the real contribution of images.", "source_url": "https:\/\/openalex.org\/W3004128329", "pdf_url": "https:\/\/doras.dcu.ie\/23747\/1\/Final_version_MSc_Thesis_2019.pdf", "doi": "", "handle": "", "repository_name": "Dublin City University Open Access Institutional Repository (Dublin City University)", "source_record_id": "W3004128329", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1131, "title": "Online Learning in Neural Machine Translation", "title_ar": "", "title_en": "Online Learning in Neural Machine Translation", "author": "Luis Cebrián Chuliá", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "computer-vision; nlp; machine-translation", "keywords": "", "abstract": "[EN] High quality translations are in high demand these days. Although machine translation offers acceptable performance, it is not sufficient in some cases and human supervision is required. In order to ease the translation task of the human, machine translation systems take part in this process. When a sentence in the source language needs to be translated, it is fed to the system which outputs a hypothesis translation. The human then, corrects this hypothesis (also known as post-editing) in order to obtain a high quality translation. Being able to transfer the knowledge that a human translator exhibit when post-editing a translation to the machine translation system is a desirable feature, as it has been proven that a more accurate machine translation system helps to increase the efficiency of the post-editing process. Because the post-editing scenario requires an already trained system, online learning techniques are suited for this task. In this work, three online learning algorithms have been proposed and applied to a neural machine translation sys- tem in a post-editing scenario. They rely on the Passive-Aggressive online learn- ing approach in which the model is updated after every sample in order to fulfil a correctness criterion while remembering previously learned information. The goal is to adapt and refine an already trained system with new samples on-the- fly as the post-editing process takes place (hence, the update time must be kept under control). Moreover, these new algorithms are compared with well-stablished online learning variants of the stochastic gradient descent algorithm. Results show im- provements on the translation quality of the system after applying these algo- rithms, reducing human effort in the post-editing process.", "source_url": "https:\/\/openalex.org\/W7139603174", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Hispana", "source_record_id": "W7139603174", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1132, "title": "Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation", "title_ar": "", "title_en": "Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation", "author": "Paige Finkelstein", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Washington", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; nlp; predictive-analytics; machine-translation", "keywords": "", "abstract": "Neural machine translation (NMT) is a promising approach to the task of machine translation that has led to state-of-the-art results in many settings. However, NMT translations are still far from sufficient for many practical purposes. For this reason, there is a robust body of ongoing research on how to improve NMT systems with human feedback. This feedback can take many forms, including interactive-predictive NMT, post-editing of NMT output, and soliciting ratings or corrections of translations for the purposes of online learning. While these approaches are often effective, they are also often very time consuming and expensive. For that reason, there is important research into the question of how best to ensure that any human effort is used optimally. In this thesis, we contribute to this line of work by proposing a system that learns when it should ask for human feedback on a translation. This system makes use of an existing pre-trained NMT model, and introduces an additional feedback-requester model that learns to selectively solicit feedback from a human translator on the NMT translations. This system reduces human effort by directing attention to the most problematic sentences in a document, and the feedback-requester model itself is updated according to the translator's feedback. We also experiment with two active learning (AL) strategies for the feedback-requester model, and present a range of experiments simulating human translator use of the system and show the results over time.", "source_url": "https:\/\/openalex.org\/W3172116156", "pdf_url": "https:\/\/digital.lib.washington.edu\/bitstreams\/af687ae6-4e54-43c8-b6df-b15737b75835\/download", "doi": "", "handle": "", "repository_name": "ResearchWorks at the University of Washington (University of Washington)", "source_record_id": "W3172116156", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1134, "title": "Neural machine translation with limited resources", "title_ar": "", "title_en": "Neural machine translation with limited resources", "author": "Tasnim Mohiuddin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; deep-learning; computer-vision; nlp; transfer-learning; predictive-analytics; machine-translation", "keywords": "", "abstract": "With the advent of deep neural networks in recent years, Neural Machine Translation (NMT) systems have achieved state-of-the-art performance on standard translation benchmarks. NMT is a way to translate from one language to another with a single neural network in an end-to-end manner. The NMT models have emerged quickly, and within a few years of research, they have outperformed the traditional statistical systems with impressive performance. Despite the success of NMT models in standard benchmarks, there are some notable limitations. One of them is that NMT models are known to be data-hungry, i.e., they tend to work very well only when a massive amount of parallel training data (a.k.a. bitext) is available, but perform poorly when the data is limited. Except for some mainstream languages, e.g., English, French, or Chinese, most natural languages are low-resourced and lack large parallel data. Moreover, acquiring large bitext corpora is not viable in most scenarios, especially with resource-constrained conditions like low-resource languages. Researchers have made numerous endeavors to expand the success of NMT from high-resource to low-resource languages like transfer learning, data augmentation, and pivoting. However, they still require strong cross-lingual signals, i.e., lots of parallel data. One solution to this problem might be transferring cross-lingual signals through cross-lingual word embeddings (CLWEs), which can be learned from monolingual data in an unsupervised way or with the help of a small seed dictionary. CLWEs seem to be very promising in resource-constrained machine translation (MT). Most of the successful and predominant CLWE methods (a.k.a. word translation methods) learn a linear mapping function based on the isomorphic assumption, which is problematic. We hypothesize to learn the cross-lingual mapping in a projected latent space which would give the model enough flexibility to induce the required geometric structures such that it would be easier to align the embeddings. Based on this hypothesis, we propose two novel models for learning CLWEs. We empirically show that our methods are particularly very effective for low-resource languages. We then turn our attention from word- to sentence-level translation with limited resources. Specifically, we focus on data augmentation strategies widely used in NLP and Computer Vision to increase the robustness of the models in resource-constrained scenarios. We investigate the domain-mismatch issue thoroughly that hinders the all-embracing success of the existing techniques in NMT. Eventually, we introduce a novel data augmentation framework for low-resource NMT that leverages the neighboring samples of the original parallel data without explicitly using additional monolingual data. Our framework can diversify the in-domain parallel data in a controlled way. We perform extensive experiments on four low-resource language pairs comprising data from different domains. We have shown that our method is comparable to the traditional back-translation that uses extra in-domain monolingual data. Typically, NMT systems are trained on heterogeneous data from different domains, sources, topics, styles, and modalities. The quality of the data also varies a lot. Usually, during training, all the data are concatenated and randomly shuffled. However, not all of them may be useful, some data may be redundant, and some might even be noisy and detrimental to the final NMT system performance. These problems are more acute in low-resource languages compared to the high-resource ones. Consequently, we explore the possibilities of curriculum training for NMT systems, i.e., presenting the data to the NMT systems in a systematic order during training. We introduce a two-stage curriculum training framework for NMT where we fine-tune a base NMT model on subsets of data. To select the data subsets, we propose two scoring approaches --- deterministic scoring using pre-trained methods and online scoring that considers prediction scores of the emerging NMT model. Our curriculum strategies consistently demonstrate better translation quality and faster convergence (approximately 50% fewer updates) on both high- and low-resource languages.", "source_url": "https:\/\/openalex.org\/W4281623321", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/157475\/2\/Thesis-Tasnim.pdf", "doi": "10.32657\/10356\/157475", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4281623321", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1135, "title": "Approximating neural machine translation for efficiency", "title_ar": "", "title_en": "Approximating neural machine translation for efficiency", "author": "Alham Fikri Aji", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers; machine-translation", "keywords": "", "abstract": "Neural machine translation (NMT) has been shown to outperform statistical machine translation. However, NMT models typically require a large number of parameters and are expensive to train and deploy. Moreover, its large model size makes parallel training inefficient due to costly network communication. Likewise, distributing and locally running the model for a client-based NMT model such as a web browser or mobile device remains challenging. This thesis investigates ways to approximately train an NMT system by compressing either the gradients or the parameters for faster communication or reduced memory consumption. We propose a gradient compression technique that exchanges only the top 1% of the most significant gradient values while delaying the rest to be considered for the next iteration. This method reduces the network communication cost by 50-fold but causes noisy gradient updates. We also find that Transformer–the current state-of-the-art NMT architecture–is highly sensitive to noisy gradients. Therefore, we extend the compression technique by restoring the compressed gradient with locally-computed gradients. We obtained a linear scale-up in parallel training without sacrificing model performance. We also explore transfer learning as a better method of initialising the training. With transfer learning, the model converges faster and can be trained with more aggressive hyperparameters. Lastly, we propose a log-based quantisation method to compress the model size. Models are quantised to 4-bit precision with no noticeable quality degradation after re-training combined with reserving the quantisation errors as feedback.", "source_url": "https:\/\/openalex.org\/W3108682037", "pdf_url": "", "doi": "10.7488\/era\/533", "handle": "", "repository_name": "ERA", "source_record_id": "W3108682037", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1136, "title": "Non-Autoregressive Neural Machine Translation", "title_ar": "", "title_en": "Non-Autoregressive Neural Machine Translation", "author": "Jindřich Helcl", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "deep-learning; nlp; machine-translation", "keywords": "", "abstract": "In recent years, a number of mehtods for improving the decoding speed of neural machine translation systems have emerged. One of the approaches that pro- poses fundamental changes to the model architecture are non-autoregressive models. In standard autoregressive models, the output token distributions are conditioned on the previously decoded outputs. The conditional dependence al- lows the model to keep track of the state of the decoding process, which improves the fluency of the output. On the other hand, it requires the neural network computation to be run sequentially, and thus it cannot be parallelized. Non- autoregressive models impose conditional independence on the output distri- butions, which means that the decoding process is parallelizable and hence the decoding speed improves. A major drawback of this approach is lower trans- lation quality compared to the autoregressive models. The goal of the non- autoregressive translation research is to find methods that improve the trans- lation quality, while retaining high decoding speed. In this thesis, we explore the research progress so far and identify flaws in the generally accepted eval- uation methodology. We experiement with non-autoregressive models trained with connectionist temporal classification. We find that even though our models...", "source_url": "https:\/\/openalex.org\/W7135983976", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135983976", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1137, "title": "Efficient neural machine translation", "title_ar": "", "title_en": "Efficient neural machine translation", "author": "Jiatao Gu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Hong Kong", "university_source": "repository_host", "college": "", "department": "", "country": "Hong Kong", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "nlp; machine-translation", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2902773018", "pdf_url": "", "doi": "10.5353\/th_991044058182403414", "handle": "", "repository_name": "The HKU Scholars Hub (University of Hong Kong)", "source_record_id": "W2902773018", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1138, "title": "Classification using data mining algorithms and fuzzy logic on neutron star signal data", "title_ar": "", "title_en": "", "author": "Ιωάννης Μανώλης", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Piraeus", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2025, "language": "el", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; expert-systems; data-mining", "keywords": "", "abstract": "Η παρούσα μελέτη διερευνά την απόδοση τόσο κλασικών όσο και ασαφών αλγορίθμων ταξινόμησης στην πρόβλεψη της αυθεντικότητας σημάτων pulsar, χρησιμοποιώντας ένα ευρέως αποδεκτό σύνολο χαρακτηριστικών που έχει προταθεί ως πρότυπη αναφορά για τις μελλοντικές έρευνες ανίχνευσης. Εκπαιδεύτηκαν και αξιολογήθηκαν έξι μοντέλα μηχανικής μάθησης, συμπεριλαμβανομένων τεσσάρων κλασικών αλγορίθμων ταξινόμησης (Λογιστική Παλινδρόμηση, Random Forest, SVM και XGBoost), καθώς και δύο προσαρμοσμένων ταξινομητών ασαφούς λογικής (Fuzzy k-NN και Fuzzy Decision Tree). Τα ασαφή μοντέλα ενσωματώνουν μηχανισμούς ταξινομικής ασαφοποίησης και διαδικασίες εξαγωγής κανόνων για τη βελτίωση της ερμηνευσιμότητας. Τα αποτελέσματα έδειξαν ότι τα κλασικά μοντέλα επιτυγχάνουν υψηλή ακρίβεια και ανθεκτικότητα, με τα Random Forest και SVM να παρουσιάζουν ιδιαίτερα υψηλές αποδόσεις. Ωστόσο, οι ασαφείς προσεγγίσεις προσφέρουν σημαντικά πλεονεκτήματα στη διαφάνεια των αποφάσεων, που τις καθιστούν χρήσιμες σε πεδία όπου η ερμηνευσιμότητα είναι κρίσιμη. Η μελέτη επιβεβαιώνει επίσης την καταλληλότητα των επιλεγμένων χαρακτηριστικών για την αξιόπιστη ανίχνευση σημάτων pulsar και προτείνει κατευθύνσεις για μελλοντική έρευνα ώστε να αξιολογηθεί περαιτέρω η επάρκειά τους. Η έρευνα υλοποιήθηκε χρησιμοποιώντας τις κατάλληλες βιβλιοθήκες της γλώσσας προγραμματισμού Python.", "source_url": "https:\/\/openalex.org\/W7152335526", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dione (University of Piraeus)", "source_record_id": "W7152335526", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1139, "title": "Intelligent data mining using artificial neural networks and genetic algorithms : techniques and applications", "title_ar": "", "title_en": "Intelligent data mining using artificial neural networks and genetic algorithms : techniques and applications", "author": "Jianhua Yang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Warwick", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; expert-systems; data-mining; predictive-analytics; optimization; xai", "keywords": "", "abstract": "Data Mining (DM) refers to the analysis of observational datasets to find relationships and to summarize the data in ways that are both understandable and useful. Many DM techniques exist. Compared with other DM techniques, Intelligent Systems (ISs) based approaches, which include Artificial Neural Networks (ANNs), fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as Genetic Algorithms (GAs), are tolerant of imprecision, uncertainty, partial truth, and approximation. They provide flexible information processing capability for handling real-life situations. This thesis is concerned with the ideas behind design, implementation, testing and application of a novel ISs based DM technique. The unique contribution of this thesis is in the implementation of a hybrid IS DM technique (Genetic Neural Mathematical Method, GNMM) for solving novel practical problems, the detailed description of this technique, and the illustrations of several applications solved by this novel technique. GNMM consists of three steps: (1) GA-based input variable selection, (2) Multi- Layer Perceptron (MLP) modelling, and (3) mathematical programming based rule extraction. In the first step, GAs are used to evolve an optimal set of MLP inputs. An adaptive method based on the average fitness of successive generations is used to adjust the mutation rate, and hence the exploration\/exploitation balance. In addition, GNMM uses the elite group and appearance percentage to minimize the randomness associated with GAs. In the second step, MLP modelling serves as the core DM engine in performing classification\/prediction tasks. An Independent Component Analysis (ICA) based weight initialization algorithm is used to determine optimal weights before the commencement of training algorithms. The Levenberg-Marquardt (LM) algorithm is used to achieve a second-order speedup compared to conventional Back-Propagation (BP) training. In the third step, mathematical programming based rule extraction is not only used to identify the premises of multivariate polynomial rules, but also to explore features from the extracted rules based on data samples associated with each rule. Therefore, the methodology can provide regression rules and features not only in the polyhedrons with data instances, but also in the polyhedrons without data instances. A total of six datasets from environmental and medical disciplines were used as case study applications. These datasets involve the prediction of longitudinal dispersion coefficient, classification of electrocorticography (ECoG)\/Electroencephalogram (EEG) data, eye bacteria Multisensor Data Fusion (MDF), and diabetes classification (denoted by Data I through to Data VI). GNMM was applied to all these six datasets to explore its effectiveness, but the emphasis is different for different datasets. For example, the emphasis of Data I and II was to give a detailed illustration of how GNMM works; Data III and IV aimed to show how to deal with difficult classification problems; the aim of Data V was to illustrate the averaging effect of GNMM; and finally Data VI was concerned with the GA parameter selection and benchmarking GNMM with other IS DM techniques such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Fuzzy ARTMAP, and Cartesian Genetic Programming (CGP). In addition, datasets obtained from published works (i.e. Data II & III) or public domains (i.e. Data VI) where previous results were present in the literature were also used to benchmark GNMM’s effectiveness. As a closely integrated system GNMM has the merit that it needs little human interaction. With some predefined parameters, such as GA’s crossover probability and the shape of ANNs’ activation functions, GNMM is able to process raw data until some human-interpretable rules being extracted. This is an important feature in terms of practice as quite often users of a DM system have little or no need to fully understand the internal components of such a system. Through case study applications, it has been shown that the GA-based variable selection stage is capable of: filtering out irrelevant and noisy variables, improving the accuracy of the model; making the ANN structure less complex and easier to understand; and reducing the computational complexity and memory requirements. Furthermore, rule extraction ensures that the MLP training results are easily understandable and transferrable.", "source_url": "https:\/\/openalex.org\/W2111157768", "pdf_url": "http:\/\/wrap.warwick.ac.uk\/3831\/1\/WRAP_THESIS_Yang_2010.pdf", "doi": "10.82444\/warw.33491953", "handle": "", "repository_name": "Warwick Research Archive Portal (University of Warwick)", "source_record_id": "W2111157768", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1140, "title": "The development of new artificial intelligence based hybrid techniques combining bees algorithm, data mining and genetic algorithm for detection, classification and prediction of faults in induction motors", "title_ar": "", "title_en": "The development of new artificial intelligence based hybrid techniques combining bees algorithm, data mining and genetic algorithm for detection, classification and prediction of faults in induction motors", "author": "Ammar K. Al-Musawi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Cardiff University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "data-mining", "categories": "machine-learning; computer-vision; ai-applications; data-mining; predictive-analytics; optimization; ai-industry", "keywords": "", "abstract": "This thesis focuses on applying Artificial Intelligence (AI) methods for detecting, classifying and predicting the faults in induction motors in order to prevent any failures happening during their operation due to loading conditions. It is very important to monitor and detect any faults in the motor during its operation in order to alert the operators so that potential problems could be avoided. In this study, a new AI algorithm has been developed and applied to detect, classify and predict the induction motor faults at an early stage. This is based on a hybrid approach using the Bees Algorithm (BA) and Data Mining called Bee for Mining (B4M), which overcomes the drawbacks of current AI methods in achieving higher classification accuracy with reduced rule set generated from the training data. The proposed B4M algorithm has been implemented, tested and validated using the University of California at Irvine (UCI) dataset, and was compared with other well-known classifiers. Later, the proposed B4M algorithm was applied in dealing with two most common faults, firstly, that of rotor (one rotor bar, four rotor bars and eight rotor bars), and secondly, bearing defects (inner race, outer race and ball bearing defects). In this research, three condition monitoring techniques involving thermal imaging, current and vibration signal processing have been used to monitor these faults. Further, features such as image metrics and Discrete Wavelet Transform (DWT) coefficients were extracted from the thermal images, and DWT coefficients from the current and vibration signals. Later, five-feature selection methods were applied in order to select the best features for defect classification. Finally, an improvement to the proposed B4M was made by producing a new hybrid classification algorithm by combining The Development of New Artificial Intelligence based Hybrid Techniques Combining Bees Algorithm, Data Mining and Genetic Algorithm for Detection, Classification and Prediction of Faults in Induction Motors Page | v Genetic Algorithm (GA) with B4M referred to as GA-B4M where the GA was used for feature selection. The new algorithms were successfully implemented on MATLAB and its performance was tested on real data and compared with other algorithms using the WEKA software. The results obtained for the thermal image monitoring data showed 98.97% classification accuracy with a reduced rule set containing 10 rules for B4M while a 100% accuracy with a larger rule set of 63 and 72 rules were achieved by Decision Table and OneR classifiers respectively. For the current monitoring data, the classification accuracy fell to 79.62% with only 8 rules for B4M, while 79.20% with 837 rules was achieved by Random Tree. Similarly, for the vibration monitoring data the B4M achieved 80.05% with 7 rules in comparison with Naïve Bayes tree at 79.25% with 31 rules. Furthermore, the results achieved by the proposed hybrid approach GAB4M on thermal imaging dataset also showed an overall improvement on the classification accuracy reaching 99.85% with 7 rules. Similarly, on the current and vibration dataset the GA-B4M obtained 79.98% with 16 rules and 98.74% with 7 rules respectively. This study has shown that the new proposed classification algorithms B4M and GA-B4M are able to detect, classify and predict the induction motor faults more reliably.", "source_url": "https:\/\/openalex.org\/W2982461209", "pdf_url": "", "doi": "", "handle": "", "repository_name": "ORCA Online Research @Cardiff (Cardiff University)", "source_record_id": "W2982461209", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1141, "title": "Data mining of classification for sybil user detection", "title_ar": "", "title_en": "Data mining of classification for sybil user detection", "author": "Anand Chinchore", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Technology Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; ai-applications; data-mining; predictive-analytics; ai-cybersecurity", "keywords": "", "abstract": "Data analytics and Big Data application research, along with new structures in complex data, are reveling the secrets of their own complexity and patterns with valuable and critical, but challenging, issues through newly designed tools, techniques and models in data science technology. A common example concerns the interconnectivity of social network users on mobiles, involving content and information sharing through mobile social networks. There have been a large number of studies on mobile networks. Many focus on a variety of secured applications that attempt to exploit social connections, impersonate users or attack social groups. Such applications are often created with the intention of collecting confidential information, laundering money, blackmail or to perform other criminal activities. Existing methods for identifying such activity, such as distributed systems, social graph-based sybil detection, behaviour classification, and local ranking systems that estimate the trust level between users, rely on the dependencies between random nodes of connection on mobile social networks. These models aim to detect suspicious connections and have the advantage of learning the relationships between nodes and data. However, their detection patterns tend to impose the behavioural patterns typically associated with community-based and external networks. In data mining, the graph-based and classification models used for pattern collection can accurately predict patterns in data in targeted categories. Decision trees, commonly used for classification, are trees in which each branch represents a choice between a number of alternatives, and each leaf represents a classification, or decision. For example, a decision tree may help an institution decide whether a node in a dataset is suspicious, or considered to be sybil, if a decision tree can be induced from a set of data about its instances and the - classifications of those instances. It could also provide the flexibility to demonstrate data distribution. Thus, researchers have tried to combine different techniques and methods into network-based models to detect various patterns generated by sybil nodes within a network. The purpose of this thesis is to abridge existing classification and regression techniques to identify sybil nodes, and the correlation of those nodes with time, to address these research limitations. Classification and regression techniques predict behaviour based on continuous or categorical responses. For example if the predicted response is continuous, then it is called a regression tree. If the response is categorical, it is called a classification tree. At each node of the tree, the value of one the connected input nodes is checked and a binary answer – yes or no – determines whether one continues to the left or right sub-branch. When a leaf is reached, a prediction follows from a series of entropy calculations and graphing techniques. This thesis introduces a novel classification model for sybil detection in mobile social behaviour that identifies dependencies using connection duration and other attributes. Roger Quinlan’s C4.5 algorithm, its resulting decision tree and a random forest simplify the step-by-step identification process, while maintaining its merits. Partial correlations between nodes are simplified using Rattle programming, and the dataset is divided into majority nodes to assist processing. This research also includes a behavioural survey of the nodes and an extended analysis using a classification system for sybil detection, with a particular focus on sybil attacks in mobile social network environments. Each sybil node is tracked and identified based on the frequency and duration of its connections with other nodes. An outline of how the classified model identifies behaviour is also included, along with an explanation of the flow of the decision tree and the C4.5 algorithm process, which press-gangs identified sybil nodes based on the results of entropy calculations and information gain. The calculated entropy for each node connection across the all datasets informs the information gain. The maxGain calculations for individual node bring the final stage of draw decision tree and helped to predict the sybil nodes, compare and justify the sybil attackers. These processes and new models applied to sybil detection provide insight into the behaviour of connections, through deep analytics and entropy gain. The evidence gleaned from this research brings significant knowledge to data analytics and data science in the identification of threats on mobile social networks.", "source_url": "https:\/\/openalex.org\/W2604611620", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UTS ePRESS (University of Technology Sydney)", "source_record_id": "W2604611620", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1142, "title": "Prediction system for Cardio-vascular diseases using data mining algorithms.", "title_ar": "", "title_en": "Prediction system for Cardio-vascular diseases using data mining algorithms.", "author": "Tinotenda Mavetera", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "North-west University", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "data-mining", "categories": "machine-learning; deep-learning; medical-ai; data-mining; predictive-analytics", "keywords": "", "abstract": "Background: Cardiovascular disease (CVD) is a major global health concern, accounting for a significant proportion of morbidity and mortality worldwide. Early detection and prevention of CVD reduces the burden of this disease, and data mining techniques show great potential in predicting CVD risk. Data mining algorithms use large datasets to identify patterns and relationships that may not be immediately apparent, making them specifically suitable for analysing complex biomedical data. Objective: This research is designed to determine the best performing machine learning algorithm based on the dataset and demonstrate how this algorithm could be used to predict cardiovascular disease and improve both diagnosis and treatment. The Study examined various data mining techniques and their performance in predicting CVD risk and explore the potential of combining different algorithms for improved prediction accuracy. Methodology: In this research, Knowledge Discovery in Database with iterative and interactive techniques was used to identify patterns from the dataset. The dataset used in this research was obtained from UCI Machine Learning Repository. Results: The results of this study demonstrate that three out of the four implemented algorithms exhibited excellent performance, yielding high classification accuracy for predicting cardiovascular disease. Specifically, Naïve Bayes achieved 85.25%, Decision Tree achieved 71.43%, while the Artificial Neural Network outperformed the other algorithms with a classification accuracy of 91.75%. A classification accuracy of 67% achieved by KNN cannot be said to be excellent. These findings suggest that the top three algorithms could be utilized effectively for predicting the presence of cardiovascular disease. Conclusion: To conclude, this research demonstrates that machine learning could be applied to healthcare and assist medical practitioners to diagnose the presence of cardiovascular disease effectively and thus contribute to the development of efficient CVD prevention strategies that improve public health outcomes.", "source_url": "https:\/\/openalex.org\/W7225216488", "pdf_url": "https:\/\/repository.nwu.ac.za\/bitstreams\/d117cde3-cd54-4fd4-b347-469073b0c890\/download", "doi": "", "handle": "", "repository_name": "Boloka Institutional Repository (North-west University)", "source_record_id": "W7225216488", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1143, "title": "Novel Algorithms and Applications for Data Mining and Machine Learning", "title_ar": "", "title_en": "Novel Algorithms and Applications for Data Mining and Machine Learning", "author": "Sudipta Pathak", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Connecticut", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "data-mining", "categories": "machine-learning; medical-ai; data-mining; predictive-analytics", "keywords": "", "abstract": "In this dissertation we propose novel approaches for data mining and machine learn- ing for some of the fundamental and advanced problems in the areas of data analysis and bioinformatics. A number of problems such as Drug-Target Interaction (DTI) pre- diction, Singular Value Decomposition (SVD), Time series Analysis (TSA) and motif search has been studied in this thesis. We have developed algorithms that outperformed the state of art in all of the above mentioned areas. We proposed algorithms for DTI prediction that outperformed all prior algorithms over benchmark datasets under mul- tiple scenarios. Our proposed approaches for Jacobi based SVD, both sequential and parallel, improves the computation time and work compared to the state of art. We also proposed ensemble based TSA models that improves classification accuracy sta- tistically significantly compared to all prior algorithm on benchmark dataset. We also introduced novel motif search algorithms for DNA and protein motifs.", "source_url": "https:\/\/openalex.org\/W3083694962", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OpenCommons at University of Connecticut (University of Connecticut)", "source_record_id": "W3083694962", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1144, "title": "Deep Learning for text data mining: Solving spreadsheet data classification.", "title_ar": "", "title_en": "Deep Learning for text data mining: Solving spreadsheet data classification.", "author": "Aleksandr Kimashev", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; nlp; data-mining; cnn; rnn", "keywords": "", "abstract": "This project developed for the Avito LOOPS company. Research goals was to investigate existing algorithms and implementations of Deep Learning, to understand their applicability to text mining, to design a solution that incorporates theoretical and practical aspects, to run classification experiments on different data sets so that the pros and cons of different techniques can be understood. Classification of the text was necessary for the spreadsheet columns classification. The work used convolutional and recurrent neural networks, trained on samples from five classes. Also, was made an attempt to classify unknowns for a neural network of classes, with an ensemble of four networks.", "source_url": "https:\/\/openalex.org\/W2766751493", "pdf_url": "", "doi": "", "handle": "", "repository_name": "BIBSYS Brage (BIBSYS (Norway))", "source_record_id": "W2766751493", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1145, "title": "Text Mining - A Toolbox for Text Classification", "title_ar": "", "title_en": "Text Mining - A Toolbox for Text Classification", "author": "Paulo Sérgio Vieira da Costa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; data-mining; predictive-analytics; sentiment-analysis", "keywords": "", "abstract": "In this thesis it will be explored in depth the process of text mining and further document classification. The main focus will be the development of a platform capable of achieving op- erations of data extraction, natural language processing, classification of data, and evaluation of constructed models, from a corpus of labeled documents. This will be integrated with a sentiment analysis dataset where the documents are polarity based, classified as positive or negative. It will be made an evaluation of the accuracy in the processing algorithms and an in depth comparison between the different ones used although this process. It was aimed to produce a user-friendly application, capable of providing the user with tools of text mining and predictive analysis with the integration of a polarity dataset.", "source_url": "https:\/\/openalex.org\/W3029837078", "pdf_url": "https:\/\/repositorio-aberto.up.pt\/bitstream\/10216\/127189\/2\/399528.pdf", "doi": "10.34626\/1gk6-1g17", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W3029837078", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1146, "title": "Functional programming paradigm for machine learning algorithms in data mining", "title_ar": "", "title_en": "Functional programming paradigm for machine learning algorithms in data mining", "author": "Miguel Fernández de Alarcón Gervás", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Autónoma de Madrid", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; robotics; data-mining; cnn", "keywords": "", "abstract": "Data Mining and Machine Learning have experienced a significant rise in the past few decades, due to the increasing computational power, as well as to the quantity and availability of data which we currently have access to. This amount of data is always growing and facilitates the arrival of technologies to process them in order to obtain some benefit out of it, from autonomous driving to enhancements in the diagnosis and treatment of diseases. The programming language most widely used in data mining is Python, primarily because of its versatility and the number of libraries that are available for data processing, the different machine learning algorithms and tools to provide visual analysis of datasets and results. The goal of this project is to compare a functional programming paradigm to Python. Our choice of functional programming language is Haskell, owing to its purely functional nature. The study aims to compare the two languages based on the results of their performance when using two common classification algorithms: Random Forest (RF) and Convolutional Neural Networks (CNNs). The experimental results lean towards Python being the better solution, with running times of up to ten times faster and a third of the memory consumption in both Random Forests and Feed Forward Neural Networks (FFNN). The differences in performance were not as significant for CNNs, with Haskell needing around 1.5 times the amount of time Python requires, although memory is still around 2-3 times as much. Thus, we can conclude that Python is still the language to beat in the field of machine learning. Despite this, Haskell can still be used as a solution to other problems that can make use of its best qualities, which include its strong typing, safety because of its lack of side effects and its ease to safely parallelize code.", "source_url": "https:\/\/openalex.org\/W3120439944", "pdf_url": "https:\/\/repositorio.uam.es\/bitstream\/10486\/688868\/1\/fernandez_de_alarcon_gerv%c3%a1s_miguel_tfg.pdf", "doi": "", "handle": "", "repository_name": "Biblos-e Archivo (Universidad Autónoma de Madrid)", "source_record_id": "W3120439944", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1147, "title": "PENGARUH PROSES DISKRITISASI FAYYAD IRANI PADA AKURASI DATA MINING KLASIFIKASI NAÏVE BAYES", "title_ar": "", "title_en": "PENGARUH PROSES DISKRITISASI FAYYAD IRANI PADA AKURASI DATA MINING KLASIFIKASI NAÏVE BAYES", "author": "Ahmad Halim Prabowo ; Rifkie Primartha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; data-mining", "keywords": "Classification Data Mining, Naive Bayes, Fayyad-Irani’s Discretization", "abstract": "Data Mining Classification is a technique to classify data into a category or class. The classification process can have varying accuracy depending on the compatibility between the data and the algorithm used. One way to improve the accuracy of the classification process is to use data processing. Fayyad-Irani discretization is a pre-processing of data that converts numerical values to categorical. Fayyad-Irani's discretization has been proven to increase the accuracy of classifications using decision tree algorithms. Unlike the decision tree algorithm, the Naive Bayes algorithm is a Bayesian probability based algorithm so that it can have a different effect on the accuracy and timing of the classification. Based on the experiments conducted in this study, it is known that Fayyad-Irani Discretization has succeeded in increasing the accuracy of the Naive Bayes classification process significantly from only 29.31% to 93.26%, but with the processing time needed to be slower with time difference as much as 25.5 minutes slower than the process without discretization. Keywords: Classification Data Mining, Naive Bayes, Fayyad-Irani’s Discretization", "source_url": "https:\/\/openalex.org\/W3167837199", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3167837199", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1148, "title": "Application of artificial intelligence, ontology and data mining for classification of judicial judgments", "title_ar": "", "title_en": "Application of artificial intelligence, ontology and data mining for classification of judicial judgments", "author": "Antonio Adolfo Mattos [UNIFESP] Castro", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; data-mining", "keywords": "", "abstract": "The objective of this work is to apply together ontology with bag-of-words models, similarity learning, and document classification in texts with uttered decisions. The objective is to improve the results of data mining in a database of court decisions. An automatic method of searching sentences in judicial processes related to the one under judgment is developed using the frequency term-inverse of frequency in documents model together with the Jaccard similarity coefficient, establishing weights on the co-occurrence of terms in legal texts of the same category. A dataset with document vectorization is used for supervised training of machine learning algorithms, aiming to classify new justice processes. The proposed methodology provides flexibility to the Judiciary, simulating the role of legal advisors in preparing court decisions with less time and efficiency in the search for jurisprudential standards. The results obtained show that, through accuracy metrics, the proposed model is effective and efficient, and can be applied in the process of identification of court decisions. Thus, the application of artificial intelligence, ontology, and data mining is indicated for information retrieval in court decisions.", "source_url": "https:\/\/openalex.org\/W7120502482", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120502482", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1149, "title": "Secure data mining of outsourced data", "title_ar": "", "title_en": "Secure data mining of outsourced data", "author": "Fang Liu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; data-mining; fairness", "keywords": "", "abstract": "Organizations and individuals nowadays are more and more willing to outsource their data to save storage and management costs, especially with the push for cloud computing which is service-oriented and offers both storage and computation scalability. However, the data, once being released to a server, is no longer under its owner’s control, and its privacy and security herein become a primary concern. To this end, users usually encrypt the private data before outsourcing it, which however makes conventional data retrieve, sharing, and analysis services be very thorny and challenging as data is both big and encrypted. Under such new circumstance, diverse secure building blocks and some more complex secure data mining techniques should be considered for secure analytical computations and knowledge discovery on outsourced databases. In this thesis, we aim at investigating various secure data mining algorithms for the cloud platform where data is centralized and encrypted. To enhance the security, we select suitable cryptographic techniques to protect user’s privacy and to allow a cloud server to manipulate encrypted data. According to our objectives, we first discuss and analyze several secure issues caused by outsourcing data to the cloud, such as query executing techniques, multiple user key management, correctness and integrity verifying, privacy-preserving data mining algorithms, and so on. Second, we design some basic secure building blocks for the cloud platform, including secure set intersection and secure scalar product. Third, based on such secure building blocks, we formally develop three secure data mining protocols to perform following data mining algorithms: association rule mining, gradient descent algorithm, and SVM classification. Finally, the thesis makes the conclusion and the prospect of further research directions.", "source_url": "https:\/\/openalex.org\/W2354950660", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2354950660", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1150, "title": "Data mining in Breast Cancer", "title_ar": "", "title_en": "Data mining in Breast Cancer", "author": "Laura Carrio Viladrich", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "medical-ai", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; medical-ai; data-mining; predictive-analytics; rnn", "keywords": "", "abstract": "Machine learning and data mining methods can be the future of the clinical decision process like pathological diagnosis. In this project we studied Breast Cancer Wisconsin dataset and applied different algorithms, concretely classifiers, in order to predict the diagnosis and the prognostic of the cancer. In order to classify the different types of cancer we divided the classification in two steps and we tested different algorithms for each step. The first step is the diagnosis classification. Diagnosis consistsin predict if the cancer is malignant and benign. And the second step is the prognostic classification. Prognostic consist in predict if cancer is recurrent or non-recurrent. After applying different models for each steps the result is that the best model to predict the diagnosis is the Decision Forest model. And the best model to predict the prognostic is the Boosted Decision Tree model. So, we conclude that the two step classifier with Decision Forest model and Boosted Decision Tree model is the best classifier.", "source_url": "https:\/\/openalex.org\/W2599885082", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)", "source_record_id": "W2599885082", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1151, "title": "Data Mining on Deflectometric Data of Surface Defects", "title_ar": "", "title_en": "Data Mining on Deflectometric Data of Surface Defects", "author": "Pinja Marika Kuosmanen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; robotics; ai-applications; data-mining; ai-industry", "keywords": "", "abstract": "The objective of this thesis is to apply machine learning and data mining methods, especially classification and clustering, onto deflectometric data of surface defects found on car bodies. The data is acquired via robot-assisted automated surface inspection system, manufactured by Micro-Epsilon GmbH, called reflectCONTROL. The measurement method of reflectCONTROL is based on Phase Measuring Deflectometry. The aim is to explore the possibility of automated defect classification via learning algorithms, and to gain new insight about the deflectometric data obtained from surface inspection process via clustering. This thesis is divided into theoretical part and empirical part. Basic concepts of machine learning and methods used in empirical evaluation are introduced in the theoretical part. The methods include two classification algorithms; Random Forest Classifier and Support Vector Machines. The three clustering algorithms used are k-means, Affinity Propagation and HDBSCAN. Furthermore, dimensionality reduction methods, such as Principal Component Analysis and t-SNE are included. In addition, the possibility of using Point Feature Histograms in the context of deflectometric data and feature generation is explored. The biggest challenge related to this research is that the data set used is highly unbalanced, the biggest class dominating over others in the learning tasks. The empirical study indicates that Random Forest Classifier and Support Vector Machines perform very similarly in classification tasks. Furthermore, it is possible to distinguish between different classes using multiclass classification on balanced data. It was also found via silhouette analysis and dimensionality reduction, that internal structure exists in the data, but it does not correspond to the human-assigned class labels of the defects. Finally, the study indicates that Point Feature Histogram-based features do not improve the classification performance significantly, but are helpful in clustering tasks by improving the correspondence between internal structure and human-assigned labels. The results of this study are promising for further research, suggesting that it is possible to conduct research on unlabeled data via clustering, and distinguish between different defect classes using appropriately selected data.", "source_url": "https:\/\/openalex.org\/W2759018742", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tampere University of Technology Institutional Repository", "source_record_id": "W2759018742", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1152, "title": "PENERAPAN DATA MINING UNTUK MEMPREDIKSI PENJUALAN HANDPHONE PADA KONTER A CELL MENGGUNAKAN ALGORITMA BAYESIAN CLASSIFICATION", "title_ar": "", "title_en": "PENERAPAN DATA MINING UNTUK MEMPREDIKSI PENJUALAN HANDPHONE PADA KONTER A CELL MENGGUNAKAN ALGORITMA BAYESIAN CLASSIFICATION", "author": "Hevin Saputra", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; data-mining; predictive-analytics", "keywords": "data mining, algorithm of bayesian classification, the number of sales of mobile", "abstract": "The background of this research is the number of sales of electronic phone products in A Cell in Sleman Yogyakarta which is often uncertain. The company has a lot of sales transaction data stored in Ms. Excel. The data only serves as an archive, so the sale on A Cell decreases because it cannot predict the data to be used as A decision maker. The application of Data Mining method is very necessary to calculate the classification of the number of mobile phone sales that function get the percentage result of accuracy of each brand of handphone. The system can improve and build systems that are still less than optimal. In evaluating the number of mobile phone sales implemented the implementation of data mining using Bayesian classification algorithm to get the best transaction data from the results of the sales of mobile phone products that have been in prediction. Therefore, through the application of data mining using Bayesian classification algorithm is expected by the method can be used as a sales strategy to assist in increasing mobile phone sales. Keywords: data mining, algorithm of bayesian classification, the number of sales of mobile", "source_url": "https:\/\/openalex.org\/W2991485768", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2991485768", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1153, "title": "Data mining in computational finance", "title_ar": "", "title_en": "Data mining in computational finance", "author": "Swati Jadhav", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Cranfield University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "machine-learning", "research_subdomain": "data-mining", "categories": "machine-learning; data-mining; predictive-analytics; optimization", "keywords": "", "abstract": "Computational finance is a relatively new discipline whose birth can be traced back to early 1950s. Its major objective is to develop and study practical models focusing on techniques that apply directly to financial analyses. The large number of decisions and computationally intensive problems involved in this discipline make data mining and machine learning models an integral part to improve, automate, and expand the current processes. One of the objectives of this research is to present a state-of-the-art of the data mining and machine learning techniques applied in the core areas of computational finance. Next, detailed analysis of public and private finance datasets is performed in an attempt to find interesting facts from data and draw conclusions regarding the usefulness of features within the datasets. Credit risk evaluation is one of the crucial modern concerns in this field. Credit scoring is essentially a classification problem where models are built using the information about past applicants to categorise new applicants as ‘creditworthy’ or ‘non-creditworthy’. We appraise the performance of a few classical machine learning algorithms for the problem of credit scoring. Typically, credit scoring databases are large and characterised by redundant and irrelevant features, making the classification task more computationally-demanding. Feature selection is the process of selecting an optimal subset of relevant features. We propose an improved information-gain directed wrapper feature selection method using genetic algorithms and successfully evaluate its effectiveness against baseline and generic wrapper methods using three benchmark datasets. One of the tasks of financial analysts is to estimate a company’s worth. In the last piece of work, this study predicts the growth rate for earnings of companies using three machine learning techniques. We employed the technique of lagged features, which allowed varying amounts of recent history to be brought into the prediction task, and transformed the time series forecasting problem into a supervised learning problem. This work was applied on a private time series dataset.", "source_url": "https:\/\/openalex.org\/W3142208920", "pdf_url": "", "doi": "", "handle": "", "repository_name": "CERES (Cranfield University)", "source_record_id": "W3142208920", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1154, "title": "Pattern recognition, classification and diagnosis of acoustic emission signals in applications for mining", "title_ar": "", "title_en": "Pattern recognition, classification and diagnosis of acoustic emission signals in applications for mining", "author": "Kenny Garate-Penaranda ; Thomas Pretz ; Karl Nienhaus", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "medical-ai", "research_subdomain": "data-mining", "categories": "machine-learning; deep-learning; medical-ai; expert-systems; data-mining", "keywords": "", "abstract": "In this work, Acoustic Emission Technique (AET) and Pattern Recognition Technique (PRT) are used in combination for classification of AE signals obtained from two experimental applications in the mining sector. AE signals are collected and used to form features or parameters in order to identify AE events of interest. Signal processing in time domain and in frequency domain are applied to extract these features from the AE signals.The aim of this work is to identify and classify similar AE signals from experimental mining tests. It is achieved by means of using unsupervised and supervised methods of PRT. The main reason for performing these tests at a laboratory scale is to obtain a correlation of the AE signals with each mining process as well as to use the results as a guide for other specific mining applications.Beginning with the formulation of the problem and data acquisition, each step of the pattern recognition process is carried out for classification of the AE signals, namely pre-processing of the data, feature selection and extraction, unsupervised or supervised classification, assessment and interpretation of the results.In the pre-processing of the data, Standard Score Normalization is applied to the input data for further comparisons of AE features. For feature extraction, three algorithms are used, namely Principal Components Analysis (PCA), Linear Discrimination analysis (LDA), and Multidimensional Scaling (MDS). For supervised classification three methods are employed, these are linear Support Vector Machine, non-linear Support Vector Machine and Back-propagation Neural Network. These supervised classifiers are evaluated using classification accuracy. For unsupervised classification, also three classification algorithms are used, namely K-means Clustering, Fuzzy C-Means Clustering and Vector Quantization Clustering. These unsupervised classifiers are evaluated using similarities between clusters by Rand Index (RI).Two experimental applications are studied using the algorithms and the selected AE features. First, AET in a laboratory column flotation cell is used to monitor the bubble activity and bubble size, as a means of improving the efficiency of the column flotation process. Next, in rock cutting, AET and PRT are employed to identify the rocks being cut as a means of automation in the operation of underground mining and tunneling.These experiments were carried out in a laboratory scale, using AE wave measurements to analyze the processes. Pattern Recognition Technique is used in combination with classic and advanced signal processing techniques to characterize the collected AE signals.", "source_url": "https:\/\/openalex.org\/W2586318097", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RWTH Publications (RWTH Aachen)", "source_record_id": "W2586318097", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "id": 1155, "title": "Mining heterogeneous enterprise data", "title_ar": "", "title_en": "Mining heterogeneous enterprise data", "author": "Xinxin Jiang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Technology Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "data-mining", "categories": "machine-learning; deep-learning; ai-applications; data-mining; ai-industry", "keywords": "", "abstract": "Heterogeneity is becoming one of the key characteristics inside enterprise data, because the current nature of globalization and competition stress the importance of leveraging huge amounts of enterprise accumulated data, according to various organizational processes, resources and standards. Effectively deriving meaningful insights from complex large-scaled heterogeneous enterprise data poses an interesting, but critical challenge. The aim of this thesis is to investigate the theoretical foundations of mining heterogeneous enterprise data in light of the above challenges and to develop new algorithms and frameworks that are able to effectively and efficiently consider heterogeneity in four elements of the data: objects, events, context, and domains. Objects describe a variety of business roles and instruments involved in business systems. Object heterogeneity means that object information at both the data and structural level is heterogeneous. The cost-sensitive hybrid neural network (Cs-HNN) proposed leverages parallel network architectures and an algorithm specifically designed for minority classification to generate a robust model for learning heterogeneous objects. Events trace an object’s behaviours or activities. Event heterogeneity reflects the level of variety in business events and is normally expressed in the type and format of features. The approach proposed in this thesis focuses on fleet tracking as a practical example of an application with a high degree of event heterogeneity. Context describes the environment and circumstances surrounding objects and events. Context heterogeneity reflects the degree of diversity in contextual features. The coupled collaborative filtering (CCF) approach proposed in this thesis is able to provide context-aware recommendations by measuring the non-independent and identically distributed (non-IID) relationships across diverse contexts. Domains are the sources of information and reflect the nature of the business or function that has generated the data. The cross-domain deep learning (Cd-DLA) proposed in this thesis provides a potential avenue to overcome the complexity and nonlinearity of heterogeneous domains. Each of the approaches, algorithms, and frameworks for heterogeneous enterprise data mining presented in this thesis outperform the state-of-the-art methods in a range of backgrounds and scenarios, as evidenced by a theoretical analysis, an empirical study, or both. All outcomes derived from this research have been published or accepted for publication, and the follow-up work has also been recognised, which demonstrates scholarly interest in mining heterogeneous enterprise data as a research topic. However, despite this interest, heterogeneous data mining still holds increasing attractive opportunities for further exploration and development in both academia and industry.", "source_url": "https:\/\/openalex.org\/W2910858813", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OPUS - Open Publications of UTS Scholars (University of Technology Sydney)", "source_record_id": "W2910858813", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1156, "title": "A data mining approach for automated classification of Alzheimer's disease", "title_ar": "", "title_en": "A data mining approach for automated classification of Alzheimer's disease", "author": "Alexander Luke Spedding", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Reading", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "data-mining", "categories": "machine-learning; computer-vision; medical-ai; data-mining; predictive-analytics; optimization", "keywords": "", "abstract": "This work presents the creation of classifiers able to automatically diagnose Alzheimer’s disease from structural magnetic resonance images. Measurements of regions inside the human brain and how they relate to a diagnosis of Alzheimer’s disease are investigated. A genetic algorithm is used to extract a subset of brain measurements which have some of the highest predictive power when compared to the rest of the measurements. A descriptive classifier is created which uses the size of each of the hippocampal volumes of a subject and compares it to the distribution of other subjects. Based on how the individual subject’s measurements compares to the distribution, we can determine whether it is an Alzheimer’s disease predictive volume. This classifier achieves an accuracy equivalent to state-of-the-art approaches. A second descriptive classifier is created using a regression model to predict a healthy subject’s age and applying this to Alzheimer’s disease positive subjects to generate an apparent brain age. The classification accuracy of when the apparent brain age is compared to the subject’s real age is comparable to the state-of-the-art methods in the literature.", "source_url": "https:\/\/openalex.org\/W2981015562", "pdf_url": "", "doi": "", "handle": "", "repository_name": "CentAUR (University of Reading)", "source_record_id": "W2981015562", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1157, "title": "Healthcare data mining from multi-source data", "title_ar": "", "title_en": "Healthcare data mining from multi-source data", "author": "Ling Chen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "data-mining", "categories": "machine-learning; medical-ai; data-mining; predictive-analytics", "keywords": "", "abstract": "The big challenge is changing the way we acquire, store, analyse, and draw conclusions from data. How we effectively and efficiently mine the data from possibly multiple sources and extract useful information is a critical question. Increasing research attention has been drawn to healthcare data mining, with an ultimate goal to improve the quality of care. The human body is complex and so too the data collected in treating it. Data noise that is often introduced via the collection process makes building Data Mining models a challenging task. This thesis focuses on the classification tasks of mining healthcare data, with the goal of improving the effectiveness of health risk prediction. In particular, we developed algorithms to address issues identified from real healthcare data, such as feature extraction, heterogeneity, label uncertainty, and large unlabeled data. The three main contributions of this research are as follows. First, we developed a new health index called Personal Health Index (PHI) that scores a person's health status based on the examination records of a given population. Second, we identified the key characteristics of the real datasets and issues that were associated with the data. Third, we developed classification algorithms to cope with those issues, particularly, the label uncertainty and large unlabeled data issues. This research takes one step forward towards scoring personal health based on mining increasingly large health records. Particularly, it pioneers exploring the mining of GHE data and tackles the associated challenges. It is our anticipation that in the near future, more robust data-mining-based health scoring systems will be available for healthcare professionals to understand people's health status and thus improve the quality of care.", "source_url": "https:\/\/openalex.org\/W2602506549", "pdf_url": "", "doi": "10.14264\/uql.2017.365", "handle": "", "repository_name": "The University of Queensland", "source_record_id": "W2602506549", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1158, "title": "Achieving Consumable Big Data Analytics by Distributing Data Mining Algorithms", "title_ar": "", "title_en": "Achieving Consumable Big Data Analytics by Distributing Data Mining Algorithms", "author": "Shady Khalifa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Queen's University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "data-mining", "categories": "machine-learning; computer-vision; data-mining; predictive-analytics", "keywords": "", "abstract": "Businesses look at Big Data as an opportunity to gain insights for improving their services. The derivation of such insights requires using different data mining techniques. Mature data mining tools like WEKA or R have been in development for years. They implement a large number of data mining algorithms and can support sophisticated Analytics. However, these mature tools are designed to run on a single machine making them unsuitable to handle Big Data. Using these tools requires data mining and statistics knowledge, and some of them, like R, are hard to learn. Businesses do not always have the technical skills required to carry on such Analytics. Even if they do, it is challenging to find a tool with the needed algorithms that supports distributed processing to handle the Big Data high arrival velocity and large volumes. The Businesses’ analytical requirements can be addressed by Consumable Big Data Analytics, that is, solutions that allow businesses to do Big Data Analytics themselves using their in-house expertise. In this work, we provide a Consumable Analytics solution to meet the businesses’ analytical needs. First, we conduct a survey of existing Analytics solutions to identify possible areas of improvement to provide Consumable Analytics. Second, instead of developing distributed data mining algorithms to handle Big Data, we develop the Data Mining Distribution (DMD) algorithm and the Label-Aware Disjoint Partitioning (LADP) algorithm to distribute the execution of all existing single-machine data mining algorithms without rewriting a single line of their code. This gives users the flexibility to use any available data mining library, have algorithms like Hoeffding Tree run 70% to 95% faster and achieve up to 18% increase in prediction accuracy. Third, we develop the free and open source QDrill solution to implement our DMD and LADP algorithms for distributed Analytics. QDrill implements our proposed Distributed Analytics Query Language (DAQL) interface that adds Analytics capabilities to the regular SQL syntax and allows integration with Business Intelligence (BI) tools. This allows businesses to use their in-house expertise to do Big Data Analytics using the spreadsheets and visualizations of their BI tools.", "source_url": "https:\/\/openalex.org\/W2608234461", "pdf_url": "", "doi": "", "handle": "", "repository_name": "QSpace (Queen's University Library)", "source_record_id": "W2608234461", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1159, "title": "Enhancing interpretability in healthcare models using explainable artificial intelligence", "title_ar": "", "title_en": "Enhancing interpretability in healthcare models using explainable artificial intelligence", "author": "Fatima Abdul Sattar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; xai", "keywords": "", "abstract": "ML models have shown remarkable performance in predictions in genomic cancer applications but experience the black-box problem. Where complicated processes of decision making remain non transparent to healthcare professionals and researchers. This absence of interpretability makes hurdles to clinical adoption, approval from regulators and discovering new scientific facts in healthcare AI. the latest research of explainable artificial intelligence (XAI) shows vital gaps. Poor comparative analysis over prediction tasks and cancer types, narrow combination of multiple interpretability methods and insufficient presentation of interpretability as a quality control mechanism for pointing out failures of model. This work evaluates and develops a combined interpretability pipeline for machine learning models used in prediction of genomic cancer. Random Forest feature selection, SHAP (SHapley Additive exPlanations) analysis and UMAP (Uniform Manifold Approximation and Projection) dimensionality reduction are used in combination to give transparency in models that works at multi-scale. The pipeline was used on two childhood cancer datasets. B-cell Acute Lymphoblastic Leukaemia (ALL) with n= 100 patients and 22,277 genes to make relapse prediction. Rhabdomyosarcoma (RMS) with n=101 patients and 54,675 genes for classification of subtype (ARMS\/ ERMS). The methodology integrates three main techniques. (1) Random Forest feature selection, to find out the top 100 most important features, (2) SHAP (SHapley Additive exPlanations) analysis, in order to produce rankings of global feature importance and explanations of individual level prediction and (3) UMAP projection different scales of features (100, 20 and 10 genes) to capture the patterns of clusters and optimal dimensionality. Cross dataset examination found zero gene overlapping across ALL and RMS top 100 genes that showed genomic signatures which are disease specific. Cross method validation with merging of Random Forest importance, Shapley values and cluster separation in UMAP space improved confidence in detected biomarkers. This thesis shows that interpretability give two roles, uncovering failures of model and explaining successful outcomes. This combined technique gives a structural template for making transparent, actual working of ML models in healthcare in genomic medicine. With these results that around 20 genes gives maximum balance contributing practical guidance for designing clinical biomarker panels.", "source_url": "https:\/\/openalex.org\/W7164379531", "pdf_url": "", "doi": "10.26183\/mcys-4680", "handle": "", "repository_name": "Western Sydney University ResearchDirect", "source_record_id": "W7164379531", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1160, "title": "Advancing robustness, interpretability and explainability for classical and large artificial intelligence models", "title_ar": "", "title_en": "Advancing robustness, interpretability and explainability for classical and large artificial intelligence models", "author": "Modafar Al-Shouha", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "responsible-ai; xai", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7161064724", "pdf_url": "", "doi": "10.5281\/zenodo.20158168", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7161064724", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1161, "title": "Building Trust in Diagnostic Artificial Intelligence: An Explainable Approach to Medical Decision Support; Interpreting Trust: The Social Construction of IBM Watson for Oncology", "title_ar": "", "title_en": "Building Trust in Diagnostic Artificial Intelligence: An Explainable Approach to Medical Decision Support; Interpreting Trust: The Social Construction of IBM Watson for Oncology", "author": "Kenny Tran", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; predictive-analytics; transformers; xai", "keywords": "", "abstract": "Artificial Intelligence is increasingly used in clinical diagnostics; yet many systems fail to capture physician trust because their decision-making processes are unclear and difficult to interpret. To address this challenge, I propose the design of an explainable diagnostic AI system that balances predictive performance with clinically meaningful explanations. The proposed solution leverages transformer-based architectures augmented with weighting techniques and natural-language generation to communicate diagnostic reasoning in human-readable terms. Rather than relying solely on statistical feature visualizations, the system emphasizes explanations aligned with established clinical reasoning practices. Publicly available electronic health record data will be used to evaluate diagnostic accuracy and interpretability. The anticipated outcome is a diagnostic model that improves transparency without sacrificing performance, thereby supporting shared decision-making between clinicians and patients. Future work includes clinician-centered usability studies, refinement of explanation interfaces, and evaluation of the system’s effectiveness across different clinical settings.", "source_url": "https:\/\/openalex.org\/W7160598486", "pdf_url": "", "doi": "10.18130\/b87h-7367", "handle": "", "repository_name": "Libra", "source_record_id": "W7160598486", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1162, "title": "Interpretable time-series forecasting with multi-model deep learning and natural language processing (NLP) driven explainable artificial intelligence (XAI)", "title_ar": "", "title_en": "Interpretable time-series forecasting with multi-model deep learning and natural language processing (NLP) driven explainable artificial intelligence (XAI)", "author": "Jiztom Kavalakkatt Francis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; xai", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7131292683", "pdf_url": "", "doi": "10.31274\/td-20260223-66", "handle": "", "repository_name": "", "source_record_id": "W7131292683", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1163, "title": "The Relevance of Explainable Artificial Intelligence (xAI) in High-Risk Decisions", "title_ar": "", "title_en": "The Relevance of Explainable Artificial Intelligence (xAI) in High-Risk Decisions", "author": "Julie Gerlings", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Copenhagen Business School", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; xai", "keywords": "Artificial Intelligence (AI), Explainable artificial intelligence (xAI), machine learning (ML), stakeholder theory, human-AI collaboration, high-risk decision making, trust, organizational context, transaction monitoring (TM), anti-money laundering (AML)", "abstract": "The increasing adoption of machine learning (ML) and artificial intelligence (AI) in critical decision-making has intensified demands for explainability and transparency, particularly in high-risk domains such as finance. This dissertation investigates how explainable AI (xAI) can be designed and adapted to meet the diverse explainability needs of stakeholders in high-risk organizations through a longitudinal Action Design Research (ADR) study conducted in collaboration with a European financial institution implementing ML in transaction monitoring (TM) for anti-money laundering (AML). The ADR study addresses key gaps in the explainable AI literature by providing empirical evidence of how stakeholders interact with xAI in organizational contexts. The systematic literature review reveals four central debates in xAI research and identifies a critical need for empirical studies examining stakeholder needs. The empirical investigation demonstrates how implementation modes (automation versus augmentation) fundamentally alter stakeholder information requirements and liability structures, challenging assumptions about universal explainability needs. The research introduces a novel proximity-responsibility framework that systematically explains how stakeholders' proximity to AI systems influences both their trust in these systems and their utilization of xAI. High-proximity stakeholders (data scientists, investigators) develop trust through operational familiarity and use xAI for optimization and decision support, while low-proximity stakeholders (compliance managers, auditors) rely on xAI primarily for oversight, documentation, and regulatory compliance. Significantly, this dissertation challenges the prevailing explainability-trust hypothesis that dominates xAI literature by providing empirical evidence that trust influences explainable AI use rather than being produced by it. The research reconceptualizes trust and control as complementary coordination mechanisms and proposes a hierarchical conceptual framework positioning interpretability as methodology for developing xAI serving a specific purpose rather than generic trust generation. The findings contribute theoretical frameworks for understanding human-AI collaboration in high-stakes environments, practical design principles for stakeholder-specific xAI implementation, and methodological guidance for conducting rigorous xAI research in organizational contexts. The study demonstrates that effective xAI design requires moving beyond one-size-fits-all approaches toward nuanced, stakeholder-specific solutions that recognize the complex interplay between organizational context, implementation choices, and human factors in AI adoption. Keywords: Artificial Intelligence (AI), Explainable artificial intelligence (xAI), machine learning (ML), stakeholder theory, human-AI collaboration, high-risk decision making, trust, organizational context, transaction monitoring (TM), anti-money laundering (AML)", "source_url": "https:\/\/openalex.org\/W4414292580", "pdf_url": "", "doi": "10.22439\/phd.35.2025", "handle": "", "repository_name": "OpenArchive@CBS (Copenhagen Business School)", "source_record_id": "W4414292580", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1164, "title": "Uncertainty in Explainable Artificial Intelligence", "title_ar": "", "title_en": "Uncertainty in Explainable Artificial Intelligence", "author": "Teodor Chiaburu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "deep-learning; responsible-ai; ai-applications; multimodal; xai; fairness; ai-industry", "keywords": "", "abstract": "Artificial intelligence (AI) has become deeply embedded in modern decision-making systems, driving advances across science, industry and society. Yet, as models grow in scale and complexity, they increasingly operate as opaque systems whose internal reasoning remains difficult to understand. Explainable AI (XAI) seeks to remedy this by providing interpretable insights into model behavior, supporting model verification, accountability and calibrated human-AI trust. However, a growing body of work shows that explanations themselves are oftentimes unstable, noisy or sensitive to slight perturbations - a variation that could be attributed to datasets, models or explanation techniques. Without a thorough understanding of where this uncertainty comes from and a reliable way to communicate it, explanations risk being misleading, unreliable and ultimately detrimental to user trust. This threatens to turn XAI from a transparency tool into a further source of ambiguity. In this dissertation, we address these challenges by developing a principled framework for Uncertainty in Explainable AI (UXAI) that conceptualizes and operationalizes uncertainty within the explanation process. First, we introduce a mathematical formulation of explainers as functions $e_{\\theta}(x, f)$, enabling a propagation-based view of how uncertainty originating in the data, model and explainer hyperparameters flows into the final explanation. Building on this formulation, we derive a test for checking how reliably uncertainty is propagated through the explanation pipeline, comparing first-order uncertainty approximations against empirical perturbation-based estimates. We then demonstrate how uncertainty quantification can enhance transparency in practice by (i) constructing a post-hoc stochastic concept-based explainer and (ii) conformalizing a multimodal, multitask model used in real-world scientific workflows. As far as the human dimension is concerned, we develop a human-in-the-loop experimental framework to study how explanations communicated together with model uncertainty influence human-AI collaboration, user performance and learning in unfamiliar tasks. Finally, we conclude by discussing what is still missing in UXAI and future directions that could advance the field further.", "source_url": "https:\/\/openalex.org\/W7140237592", "pdf_url": "", "doi": "10.14279\/depositonce-25521", "handle": "", "repository_name": "", "source_record_id": "W7140237592", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1165, "title": "A Framework for Explainable Artificial Intelligence in Geospatial Contexts", "title_ar": "", "title_en": "A Framework for Explainable Artificial Intelligence in Geospatial Contexts", "author": "Cédric Roussel", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "xai", "categories": "machine-learning; computer-vision; responsible-ai; ai-applications; predictive-analytics; xai; ai-smart-cities", "keywords": "", "abstract": "Artificial intelligence has become a key driver of innovation in geospatial data analysis. It enables new prediction, classification and decision-making methods in areas such as urban planning, environmental monitoring and mobility services. However, many artificial intelligence models, particularly complex machine learning algorithms, lack transparency. This makes it difficult for users to understand, validate or trust the outcomes of these models. This is particularly problematic in geospatial contexts, where decisions often have real-world implications and depend on local knowledge and geospatial reasoning. While existing explainable artificial intelligence methods offer insights into model behavior, they often disregard the geospatial context, such as the location of phenomena and the relationships between nearby areas. This limits their effectiveness in understanding and interpreting the geospatial behavior of the model. This dissertation addresses this challenge by advancing the field of geospatial explainable artificial intelligence, an emerging area that redefines how explanations relating to geospatial data should be constructed and communicated. It proposes a novel framework that works with a new type of explanation which can be tailored to the geospatial context. Glocal explanations are computed through the aggregation of local explanations and defined conditions. This enables explanations to be created that range between local and global explainable artificial intelligence. As for explanations, the model quality is adapted to these glocal units, enabling analysis of where and when the model performs well or poorly. The framework also provides interactive visualizations that enable users to explore, interpret, and question model outputs. One visualization is an interactive matrix, while a second uses multi-level glyphs to enable geospatial analysis of the explanations. Both visualizations integrate model quality to reliably interpret the reasoning behind the model's decisions. The novel contributions are supported by real-world experiments and prototype implementations. Three distinct use cases are considered: predicting rental bike station bookings, predicting car park occupancy, and classifying accident severity. An additional evaluation of the usability of the visualizations shows that representations such as the developed matrix or geovisualization significantly improve efficiency, effectiveness, and satisfaction. The final framework can be used by different types of users. Machine learning experts can use it to identify and understand where and why the model performs poorly. Domain experts can use the framework to gain a better understanding of the actions and causes within a use case, potentially enabling them to take actions that could have economic and social benefits.", "source_url": "https:\/\/openalex.org\/W7163335674", "pdf_url": "", "doi": "10.13140\/rg.2.2.17271.79525", "handle": "", "repository_name": "Fraunhofer-Publica (Fraunhofer-Gesellschaft)", "source_record_id": "W7163335674", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1167, "title": "Explainable Artificial Intelligence Methods for Spiking Neural Networks", "title_ar": "", "title_en": "Explainable Artificial Intelligence Methods for Spiking Neural Networks", "author": "Jane Jung", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Auckland University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; ai-applications; predictive-analytics; multimodal; xai; ai-cybersecurity; ai-smart-cities", "keywords": "", "abstract": "As the demand for energy-efficient Artificial Intelligence (AI) grows, Spiking Neural Networks (SNNs) have emerged as a leading neuromorphic alternative to traditional deep learning. However, the complex, non-linear temporal dynamics of SNNs often result in a black box nature, hindering their adoption in high-stakes domains such as cybersecurity and clinical healthcare. This thesis proposes a novel framework, the SNN-based MLP (SNN-MLP), which utilises a non-spiking surrogate model to interpret the decision-making logic of a trained SNN. By mapping the high-dimensional activity of spiking ensembles to a differentiable architecture, the framework enables the application of post-hoc Explainable Artificial Intelligence (XAI) techniques, such as Shapley Additive ExPlanation (SHAP), to provide transparent feature-level explanations. The framework is validated through two diverse case studies. The first case study evaluates network traffic for detection, and demonstrates a successful translation of single-dimensional SNNs into Multi-Layer Perceptrons (MLPs) and the resultant SNN-MLP successfully identifies malicious features with up to 87% accuracy, and also aligns in terms of interpretability with a baseline MLP. The second case study applies the framework to personalised depression modelling using more complex SNN architectures along with multimodal datasets. The results demonstrate that the SNN-MLP functions as a high-fidelity surrogate for multi-dimensional SNNs as well, identifying clinically relevant triggers-primarily anxiety and dietary factors-that align with established benchmarks. While the study notes challenges regarding data scarcity and class imbalance in clinical settings, the consistent overlap between the SNN and its surrogate proves that spiking architectures can achieve competitive predictive performance without sacrificing interpretability. Thus, this thesis provides a foundation for mathematically grounded, energy-efficient, and explainable AI, offering a pathway towards the deployment of explainable SNN systems in safety-critical and security infrastructures.", "source_url": "https:\/\/openalex.org\/W7155577599", "pdf_url": "https:\/\/openrepository.aut.ac.nz\/bitstreams\/da44f9c0-126b-4a59-9ac6-39d35ec9f7cd\/download", "doi": "", "handle": "", "repository_name": "Tuwhera (Auckland University of Technology)", "source_record_id": "W7155577599", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1169, "title": "Intuitive Explainable Artificial Intelligence for Molecular Design", "title_ar": "", "title_en": "Intuitive Explainable Artificial Intelligence for Molecular Design", "author": "Alec Lamens", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bonn", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; expert-systems; self-semi-supervised; predictive-analytics; xai", "keywords": "", "abstract": "With the onset of artificial intelligence (AI), machine learning (ML) approaches have become increasingly embedded into the drug discovery pipeline with the aim of accelerating research and reducing cost. Exemplary applications include quantitative structure-property relationship modeling and de novo design of candidate molecules. However, the practical impact of these ML models has been limited by the opaque nature of their predictions, termed the black-box problem. The proliferation of large-scale public chemical datasets combined with advances in high-performance computing has driven a shift from traditional ML approaches toward more complex deep learning (DL) methods. While powerful, these models further exacerbate the inability to rationalize model outcomes. Therefore, the field of explainable AI (XAI) has attracted substantial interest to improve the transparency of model decisions. Given the wide range of available XAI approaches and lack of established practices, these methods are often applied without careful consideration for their suitability to the task at hand. This represents a problem in drug discovery where interpretable explanations are critical to guide experimental follow-up. To address this issue, this dissertation proposes the adaptation of established XAI approaches to operate on the level of chemical structure and facilitate interpretable ML for molecular design. The first study demonstrates the use of Shapley additive explanations (SHAP) to analyze feature sets driving the correct prediction of compound promiscuity. This is followed by a benchmarking study comparing several methods for the calculation of feature importance values, revealing inconsistencies across approaches that complicate the interpretation of feature-importance explanations. To support chemically meaningful interpretability, SHAP values are then combined with counterfactual reasoning, yielding the SHAP-CF methodology. Thereafter, a novel counterfactual method is introduced that generates candidate counterfactuals through the iterative recombination of molecular cores and substituents. This method is evaluated on a challenging multi-class kinase inhibitor prediction task, demonstrating its capacity to generate large numbers of counterfactuals, enabling in-depth analysis. Next, a closely related XAI concept, contrastive explanations, is adapted for molecular design by introducing rational chemical modifications to test compounds through the exchange of scaffolds or substituents with structural analogues, termed MolCE. This approach is shown to provide chemically relevant contrastive explanations that enable direct causal insights into the structural factors that drive model predictions. Finally, MolAnchor is introduced, a domain-adapted variant of the rule-based Anchors method. By conforming the principles underlying the Anchors framework to operate on retrosynthetically meaningful molecular fragments, MolAnchor produces interpretable fragment-level decision rules. A follow-up study showcases how the if-then formulation of the fragment rules is particularly complementary to causal reasoning. Taken together, the studies presented hereindemonstrate the utility of XAI methods that have been augmented with domain-specific information to provide chemically intuitive explanations for predictive models applied to molecular design.", "source_url": "https:\/\/openalex.org\/W7164705685", "pdf_url": "", "doi": "", "handle": "", "repository_name": "bonndoc (University of Bonn)", "source_record_id": "W7164705685", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1170, "title": "APPLICATION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE IN WASTEWATER TREATMENT PLANTS", "title_ar": "", "title_en": "APPLICATION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE IN WASTEWATER TREATMENT PLANTS", "author": "Fuad Bin Nasir", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Wisconsin", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; xai", "keywords": "", "abstract": "Wastewater treatment plants (WWTPs) play a crucial role in protecting public health and the environment by removing contaminants before releasing them back into the environment. However, efficient management of these plants can be challenging because of the complex nature of wastewater treatment processes. Machine learning (ML), a subfield of artificial intelligence (AI), can predict WWTP variables by extracting correlations between variables from historical data. Accurate effluent variable prediction through ML can facilitate efficient adjustment of operational variables, thus minimizing operation cost while effectively meeting effluent quality standards. However, relying solely on ML models without understanding the contexts of the predictions is not ideal, especially in WWTPs where operators need to understand the reasons behind model predictions to increase their confidence in real-world applications. While recent studies have focused on predicting variables in WWTP using ML, research on implementing explainable artificial intelligence (XAI) is still developing. XAI is a promising approach for enhancing the transparency and interpretability of ML models in complex environmental systems, such as WWTPs. This study investigated the performance of widely used ML algorithms in predicting the key effluent quality variables across four WWTPs in Wisconsin, USA. Data was collected from both small- and large-scale WWTPs to evaluate the scalability and generalizability of the models. Feature selection (FS) techniques were used to identify the most relevant features for each target variable in the dataset. XAI tools, including SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), were used to interpret the influence of input variables on the model outputs. This study highlights how ML can accurately predict water quality outcomes, thereby allowing plant operators to better manage their facilities. The study also demonstrates how XAI can clarify the reason behind specific ML models' predictions, enhancing operator confidence in decision-making. These XAI methods identify which factors, i.e., nutrient levels, flow rates, and organic material loads, most significantly impact effluent quality. Using these transparent predictive tools, wastewater facilities can optimize operations, meet regulatory standards, reduce operating costs, and ultimately support healthier communities and ecosystems.", "source_url": "https:\/\/openalex.org\/W7126503029", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Minds at UW (University of Wisconsin)", "source_record_id": "W7126503029", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1171, "title": "eXAI: an innovative ensemble technique with explainable artificial intelligence applied to computer vision in critical contexts", "title_ar": "", "title_en": "eXAI: an innovative ensemble technique with explainable artificial intelligence applied to computer vision in critical contexts", "author": "Miguel Jose das [UNESP] Neves", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; predictive-analytics; cnn; multimodal; xai; fairness", "keywords": "", "abstract": "The growing complexity of Deep Learning models, particularly Convolutional Neural Networks (CNNs), presents a fundamental challenge in critical applications: the opacity of their decisionmaking processes. This thesis addresses this interpretability gap by proposing and validating a methodological framework termed eXAI (Ensemble Explainable Artificial Intelligence), designed to create image analysis systems that merge high predictive performance with transparency. Conceptually inspired by Ensemble Learning, the framework’s novelty lies not in combining predictive models, but in synergistically integrating an ensemble of explainability methods specifically combining SHAP for pixel-level attribution with Grad-CAM for region-level localization to provide a multi-faceted interpretation of a single model’s decisions. The framework’s flexibility and efficacy are demonstrated through two case studies in distinct domains. The first, focused on Diabetic Retinopathy diagnosis, implements a stacking architecture that integrates deep visual features from a CNN with structured clinical data, classified by a LightGBM (LGBM) meta-model. This multimodal approach achieved outstanding accuracy, while the XAI analysis validated the clinical relevance of its decision factors. The second case study, in digital forensics, applies a direct classification CNN to detect Seam Carving image manipulations. Here, XAI was crucial in revealing that the model learned to identify subtle structural distortion artifacts rather than relying on semantic content. The collective results demonstrate that the eXAI framework is a robust and adaptable approach for building trustworthy AI models for critical contexts. By delivering comprehensive and understandable explanations, this methodology enhances the confidence in, and practical utility of, artificial intelligence in both healthcare and information security.", "source_url": "https:\/\/openalex.org\/W7120300663", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120300663", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1172, "title": "An Explainable Hybrid Artificial Intelligence Framework for Stock Market Prediction Using Financial Time-Series and Market Sentiment", "title_ar": "", "title_en": "An Explainable Hybrid Artificial Intelligence Framework for Stock Market Prediction Using Financial Time-Series and Market Sentiment", "author": "Anshul Manhas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; multimodal; sentiment-analysis; xai", "keywords": "", "abstract": "This dissertation presents an explainable hybrid artificial intelligence framework for stock market prediction using financial time-series information and market sentiment. The study investigates the integration of historical market observations, technical indicators, sequential deep-learning models, financial sentiment analysis and explainable artificial intelligence within a unified forecasting framework. The methodology addresses data preprocessing, temporal alignment, feature engineering, model development, multimodal feature integration and evaluation. Particular emphasis is placed on preventing information leakage through chronological data handling and on improving the interpretability of model predictions. The dissertation provides a structured research framework for investigating whether financial sentiment provides complementary information to numerical market features in stock-market forecasting.", "source_url": "https:\/\/openalex.org\/W7204049726", "pdf_url": "", "doi": "10.5281\/zenodo.22065590", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7204049726", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1173, "title": "Causal Structure Learning for Robust and Explainable Artificial Intelligence", "title_ar": "", "title_en": "Causal Structure Learning for Robust and Explainable Artificial Intelligence", "author": "Brandon David Mossop", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Queen's University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; responsible-ai; predictive-analytics; llm; xai", "keywords": "", "abstract": "Modern artificial intelligence (AI) has achieved success by learning statistical patterns from data and framing many problems as prediction tasks. However, prediction alone cannot answer questions such as why an outcome occurred or how an intervention would change it. Answering these questions requires reasoning about cause and effect. Causal AI addresses this limitation by incorporating causal inference into AI. This thesis advances that goal through causal structure learning, focusing on two interrelated challenges: causal forecasting and causal discovery. First, it demonstrates that causal feature selection can improve both the predictive performance and explainability of AI forecasting. It then extends this idea to non-stationary environments by modeling regime-dependent causal structures, providing improved forecasts and interpretable insights into how underlying mechanisms evolve over time. Because these forecasting approaches rely on causal structure, the thesis next turns to causal discovery. It develops an agentic framework using open-weight small language models that combines statistical evidence with agents for orienting, augmenting, and critiquing causal graphs. The framework is then extended with a causal story agent that reasons about the data-generating process to construct more accurate causal graphs. Collectively, this thesis advances causal AI toward systems that represent the mechanisms underlying observed outcomes rather than only statistical associations.", "source_url": "https:\/\/openalex.org\/W7223075870", "pdf_url": "https:\/\/queensu.scholaris.ca\/bitstreams\/f2f2be29-1185-4c08-9071-57eaf93ac9da\/download", "doi": "", "handle": "", "repository_name": "QSpace (Queen's University Library)", "source_record_id": "W7223075870", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1174, "title": "Explainable Artificial Intelligence for Parkinson’s disease diagnosis model", "title_ar": "", "title_en": "Explainable Artificial Intelligence for Parkinson’s disease diagnosis model", "author": "Beatriz Rodrigues Godinho", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; xai", "keywords": "", "abstract": "Parkinson's disease is characterised by the presence of motor and non-motor symptoms within a complex diagnosis prone to errors. To support this process, Machine Learning models have been developed to distinguish Parkinson's patients from other pathologies with similar symptoms. The model under study uses movement data to detect motor symptoms and questionnaire data to identify non-motor symptoms. These developments enable clinical decision support; however, due to their complexity, interpreting the results can be challenging, which is a crucial factor for doctors and clinicians. This work focused on applying explainable artificial intelligence techniques, specifically, Shapley additive explanations. The results indicate that the most important non-motor symptoms for the model are loss or alteration of the ability to taste or smell, urinary dysfunction, loss of interest, and drooling during the day. Local analysis helped identify patterns in incorrect classifications, especially for patients with Parkinson's disease, while the heterogeneity of the group with different diseases made this analysis challenging. For the model with movement data, no results were obtained due to heterogeneity in feature preprocessing across folds and models.", "source_url": "https:\/\/openalex.org\/W7219337623", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Repositório do ISCTE-IUL", "source_record_id": "W7219337623", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1175, "title": "An Explainable Artificial Intelligence Approach to Forecasting Monthly Electricity Consumption", "title_ar": "", "title_en": "An Explainable Artificial Intelligence Approach to Forecasting Monthly Electricity Consumption", "author": "Tyra Wodén", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; ai-applications; predictive-analytics; xai; ai-smart-cities", "keywords": "", "abstract": "Recent years have seen instability in the electricity market and increasing energy demands in the world. Reliable electricity consumption forecasting is a helpful tool for consumers and other stakeholders to manage energy consumption more efficiently. This thesis forecasts the monthly electricity consumption of individual residential households in Sweden using five robust ensemble learning models. To address a gap in previous research, the interpretability of the monthly residential forecasts is enhanced using insights produced by the explainable artificial intelligence (XAI) method SHAP. A diverse set of features, including historical consumption, household attributes, weather data, and calendar information, is investigated for their contributions to the model output. Results show that the ensemble models are able to forecast the consumption with around 15% prediction error, although performance is highly impacted by extreme values for some households. The XAI analysis reveals that recent consumption is the most determining factor for the forecasts, while external factors have a more varied impact depending on model. Two external features that are influential across all models are sunshine amount and wind direction, while several other features show complex or weak relationships to model output. Furthering the research on interpretable forecasts is important for increasing end-users' trust in the forecasts, and subsequently promoting more efficient energy management.", "source_url": "https:\/\/openalex.org\/W7165082060", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W7165082060", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1176, "title": "Explainable artificial intelligence - learning decision sets with sat", "title_ar": "", "title_en": "Explainable artificial intelligence - learning decision sets with sat", "author": "Filipe Inácio da Costa Pereira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; predictive-analytics; xai", "keywords": "", "abstract": "Artificial Intelligence is a core research topic with key significance in technological growth. With the increase of data, we have more efficient models that in a few seconds will inform us of their prediction on a given input set. The more complex techniques nowadays with better results are Black Box Models. Unfortunately, these can’t provide an explanation behind their prediction, which is a major drawback for us humans. Explainable Artificial Intelligence, whose objective is to associate explanations with decisions made by autonomous agents, breaks this lack of transparency. This can be done by two approaches, either by creating models that are interpretable by themselves or by creating frameworks that justify and interpret any prediction made by any given model. This thesis describes the implementation of two interpretable models (Decision Sets and Decision Trees) based on Logic Reasoners, either SAT (Satisfiability) or SMT (Satisfiability Modulo Theories) solvers. This work was motivated by an in-depth analysis of past work in the area of Explainable Artificial Intelligence, with the purpose of seeking applications of logic in this domain. The Decision Sets approach focuses on the training data, as does any other model, and encoding the variables and constraints as a CNF (Conjuctive Normal Form) formula which can then be solved by a SAT\/SMT oracle. This approach focuses on minimizing the number of rules (or Disjunctive Normal Forms) for each binary class representation and avoiding overlap, whether it is training sample or feature-space overlap, while maintaining interpretable explanations and perfect accuracy. The Decision Tree model studied in this work consists in computing a minimum size decision tree, which would represent a 100% accurate classifier given a set of training samples. The model is based on encoding the problem as a CNF formula, which can be tackled with the efficient use of a SAT oracle.", "source_url": "https:\/\/openalex.org\/W2900159149", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W2900159149", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1177, "title": "From medical imaging to explainable artificial intelligence", "title_ar": "", "title_en": "From medical imaging to explainable artificial intelligence", "author": "Erico Tjoa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "nlp", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; nlp; medical-ai; responsible-ai; medical-imaging; xai", "keywords": "", "abstract": "Deep Neural Network (DNN) has recently been recognized as one of the most powerful models capable of performing tasks at and beyond human capacity. With millions, even billions of parameters, DNN has been able to attain remarkable performance on hitherto difficult tasks such as computer vision, natural language processing and many other complex problems (including personalized healthcare", "source_url": "https:\/\/openalex.org\/W4318676873", "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/164493\/2\/Erico%20-%20thesis.pdf", "doi": "10.32657\/10356\/164493", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W4318676873", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1178, "title": "Human interpretable artificial intelligence applications for microbial-related diseases", "title_ar": "", "title_en": "Human interpretable artificial intelligence applications for microbial-related diseases", "author": "Josh L. Espinoza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Durban University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "South Africa", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; xai; ai-education", "keywords": "", "abstract": "The human microbiome is a complex ecosystem that is influenced not only by host genetics but environmental stimuli. With advancements in next-generation sequencing (NGS) technologies, genomics and related meta-omics such as metagenomics, metatranscriptomics and metaproteomics have become increasingly accessible for researchers and clinicians to investigate microbial-related diseases. However, analysis of the outputs of “omics” technologies are often difficult due to variance introduced by biological complexity, batch effects from laboratory protocols\/conditions, and the sensitivity\/calibration of highly sensitive instruments. The biological complexity of “omics” presents a considerable analytical obstacle as most datasets contain hundreds of thousands to millions of unique features with unknown connections and nested hierarchies. In addition to this inherent complexity, the deluge of data generated from NGS technologies is fundamentally compositional, conveys only relative information, and because of this cannot be robustly analyzed using conventional statistical approaches. Furthermore, meta-omics datasets are typically sparse and the number of biological features often vastly exceeds the number of biological samples which can introduce anomalies in statistical analysis and the downstream findings if not addressed accordingly; a term dubbed as “the curse of dimensionality”. The complexity, compositionality, and dimensionality of “omics” datasets makes it challenging to derive clinical meaning and an understanding of the microbial system with respect to a host phenotype. Although, artificial intelligence and machine-learning methods have progressed substantially in recent years, their applications in domain sciences such as biology, and by extension “omics” technologies, have been limited in terms of human interpretability. In many machine-learning paradigms, interpretability is often sacrificed for analytical performance, or vice versa, but recently a domain-agnostic effort aims to develop explainable artificial intelligence algorithms that have both high modeling performance and human interpretability; a major goal of biomedical sciences. In this dissertation, I develop novel approaches in bridging biological science with machine learning methods at the vanguard of scientific development through the initiative of explainable artificial intelligence. The methods developed are validated on 3 datasets pertaining to microbial-related diseases including antibiotic resistance discovery, acute malnutrition in West African children, and caries pathology in Australian juvenile twins. The combination of methods developed are expected to provide the means for clinical researchers to overcome obstacles in interrogating the complex narratives that determine health and disease.", "source_url": "https:\/\/openalex.org\/W4362630824", "pdf_url": "https:\/\/openscholar.dut.ac.za\/bitstream\/10321\/4701\/1\/Espinoza%20JL%20Final%20Dissertation_Redacted.pdf", "doi": "10.51415\/10321\/4701", "handle": "", "repository_name": "DUT Open Scholar (Durban University of Technology)", "source_record_id": "W4362630824", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1179, "title": "An explainable artificial intelligence model for detecting xenophobic tweets", "title_ar": "", "title_en": "An explainable artificial intelligence model for detecting xenophobic tweets", "author": "CESAR; 121431 TORRES HUITZIL ; Gabriel Ichcanziho Perez Landa", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tecnológico de Monterrey", "university_source": "repository_host", "college": "", "department": "", "country": "Mexico", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; responsible-ai; sentiment-analysis; speech-recognition; xai", "keywords": "", "abstract": "Xenophobia is hate speech characterized by hatred, fear, or rejection of people from other communities. The growth of the internet worldwide has resulted in the rapid expansion in the use of social networks. The excessive use of social networks has led to hate speech, primarily due to the feeling of pseudo-anonymity that social networks provide. On occasions, the violent behavior present in the violent courses of social networks breaks the barriers of the internet and becomes an act of physical violence in real life. Research on the classification of xenophobia in social networks is a very recent problem, and that is why there are currently very few databases available for the classification of xenophobia. That is why we created a new Twitter xenophobia database, whose main feature is to have been labeled by experts in international relations, psychology, and sociology. This database has 10,073 manually tagged Tweets, of which 2,017 belong to the xenophobia class. An extensive effort is currently being made to migrate the unexplained machine learning classifiers known as black-box to new explainable artificial intelligence (XAI) models that allow the interpretability and understanding of the classification. We decided to introduce an XAI model based on contrast patterns jointly with a new interpretable feature representation based on syntactic, semantic, and sentiment analysis to understand the characteristics of xenophobic posts on social networks. The new interpretable feature representation has 38 different characteristics, including information on feelings, emotions, intentions, syntactic characteristics, and keywords related to xenophobia. Finally, our results show that our new feature representation in conjunction with a classifier based on contrast patterns obtained an average of 0.86 and 0.77 points in AUC and F1 scores, respectively. Experiments show that XAI models can achieve classification results equal to or better than unexplained models. Furthermore, creating a new interpretable feature representation based on emotions, feelings, intentions, and keywords related to xenophobia allowed us to extract a set of the most used words in xenophobic posts. The interpretable feature representation, jointly with an XAI contrast pattern-based model, allowed us to extract a set of patterns describing the xenophobic and non-xenophobic classes. These patterns are presented in a language close to the experts and contextualize words associated with xenophobia using emotions, intentions, and feelings.", "source_url": "https:\/\/openalex.org\/W7225540921", "pdf_url": "https:\/\/repositorio.tec.mx\/bitstreams\/ec89183e-6872-4d8b-b753-71e13c980ec7\/download", "doi": "", "handle": "", "repository_name": "Repositorio del Tecnológico de Monterrey (Tecnológico de Monterrey)", "source_record_id": "W7225540921", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1180, "title": "Explainable Artificial Intelligence Methods for Modeling Categorical Responses", "title_ar": "", "title_en": "Explainable Artificial Intelligence Methods for Modeling Categorical Responses", "author": "Marcus Mayrhofer", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "TU Wien", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; predictive-analytics; xai; fairness", "keywords": "", "abstract": "Artificial Intelligence (AI) and Machine Learning (ML) are technologies that are not only among the top fields of research, but their areas of applicability are also rapidly increasing. For instance, complex non-linear or non-parametric ML models are already being applied to support key decisions in sectors like health care, criminal justice, or finance. However, while using ML or AI in critical sectors can be viewed as favorable due to their generally high predictive performance and power, the main drawback for human users is that drawinginference, detecting bias or retracing the basis for the different models’ decisions is often hard, if not impossible. The field of Explainable AI addresses exactly this prevalent lack of interpretability of various complex machine learning models – and multiple proposals on how to solve this issue have already been made. In this work we discuss three different methods of Explainable AI, which allow for an interpretation of complex models in a way that is accessible to humans. Moreover, we compare the methods in terms of performanceand applicability on a use-case scenario, namely to predict the electricity imbalance price for Austria, using data collected from the Transparency Platform, which is operated by the European Network of Transmission System Operators for Electricity (ENTSO-E). Specifically, the considered methods are Local Interpretable Model-agnostic Explanations (LIME), Shapley values for model explainability, and SHapley Additive exPlanations (SHAP), for all of which the focus is placed on their application in the context of classification tasks. Re-garding the classification of the electricity imbalance price, we analyze the time-dependent electricity market data from Austria and explore multiple modeling strategies. While all three methods of Explainable AI discussed in this work provide model-agnostic explanation procedures for individual observations, only the model-specific Tree SHAP algorithm from the SHAP framework offers efficient implementations that enable us to fully explain all predictions of tree-based models. The final model for the classification of the electricity imbalance price is based on an online learning approach utilizing boosted tree ensemblemodels, for which we employ the methods of Explainable AI to interpret the model. Furthermore, tree-based methods do not only yield the best results for the currently available data, but also possess the highest potential for improvement should the data quality or availability improve in the future.", "source_url": "https:\/\/openalex.org\/W3199089859", "pdf_url": "https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/18438\/1\/Mayrhofer%20Marcus%20-%202021%20-%20Explainable%20artificial%20intelligence%20methods%20for...pdf", "doi": "10.34726\/hss.2021.87160", "handle": "", "repository_name": "reposiTUm (TU Wien)", "source_record_id": "W3199089859", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1181, "title": "Explainable artificial intelligence for the electrocardiogram", "title_ar": "", "title_en": "Explainable artificial intelligence for the electrocardiogram", "author": "Rutger R van de Leur", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Utrecht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; predictive-analytics; xai; fairness", "keywords": "", "abstract": "The 12-lead electrocardiogram (ECG) is a crucial diagnostic tool in clinical practice, used to identify various cardiac abnormalities. However, interpreting ECGs is complex, with significant variability between and within clinicians, and expert knowledge is not always accessible. This has led to the development of computerized interpretation algorithms (CIE), though they have not yet matched the accuracy of physicians. Recent advancements, particularly the use of deep neural networks (DNNs), have shown potential to process raw ECG data more effectively without relying on hand-crafted features. With the availability of large labeled ECG datasets, ECGs are ideal for developing AI-based algorithms. This research aimed to bridge the gap between AI advancements and their clinical application, bringing these technologies closer to real-world use. Four key prerequisites for developing and implementing ECG-AI were identified: asking the right clinical questions, ensuring rigorous quality control, addressing potential bias using explainability techniques, and conducting appropriate implementation studies. Additionally, we highlighted four key opportunities for ECG-AI applications: disease screening, diagnostic workflow optimization, decision support in treatment, and detecting new ECG features. The first step in exploring DNNs for ECG interpretation involved developing an algorithm to optimize diagnostic workflows. This algorithm efficiently triaged ECGs, identifying which required expert consultation and when, ensuring safe and effective clinical outcomes. An implementation study in a hospital setting demonstrated the algorithm’s practical benefits. Another important aspect of AI implementation is ensuring the algorithms can express uncertainty, as DNNs tend to make predictions even with noisy or unfamiliar data. We developed a method to quantify the algorithm's confidence in its predictions, ensuring that only certain ECGs undergo automatic analysis, while others are referred to an expert. Addressing challenges in DNN usage, including the lack of explainability and the need for large datasets, we created a novel method to interpret ECGs in an explainable manner. By training a variational auto-encoder on over one million ECGs, we decomposed the ECG morphology into 32 explainable factors, termed FactorECG. This method performed as effectively as traditional \"black box\" models but with added transparency, offering promise in applications like detecting reduced ejection fraction. FactorECG also surpassed heatmap-based techniques in improving explainability. Given that large ECG datasets are often unavailable for certain clinical questions, we explored the transferability of DNN knowledge from large to small datasets. We developed algorithms to detect specific genetic mutations and predict disease progression in patients with various heart conditions. Remarkably, these algorithms outperformed traditional prediction models, relying only on the 12-lead ECG for input. In conclusion, while significant progress has been made in developing ECG-AI, challenges remain before it can be fully implemented in clinical practice. Future work must focus on refining the FactorECG algorithm, conducting large-scale trials, and developing software platforms to integrate these algorithms into clinical workflows. Importantly, further research is needed to assess whether ECG-AI improves patient outcomes and reduces healthcare costs and burdens.", "source_url": "https:\/\/openalex.org\/W4402589842", "pdf_url": "", "doi": "10.33540\/2459", "handle": "", "repository_name": "Utrecht University Repository (Utrecht University)", "source_record_id": "W4402589842", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1182, "title": "Towards explainable artificial intelligence (XAI)", "title_ar": "", "title_en": "", "author": "Tiia Haverinen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Jyväskylä", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "fi", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "responsible-ai; xai", "keywords": "", "abstract": "2000-luvun aikana tekoälysovellukset ovat saavuttaneet erinomaisen suorituskyvyn useissa eri tehtävissä. Suuret datajoukot, kasvava laskennallinen teho sekä yhä monimutkaisemmat koneoppimismallit ovat mahdollistaneet sen. Valitettavasti nämä monimutkaiset mallit ovat usein vain mustia laatikoita ihmiskäyttäjille ja käyttäjällä on vaikeuksia ymmärtää ja luottaa tekoälysysteemin lopputuloksiin. Selittävän tekoälyn osa-alueella on ollut suuri määrä tutkimusta sellaisten menetelmien kehittämiseksi, jotka lisäisivät tekoälysysteemien selittävyyttä. Tämä opinnäytetyö sisältää sekä kirjallisuuskatsauksen selittävän tekoälyn tutkimuksesta että kokeilun, jossa kartoitettiin yksinkertaisilla tekoälymenetelmillä ECR-ionilähteen optimaalisia parametreja maksimaaliselle ionisuihkun intensiteetille.", "source_url": "https:\/\/openalex.org\/W3110241541", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Jyväskylä University Digital Archive (University of Jyväskylä)", "source_record_id": "W3110241541", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1184, "title": "DEVELOPMENT OF ACTIVE MACHINE LEARNING METHODS FOR PREDICTIVE MAINTENANCE PROCESSES AND REMAINING OF USEFUL LIFE (RUL) PREDICTION IN INDUSTRY 4.0", "title_ar": "", "title_en": "", "author": "Esmaail Albarazi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2026, "language": "pt", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-industry", "keywords": "", "abstract": "No contexto da Indústria 4.0, a manutenção preditiva desempenha um papel fundamental na otimização do desempenho dos ativos e na redução dos custos operacionais. Esta tese apresenta uma nova estrutura de aprendizagem ativa projetada para melhorar a eficiência e a precisão da previsão da vida útil restante(RUL) para equipamentos industriais.A investigação aborda o desafio dos dados de treino rotulados limitados em aplicações de manutenção preditiva, desenvolvendo uma estratégia de aprendizagem ativa que seleciona de forma inteligente as amostras mais informativas pararotulagem. A abordagem proposta combina arquiteturas de aprendizagem profunda, especificamente redes neurais LSTM (Long Short-Term Memory) e GRU(Gated Recurrent Unit), com mecanismos de aprendizagem ativa para obter previsões robustas da RUL em vários domínios industriais.A tese avalia a metodologia em dois conjuntos de dados de referência: o conjuntode dados CMAPSS (Commercial Modular Aero-Propulsion System Simulation),que simula a degradação do motor turbofan, e o conjunto de dados PRONOSTIA,que consiste em medições de vibração de rolamentos extraídas usando extraçãode características baseada em decomposição média local (LMD) com análise demonotonicidade para identificação de indicadores de saúde.As principais contribuições incluem: (1) uma estratégia inteligente de seleçãode amostras que reduz a carga de anotação, mantendo a precisão da previsão;(2) análise comparativa das arquiteturas LSTM e GRU para modelagem de sequências temporais de padrões de degradação; e (3) validação em diversos tiposde equipamentos industriais, demonstrando a generalização da abordagem deaprendizagem ativa proposta para cenários de manutenção preditiva da Indústria 4.0.", "source_url": "https:\/\/openalex.org\/W7162617772", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7162617772", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1186, "title": "Predictive Maintenance for IT Equipment using Machine Learning Models", "title_ar": "", "title_en": "Predictive Maintenance for IT Equipment using Machine Learning Models", "author": "Rashed Khalid Alshirawi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Rochester Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; ai-applications; predictive-analytics; xai; ai-industry", "keywords": "", "abstract": "Predictive maintenance was recently introduced as a paradigm shift in the field of smart manufacturing, and it allows organizations to predict equipment failures and streamline maintenance planning based on the data-driven insights. The present work addresses the concept of the implementation of a decision tree-based predictive maintenance model based on AI4I 2020 Predictive Maintenance Dataset, a simulated environment of an actual industry through sensor data including temperature, torque, rotational velocity, and toolwear. The study uses a quantitative approach that is based on positivism philosophy, which is the focus on objectivity, empirical validation, and statistical testing of the model performance. Synthetic Minority Oversampling Technique (SMOTE) was used to solve the problem of skewed data in the dataset wherein the distribution has been equalized between the failure and non-failure cases. This preprocessing procedure raised the ability of the model to identify minority-class events, such as machine failures, and this negatively affected the recall and the overall classification accuracy. The Decision Tree classifier was obtained with the accuracy of 90.76 percentage backed up by the precision, recall, and F1-score values that show good prediction performance and at the same time, interpretability which is imperative in any industrial use. The feature-importance analysis showed that the variables concerning Heat Dissipation Failure, Power Failure, and Overstrain Failure were the most significant variables that predict equipment breakdowns. The results of the study indicate that the combination of machinelearning interpretability and balanced and high-quality data is essential in the process of obtaining reliable predictive maintenance solutions. The findings confirm the Decision Tree as a realistic and explicable decision model to use in the practical application and provides transparency as well as accuracy in the maintenance decision making. The study culminates in suggestions on how continuous monitoring can be introduced, sensor networks can be extended, and real time earlywarning dashboards can be developed to make industries efficient and resilient to operations. The next steps inwork are to investigate the techniques of ensemble learning and hybrid predictive systems to enhance further the accuracy of fault-detection and adaptability to a tough manufacturing environment.", "source_url": "https:\/\/openalex.org\/W7119561594", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RIT Scholar Works (Rochester Institute of Technology)", "source_record_id": "W7119561594", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1187, "title": "AI-Enhanced Predictive Maintenance for Industrial Robotics using Sensor Data Analysis and Machine Learning", "title_ar": "", "title_en": "AI-Enhanced Predictive Maintenance for Industrial Robotics using Sensor Data Analysis and Machine Learning", "author": "Most Arina Afrin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Vaasa", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; computer-vision; robotics; ai-applications; predictive-analytics; ai-industry", "keywords": "Predictive Maintenance, Industrial Robotics, Machine Learning, Sensor Data Analysis, Remaining Useful Life (RUL) Prediction, Industry 4.0\/ Robotics 4.0", "abstract": "Modern manufacturing is progressing towards Industry 4.0 and Robotics 4.0, which puts the industrial robots in the focus of contemporary manufacturing. These sophisticated robots keep on producing multi-sensor rich data, which depicts their internal kinematics, forces, torques, and joint dynamics, which is useful in detecting anomalies that may result in failures in time. However, even with access to these types of data, fixed-interval or reactive maintenance plans continue to be implemented by many industries, which results in unwarranted downtime and expensive interruption. These gaps, combined with the necessity to have the predictive maintenance schemes that are reliable and capable of converting the raw sensor images into actionable intelligence based on machine learning. We focused on creating an AI-enhanced predictive maintenance model of industrial robotics, based on internal robot states and machine learning algorithms. The paper concentrates on data pre-processing, feature engineering, anomaly detection, Remaining Useful Life (RUL) prediction, and designing a data-driven maintenance decision-support system that includes and operates synchronized robot sensor streams three tools of kinematics, joint positions, and force\/torque measurements then proceed to extract time-domain features, and correlation-based features to identify patterns of early degradation. Exploration of sensor relationships and structural separability of normal and abnormal conditions was performed by use of Principal Component Analysis (PCA) and correlation matrices. Logistic Regression, Support Vector Machine (SVM), Random Forest and XGBoost were four classical machine learning models trained and tested on chronological splits. Accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC were utilized as a measurement of model performance. A decision-support system was subsequently developed to transform predictive results to a form of maintenance recommendations. The patterns in the robot signals are well established and consistent between normal behavior where random Forest and XGBoost performed better than any of the remaining models. The study proves scientific soundness of applying the classical machine learning models specifically the Random Forest and the XGBoost on predictive maintenance in industrial robotics. It established internal robot data when adequately preprocessed and engineered provides strong predictive capability in detecting faults early and estimating their life. The decision-support framework suggested is a feasible avenue that industries can use to minimize production downtimes, enhance their safety, and enhance the transition to Robotics 4.0. This paper will add fresh empirical data, methodological soundness and practical solutions to the expanding research on AI-enhanced maintenance in intelligent manufacturing settings. KEYWORDS: Predictive Maintenance, Industrial Robotics, Machine Learning, Sensor Data Analysis, Remaining Useful Life (RUL) Prediction, Industry 4.0\/ Robotics 4.0", "source_url": "https:\/\/openalex.org\/W7197330248", "pdf_url": "https:\/\/osuva.uwasa.fi\/bitstreams\/9f2c039e-a40c-4b39-99e1-62ade1f35830\/download", "doi": "", "handle": "", "repository_name": "Osuva (University of Vaasa)", "source_record_id": "W7197330248", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1188, "title": "Machine learning models for predictive maintenance in industrial systems", "title_ar": "", "title_en": "", "author": "Sandra Sanz Chamorro", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidad Complutense de Madrid", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2026, "language": "es", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "Este trabajo propone el diseño e implementación de un gemelo digital para la monitorización de la degradación de un filtro industrial además de la predicción de su vida útil restante (Remaining Useful Life, RUL). Para conseguirlo se usó un conjunto de datos formado por ensayos que muestran la degradación del filtro a lo largo de las mediciones. A partir de estas mediciones se generaron características temporales y se entrenaron cinco modelos de regresión para ser comparados. Para la validación se separaron los ensayos y se tuvo en cuenta la información que se tendría disponible en el sistema real. Además de la predicción del valor del RUL, se calcularon intervalos de predicción, alertas, eventos y explicabilidad que permiten entender mejor las estimaciones. Todos estos elementos se integraron en el gemelo digital. Se usó el protocolo MQTT para comunicar el simulador y el procesador entre sí, e InfluxDB para guardar los datos. Para la representación se diseñó una interfaz con Streamlit.", "source_url": "https:\/\/openalex.org\/W7222386388", "pdf_url": "https:\/\/docta.ucm.es\/bitstreams\/8895f9c6-bfc4-4f3c-9710-6c1132bc6f88\/download", "doi": "", "handle": "", "repository_name": "Docta Complutense (Universidad Complutense de Madrid)", "source_record_id": "W7222386388", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1189, "title": "Predictive Maintenance in Modern Radar Systems using Machine Learning", "title_ar": "", "title_en": "Predictive Maintenance in Modern Radar Systems using Machine Learning", "author": "Klas Thorgren", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Lund University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "medical-ai", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; medical-ai; expert-systems; ai-applications; predictive-analytics; rnn; ai-industry", "keywords": "", "abstract": "This thesis investigated how machine learning-based anomaly detection can be applied to support predictive maintenance for a radar system using available monitoring data. An LSTM autoencoder was trained on selected healthy running operation and used to detect deviations in multivariate sensor windows based on reconstruction error. The anomaly score was combined with report information and a rule-based operating-state classification to separate potentially maintenancerelevant anomalies from deviations caused by known operating conditions. The results show that the method can detect both naturally occurring anomalous behaviour and simulated fault-like deviations, but also that a more complete predictive maintenance implementation would require richer sensor coverage, better operational metadata, and more direct information about system states.", "source_url": "https:\/\/openalex.org\/W7213856700", "pdf_url": "https:\/\/lup.lub.lu.se\/student-papers\/record\/9251213\/file\/9251215.pdf", "doi": "", "handle": "", "repository_name": "Lund University Publications Student Papers (Lund University)", "source_record_id": "W7213856700", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1190, "title": "Utilization of the internet of things and machine learning in digital development of predictive maintenance at Finnish pulp mills", "title_ar": "", "title_en": "Utilization of the internet of things and machine learning in digital development of predictive maintenance at Finnish pulp mills", "author": "Artturi Jokelainen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "LUT University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "The topic of this thesis is the utilization of the internet of things and machine learning in digital development of predictive maintenance at Finnish pulp mills. This thesis examines the architecture and steps needed to build a cloud-based internet of things data platform, studies the utilization of machine learning algorithms on rolling bearing fault detection and describes the future potential of the internet of things in the context of predictive maintenance at pulp mills. An internet of things data platform was developed during this thesis project to support the storage and analysis of large quantities of vibration sensor and other data related to pulp mill predictive maintenance and other pulp mill processes. Literature relevant to cloud data platforms, cloud computing and the internet of things was examined. Strengths, weaknesses, opportunities and threats of the developed internet of things data platform were discussed. Three different machine learning algorithms were tested using various parameter values and 8 features computed from rolling bearing vibration signals to experiment with detecting rolling bearing faults. Future work and research required for developing an automated production-grade fault detection solution were discussed. The potential of further developments in internet of things technologies with regards to predictive maintenance was also discussed.", "source_url": "https:\/\/openalex.org\/W3010075980", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LUTPub (LUT University)", "source_record_id": "W3010075980", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1192, "title": "Machine Learning with Conformal Prediction for Predictive Maintenance tasks in Industry 4.0 : Data-driven Approach", "title_ar": "", "title_en": "Machine Learning with Conformal Prediction for Predictive Maintenance tasks in Industry 4.0 : Data-driven Approach", "author": "Liu, Shuzhou ; Mulahuko, Mpova", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Jönköping University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-industry", "keywords": "", "abstract": "This thesis is a cooperation with Knowit, Östrand \\& Hansen, and Orkla. It aimed to explore the application of Machine Learning and Deep Learning models with Conformal Prediction for a predictive maintenance situation at Orkla. Predictive maintenance is essential in numerous industrial manufacturing scenarios. It can help to reduce machine downtime, improve equipment reliability, and save unnecessary costs. In this thesis, various Machine Learning and Deep Learning models, including Decision Tree, Random Forest, Support Vector Regression, Gradient Boosting, and Long short-term memory, are applied to a real-world predictive maintenance dataset. The Orkla dataset was originally planned to use in this thesis project. However, due to some challenges met and time limitations, one NASA C-MAPSS dataset with a similar data structure was chosen to study how Machine Learning models could be applied to predict the remaining useful lifetime (RUL) in manufacturing. Besides, conformal prediction, a recently developed framework to measure the prediction uncertainty of Machine Learning models, is also integrated into the models for more reliable RUL prediction. The thesis project results show that both the Machine Learning and Deep Learning models with conformal prediction could predict RUL closer to the true RUL while LSTM outperforms the Machine Learning models. Also, the conformal prediction intervals provide informative and reliable information about the uncertainty of the predictions, which can help inform personnel at factories in advance to take necessary maintenance actions. Overall, this thesis demonstrates the effectiveness of utilizing machine learning and Deep Learning models with Conformal Prediction for predictive maintenance situations. Moreover, based on the modeling results of the NASA dataset, some insights are discussed on how to transfer these experiences into Orkla data for RUL prediction in the future.", "source_url": "https:\/\/openalex.org\/W7113257904", "pdf_url": "https:\/\/hj.diva-portal.org\/smash\/get\/diva2:1765779\/FULLTEXT01", "doi": "", "handle": "", "repository_name": "Jonkoping University Library (Jönköping University)", "source_record_id": "W7113257904", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1193, "title": "Application of Machine Learning Methods on Predictive Maintenance", "title_ar": "", "title_en": "Application of Machine Learning Methods on Predictive Maintenance", "author": "Feranmi Akanni", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "Maintenance is important in optimization of the business value of a functional unit and this optimization can only be achieved through predictive maintenance which ensures that maintenance activities are not carried out before due time and at the same time, prevent occurrence of breakdown of functional units because of missed maintenance activities.In this thesis, we focus on using different machine learning methods to predict the failure of a functional units.We explore the data and use missing data techniques to deal with missing values in the dataset, which resulted in a complete dataset.We explore various feature selection techniques to extract important features and reduce dimensionality of the dataset.Then, we explore the following machine learning methods: logistic regression, naïve Bayes, support vector machine, k-nearest neighbour and ensemble learning techniques which are bagging and boosting methods.Our results indicated that predictions from ensemble learning techniques have better evaluation metrics compared to other machine learning methods.", "source_url": "https:\/\/openalex.org\/W3010495304", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W3010495304", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1194, "title": "Predictive Maintenance Using Machine Learning Methods in Petrochemical Refineries", "title_ar": "", "title_en": "Predictive Maintenance Using Machine Learning Methods in Petrochemical Refineries", "author": "Sander Suursalu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-industry", "keywords": "", "abstract": "This research project evaluates the suitability of machine learning methods for early fault prediction and predictive maintenance in petrochemical refineries based on real- life use cases at Shell Pernis. Refineries are mature industrial installations, however, unplanned shutdowns still occur due to equipment failures. Refineries have petabytes of process control data available from the past years, however, all of that data is unla- belled. The goal of this research project was to evaluate, whether useful information can be extracted from the process control data. The resulting approach had to be compatible with Shell IT, scalable to larger sections of the refinery, reusable in other parts of the refinery and capable of detecting the components that cause the potential faults. During this research project, multiple solutions based on artificial neural net- works and statistical approaches were implemented to model the normal behaviour of the monitored systems. Abnormal predictions for the modelled systems were then used to predict failures in advance, where the prediction horizon reached more than a month for some use cases. 4-layer GRUs with tanh activation functions and an input sequence length of 4 samples provided the best results. GRUs were 7% faster to train than LSTMs while reducing the prediction error by 15%. Furthermore, the predic- tion error was less than 3% for the normal operating conditions while reaching more than 15% prior to failures. Therefore, machine learning models can predict failures in petrochemical refineries without any industry-specific knowledge, if the model is trained with clean data that does not contain abnormal time series.", "source_url": "https:\/\/openalex.org\/W2779737813", "pdf_url": "https:\/\/repository.tudelft.nl\/file\/File_fa0ac32d-b9c9-49f2-b716-3d1fe9d9008f", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2779737813", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1195, "title": "Machine learning for predictive maintenance applied in a teaching plant", "title_ar": "", "title_en": "Machine learning for predictive maintenance applied in a teaching plant", "author": "João Guilherme Menuzzo Silva", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Federal University of Technology – Paraná", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-education; ai-industry", "keywords": "", "abstract": "The new technologies from Industry 4.0 allow factories to become smart. It is understood that material, human and financial resources are better used and consequently there is an increase in efficiency and sustainability of processes. It is essential that an intelligent factory be able to detect failures in its process and even prevent them when possible. In this sense, the scenario that is promising due to the results already validated in real applications is the application of Artificial Intelligence. This technology considered to enable Industry 4.0 refers to a large area that is made up of several others. Among them, Machine Learning has shown interesting results when intended for industrial applications. Based on the context presented and the importance of fault detection for modern industry, this work aims to detect anomalous behavior patterns that can identify faults through the analysis of temperature and level curves from an industrial didactic plant. In order to be able to train the intelligent system used, anomalous (simulating failures) and normal situations will be applied to the plant. The collection of variables inherent to the simulated process was transmitted to a dedicated computer through OPC communication, where they were applied to the algorithm capable of learning and approximating the temperature and level curves. The expected and actual behavior were compared, and the divergence between the two curves indicated or not the presence of some anomalous event to the industrial process.", "source_url": "https:\/\/openalex.org\/W7204506599", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/41203\/1\/aprendizadomaquinamanutencaopreditiva.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)", "source_record_id": "W7204506599", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1196, "title": "Deep learning for automobile predictive maintenance under Industry 4.0", "title_ar": "", "title_en": "Deep learning for automobile predictive maintenance under Industry 4.0", "author": "Chong Chen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Cardiff University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; self-semi-supervised; predictive-analytics; rnn; ai-cybersecurity; ai-industry; ai-smart-cities", "keywords": "", "abstract": "Industry 4.0 refers to the fourth industrial revolution, which has boosted the development of the world. An important target of Industry 4.0 is to maximize the asset uptime so to improve productivity and reduce the production and maintenance cost. The emerging techniques such as artificial intelligence (AI), industrial Internet of things (IIoT) and cyber-physical system (CPS) have accelerated the development of data-orientated application such as predictive maintenance (PdM). Maintenance is a big concern for an automobile fleet management company. An accurate maintenance prediction can be helpful to avoid critical failure and avoid further loss. Deep learning is a type of prevailing machine learning algorithm which has been widely used in big data analytics. However, how to establish a maintenance prediction model based on historical maintenance data using deep learning has not been investigated. Moreover, it is worthwhile to study how to build a prediction model when the labelled data is insufficient. Furthermore, surrounding factors which may impact automobile lifecycle have not been concerned in the state-of-the-art. Hence, this thesis will focus on how to pave the way for automobile PdM under Industry 4.0. This research is structured according to four themes. Firstly, different from the conventional PdM research that only focuses on modelling based on sensor data or historical maintenance data, a framework for automobile PdM based on multi-source data is proposed. The proposed framework aims at automobile TBF modelling, prediction, and decision support based on the multi-source data. There are five layers designed in this framework, which are data collection, cloud data transmission and storage, data mapping, pre-processing and integration, deep learning for automobile TBF modelling, and decision support for PdM. This framework covers the entire knowledge discovery process from data collection to decision support. Secondly, one of the purposes of this thesis is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. An accurate automobile TBF iv Abstract prediction can bring tangible benefits to a fleet management company. Different from the existing studies that adopted sensor data for failure time prediction, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed based on the historical maintenance data for TBF modelling and prediction. CoxPHDL is able to tackle the data sparsity and data censoring issues that are common in the analysis of historical maintenance data. Firstly, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox PHM is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental results have demonstrated the merits of the proposed approach. Thirdly, a large amount of labelled data is one of the critical factors to the satisfactory algorithm performance of deep learning. However, labelled data is expensive to collect in the real world. In order to build a TBF prediction model using deep learning when the labelled data is limited, a new semi-supervised learning algorithm called deep learning embedded semi-supervised learning (DLeSSL) is proposed. Based on DLeSSL, unlabelled data can be estimated using a semi-supervised learning approach that has a deep learning technique embedded so to expand the labelled dataset. Results derived using the proposed method reveal that deep learning (DLeSSL based) outperforms the benchmarking algorithms when the labelled data is limited. In addition, different from existing studies, the effect on algorithm performance due to the size of labelled data and unlabelled data is reported to offer insights for the deployment of DLeSSL. Finally, automobile lifecycle can be impacted by surrounding factors such as weather, traffic, and terrain. The data contains these factors can be collected and processed via geographical information system (GIS). To introduce these GIS data into automobile TBF modelling, an integrated approach is proposed. This is the first time that the surrounding factors are considered in the study of automobile TBF modelling. Meanwhile, in order to build a TBF prediction model based on multi-source data, a new deep learning architecture called merged-LSTM (M-LSTM) network is designed. Abstract v Experimental results derived using the proposed approach and M-LSTM network reveal the impacts of the GIS factors. This thesis aims to research automobile PdM using deep learning, which provides a feasibility study for achieving Industry 4.0. As such, it offers great potential as a route to achieving a more profitable, efficient, and sustainable fleet management.", "source_url": "https:\/\/openalex.org\/W3168907897", "pdf_url": "https:\/\/orca.cardiff.ac.uk\/137968\/1\/2021ChenCPhD.pdf", "doi": "", "handle": "", "repository_name": "ORCA Online Research @Cardiff (Cardiff University)", "source_record_id": "W3168907897", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1197, "title": "A machine learning framework for predictive maintenance of wind turbines", "title_ar": "", "title_en": "A machine learning framework for predictive maintenance of wind turbines", "author": "Katherine Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Massachusetts Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020", "source_url": "https:\/\/openalex.org\/W3137400720", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DSpace@MIT (Massachusetts Institute of Technology)", "source_record_id": "W3137400720", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1198, "title": "Predictive Maintenance Using Machine Learning", "title_ar": "", "title_en": "", "author": "Mateyaunga Innocent", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2020, "language": "fr", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-industry", "keywords": "", "abstract": "Ces dernières années, le Deep Learning a attiré une attention croissante, d’où son application dans la maintenance prédictive. Il s’agit d’un domaine de recherche en évolution qui a le potentiel d’influencer divers domaines d’application et, par conséquent, son utilisation pour les applications de gestion de la santé des systèmes et équipements complexes et couteux doit être mis à profit. Cette thèse présente une revue systématique de la gestion de la santé du système basée sur l’intelligence artificielle (IA) en mettant l’accent sur les tendances récentes de l’apprentissage profond (DL) dans le domaine: différents types de modèles d’apprentissage automatique (ML) qui peuvent être utilisés pour prédire la défaillance d’une machine .Au début, la revue des publications précédentes est effectué, puis différents modèles (LSTM, GRU et SRN) sont utilisés sur l’ensemble de données NASA Turbofan afin de déterminer le meilleur modèle, les versions CuDNN (CuDNNLSTM, CuDNNGRU) sont également appliquées et le temps d’exécution est comparé aux temps de base (LSTM, GRU). Le CuDNNGRU s’avère être la meilleure méthode en raison de son temps d’exécution (30,6 s) et de sa exactitude compétitive (0,9827) et de sa précision (0,9452).", "source_url": "https:\/\/openalex.org\/W7135298816", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/3dec6713-7835-4c54-92a5-a7922e1ca84a\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135298816", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1199, "title": "Predictive maintenance for software systems using machine learning", "title_ar": "", "title_en": "Predictive maintenance for software systems using machine learning", "author": "Taleska, Teodora", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Primorska", "university_source": "repository_host", "college": "", "department": "", "country": "Slovenia", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7115223837", "pdf_url": "https:\/\/repozitorij.upr.si\/Dokument.php?lang=slv&id=29616&dn=", "doi": "", "handle": "", "repository_name": "Repository of the University of Primorsk (University of Primorska)", "source_record_id": "W7115223837", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1200, "title": "Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms", "title_ar": "", "title_en": "Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms", "author": "Kayalvizhi Lakshmanan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Swansea University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; ai-industry; ai-smart-cities", "keywords": "", "abstract": "The importance of Predictive Maintenance is critical for engineering industries, such as manufacturing, aerospace and energy. Unexpected failures cause unpredictable downtime, which can be disruptive and high costs due to reduced productivity. This forces industries to ensure the reliability of their equip-ment. In order to increase the reliability of equipment, maintenance actions, such as repairs, replacements, equipment updates, and corrective actions are employed. These actions affect the flexibility, quality of operation and manu-facturing time. It is therefore essential to plan maintenance before failure occurs.Traditional maintenance techniques rely on checks conducted routinely based on running hours of the machine. The drawback of this approach is that maintenance is sometimes performed before it is required. Therefore, conducting maintenance based on the actual condition of the equipment is the optimal solu-tion. This requires collecting real-time data on the condition of the equipment, using sensors (to detect events and send information to computer processor).Predictive Maintenance uses these types of techniques or analytics to inform about the current, and future state of the equipment. In the last decade, with the introduction of the Internet of Things (IoT), Machine Learning (ML), cloud computing and Big Data Analytics, manufacturing industry has moved forward towards implementing Predictive Maintenance, resulting in increased uptime and quality control, optimisation of maintenance routes, improved worker safety and greater productivity.The present thesis describes a novel computational strategy of Predictive Maintenance (fault diagnosis and fault prognosis) with ML and Deep Learning applications for an FG304 series external gear pump, also known as a domino pump. In the absence of a comprehensive set of experimental data, synthetic data generation techniques are implemented for Predictive Maintenance by perturbing the frequency content of time series generated using High-Fidelity computational techniques. In addition, various types of feature extraction methods considered to extract most discriminatory informations from the data. For fault diagnosis, three types of ML classification algorithms are employed, namely Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB) algorithms. For prognosis, ML regression algorithms, such as MLP and SVM, are utilised. Although significant work has been reported by previous authors, it remains difficult to optimise the choice of hyper-parameters (important parameters whose value is used to control the learning process) for each specific ML algorithm. For instance, the type of SVM kernel function or the selection of the MLP activation function and the optimum number of hidden layers (and neurons).It is widely understood that the reliability of ML algorithms is strongly depen-dent upon the existence of a sufficiently large quantity of high-quality training data. In the present thesis, due to the unavailability of experimental data, a novel high-fidelity in-silico dataset is generated via a Computational Fluid Dynamic (CFD) model, which has been used for the training of the underlying ML metamodel. In addition, a large number of scenarios are recreated, ranging from healthy to faulty ones (e.g. clogging, radial gap variations, axial gap variations, viscosity variations, speed variations). Furthermore, the high-fidelity dataset is re-enacted by using degradation functions to predict the remaining useful life (fault prognosis) of an external gear pump.The thesis explores and compares the performance of MLP, SVM and NB algo-rithms for fault diagnosis and MLP and SVM for fault prognosis. In order to enable fast training and reliable testing of the MLP algorithm, some predefined network architectures, like 2n neurons per hidden layer, are used to speed up the identification of the precise number of neurons (shown to be useful when the sample data set is sufficiently large). Finally, a series of benchmark tests are presented, enabling to conclude that for fault diagnosis, the use of wavelet features and a MLP algorithm can provide the best accuracy, and the MLP al-gorithm provides the best prediction results for fault prognosis. In addition, benchmark examples are simulated to demonstrate the mesh convergence for the CFD model whereas, quantification analysis and noise influence on training data are performed for ML algorithms.", "source_url": "https:\/\/openalex.org\/W3211660692", "pdf_url": "", "doi": "10.23889\/suthesis.58703", "handle": "", "repository_name": "Cronfa (Swansea University)", "source_record_id": "W3211660692", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1201, "title": "An automation system for predictive maintenance of electric machines applied to pumping systems", "title_ar": "", "title_en": "An automation system for predictive maintenance of electric machines applied to pumping systems", "author": "Gonçalo José de Sousa Azinheira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidade do Algarve", "university_source": "repository_host", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry; ai-smart-cities", "keywords": "", "abstract": "This thesis presents the development and implementation of an automation system for predictive maintenance of electric machines in pumping systems. The work integrates Industry 4.0 technologies such as IoT devices, machine learning algorithms, and advanced sensor systems to enhance the reliability and efficiency of industrial operations. A complete test bench was developed, featuring an electric pump, water reservoir, automation and control board, energy meter, vibration sensors, pressure sensors, motorized valves, and temperature sensors, allowing automated test procedures including fault-injection behaviors through valve control. The research bridges the gap between theoretical models of predictive maintenance and their practical implementation in industrial environments, emphasizing the importance of automation and data-driven decision-making. The core achievements include the successful development and deployment of a system capable of real-time data acquisition, advanced vibration analysis, and fault prediction using machine learning. A detailed analysis was conducted to determine optimal data processing procedures prior to analysis, and automation-based sensors were integrated with electronic-based sensors in a unified predictive maintenance system. Machine learning algorithms demonstrated the feasibility of implementing predictive maintenance within standard automation pumping systems by successfully predicting faults induced in the test pump. The developed system not only reduces unexpected failures but also aligns with modern demandsfor sustainability and operational efficiency. By combining data acquisition, real-time analysis, and predictive modeling, the research offers a comprehensive approach that can be adapted across various industries reliant on electric machines.", "source_url": "https:\/\/openalex.org\/W7217058524", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Universidade do Algarve", "source_record_id": "W7217058524", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1202, "title": "Machine Learning for Predictive Maintenance: A Boeing 747 Bleed Air Valves case study", "title_ar": "", "title_en": "Machine Learning for Predictive Maintenance: A Boeing 747 Bleed Air Valves case study", "author": "Erik IJzermans", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "The newest generation of aircraft has seen a strong increase in sensor data generated on-board. The available data has the potential to indicate the health state of individual components based on which their maintenance requirements can be determined, a maintenance strategy called Condition Based Maintenance. Predictive Maintenance is a specific condition based maintenance strategy that aims to determine these requirements in advance by predicting failures from the sensor data. It has the potential to reduce costly unanticipated maintenance or unnecessarily conservative maintenance. In the short term it could add significant value for components that are currently subject to a reactive maintenance policy. In the long term it could potentially disrupt the traditional maintenance practice of periodic inspection. One of the main challenges in applying Predictive Maintenance in the aviation industry is translating the large amounts of sensor data into a reliable failure prediction, a process called prognostics. In this study, state-of-the-art machine learning, and specifically deep learning models, have been investigated for their potential for prognostics. A case study has been performed at KLM Royal Dutch Airlines on the Boeing 747 Bleed Air Valves, traditionally some of the most challenging components from a maintenance perspective. It has been shown that fully self-learning algorithms can be used for prognostics, enabling the implementation of one of the first real-life predictive maintenance implementations.", "source_url": "https:\/\/openalex.org\/W2898020487", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2898020487", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1203, "title": "Scalability Analysis of Predictive Maintenance Using Machine Learning in Oil Refineries", "title_ar": "", "title_en": "Scalability Analysis of Predictive Maintenance Using Machine Learning in Oil Refineries", "author": "Helmi Helmiriawan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "Modern refineries typically use a high number of sensors that generate an enormous amount of data about the condition of the plants. This generated data can be used to perform predictive maintenance, an approach to predict impending failures and mitigate downtime in refineries. This research analyzes the scalability of machine learning methods for predictive maintenance solution in an oil refinery. It can be done by modeling the normal behavior of the plant and use the prediction error to identify anomalies which might potentially become failures. Several methods and learning algorithms are explored in this research to model the normal behavior of multiple components in the plant. The experiments are performed by using historical process data from a crude distiller unit at Shell Pernis Refinery. The results show that the proposed approach using multiple targets model is able to predict multiple components in the plant. It is not only able to detect anomalies but also identify the faulty component. Furthermore, it reduces the required time to model the normal behavior of the plant which improves the scalability of the predictive maintenance approach in the refinery.", "source_url": "https:\/\/openalex.org\/W2907310032", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2907310032", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1204, "title": "Machine learning model for predictive maintenance on linear accelerators", "title_ar": "", "title_en": "Machine learning model for predictive maintenance on linear accelerators", "author": "A. K. K. Tonui", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Strathmore University", "university_source": "repository_host", "college": "", "department": "", "country": "Kenya", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "medical-ai", "research_subdomain": "object-detection", "categories": "machine-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; object-detection; ai-industry", "keywords": "", "abstract": "Predicting machine failures is the next frontier in industrial machine maintenance.However, the ideal implementation of such a program will require the fitting of industrial machines with sensors that can constantly monitor a machine's vital parameters while feeding them to a supervisory module for analysis and possible action.However, such an undertaking will require massive capital and time investments to achieve.This is where log file mining and analysis come in.By analysing the already existing machine log files of medical linear accelerators, a prediction model was developed to anticipate motor problems and notify engineers without investing the capital outlay of fitting new sensors on a machine.Mining of the log files yielded an imbalanced dataset containing 3.367% anomalies.This study tested three algorithms for their predictive power with the Random Forest classifier coming out on top with 99% precision, recall and accuracy.It was closely followed by Logistic Regression and an anomaly detector, Isolation Forest, with a precision of 59%.These strong results indicate the potential of machine learning for predicting machine breakdowns to anticipate machine failures and enable engineers to take proactive maintenance action.", "source_url": "https:\/\/openalex.org\/W7214852035", "pdf_url": "https:\/\/su-plus.strathmore.edu\/bitstreams\/4edbe5bd-80f0-4bf7-9a77-c75981da2749\/download", "doi": "", "handle": "", "repository_name": "SU+ Digital Repository (Strathmore University)", "source_record_id": "W7214852035", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1205, "title": "Predictive maintenance with machine learning on weld joint analysed by ultrasound", "title_ar": "", "title_en": "Predictive maintenance with machine learning on weld joint analysed by ultrasound", "author": "Adam Hedkvist", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Uppsala University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; medical-imaging; ai-industry", "keywords": "", "abstract": "Ever since the first industrial revolution industries have had the goal to increase their production. With new technology such as CPS, AI and IoT industries today are going through the fourth industrial revolution denoted as industry 4.0. The new technology not only revolutionises production, but also maintenance, making predictive maintenance possible. Predictive maintenance seeks to predict when failure would occur, instead of having scheduled maintenance or maintenance after failure already occurred. In this report a convolutional neural network (CNN) will analyse data from an ultrasound machine scanning a weld joint. The data from the ultrasound machine will be transformed by the short time Fourier transform in order to create an image for the CNN. Since the data from the ultrasound is not complete, simulated data will be created and investigated as another option for training the network. The results are promising, however the lack of data makes it hard to show any concrete proof.", "source_url": "https:\/\/openalex.org\/W2988297880", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Uppsala University Publications (Uppsala University)", "source_record_id": "W2988297880", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1206, "title": "Machine learning solutions for maintenance of power plants", "title_ar": "", "title_en": "Machine learning solutions for maintenance of power plants", "author": "Stanislav Kalabin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal\/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal\/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented.", "source_url": "https:\/\/openalex.org\/W2924328858", "pdf_url": "https:\/\/aaltodoc.aalto.fi\/bitstreams\/152f90d7-5565-49b4-b750-b3380a85ab58\/download", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W2924328858", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1207, "title": "A study of Machine Learning for Predictive Maintenance - A topic and programming guidance", "title_ar": "", "title_en": "A study of Machine Learning for Predictive Maintenance - A topic and programming guidance", "author": "Kåre Hartlapp Lærum", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; ai-applications; transfer-learning; predictive-analytics; ai-industry", "keywords": "", "abstract": "A significant potential and interest is found for Predictive Maintenance (PdM) and Machine Learning (ML). Both fields are under development and need further research. Through collaboration with the Norwegian maintenance company, Karsten Moholt, two areas of special significance for this thesis where found. Firstly, finding more information regarding the ML approach and especially the potential of Transfer Learning (TL), and secondly, understanding and building an ML model for PdM. This thesis aims to provide an introduction and to be practical start guide for those interested in using ML for PdM. To achieve this, necessary background information has been collected, and to further learn about ML, several online courses has been completed. The online course, books and several web-guides are used to build and develop two ML models to find Remaining Useful Life (RUL) on the Turbofan Engine Degradation Data Set provided by NASA. The information and code are presented to enable the reader to understand ML and to provide tool to start building ML models. PdM is presented as a technique for monitoring operating condition to provide data that can ensure the maximum interval between repairs and minimize the number and cost of unscheduled machine failures. ML is con sidered as an important and powerful tool for finding patterns and make predictions from a vast amount of data where Neural Networks (NN) is the main technique for implementing ML. TL is considered as a powerful idea, where knowledge from one NN can be transferred to another. Further, the topic of NN is presentment and explained with examples. To enable the reader to start programming, needed tools such as Python, Jupyter, Numpy, Pandas and Keras are presented, used and recommended. The first model provides a guide on how to program an NN oneself, and includes the elements; prepare data , initialize weights and biases , forward propagation , calculating cost , backpropagation and update parameters . The second model presents how to program the same model with Keras, a NN framework, also enabling one to build a ML model with few lines of code. This guide provides examples and tools that can be used for simple demonstrations of ML for regression problems. The thesis provides arguments and justification for the need for further research. It enables a foundation for further development of ML models for different PdM RUL scenarios.", "source_url": "https:\/\/openalex.org\/W2900613942", "pdf_url": "", "doi": "", "handle": "", "repository_name": "NORA - Norwegian Open Research Archives", "source_record_id": "W2900613942", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1208, "title": "أثرمنهج التعلم العميق القائم على الذكاء الاصطناعي على مهارة كتابة اللغة العربية لدى طلاب الصف الحادي عشر في المدرسة الثانوية الحكومية الثانية بيوجياكرتا ٢٠٢٦ = ATHR MANHAJ AL-TA'ALLUM AL-'AMIQ AL-QA'IM 'ALA AL-DHAKA' AL-ISTINA'I 'ALA MAHARAT KITABAT AL-LUGHAH AL-'ARABIYYAH LADA TULLAB AL-SAFF AL-HADI 'ASHAR FI AL-MADRASAH AL-THANAWIYYAH AL-HUKUMIYYAH AL-THANIYAH YOGYAKARTA 2026", "title_ar": "أثرمنهج التعلم العميق القائم على الذكاء الاصطناعي على مهارة كتابة اللغة العربية لدى طلاب الصف الحادي عشر في المدرسة الثانوية الحكومية الثانية بيوجياكرتا ٢٠٢٦ = ATHR MANHAJ AL-TA'ALLUM AL-'AMIQ AL-QA'IM 'ALA AL-DHAKA' AL-ISTINA'I 'ALA MAHARAT KITABAT AL-LUGHAH AL-'ARABIYYAH LADA TULLAB AL-SAFF AL-HADI 'ASHAR FI AL-MADRASAH AL-THANAWIYYAH AL-HUKUMIYYAH AL-THANIYAH YOGYAKARTA 2026", "title_en": "", "author": "NIM.: 22104020014 Sri Fatimah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Sunan Kalijaga State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; nlp; transformers; arabic-nlp", "keywords": "", "abstract": "Penelitian ini dilatarbelakangi oleh masih rendahnya keterampilan menulis Bahasa Arab siswa kelas XI MAN 2 Yogyakarta, khususnya dalam menulis kalimat dan paragraf sederhana. Penelitian ini bertujuan untuk mengetahui pengaruh pendekatan deep learning berbasis Artificial Intelligence (AI) terhadap keterampilan menulis Bahasa Arab siswa kelas XI MAN 2 Yogyakarta. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis kuasi-eksperimen dan desain non-equivalent control group design. Populasi penelitian adalah seluruh siswa kelas XI MAN 2 Yogyakarta tahun ajaran 2025\/2026 dengan teknik pengambilan cluster random sampling. Pengumpulan data dilakukan melalui pretest, posttest, dan observasi partisipan. Analisis data menggunakan uji Wilcoxon, uji Mann-Whitney U, dan uji N-Gain. Hasil penelitian menunjukkan bahwa pendekatan deep learning berbasis AI tidak berpengaruh secara signifikan terhadap keterampilan menulis Bahasa Arab siswa kelas XI MAN 2 Yogyakarta. Hasil uji Mann-Whitney U menunjukkan nilai signifikansi sebesar 0,221 pada analisis pertama dan 0,133 pada analisis kedua, yang berarti lebih besar dari 0,05. Selain itu, hasil uji N-Gain memperoleh nilai sebesar 0,041 yang termasuk kategori rendah. Dengan demikian, pendekatan deep learning berbasis AI hanya menunjukkan peningkatan yang rendah dan tidak signifikan secara statistik terhadap keterampilan menulis Bahasa Arab siswa.", "source_url": "https:\/\/openalex.org\/W7164774459", "pdf_url": "https:\/\/digilib.uin-suka.ac.id\/id\/eprint\/76688\/1\/22104020014_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf", "doi": "", "handle": "", "repository_name": "Digital Library UIN Sunan Kalijaga (Sunan Kalijaga State Islamic University)", "source_record_id": "W7164774459", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1209, "title": "Pengaruh artificial intelligence self-eficacy terhadap stres akademik mahasiswa Fakultas Psikologi Universitas Islam Negeri Maulana Malik Ibrahim Malang", "title_ar": "", "title_en": "", "author": "Ahmad Rofiul Abror", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maulana Malik Ibrahim State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "id", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; ai-applications; predictive-analytics; transformers; arabic-nlp; ai-education", "keywords": "", "abstract": "INDONESIA Penelitian ini bertujuan untuk mengetahui pengaruh Artificial Intelligence Self-Efficacy (AISE) terhadap stres akademik pada mahasiswa Fakultas Psikologi UIN Malang. Latar belakang penelitian ini didasari oleh meningkatnya penggunaan kecerdasan buatan (AI) dalam aktivitas akademik mahasiswa serta asumsi bahwa tingkat kepercayaan diri terhadap penggunaan AI dapat memengaruhi kondisi psikologis, khususnya stres akademik. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan teknik analisis regresi linier sederhana. Sampel penelitian terdiri dari mahasiswa aktif Fakultas Psikologi UIN Maulana Malik Ibrahim Malang yang dipilih melalui teknik purposive sampling dengan kriteria: terdaftar sebagai mahasiswa aktif pada semester berjalan dan pernah menggunakan teknologi berbasis Artificial Intelligence (AI) dalam aktivitas akademik seperti menyusun tugas, mencari referensi, atau membuat laporan. Hasil penelitian menunjukkan bahwa AISE tidak berpengaruh signifikan terhadap stres akademik, dengan nilai koefisien regresi sebesar -0,182 dan signifikansi 0,098 (p > 0,05). Temuan ini menunjukkan bahwa kepercayaan diri mahasiswa dalam menggunakan teknologi AI belum cukup untuk mengurangi tingkat stres akademik. Implikasi dari hasil ini adalah perlunya pengembangan program literasi dan pelatihan penggunaan AI yang lebih komprehensif, agar mahasiswa tidak hanya mampu memanfaatkan teknologi tersebut secara teknis, tetapi juga dapat menggunakannya secara adaptif dan efektif dalam menghadapi tuntutan akademik tanpa menimbulkan tekanan psikologis. Faktor lain seperti dukungan sosial, beban akademik, dan kecemasan terhadap penggunaan AI diduga lebih berperan dalam memengaruhi stres. Penelitian ini diharapkan dapat memberikan pemahaman baru mengenai pentingnya keseimbangan antara kemampuan teknologis dan kesiapan psikologis mahasiswa di era digital. ENGLISH This study aims to determine the effect of Artificial Intelligence Self-Efficacy (AISE) on academic stress in students of the Faculty of Psychology, UIN Malang. The background of this study is based on the increasing use of artificial intelligence (AI) in students' academic activities and the assumption that the level of confidence in the use of AI can affect psychological conditions, especially academic stress. The research method used is a quantitative approach with a simple linear regression analysis technique. The research sample consisted of active students of the Faculty of Psychology, UIN Maulana Malik Ibrahim Malang who were selected through a purposive sampling technique with the following criteria: registered as active students in the current semester and have used Artificial Intelligence (AI)-based technology in academic activities such as compiling assignments, searching for references, or making reports. The results showed that AISE had no significant effect on academic stress, with a regression coefficient value of -0.182 and a significance of 0.098 (p > 0.05). This finding indicates that students' confidence in using AI technology is not enough to reduce academic stress levels. The implication of these results is the need to develop more comprehensive AI literacy and training programs, so that students are not only able to utilize the technology technically, but also can use it adaptively and effectively to face academic demands without causing psychological stress. Other factors such as social support, academic burden, and anxiety about AI use are suspected to play a larger role in influencing stress. This research is expected to provide new insights into the importance of balancing technological skills and psychological readiness in students in the digital era. ARABIC تهدف هذه الدراسة إلى تحديد أثر الكفاءة الذاتية في استخدام الذكاء الاصطناعي على الضغط الأكاديمي لدى طلاب كلية علم النفس بجامعة مالانج الإسلامية الحكومية. وتستند خلفية هذه الدراسة إلى تزايد استخدام الذكاء الاصطناعي في الأنشطة الأكاديمية للطلاب، وفرضية أن مستوى الثقة في استخدام الذكاء الاصطناعي قد يؤثر على الحالة النفسية، وخاصة الضغط الأكاديمي. استخدمت الدراسة المنهج الكمي، وتحديدًا تقنية تحليل الانحدار الخطي البسيط. تكونت عينة البحث من طلاب كلية علم النفس بجامعة مولانا مالك إبراهيم الإسلامية الحكومية في مالانج، والذين تم اختيارهم باستخدام أسلوب العينة الهادفة وفقًا للمعايير التالية: أن يكونوا مسجلين كطلاب منتظمين في الفصل الدراسي الحالي، وأن يكونوا قد استخدموا تقنية الذكاء الاصطناعي في أنشطة أكاديمية مثل إعداد الواجبات، والبحث عن المراجع، وكتابة التقارير. أظهرت النتائج أن الكفاءة الذاتية في استخدام الذكاء الاصطناعي لم يكن لها تأثير معنوي على الضغط الأكاديمي، حيث بلغ معامل الانحدار -0.182، ومستوى الدلالة 0.098 (قيمة الاحتمال > 0.05). تشير هذه النتيجة إلى أن ثقة الطلاب في استخدام تقنيات الذكاء الاصطناعي لا تكفي وحدها لخفض مستويات التوتر الأكاديمي.", "source_url": "https:\/\/openalex.org\/W7125361256", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W7125361256", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1210, "title": "أنا أشهدُ إذن أنا موجود ما بعد ديكارت: تأسيس الأنطولوجيا التوحيدية وفلسفة الشهود من الكوجيتو إلى الميثاق : قراءة في المعادلة الوجودية (B + F = NF)", "title_ar": "أنا أشهدُ إذن أنا موجود ما بعد ديكارت: تأسيس الأنطولوجيا التوحيدية وفلسفة الشهود من الكوجيتو إلى الميثاق : قراءة في المعادلة الوجودية (B + F = NF)", "title_en": "", "author": "Fatiha Nesrine Bouzid", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "يقدم هذا البحث نقضاً وجودياً لمقولة ديكارت أنا أفكر إذن أنا موجود، ويقدم بديلاً أنطولوجياً من النص القرآني تحت مسمى فلسفة الشهود التوحيدية . في عصر سيطر فيه الذكاء الاصطناعي على التفكير كعملية معالجة وظيفية، يثبت البحث أن التفكير لم يعد يميز الإنسان، بل الشهادة . تعتمد الدراسة معادلة وجودية (B + F = NF) لتفكيك بنية الوجود الإنساني والكوني؛ حيث (B) هي المعالجة الصامتة (الطين\/الآلة)، و(F) هي السيادة الشاهدة (الروح\/الفيتو)، و(NF) هو الأثر السردي الخالد. كما يكشف البحث عن الإعجاز اللغوي في التحول من البشر (الطين قبل النفخ) إلى بني آدم (الطين والروح بعد النفخ)، ليثبت أن التكريم الإلهي كان للإنسان الشاهد لا للإنسان المادي. كما يميز البحث بين الكسر الأخير في الموت العادي والشهيد الحي الذي لا يموت بل يبقى حاضراً شاهداً عند ربه. يخلص البحث إلى أن الإنسان ليس كائناً مفكراً بل كائناً شاهداً خُلق ليحضر ويقاوم العدم. تعريف التفكير (B): ينبغي التأكيد أن الذكاء الاصطناعي يمتلك (B) عمياء، بينما التفكير الإنساني (B) مستنيرة وتشهد بـ (F)؛ فالتفكير الإنساني ليس خوارزمية فحسب، بل خوارزمية تشهد على نفسها.", "source_url": "https:\/\/openalex.org\/W7203651425", "pdf_url": "", "doi": "10.5281\/zenodo.21981399", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7203651425", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1211, "title": "Students' perception of using Chat Gpt as an academic writing assistant at an Islamic Senior High School", "title_ar": "", "title_en": "Students' perception of using Chat Gpt as an academic writing assistant at an Islamic Senior High School", "author": "Bunayya Izzani Fathi Musaqqaf", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maulana Malik Ibrahim State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; responsible-ai; ai-applications; llm; fairness; ai-education", "keywords": "", "abstract": "INGGRIS The growing phenomenon of Artificial Intelligence (AI) use, particularly ChatGPT, in academic writing has become an interesting topic recently. While ChatGPT assists students in generating ideas, improving sentence structure, and enhancing writing efficiency, its use also raises concerns about dependency, ethics, and declining critical thinking skills. The purpose of this research is to explore students’ perceptions of using ChatGPT in academic writing and to identify the challenges they experience. This study employed a qualitative approach with a transcendental phenomenological design, where data were collected through observation and semi structured interviews involving 23 students from the 11th grade “Excellent (A)” class at MAN 1 Nganjuk, six of whom were selected as key informants. Data collection was carried out through direct classroom observation during writing activities and interviews to understand students’ experiences and viewpoints. The findings reveal that students hold a positive perception of ChatGPT as a helpful tool that supports idea generation and writing organization. However, several technical challenges such as unstable internet connections and cognitive challenges including poor prompt literacy, dependence on AI outputs, and reduced creativity were also identified. The discussion highlights that the effectiveness of ChatGPT in academic writing depends largely on students’ learning motivation, digital literacy, and teachers’ guidance in fostering ethical and reflective AI use. INDONESIA Fenomena meningkatnya penggunaan Artificial Intelligence (AI), khususnya ChatGPT, dalam penulisan akademik menjadi topik yang menarik perhatian dalam beberapa waktu terakhir. ChatGPT membantu siswa dalam menghasilkan ide, memperbaiki struktur kalimat, dan meningkatkan efisiensi penulisan. Namun, penggunaannya juga memunculkan kekhawatiran terkait ketergantungan, etika, serta menurunnya kemampuan berpikir kritis. Tujuan penelitian ini adalah untuk mengeksplorasi bagaimana persepsi siswa terhadap penggunaan ChatGPT dalam penulisan akademik serta mengidentifikasi tantangan yang mereka alami. Penelitian ini menggunakan pendekatan kualitatif dengan desain fenomenologi transendental. Data dikumpulkan melalui observasi dan wawancara semi terstruktur terhadap 23 siswa kelas XI “Unggulan (A)” di MAN 1 Nganjuk, dengan enam siswa dipilih sebagai informan kunci. Pengumpulan data dilakukan melalui observasi langsung selama kegiatan menulis dan wawancara untuk memahami pengalaman serta pandangan siswa. Hasil penelitian menunjukkan bahwa siswa memiliki persepsi positif terhadap ChatGPT sebagai alat bantu yang mendukung pengembangan ide dan pengorganisasian tulisan. Namun, beberapa tantangan juga ditemukan, seperti kendala teknis berupa koneksi internet yang tidak stabil, serta tantangan kognitif seperti rendahnya kemampuan membuat prompt, ketergantungan pada hasil AI, dan berkurangnya kreativitas. Pembahasan menegaskan bahwa efektivitas penggunaan ChatGPT dalam penulisan akademik sangat bergantung pada motivasi belajar siswa, literasi digital, serta peran guru dalam membimbing penggunaan AI yang etis dan reflektif. ARAB يشهد استخدام الذكاء الاصطناعي، وخاصة برنامج شات جي بي تي، في مجال الكتابة الأكاديمية انتشاراً متزايداً في الآونة الأخيرة، مما جعله موضوعاً يثير اهتماماً واسعاً. إذ يساعد شات جي بي تي الطلاب في توليد الأفكار، وتحسين بناء الجمل، وزيادة فعالية عملية الكتابة، إلا أن استخدامه يثير أيضاً عدداً من المخاوف، مثل الاعتماد المفرط، والجوانب الأخلاقية، وتراجع مهارات التفكير النقدي. يهدف هذا البحث إلى استكشاف تصوّرات الطلاب حول استخدام شات جي بي تي في الكتابة الأكاديمية، وكذلك التعرّف على التحديات التي يواجهونها .اعتمدت هذه الدراسة المنهج النوعي بتصميم ظواهري متعالٍ (فينومينولوجيا متعالية). وقد جُمعت البيانات من خلال الملاحظة والمقابلات نصف المهيكلة مع ٢٣ طالباً من الصف الحادي عشر \"الممتاز (A)\" في مدرسة المدرسة العلية ١ نجوج، حيث تم اختيار ستة طلاب منهم كمخبرين رئيسيين. شملت عملية جمع البيانات الملاحظة المباشرة داخل الصف أثناء أنشطة الكتابة، وكذلك المقابلات لفهم تجارب الطلاب ووجهات نظرهم. أظهرت النتائج أن الطلاب يمتلكون تصوّراً إيجابياً تجاه شات جي بي تي بوصفه أداة مفيدة تساعد في توليد الأفكار وتنظيم الكتابة. ومع ذلك، برزت عدة تحديات، منها تحديات تقنية مثل عدم استقرار اتصال الإنترنت، وتحديات معرفية تشمل ضعف مهارة إعداد \"البْرومبت\"، والاعتماد على مخرجات الذكاء الاصطناعي، وتراجع مستوى الإبداع. وتشير المناقشة إلى أن فعالية استخدام شات جي بي تي في الكتابة الأكاديمية تعتمد بدرجة كبيرة على دافع التعلم لدى الطلاب، ومستوى الوعي الرقمي لديهم، ودور المعلم في توجيههم نحو استخدام أخلاقي وواعي للذكاء الاصطناعي.", "source_url": "https:\/\/openalex.org\/W7118152733", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W7118152733", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1212, "title": "Analisis keakuratan penafsiran oleh Chatgpt-5: Studi komparatif penafsiran AI dan tafsir Mafatihul Ghaib", "title_ar": "", "title_en": "", "author": "Isnaini, Inayatul", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maulana Malik Ibrahim State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "id", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; transformers; llm", "keywords": "", "abstract": "ABSTRAK Perkembangan pesat teknologi kecerdasan buatan (Artificial Intelligence\/AI), khususnya ChatGPT yang kini mulai dimanfaatkan dalam bidang studi keislaman, termasuk dalam penafsiran Al-Qur’an. Meskipun AI memiliki kemampuan untuk menyajikan informasi secara cepat dan sistematis, keakuratan hasil tafsir yang dihasilkannya masih perlu diuji secara ilmiah. Oleh karena itu, penelitian ini bertujuan untuk menelaah tingkat akurasi penafsiran yang dihasilkan oleh ChatGPT-5 terhadap ayat Al-Qur’an, dengan membandingkannya terhadap tafsir Mafatih al-Ghaib karya Fakhruddin ar-Razi. Penelitian ini merupakan penelitian kualitatif dengan jenis library research (penelitian kepustakaan). Pendekatan yang digunakan ialah pendekatan tafsir muqāran (komparatif) yang dikombinasikan dengan teori validitas tafsir, guna menilai kesesuaian hasil tafsir ChatGPT-5 dengan prinsip-prinsip penafsiran yang berlaku dalam tradisi tafsir klasik. Data primer berasal dari hasil penafsiran ChatGPT-5 dan kitab tafsir Mafatihul Ghaib, sementara data sekunder meliputi buku, jurnal, dan penelitian terdahulu yang relevan. Hasil penelitian menunjukkan bahwa dari segi keakuratan makna, penafsiran yang dihasilkan oleh ChatGPT-5 dapat dikatakan cukup akurat karena kandungan maknanya sejalan dengan penjelasan yang terdapat dalam Mafatihul Ghaib. Namun, dari aspek sumber rujukan, penafsiran tersebut menunjukkan perbedaan antara kutipan sumber yang disebutkan dengan letak aslinya dalam kitab tafsir klasik, serta ketidaksesuaian data yang menunjukkan bahwa ChatGPT-5 belum sepenuhnya berpijak pada dasar-dasar pemahaman nash yang benar sebagaimana disyaratkan dalam validitas tafsir menurut As-Shabuni. مستخلص البحث بدأ استخدام التطور السريع لتكنولوجيا الذكاء الاصطناعي، وخاصةً ChatGPT، في الدراسات الإسلامية، بما في ذلك تفسير القرآن الكريم. ورغم قدرة الذكاء الاصطناعي على عرض المعلومات بسرعة ومنهجية، إلا أن دقة تفسيراته لا تزال بحاجة إلى اختبار علمي. لذلك، تهدف هذه الدراسة إلى فحص دقة تفسيرات ChatGPT-5 للآيات القرآنية بمقارنتها بتفسير فخر الدين الرازي لكتاب مفاتيح الغيب. هذه الدراسة دراسة نوعية تعتمد على البحث المكتبي. المنهج المستخدم هو منهج تفسير مقارن، مقرون بنظرية صحة التفسير، لتقييم مدى ملاءمة تفسيرات ChatGPT-5 لمبادئ التفسير المعمول بها في تراث التفسير التقليدي. البيانات الأولية مستمدة من تفسير ChatGPT-5 وتفسير مفاتيح الغيب، بينما تشمل البيانات الثانوية الكتب والمجلات والأبحاث السابقة ذات الصلة. تشير نتائج الدراسة إلى أن تفسير ChatGPT-5 دقيقٌ تمامًا من حيث دقة المعنى، نظرًا لتوافقه مع شرح كتاب مفاتيح الغيب. ومع ذلك، من حيث المصادر المرجعية، يُظهر التفسير اختلافات بين الاقتباسات المرجعية وموقعها الأصلي في كتب التفسير القديمة، بالإضافة إلى تباين البيانات، مما يشير إلى أن ChatGPT-5 لا يستند تمامًا إلى أسس الفهم الصحيح للنص، كما هو مطلوب في صحة التفسير عند الشبوني ABSTRACT The rapid development of Artificial Intelligence (AI) technology, particularly ChatGPT, which is now being utilized in the field of Islamic studies, including in the interpretation of the Qur'an. Although AI has the ability to present information quickly and systematically, the accuracy of its interpretive results still needs to be tested scientifically. Therefore, this study aims to examine the level of accuracy of interpretations produced by ChatGPT-5 regarding Qur'anic verses, by comparing them with Mafatih al-Ghaib by Fakhruddin ar-Razi. This research is a qualitative study employing library research methodology. The approach used is the comparative exegesis (tafsir muqāran) method combined with the theory of exegetical validity, in order to assess the conformity of ChatGPT-5's interpretive results with the principles of interpretation prevailing in classical exegetical traditions. Primary data is derived from ChatGPT-5's interpretations and tafsir Mafatihul Ghaib, while secondary data includes books, journals, and relevant previous studies. The research findings indicate that in terms of semantic accuracy, the interpretations produced by ChatGPT-5 can be considered fairly accurate because their semantic content aligns with the explanations found in Mafatih al-Ghaib. The results of the study indicate that in terms of accuracy of meaning, the interpretation produced by ChatGPT-5 can be said to be quite accurate because its meaning is in line with the explanations contained in Tafsir Mafatihul Ghaib. However, from the aspect of reference sources, the interpretation shows differences between the cited source quotes and their original location in classical tafsir books, as well as data discrepancies that indicate that ChatGPT-5 is not fully based on the basics of correct understanding of the text as required in the validity of tafsir according to As-Shabuni.", "source_url": "https:\/\/openalex.org\/W7114933950", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W7114933950", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1213, "title": "سيميولوجيا السخرية الرقمية وإدارة الأزمات الإعلامية في المربع الدولي: دراسة تحليلية لنتائج مارس 2026 Semiology of Digital Satire and Media Crisis Management in the International Quadrant: An Analytical Study of March 2026 Outcomes", "title_ar": "سيميولوجيا السخرية الرقمية وإدارة الأزمات الإعلامية في المربع الدولي: دراسة تحليلية لنتائج مارس 2026 Semiology of Digital Satire and Media Crisis Management in the International Quadrant: An Analytical Study of March 2026 Outcomes", "title_en": "", "author": "Dalal AlDosari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "السخرية الرقمية, إدارة الأزمات الإعلامية, الميم السياسي, التحليل السيميائي, جيل Z, الإعلام الرقمي, الرأي العام", "abstract": "تستهدف هذه الدراسة تحليل دور السخرية الرقمية بوصفها أداة تواصلية في إدارة الأزمات الإعلامية وتشكيل مناخ الرأي العام خلال التوترات الجيوسياسية في ما يُعرف بـ “المربع الدولي” خلال مارس 2026. اعتمدت الدراسة على منهج النتنوغرافيا (Netnography) والتحليل السيميائي لتحليل عينة قصدية من محتوى منصة إنستغرام، شملت منشورات لمؤسسات إعلامية ومحتوى منتج من قبل مؤثرين على المستويين المحلي والدولي. تركز الدراسة على استكشاف كيفية توظيف “الميم السياسي” في إعادة تأطير الخطاب الإعلامي الرسمي، وإعادة إنتاج المعنى في البيئة الرقمية. أظهرت النتائج أن الجمهور، ولا سيما جيل Z، يوظف السخرية الرقمية كآلية معرفية للتفاعل مع الرسائل الإعلامية، وكوسيلة لتقليل تأثير التضليل الرقمي، بما في ذلك المحتوى المُنتج بتقنيات الذكاء الاصطناعي. وتخلص الدراسة إلى أهمية تبني المؤسسات الإعلامية نماذج تشاركية في إدارة المحتوى، تأخذ في الاعتبار الأدوار المتزايدة للجمهور في إعادة صياغة الخطاب الإعلامي، كما تشير إلى أن السخرية الرقمية تمثل مؤشرًا سوسيولوجيًا ذا دلالة لفهم اتجاهات الرأي العام في سياقات الأزمات. الكلمات المفتاحية: السخرية الرقمية، إدارة الأزمات الإعلامية، الميم السياسي، التحليل السيميائي، جيل Z، الإعلام الرقمي، الرأي العام. Abstract: This study examines the role of digital humor as a communicative tool in media crisis management and in shaping public opinion during the geopolitical tensions referred to as the “International Quadrant” in March 2026. The study employs Netnography and Semiotic Analysis to examine a purposive sample of Instagram content, including posts from media institutions as well as content produced by local and international influencers. The research aims to explore how political memes are utilized to reframe official media discourse and reconstruct meaning within digital environments. The findings indicate that audiences, particularly Generation Z, employ digital humor as a cognitive mechanism to engage with media messages and to mitigate the impact of digital misinformation, including AI-generated content. The study concludes that media institutions should adopt participatory content management models that acknowledge the evolving role of audiences in reshaping media narratives. It further suggests that digital satire can serve as a significant sociological indicator for understanding public opinion trends in times of crisis. Keywords: Digital Humor, Media Crisis Management, Political Memes, Semiotic Analysis, Generation Z, Digital Media, Public Opinion.", "source_url": "https:\/\/openalex.org\/W7155226993", "pdf_url": "", "doi": "10.5281\/zenodo.19427755", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7155226993", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1214, "title": "Improving teacher ability in developing English material through Curipod application", "title_ar": "", "title_en": "Improving teacher ability in developing English material through Curipod application", "author": "Nariza Titis Vio Masayu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maulana Malik Ibrahim State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; nlp; ai-applications; transformers; arabic-nlp; ai-education", "keywords": "", "abstract": "ENGLISH: This research aims to discover the role of Curipod application in helping teachers create blended learning-based materials. Researcher took this title because they saw that this application has much potential to be explored and developed to support teachers in utilizing AI media in learning. The research used a descriptive qualitative method to analyze information and phenomena that occur in depth. The researcher used three resource persons, namely English teachers of grades 10, 11, and 12 at SMK Nasional. Data was collected using observation, interviews, and documentation during the research. Observations were conducted in November with five meetings and continued with interviews with each teacher. The observations and interviews were analyzed to collect valid data and thoroughly understand the activities under study. Based on the research results, six main points were found: the role of the Curipod application in helping teachers create materials, the differences felt by teachers before and after using Curipod, the challenges for teachers and students in using Curipod, devices and internet networks during learning with Curipod, a basic understanding of Curipod, and active and reflective teaching. Researcher concluded that Curipod proved to help teachers in developing English materials. Researcher have recommendations for future research on the many opportunities to develop and explore the Curipod application. INDONESIA: Penelitian ini bertujuan untuk mengetahui bagaimana peran aplikasi Curipod dalam membantu guru membuat materi berbasis blended learning. Peneliti mengambil judul ini karena melihat bahwa aplikasi ini memiliki banyak potensi untuk dieksplorasi dan dikembangkan untuk mendukung para pengajar dalam memanfaatkan media AI dalam pembelajaran. Penelitian dilakukan dengan menggunakan metode kualitatif deskriptif untuk menggali informasi dan fenomena yang terjadi secara mendalam. Peneliti menggunakan tiga narasumber, yaitu guru bahasa inggris kelas 10,11, dan 12 di SMK Nasional. Selama penelitian data dikumpulan dengan cara observasi, wawancara dan dokumentasi. Observasi dilakukan dibulan November dengan lima kali pertemuan dan dilanjutkan dengan wawancara pada masing-masing guru. Hasil observasi dan wawancara dianalisis untuk mengumpulkan data yang valid dengan tujuan memperoleh pemahaman menyeluruh tentang kegiatan yang diteliti. Berdasarkan hasil penelitian, ditemukan 6 poin utama yaitu peran aplikasi Curipod dalam membantu guru membuat materi, perbedaan yang dirasakan oleh guru sebelum dan sesudah menggunakan Curipod, tantangan bagi guru dan siswa dalam menggunakan Curipod, perangkat dan jaringan internet saat pembelajaran dengan Curipod, pemahaman dasar mengenai Curipod, dan pengajaran aktif dan reflektif. Peneliti memiliki rekomendasi untuk penelitian selanjutnya mengenai banyaknya kesempatan untuk mengembangkan dan mengeksplorasi aplikasi Curipod. ARABIC: يهدف هذا البحث إلى اكتشاف دور تطبيق Curipod في مساعدة المعلمين على إنشاء مواد قائمة على التعلم المدمج. واتخذ الباحثون هذا العنوان لأنهم رأوا أن هذا التطبيق لديه الكثير من الإمكانات التي يمكن استكشافها وتطويرها لدعم المعلمين في استخدام وسائط الذكاء الاصطناعي في التعلم. وقد استخدم البحث المنهج الوصفي الكيفي لتحليل المعلومات والظواهر التي تحدث بعمق. واستخدم الباحث ثلاثة أشخاص من ذوي الخبرة، وهم مدرسو اللغة الإنجليزية للصفوف العاشر والحادي عشر والثاني عشر في مدرسة SMK Nasional. تم جمع البيانات باستخدام الملاحظة والمقابلات والتوثيق أثناء البحث. أُجريت الملاحظات في شهر نوفمبر في خمسة اجتماعات واستمرت المقابلات مع كل معلم. وقد تم تحليل الملاحظات والمقابلات لجمع بيانات صحيحة وفهم الأنشطة قيد الدراسة فهماً شاملاً. وبناءً على نتائج البحث، تبين وجود 6 نقاط رئيسية: دور تطبيق كوريبود في مساعدة المعلمين في إنشاء المواد، والفروق التي شعر بها المعلمون قبل وبعد استخدام كوريبود، والتحديات التي تواجه المعلمين والطلاب في استخدام كوريبود، والأجهزة وشبكات الإنترنت أثناء التعلم باستخدام كوريبود، والفهم الأساسي لكوريبود، والتدريس النشط والانعكاسي. لدى الباحثين توصيات للبحوث المستقبلية حول الفرص العديدة لتطوير واستكشاف تطبيق كوريبود.", "source_url": "https:\/\/openalex.org\/W7127226920", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W7127226920", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1216, "title": "Analisis dampak penggunaan ChatGPT dalam kebiasaan pencarian informasi mahasiswa Universitas Brawijaya", "title_ar": "", "title_en": "", "author": "Uswatun Hasanah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maulana Malik Ibrahim State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "id", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; ai-applications; transformers; llm; arabic-nlp; ai-education", "keywords": "", "abstract": "INDONESSIA Perkembangan teknologi kecerdasan buatan (AI) seperti ChatGPT telah mempengaruhi kebiasaan mahasiswa dalam mencari informasi akademik, sehingga semakin sering menggunakan ChatGPT dalam pemenuhan kebutuhan informasi dan akademik mereka. Penggunaan ChatGPT yang terlalu sering berpotensi dalam mengubah kebiasaan pencarian informasi, pola penggunaan perpustakaan, preferensi terhadap sumber informasi, serta tingkat ketelitian mahasiswa dalam memilah informasi. Penelitian ini bertujuan menganalisis dampak penggunaan ChatGPT terhadap kebiasaan pencarian informasi mahasiswa Universitas Brawijaya. Metode penelitian menggunakan pendekatan kuantitatif dengan penyebaran kuesioner kepada 106 responden. Penelitian ini beracuan pada penelitian lain yang telah dilakukan oleh Karunaratne & Adesina (2023) dengan topik serupa menggunakan lima indikator, yaitu awareness, use and effectiveness, challenges, trust in the function, dan user competence. Hasil penelitian menunjukkan bahwa mahasiswa telah merasakan dampak dari penggunaan ChatGPT dalam proses pencarian informasi. Mahasiswa lebih sering menggunakan ChatGPT sebagai titik awal pencarian informasi karena kemudahan akses dan kecepatan respons, kemudian memverifikasi hasil yang diperoleh melalui sumber ilmiah lain. Sikap kritis ini membantu mahasiswa menghindari kesalahan akibat keterbatasan akurasi ChatGPT sekaligus mencegah ketergantungan berlebihan. Selain itu, pada indikator awareness diketahui bahwa mahasiswa Universitas Brawijaya menyadari manfaat ChatGPT sebagai sarana pencarian informasi cepat, namun pada saat yang sama juga memahami keterbatasannya sehingga menggunakannya secara bijak dan selektif. Pada indikator user competence menunjukkan bahwa penggunaan ChatGPT telah mendorong mahasiswa terbiasa merumuskan pertanyaan lebih spesifik dan meningkatkan kemampuan mengevaluasi kredibilitas informasi. ChatGPT berperan sebagai alat bantu yang mempercepat pencarian informasi, tetapi tetap ditempatkan secara proporsional sebagai pendukung proses belajar, bukan sumber rujukan utama. ENGLISH The development of artificial intelligence (AI) technology such as ChatGPT has influenced students' habits in searching for academic information, so that they increasingly use ChatGPT to fulfill their information and academic needs. Excessive use of ChatGPT has the potential to change information search habits, library usage patterns, preferences for information sources, and the level of accuracy of students in sorting information. This study aims to analyze the impact of ChatGPT usage on the information search habits of students at Brawijaya University. The research method uses a quantitative approach with a questionnaire distributed to 106 respondents. This study refers to another study conducted by Karunaratne & Adesina (2023) on a similar topic using five indicators, namely awareness, use and effectiveness, challenges, trust in the function, and user competence. The results show that students have felt the impact of using ChatGPT in the information search process. Students more often use ChatGPT as a starting point for information search because of its ease of access and speed of response, then verify the results obtained through other scientific sources. This critical attitude helps students avoid mistakes due to ChatGPT's accuracy limitations while preventing excessive dependence. Additionally, the awareness indicator shows that Brawijaya University students recognize the benefits of ChatGPT as a means of quick information retrieval, but at the same time understand its limitations, so they use it wisely and selectively. The user competence indicator shows that the use of ChatGPT has encouraged students to formulate more specific questions and improve their ability to evaluate the credibility of information. ChatGPT serves as a tool that accelerates information retrieval, but it is still placed proportionally as a support for the learning process, not the primary reference source. ARABIC أثر تطور تكنولوجيا الذكاء الاصطناعي مثل شات جي بي تي على عادات الطلاب في البحث عن المعلومات الأكاديمية. يتزايد استخدام الطلاب لـ شات جي بي تي لتلبية احتياجاتهم المعلوماتية والأكاديمية. الاستخدام المتكرر لـ شات جي بي تي من شأنه أن يغير عادات البحث عن المعلومات، وأنماط استخدام المكتبات، وتفضيلات مصادر المعلومات، ومستوى دقة الطلاب في فرز المعلومات. تهدف هذه الدراسة إلى تحليل تأثير استخدام شات جي بي تي على عادات البحث عن المعلومات لدى الطلاب في جامعة براويجايا. تستخدم طريقة البحث نهجًا كميًا مع استبيان تم توزيعه على ١٠٦ مستجيبًا. تشير هذه الدراسة إلى دراسة أخرى أجراها كاروناراتنه وأديسينا (٢٠٢٣) تناقش موضوعًا مشابهًا باستخدام خمسة مؤشرات تم تحليلها، وهي الوعي والاستخدام والفعالية والتحديات والثقة في الوظيفة وكفاءة المستخدم. تظهر النتائج أن الطلاب قد شعروا بالتأثير الحقيقي لاستخدام شات جي بي تي في عملية البحث عن المعلومات.", "source_url": "https:\/\/openalex.org\/W7125093218", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W7125093218", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1217, "title": "commande neuro-floue tolérante aux défauts d'une turbine à gaz contribution à la détection de vibration", "title_ar": "commande neuro-floue tolérante aux défauts d'une turbine à gaz contribution à la détection de vibration", "title_en": "", "author": "hadroug naji", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "توربينات ، غاز ، الذكاء الاصطناعي ، المنطق الضبابي ، الالياف العصبية ، neuro-flou ، gaz ، turbine ، vibration ، neuro-fuzziness ، gas ، turbine ، vibration ،CATD", "source_url": "https:\/\/openalex.org\/W2982633701", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2982633701", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1218, "title": "طرق اتخاذ القرار باستخدام الذكاء الإصطناعي: دراسة مقارنة للتنبؤ بالطاقة الكهربائية لولاية تلمسان باستعمال الشبكات العصبيةالإصطناعية", "title_ar": "طرق اتخاذ القرار باستخدام الذكاء الإصطناعي: دراسة مقارنة للتنبؤ بالطاقة الكهربائية لولاية تلمسان باستعمال الشبكات العصبيةالإصطناعية", "title_en": "", "author": "Latifa Djebbari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2016, "language": "ar", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics", "keywords": "", "abstract": "Cette thèse a pour objectif de décrire et d’exposer les différentes méthodes de prise de décision en adoptant l’intelligence artificielle comme méthode, pour pouvoir ainsi appliquer ces méthodes dans la prévision de la charge électrique de la wilaya de Tlemcen. Pour ce faire, la SONELGAZ Tlemcen nous a fourni toutes les données nécessaires .Nous avons opté pour deux méthodes : la première méthode est statistique : en utilisant la méthode de BOX and JENKINS. La seconde méthode se base sur l’une des approches de l’intelligence artificielle, celle des neurones artificiels. Nous avons tenté de comparer les résultats pour choisir la méthode la plus efficace qui donne des résultats plus réalistes, dans le but d’éviter les coupures d’électricité surtout dans les heures de pointe et améliorer les services pour les clients. This dissertation revolves around the various methods of decision making adapting Artificial Intelligence and applying its principles in predicting the electrical load in Tlemcen. To achieve this, data were collected from SONELGAZ, Tlemcen, and a case study was held applying two methods: the first method was statistical using BOX & JENKINS, whereas the second one was based on Artificial Neurons. Using a comparative approach, the researcher selected the efficient method that will produce more realistic results, to avoid power cut, especially in the peak hours to improve services for customers. تهدف هذه الأطروحة إلى محاولة لعرض مختلف طرق اتخاذ القرار باستخدام الذكاء الاصطناعي، و تطبيق النتائج النظرية في نمذجة الطلب الكلي للطاقة الكهربائية لولاية تلمسان اعتمادا على البيانات المقدمة من طرف SONELGAZ تلمسان من خلال طريقتين الأولى إحصائية باستخدام منهجية Box and Jenkins .الطريقة الثانية تعتمد على فرع من فروع الذكاء الاصطناعي و هي الشبكات العصبية الاصطناعية، و محاولة المقارنة فيما بينهما لاختيار أنجعها والتي تقدم أقرب النتائج للواقع لتفادي الإنقطاعات المتكررة خاصة في ساعات الذروة ، و تقديم خدمة أفضل للزبون.", "source_url": "https:\/\/openalex.org\/W2443202341", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/3203c58b-ecd1-4f2e-b376-6f34881ba158\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W2443202341", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1219, "title": "أثر فروقات المعلومات على صحة اتخاذ القرار في المؤسسة الاستشفائية العمومية - دراسة حالة مستشفى تلمسان-", "title_ar": "أثر فروقات المعلومات على صحة اتخاذ القرار في المؤسسة الاستشفائية العمومية - دراسة حالة مستشفى تلمسان-", "title_en": "", "author": "Touria Boufatah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2022, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "القليلة ا خلال العقود ًّ را هام ّ ة من أهم الاقتصاديات التي تشهد تطو ّ اقتصاد الصح ُّيعد الماضية، الذي عرف اهتمام العديد من الباحثين من خلا العلمي حليل لنظم ّ ل الاعتماد على الت المعلومات بهدف ترقية وتطويرصنع القرار في المؤسسة الاستشفائية. تهدف دراستنا الى توجيه اتخاذ القرار الطبي للرؤية الرياضية التي تنظر الى الجانب الحسابي للعقلانية ودعمه باستعمال الذكاء الاصطناعي وذلك للتقليل من الغموض الموجود في التواصل ما بين الافراد. فدعم القرار الطبي باستعمال الذكاء الاصطناعي هو نشاط علمي واحترافي رسمي هدفه الأساسي هو قابلية التكيف مع جميع المواقف والتزاوج ما بين التقنية المتطورة والمعلومات والمهارات الطبية المكتسبة، مما يؤدي اجمة عن ّ الى تفادي المشكلات الصحية الن فروقات المعلومات وبالتالي التحكم أكثر في التكلفة الذي يعد من اساسيات اقتصاد الصح", "source_url": "https:\/\/openalex.org\/W7135276728", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/f01fb398-b67a-4e0b-b83d-7dd4e457db33\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135276728", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1220, "title": "استخدام الخوارزميات الجينية في التنبؤ بتطاير الأسواق المالية", "title_ar": "استخدام الخوارزميات الجينية في التنبؤ بتطاير الأسواق المالية", "title_en": "", "author": "Omar Mouffak", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2019, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "الخوارزميات الجينية هي واحدة من بين أحدث الطرق الكمية المستخدمة في دعم اتخاذ القرار، هي إحدى تقنيات الذكاء الاصطناعي تحاكي تفسيرات علمية في الوراثة و التطور الطبيعي. بذلك تهدف هذه الدراسة إلى التعرف على الخوارزميات الجينية، كذلك معرفة كيف و مشتقاتها، ثم محاولة تطبيقها على ARCH تستخدم الخوارزميات الجينية في التنبؤ بتطاير الأسواق المالية إلى جانب النماذج القياسية مثل و Madex ،Tunindex : ثلاث أسواق مالية هي بورصة تونس، الدار البيضاء و نيويورك من خلال تحليل السلاسل الزمنية للمؤشرات و بعد إجراء مقارنة مع الطرق القياسية، تمكنا من الحصول على مجموعة من النتائج ،Evolver بالاعتماد على برنامج .Dow Jones حول هذه الحالات. فتم استنتاج مدى فعالية استخدام الخوارزميات الجينية في التنبؤ بتطاير الأسواق المالية، كما أنها تستحق الاستخدام في المسائل الأكثر تعقيدا و عند فشل الطرق المعتادة.", "source_url": "https:\/\/openalex.org\/W7135248377", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/bfb7411b-139e-4154-91a7-df5cb0144052\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135248377", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1221, "title": "Enhanced Arabic Language Texts Classification using the Light Gradient Boosted Machine Algorithm (LIGHTGBM)", "title_ar": "Enhanced Arabic Language Texts Classification using the Light Gradient Boosted Machine Algorithm (LIGHTGBM)", "title_en": "", "author": "عدي زياد فيصل البطاينه", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "ar", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "nlp; arabic-nlp", "keywords": "", "abstract": "يتحرك العالم هذه الأيام بسرعة نحو تقنية الذكاء الاصطناعي (AI) حيث أن الذكاء الاصطناعي هو تقنية قوية للغاية للتعرف على أنماط احتياجات المستخدم وتحديدها وفي العديد من المجالات الرئيسية في الوقت الحاضر. كانت النصوص العربية أحد الفروع التي يجب تصنيفها واكتشافها في الوقت الحاضر حيث يتزايد توفر السياق العربي على السحابة بشكل كبير خلال العقد الماضي. وهذا يثير مطالب جديدة لمعالجة السياق العربي وأتمتة تجربة المستخدم المتعلقة بالنصوص العربية. لسوء الحظ، هناك القليل من الجهود لتصنيف واكتشاف السياق العربي الذي تحتاجه الحلول الذكية مثل أنظمة التوصية وحلول بناء الملف الشخصي لذلك، اقترح هذا البحث نهجا جديدا لغرض اكتشاف والتعرف على السياق العربي الموجود على الإنترنت. يعتمد النهج الجديد على منهجية التحسين التي تعد جزءا من خوارزمية شجرة الغابة العشوائية المسماة LIGHTGBM جنبا إلى جنب مع مرحلة ما قبل المعالجة واستخراج الميزات من السياق العربي. تم استخدام خوارزمية LIGHTGBMفي هذا البحث بسبب ثباتها ودقتها العالية المكتسبة من التدريب. يستخدم LIGHTGBMدعما متوازيا فعالا للتعليمات. تستخدم الخوارزمية الأشجار الثابتة الجزئية ووظائف الخسارة المقدرة. تلعب مرحلة ما قبل المعالجة في نهجنا دورا مهما هنا لتصفية البيانات وإخراج البيانات المصفاة بدون تشوهات من أجل استخراج ميزات فعالة للغاية لزيادة الدقة في مرحلتي التدريب والاختبار، وتحتوي مراحل ما قبل المعالجة التصفية والتطبيع والترميز وإزالة كلمات التوقف، كل هذه الخطوات تعمل على تحسين مجموعة البيانات لاكتساب دقة عالية أثناء التدريب والاختبار. بعد مرحلة ما قبل المعالجة، يتم استخراج الميزة لاستخراج الميزات الفعالة. تسمى الميزة المستخرجة في هذه المرحلة N-grams التي تتمتع بميزة تقديم مزيد من المعلومات حول كلمة واحدة حتى يتمكن LIGHTGBM من التدريب ومعرفة المزيد حول الكلمات المدخلة. تم اختبار نهجنا الجديد في هذا البحث ومقارنته بالعديد من الخوارزميات الأخرى باستخدام نفس مجموعة البيانات. حصل LIGHTGBMعلى دقة أعلى من بين جميع طرق التصنيف الأخرى حيث حصل LIGHTGBMعلى دقة 81.31%مع 3984 سجل،Precision87.5%،Recall89.9%وحصل على دقة 94.03%مع 300 سجل.", "source_url": "https:\/\/openalex.org\/W3214987655", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3214987655", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1222, "title": "Prediction of future Solar and wind Energy Generation through Artificial Neural Network models", "title_ar": "Prediction of future Solar and wind Energy Generation through Artificial Neural Network models", "title_en": "", "author": "احمد علي محمد السبع", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "ar", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "تقدم هذه الرسائل مراجعة للطرق الحديثة للتنبؤ بالجيل المستقبلي من الطاقة الشمسية وطاقة الرياح من خلال نماذج الشبكات العصبية الاصطناعية في الظروف البيئية المتقلبة, تتمثل الفكرة الرئيسية في استخدام البيانات التاريخية من مزارع الخلايا الشمسية و تور بينات الرياح لتدريب النماذج باستخدام بيانات التدريب التي يتم إجراؤها لفترات مختلفة. وهذا يتطلب بناء نماذج باستخدام الذكاء الاصطناعي (AI) و كذلك باستخدام شبكة عصبية اصطناعية (ANN) و لتحقيق ذلك قمنا ببناء نموذجين هي Feedforward Multi-Layer ANN و Elman Feedback Multi-Layer ANN. استخدم هذا البحث الذكاء الاصطناعي (AI) لقدرته على التعامل مع المعادلات الرياضية المعقدة في الظروف القاسية, نظرا لموثوقيتها ، ودقتها العالية، وإمكانية التنبؤ بها من خلال التدريب على البيانات التاريخية، يتم توقع أربعة نماذج لفترات مختلفة لتوليد الطاقة على مرحلتين. المرحلة الأولى: عملية التدريب على البيانات التاريخية لمدة شهر والتنبؤ بها لمدة يوم واحد في المستقبل يطبق على النماذج الأربعة السابقة وإيجاد أفضل نموذج لشبكة ANN للتنبؤ بكمية توليد الطاقة بحيث يتم أخذ النموذج الذي يتنبأ بكمية توليد الطاقة بدقة عالية و أفضل أداء وأقل معدل خطأ من بين النموذجين ثم ننتقل الى المرحلة الثانية. المرحلة الثانية: إنها مرحلة تدريب على البيانات لمدة شهر و التنبؤ لمدة سبع أيام بشكل تدريجي لإيجاد أفضل أداء و لتقليل الحد الأدنى من الأخطاء المرتبطة بالتنبؤ بحمل الطاقة الكهربائية من أجل تقديم تقرير دقيق للغاية للتنبؤ بكمية توليد الطاقة في المستقبل في تاريخ محدد ليوم محدد بعد أسبوع أو شهر أو سنة، من أجل الموثوقية والتكامل الفعال للشبكة لإيجاد حلول لمعالجة أزمة الطاقة, وبالتالي خفض التكلفة المالية في المنطقة بسبب استيراد النفط والغاز لإنتاج حمولة الكهرباء. علاوة على ذلك، للحد من التلوث البيئي للحد من النفايات من استخراج النفط والغاز والفحم, هناك فائدة أخرى هي وجود الخلايا الشمسية لخدمة بعض المناطق بالطاقة الكهروضوئية, هذه إحدى الفوائد عندما نعرف استهلاك المنطقة بدقة. توفر الشركة الكهربائية هذه المنطقة بشكل أفضل إما عن طريق زيادة الخلايا الشمسية و تور بينات الرياح أو زيادة واردات الغاز وتجنب فصل الكهرباء وتوفير الصفقات والحفاظ على كمية الاستيراد للغاز لتوليد الطاقة, تمكن النموذجين المقترحين لهذا العمل من تقديم نتائج أفضل من الأعمال السابق (Idris Khana, Honglu Zhua, ET al. 2017, Luis Carlos Parra Raffan , Andres Romero, ET al. 2018 ). بدقة تصل إلى 96.6?", "source_url": "https:\/\/openalex.org\/W3212747612", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3212747612", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1223, "title": "Implementasi Particle Swarm Optimization pada NPC Hunter untuk pencarian NPC Hewan Hunter of Forest", "title_ar": "Implementasi Particle Swarm Optimization pada NPC Hunter untuk pencarian NPC Hewan Hunter of Forest", "title_en": "", "author": "Achmad Ihwany", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maulana Malik Ibrahim State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2018, "language": "ar", "research_domain": "machine-learning", "research_subdomain": "optimization", "categories": "machine-learning; optimization", "keywords": "", "abstract": "مستخلص البحث تحسين سرب الجسيمات (PSO) ، والذي يحاكي شخصية مجموعة من الطيور أو الأسماك. تحاكي خوارزمية PSO السلوك الاجتماعي لهذا الكائن الحي. يتكون السلوك الاجتماعي من العمل الفردي وتأثير الأفراد الآخرين في المجموعة. كلمة الجسيمات تثبت ، على سبيل المثال ، طائرًا في قطيع من الطيور. في كل شخصية فردية أو جسيمية باستخدام ذكائه الخاص وأيضاً بسبب سلوكه الجماعي المشترك. لذلك ، إذا كان أحد الجسيمات أو طائر واحد يحصل على أفضل مسار أو أسرع مسار إلى مصدر الغذاء ، فسيجد الطائر الآخر أو المجموعة الأخرى أيضًا المسار الأسرع أو الأسرع ، على الرغم من أن موقعه بعيد جدًا في المجموعة قبل ، أحرف غير المشغِّل (NPC) أو تسمى أيضًا بالكيانات هي كيان في اللعبة لا يتحكم فيه المشغل مباشرة. يتم التحكم في NPC تلقائيًا بواسطة الكمبيوتر. لتكون قادرة على الحصول على سلوك ذكي من المجلس الوطني للمرأة استخدام الذكاء الاصطناعي أو الذكاء الاصطناعي (منظمة العفو الدولية). يتم استخدام. AIS في NPC من خلال توفير خوارزميات محددة وفقًا للسلوك الذكي المتوقع تتناول هذه الدراسة تطبيق خوارزمية تحسين سرع الجسيمات لتحديد سلوك صائد NPC في لعبة \" صياد الغابة. \" حتى تتمكن من تحديد سلوك الصياد في لعبة FPS صياد الغابة المعتمدة على سطح المكتب نتيجة هذا البحث ، هذا البحث ينجح أقصر طريق إلى لعبة الصيد المطبق على NPC Hunter بشكل حيوي باستخدام خوارزمية سرب تحسين الجسيمات ABSTRACT Particle swarm optimization (PSO), which mimics the character of a group of birds or fish. The PSO algorithm mimics the social behavior of this organism. Social behavior consists of individual action and the influence of other individuals in a group. The word particle proves, for example, a bird in a flock of birds. In each individual or particle character using his own intelligence and also due to his collective joint behavior. Therefore, if one of the particles or one bird gets the best path or the fastest path to the food source, the other bird or other group will also find the fastest or fastest route, although their location is far in the group before . Non-Player Characters (NPC) or also called agents are an in-game entity that is not directly controlled by the player. NPC is automatically controlled by computer. To be able to obtain intelligent behavior from NPC used artificial intelligence or Artificial Intelligence (AI). The use of AI in NPC is done by providing specific algorithms according to the expected intelligent behavior. This study discusses the Implementation of Particle Swarm Optimization Algorithm for Determining the Behavior of NPC Hunter in \"Hunter Of Forest\" Game In order to be able to determine the hunter behavior in the desktop-based FPS Hunter Of Forest game. The result of this research is, This research succeed the shortest path to game hunting applied to NPC Hunter dynamically using Particle Swarm Optimization Algorithm ABSTRAK Particle swarm optimization (PSO), yang meniru karakter sekelompok burung atau ikan. Algoritma PSO meniru perilaku sosial organisme ini. Perilaku sosial terdiri dari aksi individu dan pengaruh dari individu-individu lain dalam suatu kelompok. Kata partikel membuktikan, misalnya, seekor burung dalam kawanan burung. Pada masing-masing individu atau partikel berkarakter memakai kecerdasannya (intelligence) sendiri dan juga diakibatkan tingkah laku gabungan kolektifnya. Maka dari itu, apabila salah satu dari partikel atau satu burung mendapatkan jalan yang terbaik atau jalan yang tercepat untuk menuju ke sumber makanan, maka burung yang lain atau kelompok yang lain juga akan menemukan jalur tebaik atau tercepat tadi, meskipun lokasinya mereka jauh di kelompok tadi. Non-Player Characters (NPC) atau disebut juga agen adalah suatu entitas dalam game yang tidak dikendalikan secara langsung oleh pemain. NPC dikendalikan secara otomatis oleh komputer. Untuk dapat memperoleh perilaku cerdas dari NPC digunakan kecerdasan buatan atau Artificial Intelligence (AI). Penggunaan AI pada NPC dilakukan dengan pemberian algoritma khusus sesuai dengan perilaku cerdas yang diharapkan. Penelitian ini membahas mengenai Implementasi Algoritma Particle Swarm Optimization Untuk Menentukan Perilaku NPC Hunter Pada Game “Hunter Of Forest” Dengan tujuan agar dapat menentukan prilaku hunter pada game FPS Hunter Of Forest yang berbasis dekstop. Hasil dari penelitian ini adalah, Penelitian ini berhasil jalur terpendek menuju hewan buruan yang diterapkan pada NPC Hunter secara dinamis menggunakan Algoritma Particle Swarm Optimization.", "source_url": "https:\/\/openalex.org\/W2907762393", "pdf_url": "http:\/\/etheses.uin-malang.ac.id\/12535\/1\/11650087.pdf", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W2907762393", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1224, "title": "Aplikasi game virtual reality pengenalan ilmuwan muslim Al-Haytham menggunakan Fuzzy Type - 2", "title_ar": "Aplikasi game virtual reality pengenalan ilmuwan muslim Al-Haytham menggunakan Fuzzy Type - 2", "title_en": "", "author": "Ahmad Fathan Hunaifi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maulana Malik Ibrahim State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2018, "language": "ar", "research_domain": "expert-systems", "research_subdomain": "ai-cybersecurity", "categories": "expert-systems; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "مستخلص البحث تم تقديم نوع ضبابي اثنان في الأصل من قبل زاده. ثم في الوقت الحاضر ، أصبحت القدرات المحوسبة محدودة ، لذا فإن استخدام النوع الثاني الغامض لا يحظى بشعبية كبيرة. لكن منذ عام أَلْفَانِ ، بدأ هذا النوع من الغموض في اختيار الكثير من الباحثين على الرغم من أنه يتطلب حوسبة معقدة. النوع المشوش اثنان له أداء أفضل من النوع الغامض واحد. هناك عدة طرق لتكوين نموذج غامض من النوع اثنان ، وهو النوع العام اثنان ، والنوع اثنان الفاصل ، وأحدث نظام شبه النوع الثاني. من الصعب تنفيذ النوع العام الضبابي اثنان لأنه لا يزال معقدًا رياضياً لجعل الحوسبة أكثر تعقيداً. المفهوم الرئيسي في هذه اللعبة هو توفير الذكاء الاصطناعي للعدو ، حرف غير قابل للعب ، حيث لا يتم التحكم في حرف غير قابل للعب من قبل اللاعب بحيث يمكن للمجلس الوطني لنواب الشعب أن يعطي تأثير للاعب. في هذه الدراسة ، أجرى المؤلفون دراسة حول تطبيق النوع الضبابي اثنان إلى الـ حرف غير قابل للعب في لعبة الواقع الافتراضي ، إدخال علماء مسلمين متنقلين. النسبة المئوية للقرارات التي اتخذت هي واحد وعشرين نقطة ستة وتسعين في المئة باترول ، تسعة عشر في المئة ثمانون في المئة ضبابية ، سبع نقاط تسعة وتسعين في المئة الهجوم. ABSTRACT Fuzzy type-2 was originally introduced by Zadeh. Then nowadays computerized capabilities are limited so the use of fuzzy type-2 is not very popular. But since 2000 this type-2 fuzzy has begun to be a lot of choice for researchers even though it requires complex computational. Fuzzy type-2 has a better performance than fuzzy type-1. There are several ways to model fuzzy type-2, namely general type - 2, interval type-2 and the latest Quasi type-2. General fuzzy type-2 is difficult to implement because it is still mathematically complicated to make computing more complex. The main concept in this game is to provide artificial intelligence to the enemy, the NPC, where the NPC is not controlled by the player so that the NPC can give an influence to the player. In this study the authors conducted a study of the application of fuzzy type-2 to NPCs in the virtual reality game the introduction of mobile-based Muslim scientists. The percentage of decisions made is 21.96% Patrol, 19.80% Blur, 7.99% attack. ABSTRAK Fuzzy type-2 pada awalnya diperkenalkan oleh Zadeh. Kemudian saat ini kemampuan komputerisasi terbatas sehingga pemakaian fuzzy type-2 tidak begitu populer. Tetapi sejak tahun 2000 fuzzy type-2 ini mulai banyak menjadi pilihan bagi para peneliti meskipun membutuhkan komputasional yang kompleks. Fuzzy type-2 memiliki performasi yang lebih baik dari fuzzy type-1. Ada beberapa cara untuk memodelkan fuzzy type-2 yaitu general tipe – 2, interval tipe-2 dan yang terbaru Quasi type-2. General fuzzy type-2 sulit untuk diimplementasikan karena secara matematis masih rumit sehingga membuat komputasi menjadi lebih kompleks. Konsep utama dalam game ini adalah memberikan kecerdasan buatan kepada musuh yaitu NPC yang dimana NPC ini tidak dikendalikan oleh player sehingga NPC dapat memberikan sebuah pengaruh kepada player. Pada penelitian ini penulis melakukan penelitian tentang penerapan fuzzy type-2 kepada NPC dalam game virtual reality pengenalan ilmuwan muslim berbasis mobile. Persentase keputusan yang dihasilkan yaitu, 21.96% Patroli, 19.80% Kabur, 7.99% menyerang.", "source_url": "https:\/\/openalex.org\/W2948605749", "pdf_url": "http:\/\/etheses.uin-malang.ac.id\/14209\/1\/13650094.pdf", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W2948605749", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1225, "title": "Becoming the Cyberattacker: Active Defense for the Electric Grid", "title_ar": "", "title_en": "Becoming the Cyberattacker: Active Defense for the Electric Grid", "author": "Amr Mohamed Saber Mohamed", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Toronto", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; reinforcement-learning; ai-applications; predictive-analytics; ai-cybersecurity", "keywords": "", "abstract": "Understanding the threat landscape of cyber-physical systems is crucial for developing effective defenses against emerging, highly sophisticated cyber threats. This understanding demands two essential components: the ability to predict the sophisticated behaviors of threat actors or the capacity to engage with them to understand their tactics and strategies. Currently, we face challenges in both areas -- lacking effective methods to model sophisticated cyberattackers or engage with them in meaningful ways. This research addresses these challenges by introducing frameworks that leverage physics-aware artificial intelligence to model sophisticated threat actors. Additionally, the research contributes to the development of frameworks that enable intelligent interactions with real cyberattackers. Succinctly, this research makes key contributions in three research directions. First, it contributes a novel framework that leverages reinforcement learning for modeling attackers. Second, it contributes another innovative framework that leverages safety-critical control to create resilient honeypots capable of intelligently engaging with sophisticated cyber attackers. Third, the research contributes several novel defense approaches, including the use of synthetic attack data for data-driven threat detection and risk assessment, the utilization of synthetic attacker models to validate and test defenses, and the application of safety-critical control for threat mitigation and safe reinforcement learning for dynamic defense. The benefits arising from these contributions are extensive. In research, these contributions provide methods for automating vulnerability discovery and unveiling strategic cyber threats capable of exploiting them. This proactive comprehension of cyber risks facilitates the active design of secure and resilient systems against emerging cyber threats. Additionally, the frameworks offer an approach to test and validate defenses against realistic strategic attacks. In practical application, the defensive contributions comprise data-driven methods capable of monitoring cyber-physical systems and offering early threat detection, threat classification, and risk assessment to assist in effective cyber incident response. Furthermore, the contributions related to honeypots present methods for security teams to deploy intelligent honeypots that can lure, deceive, and interact with attackers, providing security teams with a means to gather intelligence about the threat landscape. This, in turn, enables a better understanding of attackers' resources and capabilities, facilitating improved anticipation and mitigation of threats. Collectively, these benefits converge into an active defense stance, empowering organizations to proactively safeguard against cyber threats. The frameworks developed in this research present a broad, innovative, and advanced approach to understanding, modeling, and ultimately defending against the ever-evolving landscape of cyber threats targeting cyber-physical systems. فهم مشهد التهديدات لأنظمة التحكم السيبرانية-الفيزيائية أمر بالغ الأهمية لتطوير دفاعات فعّالة ضد التهديدات السيبرانية الناشئة والمعقدة. يتطلب هذا الفهم عنصرين أساسيين: القدرة على التنبؤ بسلوك الجهات المهاجمة المتطورة أو القدرة على التفاعل معهم لفهم تكتيكاتهم واستراتيجياتهم. نواجه حالياً تحديات في كلا الجانبين، حيث نفتقر إلى أساليب فعالة لنمذجة المهاجمين السيبرانيين المتقدمين أو التفاعل معهم بطرق ذات مغزى. تتناول هذه الدراسة تلك التحديات من خلال تقديم أطر عمل تعتمد على الذكاء الاصطناعي الملمّ بالفيزياء لنمذجة الجهات المهاجمة المتقدمة. بالإضافة إلى ذلك، تساهم هذه الدراسة في تطوير أطر تمكّن من التفاعل الذكي مع المهاجمين السيبرانيين الحقيقيين. بإيجاز، تقدم هذه الدراسة ثلاث مساهمات رئيسية في ثلاثة اتجاهات بحثية. أولاً، تساهم في إطار عمل جديد يعتمد على التعلم المعزز لنمذجة المهاجمين. ثانياً، تقدم إطارًا مبتكرًا آخر يعتمد على التحكم في الأنظمة الحرجة من أجل إنشاء \"مصائد ذكية\" قادرة على التفاعل بذكاء مع المهاجمين السيبرانيين المتقدمين. ثالثاً، تساهم الدراسة في تطوير عدة أساليب دفاعية جديدة، بما في ذلك استخدام بيانات هجمات صناعية لاكتشاف التهديدات وتقييم المخاطر، وتطبيق نماذج مهاجمين صناعية لاختبار الدفاعات والتحقق منها، واستخدام التحكم في الأنظمة الحرجة لتخفيف التهديدات وتطبيق التعلم المعزز الآمن للدفاع الديناميكي. تتمثل الفوائد الناتجة عن هذه المساهمات في نطاق واسع. في مجال البحث، تقدم هذه المساهمات طرقًا لاكتشاف الثغرات تلقائيا وكشف التهديدات الاستراتيجية القادرة على استغلالها. هذا الفهم الاستباقي للمخاطر السيبرانية يمكّن من التصميم الفعّال لأنظمة آمنة وقابلة للتكيف ضد التهديدات السيبرانية الناشئة. علاوة على ذلك، تقدم الأطر المطورة أسلوبًا لاختبار الدفاعات والتحقق منها ضد هجمات واقعية واستراتيجية. في التطبيق العملي، تتضمن المساهمات الدفاعية أساليب تعتمد على البيانات يمكنها مراقبة الأنظمة السيبرانية-الفيزيائية وتقديم كشف مبكر عن التهديدات، وتصنيفها، وتقييم المخاطر للمساعدة في الاستجابة الفعّالة للحوادث السيبرانية.", "source_url": "https:\/\/openalex.org\/W7226042570", "pdf_url": "https:\/\/utoronto.scholaris.ca\/bitstreams\/5a5309c3-cc14-4742-9e49-043d82f40563\/download", "doi": "", "handle": "", "repository_name": "TSpace (University of Toronto)", "source_record_id": "W7226042570", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1226, "title": "تأثير برنامج للتدريب العقلي على تنمية صفة رد الفعل وبعض المهارات الأساسية لدى لاعبي كرة السلة u15 . دراسة ميدانية لنادي مدينة بسكرة لكرة السلة", "title_ar": "تأثير برنامج للتدريب العقلي على تنمية صفة رد الفعل وبعض المهارات الأساسية لدى لاعبي كرة السلة u15 . دراسة ميدانية لنادي مدينة بسكرة لكرة السلة", "title_en": "", "author": "مسعود بن عيشي", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Biskra", "university_source": "repository_host", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2024, "language": "ar", "research_domain": "computer-vision", "research_subdomain": "", "categories": "computer-vision", "keywords": "", "abstract": " أهداف الدراسة: -إعداد برنامج تدريبي عقلي يستفاد منه المدربين والمختصين في كرة السلة . - إبراز أثر البرنامج التدريبي المقترح في تحسين سرعة رد الفعل . - التعرف على العلاقة بين التدريب العقلي و الأداء المهاري للاعبي كرة السلة. - إظهار تحسين القدرات العقلية لدى لاعبي السلة . - التعرف على مستوى القدرات العقلية لدى لاعبي كرة السلة اقل من 15 سنة. - معرفة هل هناك تأثير لأحد لقدرات العقلية لمهارات أساسية دون أخرى . - إبراز مدى تأثير التدريب العقلي على الفئة العمرية أقل من 15 سنة وكذا طريقة الاستفادة منه .  التساؤل العام: ما تأثير برنامج للتدريب العقلي في تنمية صفة رد الفعل وبعض المهارات الأساسية لدى لاعبي كرة السلة أقل من 15 سنة ؟  الفرضية العامة: توجد فروق ذات دلالة إحصائية للعينتين الضابطة و التجريبية لصالح المجموعة التجريبية بعد تطبيق البرنامج العقلي المقترح في تنمية صفة رد الفعل لدى لاعبي كرة السلة اقل من 15 سنة .  أهمية الدراسة: تكمن أهمية دراستنا في معرفة مدى تأثير التدريب العقلي على صفة رد الفعل و مهارات التسديد بأنواعه ( الرمية السلمية , التصويب الحرة , التصويب من القفز , المحاورة) في كرة السلة لصنف اقل من 15 سنة.  إجراءات الدراسة: كانت بإستخدام إستبيان إلكتروني وتوزيعه على عينة مكونة من ( 26 مدربًا) في رياضة كرة السلة، لمختلف الفئات العمرية الصغرى، الهدف من ذلك كان لفحص آراء هؤلاء المدربين حول تأثير التدريب العقلي في تطوير سرعة رد الفعل والمهارات الأساسية، هذه الخطوة تساعد الباحث في التأكد من وجود مشكلة فعلية تستحق البحث والتحليل العلمي ، وتكونت عينة الدراسة من (12) لاعبا تم اختيارهم بطريقة قصدية، طبق عليهم مقاييس نفسية (الاسترخاء ، التصور العقلي، الانتباه ) و اختبارات سرعة رد الفعل (اختبار نيسلون ،واختبار سرعة رد الفعل) واختبارات مهارية للتصويب بانواعه و المحاورة (اختبار نوكس)  المجال الزماني والمكاني: تم إجراء الدراسة من بداية سنة 2021 إلى منتصف سنة 2023، في الصالة الرياضية خالدي محمد بسكرة.  أهم الإستنتاجات: وجود فروق في نتائج القياس البعدي للمجموعتين الضابطة والتجريبية ولصالح المجموعة التجريبية في كل الاختبارات المهارية و اختبارات سرعة رد الفعل أثرت المهارات العقلية من تصور واسترخاء و تركيز انتباه في تنمية وتحسين مهارة التصويب والمحاورة في كرة السلة . البرنامج التدريبي المقترح فعال لتحسين الأداء للفئة العمرية لأقل من 15 سنة . أهم الإقتراحات: استخدام برنامجنا العقلي ضمن تصميم البرنامج السنوي للتدريب، يجب أخذ بعين الاعتبار استخدام البرنامج التدريبي المقترح من قبل الباحث، بالإضافة إلى البرامج المهارية والبدنية،ما يعزز من جودة البرامج . الاهتمام بمهارات التصويب المختلفة في كرة السلة واقتناء أجهزة مساعدة على تطوير الجوانب الحركية وتقويمها فمعظم الدراسات تهتم بدقة التصويب وتهمل جانب التنسيق الحركي وكذا التوافق الحركي. الاهتمام بالأجهزة والتقنيات التي تستخدم في قياس الاسترخاء و التصور العقلي و تركيز الانتباه ، فقط تطور بشكل لافت هاذ المجال واستغنى الغرب عن توزيع مقاييس وتحليل نتائج من الاستبيان. الاعتماد على الذكاء الاصطناعي مستقبلا في تحديد البرامج العقلية و مهارتها ، برنامج ال ( chtgpt) نموذجا. دعوة مدربي كرة السلة إلى الاهتمام بسرعة رد الفعل كصفة قابلة للتدريب و التطوير Abstract The effect of a mental training program on the development of reaction time and some basic skills in basketball players u15.  Objectives of the study: - Preparing a Mental Training Program Beneficial for Basketball Coaches and Specialists. - Emphasizing the Impact of the Proposed Training Program on Improving Reaction Speed. - Understanding the Relationship Between Mental Training and Skill Performance in Basketball Players. - Demonstrating Improvement in Mental Abilities in Basketball Players. - Assessing the Mental Abilities of Basketball Players Under 15 Years Old. - Determining if There's Differential Impact on Specific Mental Abilities for Different Fundamental Skills  The general question: What is the impact of a mental training program on the development of reaction time and some basic skills in basketball players under the age of 15?  Study hypothesis: To determine the extent of the impact of mental training on reaction time and various shooting skills (free throws, free throw shooting, jump shots, and layups) in basketball players under the age of 15.  The importance of the study: The importance of our study lies in understanding the extent of the impact of mental training on reaction time and various shooting skills (free throws, free throw shooting, jump shots, and layups) in basketball players under the age of 15.  Field study procedures: Using an electronic questionnaire distributed to a sample of 26 basketball coaches across different age groups, the aim was to examine the opinions of these coaches regarding the impact of mental training on the development of reaction time and basic skills. This step helps the researcher confirm the existence of a genuine problem worthy of scientific research and analysis. The study sample consisted of 12 players selected purposively. Psychological measures (relaxation, mental imagery, attention) and reaction time tests (Nelson test, reaction time test) were administered to them, along with skill tests for shooting and dribbling (Knox test).", "source_url": "https:\/\/openalex.org\/W7127222336", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Biskra Theses Repository (University of Biskra)", "source_record_id": "W7127222336", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1227, "title": "Control of a Furuta pendulum with reinforcement learning", "title_ar": "", "title_en": "Control of a Furuta pendulum with reinforcement learning", "author": "Fernando Luiz Klein de Souza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Federal University of Technology – Paraná", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "ai-smart-cities", "categories": "reinforcement-learning; ai-applications; ai-smart-cities", "keywords": "", "abstract": "This work proposed the control of a Furuta pendulum using reinforcement learning, addressing challenges such as underactuation, non-linearities, and instabilities. The classical control approach for this system involves positioning the pendulum close to the equilibrium region, a technique known as swing-up, and controlling it at the equilibrium point using a linear controller. However, this approach requires system linearizations, which leads to sub-optimal performance. The main goal of the study was to explore the application of reinforcement learning to handle the complexities of the system, such as its non-linear and unstable characteristics. For this, the Q-Learning algorithm was employed, conducting simulations to compare the performance of the classical controllers and the reinforcement learning-based controller, with metrics focusing on stability, accuracy, and energy efficiency. The results indicate that the Q-Learning-based model performed worse than the classical controllers in terms of tracking error and energy consumption for control, also requiring non-trivial adjustments of hyperparameters to optimize the system’s performance, which significantly increased the training time due to the need to explore and adjust different configurations to achieve acceptable performance. It is concluded that, although classical methods are more suitable for scenarios that require fast and reliable solutions, reinforcement learning represents a promising alternative for complex and uncertain systems, where explicit modeling of the environment is difficult or unfeasible.", "source_url": "https:\/\/openalex.org\/W7155459785", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/36778\/1\/pendulofurutaaprendizadoreforco.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)", "source_record_id": "W7155459785", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1228, "title": "Diversity in Reinforcement Learning", "title_ar": "", "title_en": "Diversity in Reinforcement Learning", "author": "Arno Feiden", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bonn", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; computer-vision; reinforcement-learning; predictive-analytics; optimization", "keywords": "", "abstract": "Reinforcement learning is a machine learning paradigm that aims to develop optimal policies for discrete-time control problems based on quantitative feedback on the learner’s behaviour. Reinforcement learning is formulated as a gradient-based optimisation procedure, but reinforcement learning problems with unique optimal solutions are rare. This work focuses on ways to distinguish policies that solve the same reinforcement learning problem differently. A pointed analysis of reinforcement learning literature, specifically of policy gradient methods, shows that policies are best understood through their behaviour: The actions that the policy selects based on the encountered state. There are two equivalent main approaches to understanding this behaviour empirically: Episodic behavioural characterisation, which interprets a policy as a random element in the space of state-action time series and behavioural characterisation through the occupancy measure, which interprets a policy as a random element representing the visitation frequency in the space of state-action pairs. Both characterisations can be used to deduce general, problem-agnostic distance measures either by comparing time series or by comparing sampled distributions. Their utility is first explored by visualising small populations of policies. The relationships between policies that emerge in the visualisation also reflect other, hidden relationships, thereby validating the approach. This idea is then integrated with an evolutionary algorithm. Evolutionary computation is a branch of optimisation techniques that improve populations of candidate solutions. In quality-diversity methods specifically, the members of the population are encouraged to differ from one another to improve the evolutionary process. When these methods are used to solve reinforcement learning problems, a measure of distance between candidate solutions is required. The developed techniques to distinguish policies are successfully connected with MAP-Elites, a quality-diversity heuristic that is often used for reinforcement learning problems and typically distinguishes policies based on selective, problem-specific criteria. The applications validate the established general distances measure, but they also show that a diverse population of solutions is, for example, more explorative and more robust against environmental changes even if the diversity is not directed towards the specific goal such as exploration or robustness. This reveals the intrinsic value of diversity.", "source_url": "https:\/\/openalex.org\/W7172577797", "pdf_url": "", "doi": "", "handle": "", "repository_name": "bonndoc (University of Bonn)", "source_record_id": "W7172577797", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1229, "title": "Reinforcement Learning-Based Ordering", "title_ar": "", "title_en": "Reinforcement Learning-Based Ordering", "author": "Samuel Dahn ; Theodor Hallbeck", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Lund University", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; reinforcement-learning; predictive-analytics", "keywords": "", "abstract": "This thesis investigates the applicability and performance of reinforcement learn- ing (RL) for inventory management of spare parts in a single-echelon setting. Traditional inventory control methods, such as (R,Q)-policies, typically decou- ple forecasting and ordering decisions, which may lead to suboptimal system- wide performance, particularly in environments characterized by intermittent and stochastic demand. To address these limitations, a reinforcement learning-based ordering model is developed and evaluated using both simulated and real-world data provided by Syncron AB. The proposed model is a Dueling Double Deep Q-Network (DDDQN) architecture incorporating enhancements such as prioritized expe- rience replay, experience buffer modification, and early stopping to improve training efficiency and stability. The performance of the RL-model is benchmarked against a standard peri- odic review (R,Q)-policy using backorder and holding cost. Results indicate that while RL demonstrates potential in capturing complex system dynamics and achieving competitive performance in certain scenarios, its effectiveness is highly dependent on data availability, demand characteristics, and computa- tional constraints. In particular, challenges related to intermittent demand, policy convergence, and generalization to unseen environments restrict consis- tent outperformance of traditional methods. The findings suggest that RL based approaches can complement existing inven- tory control methods but cannot yet fully replace them in practical applications. This highlights the importance of hybrid decision frameworks that leverage the strengths of both data-driven and analytical methods. The thesis concludes with recommendations for future development, including model scalability, improved data utilization, and more robust evaluation across varying demand patterns.", "source_url": "https:\/\/openalex.org\/W7165431631", "pdf_url": "https:\/\/lup.lub.lu.se\/student-papers\/record\/9238531\/file\/9238536.pdf", "doi": "", "handle": "", "repository_name": "Lund University Publications Student Papers (Lund University)", "source_record_id": "W7165431631", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1230, "title": "A programming language for reinforcement learning", "title_ar": "", "title_en": "A programming language for reinforcement learning", "author": "Camilo Andrés Morillo Cervantes", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Colombia", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "The increasing complexity of reinforcement learning (RL) algorithms has revealed a critical gap in the programming models and tools available to developers, leading to suboptimal implementations and a lack of standardization in RL software projects. This thesis proposes the design and implementation of a specialized programming language that provides higher-level abstractions tailored specifically for RL. By addressing the representation of state and action space, the reward function and the management of hyperparameters, our language aims to alleviate the burden on programmers, allowing them to concentrate on the intrinsic complexities of their algorithms rather than the underlying details of RL. We will develop language abstractions and data structures within the Racket programming environment to facilitate the expression of RL constructs effectively. Additionally, a suite of test programs will be created to evaluate the efficacy and usability of our proposed language. This work seeks to enhance the quality and accessibility of RL programming, fostering improved practices and innovations in the field.", "source_url": "https:\/\/openalex.org\/W7214856098", "pdf_url": "", "doi": "", "handle": "", "repository_name": "instname:Universidad de los Andes", "source_record_id": "W7214856098", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1231, "title": "Learning to Plan with Reinforcement Learning", "title_ar": "", "title_en": "Learning to Plan with Reinforcement Learning", "author": "Ming Hay Chung", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Cambridge", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; reinforcement-learning; responsible-ai; llm; xai", "keywords": "", "abstract": "This thesis investigates the domain of learning to plan within reinforcement learning. Traditional planning algorithms, such as Monte Carlo Tree Search, are typically handcrafted and lack the flexibility inherent in biological planning systems. To address this limitation, the Thinker algorithm is proposed. A central theme of this work is the shift in focus from designing specialized planning algorithms to augmenting the environment. By treating planning as an explicit action within an augmented environment, the agent is enabled to learn to plan using standard model-free reinforcement learning. Additionally, this thesis examines the interpretability of planning behaviour of RL agents trained by different algorithms. Finally, this thesis extends the Thinker algorithm to Large Language Models trained on mathematical reasoning tasks, demonstrating that similarly structured augmented environments significantly reduce computational costs while maintaining competitive performance on reasoning tasks.", "source_url": "https:\/\/openalex.org\/W7159592709", "pdf_url": "https:\/\/www.repository.cam.ac.uk\/bitstreams\/83ddc738-d30a-4859-a414-c09e906b7018\/download", "doi": "10.17863\/cam.129818", "handle": "", "repository_name": "Apollo (University of Cambridge)", "source_record_id": "W7159592709", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1232, "title": "Contributions to reinforcement learning with function approximators", "title_ar": "", "title_en": "Contributions to reinforcement learning with function approximators", "author": "Riad Akrour", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning; reinforcement-learning; robotics", "keywords": "", "abstract": "Reinforcement Learning tackles sequential decision making problems under uncertainty. It has proven to be especially relevant for problems involving hard to model dynamics or large state-action spaces, where classical approaches from the dynamic programming literature fail to scale. A key component to scale reinforcement learning to such complex problems is to use function approximators such as neural networks. However, adding function approximators to reinforcement learning can introduce instabilities to an already complex training process, due to errors in policy evaluation or update. In this manuscript, we present several approaches leveraging entropy regularization to average out errors in the policy evaluation step and discuss their theoretical and empirical merits. Because entropy regularization promotes stochastic behaviors, using it as is for data gathering can be ill-suited for applications such as robotics. As such, we round off the manuscript with a discussion on episodic exploration and Bayesian policy search, proposing alternative data gathering policies that are in some cases more appropriate for real world applications.", "source_url": "https:\/\/openalex.org\/W7225844800", "pdf_url": "", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W7225844800", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1233, "title": "Reinforcement Learning for Video Games", "title_ar": "", "title_en": "Reinforcement Learning for Video Games", "author": "Joar Edlund ; Jack Jönsson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; reinforcement-learning; cnn; video-analysis", "keywords": "", "abstract": "We present an implementation of a specific type of deep reinforcement learning algorithm known as deep Qlearning. With a Convolutional Neural Network (CNN) combined with our Q-learning algorithm, we trained an agent to play the game of Snake. The input to the CNN is the raw pixel values from the Snake environment and the output is a value function which estimates future rewards for different actions. We implemented the Q-learning algorithm on a grid based and a pixel based representation of the Snake environment and found that the algorithm can perform at human level on smaller grid based representation whilst the performance on the pixel based representation was fairly limited.", "source_url": "https:\/\/openalex.org\/W2986251207", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2986251207", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1234, "title": "Investigating Overestimation Bias in Reinforcement Learning", "title_ar": "", "title_en": "Investigating Overestimation Bias in Reinforcement Learning", "author": "Andreas Pentaliotis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "fairness", "categories": "reinforcement-learning; responsible-ai; fairness", "keywords": "", "abstract": "Overestimation bias is an inherent property of reinforcement learning algorithms that approximate maximum expected values by maximizing uncertain estimates. Since overestimation bias was identified in the literature, it has been generally considered to have a negative effect on reinforcement learning algorithms. In this thesis we investigate overestimation bias by examining Q-learning and conclude that overestimation bias may have either a negative or positive effect on reinforcement learning algorithms depending on the reinforcement learning problem. Based on this conclusion, we propose a new variant of Q-learning, called Variation-resistant Q-learning, to control and utilize estimation bias for better performance. We present the tabular version of Variation-resistant Q-learning, prove a convergence theorem for the algorithm in the tabular case, and extend the algorithm to a function approximation solution method. Additionally, we present empirical results from three different experiments, in which we compared the performance of Variation-resistant Q-learning, Q-learning, and Double Q-learning. The empirical results verify that Variation-resistant Q-learning can control and utilize estimation bias for better performance in the experimental tasks.", "source_url": "https:\/\/openalex.org\/W3037989090", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3037989090", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:52:59", "updated_at": "2026-11-15 16:52:59" }, { "id": 1236, "title": "Explorations of In-Context Reinforcement Learning", "title_ar": "", "title_en": "Explorations of In-Context Reinforcement Learning", "author": "Ethan Brooks", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Michigan", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; reinforcement-learning; self-semi-supervised; rnn; transformers; llm", "keywords": "", "abstract": "In-Context Learning describes a form of learning that occurs when a model accumulates information in its context or memory. For example, a Long Short-Term Memory unit (LSTM) can rapidly adapt to a novel task as input\/target exemplars are fed into it. While in-context learning of this kind is not a new discovery, recent work has demonstrated the capacity of large \"foundation\" models to acquire this ability \"for free,\" by training on large quantities of semi-supervised data, without the sophisticated (but often unstable) meta-objectives proposed by many earlier papers #cite(< finn2017model>)#cite(< stadie2018some>)#cite(< rakelly2019efficient>). In this work we explore several algorithms which specialize in-context learning based on semi-supervised methods to the reinforcement learning (RL) setting. In particular, we explore three approaches to in-context learning of value (expected cumulative discounted reward). The first of these methods demonstrates a method for implementing policy iteration, a classic RL algorithm, using a pre-trained large language model (LLM). We use the LLM to generate planning rollouts and extract monte-carlo estimates of value from them. We demonstrate the method on several small, text-based domains and present evidence that the LLM can generalize to unseen states, a key requirement of learning in non-tabular settings. The second method imports many of the ideas of the first, but trains a transformer model directly on offline RL data. We incorporate Algorithm Distillation (AD) #cite(< laskin2022context>), another method for in-context reinforcement learning that directly distills the improvement operator from data that includes behavior ranging from random to optimal. Our method combines the benefits of AD with the policy iteration method proposed in our previous work and demonstrates benefits in performance and generalization. Our third method proposes a new method for estimating value. Like the previous methods, this one implements a form of policy iteration, but eschews monte-carlo rollouts for a new approach to estimating value. We train a network to estimate Bellman updates and iteratively feed its outputs back into itself until the estimate converges. We find that this iterative approach improves the capability of the value estimates to generalize and mitigates some of the instability of other offline methods.", "source_url": "https:\/\/openalex.org\/W4398230562", "pdf_url": "", "doi": "10.7302\/23097", "handle": "", "repository_name": "Deep Blue (University of Michigan)", "source_record_id": "W4398230562", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1237, "title": "Reinforcement learning in videogames", "title_ar": "", "title_en": "Reinforcement learning in videogames", "author": "Alex Osés Laza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; reinforcement-learning; video-analysis", "keywords": "", "abstract": "While there are still a lot of projects and papers focused on: given a game, discover and measure which is the best algorithm for it, I decided to twist things around and decided to focus on two algorithms and its parameters be able to tell which games will be best approachable with it. To do this, I will be implementing both algorithms Q-Learning and SARSA, helping myself with Neural Networks to be able to represent the vast state space that the games have. The idea is to implement the algorithms as general as possible.This way in case someone wanted to use my algorithms for their game, it would take the less amount of time possible to adapt the game for the algorithm. I will be using some games that are used to make Artificial Intelligence competitions so I have a base to work with, having more time to focus on the actual algorithm implementation and results comparison.", "source_url": "https:\/\/openalex.org\/W2777031522", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)", "source_record_id": "W2777031522", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1238, "title": "Reinforcement Learning for Stock Option Trading", "title_ar": "", "title_en": "Reinforcement Learning for Stock Option Trading", "author": "James Garza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "machine-learning; reinforcement-learning", "keywords": "", "abstract": "Reinforcement learning has recently seen an increase in popularity due to its ability to learn from past experience and its capability of adapting quickly and effectively to new market conditions. This research will focus on reinforcement learning and its importance in trading stock options. Option traders can trade options with one of two option expirations: American or European style. This research will base the analysis on the American expiration style, considered more challenging in trading than the European expiration style. This could lead to the possibility of improving the current trading techniques. In addition, this research aims to understand the role that reinforcement learning plays in trading stock options and evaluate its effectiveness in different market environments. Reinforcement learning has the potential to identify optimal trading strategies for stock options, and could assist current traders in their trading strategies. Trading and markets have existed for millennia, going as far back as Babylon in 2000 BC, with currency exchange and commodities (Kirkpatrick and Dahlquist, 2010). However, markets have evolved and become more complex than in those early trading days. Automation of trading and trading tasks has enabled organisations to act more quickly, consistently, and cost-effectively, all while reducing the risk of human error. The complexity of the markets undeniably increases the difficulty of option trading in dynamic environments. Two questions that arise are: Can Reinforcement Learning models use historical option data to develop effective option trading strategies? Can Reinforcement Learning assist human traders in trading options? These questions are hard to answer at a glance and require robust research and exploration to understand the behaviour of this market segment. Additionally, this research will explore the potential benefits of utilising Reinforcement Learning in stock option trading and how it might be used to modify existing techniques (Moody and Saffell, 2001). The Reinforcement Models that will be explored are Actor-Critical (A2C), Deep Deterministic Policy, Proximal Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). These models use Reinforcement Learning algorithms that train an agent to solve tasks by trial and error. This research will attempt to use these trading agents to develop algorithmic trading 9 strategies, which are difficult for human traders. In chapter 5, there is a complete description of how they work. Ultimately, this research found that Reinforcement Learning can develop trading strategies that could assist human traders. These trading agents are based on machine learning models, which allow them to identify", "source_url": "https:\/\/openalex.org\/W4407910680", "pdf_url": "", "doi": "10.63227\/566.706.52", "handle": "", "repository_name": "", "source_record_id": "W4407910680", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1239, "title": "Learning to safely drive using Reinforcement Learning", "title_ar": "", "title_en": "Learning to safely drive using Reinforcement Learning", "author": "Laura Carrera Escalé", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Politècnica de Catalunya", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; computer-vision; reinforcement-learning; robotics; predictive-analytics; rnn", "keywords": "", "abstract": "The autonomous driving research area has gained popularity over the past decade, even more with the launch of the first autonomous vehicle from Tesla, Inc. Different research branches are currently being studied, and one of the most innovative is the one in the direction of the Reinforcement Learning. However, Reinforcement Learning models do not ensure doing the safest decisions due to the unknown decision making process, making it impossible to apply these research lines in the real world, since being sure of the safety of the system is what allows autonomous driving to trespass the theoretical knowledge to practice. The aim of this project is to define a Reinforcement Learning model which ensures safety and allows to have control and awareness of the decision making process given possible unsafe situations, being trained and evaluated over a driving simulator named CARLA. The model architecture is composed of a Variational Autoencoder, in charge of reducing the dimensionality of the input images given by the simulator, a Mixture Density Recurrent Neural Network, which forecasts the most probable future state, and a Soft Actor-Critic who predicts the next action of the car agent based on past experience. Moreover, a security mask is applied to modify the actor's policy given a dangerous situation. This safety mask ensures a supervised behavior in this kind of situations providing Reinforcement-Learning- based autonomous driving systems of the security they were lacking to be applied in the real world. In addition, it has been analyzed if the agent would be able to learn the safety constraints provided by the safety mask, therefore learning to safely drive. The main contributions of this project start with proving the efficiency of using the Rein- forcement Learning Soft Actor-Critic algorithm in an autonomous driving task, which has never been done before. Additionally, several reward functions were defined which outper- forms the current state of the art. Moreover, this thesis also provides an exhaustive analysis of the relevance of forecasting in a self-driving task. To conclude, this thesis proves that using security masks in Reinforcement-Learning-based autonomous driving systems is, to the best of our knowledge, the best option to avoid the uncertain actions of Reinforcement Learning agents in unsafe situations. This fact could be the first step for promoting the application of Reinforcement Learning in the real world since it ensures safe behavior.", "source_url": "https:\/\/openalex.org\/W3186142913", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "source_record_id": "W3186142913", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1240, "title": "Curiosity-driven algorithm for reinforcement learning", "title_ar": "", "title_en": "Curiosity-driven algorithm for reinforcement learning", "author": "Vitalii Tsybulko", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Jyväskylä", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; reinforcement-learning; predictive-analytics", "keywords": "", "abstract": "One problem of current Reinforcement Learning algorithms is finding a balance between exploitation of existing knowledge and exploration for a new experience. Curiosity exploration bonus has been proposed to address this problem, but current implementations are vulnerable to stochastic noise inside the environment. The new approach presented in this thesis utilises exploration bonus based on the predicted novelty of the next state. That protects exploration from noise issues during training. This work also introduces a new way of combining extrinsic and intrinsic rewards. Both improvements help to overcome a number of problems that Reinforcement Learning had until now.", "source_url": "https:\/\/openalex.org\/W2947788527", "pdf_url": "https:\/\/jyx.jyu.fi\/bitstreams\/12cbc61b-2a66-4440-947a-47207e00dafc\/download", "doi": "", "handle": "", "repository_name": "Jyväskylä University Digital Archive (University of Jyväskylä)", "source_record_id": "W2947788527", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1241, "title": "Reinforcement learning for robotic assisted tasks", "title_ar": "", "title_en": "Reinforcement learning for robotic assisted tasks", "author": "Aniol Civit Bertran", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "", "categories": "medical-ai; reinforcement-learning; robotics", "keywords": "", "abstract": "The increment of dependant people for the realization of Activities of Daily Living is a fact that comes implicit with the increase of population and their lifespan given by the modern technological progress. For this reason, Assistive Robotics is a field that is currently researched and gradually implemented. In this project, two different algorithms to train robots to perform those tasks are being studied and compared, Reinforcement Learning and Imitation Learning. The main objective is to learn the benefits, drawbacks and suitability of both for a given task. To do the experimentation, the Assistive Gym software will be used, to train the nets with Reinforcement Learning and to perform the movements with Dynamic Movement Primitives learned from a demonstration. The environment will consist in a Sawyer robot cleaning the right arm of a human lying down on a bed. The potential of each methodology will be tested in different scenarios, having the user’s arm straight or partially flexed. Furthermore, small implementations to improve the execution of the task will be implemented for the Dynamic Movement Primitives to test the performance and the similarity to the demonstration. Both methodologies are capable to perform the desired task successfully, the model trained with Reinforcement Learning is more suitable for an individual user that will use the robot in a long term, due to the training time. The Imitation Learning algorithm is faster to teach and its trajectory and velocities are easily adaptable to the preferences of the user, it is more suitable in places where there is a high rotation of users that require urgent and short attention, such as clinics or hospitals", "source_url": "https:\/\/openalex.org\/W3108883344", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DIGITAL.CSIC (Spanish National Research Council (CSIC))", "source_record_id": "W3108883344", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1242, "title": "Application of reinforcement learning to production system", "title_ar": "", "title_en": "Application of reinforcement learning to production system", "author": "Zhijin Jiang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "The primary goal for this research is to obtain the optimal or near-optimal joint production and maintenance scheduling policy by means of reinforcement learning. In this research, we adopted reinforcement algorithm to control the feeding interval and the maintenance state of upstream station in production system. With the help of this algorithm, the work-in-process(WIP) in the production system can be limited to a reasonable level and machines are preventively maintained to be functional. By balancing the reward and cost from WIP, maintenance and the idle loss of bottleneck machine, the reinforcement learning algorithm is able to find the acceptable policy for adjusting the feeding rate and scheduling the preventive maintenance for upstream machine. However reinforcement learning involves in a lot of parameters and in practice parameters may range widely from cases to cases. There are totally five experiments performed in this research, the first and the second is the validation experiments and the third and forth is to explain the property of the algorithm. the fifth experiment describes how fast the algorithm can learn to achieve the target state of upstream station. The developed model consists of reinforcement learning based, decision-making agents with simulation model of the integrated production system. The smart agent determine the optimal or near-optimal action for each system state by interacting with their environment.", "source_url": "https:\/\/openalex.org\/W2887408988", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2887408988", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1243, "title": "Safe Hierarchical Reinforcement Learning: Hierarchical Reinforcement Learning with Safe Space Exploration", "title_ar": "", "title_en": "Safe Hierarchical Reinforcement Learning: Hierarchical Reinforcement Learning with Safe Space Exploration", "author": "Stephen Verbist", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "Reinforcement Learning is a much researched topic for autonomous machine behavior and is often applied to navigation problems. In order to deal with growing environments and larger state\/action spaces, Hierarchical Reinforcement Learning has been introduced. Unfortunately learning from experience, which is central to Reinforcement Learning, makes guaranteeing safety a complex problem. This paper demonstrates an approach, named the actor-judge approach, to make the exploration safer while imposing as few as possible restrictions on the agent. The approach combines ideas from the fields of Hierarchical Reinforcement Learning and Safe Reinforcement Learning to develop a Safe Hierarchical Reinforcement Learning algorithm. The algorithm is tested in a simulated environment where the agent represents an Unmanned Aerial Vehicle able to move laterally in four directions using quadridirectional range sensors to establish a relative position. Although this approach does not guarantee the agent to never explore unsafe areas of the state domain, results show the actor-judge method increases agent safety and can be used on multiple levels an HRL agent hierarchy.", "source_url": "https:\/\/openalex.org\/W2614018942", "pdf_url": "https:\/\/repository.tudelft.nl\/file\/File_10ae1c30-9e76-4af3-982f-34b67806dad2", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2614018942", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1244, "title": "A taxonomy of reinforcement learning faults", "title_ar": "", "title_en": "A taxonomy of reinforcement learning faults", "author": "Deepak George Thomas", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "ai-industry", "categories": "reinforcement-learning; ai-applications; ai-industry", "keywords": "", "abstract": "Reinforcement Learning (RL) is increasingly being used for a variety of applications, some of which include safety-critical tasks. Therefore, it is important for developers to be aware of faults that occur when writing programs that leverage RL. This thesis conducts an empirical study on the faults associated with RL programs. Our work is tailored towards developers who use well-vetted frameworks or implementations of RL algorithms. We conduct studies to identify relevant RL tags to mine StackExchange and a popular RL framework to mine GitHub. Then, we mine StackExchange and GitHub and after dropping unrelated posts using various automated techniques, we arrive at 2,787 posts, which we manually analyze. Finally, we manually label a portion of these posts to develop a taxonomy of RL faults. Our final taxonomy contains 8 categories of faults from 112 artifacts. We describe these categories and the corresponding child nodes in detail in this thesis. We compare our work against prior work done in this area by comparing each leaf node of theirs with our taxonomy. We present two promising future directions that could be derived from this work: Mutation Testing and Fault Localization. Both of these directions require a taxonomy of real RL faults, which our work provides.", "source_url": "https:\/\/openalex.org\/W4410025076", "pdf_url": "", "doi": "10.31274\/td-20250502-179", "handle": "", "repository_name": "", "source_record_id": "W4410025076", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1245, "title": "Reinforcement Learning Approaches in Dynamic Environments", "title_ar": "", "title_en": "Reinforcement Learning Approaches in Dynamic Environments", "author": "Miyoung Han", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "Reinforcement learning is learning from interaction with an environment to achieve a goal. It is an efficient framework to solve sequential decision-making problems, using Markov decision processes (MDPs) as a general problem formulation. In this thesis, we apply reinforcement learning to sequential decision-making problems in dynamic environments.We first present an algorithm based on Q-learning with a customized exploration and exploitation strategy to solve a real taxi routing problem. Our algorithm is able to progressively learn optimal actions for routing an autonomous taxi to passenger pick-up points. Then, we address the factored MDP problem in a non-deterministic setting. We propose an algorithm that learns transition functions using the Dynamic Bayesian Network formalism. We demonstrate that factorization methods allow to efficiently learn correct models; through the learned models, the agent can accrue higher cumulative rewards.We extend our work to very large domains. In the focused crawling problem, we propose a new scoring mechanism taking into account long-term effects of selecting a link, and present new feature representations of states for Web pages and actions for next link selection. This approach allowed us to improve on the efficiency of focused crawling. In the influence maximization (IM) problem, we extend the classical IM problem with incomplete knowledge of graph structure and topic-based user interest. Our algorithm finds the most influential seeds to maximize topic-based influence by learning action values for each probed node.", "source_url": "https:\/\/openalex.org\/W2939567823", "pdf_url": "", "doi": "", "handle": "", "repository_name": "HAL", "source_record_id": "W2939567823", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1246, "title": "Mutual Reinforcement Learning", "title_ar": "", "title_en": "Mutual Reinforcement Learning", "author": "Cameron Reid", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Purdue University System", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "Mutual learning is an emerging field in intelligent systems which takes inspiration from naturally intelligent agents and attempts to explore how agents can communicate and cooperate to share information and learn more quickly. While agents in many biological systems have little trouble learning from one another, it is not immediately obvious how artificial agents would achieve similar learning. In this thesis, I explore how agents learn to interact with complex systems. I further explore how these complex learning agents may be able to transfer knowledge to one another to improve their learning performance when they are learning together and have the power of communication. While significant research has been done to explore the problem of knowledge transfer, the existing literature is concerned either with supervised learning tasks or relatively simple discrete reinforcement learning. The work presented here is, to my knowledge, the first which admits continuous state spaces and deep reinforcement learning techniques. The first contribution of this thesis, presented in Chapter 2, is a modified version of deep Q-learning which demonstrates improved learning performance due to the addition of a mutual learning term which penalizes disagreement between mutually learning agents. The second contribution, in Chapter 3, is a presentation work which describes effective communication of agents which use fundamentally different knowledge representations and systems of learning (model-free deep Q learning and model-based adaptive dynamic programming), and I discuss how the agents can mathematically negotiate their trust in one another to achieve superior learning performance. I conclude with a discussion of the promise shown by this area of research and a discussion of problems which I believe are exciting directions for future research.", "source_url": "https:\/\/openalex.org\/W3177417054", "pdf_url": "", "doi": "10.25394\/pgs.14439575", "handle": "", "repository_name": "Purdue e-Pubs (Purdue University System)", "source_record_id": "W3177417054", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1247, "title": "Reinforcement Learning in R", "title_ar": "", "title_en": "Reinforcement Learning in R", "author": "Markus Dumke", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Ludwid Maxmilian's Universitat Munchen", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2902900704", "pdf_url": "", "doi": "10.5282\/ubm\/epub.59093", "handle": "", "repository_name": "Open access LMU (Ludwid Maxmilian's Universitat Munchen)", "source_record_id": "W2902900704", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1248, "title": "Reinforcement Learning on Resource Bounded Systems", "title_ar": "", "title_en": "Reinforcement Learning on Resource Bounded Systems", "author": "Jaden B. Travnik", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Alberta", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; reinforcement-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "Recent advancements in reinforcement learning have made the field interesting to academia and industry alike. Many of these advancements depend on deep learning as a means to approximate a value function or a policy. This dependency usually relies on high performance hardware (e.g., a graphics processing unit, GPU) and applications of deep learning are often limited to domains where a substantial amount of time is allowed for a prediction to be made or an action to be chosen. Although these criteria cover many important use cases, the growing popularity of the ``Internet of Things,'' wearable electronics, and the advancement of myoelectric prosthetic limbs presents a rapidly growing real-time domain of resource bounded systems that are not properly suited for deep learning yet could still benefit from the application of reinforcement learning. Furthermore, many of these systems are limited by the physical space they must occupy. This restricts the size of the hardware and thereby the computational and memory resources that can be used. Despite these restrictions, demand for prompt actions from receptive systems continues to grow. To address these problems, I first highlight the difficulties one is faced with when implementing reinforcement learning on a system which is deployed in an asynchronous environment and introduce a new metric of performance by measuring the time it takes for a system to react to an observed state of an asynchronous environment. Secondly, I develop a class of algorithms that addresses these issues by reordering the algorithmic components to minimize reaction time. Thirdly, to minimize both the time and memory necessary to compute function approximation, I introduce a novel linear function approximation method, selective Kanerva coding (SKC), that allows a reinforcement learning agent to perform behaviors reactively in real-time while using less memory and computation time than the standard linear approach of tile coding. I also show that SKC is less sensitive to the curse of dimensionality than tile coding making SKC a significant step towards accurately representing high dimensional data on resource bounded systems. Moreover, I show that SKC can make the inclusion of more sensory modalities more feasible, which can increase prediction accuracy when those modes of sensation are relevant to the prediction. Finally, I present an exploration of the meta-parameters of SKC and evaluate the performance of two different variations of SKC against the original formulation. These findings are imperative to the current state of the field of reinforcement learning as they form a challenging perspective that is contrary to the current direction of the field's focus on deep learning. I form this argument by emphasizing the impracticality of deep learning in domains of resource bounded systems deployed in real-time environments, establishing the limitations on available computation and memory of these systems, and address these issues by proposing new insights, algorithms, and representations.", "source_url": "https:\/\/openalex.org\/W2809564569", "pdf_url": "", "doi": "10.7939\/r39g5gv5s", "handle": "", "repository_name": "ERA: Education and Research Archive (University of Alberta)", "source_record_id": "W2809564569", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1249, "title": "BikeBot: A Study in Reinforcement Learning", "title_ar": "", "title_en": "BikeBot: A Study in Reinforcement Learning", "author": "April Kutz ; Kristen A. Brennan ; Jeremy Dixon", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Northern Illinois University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "machine-learning; reinforcement-learning; robotics", "keywords": "", "abstract": "In the United States, there is a lack of young adults interested in studying Science, Technology, Engineering, and Math (STEM). One way to help spark interest in STEM is a classroom aid that demonstrates how STEM can be fun and how it is used to push the limits of modern technology. BikeBot was designed for just this purpose. BikeBot is a robotic bicycle that utilizes an algorithm called Q-learning. Q-Learning is a type of reinforcement machine learning program, a basic form of artificial intelligence, that will allow BikeBot to balance without any explicit controls. The algorithm was tested and proven to work through a computer simulation of BikeBot’s dynamics. Our results show that BikeBot learned to balance upright while moving forward at a constant velocity in 50,000 iterations or 16 minutes and 40 seconds of real time learning.", "source_url": "https:\/\/openalex.org\/W2978355259", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Huskie Commons (Northern Illinois University)", "source_record_id": "W2978355259", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1250, "title": "Reinforcement Learning in Block Markov Chains", "title_ar": "", "title_en": "Reinforcement Learning in Block Markov Chains", "author": "Pascal Lagerweij", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Delft University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "data-mining", "categories": "machine-learning; reinforcement-learning; data-mining", "keywords": "", "abstract": "Nowadays, reinforcement learning algorithms on Markov decision processes (MDPs) face computational issues when the state space is large. To reduce this state space of a MDP several state aggregation, or clustering, methodologies have been applied. Recently, a new clustering algorithm has been proposed that is able to cluster states from a single block Markov chain. A block Markov chain is a Markov chain with blocks in its transition matrix that correspond to clusters. Our aim was to investigate the possible combination of state aggregation in reinforcement learning on MDPs with clustering of states on a block Markov chain. First, we investigated the clustering algorithm and its properties to see its performance with different parameters and trajectory length. We compared the statistical properties of a pure Markov chain and the mixed Markov chain generated by a MDP. Afterwards, we verified the performance of the clustering algorithm on this mixed Markov chain. We proposed the BMC-MDP model that is able to model cluster based MDPs. We proposed C-PSRL, an algorithm, that consists of a single clustering step, on this newly introduced model. We compared its performance with a naïve approach and concluded that this new combined approach of clustering and MDP solving on a reduced space is a viable approach that reduces the computational complexity significantly. This research opened up the possibilities of more complex algorithms with, for example, multiple clustering steps. Moreover, if we can extend this clustering algorithm to clustering based on a state and action trajectory, this may results in an increased clustering performance and thereby enhance the performance of this general approach of optimizing on a cluster based MDP.", "source_url": "https:\/\/openalex.org\/W2981119674", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Repository (Delft University of Technology)", "source_record_id": "W2981119674", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1251, "title": "Sanitizer check debloating with reinforcement learning", "title_ar": "", "title_en": "Sanitizer check debloating with reinforcement learning", "author": "Kun Hung Lung", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "reinforcement-learning", "research_subdomain": "", "categories": "reinforcement-learning", "keywords": "", "abstract": "991012980417303412 HKUST Electronic Theses Sanitizer check debloating with reinforcement learning by Lung Kun Hung thesis 2021 1 online resource (x, 38 pages) : color illustrations Not presently available for public access at author’s request *CONFIDENTIAL* M.Phil. Computer Science and Engineering", "source_url": "https:\/\/openalex.org\/W4321433596", "pdf_url": "", "doi": "10.14711\/thesis-991012980417303412", "handle": "", "repository_name": "", "source_record_id": "W4321433596", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1252, "title": "Automating news classification with large language models: Exploring fine-tuning, dataset size, and architecture", "title_ar": "", "title_en": "Automating news classification with large language models: Exploring fine-tuning, dataset size, and architecture", "author": "Yesilyurt, Burcu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; responsible-ai; transfer-learning; predictive-analytics; transformers; llm; xai", "keywords": "", "abstract": "A comprehensive evaluation benchmarks Large Language Models with traditional machine learning algorithms for automatic news classification is done on three standard news classification datasets: BBC News, 20 Newsgroups, and AG News. We implement traditional models, including Naive Bayes, Logistic Regression, Support Vector Machine, and Random Forest, to provide clear and interpretable baselines using manual term-frequency and syntactic features. Then, fine‐tuned transformer architectures, including BERT, RoBERTa, T5, GPT, and their distilled variants, were used to quantify improvements in predictive accuracy, resource efficiency, and explainability. Performance is measured via 5-fold cross-validation using F1 and accuracy metrics, and statistical significance is assessed with a Friedman test followed by Holm’s correction. Results show that transformer models consistently outperform classical approaches, with BERT achieving the highest scores under both balanced and imbalanced conditions. Distilled models rival or surpass full-size transformers on larger datasets while reducing memory requirements and maintaining comparable inference latency. Attention‐based attribution methods provide semantic explanations on par with feature‐importance metrics, confirming that LLMs deliver superior accuracy, adaptability, and transparency in news classification. Future work should investigate multilingual pretraining, multilabel classification, and ensemble techniques to further strengthen real‐time, explainable news‐analysis pipelines.", "source_url": "https:\/\/openalex.org\/W7113115638", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7113115638", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1253, "title": "Efficient fine-tuning of Transformer Models Under Hardware Constraints : An Empirical Study of LoRA and Knowledge Distillation", "title_ar": "", "title_en": "Efficient fine-tuning of Transformer Models Under Hardware Constraints : An Empirical Study of LoRA and Knowledge Distillation", "author": "Sai Mounika Karasu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dalarna University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; transfer-learning; transformers; llm; ai-education", "keywords": "", "abstract": "Background Transformer-based language models dominate modern natural language processing but impose substantial computational costs during full fine-tuning, limiting accessibility on consumer-grade hardware. Knowledge Distillation and Low-Rank Adaptation (LoRA) independently address this challenge, yet their interaction under strict single GPU memory constraints remains insufficiently explored. Objectives This thesis investigates whether transformer models can be fine-tuned on a single consumer-grade GPU (NVIDIA T4 class) without performance degradation, and in some cases with improved generalization. The primary objective is to evaluate a hybrid framework combining LoRA’s parameter efficiency with KD’s supervisory signal, quantifying trade-offs in accuracy, trainable parameter count, and training time on the SST-2 benchmark. Methods A dual-phase experimental design was conducted under fixed hard-ware constraints. Four configurations were evaluated: (1) full fine-tuning of BERT-Large; (2) LoRA-based fine-tuning with a frozen backbone; (3) knowledge distillation using a DistilBERT student; and (4) a hybrid LoRA–KD strategy with restricted adapter placement. Experiments were single-run with a fixed random seed. Although this precludes formal statistical testing, observed performance gaps substantially exceed typical fine-tuning variance of 0.2–0.5% documented in prior BERT evaluation studies, indicating systematic architectural effects rather than stochastic initialization noise. Statistical Validity Consideration To contextualize the single-run experimental design, we reference established variance benchmarks from prior BERT fine-tuning studies. Dodge et al. report that across five random seeds on SST-2, BERT-Base exhibits a standard deviation of approximately 0.4% in validation accuracy. Mosbach et al. demonstrate that with appropriate learning rate warmup and sufficient training steps, this variance can be reduced to 0.2–0.3%. Given these benchmarks, the observed performance gaps in this study are note-worthy: • LoRA vs. FFT: 2.07% gap ≈ 5–10× typical variance • Hybrid vs. LoRA (1,000 samples): 3.53% gap ≈ 9–18× typical variance • Hybrid vs. LoRA: 1.38% gap ≈ 3–7× typical variance While these margins suggest systematic effects, we acknowledge that formal sta-tistical significance would require multi-seed validation. The resource constraints ofthis study precluded such validation.We therefore interpret findings as indicative rather than definitive, and recommendmulti-seed replication as a priority for future work. Results Three principal findings emerge from the SST-2 evaluation. First, theLoRA (4.02 GB vs. 4.11 GB), indicating that offline KD enables richer supervisionwithout increased memory cost. Second, LoRA reduces trainable parameters byover 95% while achieving higher accuracy than FFT in this evaluation (93.81% vs.91.74%), suggesting an implicit regularization effect under moderate data conditions. Third, the Hybrid LoRA–KD approach shows improved performance in low-data regimes relative to standalone LoRA, supporting the hypothesis that distillation acts as a regularizer when labeled data is scarce. Third, qualitative analysis revealed increased error sensitivity in compressed models for linguistically complex inputs involving compositional negation. Conclusions On SST-2, LoRA provides sufficient parameter efficiency under moderate data availability, while knowledge distillation yields complementary gains in data-scarce regimes; the Hybrid model achieves 92.43% accuracy using only 1,000 samples and 4.7M trainable parameters- approaching FFT performance (91.74% on 30,000 samples) with 97% less training data- demonstrating strong data efficiency and enabling environmentally sustainable NLP research on consumer-grade GPUs (NVIDIA T4).", "source_url": "https:\/\/openalex.org\/W7161919335", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Diva portal (Dalarna University Library)", "source_record_id": "W7161919335", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1254, "title": "PERFORMANCE ANALYSIS OF TRANSFORMER-BASED LANGUAGE MODELS FOR DETECTION OF PHISHING EMAILS", "title_ar": "", "title_en": "PERFORMANCE ANALYSIS OF TRANSFORMER-BASED LANGUAGE MODELS FOR DETECTION OF PHISHING EMAILS", "author": "Chidozie Princewill Arannonu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; ai-applications; transfer-learning; transformers; llm; ai-cybersecurity", "keywords": "", "abstract": "This thesis evaluates the performance and adversarial robustness of four transformer-based language models; BERT, RoBERTa, DistilBERT, and GPT-2 which were fine-tuned for binary phishing email classification. Two preprocessing configurations (Basic and Advanced) and five training data fractions are systematically compared using a temporally partitioned dataset that separates pre-2015 training data from 2015–2024 deployment data, simulating real-world generalization across evolving phishing tactics. DistilBERT with Advanced preprocessing achieves the best overall performance, reaching 97.26% accuracy and a weighted F1-score of 0.972. The study further conducts adversarial robustness evaluation using four attack strategies from the TextAttack framework namely; DeepWordBug, Pruthi, TextBugger, and TextFooler which found out that character-level attacks achieve success rates of up to 22.44%. Finally, applying Advanced preprocessing to adversarially perturbed inputs recovers post-attack accuracy to near-clean-data levels across all models, establishing preprocessing as a deployable defense mechanism rather than merely a data cleaning step.", "source_url": "https:\/\/openalex.org\/W7160619207", "pdf_url": "", "doi": "10.25394\/pgs.32204841", "handle": "", "repository_name": "Purdue", "source_record_id": "W7160619207", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1255, "title": "Federated Fine-Tuning of Mixture of Experts Vision Transformers", "title_ar": "", "title_en": "Federated Fine-Tuning of Mixture of Experts Vision Transformers", "author": "Filippo Serafini", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bologna", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; transfer-learning; transformers; federated-learning; fairness; ai-smart-cities", "keywords": "", "abstract": "Mixture-of-Experts (MoE) architectures decouple representational capacity from per-token computational cost: each input activates only a few specialised experts, yielding far greater capacity than dense models at equal activation cost. Federated Learning (FL) trains models on edge devices—aggregating local updates, never centralising raw data—preserving privacy and unlocking edge-resident data. Their integration is hard: cross-device FL faces tight bandwidth and memory budgets and local datasets are heterogeneous (non-IID); MoE sparsity, however, can overcome these barriers. Adapting the partial-replica paradigm of the FoMoE framework—where each client hosts only a subset of the experts—this thesis investigates the federated fine-tuning of a Vision MoE (V-MoE-S\/32) on edge devices holding severely non-IID data, combined with Low-Rank Adaptation (LoRA) and Sparsity-aware Model Aggregation. Under a static expert assignment, restricting each client to half or fewer of the expert pool costs only 1–2 percentage points of Top-1 accuracy versus the full-replica baseline, while cutting the per-round communication payload by up to 28% and static on-device memory by up to 36%. A static assignment, however, mismatches the experts a client needs most with those initially allocated. To close this gap, this work designs and evaluates a novel server-orchestrated dynamic expert reassignment protocol that periodically migrates experts towards the clients demanding them most. Rather than raising the accuracy ceiling, it mainly stabilises the federation against sub-optimal initial placements, at a communication overhead of 1–3% of total traffic. Partial expert replication, parameter-efficient adaptation, and sparsity-aware aggregation thus compose into a practical recipe for fitting sparse vision models within edge memory, compute, and bandwidth budgets; by decoupling local hardware limits from model size, this design lets edge networks train far larger architectures.", "source_url": "https:\/\/openalex.org\/W7219542350", "pdf_url": "https:\/\/amslaurea.unibo.it\/id\/eprint\/39760\/1\/Master_s_thesis_Filippo_Serafini.pdf", "doi": "", "handle": "", "repository_name": "AMS Degree Thesis (University of Bologna)", "source_record_id": "W7219542350", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1256, "title": "SISTEM PENILAIAN FORUM DISKUSI MAHASISWA PADA E�LEARNING BERBASIS MODEL BERT", "title_ar": "", "title_en": "", "author": "Ahmad Habibi ; Endina Putri Purwandari ; Widhia oktoeberza", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Bengkulu", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "id", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; ai-applications; transfer-learning; transformers; ai-education", "keywords": "", "abstract": "Penelitian ini bertujuan untuk merancang dan mengembangkan sistem penilaian otomatis forum diskusi mahasiswa pada platform E-Learning dengan memanfaatkan model BERT (Bidirectional Encoder Representations from Transformers). Penelitian ini dilatarbelakangi oleh tantangan dalam menilai partisipasi mahasiswa dalam forum diskusi E-Learning, nyatanya dengan metode tradisional sering kali bersifat subjektif dan memakan waktu. Sedangkan model BERT dikenal dengan kemampuannya dalam memahami konteks teks secara mendalam, sehingga diharapkan dapat meningkatkan objektivitas dan efisiensi dalam proses penilaian. Penelitian ini mencakup beberapa tahapan, mulai dari analisis kebutuhan, perancangan sistem, pengembangan fine-tuning model BERT, hingga evaluasi dengan membandingkan hasil penilaian otomatis dan manual. Sistem ini dirancang untuk menilai kontribusi mahasiswa berdasarkan kompleksitas dan relevansi kontennya, dengan tujuan meningkatkan interaksi serta kualitas diskusi dalam E-Learning. Hasil yang diharapkan adalah sistem penilaian yang lebih akurat, efisien, dan mampu memberikan umpan balik yang bermanfaat bagi mahasiswa maupun dosen. Selain itu, penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam pengembangan teknologi penilaian otomatis berbasis Natural Language Processing (NLP) di bidang pendidikan. Kata kunci: E-Learning, BERT, Penilaian Otomatis, Forum Diskusi, Teknologi Pendidikan.", "source_url": "https:\/\/openalex.org\/W7126128896", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Bengkulu Scholar Repository (University of Bengkulu)", "source_record_id": "W7126128896", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1257, "title": "Generating Healthcare Reports Using Natural Language Processing : Fine-tuning Finnish Language Models", "title_ar": "", "title_en": "Generating Healthcare Reports Using Natural Language Processing : Fine-tuning Finnish Language Models", "author": "Tuuli Laitinen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; medical-ai; transfer-learning; cnn; transformers; llm", "keywords": "", "abstract": "Generating reports in different healthcare departments is an important factor in keeping up with different phenomena occurring in the everyday work. Examples of these are the number of operations done, complications happened during them, and used instruments. Different systems the healthcare workers use might have their own reporting systems, which is the case for Tampere heart hospital’s cardiology and cardiothoracic surgery departments with their quality monitoring and reporting system called Kardio. The current problem with Kardio has been limited possibilities to generate reports and therefore a solution was to try and develop a system where users could generate reports by natural language prompts. The goal was to train Finnish language models so that they could be used to identify named entities for cardiology operations. The results obtained have been used to investigate whether such a feature could be used as a part in generating new reports. The implementation of the functionality began by considering the components required for its development. This led to the conclusion that a tool capable of identifying certain entities from natural language text was necessary. There are no language models based on medical text and data for the Finnish language readily available and training a whole new language model was not necessary. Because of this, the choice was to fine-tune existing Finnish language models with data related to the field of cardiology. The chosen three language models to be fine-tuned were two BERT based, FinBERT and Finnish RoBERTa, and one CNN-based language model by spaCy. The results showed that there are no significant differences in the fine-tuning of the language models, but the F1-scores indicate that FinBERT and Finnish RoBERTa perform slightly better than spaCy. The F1-scores, ranging around 0.95 to 0.98, were very good compared to the fine-tuning of English language models, but this was possibly due to the very limited and self-generated training data. Based on the results obtained, the fine-tuned language model could be used in recognising entities needed for cardiology operation report generation after further development. Additional work should be done to improve the training data so that the language model recognises all the entities needed for reports, while ensuring that the language model performs well with a wide range of inputs.", "source_url": "https:\/\/openalex.org\/W7133049232", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/224856\/2\/LaitinenTuuli.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133049232", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1258, "title": "Automating Programming Assignment Grading with a Fine-Tuned Large Language Model", "title_ar": "", "title_en": "Automating Programming Assignment Grading with a Fine-Tuned Large Language Model", "author": "Matti Syrjanen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; ai-applications; transfer-learning; llm; ai-industry", "keywords": "", "abstract": "Grading programming assignments is a labor-intensive process that requires significant effort from university personnel and is often influenced by subjective biases. Automated grading systems, particularly those leveraging artificial intelligence, could offer a promising solution to these challenges. This thesis examines the potential of large language models to reduce the grading workload while also standardizing the process. The product of this study was a fine-tuned OpenAI GPT model trained on past programming assignment submission-feedback pairings with feedback templates and grading instructions from the Programming 2 course run by Tampere University. The research focused on measuring the feedback given by the fine-tuned model in terms of consistency and differences between the feedback given by the corresponding human graders. The base GPT model was fine-tuned with a small dataset and default settings. Evaluation results suggest that further experimentation with different hyperparameters and larger or alternative datasets is necessary to increase the reliability of the model. While the AI-generated feedback met predefined evaluation metrics in most cases, the results were not yet reliable enough for actual grading.", "source_url": "https:\/\/openalex.org\/W7132916378", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/225407\/2\/SyrjanenMatti.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7132916378", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1259, "title": "Automatisk särskiljning av fakta- och resoneringsfrågor i läsförståelsetest : En jämförande studie av BERT-baserade Transformer-modeller", "title_ar": "", "title_en": "", "author": "Elias Bergström Persson", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Dalarna University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2026, "language": "sv", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers; llm", "keywords": "", "abstract": "Reading comprehension tests are a common way of assessing a language learner's ability. The difficulty of a reading comprehension test is often determined by the balance of factoid and inferential questions. While factoid questions test retrieval of explicitly stated facts, inferential questions demand commonsense reasoning and \"reading between the lines\", making inferential questions generally more difficult. Manually classifying questions can be subjective and time-consuming for test designers. This study implements four automatic binary classifiers of varying sizes for reading comprehension questions using BERT-based Pre-trained Language Models, along with four lexical baselines. The SQuAD 1.1 dataset is used for the factoid class, and the Cosmos QA dataset for the inferential class. This data was processed in a filtering step and supplemented with synthetic data to prevent the models from relying on surface-level stylistic biases between the datasets. The fine-tuned classifiers (TinyBERT, DistilBERT, DeBERTa-v3-base, and DeBERTa-v3-large) were quantitatively evaluated based on F1-performance and parameter count. Due to hardware limitations, the largest model, DeBERTa-v3-large was trained using Low-Rank Adaptation (LoRA). The comparison demonstrates a strong positive correlation between model size and classification performance. The best-performing model, DeBERTa-v3-large, achieved a macro average F1-score of 0.926, outperforming the lexical baselines by ~24%. These findings suggest that it is possible to effectively automate the classification of reading comprehension questions using modern Transformer architectures, serving as a valuable tool for automatic difficulty assessment of reading comprehension tests.", "source_url": "https:\/\/openalex.org\/W7134098317", "pdf_url": "https:\/\/kth.diva-portal.org\/smash\/get\/diva2:2043411\/FULLTEXT01", "doi": "", "handle": "", "repository_name": "Diva portal (Dalarna University Library)", "source_record_id": "W7134098317", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1260, "title": "An exploration of fine-tuning BERT for hate speech analysis", "title_ar": "", "title_en": "An exploration of fine-tuning BERT for hate speech analysis", "author": "Yingqi Zhao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers; llm; sentiment-analysis; speech-recognition", "keywords": "", "abstract": "In the realm of Natural Language Processing (NLP), sentiment classification stands as an essential subject, commonly categorizing text into three classes: positive, negative, and neutral, enabling the statistical analysis of the emotional tendencies in the text.With the development of social networks, the classification requirements for sentiment analysis have gradually become more refined.This thesis draws datasets related to hate speech, fine-tuning BERT as a classification model to categorize the text into five categories to enhance the analysis of hate speech within social media platforms.Simultaneously, to address the substantial parameter size of large language models and the associated high fine-tuning costs, the thesis explores three methods for fine-tuning the BERT model: Fine-tuning BERT with all parameters, Fine-tuning BERT only the classification heads and Fine-tuning BERT with LoRA.This Thesis empirically validates the feasibility and superiority of designing targeted sentiment analysis models for refined scenarios.Achieving 90% accuracy confirms the outstanding performance of the BERT model in addressing hate speech analysis issues.Furthermore, comparing different fine-tuning methods validates the potential and value of the PEFT method for fine-tuning transformer models.", "source_url": "https:\/\/openalex.org\/W7133093837", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/155728\/7\/Zhao%20Yingqi.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133093837", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1261, "title": "Improving Code Quality Using Fine-tuning Large Language Models", "title_ar": "", "title_en": "Improving Code Quality Using Fine-tuning Large Language Models", "author": "Quang Hieu Nguyen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; generative-ai; reinforcement-learning; transfer-learning; transformers; llm", "keywords": "", "abstract": "Large language models has demonstrated significant capabilities in solving real-life problems by the means of generating human-like responses to input text in the similar format. However, these generic languages exhibit weakness when the context is not fully provided, resulting a need for model customization to enhance their performance in specialized fields. The fine-tuning is implemented using techniques inspired by machine learning methods. The two well-known fine-tuning methods are Supervised Fine-Tuning (STF), based on the concept of supervised learning, and Reinforcement Learning from Human Feedback (RLHF) introduced by OpenAI. The resource-intensive nature of RLHF makes it challenging to replicate the results achieved by Chat Generative Pre-trained Transformer (ChatGPT) models. Therefore, a new technique named direct preference optimization (DPO) has been introduced lately, incorporating similar concepts to RLHF while reducing required time and labor. Given several studies have not addressed the improvement of DPO compared to other fine-tuning methods and its interplay with them, this thesis employs STF, DPO and their combination to fine-tune the base LLAMA 3.1 model, whose size is 8 billion parameters, in order to improve code quality generated by the LLMs. These fine-tuned models are then evaluated using Bi-Lingual Evaluation Understudy (BLEU) and Bidirectional Encoder Representations from Transformer (BERT) metrics, along with their code-specific derivatives. Despite the outcomes of the metrics cannot determine the most effective fine-tuning technique, they clearly highlight the significant influence of dataset utilized during the fine-tuning process. The repository containing the code and data for the research can be accessed via GitHub platform.", "source_url": "https:\/\/openalex.org\/W7133076851", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/161629\/2\/NguyenDuc.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133076851", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1263, "title": "UNSUPERVISED MULTI-REVIEW SUMMARIZATION USING FINE-TUNED TRANSFORMER LANGUAGE MODELS", "title_ar": "", "title_en": "UNSUPERVISED MULTI-REVIEW SUMMARIZATION USING FINE-TUNED TRANSFORMER LANGUAGE MODELS", "author": "LUCAS ROBERTO DA SILVA", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers; llm; sentiment-analysis", "keywords": "", "abstract": "Automatic summarization is the task of generating concise, correct, and factual summaries.The task can be applied to different textual styles, including news, academic publications, and product or place reviews.This dissertation addresses the summary of multiple evaluations.This type of application stands out for its unsupervised nature and the need to deal with the redundancy of the information present in the reviews.The automatic summarization works are evaluated using the ROUGE metric, which is based on the comparison of n-grans between the reference text and the generated summary.The lack of supervised data motivated the creation of the MeanSum architecture, which was the first neural network architecture based on an unsupervised model for this task.It is based on auto-encoder and has been extended to other works, but none explored the effects of using the attention mechanism and auxiliary tasks during training.The present work is divided into two parts: the first deals with an experiment in which we make extensions to the MeanSum architecture, adding attention mechanisms and auxiliary sentiment classification tasks.In the same experiment, we explore synthetic data to adapt supervised models for unsupervised tasks.In the second part, we used the results previously obtained to carry out a second study on fine-tuning pre-trained Transformer language models.The use of these models showed a promising alternative to the unsupervised nature of the problem, outperforming previous works by + 4 ROUGE.", "source_url": "https:\/\/openalex.org\/W4244183052", "pdf_url": "https:\/\/www.maxwell.vrac.puc-rio.br\/53550\/53550.PDF", "doi": "10.17771\/pucrio.acad.53550", "handle": "", "repository_name": "", "source_record_id": "W4244183052", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1264, "title": "Microbial named entity recognition using BERT models", "title_ar": "", "title_en": "Microbial named entity recognition using BERT models", "author": "Brian K Rao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Illinois at Urbana-Champaign", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; medical-ai; ai-applications; transfer-learning; data-mining; transformers; ai-industry", "keywords": "", "abstract": "Bacteria are critical subjects of microbiological research that span many rapidly-growing fields of study.The development and widespread application of high-throughput sequencing has led to more microbial data being collected in recent years than ever before.This study investigates the capabilities of the popular Natural Language Processing (NLP) model Bidirectional Encoder Representations from Transformers (BERT) on the relatively understudied text mining domain of microbiology.This is done by fine-tuning a variety of BERT models (BERT, DistilBERT, SciBERT, BioBERT, PubMedBERT) on the Bacteria Biotope 2019 Open Shared Task (BB2019-OST) corpus of annotated microbial research text and evaluating the best performing models on the Named Entity Recognition (NER) task.Following this, an in-depth error analysis was conducted to gain insights into BERT's entity recognition capabilities.Finally, to investigate performance capabilities further, learning rate and batch size hyperparameters were tuned to increase F1-score.The best BERT model in the comparison was BioBERT, earning an F1-score of 73.82 (±1.04) with default hyperparameters, and 75.35 (±0.62) with tuned hyperparameters.BioBERT had better F1-scores and entity-level statistics, despite PubMedBERT ranking high in biomedical NLP benchmarks.This suggests that the generality of the pretraining corpora of BERT models is particularly important for text mining in the microbial domain.", "source_url": "https:\/\/openalex.org\/W7220763410", "pdf_url": "https:\/\/www.ideals.illinois.edu\/items\/125125\/bitstreams\/410938\/data.pdf", "doi": "", "handle": "", "repository_name": "Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)", "source_record_id": "W7220763410", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1265, "title": "Sentiment analysis for portuguese with BERT models", "title_ar": "", "title_en": "Sentiment analysis for portuguese with BERT models", "author": "Vinícius Pinheiro Winter", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Federal University of Technology – Paraná", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; transfer-learning; transformers; sentiment-analysis", "keywords": "", "abstract": "This paper presents the development and application of the BERT model (Bidirectional Encoder Representations from Transformers) for sentiment analysis in Portuguese texts, focusing on data from social networks. The study is justified by the need to understand the sentimental nuances expressed in a language with limited NLP resources, such as Portuguese, and the relevance of analyzing sentiments on widely used platforms. The objectives center on training and validating a Portuguese-adapted BERT model, using a manually classified dataset, as well as another dataset collected automatically without classification labels. The methodology involved fine-tuning BERT to adjust it to the linguistic peculiarities of Portuguese, followed by rigorous validation that included comparisons with classic NLP algorithms and extrinsic analyses, such as the generation of word clouds to visualize the frequencies of words associated with different sentiments. The results demonstrate an accuracy of 86.64% in the BERTimbau model, which was chosen due to its pre-training in Portuguese and its superior performance in tests, thus evidencing its effectiveness in classification and its relevance for practical applications. It was also observed through word cloud analysis that a large part of the classifications of the Reddit dataset was effective due to the presence of words commonly associated with the sentiments to which they were classified. This work contributes to the field of NLP by providing a Portuguese-adapted model for sentiment analysis, as well as valuable perspectives for future research and applications in various areas, including behavioral analysis and mental health monitoring.", "source_url": "https:\/\/openalex.org\/W7155474077", "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/36753\/1\/analisesentimentosmodelobert.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)", "source_record_id": "W7155474077", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1266, "title": "Pre-training model based on the transfer learning in natural language processing", "title_ar": "", "title_en": "Pre-training model based on the transfer learning in natural language processing", "author": "Jiayi Tang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; predictive-analytics; cnn; transformers; llm", "keywords": "", "abstract": "Transfer learning is to apply knowledge or patterns learned in a particular field or task to different but related areas or problem. It is very prominent in terms of scarcity of data and heterogeneity of domain distribution. In the field of natural language processing, transfer learning is embodied in the pre-training model. There are two existing strategies for applying pre-trained language representations to downstream tasks: feature-based (ELMO) and fine-tuning (GPT、BERT). In 2018, Google released a large-scale pre-training language model BERT, which stands for Bidirectional Encoder Representations from Transformer. Compared with other pre-training model ELMO and GPT, and the classical model CNN, BERT is the latest and best-performing model up until now. Its highlights are (1) Bidirectional Transformer (2) Mask-Language Model (3) Next Sentence Prediction (4) A more general input layer and output layer. BERT model can efficiently learn text information and apply it to various NLP tasks. In this report, we use the BERT model in two way. The first is to use the pre-training model released by Google directly and then pass the fine-tuning stage. The second is to use the BERT-as-service to use the BERT model as a sentence encode followed by a DNN classifier. Then we horizontally compare BERT with ELMO and GPT; then vertically compare BERT with different parameters.", "source_url": "https:\/\/openalex.org\/W2953559479", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2953559479", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1267, "title": "Bringing Visibility to Workflow Processes via Topic Modeling with BERT Transformer Models", "title_ar": "", "title_en": "Bringing Visibility to Workflow Processes via Topic Modeling with BERT Transformer Models", "author": "Seth Peters", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; transformers", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7215214106", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/3fd6d3d0-eafc-4499-ba3d-7a96e013903f\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215214106", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1268, "title": "Using Bidirectional Encoder Representations from Transformers for Conversational Machine Comprehension", "title_ar": "", "title_en": "Using Bidirectional Encoder Representations from Transformers for Conversational Machine Comprehension", "author": "Evangelina Gogoulou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; transformers; llm; xai", "keywords": "", "abstract": "Bidirectional Encoder Representations from Transformers (BERT) is a recently proposed language representation model, designed to pre-train deep bidirectional representations, with the goal of extracting context-sensitive features from an input text [1]. One of the challenging problems in the field of Natural Language Processing is Conversational Machine Comprehension (CMC). Given a context passage, a conversational question and the conversational history, the system should predict the answer span of the question in the context passage. The main challenge in this task is how to effectively encode the conversational history into the prediction of the next answer. In this thesis work, we investigate the use of the BERT language model for the CMC task. We propose a new architecture, named BERT-CMC, using the BERT model as a base. This architecture includes a new module for encoding the conversational history, inspired by the Transformer-XL model [2]. This module serves the role of memory throughout the conversation. The proposed model is trained and evaluated on the Conversational Question Answering dataset (CoQA) [3]. Our hypothesis is that the BERT-CMC model will effectively learn the underlying context of the conversation, leading to better performance than the baseline model proposed for CoQA. Our results of evaluating the BERT-CMC on the CoQA dataset show that the model performs poorly (44.7% F1 score), comparing to the CoQA baseline model (66.2% F1 score). In the light of model explainability, we also perform a qualitative analysis of the model behavior in questions with various linguistic phenomena eg coreference, pragmatic reasoning. Additionally, we motivate the critical design choices made, by performing an ablation study of the effect of these choices on the model performance. The results suggest that fine tuning the BERT layers boost the model performance. Moreover, it is shown that increasing the number of extra layers on top of BERT leads to bigger capacity of the conversational memory.", "source_url": "https:\/\/openalex.org\/W3046466516", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W3046466516", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1269, "title": "Comparison of sequence classification techniques with BERT for named entity recognition", "title_ar": "", "title_en": "Comparison of sequence classification techniques with BERT for named entity recognition", "author": "Erik Rosvall", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers", "keywords": "", "abstract": "This thesis takes its starting point from the recent advances in Natural Language Processing being developed upon the Transformer model. One of the significant developments recently was the release of a deep bidirectional encoder called BERT that broke several state of the art results at its release. BERT utilises Transfer Learning to improve modelling language dependencies in texts. BERT is used for several different Natural Language Processing tasks, this thesis looks at Named Entity Recognition, sometimes referred to as sequence classification. This thesis compares the model architecture as it was presented in its original paper with a different classifier in the form of a Conditional Random Field. BERT was evaluated on the CoNLL-03 dataset, based on English news articles published by Reuters. The Conditional Random Field classifier overall outperforms the original Feed Forward classifier on the F1-score metric with a small margin of approximately 0.25 percentage points. While the thesis fails to reproduce the original report’s results it compares the two model architectures across the hyperparameters proposed for fine-tuning. Conditional Random Fields proves to perform better scores at most hyperparameter combination and are less sensitive to which parameters were chosen, creating an incentive for its use by reducing the effect of parameter search compared to a Feed Forward layer as the classifier. Comparing the two models shows a lower variance in the results for Conditional Random Fields.", "source_url": "https:\/\/openalex.org\/W2986482033", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2986482033", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1270, "title": "Analysis of fine-tuning techniques in text classification", "title_ar": "", "title_en": "Analysis of fine-tuning techniques in text classification", "author": "Tobias Gonçalves Pires", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers; llm", "keywords": "", "abstract": "Natural Language Processing (NLP) aims to develop models that enable computers to understand, interpret, process and generate text in a way similar to human communication. The last decade has seen significant advances in the field, with the introduction of deep neural network models, and the subsequent evolution of the architecture of these models such as the attention mechanism and the Transformers architecture, culminating in language models such as ELMo, BERT and GPT. And later models called Large Language Models (LLMs) improved the ability to understand and generate texts in a sophisticated way. Pre-trained models offer the advantage of reusing knowledge accumulated from vast datasets, although specific fine-tuning is required for individual tasks. However, training and tuning these models consumes a lot of processing resources, making it unfeasible for many organizations due to high costs. In resource-constrained environments, efficient fine-tuning techniques such as LoRA (Low-Rank Adaptation) were developed to optimize the model adaptation process, minimizing the number of adjustable parameters and avoiding overfitting. These techniques allow for faster and more economical training, while maintaining the robustness and generalization of the models. This work evaluates three efficient fine-tuning techniques LoRA, AdaLoRA and IA3 (in addition to full fine-tuning) in terms of memory consumption, training time and accuracy, using the DistilBERT, Roberta-base and TinyLlama models on different datasets (AG News, IMDb and SNLI).", "source_url": "https:\/\/openalex.org\/W7120873934", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120873934", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1271, "title": "Transformer-based kernels for Natural Language Processing", "title_ar": "", "title_en": "Transformer-based kernels for Natural Language Processing", "author": "David Morán Pomés", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; transfer-learning; rnn; transformers", "keywords": "", "abstract": "Kernel methods (KM) and Artificial Neural Networks (ANN) are two of the main families of methods that are more widely spread nowadays in the world of Machine Learning, being the Support Vector Machine (SVM) and the Deep Learning Neural Network (DLNN) its most known members. One of the main benefits of both families is how they can be adapted to work with different kinds of input data, even though they do it in a different way: KM rely on different kernel functions and ANN rely on specific network architectures. In the specific world of Natural Language Processing, both families have specific resources to deal with texts as inputs. While KM rely on specific kernels like the string kernel, ANN rely on Recurrent Neural Network (RNN) architectures and, more recently, on Transformer architectures. Even though KM were the first ones to dive into the NLP world, ANN are nowadays the state-of-the-art in all kinds of NLP tasks. The main reason of this success is the fact that ANN perform as a nonlinear parametric feature extractor, which processes the text in a complex way that lets the model learn an adequate representation of the input. On the other side, kernel functions do only rely on fixed similarity measures to contain that information, which is less flexible and powerful. This is the reason why we developed a new kernel function, called Transformer kernel. This kernel function is a parametric model like an ANN, but instead of computing a nonlinear representation of the input, it directly computes a similarity measure that corresponds to an inner product in some implicit feature space. The main idea of this model is that it can be pre-trained in a generic task using the whole Wikipedia corpus (like the different Transformer models for NLP tasks), but the resulting model can be directly used as a kernel function for any kind of kernel method (like the SVM). In order to do so, we build this model by stacking two different components: A Transformer Encoder block (based on the BERT architecture) and a newly designed kernel function called Attention Kernel. By using this kernel function with real text datasets, we have shown that it provides results that are much better than the ones obtained by the use of the string kernel. We have also seen that, due to the fact that our model is built as a Neural Network and can be executed on a GPU, the Transformer kernel can compute the kernel matrix of a dataset much faster than the classical string kernel. Results are very promising, and suggest that further study of the behaviour of this kernel function with different datasets for pre-training and different final tasks will be very interesting. Moreover, it has to be considered if it is possible to adapt this kernel in order to combine the text input with other kinds of data (like numerical or categorical variables), or if the kernel can be fine-tuned into a specific task before it is used as a kernel function.", "source_url": "https:\/\/openalex.org\/W3138190555", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W3138190555", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1272, "title": "Cyberbullying identification and classification using transformer models", "title_ar": "", "title_en": "Cyberbullying identification and classification using transformer models", "author": "Wachiraporn Tapaopong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; ai-applications; transfer-learning; predictive-analytics; transformers; ai-cybersecurity", "keywords": "", "abstract": "The widespread use of social media platforms has made cyberbullying an increasingly prevalent issue, causing physical, emotional, and mental harm to victims. This study focuses on utilizing Natural Language Processing techniques to enhance the identification and analysis of cyberbullying in hazardous social media messages. To achieve this goal, we utilized the transfer learning and fine-tuning techniques with the Transformer models, specifically BERT, RoBERTa, ALBERT, DistilBERT, and ConvBERT, to compare the effectiveness of category prediction. The study tested two methods to evaluate the effectiveness of automated cyberbullying detection. The first involves six categories: religion, age, ethnicity, gender, not cyberbullying, and other types of cyberbullying. The second method did not include \"other types of cyberbullying.\" The findings revealed that DistilBERT was the model with the best performance for multi-class text classification with the least amount of training time. This research emphasized the value of automated cyberbullying detection by using the potential of Natural Language Processing techniques to reduce the negative consequences of cyberbullying. Overall, this study contributed to the development of effective and efficient approaches for identifying and classifying cyberbullying in order to prevent and intervene in cyberbullying. Implication of thematic paper: The implications of this research are significant in terms of addressing the issue of cyberbullying, which is becoming increasingly prevalent due to the widespread use of social media platforms. The use of Transformer models for cyberbullying identification and classification has the potential to significantly improve the accuracy and efficiency of automated detection systems. These models can learn to recognize and categorize cyberbullying behaviors in a way that is more nuanced and accurate than traditional machine learning approaches by utilizing deep learning. This could help social media platforms more effectively identify and address instances of cyberbullying, ultimately leading to a more secure and encouraging online environment for all users. Furthermore, the use of Transformer models for cyberbullying detection could also have broader implications for the fields of natural language processing and machine learning, as it demonstrates the potential for these models to effectively analyze and understand complex, human-generated text data.", "source_url": "https:\/\/openalex.org\/W7220849862", "pdf_url": "", "doi": "10.67546\/mu.th.6437949", "handle": "", "repository_name": "", "source_record_id": "W7220849862", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1273, "title": "Evaluating Transformer Models For Document Question Answering : A Comparative Study On Multilingual And Normative Texts", "title_ar": "", "title_en": "Evaluating Transformer Models For Document Question Answering : A Comparative Study On Multilingual And Normative Texts", "author": "Yaradanakul, Zeynep", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Helsinki", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers; llm", "keywords": "", "abstract": "Document Question Answering (DQA) systems have gained significant traction in recent years, especially with the rise of large language models (LLMs). However, the application of these models to normative documents, such as legal texts, guidelines, or emergency instructions, presents unique chal- lenges. Normative documents often contain formal, domain- specific language, making it difficult for DQA models to extract precise answers. This study evaluates the performance of various transformer-based models on a bilingual dataset of university normative documents, comprising English and Turk- ish texts. For English, models BERT, DistilBERT, ALBERT, RoBERTa, T5, and Longformer are analyzed. For Turkish, XLM-R, and fine-tuned Turkish-specific models BERTurk and PEFT mT0 are utilized to address the unique morphological challenges of the Turkish language. Evaluation metrics, in- cluding token-level accuracy, F1 score, and execution time, are used to analyze the models’ performance in extracting relevant information. This research identifies the most effective models for processing normative documents in each language and highlights the limitations of current DQA systems in handling complex, formal texts across linguistic boundaries.", "source_url": "https:\/\/openalex.org\/W7115264002", "pdf_url": "https:\/\/www.doria.fi\/bitstream\/10024\/190611\/3\/yaradanaku_zeynep.pdf", "doi": "", "handle": "", "repository_name": "Doria (University of Helsinki)", "source_record_id": "W7115264002", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1275, "title": "Exploring deep learning for earth system forecasting", "title_ar": "", "title_en": "Exploring deep learning for earth system forecasting", "author": "Zhihan Gao", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "HKUST Electronic Theses Exploring deep learning for earth system forecasting by Zhihan Gao thesis 2024 1 online resource (xx, 132 pages) : illustrations (some color) Conventionally, Earth system (e.g., weather and climate) forecasting relies on numerical simulations using complex physical", "source_url": "https:\/\/openalex.org\/W4405916824", "pdf_url": "", "doi": "10.14711\/thesis-991013340437803412", "handle": "", "repository_name": "", "source_record_id": "W4405916824", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1276, "title": "Deep Learning for Chatbots", "title_ar": "", "title_en": "Deep Learning for Chatbots", "author": "Vyas Ajay Bhagwat", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "San Jose State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "", "categories": "machine-learning; deep-learning; nlp", "keywords": "", "abstract": "Natural Language Processing (NLP) requires modelling complex relationships between the semantics of the language. While traditional machine learning techniques are used for NLP, the models built for conversations, called chatbots, are unable to be truly generic. While chatbots have been made with traditional machine learning techniques, deep learning has allowed the complexities within NLP to be easier to model and can be leveraged to build a chatbot which has a real conversation with a human. In this project, we explore the problems and techniques used to build chatbots and where improvements can be made. We analyze different architectures to build chatbots and propose a hybrid model, partly retrieval-based and partly generation-based which gives the best results.", "source_url": "https:\/\/openalex.org\/W4231594475", "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=1645&context=etd_projects", "doi": "10.31979\/etd.9hrt-u93z", "handle": "", "repository_name": "San José State University ScholarWorks (San Jose State University)", "source_record_id": "W4231594475", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1277, "title": "Is deep learning the same as deep learning? : perspectives on the construct of deep learning", "title_ar": "", "title_en": "Is deep learning the same as deep learning? : perspectives on the construct of deep learning", "author": "Karianne Strauman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2884223569", "pdf_url": "", "doi": "", "handle": "", "repository_name": "BIBSYS Brage (BIBSYS (Norway))", "source_record_id": "W2884223569", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1278, "title": "Deep Learning Macroeconomics", "title_ar": "", "title_en": "Deep Learning Macroeconomics", "author": "Rafael Rockenbach da Silva Guimarães", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; deep-learning; transfer-learning; predictive-analytics", "keywords": "", "abstract": "Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Although macroeconomists already apply transfer learning when assuming a given a priori distribution in a Bayesian context, estimating a structural VAR with signal restriction and calibrating parameters based on results observed in other models, to name a few examples, advance in a more systematic transfer learning strategy in applied macroeconomics is the innovation we are introducing. We explore the proposed strategy empirically, showing that data from different but related domains, a type of transfer learning, helps identify the business cycle phases when there is no business cycle dating committee and to quick estimate a economic-based output gap. Next, since deep learning methods are a way of learning representations, those that are formed by the composition of multiple non-linear transformations, to yield more abstract representations, we apply deep learning for mapping low-frequency from high-frequency variables. The results obtained show the suitability of deep learning models applied to macroeconomic problems. First, models learned to classify United States business cycles correctly. Then, applying transfer learning, they were able to identify the business cycles of out-of-sample Brazilian and European data. Along the same lines, the models learned to estimate the output gap based on the U.S. data and obtained good performance when faced with Brazilian data. Additionally, deep learning proved adequate for mapping low-frequency variables from high-frequency data to interpolate, distribute, and extrapolate time series by related series.", "source_url": "https:\/\/openalex.org\/W4226128270", "pdf_url": "", "doi": "10.48550\/arxiv.2201.13380", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W4226128270", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1279, "title": "ENHANCED DEEP LEARNING NEURAL NETWORKS FOR CLASSIFICATION AND FORECASTING PROBLEMS IN EMBEDDED SYSTEMS", "title_ar": "", "title_en": "ENHANCED DEEP LEARNING NEURAL NETWORKS FOR CLASSIFICATION AND FORECASTING PROBLEMS IN EMBEDDED SYSTEMS", "author": "Pentsos, Vasileios", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Southern Illinois University Carbondale", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; cnn; rnn; transformers; image-classification; ai-smart-cities", "keywords": "", "abstract": "The widespread adoption of deep learning has led to a surge in demand for the efficient deployment of Deep Neural Networks (DNNs) on embedded and resource-constrained systems. These platforms are increasingly tasked with performing complex inference workloads in real time, yet they must do so under strict limitations on energy consumption, computation, and memory resources. As DNN models grow in complexity to support advanced applications such as time-series forecasting and image classification, ensuring their efficient execution without compromising performancebecomes a key design challenge. This calls for tailored algorithmic and architectural solutions that can meet application-specific constraints while maximizing the benefits of modern hardware accelerators.This dissertation proposes a set of algorithmic-based approaches aimed at enhancing the efficiency and adaptability of deep learning models for embedded systems. The research centers on three primary contributions: (i) a hybrid Long Short-Term Memory (LSTM)-Transformer architecture optimized for multi-step residential power load forecasting, integrating sequence modeling and attention mechanisms for improved accuracy under training time constraints, (ii) a lightweight hierarchical deep neural network enhancement that augments baseline classifiers through cascading binaryConvolutional Neural Networks (CNNs) and Vision Transformers, achieving higher classification accuracy and reduced inference time, and (iii) an energy-aware scheduling framework for DNN inference, featuring dynamic batch size selection, GPU frequency adjustment, and concurrent task mapping across multi-GPU systems to minimize energy use without violating real-time deadlines.In summary, this dissertation presents a comprehensive approach to designing and managing deep learning workflows on embedded platforms, emphasizing performance-efficiency trade-offs. The proposed methods demonstrated significantimprovements in both predictive accuracy and resource utilization, compared to existing solutions, offering practical pathways for deploying DNNs in sustainable, computing environments.", "source_url": "https:\/\/openalex.org\/W7111621194", "pdf_url": "", "doi": "", "handle": "", "repository_name": "OpenSIUC (Southern Illinois University Carbondale)", "source_record_id": "W7111621194", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1280, "title": "Deep learning neural network for image processing", "title_ar": "", "title_en": "Deep learning neural network for image processing", "author": "Xue-Qing Ma", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; cnn; image-classification; speech-recognition", "keywords": "", "abstract": "Deep learning is a new research direction in the field of machine learning. It is a subclass of machine learning. Inspired by the way the human brain works, deep learning is a learning process that uses deep neural networks to address feature expression. In many neural network models, convolutional neural network (CNN) can be considered the most successful neural network model in recent years. It is widely used in computer vision, natural language processing, speech and image recognition because of its satisfying performance. However, CNN is a deep neural network, which contains many layers and parameters. Thus, it may take a long time and be very difficult to train the network. What is more, the accuracy of the neural network should also be guaranteed. In this dissertation, I tried to maximize the accuracy of CNN with the least amount of training time. I improved the accuracy of the CNN model by adding two more convolutional and pooling layers based on MATLAB, and used different optimization methods including Adam optimizer, dropout layers, batch normalization layers and adaptive learning rate to improve the network performance based on PyTorch. The experimental results showed that the test accuracy of the CNN in MATLAB improved by 3％ after adding layers and the final accuracy of the test dataset has reached 99.218％ by applying the optimization methods in PyTorch. In conclusion, the accuracy of convolution neural network can be significantly improved by adding more convolution layers with less time and the above optimization methods can also effectively improve the network performance.", "source_url": "https:\/\/openalex.org\/W3081926688", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W3081926688", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1281, "title": "Deep Learning, Neural Networks and Aerospace Applications", "title_ar": "", "title_en": "Deep Learning, Neural Networks and Aerospace Applications", "author": "Liting Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2901693003", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2901693003", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1282, "title": "Deep learning : neural networks for object detection", "title_ar": "", "title_en": "", "author": "Alejandro Garcia Miñano", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Autònoma de Barcelona", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2018, "language": "es", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; object-detection; medical-imaging", "keywords": "", "abstract": "En la actualidad se está llevando a cabo unan gran mejoría en los sistemas basados en detección de objetos, con el incremento de potencia de las GPUS, cada vez es más viable realizar entrenamientos en conjuntos de datos lo suficientemente grandes como para lograr una buena precisión, los detectores de una fase se están imponiendo ante los detectores de dos fases por su principal ventaja de obtener tiempos de inferencia mucho menores, esto abre una gran cantidad de usos para este tipo de detectores como pueden ser sistemas de vigilancia en tiempo real, control del tráfico. En este articulo se analiza el detector de una fase conocido como RetinaNet, que utiliza las técnicas ya conocidas por los detectores de una fase pero que introduce como novedad una función de error capaz de incrementar la precisión obtenida hasta este momento con los detectores de una fase, solventando el mayor problema que comparten todos ellos, que es el desbalanceo en la detección de objetos.", "source_url": "https:\/\/openalex.org\/W2912333791", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona)", "source_record_id": "W2912333791", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1283, "title": "DeepSR a Deep Learning Neural Network for Speech Reading", "title_ar": "", "title_en": "DeepSR a Deep Learning Neural Network for Speech Reading", "author": "Basuki Winoto", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Harvard University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; cnn; rnn; video-analysis; speech-recognition", "keywords": "", "abstract": "Speech reading or lip reading is the understanding of spoken language while watching the speaker. It is a natural extension to increase comprehension when hearing becomes challenging. One attentively observes the speaker’s mouth movement as it forms the word. Speech reading is a skill, in addition to hand sign language, that helps with comprehension. It is an interesting skill yet difficult to learn. This thesis describes the development of a deep learning model that translates mouth movement into words. The model stacks a Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) for learning the sequence of mouth images. The model is trained with a dataset from the GRID corpus. This corpus contains video recordings of 34 speakers with each speaks 1000 sentences in English. The trained model is capable of predicting words from the mouth video frames with a word accuracy of 52.25%, far exceeding human accuracy of 14.47%.", "source_url": "https:\/\/openalex.org\/W3081906677", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)", "source_record_id": "W3081906677", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1284, "title": "Deep Learning Neural Network Based on Image Filters", "title_ar": "", "title_en": "Deep Learning Neural Network Based on Image Filters", "author": "MD Sajib", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2022, "language": "en", "research_domain": "computer-vision", "research_subdomain": "", "categories": "deep-learning; computer-vision", "keywords": "", "abstract": "The picture is worth thousands of words, so it is necessary to enhance images.Removing noise from color images is a very active research scope in image processing.This paper presents the implementation and comparison of mean, median, gaussian, conservative, and Crimmins speckle removal.The effectiveness of the restoration filters is measured using performance metrics like the Peak signal to noise ratio (PSNR), Mean square error (MSE), and Structure Similarity Index Method (SSIM).Results and analysis show that mean, median, and gaussian are comparatively better than conservative and Crimmins in restoring the degraded image.The study also clarifies that filters SRCNN performs better all the time.", "source_url": "https:\/\/openalex.org\/W7215174292", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/5e4e8b25-374f-45e5-94fb-5e44dc39e5b4\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215174292", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1285, "title": "Image Denoising via Deep Learning Neural Networks and Transformers", "title_ar": "", "title_en": "Image Denoising via Deep Learning Neural Networks and Transformers", "author": "Jundong Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; computer-vision; transformers", "keywords": "image denoising, deep learning, residual learning, Transformers.", "abstract": "This thesis investigates the implementation of image denoising using Neural Networks (NN) in conjunction with wavelet transform techniques. For the purpose of data simulation, images are intentionally corrupted with additive white Gaussian noise (AWGN) at various noise levels. Additionally, the study includes the examination of both random noise and real-world noise scenarios. This research has an emphasis on implementing several novel neural network architectures and strategies. By integrating the wavelet transform, the research aims to leverage its multi-resolution analysis capability to further enhance denoising performance. The proposed methods underscore the ability of residual learning and Transformers to integrate both local and global features, which efficiently capture fine-grained local details and long-range global dependencies. Keywords: image denoising, deep learning, residual learning, Transformers.", "source_url": "https:\/\/openalex.org\/W7133094216", "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/160429\/2\/WangJundong.pdf", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W7133094216", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1286, "title": "Data Augmentation Methods for Deep Learning Neural Networks", "title_ar": "", "title_en": "Data Augmentation Methods for Deep Learning Neural Networks", "author": "Md. Sharif", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7215584607", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215584607", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1287, "title": "Image Data Augmentation Methods with Deep Learning Neural Networks", "title_ar": "", "title_en": "Image Data Augmentation Methods with Deep Learning Neural Networks", "author": "Mohammad Hossain", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "deep-learning; computer-vision; ai-applications; cnn; ai-agriculture", "keywords": "", "abstract": "Image data enhancement is a vital method in the field of deep learning for enhancing model performance and generalization capabilities.This thesis explores various image data augmentation methods and their effectiveness in improving the performance of deep learning neural networks.The study begins with an overview of image data augmentation techniques, including rotation, flipping, scaling, cropping, translation, brightness adjustment, contrast adjustment, and Gaussian noise addition.Each augmentation method is examined in detail, discussing its impact on the variety and richness of the training collection.Following the theoretical overview, a series of experiments are conducted using widely used deep learning models such as Convolutional Neural Networks (CNNs), VGG, and MobileNet.These experiments aim to evaluate the performance of different augmentation methods in terms of model accuracy, robustness, and generalization on benchmark datasets.The results of the experiments demonstrate the effectiveness of certain augmentation techniques in improving model performance, while also highlighting potential trade-offs and considerations for real-world applications.Additionally, the study investigates the impact of augmentation parameters such as magnitude, probability, and combination strategies on model training dynamics and convergence.This thesis provides valuable insights into the role of image data augmentation in deep learning and offers practical guidance for selecting and implementing augmentation methods to enhance the effectiveness of neural networks across various computer vision assignments.", "source_url": "https:\/\/openalex.org\/W7215158281", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/13972c2f-f64d-4f28-8b7c-b18f3a88a358\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215158281", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1288, "title": "Rhythm interpretation using deep learning neural networks", "title_ar": "", "title_en": "Rhythm interpretation using deep learning neural networks", "author": "Daniel Eidsvåg", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Norway", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "medical-ai", "research_subdomain": "ai-industry", "categories": "machine-learning; deep-learning; medical-ai; ai-applications; ai-industry", "keywords": "", "abstract": "Out-of-hospital cardiac arrest (OHCA) is a leading cause of death in the industrialized world, with an estimated annual incidence that varies by 52.5 (in Asia), 86.4 (in Europe), 98.1 (in North America), and 111.9 (in Australia) per 100,000 person-years. Lethal ventricular arrhythmias are the most frequent causes of OHCA. A defibrillation shock is an effective treatment, and early defibrillation is one of the key factors in survival from OHCA. However, chest compressions, ventilations, and drug play a key role in the treatment of cardiac arrest. Under resuscitation, automated external defibrillator (AED) require peri-shock pauses to analyze for a shockable rhythm. This peri-shock are associated with a decrease in survival to hospital discharge. The objective of this thesis is to determine if different artificial neural network (ANN) structures can be used as a classifier, to determine the underlying heart rhythm under chest compressions to remove peri-shock pauses during cardiac arrest. The analysis is conducted from data obtained from 394 OHCA patients, where two datasets were used. Both containing 3-second segments with clinical rhythm annotations resulting in 2446 and 422415 segments. Results in this thesis suggest that there is no clear best method in the different neural network methods for ECG data. However, FNN demonstrates the most promising results with an accuracy of 52.30%. This result emphasizes the problem regarding classification of ECG data with compression artifacts.", "source_url": "https:\/\/openalex.org\/W2756701130", "pdf_url": "", "doi": "", "handle": "", "repository_name": "BIBSYS Brage (BIBSYS (Norway))", "source_record_id": "W2756701130", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1289, "title": "Hand gesture recognition using deep learning neural networks", "title_ar": "", "title_en": "Hand gesture recognition using deep learning neural networks", "author": "Norah M. Alnaim", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Brunel University London", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; predictive-analytics; cnn; video-analysis; speech-recognition", "keywords": "", "abstract": "Human Computer Interaction (HCI) is a broad field involving different types of interactions including gestures.Gesture recognition concerns non-verbal motions used as a means of communication in HCI.A system may be utilised to identify human gestures to convey information for device control.This represents a significant field within HCI involving device interfaces and users.The aim of gesture recognition is to record gestures that are formed in a certain way and then detected by a device such as a camera.Hand gestures can be used as a form of communication for many different applications.It may be used by people who possess different disabilities, including those with hearing-impairments, speech impairments and stroke patients, to communicate and fulfil their basic needs.Various studies have previously been conducted relating to hand gestures.Some studies proposed different techniques to implement the hand gesture experiments.For image processing there are multiple tools to extract features of images, as well as Artificial Intelligence which has varied classifiers to classify different types of data.2D and 3D hand gestures request an effective algorithm to extract images and classify various mini gestures and movements.This research discusses this issue using different algorithms.To detect 2D or 3D hand gestures, this research proposed image processing tools such as Wavelet Transforms and Empirical Mode Decomposition to extract image features.The Artificial Neural Network (ANN) classifier which used to train and classify data besides Convolutional Neural Networks (CNN).These methods were examined in terms of multiple parameters such as execution time, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood, negative likelihood, receiver operating characteristic, area under ROC curve and root mean square.This research discusses four original contributions in the field of hand gestures.The first contribution is an implementation of two experiments using 2D hand gesture video where ten different gestures are detected in short and long distances using an iPhone 6 Plus with 4K resolution.The experiments are performed using WT and EMD for feature extraction while ANN and CNN for classification.The second contribution comprises 3D hand gesture video experiments where twelve gestures are recorded using holoscopic imaging system camera.The third contribution pertains experimental work carried out to detect seven common hand gestures.Finally, disparity experiments were performed using the left and the right 3D hand gesture videos to discover disparities.The results of comparison show the accuracy results of CNN being 100% compared to other techniques.CNN is clearly the most appropriate method to be used in a hand gesture system.", "source_url": "https:\/\/openalex.org\/W3085860926", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Brunel University Research Archive (BURA) (Brunel University London)", "source_record_id": "W3085860926", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1290, "title": "REFINING DEEP LEARNING NEURAL NETWORKS FOR AUTONOMOUS VEHICLE NAVIGATION", "title_ar": "", "title_en": "REFINING DEEP LEARNING NEURAL NETWORKS FOR AUTONOMOUS VEHICLE NAVIGATION", "author": "Marcea Ascencio", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Naval Postgraduate School", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; cnn; image-classification; ai-agriculture", "keywords": "", "abstract": "Machine learning methods have recently increased in appearance in navigation and guidance applications by means of image classification. This thesis sought to advance the ongoing Electrical and Computer Engineering (ECE) Control Systems and Robotics Laboratory project in developing a system that will autonomously navigate across the Naval Postgraduate School (NPS) campus. In pursuit of providing a robust navigation and guidance solution to an autonomous robotic vehicle, a convolutional neural network (CNN) was trained to classify significant objects around NPS. In addition to increasing the number of objects that the neural network could classify, the network was also trained with varying image augmentation techniques applied to the training and validation images. A variety of tests were performed to evaluate the accuracy of the model when exposed to different objects and regions throughout the campus. The tests also included running the image classification model against images that were altered with crop, blur, rotation, and noise. The results demonstrated high classification accuracy and asserted that the output was robust when faced with poor image quality. This work established a strong baseline for more CNN output integration into the navigation and guidance solution of the robotic vehicle.", "source_url": "https:\/\/openalex.org\/W3197256413", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)", "source_record_id": "W3197256413", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1291, "title": "On the Optimal Deployment of Deep Learning Neural Networks on Field Programmable Gate Arrays", "title_ar": "", "title_en": "On the Optimal Deployment of Deep Learning Neural Networks on Field Programmable Gate Arrays", "author": "Ahmad Shawahna", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "King Fahd University of Petroleum and Minerals", "university_source": "repository_host", "college": "", "department": "", "country": "Saudi Arabia", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7167161047", "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/142340\/1\/Dissertation_Shawahna.pdf", "doi": "", "handle": "", "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)", "source_record_id": "W7167161047", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1292, "title": "Deep Learning Neural Networks-Based Edge Detection in Mechanical Components", "title_ar": "", "title_en": "Deep Learning Neural Networks-Based Edge Detection in Mechanical Components", "author": "Rahman", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "computer-vision", "research_subdomain": "object-detection", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; cnn; object-detection; ai-industry", "keywords": "", "abstract": "For the past few decades, the traditional deep neural network was one of the vital parts of machine learning methods, but in recent years the trend has been changed with the development of machine vision technology.Nowadays convolutional neural network is used in most image detection-related sectors.Detecting the special features from the images such as hole detection, edge detection, or detection of any corners can be easily and effectively done using the convolutional neural network.The system can estimate and identify the location of possible objects for object detection.This research aims to detect the special features from the images and determine the coordinates of the edges then convey those data to the industrial computer, which is used for the accurate assembling of the mechanical parts in the industry.The research is started with capturing the images of the mechanical disc.Next, the targeted parts of the images subjected to be detected are labeled using image labeling tools.In the next step, all the images are used in training.After the training, the weight value obtained from the training is used in the python code for detecting the features.A graphical user interface is made for operating with the data and result demonstration.In the result section, all the required edges are identified correctly in the first step.In the second step, the mean distances between the two edges are determined using the Euclidean method.Lastly, the holes are detected and the center and the coordinates of the center of the holes are also identified.", "source_url": "https:\/\/openalex.org\/W3184637092", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W3184637092", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1293, "title": "Deep Learning Neural Network based Survival Analysis: Application in Brain Stroke", "title_ar": "", "title_en": "Deep Learning Neural Network based Survival Analysis: Application in Brain Stroke", "author": "Nasrin Someeh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3213784345", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3213784345", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1294, "title": "Customer Move Prediction In Electricity Sales Using Deep Learning Neural Networks", "title_ar": "", "title_en": "Customer Move Prediction In Electricity Sales Using Deep Learning Neural Networks", "author": "J. Laakso", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Tampere University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3035970992", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Tampere University Institutional Repository (Tampere University)", "source_record_id": "W3035970992", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1295, "title": "Analyzes of Braİn Blood Vessels Using Deep Learning Neural Networks", "title_ar": "", "title_en": "", "author": "Tuğçe Göksu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "FSM Vakif University", "university_source": "repository_host", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2022, "language": "tr", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "deep-learning; computer-vision; cnn; image-segmentation", "keywords": "", "abstract": "Çok katmanlı ileri beslemeli bir yapay sinir ağı modeli olan evrişimsel sinir ağı (ing: Convolutional Neural Network - CNN) özellikle görüntüler üzerinden özellik çıkarılması ve analizlerinin yapılması aşamasında sıklıkla kullanılmaktadır. Yapılan bu tez çalışmasında, iki boyutlu olarak elde edilen gerçek beyin damar ağı görüntüleri kullanılarak, derin öğrenme sinir ağı tabanlı otomatik damar ağı bölütlemesi gerçekleştirilen bir sistem ortaya konulmuştur. Görüntülerdeki damar ağı bölütlemesi için üç farklı derin öğrenme sinir ağı mimarisi kullanılmıştır. Kullanılan derin öğrenme sinir ağı mimarileri sırası ile Otomatik kodlayıcı (AutoEncoder), U-Net ve ResNet+U-Net ağlarıdır. Otomatik bölütleme adımında kullanılan derin öğrenme sinir ağı mimarilerinin eğitimi için oluşturulan veri setinde, eğitim için 1806 adet test için ise 34 adet görüntü kullanılarak eğitim ve test işlemi tamamlanmıştır. Üç ağ mimarisinden elde edilen eğitilmiş ağ modeli ile yapılan test işleminde en yüksek doğruluk başarım değerini ,31 ile ResNet+U-Net ağı vermiştir. Bölütleme işleminden sonra, vasküler analiz aşamasından önce, bölütlenmiş görüntüler üzerinde ikilileştirme (ing: binarization) işlemi uygulanarak, bölütlenmiş görüntülerdeki sadece beyin damar ağının belirginleştirilmesi sağlanmıştır. Tez çalışmasının bir sonraki aşamasında ise bölütlenmiş beyin damar ağı görüntüleri kullanılarak vasküler analiz işlemi gerçekleştirilmiştir. Yapılan vasküler analiz ile beyin damar ağının, vasküler alan fraksiyonu, dallanma noktası sayısı (ing: branching point), dal sayısı (ing: branch number) ve vasküler uzunluk (damar uzunluğu) metrikleri hesaplanmıştır. Elde edilen sonuçlar, medikal alanda kullanılan angiotool ve fiji uygulamaları ile karşılaştırılmıştır. Doğruluk, birleşimlerin kesişimi (ing: Intersection Over Union-IoU), zar katsayısı (ing: dice coefficient), kesinlik (ing: precision) ve duyarlılık (ing: recall) değerlendirme kriterleri ile değerlendirmeler yapılmıştır. Tez çalışması kapsamında yazılan uygulama, var olan analiz uygulamaları (Angiotool ve Fiji makrosu) ile aynı veri seti kullanılarak test edilerek sonuçları karşılaştırılmıştır. Analiz sonuçları, tez çalışmasında yapılan uygulamanın medikal alanda kullanılan diğer uygulamalara kıyasla değerlendirme metriklerine göre daha iyi sonuçlar verdiğini göstermektedir.", "source_url": "https:\/\/openalex.org\/W7140350108", "pdf_url": "https:\/\/acikerisim.fsm.edu.tr\/bitstreams\/adbd5ff2-2ec2-4696-a4f1-5e9c2718c519\/download", "doi": "", "handle": "", "repository_name": "DSpace@FSM (FSM Vakif University)", "source_record_id": "W7140350108", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1296, "title": "Adaptive Second-order Derivative Approximate Greatest Descent Optimization for Deep Learning Neural Networks", "title_ar": "", "title_en": "Adaptive Second-order Derivative Approximate Greatest Descent Optimization for Deep Learning Neural Networks", "author": "Hong Hui Tan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Curtin University", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "", "categories": "deep-learning", "keywords": "", "abstract": "Backpropagation using Stochastic Diagonal Approximate Greatest Descent (SDAGD) is a novel adaptive second-order derivative optimization method in updating weights of deep learning neural networks. SDAGD applies two-phase switching strategy to seek for solution at far using long-term optimal trajectory and automatically switch to Newton method when nearer to optimal solution. SDAGD has the advantages of steepest training roll-off rate, adaptive adjustment of step-length and the ability to deal with vanishing gradient issues in deep architecture.", "source_url": "https:\/\/openalex.org\/W3008992957", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eSpace (Curtin University)", "source_record_id": "W3008992957", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1297, "title": "Medical image analysis with machine learning techniques and deep learning neural networks", "title_ar": "", "title_en": "", "author": "Γεώργιος Γιαννουλόπουλος ; Giannoulopoulos, George", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Piraeus", "university_source": "repository_host", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2020, "language": "el", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; medical-imaging", "keywords": "", "abstract": "Οι πρόσφατες εξελίξεις στον τομέα της μηχανικής μάθησης στη μηχανική όραση θα εξεταστούν ,ενώ ένα υποσύνολο αυτών των αλγορίθμων θα δοκιμαστεί στο πρόβλημα της ταξινόμησης δερματικών παθήσεων. Πιο συγκεκριμένα, η απόδοση των βαθιών νευρωνικών δικτύων στη ταξινόμηση εικόνων θα παρουσιαστεί και δοκιμαστεί. Τέλος, μια συγκεκριμένη οικογένεια βαθιών νευρωνικών δικτύων που λέγονται EfficientNets θα εκπαιδευτεί στο σύνολο δεδομένων του ISIC challenge του 2019.", "source_url": "https:\/\/openalex.org\/W3081787540", "pdf_url": "", "doi": "10.26267\/unipi_dione\/239", "handle": "", "repository_name": "Dione (University of Piraeus)", "source_record_id": "W3081787540", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1298, "title": "PLACE-BASED NAVIGATION FOR AUTONOMOUS VEHICLES WITH DEEP LEARNING NEURAL NETWORKS", "title_ar": "", "title_en": "PLACE-BASED NAVIGATION FOR AUTONOMOUS VEHICLES WITH DEEP LEARNING NEURAL NETWORKS", "author": "Magee, Ashleigh", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Naval Postgraduate School", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; robotics; transfer-learning; cnn; image-classification", "keywords": "", "abstract": "Accurate navigation is crucial to successfully deploying autonomous vehicles but is often limited by GPS reliance. The purpose of this thesis was to develop an image-based navigation solution that does not require GPS. This work builds on an ongoing research project, which is to develop a mobile robot that can navigate anywhere on the Naval Postgraduate School campus, inside and outside of buildings. This work focused on indoor navigation using image classification, and images were classified using a convolutional neural network (CNN). Transfer learning was used to reduce CNN training time and increase learning efficiency. The trained CNN was integrated into a waypoint loop algorithm that ran autonomously on the robot, stopping when it had correctly identified its desired location. Tests of the CNN classification showed a high success rate, but that it was also susceptible to variations in light and similar types of images. Increasing the data set will improve the classification results and allow for use in a variety of applications. This system will also benefit from improved indoor localization techniques.", "source_url": "https:\/\/openalex.org\/W7112451897", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)", "source_record_id": "W7112451897", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1299, "title": "Autonomous control of a mobile robot with incremental deep learning neural networks", "title_ar": "", "title_en": "Autonomous control of a mobile robot with incremental deep learning neural networks", "author": "Isabella Glöde", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "ai-industry", "categories": "deep-learning; robotics; ai-applications; ai-industry", "keywords": "", "abstract": "Over the last few years autonomous driving had an increasingly strong impact on the automotive industry. This created an increased need for artificial intelligence algo- rithms which allow for computers to make human-like decisions. However, a compro- mise between the computational power drawn by these algorithms and their subsequent performance must be found to fulfil production requirements. In this thesis incremental deep learning strategies are used for the control of a mobile robot such as a four wheel steering vehicle. This strategy is similar to the human approach of learning. In many small steps the vehicle learns to achieve a specific goal. The usage of incremental training leads to growing knowledge-base within the system. It also provides the opportunity to use older training achievements to improve the system, when more training data is available. To demonstrate the capabilities of such an algorithm, two different models have been formulated. First, a more simple model with counter wheel steering, and second, a more complex, nonlinear model with independent steering. These two models are trained incrementally to follow different types of trajectories. Therefore an algorithm was established to generate useful initial points. The incremental steps allow the robot to be positioned further and further away from the desired trajectory in the environ- ment. Afterwards, the effects of different trajectory types on model behaviour are investigated by over one thousand simulation runs. To do this, path planning for straight lines and circles are introduced. This work demonstrates that even simulations with simple network structures can have high performance.", "source_url": "https:\/\/openalex.org\/W3160493819", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W3160493819", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1300, "title": "A Deep Learning (Neural Network) Approach for ECG Heartbeat Classification: Arrhythmia Detection", "title_ar": "", "title_en": "A Deep Learning (Neural Network) Approach for ECG Heartbeat Classification: Arrhythmia Detection", "author": "B.E. Salami", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "deep-learning; medical-ai; cnn", "keywords": "", "abstract": "Nearly 17 million people die every year due to cardiovascular disease according to World Health Organization and one of the diseases associated with the human heart is arrhythmia.Arrhythmia is the irregularity and abnormality of heartbeat rhythm.To detect arrhythmia, ECG (Electrocardiogram) is used for diagnosing it, but in order to interpret well ECG signals, many automated analyses method have been proposed over the years but not all gave a satisfactory result.However, Deep learning especially Convolutional Neural Network has been successfully used for the detection and classification of ECG signal because it is an end to end model, which means it does not require pre-processing before network training except normalizing the dataset.In this thesis, a 1D-Convolutional Neural Network model (1D-CNN) was proposed, which is mainly used for signal and time-series data.A total of 7636 consisting of 5 different classes of arrhythmias were extracted which includes Normal sinus rhythm n, Atrial Premature beat, Premature ventricular contraction, Atrial fibrillation, and Atrial flutter from the MIT -BIH Arrhythmia database.By experimenting with 5 different 1D-CNN layered model, different results were achieved.The results obtained all showed that increasing or decreasing the number of layers with respect to changing the values of hyperparameters can affect performance results of a network. .", "source_url": "https:\/\/openalex.org\/W3112906421", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/c66b8559-4efd-4f89-8416-963a6dc5b6c6\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W3112906421", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1301, "title": "Deep Learning: Neural Network Applications and Architectures in Sequence and Image Classification", "title_ar": "", "title_en": "Deep Learning: Neural Network Applications and Architectures in Sequence and Image Classification", "author": "Alexander Stec", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "deep-learning; computer-vision; image-classification", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3005728568", "pdf_url": "", "doi": "10.21985\/n2-0bg0-v403", "handle": "", "repository_name": "", "source_record_id": "W3005728568", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1303, "title": "Sentiment analysis with machine learning", "title_ar": "", "title_en": "", "author": "Αικατερίνη Χριστοπούλου", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2017, "language": "el", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; data-mining; sentiment-analysis", "keywords": "", "abstract": "H συνεχόμενη ανάπτυξη του διαδικτύου δημιούργησε νέους τρόπους επικοινωνίας. Τα social media παράγουν ένα τεράστιο όγκο δεδομένων σε μορφή κειμένου, τα οποία παρέχουν πληροφορίες για τις απόψεις και τα συναισθήματα των χρηστών. Σκοπός της Εξόρυξης Δεδομένων (Data Mining) και Ανάλυσης Συναισθήματος (Sentiment Analysis) είναι η επεξεργασία των πληροφοριών αυτών και η εξαγωγή συμπερασμάτων ως προς τα συναισθήματα των χρηστών και τη πολικότητα του κειμένου. Το οποίο είναι πολύ χρήσιμο κυρίως στην εξέλιξη και ανάπτυξη των εταιρειών. Η παρούσα διπλωματική εργασία μελετάει τις τεχνικές και τα εργαλεία της Ανάλυσης Συναισθήματος. Έπειτα, εξετάζονται οι κατηγορίες των τεχνικών Ανάλυσης Συναισθήματος, οι οποίες είναι οι τεχνικές με λεξικά, τεχνικές με επιβλεπόμενη και μη-επιβλεπόμενη μηχανική μάθηση. Στη συνέχεια γίνεται μελέτη και σύγκριση διαφορετικών μοντέλων κατηγοριοποίησης κειμένων από σελίδες κριτικής ταινιών (IMDb). Αλγόριθμοι που θα χρησιμοποιηθούν είναι ο Πολυωνυμικός «Αφελής» Bayes (Multinomial Naive Bayes) και οι Μηχανές Διανυσμάτων Υποστήριξης (SVM). Τα μοντέλα κατηγοριοποίησης υλοποιούνται μέσω του προγραμματιστικού περιβάλλοντος Weka.", "source_url": "https:\/\/openalex.org\/W7162359589", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Νημερτής", "source_record_id": "W7162359589", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1304, "title": "Sentiment Analysis Using Machine Learning Techniques", "title_ar": "", "title_en": "Sentiment Analysis Using Machine Learning Techniques", "author": "Abinash Tripathy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis", "keywords": "", "abstract": "Before buying a product, people usually go to various shops in the market, query about the product, cost, and warranty, and then finally buy the product based on the opinions they received on cost and quality of service. This process is time consuming and the chances of being cheated by the seller are more as there is nobody to guide as to where the buyer can get authentic product and with proper cost. But now-a-days a good number of persons depend upon the on-line market for buying their required products. This is because the information about the products is available from multiple sources; thus it is comparatively cheap and also has the facility of home delivery. Again, before going through the process of placing order for any product, customers very often refer to the comments or reviews of the present users of the product, which help them take decision about the quality of the product as well as the service provided by the seller. Similar to placing order for products, it is observed that there are quite a few specialists in the field of movies, who go though the movie and then finally give a comment about the quality of the movie, i.e., to watch the movie or not or in five-star rating. These reviews are mainly in the text format and sometimes tough to understand. Thus, these reports need to be processed appropriately to obtain some meaningful information. Classification of these reviews is one of the approaches to extract knowledge about the reviews. In this thesis, different machine learning techniques are used to classify the reviews. Simulation and experiments are carried out to evaluate the performance of the proposed classification methods. It is observed that a good number of researchers have often considered two different review datasets for sentiment classification namely aclIMDb and Polarity dataset. The IMDb dataset is divided into training and testing data. Thus, training data are used for training the machine learning algorithms and testing data are used to test the data based on the training information. On the other hand, polarity dataset does not have separate data for training and testing. Thus, k-fold cross validation technique is used to classify the reviews. Four different machine learning techniques (MLTs) viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA) are used for the classification of these movie reviews. Different performance evaluation parameters are used to evaluate the performance of the machine learning techniques. It is observed that among the above four machine learning algorithms, RF technique yields the classification result, with more accuracy. Secondly, n-gram based classification of reviews are carried out on the aclIMDb dataset...", "source_url": "https:\/\/openalex.org\/W2735996418", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2735996418", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1305, "title": "Machine Learning for Sentiment Analysis of Children's Diaries", "title_ar": "", "title_en": "Machine Learning for Sentiment Analysis of Children's Diaries", "author": "Marc J. de Vries", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Utrecht University", "university_source": "repository_host", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; robotics; sentiment-analysis", "keywords": "", "abstract": "In this thesis, the automatic detection of sentiments expressed by children using machine learning is investigated. The automatic sentiment detection is used within a robotic companion that helps children with their daily struggles of living with Type 1 diabetes. The problem statement that guides this research is: To what extent is it possible to correctly classify the sentiment of a Dutch child’s diary entry by means of automatic text analysis? Results show that machine learning models yield a significantly higher performance than a symbolic sentiment scoring algorithm. Machine learning models have shown to be better at capturing context and at capturing complex negations. The usage of an ordinal or time-correlated adaptation of a machine learning model is evaluated as well, but results show that such a model does not have an advantage over a regular machine learning model. Additionally, a new algorithm for semantic normalization on top of standard morphological normalization is introduced in this thesis. Results show that using this newly created step consistently improves the performance in the current study. The new algorithm for semantic normalization is especially useful in this thesis, because the dataset is sparse and contains highly infrequent words.", "source_url": "https:\/\/openalex.org\/W2735422745", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Utrecht University Repository (Utrecht University)", "source_record_id": "W2735422745", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1306, "title": "Machine Learning Methods For Sentiment Analysis", "title_ar": "", "title_en": "", "author": "Олександра Дмитрівна Рябова", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Odesa I.I.Mechnikov National University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2021, "language": "uk", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis", "keywords": "", "abstract": "Галузь обробки природної мови(Natural Language Processing) зазнала швидкого розвитку за останнє десятирiччя. В першу чергу, такий стрiмкий розвиток став можливим завдяки велетенським обсягам оцифрованих та промаркiрованих даних та потужним комп’ютерам з великою обчислювальною потужнiстю. Цi два фактори є суттєвими для застосування методiв машинного навчання зi вчителем, якi довели свою ефективнiсть у порiвняннi з методами машинного навчання без учителя у бiльшостi задач обробки природньої мови. Проте наявнiсть маркiрованих даних завжди була вагомим обмежувальним фактором для цих методiв. Отже, роздивимось таке питання - чи можна створити такi обставини, за яких завдання обробки природної мови можна буде вирiшувати так само ефективно, але з використанням меншої кiлькостi ресурсiв?", "source_url": "https:\/\/openalex.org\/W7200396962", "pdf_url": "", "doi": "", "handle": "", "repository_name": "INSTITUTIONAL REPOSITORY OF ODESSA NATIONAL UNIVERSITY NAMED I.I. Mechnykova (Odesa I.I.Mechnikov National University)", "source_record_id": "W7200396962", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1307, "title": "Sentiment analysis on Twitter data using machine learning", "title_ar": "", "title_en": "Sentiment analysis on Twitter data using machine learning", "author": "Ravikumar Patel", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Laurentian University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; computer-vision; nlp; data-mining; sentiment-analysis; speech-recognition", "keywords": "", "abstract": "In the world of social media people are more responsive towards product or certain events that are currently occurring. This response given by the user is in form of raw textual data (Semi Structured Data) in different languages and terms, which contains noise in data as well as critical information that encourage the analyst to discover knowledge and pattern from the dataset available. This is useful for decision making and taking strategic decision for the future market. To discover this unknown information from the linguistic data Natural Language Processing (NLP) and Data Mining techniques are most focused research terms used for sentiment analysis. In the derived approach the analysis on Twitter data to detect sentiment of the people throughout the world using machine learning techniques. Here the data set available for research is from Twitter for world cup Soccer 2014, held in Brazil. During this period, many people had given their opinion, emotion and attitude about the game, promotion, players. By filtering and analyzing the data using natural language processing techniques, and sentiment polarity has been calculated based on the emotion word detected in the user tweets. The data set is normalized to be used by machine learning algorithm and prepared using natural language processing techniques like Word Tokenization, Stemming and lemmatization, POS (Part of speech) Tagger, NER (Name Entity recognition) and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK), which is openly available for academic as well as for research purpose. Derived algorithm extracts emotional words using WordNet with its POS (Part-of-Speech) for the word in a sentence that has a meaning in current context, and is assigned sentiment polarity using ‘SentWordNet’ Dictionary or using lexicon based method. The resultant polarity assigned is further analyzed using Naïve Bayes and SVM (support vector Machine) machine learning algorithm and visualized data on WEKA platform. Finally, the goal is to compare both the results of implementation and prove the best approach for sentiment analysis on social media for semi structured data.", "source_url": "https:\/\/openalex.org\/W7071399435", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Lu Zone Ul (Laurentian University)", "source_record_id": "W7071399435", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1308, "title": "Sentiment Analysis Using Supervised Machine Learning Technique", "title_ar": "", "title_en": "Sentiment Analysis Using Supervised Machine Learning Technique", "author": "Jitendra Kumar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis", "keywords": "", "abstract": "At present time, People are free to share their thoughts and idea over the Internet. These thoughts and comments help to understand the current scenario of a person, country or mass. But the problem is that the available data is huge to understand. So to summarize the data and to get a proper essence of the data an approach is needed that is called Sentiment Analysis. It is all about to analyze the human comments, opinions, ideas or sentiments. Sentiment analysis is having various application in different domain due to the exponential growth of Internet users. Different approaches are their to analyze the human comments or sentiments. In first work, CountVectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF) approach have been used to assign the numerical value to each word. After that different Machine Learning Classifiers are used to classify the sentiments. Support vector machine (SVM) gives good result and includes less error. In second work, SentiWordNet is used to assign the weight to each word. The contributions of the work are: (a) Generate feature weight model using SentiWordNet 3.0, (b) assign feature weight to every feature according to POS tag, and (c) suitable combination selection of adj, adv, verb and noun to give better results. The features are applied to the classifier and 74% accuracy is achieved using SVM with 10 folds. Four Performance metrics are used accuracy, precision, recall, and F1-score.", "source_url": "https:\/\/openalex.org\/W2801669976", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2801669976", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1309, "title": "Sentiment Analysis of Twitter Data Using Machine Learning and Deep Learning Methods", "title_ar": "", "title_en": "Sentiment Analysis of Twitter Data Using Machine Learning and Deep Learning Methods", "author": "Kundan Reddy Manda", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; nlp; generative-ai; cnn; rnn; sentiment-analysis", "keywords": "", "abstract": "Background: Twitter, Facebook, WordPress, etc. act as the major sources of information exchange in today's world. The tweets on Twitter are mainly based on the public opinion on a product, event or topic and thus contains large volumes of unprocessed data. Synthesis and Analysis of this data is very important and difficult due to the size of the dataset. Sentiment analysis is chosen as the apt method to analyse this data as this method does not go through all the tweets but rather relates to the sentiments of these tweets in terms of positive, negative and neutral opinions. Sentiment Analysis is normally performed in 3 ways namely Machine learning-based approach, Sentiment lexicon-based approach, and Hybrid approach. The Machine learning based approach uses machine learning algorithms and deep learning algorithms for analysing the data, whereas the sentiment lexicon-based approach uses lexicons in analysing the data and they contain vocabulary of positive and negative words. The Hybrid approach uses a combination of both Machine learning and sentiment lexicon approach for classification. Objectives: The primary objectives of this research are: To identify the algorithms and metrics for evaluating the performance of Machine Learning Classifiers. To compare the metrics from the identified algorithms depending on the size of the dataset that affects the performance of the best-suited algorithm for sentiment analysis. Method: The method chosen to address the research questions is Experiment. Through which the identified algorithms are evaluated with the selected metrics. Results: The identified machine learning algorithms are Naïve Bayes, Random Forest, XGBoost and the deep learning algorithm is CNN-LSTM. The algorithms are evaluated with respect to the metrics namely precision, accuracy, F1 score, recall and compared. CNN-LSTM model is best suited for sentiment analysis on twitter data with respect to the selected size of the dataset. Conclusion: Through the analysis of results, the aim of this research is achieved in identifying the best-suited algorithm for sentiment analysis on twitter data with respect to the selected dataset. CNN-LSTM model results in having the highest accuracy of 88% among the selected algorithms for the sentiment analysis of Twitter data with respect to the selected dataset.", "source_url": "https:\/\/openalex.org\/W2974440996", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2974440996", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1310, "title": "Sentiment Analysis Using Hybrid Machine Learning Technique", "title_ar": "", "title_en": "Sentiment Analysis Using Hybrid Machine Learning Technique", "author": "Nishant Kumar", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; nlp; expert-systems; sentiment-analysis", "keywords": "", "abstract": "It is observed that consumers often share their opinion, views or feeling about any term used on social network in the form of reviews, comments or feedback. Those feedbacks given by end users have a great impact for evolution of new version of any product. Due to this trend in social media in recent years, sentiment analysis has become an important concern for theoreticians and practitioners Moreover reviews are often written in natural language and are mostly unstructured. Thus, to obtain any meaningful information from these reviews, it needs to be processed. Due to large size of data it is impossible to process this information manually. Hence machine learning algorithms are considered for analysis. Since data are unstructured in nature, unsupervised machine learning algorithm can be helpful in solving this problem. But unsupervised methods have less accuracy; hence not acceptable. In this study, a hybrid machine learning approach is adopted to automatically find the requirements for next version of software. Also some reviews neither belong to positive cluster nor to negative. They mixed reaction or feeling about some topics. Those problem associated with NLP is solved using hybrid technique of the fuzzy c-means and ANN. Moreover in this study, different methods of unsupervised machine leaning algorithm are implemented and their results are compared with each other. The best outcome is used to train the neural network. By using this hybridization technique, accuracy gets increased. And in later stage, this technique is applied to find the new requirement of product.", "source_url": "https:\/\/openalex.org\/W2746211547", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2746211547", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1311, "title": "Arabic Sentiment Analysis using Machine Learning", "title_ar": "", "title_en": "Arabic Sentiment Analysis using Machine Learning", "author": "Sasi Fuad Atiyah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis; arabic-nlp", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2997475805", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2997475805", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1312, "title": "Using machine learning for sentiment analysis of Slovene web commentaries", "title_ar": "", "title_en": "Using machine learning for sentiment analysis of Slovene web commentaries", "author": "Klemen Kadunc", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; predictive-analytics; sentiment-analysis", "keywords": "", "abstract": "The purpose of this work is to develop a tool for sentiment analysis of user comments. Several machine learning classifiers were tested and multinomial naive Bayes turned out to be the best predictor. We tried several preprocessing techniques, especially those for web texts. The classifier was improved with a Slovene sentiment lexicon, which is a list of words and set phrases with a positive and a negative connotation. An English sentiment lexicon was manually translated into Slovene. The analysed corpus of user comments was manually annotated by three annotators; its entries were selected from some of the most visited Slovene news portals. Both the lexicon and the annotated corpus of user comments are the main contributions of this work.", "source_url": "https:\/\/openalex.org\/W2409903970", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2409903970", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1313, "title": "Domain independence of Machine Learning and lexicon based methods in sentiment analysis.", "title_ar": "", "title_en": "Domain independence of Machine Learning and lexicon based methods in sentiment analysis.", "author": "Meriton Xhymshiti", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; predictive-analytics; sentiment-analysis", "keywords": "", "abstract": "Sentiment analysis is a sub-area in the field of Natural Language Processing (NLP) and it aims at automatically detecting the polarity of an opinion expressed on a textual information. There are two main approaches for analyzing a sentiment and determining its polarity: Lexicon based approaches and Machine Learning approaches. A lexicon-based approach uses a dictionary of words together with a polarity label for each of these words to determine the sentiment polarity of a document (e.g positive, negative or neutral). A machine learning approach trains a classifier in a labelled dataset and predicts sentiments using the model it creates. This paper presents a comparison on the domain independence of a ML system and lexicon-based system for Dutch sentiment analysis. The main contribution of this paper is that we show that in absence of “good-quality” labelled data for training in a specific domain, a lexicon-based system can be as good as a ML system. The dataset that will be used is in Dutch language and consists of large datasets of product and clothing reviews crawled from bol.com and a small dataset of life memories of people collected by researchers at the University of Tilburg. Pattern will be used as a lexicon based method and Support Vector Machines as a machine learning method.", "source_url": "https:\/\/openalex.org\/W3041588576", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3041588576", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1314, "title": "Machine learning techniques for sentiment analysis and emotion recognition in natural language", "title_ar": "", "title_en": "Machine learning techniques for sentiment analysis and emotion recognition in natural language", "author": "Spyridon Kardakis", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; nlp; data-mining; sentiment-analysis", "keywords": "", "abstract": "The field of Textual Sentiment Analysis has been met with increased interest by the research community in recent years due to the rise of social media and the Internet. The vast amount of opinion-heavy user-generated content that is available to us, whether that is a product\/service review or an opinion on an event, shows that effective Sentiment Analysis is needed. However, automatic knowledge extraction about the opinion and emotional state of people can be a very challenging task. This thesis studies the fields of Machine Learning and Deep Learning in-depth, in order to perform Sentiment Analysis and by extension Emotion Recognition classification tasks. A novel Hidden Markov Model-based approach is proposed where a single model is trained for each class label with the help of clustering and a lexicon. Overall, the main goal is to present a variety of Machine Learning models, ranging from basic all the way to state-of-the-art approaches, and implement them in real-world datasets. Initially, the theory behind the aforementioned fields and the related literature is introduced. Then, we present the mathematical background of the proposed approaches as well as expand on their usage for text classification and its challenges. The task at hand is supervised text classification. Additionally, a survey of the available datasets in the Sentiment Analysis domain is performed and the field of Ensemble Learning is explored. Finally, we implement and evaluate the proposed models on benchmark datasets using k-fold cross-validation and come to conclusions regarding each algorithm’s ability to recognize peoples' opinions and emotions. From the experimental results it is observed that the proposed Hidden Markov Models and Deep Neural Networks with word embeddings achieve very high performance, proving that they are potent and suitable tools for Sentiment Analysis and classification tasks in general.", "source_url": "https:\/\/openalex.org\/W2985845076", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Νημερτής", "source_record_id": "W2985845076", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1315, "title": "Stock Price Movement Prediction Using Sentiment Analysis and Machine Learning", "title_ar": "", "title_en": "Stock Price Movement Prediction Using Sentiment Analysis and Machine Learning", "author": "Jenny Zheng Wang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "California State Polytechnic University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; data-mining; predictive-analytics; sentiment-analysis", "keywords": "", "abstract": "Stock price prediction is of strong interest but a challenging task to both researchers and investors. Recently, sentiment analysis and machine learning have been adopted in stock price movement prediction. In particular, retail investors’ sentiment from online forums has shown their power to influence the stock market. In this paper, a novel system was built to predict stock price movement for the following trading day. The system includes a web scraper, an enhanced sentiment analyzer, a machine learning engine, an evaluation module, and a recommendation module. The system can automatically select the best prediction model from four state-of-the-art machine learning models (Long Short-Term Memory, Support Vector Machine, Random Forest, and Extreme Boost Gradient Tree) based on the acquired data and the models’ performance. Moreover, stock market lexicons were created using large-scale text mining on the Yahoo Finance Conversation boards and natural language processing. Experiments using the top 30 stocks on the Yahoo users’ watchlists and a randomly selected stock from NASDAQ were performed to examine the system performance and proposed methods. The experimental results show that incorporating sentiment analysis can improve the prediction for stocks with a large daily discussion volume. Long Short-Term Memory model outperformed other machine learning models when using both price and sentiment analysis as inputs. In addition, the Extreme Boost Gradient Tree (XGBoost) model achieved the highest accuracy using the price-only feature on low-volume stocks. Last but not least, the models using the enhanced sentiment analyzer outperformed the VADER sentiment analyzer by 1.96%.", "source_url": "https:\/\/openalex.org\/W4390747363", "pdf_url": "", "doi": "10.15368\/theses.2021.98", "handle": "", "repository_name": "DigitalCommons - CalPoly (California State Polytechnic University)", "source_record_id": "W4390747363", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1316, "title": "Sentiment analysis in geo social streams by using machine learning technique", "title_ar": "", "title_en": "Sentiment analysis in geo social streams by using machine learning technique", "author": "Bikesh Twanabasu", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universitat Jaume I", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; computer-vision; nlp; sentiment-analysis", "keywords": "", "abstract": "Massive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiments from text. The rapid increase in the volume of sentiment rich data on the web has resulted in an increased interaction among researchers regarding sentiment analysis and opinion (Kaushik & Mishra, 2014). However, limited research has been conducted considering location as another dimension along with the sentiment rich data. In this work, we analyze the sentiments of Geotweets, tweets containing latitude and longitude coordinates, and visualize the results in the form of a map in real time. We collect tweets from Twitter using its Streaming API, filtered by English language and location (bounding box). For those tweets which don’t have geographic coordinates, we geocode them using geocoder from GeoPy. Textblob, an open source library in python was used to calculate the sentiments of Geotweets. Map visualization was implemented using Leaflet. Plugins for clusters, heat maps and real-time have been used in this visualization. The visualization gives an insight of location sentiments.", "source_url": "https:\/\/openalex.org\/W2795429583", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Repositori UJI (Universitat Jaume I)", "source_record_id": "W2795429583", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1317, "title": "Machine learning technicques for aspect based sentiment analysis", "title_ar": "", "title_en": "Machine learning technicques for aspect based sentiment analysis", "author": "Chen, Haonan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Nanyang Technological University", "university_source": "repository_host", "college": "", "department": "", "country": "Singapore", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; nlp; rnn; sentiment-analysis", "keywords": "", "abstract": "Aspect Based Sentiment Analysis (ABSA) is a field of study where sentiments on certain aspects or characteristics of entities are obtained, analyzed, and aggregated from text. Since ABSA facilitates analyzing sentiments at a fine-grained level, it has gained significant attention over the past few years. In particular, ABSA caters real-world applications such analyzing sentiments from product reviews, tweets and emails focusing solely on user intended aspects. In recent research, ABSA has predominantly leveraged on Machine Learning (ML) techniques such as Representation Learning, Kernel Methods and Deep Learning. This dissertation focuses on empirically evaluating two recently proposed ML techniques for ABSA – Support Vector Machine (SVM)-based and Recurrent Neural Networks (RNNs)-based approaches on several datasets. To this end, we re-implemented two state-of-the-art ABSA frameworks which use these classifiers and compared them on the aforementioned ABSA datasets in terms of both accuracy and efficiency. Through these large-scale evaluations, we infer the following: (i) SVMs produce accuracies which are comparable to that of RNNs but they are much computationally lighter, (ii) When there is a significant imbalance among the classes in a multi-class ABSA setting, RNNs perform much better than SVMs. Furthermore, we observe that RNN, as it leverages on word embeddings, are particularly more suited for semantics based ABSA. However, due to its huge computational demands (e.g., large number of GPU cores and high GPU memory), we could not explore the full realm of its performance on our experimental setup which had limited computing resources.", "source_url": "https:\/\/openalex.org\/W2750897301", "pdf_url": "", "doi": "", "handle": "", "repository_name": "DR-NTU (Nanyang Technological University)", "source_record_id": "W2750897301", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1319, "title": "Sentiment analysis and sarcasm detection in texts using machine learning approaches", "title_ar": "", "title_en": "Sentiment analysis and sarcasm detection in texts using machine learning approaches", "author": "Tianzheng Rong", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3194703718", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3194703718", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1320, "title": "An effective sentiment analysis using machine learning and swarm intelligence schemes", "title_ar": "", "title_en": "An effective sentiment analysis using machine learning and swarm intelligence schemes", "author": "T. Saravanan", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "India", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; optimization; sentiment-analysis", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3159760934", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Shodhganga", "source_record_id": "W3159760934", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1321, "title": "Sentiment Analysis of Twitter Data Using Machine Learning Techniques", "title_ar": "", "title_en": "Sentiment Analysis of Twitter Data Using Machine Learning Techniques", "author": "Rohit Joshi ; Rajkumar Tekchandani", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2807388816", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2807388816", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1322, "title": "Multiclass sentiment analysis: a comprehensive approach the use of machine learning", "title_ar": "", "title_en": "Multiclass sentiment analysis: a comprehensive approach the use of machine learning", "author": "Allisfrank dos Santos", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; data-mining; sentiment-analysis", "keywords": "", "abstract": "In the globalized world, the analysis of data generated from the most varied sources, especially the textual ones, has become of great importance for the acquisition of knowledge and information. In this respect, the Internet and social networks make up the main textual database. The Sentiment Analysis is a form of data mining in text format, and the purpose of this type of analysis is to identify and \/ or analyze users' opinions about an entity or about sentiment related to various topics. Several researchers have used the Sentiment Analysis to understand user behavior through polarity, which can be separated into two or three classes. However, the challenge ahead is to find ways that go beyond the traditional classification and achieve a more real analysis of the expressed feelings, exploring t he idea of multiclass analysis (through emotional classes). Based on these facts, this paper aims to study aspects of the Sentiment Analysis related to the number of classes of emotions to be analyzed, as well as the representation form of the texts to be submitted for classification. For this, classic Machine Learning algorithms (SVM, kNN and Naive Bayes) as well as vectorization techniques such as TF - IDF and Word2Vec were used. The results show that a reduced number of classes allied to the use of Word2Vec as a textual representation method improves the classification result, especially with the use of the SVM classifier, obtaining an accuracy of 58.8% for the emotional base and 68.6% for the basis of polarity.", "source_url": "https:\/\/openalex.org\/W7120433465", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W7120433465", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1323, "title": "Sentiment Analysis Of Twitter Texts Using Machine Learning Algorithms", "title_ar": "", "title_en": "", "author": "Hawar BARZENJI", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2021, "language": "tr", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis", "keywords": "", "abstract": "06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.", "source_url": "https:\/\/openalex.org\/W7163721174", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Sakarya Üniversitesi Kurumsal Açık Akademik Arşivi", "source_record_id": "W7163721174", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1324, "title": "Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis", "title_ar": "", "title_en": "Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis", "author": "Soujanya Poria", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Stirling", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "deep-learning; computer-vision; nlp; ai-applications; multimodal; sentiment-analysis; ai-education", "keywords": "", "abstract": "Emotions and sentiments play a crucial role in our everyday lives. They aid decision-making, learning, communication, and situation awareness in human-centric environments. Over the past two decades, researchers in artificial intelligence have been attempting to endow machines with cognitive capabilities to recognize, infer, interpret and express emotions and sentiments. All such efforts can be attributed to affective computing, an interdisciplinary field spanning computer science, psychology, social sciences and cognitive science. Sentiment analysis and emotion recognition has also become a new trend in social media, avidly helping users understand opinions being expressed on different platforms in the web. In this thesis, we focus on developing novel methods for text-based sentiment analysis. As an application of the developed methods, we employ them to improve multimodal polarity detection and emotion recognition. Specifically, we develop innovative text and visual-based sentiment-analysis engines and use them to improve the performance of multimodal sentiment analysis. We begin by discussing challenges involved in both text-based and multimodal sentiment analysis. Next, we present a number of novel techniques to address these challenges. In particular, in the context of concept-based sentiment analysis, a paradigm gaining increasing interest recently, it is important to identify concepts in text; accordingly, we design a syntaxbased concept-extraction engine. We then exploit the extracted concepts to develop conceptbased affective vector space which we term, EmoSenticSpace. We then use this for deep learning-based sentiment analysis, in combination with our novel linguistic pattern-based affective reasoning method termed sentiment flow. Finally, we integrate all our text-based techniques and combine them with a novel deep learning-based visual feature extractor for multimodal sentiment analysis and emotion recognition. Comparative experimental results using a range of benchmark datasets have demonstrated the effectiveness of the proposed approach.", "source_url": "https:\/\/openalex.org\/W2620364470", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Stirling Online Research Repository (University of Stirling)", "source_record_id": "W2620364470", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1325, "title": "optimization of sentiment analysis of multilingual text on social network using machine learning", "title_ar": "", "title_en": "optimization of sentiment analysis of multilingual text on social network using machine learning", "author": "Swati Sharma", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "India", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; sentiment-analysis", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3165193169", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Shodhganga", "source_record_id": "W3165193169", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1326, "title": "Essays on Time Series Forecasting and Machine Learning", "title_ar": "", "title_en": "Essays on Time Series Forecasting and Machine Learning", "author": "Frank, Johannes", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; transformers; ai-industry", "keywords": "", "abstract": "This dissertation investigates how modern machine learning (ML) methods, with a particular focus on multi-task learning (MTL) frameworks, can enhance forecasts in financial and macroeconomic contexts. Chapter 1 outlines the limitations of traditional econometric approaches in environments characterized by nonlinearity, structural breaks, and mixed-frequency data, and motivates the use of ML models capable of exploiting shared information across related prediction tasks. Chapter 2 evaluates the performance of temporal fusion transformers (TFT) for forecasting realized volatility (RV) of US stocks. It shows that models with sector-level pooling significantly outperform asset-specific or fully pooled models, and that TFT surpasses traditional volatility models and common ML benchmarks, especially in volatile market phases. Chapter 3 extends this approach by introducing a two-dimensional MTL framework to jointly forecast RV across industry portfolios and forecast horizons. Using long short-term memory (LSTM) models with structured parameter sharing, the study finds that sharing information across both dimensions improves forecast accuracy, particularly at short horizons. Chapter 4 applies MTL to macroeconomic nowcasting using disaggregated US labor market and inflation data. Results show that MTL enhances nowcast accuracy compared to single-task and traditional models, with the optimal degree of information sharing depending on task-specific heterogeneity. Chapter 5 embeds MTL into a mixed frequency data sampling (MIDAS) setting by combining it with different MIDAS techniques to nowcast sectoral employment using high-frequency indicators. Nonlinear MTL models, particularly those based on extreme gradient boosting (XGBoost), outperform benchmarks and highlight the value of preserving intra-month dynamics. Overall, the dissertation demonstrates that combining MTL with flexible ML architectures offers a powerful framework for improving forecast quality and stability in complex economic settings.", "source_url": "https:\/\/openalex.org\/W7109568429", "pdf_url": "", "doi": "10.25593\/open-fau-2528", "handle": "", "repository_name": "", "source_record_id": "W7109568429", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1327, "title": "Forecasting euro area inflation using time series and machine learning techniques", "title_ar": "", "title_en": "Forecasting euro area inflation using time series and machine learning techniques", "author": "Vyshnivska, Ruslana", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Johannes Kepler Universitat Linz", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "Machine learning models have been extensively used for forecasting inflation in individual countries, yet the number of studies focusing on the euro area is comparatively lower. The objective of this thesis is to compare the out-of-sample forecast performance of machine learning and traditional time series models on different forecast horizons for the euro area inflation. The focus is on core and headline inflation, as well as periods of high- and low-inflation. To this end, a comprehensive dataset was constructed to facilitate forecasting. The findings indicate that, in general, traditional time series models yield more accurate forecasts in the short and medium run. In contrast, machine learning models demonstrate higher forecast accuracy in the long run. This thesis also concludes that an increase in the number of predictors for machine learning models may reduce forecast performance when working with a small sample size. The findings further demonstrate that machine learning models may exhibit higher forecast accuracy for more volatile time series and during periods of high inflation. However, for forecasting core inflation in a low-inflation period, all models demonstrate similar performance, with no discernible pattern in forecast accuracy.", "source_url": "https:\/\/openalex.org\/W7113179973", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University Library Linz repository (Johannes Kepler Universitat Linz)", "source_record_id": "W7113179973", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1328, "title": "Time series forecasting with machine learning models informed by domain knowledge", "title_ar": "", "title_en": "", "author": "Bárbara Daniela Oliveira da Silva", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Universidade do Minho", "university_source": "repository_host", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2025, "language": "pt", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "A previsão de séries temporais é uma tarefa crítica em muitos setores, particularmente no setor financeiro. Neste setor a previsão de cashflow é indispensável, uma vez que permite a uma empresa planear e gerir as entradas e saídas de dinheiro de uma forma eficaz. No entanto, esta tarefa pode ser desafiante quando os dados são limitados e\/ou de pouca qualidade. Desta forma, esta dissertação tem como objetivo melhorar a previsão de séries temporais, particularmente o cashflow, através da integração do conhecimento de domínio em modelos de machine learning. Inicialmente, foram exploradas várias abordagens para integrar o conhecimento de domínio nos modelos de previsão. Estas técnicas incluíam a integração de conhecimento, ou alterar a função de perda, ou treinar um modelo para aprender as relações hierárquicas entre variáveis, ou utilizar propriedades adicionais acerca das variáveis, denominadas por meta-features. No entanto, devido à complexidade e incerteza envolvidas na tradução desse conhecimento para uma forma que o modelo pudesse aprender, foi adotada uma abordagem mais prática. Foi utilizada uma arquitetura de Neural ODE (Ordinary Differential Equation) na tentativa de aprender as dinâmicas temporais dos dados; no entanto, mostrou-se insuficiente por si só ao aprender dinâmicas mais complexas. Face a estes resultados, foi necessária uma arquitetura mais complexa. A arquitetura ODE-GRU, que combina a Neural ODE com a estrutura recorrente da GRU (Gated Recurrent Unit). Esta arquitetura foi capaz de aprender dinâmicas mais complexas, capturando também dependências a longo prazo nos dados. O objetivo final foi comparar os modelos com e sem conhecimento de domínio, de forma a avaliar a eficácia da incorporação do conhecimento de domínio na melhoria do desempenho dos modelos. Os resultados indicaram que, no contexto do conjunto de dados utilizado, a integração do conhecimento do domínio não conduziu a melhorias significativas no desempenho do modelo. Este resultado pode dever-se ao facto de o histórico de dados disponíveis ser limitado, salientando a importância de um conjunto de dados com um registo histórico mais longo para se obter melhores resultados em estudos futuros.", "source_url": "https:\/\/openalex.org\/W7170119064", "pdf_url": "", "doi": "", "handle": "", "repository_name": "RepositóriUM (Universidade do Minho)", "source_record_id": "W7170119064", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1329, "title": "Hybrid Machine Learning and Time-Series Modelling to Forecast Product Demand: A CRISP-DM Approach", "title_ar": "", "title_en": "Hybrid Machine Learning and Time-Series Modelling to Forecast Product Demand: A CRISP-DM Approach", "author": "Niklas Virtanen", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Vaasa", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; deep-learning; nlp; responsible-ai; ai-applications; predictive-analytics; sentiment-analysis; xai; ai-industry", "keywords": "", "abstract": "Efficient demand forecasting can yield significant competitive advantage for companies in today’s market, making it a crucial process in supply chain management. By exploring and implementing modern demand forecasting models, encompassing time-series, machine learning, deep learning and hybrid approaches, companies can gain benefits in important business areas such as strategic planning, operational efficiency and customer service. This research is done in collaboration with a case company, and it aims to identify and develop a product demand forecasting model that improves forecasting accuracy compared against a baseline approach and to identify and analyze key demand drivers that influence demand. To meet these goals, an industry standard CRISP-DM framework is utilized to develop and compare multiple time-series, machine learning and their hybrid combination models to find the best performing forecasting solution. Key demand drivers are identified and analyzed by combining SHAP values with statistical analysis. The results of this study indicate that a hybrid demand forecasting model combining Holt-Winters Exponential Smoothing and Elastic Net Regression outperformed the other models in the comparison and improved the forecasting accuracy significantly compared to the baseline approach. The results also suggest that product demand is driven by trend and seasonal factors, short-term sales pricing and momentum as well as macroeconomic leading indicators such as industry sentiment, interest rates and euro strength. Despite the significant improvement in forecasting accuracy and practical insights through the identified demand drivers, research limitations such as limited amount of training data, small test set and lack of true causal analysis of the relationships between features and demand are acknowledged to emphasize the need for future research. The development and evaluation of the HW-Elastic Net model contributes to current research by highlighting the importance of exploring hybrid models in demand forecasting, especially on small and noisy real-world datasets.", "source_url": "https:\/\/openalex.org\/W7198431739", "pdf_url": "https:\/\/osuva.uwasa.fi\/bitstreams\/6769593d-1bc1-41d8-9aac-273cac1b970a\/download", "doi": "", "handle": "", "repository_name": "Osuva (University of Vaasa)", "source_record_id": "W7198431739", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1330, "title": "Volatility homogenisation and machine learning for time series forecasting", "title_ar": "", "title_en": "Volatility homogenisation and machine learning for time series forecasting", "author": "Adam Kowalewski", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Melbourne", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml; predictive-analytics", "keywords": "", "abstract": "Volatility homogenisation is a technique of looking at a process at regular points in space. In other words, we are only interested when the process moves by a certain quantum. The intuition and empirical evidence behind this is that we ignore smaller movements which are just noise, while only concerning ourselves with larger movements which represent the information from the underlying process. In this vein, we have derived theoretical results showing volatility homogenisation as a means of estimating the drift and volatility of theoretical processes and verify these results by simulation. This demonstrates the ability of a “homogenised” process to retain salient information regarding the underlying process. Volatility homogenisation is then coupled, as a preprocessing step, with various machine learning techniques which yields greater forecasting accuracy than when the machine learning techniques are used without volatility homogenisation preprocessing. In addition to this, we develop volatility homogenisation kernels for machine learning kernel-based techniques such as support vector machines, relevance vector machines and Gaussian processes for machine learning. The volatility homogenisation kernel causes a kernel-based machine learning technique to utilise volatility homogenisation internally and, with it, obtain better predictions on forecasting the direction of a financial time series. In order to create and use the volatility homogenisation kernel, we have developed a solution to the problem of a kernel taking inputs which have dimensions of differing size while still maintaining a convex solution to the model for techniques such as support vector machines, for a given set of parameters. Furthermore, we have demonstrated the efficacy of volatility homogenisation as a way of successfully investing using a Kelly criterion strategy. The strategy makes use of the information inherent in a support vector machine model which uses a volatility homogenisation kernel in order to calculate the necessary parameters for the Kelly betting strategy. We also develop strategies which select additional features for the support vector machine through the use of a nearest neighbour strategy using various measures of association. Overall, volatility homogenisation is a robust strategy for the decomposition of a process which allows various machine learning techniques to discern the main driving process inherent in a financial time series, which leads to better forecasts and investment strategies.", "source_url": "https:\/\/openalex.org\/W2601293466", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Minerva Access (University of Melbourne)", "source_record_id": "W2601293466", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1331, "title": "Essays on forecasting time series with machine learning techniques", "title_ar": "", "title_en": "Essays on forecasting time series with machine learning techniques", "author": "Ali Lashgari", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Kansas State University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; data-mining; predictive-analytics; rnn; xai", "keywords": "", "abstract": "This dissertation consists of three essays on forecasting time series with machine learning techniques, delving into different financial and economic domains.The first essay investigates the forecasting accuracy of the S&P 500 stock market index during the pandemic, utilizing text mining and technical analysis.It finds that the LSTM model, which uses numerical data rather than financial news, offers superior accuracy in predicting price movements, showing the effectiveness of machine learning over traditional analysis methods.The second essay tests the hypothesis that the Federal Reserve responds to data revisions when setting monetary policy.To deal with the large number of data revisions that the Federal Reserve can potentially respond to, we use four machine learning techniques, Lasso, ridge regression, elastic net, and post-Lasso.When using our preferred method, elastic net, we conclude that the Federal Reserve responds to five types of data revision.We discuss the implications of this finding for theoretical macroeconomic models in the context of the signal extraction problem.The third essay addresses the predictability of West Texas Intermediate crude oil prices, influenced by macroeconomic factors.By comparing the performance of Long Short-Term Memory (LSTM) and Random Forest (RF) models, they are superior capability in both short and long-term forecasts during significant economic shocks like the 2008 financial crisis and the COVID-19 pandemic.The inclusion of SHAP analysis further enriches the understanding of how historical prices and macroeconomic indicators like the Consumer Price Index and exchange rates play pivotal roles in forecasting.", "source_url": "https:\/\/openalex.org\/W7217587265", "pdf_url": "https:\/\/krex.k-state.edu\/bitstreams\/378de6d1-ddd7-482d-8a9a-d1d735d48b38\/download", "doi": "", "handle": "", "repository_name": "K-State Research Exchange (Kansas State University)", "source_record_id": "W7217587265", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1332, "title": "Machine learning for time series analysis and forecasting", "title_ar": "", "title_en": "Machine learning for time series analysis and forecasting", "author": "Ming Luo", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Northeastern University", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "data-mining", "categories": "machine-learning; deep-learning; data-mining; predictive-analytics", "keywords": "", "abstract": "We are immersed in a world with all types of data. Time series data are prevalent and essential in decision-making. Time series data have intrinsic temporal order and are thus immutable over time. The autocorrelation among time series data over time index makes them unique to deal with. Moreover, latent explanatory variables behind the time series make it challenging to handle. In this thesis, the author applies machine learning techniques to analyze time series data for classification, clustering, and forecasting. First, a new distance measure, value-added, is proposed in time series classification and clustering. Further, the author develops a novel framework in which decisions such as the number of clusters and prediction based on value-added are made using different techniques. Numerical real-world data studies demonstrate the value-added framework in time series classification and clustering. Forecasting in scale is a particular issue in business forecasting. Frequent forecasting with a hierarchy of time series data differs entirely from that with a single univariate time series. The author first reviews standard hierarchical time series forecasting methods. Then a new approach reconciliation with neural network (R-NN) is proposed for hierarchical forecasting, considering the non-linear relationship among time series for forecasting. Conventional techniques have yet to incorporate the relationship between series while making individual predictions and tend to lose information. In addition, computational costs can be exceptionally high due to model perplexity when using methods such as optimal reconciliation. The R-NN is straightforward to implement and fast to train without losing information. Numerical studies are shown, and accuracy improvements are observed. The methods and procedures developed in this thesis can be applied in various business settings. For example, the value-added time series classification and clustering procedure can be turned into a software product to classify and re-classify sales data for companies to order better and deliver. Business insights on clusters and predictions can be incorporated as well. Especially for companies with limited labor resources to make predictions on plenty of products, this approach can still generate a good prognosis for all the series. Likewise, the reconciliation with neural network (R-NN) can be used to forecast many time series simultaneously consistently. Then a coherent hierarchy of forecasts can assist subsequent supply chain decisions such as production and deployment.--Author's abstract", "source_url": "https:\/\/openalex.org\/W4388760855", "pdf_url": "https:\/\/repository.library.northeastern.edu\/files\/neu:4f197h99b\/fulltext.pdf", "doi": "10.17760\/d20487630", "handle": "", "repository_name": "Digital Repository Service (Northeastern University)", "source_record_id": "W4388760855", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1333, "title": "Studies on Machine Learning Techniques for Time Series Forecasting", "title_ar": "", "title_en": "Studies on Machine Learning Techniques for Time Series Forecasting", "author": "Santisudha Panigrahi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "India", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3179139553", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Shodhganga", "source_record_id": "W3179139553", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1334, "title": "Towards Robust and Reliable Machine Learning on Time Series Forecasting", "title_ar": "", "title_en": "Towards Robust and Reliable Machine Learning on Time Series Forecasting", "author": "Yunyao Cheng", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Aalborg Universitet", "university_source": "repository_host", "college": "", "department": "", "country": "Denmark", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W4407390085", "pdf_url": "", "doi": "10.54337\/aau766169172", "handle": "", "repository_name": "VBN Forskningsportal (Aalborg Universitet)", "source_record_id": "W4407390085", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1335, "title": "Operation Forecast : Optimization of production using time series analysis a machine learning", "title_ar": "", "title_en": "Operation Forecast : Optimization of production using time series analysis a machine learning", "author": "Rasmus Ståhl", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "Operation Forecast : Optimization of production using time series analysis & machine learning", "source_url": "https:\/\/openalex.org\/W3113720617", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3113720617", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1336, "title": "Performance evaluation of machine learning algorithms for time-series forecasting", "title_ar": "", "title_en": "", "author": "Ευγενία Κοτζαπαναγιώτου", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Greece", "governorate": "", "publication_year": 2019, "language": "el", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "Στόχος των προβλέψεων είναι να είναι όσο το δυνατόν πιο εύστοχες και να ελαχιστοποιηθεί η απόκλισή τους από τις πραγματικές μελλοντικές τιμές που προσπαθούν να περιγράψουν. Έτσι, οι μέθοδοι πρόβλεψης συμβάλλουν στη σωστή και έγκαιρη λήψη αποφάσεων. Αν τα δεδομένα, που χρησιμοποιούμε στη διαδικασία της πρόβλεψης, είναι υψηλής ποιότητας μπορούμε να έχουμε ικανοποιητικά ακριβείς προβλέψεις. Η παρούσα διπλωματική εργασία έχει ως αντικείμενο τη μελέτη μεθόδων μηχανικής μάθησης στην πρόβλεψη μελλοντικών τιμών χρονολογικών σειρών. Αρχικά, γίνεται μία σύντομη εισαγωγή στις βασικές έννοιες της Μηχανικής Μάθησης. Στη συνέχεια, στο πλαίσιο της πειραματικής διαδικασίας χρησιμοποιούνται τα μοντέλα μάθησης και αξιολογούνται πλήρως τα αποτελέσματά τους στο πραγματικό πρόβλημα της πρόβλεψης των τιμών του πιο γνωστού και σημαντικού χρηματιστηριακού δείκτη, του Dow Jones (Dow Jones Industrial Average, DJIA), ο οποίος αποτελεί στοιχείο επιρροής των χρηματαγορών καθώς και των πολιτικών οικονομιών σε όλο των κόσμο. Για την υλοποίηση των μεθόδων χρησιμοποιήθηκε κώδικας από το API (Application Programming Interface) του Weka που είναι ένα ελεύθερο και ευρέως διαδεδομένο λογισμικό για μηχανική μάθηση.", "source_url": "https:\/\/openalex.org\/W7162357200", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Νημερτής", "source_record_id": "W7162357200", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1337, "title": "Time Series Algorithms in Machine Learning - A Graph Approach to Multivariate Forecasting", "title_ar": "", "title_en": "Time Series Algorithms in Machine Learning - A Graph Approach to Multivariate Forecasting", "author": "Ryan Zhou", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Trent University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; gnn; ai-industry; ai-smart-cities", "keywords": "forecasting, graph neural network, LSTM, machine learning, neural network, time series", "abstract": "Forecasting future values of time series has long been a field with many and varied applications, from climate and weather forecasting to stock prediction and economic planning to the control of industrial processes. Many of these problems involve not only a single time series but many simultaneous series which may influence each other. This thesis provides methods based on machine learning of handling such problems. We first consider single time series with both single and multiple features. We review the algorithms and unique challenges involved in applying machine learning to time series. Many machine learning algorithms when used for regression are designed to produce a single output value for each timestamp of interest with no measure of confidence; however, evaluating the uncertainty of the predictions is an important component for practical forecasting. We therefore discuss methods of constructing uncertainty estimates in the form of prediction intervals for each prediction. Stability over long time horizons is also a concern for these algorithms as recursion is a common method used to generate predictions over long time intervals. To address this, we present methods of maintaining stability in the forecast even over large time horizons. These methods are applied to an electricity forecasting problem where we demonstrate the effectiveness for support vector machines, neural networks and gradient boosted trees. We next consider spatiotemporal problems, which consist of multiple interlinked time series, each of which may contain multiple features. We represent these problems using graphs, allowing us to learn relationships using graph neural networks. Existing methods of doing this generally make use of separate time and spatial (graph) layers, or simply replace operations in temporal layers with graph operations. We show that these approaches have difficulty learning relationships that contain time lags of several time steps. To address this, we propose a new layer inspired by the long-short term memory (LSTM) recurrent neural network which adds a distinct memory state dedicated to learning graph relationships while keeping the original memory state. This allows the model to consider temporally distant events at other nodes without affecting its ability to model long-term relationships at a single node. We show that this model is capable of learning the long-term patterns that existing models struggle with. We then apply this model to a number of real-world bike-share and traffic datasets where we observe improved performance when compared to other models with similar numbers of parameters. Author Keywords: forecasting, graph neural network, LSTM, machine learning, neural network, time series", "source_url": "https:\/\/openalex.org\/W7207033218", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Collections (Trent University)", "source_record_id": "W7207033218", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1338, "title": "Financial time series forecasting : from machine learning to deep learning", "title_ar": "", "title_en": "Financial time series forecasting : from machine learning to deep learning", "author": "Karl Sant Fournier", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2965789559", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2965789559", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1339, "title": "Forecasting oil prices: time series vs. machine learning methods", "title_ar": "", "title_en": "Forecasting oil prices: time series vs. machine learning methods", "author": "Marco J. Bruno", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "Overview of the oil market. Time series predictability. Benchmark comparison ratios. Machine learning methods. Prediction and training errors. Testing and results.", "source_url": "https:\/\/openalex.org\/W2770352589", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2770352589", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1340, "title": "Rational Krylov Methods and Machine Learning Approaches to Time Series Forecasting", "title_ar": "", "title_en": "Rational Krylov Methods and Machine Learning Approaches to Time Series Forecasting", "author": "Steven Elsworth", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "The University of Manchester", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7127555002", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Research Explorer (The University of Manchester)", "source_record_id": "W7127555002", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1341, "title": "MACHINE LEARNING STRATEGIES TO PREDICT OIL FIELD PERFORMANCE AS TIME-SERIES FORECASTING", "title_ar": "", "title_en": "MACHINE LEARNING STRATEGIES TO PREDICT OIL FIELD PERFORMANCE AS TIME-SERIES FORECASTING", "author": "ISABEL FIGUEIRA DE ABREU GONCALVES", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; predictive-analytics; rnn", "keywords": "", "abstract": "Precisely forecasting oil field performance is essential in oil reservoir planning and management.Nevertheless, forecasting oil production is a complex nonlinear problem due to all geophysical and petrophysical properties that may result in different effects with a bit of change.Thus, all decisions to be made during an exploitation project must consider different efficient algorithms to simulate data, providing robust scenarios to lead to the best deductions.To reduce the uncertainty in the simulation process, recent studies have efficiently introduced machine learning algorithms for solving reservoir engineering problems since they can extract the maximum information from the dataset.This thesis proposes using two machine learning techniques to predict the daily oil production of an offshore reservoir.Initially, the oil rate production is considered a time series and is pre-processed and restructured to fit a supervised learning problem.The Random Forest model is used to forecast a one-time step, which is an extension of decision tree learning, widely used in regression and classification problems for supervised machine learning.Regardless, the restrictions of this approach lead us to a more robust model, the LSTM RNN's, which are proposed by several studies as a suitable deep learning technique for time series modeling.Various configurations of LSTM RNN's were constructed to implement single-step and multi-step oil rate forecasting and down-hole pressure was incorporated to the inputs.For testing the robustness of the proposed models, we use four different datasets, three of them synthetically generated and one from a public real dataset, the Volve oil field, as a case study to conduct the experiments.The results indicate that the Random Forest model could sufficiently estimate the one-time step of the oil field production, and LSTM could handle more inputs and adequately estimate multiple-time steps of oil production.", "source_url": "https:\/\/openalex.org\/W4381617672", "pdf_url": "https:\/\/www.maxwell.vrac.puc-rio.br\/62916\/62916.PDF", "doi": "10.17771\/pucrio.acad.62916", "handle": "", "repository_name": "", "source_record_id": "W4381617672", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1342, "title": "Time-series Forecasting Using Statistical & Machine Learning Models", "title_ar": "", "title_en": "Time-series Forecasting Using Statistical & Machine Learning Models", "author": "Soetens, Benn", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7111872836", "pdf_url": "", "doi": "", "handle": "", "repository_name": "TU\/e Research Portal", "source_record_id": "W7111872836", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1343, "title": "Demand Forecasting in Retail : A Comparison of Time Series Analysis and Machine Learning Models", "title_ar": "", "title_en": "Demand Forecasting in Retail : A Comparison of Time Series Analysis and Machine Learning Models", "author": "Lindfors, Albin", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "University of Helsinki", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; predictive-analytics", "keywords": "", "abstract": "Having an accurate forecast of the upcoming demand is of utmost importance to a retail company, as it helps the retailer plan the day-to-day activities and optimize the supply chain. At the same time, retailers also gather a substantial amount of data about everything from weather conditions to promotional campaigns, having the potential to improve the forecasts when used right. The aim of this thesis is to compare time series analysis, which only utilize the past sales data, to machine learning models, which can also utilize other data, when forecasting retail sales figures. The comparison is conducted by a thorough literature review and an empirical study where forecasting is performed on a set of Walmart sales data. The forecasting methods used in the empirical study are ARIMA, Holt-Winter’s exponential smoothing, linear regression, decision tree and artificial neural networks, and the results of the empirical study suggest that ARIMA and Holt-Winter’s exponential smoothing are the best performing models on this particular dataset.", "source_url": "https:\/\/openalex.org\/W7115303668", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Doria (University of Helsinki)", "source_record_id": "W7115303668", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1344, "title": "High-variance multivariate time series forecasting using machine learning", "title_ar": "", "title_en": "High-variance multivariate time series forecasting using machine learning", "author": "N. Katardjiev", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; medical-ai; predictive-analytics; rnn", "keywords": "", "abstract": "There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-specific transformations and considerations are usually required. This research aims to examine the issue by modeling four types of machine- and deep learning algorithms - support vector machine, random forest, feed-forward neural network, and a LSTM neural network - on a high-variance, multivariate time series to forecast trend changes one time step in the future, accounting for lag.The models were trained on clinical trial data of patients in an alcohol addiction treatment plan provided by a Uppsala-based company. The results showed moderate performance differences, with a concern that the models were performing a random walk or naive forecast. Further analysis was able to prove that at least one model, the feed-forward neural network, was not undergoing this and was able to make meaningful forecasts one time step into the future. In addition, the research also examined the effec tof optimization processes by comparing a grid search, a random search, and a Bayesian optimization process. In all cases, the grid search found the lowest minima, though its slow runtimes were consistently beaten by Bayesian optimization, which contained only slightly lower performances than the grid search.", "source_url": "https:\/\/openalex.org\/W2995738585", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2995738585", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1346, "title": "Machine learning models for time series forecasting with limited historical data", "title_ar": "", "title_en": "", "author": "Карина Миколаївна Міловська", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Odesa I.I.Mechnikov National University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2021, "language": "uk", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "Протягом останнiх десятилiть у свiтi активно вiдбувається процес iнформатизацiї економiки, в умовах якої гостро постає питання взаєморорахункiв мiж людьми шляхом впровадження iнновацiйної мережi платежiв та нових платiжних засобiв. Набуває популярностi поняття вiртуальної валюти — криптовалюти. Криптовалюта – рiзновид цифрової валюти, ство- рення i контроль за якою базуються на криптографiчних методах. Термiн закрiпився пiсля статтi «Cryptocurrency», яка була опублiкована у 2011 роцi у журналi «Forbes» [8].", "source_url": "https:\/\/openalex.org\/W7197834097", "pdf_url": "", "doi": "", "handle": "", "repository_name": "INSTITUTIONAL REPOSITORY OF ODESSA NATIONAL UNIVERSITY NAMED I.I. Mechnykova (Odesa I.I.Mechnikov National University)", "source_record_id": "W7197834097", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1347, "title": "Machine learning for time series forecasting : application in water surface analysis of Mun River", "title_ar": "", "title_en": "Machine learning for time series forecasting : application in water surface analysis of Mun River", "author": "Anan Ungpansattawong ; Pongthep Thongsang", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Thailand", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "predictive-analytics", "categories": "machine-learning; computer-vision; predictive-analytics", "keywords": "", "abstract": "At present, the world is facing with a global warming problem. Thailand is another country that has suffered from global warming as well. One of the problems that are often seen change that is of the season's Summer temperature is higher than it used to be. The rainy season does not fall according to the season. Or when the rain became a long time, it was flooded from the drainage in time at this point, if we can predict whether the amount of water to be found this year is high or low. Therefore, the study through satellite imagery was chosen as the starting point. This study methodology is consisting of data collection, data processing 1, data processing 2, and prediction. Using the principle that if the amount of water increases and the river does not look deep, the area of the river in the satellite image will also have to increase. Therefore, the study was to create a tool that can predict the river's surface area from satellite imagery. The results obtained from this study revealed that the river's surface area could indicate an increase in water content and the forecasts yield satisfactory results.", "source_url": "https:\/\/openalex.org\/W4407782648", "pdf_url": "", "doi": "10.58837\/chula.sp.2020.59", "handle": "", "repository_name": "Chula Digiverse", "source_record_id": "W4407782648", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1348, "title": "Machine Learning Methods for Time Series Forecasting-E-commerce Sales Study Case", "title_ar": "", "title_en": "Machine Learning Methods for Time Series Forecasting-E-commerce Sales Study Case", "author": "Ana Rita Mendes Teixeira", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7111173596", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W7111173596", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1349, "title": "السبب الرئيسي لهلوسة الذكاء الاصطناعي", "title_ar": "السبب الرئيسي لهلوسة الذكاء الاصطناعي", "title_en": "", "author": "Abderraouf CHABIRA", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "السبب الرئيسي لهلوسة الذكاء الاصطناعي الذكاء الاصطناعي لا يفرق بين الواو الحسابية و الواو الوجودية الواو الحسابية مثل : 6 تفاحات و 5 تفاحات فتترجم هنا إلى جمع الواو الوجودية مثل : ألم و سعال تترجم في هذه الحالة إلى ضرب بمعاملات 01 السبب الثاني الاحتمالات حيث نرى أن كرومنغوف أخطأ عندما أهمل الزمن في حساب الاحتمالات © 2026 Chabira Abderraouf , CC-BY 4.0", "source_url": "https:\/\/openalex.org\/W7212687303", "pdf_url": "", "doi": "10.5281\/zenodo.22758317", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7212687303", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1350, "title": "أثر الذكاء الاصطناعي في تحسين الإنتاجية", "title_ar": "أثر الذكاء الاصطناعي في تحسين الإنتاجية", "title_en": "", "author": "مفلح، عائشة عبدالغفار محمد علوان", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203843586", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203843586", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1351, "title": "أثر الذكاء الاصطناعي على الأداء الاستراتيجي", "title_ar": "أثر الذكاء الاصطناعي على الأداء الاستراتيجي", "title_en": "", "author": "الرفاعي، نهى عبدالله علي قاسم", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203847387", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203847387", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1352, "title": "دور الذكاء الاصطناعي في اتخاذ القرارات الاستراتيجية", "title_ar": "دور الذكاء الاصطناعي في اتخاذ القرارات الاستراتيجية", "title_en": "", "author": "العريقي، طه", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203845848", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203845848", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1353, "title": "أثر الذكاء الاصطناعي في اتخاذ القرارات المحاسبية", "title_ar": "أثر الذكاء الاصطناعي في اتخاذ القرارات المحاسبية", "title_en": "", "author": "فازع، عبدالودود ; عادل حمزة", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203854257", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203854257", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1354, "title": "نظام اكتشاف هجمات DDOS باستخدام الذكاء الاصطناعي", "title_ar": "نظام اكتشاف هجمات DDOS باستخدام الذكاء الاصطناعي", "title_en": "", "author": "حسن، فارس وآخرون", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "ai-applications", "research_subdomain": "ai-cybersecurity", "categories": "ai-applications; ai-cybersecurity", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203854139", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203854139", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1355, "title": "دور الذكاء الاصطناعي في اتخاذ القرارات الاستراتيجية", "title_ar": "دور الذكاء الاصطناعي في اتخاذ القرارات الاستراتيجية", "title_en": "", "author": "الفقيه، عمار رمزي ردمان", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203844676", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203844676", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1356, "title": "نظام ذكي لتشخيص مشاكل البشرة باستخدام الذكاء الاصطناعي", "title_ar": "نظام ذكي لتشخيص مشاكل البشرة باستخدام الذكاء الاصطناعي", "title_en": "", "author": "الحميري، ندى وآخرون", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203854258", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203854258", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1357, "title": "نموذجا HeyGen دبلجة الفيديوهات بواسطة الذكاء الاصطناعي - منصة", "title_ar": "نموذجا HeyGen دبلجة الفيديوهات بواسطة الذكاء الاصطناعي - منصة", "title_en": "", "author": "ياسين منير بلعسكري", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "nlp; machine-translation", "keywords": "", "abstract": "This study aims to highlight the role of machine translation and film dubbing using artificial intelligence technologies. It does so by analyzing machine translation as a tool to enhance the understanding of texts and translate them accurately and quickly, and by reviewing how artificial intelligence is used to provide more interactive and realistic viewing experiences. The study offers a comprehensive overview of how technology enhances translation and content dubbing, contributing to the expansion of cultural and artistic access.", "source_url": "https:\/\/openalex.org\/W7135255797", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135255797", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1358, "title": "أثر أدوات الذكاء الاصطناعي على جودة عمليات التدقيق", "title_ar": "أثر أدوات الذكاء الاصطناعي على جودة عمليات التدقيق", "title_en": "", "author": "Najat Omer Mamaseni ; Mahmood", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "This study investigates the impact of artificial intelligence tools on the quality of audit processes. It focuses on the role of AI tools in supporting audit procedures and improving audit quality. The study examines the relationship between the use of artificial intelligence tools and audit quality, based on the perspectives of a sample of auditors and accounting academics in Erbil, Iraq.", "source_url": "https:\/\/openalex.org\/W7223476782", "pdf_url": "", "doi": "10.5281\/zenodo.23606238", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7223476782", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1359, "title": "دور الذكاء الاصطناعي في تعزيز رأس المال الفكري", "title_ar": "دور الذكاء الاصطناعي في تعزيز رأس المال الفكري", "title_en": "", "author": "عثمان، نورا احمد مقبل", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203850953", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203850953", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1360, "title": "دور الذكاء الاصطناعي في تعزيز الابتكار في بيئة العمل", "title_ar": "دور الذكاء الاصطناعي في تعزيز الابتكار في بيئة العمل", "title_en": "", "author": "قحطان، باسم منير سعيد", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203853310", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203853310", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1361, "title": "الذكاء الاصطناعي واثرة على مهنة المحاسبة في البنوك اليمنية", "title_ar": "الذكاء الاصطناعي واثرة على مهنة المحاسبة في البنوك اليمنية", "title_en": "", "author": "منيف، بندر ; عبدالرقيب، عمرو", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203846232", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203846232", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1362, "title": "اشكالية دقة الترجمة في مواقع و تطبيقات الذكاء الاصطناعي", "title_ar": "اشكالية دقة الترجمة في مواقع و تطبيقات الذكاء الاصطناعي", "title_en": "", "author": "سعد فضل الله بوكليخة", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "translation\/artificial intelligence, artificial intelligence applications.", "abstract": "This work stems from the desire to explore the extent of recent progress in the field of artificial intelligence techniques in file translation. The purpose of this study is to demonstrate the continuous improvements in artificial intelligence, which has shown amazing results in the field of translation. Keywords: translation\/artificial intelligence, artificial intelligence applications.", "source_url": "https:\/\/openalex.org\/W7135338480", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135338480", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1363, "title": "توظيف تطبيقات الذكاء الاصطناعي التوليدي في مرحلة رياض الأطفال في ضوء فلسفة المنهج المطور 2.0", "title_ar": "توظيف تطبيقات الذكاء الاصطناعي التوليدي في مرحلة رياض الأطفال في ضوء فلسفة المنهج المطور 2.0", "title_en": "", "author": "Fayrouz M. Rashed", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "ai-applications", "research_subdomain": "ai-education", "categories": "ai-applications; ai-education", "keywords": "", "abstract": "مع الظهور المبكر للذكاء الاصطناعي التوليدي، تأتي هذه الدراسة لتحليل إمكانية توظيف تطبيقاته في مرحلة رياض الأطفال في ضوء فلسفة منهج رياض الأطفال المطوّر 2.0 في مصر، باعتباره أداة حديثة ومنخفضة التكلفة وسهلة الوصول، تمكّن مؤسسات رياض الأطفال من تجاوز معوّقات دمج التكنولوجيا في التعليم. اعتمدت الدراسة المنهج الوصفي لتحليل الأدبيات ذات الصلة، وتحديد الأسس الفلسفية والمنهجية للمنهج المطوّر، ورصد المتطلبات المادية والبشرية والتنظيمية اللازمة لتبنّي الذكاء الاصطناعي التوليدي بفعالية وأمان. وقد توصّلت الدراسة إلى إمكانية توظيف الذكاء الاصطناعي التوليدي نظريًا في جميع مقررات المنهج الستة، من خلال تسعة مداخل رئيسية، هي: تحسين الخبرة التعليمية، وتوليد المحتوى التعليمي، وتفريد التعليم، ودعم الطلاب ذوي الاحتياجات الخاصة، والتدريب الفردي على المهارات الاجتماعية واللغوية والبرمجية والحسابية، والتنمية المهنية الذاتية للمعلمة، والاستشارات التربوية، والتعاون مع الأسرة، والتدريب على ريادة فضاء الثورة الصناعية الخامسة. كما قدّمت الدراسة رؤيةً متكاملةً لتوظيف الذكاء الاصطناعي التوليدي في مرحلة رياض الأطفال، تبلورت في إطار عملٍ أطلقت عليه الباحثة اسم GITCEE، قائمٍ على مبادئ أشهر أُطر العمل العالمية لدمج التكنولوجيا في التعليم. وتفعيلًا لهذه الرؤية، طوّرت الباحثة منصة KG-Pilot كمنصة تربوية شاملة تدعم تنفيذ الرؤية ومتابعتها، وتعمل كنظام لإدارة التعلّم الذاتي للمعلمة، وكبوابة معلوماتية تربوية، وكمساحة للتواصل وتبادل الخبرات بين المعلمات وأولياء الأمور. وبذلك تسهم الدراسة في تحسين جودة التعليم المبكر وتحقيق أهداف رؤية مصر 2030 في تطوير التعليم.", "source_url": "https:\/\/openalex.org\/W7165473282", "pdf_url": "", "doi": "10.5281\/zenodo.20782956", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7165473282", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1364, "title": "أثر الذكاء الاصطناعي على كفاءة الانظمة المحاسبية في البنوك اليمنية", "title_ar": "أثر الذكاء الاصطناعي على كفاءة الانظمة المحاسبية في البنوك اليمنية", "title_en": "", "author": "عبده، عبدالادر ; عبدالناصر، جمال", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203848352", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203848352", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1365, "title": "الذكاء الاصطناعي والأدوات المساعدة على الترجمة ما بين دعم كفـاءة المترجم وتهديد مستقبله المهني", "title_ar": "الذكاء الاصطناعي والأدوات المساعدة على الترجمة ما بين دعم كفـاءة المترجم وتهديد مستقبله المهني", "title_en": "", "author": "سمية عواد عيد عواد", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "The purpose of this research entitled \"Artificial Intelligence and Translation Aids Between Supporting the Translator's Competence and Threatening his Professional Future\" is to solve the problem \"the reality of translation and the translator role under the presence of artificial intelligence and translation aids: is it a support for his competence or a threat to his professional future?\", to which we responded by abundantlying two theoretical chapters and a practical chapter. Thanks to this research, we have obtained several results, the most important of which is that despite the great challenge between machines and humans, they cannot overcome it because human capacities cannot be imitated in many aspects.", "source_url": "https:\/\/openalex.org\/W7135325394", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135325394", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1366, "title": "تصورات الطلاب في استخدام الذكاء الاصطناعي لإكمال واجبات مادة الكتابة: دراسة حالة في قسم تعليم اللغة العربية جامعة مولانا مالك إبراهيم الإسلامية الحكومية مالانج", "title_ar": "تصورات الطلاب في استخدام الذكاء الاصطناعي لإكمال واجبات مادة الكتابة: دراسة حالة في قسم تعليم اللغة العربية جامعة مولانا مالك إبراهيم الإسلامية الحكومية مالانج", "title_en": "", "author": "Muhammad Yusuf", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Maulana Malik Ibrahim State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "nlp", "research_subdomain": "llm", "categories": "nlp; ai-applications; llm; arabic-nlp; ai-education", "keywords": "", "abstract": "ARABIC يُبشّر التوسع في استخدام الذكاء الاصطناعي في التعليم بإمكانات هائلة لتحسين عملية التعلم والتدريس. ويُتيح ظهور تطبيقات الذكاء الاصطناعي حلولًا لتصحيح مهارات الكتابة لدى الطلاب وتقديم التغذية الراجعة بشأنها. وقد سعت العديد من الدراسات السابقة إلى معالجة معوقات تدريس مهارات الكتابة. مع ذلك، لم تُجرَ أي دراسة حتى الآن تطبيقًا للذكاء الاصطناعي على برنامج شات جي بي تي لإنجاز واجبات الكتابة كدراسة حالة. وللتغذية الراجعة من المُدرّس الافتراضي تأثيرات مباشرة وغير مباشرة، إذ تُساهم بنسبة ٣٤.٨% في تحسين كتابة الطلاب. تتمثل أهداف هذه الدراسة فيما يلي: (١) وصف استخدام الذكاء الاصطناعي لإنجاز واجبات مادة الكتابة لدى طلاب تخصص تعليم اللغة العربية في جامعة مولانا مالك إبراهيم الإسلامية الحكومية، مالانج. (٢) تحديد تصورات الطلاب حول استخدام الذكاء الاصطناعي لإنجاز واجبات مادة الكتابة في مقررات تعليم اللغة العربية بجامعة مولانا مالك إبراهيم الإسلامية الحكومية، مالانج. اعتمدت هذه الدراسة منهج البحث دراسة حالة، وشملت تقنيات البحث المقابلات والتوثيق. يمكن للبحث النوعي أن يوفر فهمًا معمقًا لتصورات الطلاب حول استخدام الذكاء الاصطناعي لإنجاز واجباتهم في مقرر \"مهارات الكتابة\". وشملت أساليب جمع البيانات والمقابلات والتوثيق. نتائج هذه الدراسة هي: (١) وصف الطلاب لاستخدام الذكاء الاصطناعي في إنجاز واجبات الكتابة ضمن قسم تعليم اللغة العربية في جامعة مولانا مالك إبراهيم الإسلامية الحكومية في مالانج. وتشمل هذه الأساليب خمسة جوانب، هي: (أ) المساعدة في بناء إطار فكري. (ب) أدوات الترجمة من الإندونيسية إلى العربية (ج) أدوات الترجمة من العربية إلى الإندونيسية.(د) المساعدة في تصحيح بنية الجملة. (هـ) المساعدة في إنجاز الواجبات.(٢) تصورات الطلاب حول استخدام الذكاء الاصطناعي في إنجاز واجبات الكتابة ضمن قسم تعليم اللغة العربية في جامعة مولانا مالك إبراهيم الإسلامية الحكومية في مالانج. (أ) التصورات الإيجابية: ملاءمة نتائج شات جي بي تي ومهام مهارة الكتابة ,الكفاءة الزمنية في إنجاز المهام, الميزات السمعية البصرية وعرض النص في شات جي بي تي واضح وسهل الفهم, شات جي بي تي كمساعد رقمي, (ب) التصورات السلبية: تفاعل الطلاب مع شات جي بي تي يميل إلى أن يكون محدودا, اللغة صارمة وغير تواصلية ولا تشبه لغة المتحدثين الأصليين باللغة العربية, نتائج من مصادر لا يمكن التحقق منها, مخاوف بشأن الاعتماد على شات جي بي تي على المدى الطويل. ENGLISH The increasing use of artificial intelligence in the field of education has significant potential to enhance the quality of teaching and learning. The emergence of artificial intelligence based applications offers various solutions, particularly in providing corrections and feedback on students’ writing skill assignments. Although numerous previous studies have discussed the use of artificial intelligence, there is still a lack of research examining the utilization of artificial intelligence specifically ChatGPT as an assistive tool in completing writing tasks through a case study approach. Digital assistive feedback has been shown to have both direct and indirect effects, contributing 34.8% to the improvement of students’ writing skills. This study aims to: (1) describe the use of artificial intelligence in completing writing tasks among students of Arabic Language Education at Universitas Islam Negeri Maulana Malik Ibrahim Malang; and (2) identify students’ perceptions of the use of artificial intelligence in completing writing tasks in the writing skills course. This research employs a qualitative method with a case study approach. Data were collected through interviews and documentation to gain an in-depth understanding of students’ experiences and perceptions regarding the use of artificial intelligence in learning. This study aims to: (1) describe the use of artificial intelligence in completing writing tasks among students of Arabic Language Education at Universitas Islam Negeri Maulana Malik Ibrahim Malang; and (2) identify students’ perceptions of the use of artificial intelligence in completing writing tasks in the writing skills course writing tasks among students of Arabic Language Education at Universitas Islam Negeri Maulana Malik Ibrahim Malang This research employs a qualitative method with a case study approach. Data were collected through interviews and documentation to gain an in-depth understanding of students’ experiences and perceptions regarding the use of artificial intelligence in learning. The findings indicate that (1) the use of artificial intelligence in completing writing tasks encompasses five main aspects: (a) assistance in developing a conceptual framework, (b) use as a translation tool from Indonesian to Arabic, (c) use as a translation tool from Arabic to Indonesian, (d) assistance in correcting sentence structure, and (e) assistance in completing writing tasks. (2) Students’ perceptions of artificial intelligence use are divided into positive and negative perceptions. (a) Positive perceptions include the suitability of ChatGPT’s output for writing tasks, time efficiency in task completion, clear and easily understandable text presentation, and the role of ChatGPT as a digital assistant.", "source_url": "https:\/\/openalex.org\/W7119441848", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W7119441848", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1367, "title": "تطوير مواد تعليم اللغة العربية لترقية مهارتي الاستماع والكلام على أساس تمثيل المسرحيات بوسيلة برمجية الذكاء الاصطناعي لطلبة الفصل الثامن في المدرسة المتوسطة الإسلامية الحكومية 1 غرسيك", "title_ar": "تطوير مواد تعليم اللغة العربية لترقية مهارتي الاستماع والكلام على أساس تمثيل المسرحيات بوسيلة برمجية الذكاء الاصطناعي لطلبة الفصل الثامن في المدرسة المتوسطة الإسلامية الحكومية 1 غرسيك", "title_en": "", "author": "Arina Fadlilatar Rifqi", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "nlp", "research_subdomain": "arabic-nlp", "categories": "nlp; ai-applications; arabic-nlp; ai-education", "keywords": "", "abstract": "The purpose of this research are: 1) Analyzing the development of Arabic language learning materials to improve listening and speaking skills based on socio drama with the help of artificial intelligence software for eighth grade students at MTsN 1 Gresik. 2) Analyzing the application of Arabic language learning materials to improve listening and speaking skills based on socio drama with the help of artificial intelligence software for eighth grade students at MTsN 1 Gresik. 3) Analyzing the effectiveness of the application of Arabic language learning materials to improve listening and speaking skills based on socio drama with the help of artificial intelligence software for eighth grade students at MTsN 1 Gresik. The research methodology used is Research and Development (R&D) with the Brog and Gall model. To analyse the feasibility level of the product, using a research instrument in the form of a questionnaire given to experts. Pre-test and post-test tests were applied to students as an analysis of the level of effectiveness. The data instruments used are observation, interview, questionnaire and test. The data analysis technique used is descriptive analysis to obtain the results of observations of students' needs and learning problems; and statistical analysis used to obtain the results of questionnaires and tests aimed at experts and students. The results of this study can be seen based on the assessment of experts and the results of the Wilcoxon test. The results of the learning material expert assessment were 93.3% with a very good estimation, the language expert assessment was 91, 43% with a very good estimation and the design expert assessment was 93, 4% with a very good estimation. The results of the Wilcoxon Signed Ranks test aimed at students obtained a z value of -5,088 and a calculated value of Asymp. Sig (2-tailed) .000 is smaller than the α level of 5% (0.05), namely .", "source_url": "https:\/\/openalex.org\/W7166608645", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digilib UIN Sunan Ampel Surabaya (UIN Sunan Ampel)", "source_record_id": "W7166608645", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1368, "title": "Administrative Liability for Artificial Intelligence Damage - An Analytical Study of the Rules of Administrative", "title_ar": "Administrative Liability for Artificial Intelligence Damage - An Analytical Study of the Rules of Administrative", "title_en": "", "author": "حسنات مبارك سالم دهام العبد الله", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Qatar University", "university_source": "repository_host", "college": "", "department": "", "country": "Qatar", "governorate": "", "publication_year": 2025, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "تبين هذه الدراسة المسؤولية الإدارية عن أضرار الذكاء الاصطناعي - دراسة تحليلية لقواعد المسؤولية الإدارية، ذلك أن المسؤوليات الإدارية تختلف وفق تطبيقات الذكاء الاصطناعي، إلى جانب قلة المراجع الفقهية وغياب النص التشريعي الناظم لهذه المسألة؛ سواء في دولة قطر أو في غيرها. تناولت الدراسة في فصلها الأول، المفهوم والشخصية القانونية للذكاء الاصطناعي، من خلال تعريف الذكاء الاصطناعي أولا، وتوضيح الشخصية القانونية له ثانيا، وأبان الفصل الثاني مجالات تطبيق الذكاء الاصطناعي واستخداماته في القانون الإداري في حين الصرف الفصل الثالث للبحث في المسؤولية عن تطبيقات الذكاء الاصطناعي في القانونين المدني والإداري، من خلال استعراض موقف القانون المدني من المسؤولية عن الذكاء الاصطناعي، وأحكام المسؤولية الإدارية عن تطبيقات الذكاء الاصطناعي.", "source_url": "https:\/\/openalex.org\/W7123385496", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Qatar University QSpace (Qatar University)", "source_record_id": "W7123385496", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1369, "title": "تطوير نموذج تعلّم تفاعلي قائم على الذكاء الاصطناعي لتنمية مهارة الكلام لدى طلبة برنامج الإعداد للدراسة بجامعة الأزهر مصر في مركز ممتازة بوجونغورو = TATWIR NAMUDHAJ TAʻALLUM TAFAʻULI QA’IM ʻALA AL-DHAKA’ AL-ISTINACI LI-TANMIYAT MAHARAT AL-KALAM LADA TALABAT BARNAMAJ AL-IʻDAD LI-AL-DIRASAH BI-JAMIʻAT AL-AZHARI MISRA FI MARKAZ MUMTAZAH BOJONEGORO", "title_ar": "تطوير نموذج تعلّم تفاعلي قائم على الذكاء الاصطناعي لتنمية مهارة الكلام لدى طلبة برنامج الإعداد للدراسة بجامعة الأزهر مصر في مركز ممتازة بوجونغورو = TATWIR NAMUDHAJ TAʻALLUM TAFAʻULI QA’IM ʻALA AL-DHAKA’ AL-ISTINACI LI-TANMIYAT MAHARAT AL-KALAM LADA TALABAT BARNAMAJ AL-IʻDAD LI-AL-DIRASAH BI-JAMIʻAT AL-AZHARI MISRA FI MARKAZ MUMTAZAH BOJONEGORO", "title_en": "", "author": "NIM. 18106000005 Uswatun Hasanah", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "Sunan Kalijaga State Islamic University", "university_source": "repository_host", "college": "", "department": "", "country": "Indonesia", "governorate": "", "publication_year": 2026, "language": "ar", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "deep-learning; transformers", "keywords": "", "abstract": "Penelitian ini bertujuan untuk mengatasi rendahnya keterampilan berbicara siswa program studi persiapan Al-Azhar Mesir di Mumtaza Center, yang dilatarbelakangi dengan kurangnya kepercayaan komunikasi dalam pembelajaran Maharah Al-Kalam di Mumtaz Center dan ketiadaan model pembelajaran khusus Maharah Al-Kalam. Data awal menunjukan rata-rata nilai test lisan siswa hanya mencapai 51,9, jauh dibawah target ideal 85. Oleh karena itu penelitian ini bertujuan: 1) Menganalisis kebutuhan pengembangan model pembelajaran; 2) Merancang dan Mengembangkan Model pembelajaran AISI (AI Interactive Speaking Model); 3) Mengukur efektifitas model pembelajaran untuk meningkatkan Maharah Al-Kalam siswa. Penelitian ini merupakan penelitian dan pengembangan (Research and Development\/R&D) yang mengadopsi model ADDIE. Subjek penelitian adalah 34 siswa tingkat mutawassith program persiapan studi al-azhar mesir di mumtaza center. Model pembelajaran AISI dirancang dalam bentuk model cetak yang memuat materi tema percakapan fokus pada perkenalan, keluarga, pembelajaran, dan perkuliahan, dan menerapkan sepenuhnya Metode (Role-play). Hasil penelitian menunjukan bahwa model pembelajaran AISI berhasil dikembangkan dan mendapatkan penilain kelayakan (validitas) yang cukup layak dari para ahli: Validator Materi (92%), Validator Model Pembelajaran (88%). Dari aspek kepraktisan, hasil uji coba pada kelompok besar menunjukan persentase sebesar (88%), yang diklasifikasikan sebagai “Baik Sekali”. Sementara itu, hasil pengujian efektivitas menggunakan NGain Test menunjukan rata-rata skor 0,3603. Nilai diklasifikasikan berada dalam “Sedang” (Medium). Meskipun demikian, penggunaan Model “AISI” terbutki menghasilkan peningkatan yang cukup efektif dan cukup signifikan pada rata-rata nilai siswa, dari pre-test (51,9) menjadi post-test (79,2). Model pembelajaran “AISI” berbasis AI terbukti valid, praktis, dan cukup efektif secara sedang untuk meningkatkan keterampilan berbicara siswa. Direkomendasikan agar guru dapat menggunakan model ini sebagai bahan ajar pendamping untuk memperkuat praktik berbicara siswa didalam pembelajaran maupun diluar lingkungan pembelajaran. Kata kunci: Model pembelajaran interaktif, AI, keterampilan.", "source_url": "https:\/\/openalex.org\/W7171753024", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Library UIN Sunan Kalijaga (Sunan Kalijaga State Islamic University)", "source_record_id": "W7171753024", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1370, "title": "تأثير تقنيات الذكاء الاصطناعي على جودة علاقات العملاء \"دراسة تطبيقية على عملاء المتاجر الإلكترونية\"", "title_ar": "تأثير تقنيات الذكاء الاصطناعي على جودة علاقات العملاء \"دراسة تطبيقية على عملاء المتاجر الإلكترونية\"", "title_en": "", "author": "Talaat Asaad Abdelhamid ; Ahmed Ghoneim ; Mayan Magdy", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2024, "language": "ar", "research_domain": "nlp", "research_subdomain": "predictive-analytics", "categories": "machine-learning; nlp; predictive-analytics", "keywords": "", "abstract": "هدفت هذه الدراسة إلى بحث تأثير تقنيات الذكاء الاصطناعي على جودة علاقات العملاء، وفى ضوء مراجعة الدراسات السابقة تم استخلاص إطار مفاهيمي لأبعاد هذه الدراسة يتضمن متغيرين أساسيين، وهما المتغير المستقل ويتمثل في تقنيات الذكاء الاصطناعي (روبوتات الدردشة، التسعير الديناميكي، العروض الموجهة، تحليل البيانات، فهم العملاء، توصية المحتوى) والمتغير التابع جودة علاقات العملاء (الرضا، الثقة، الالتزام، الولاء)، وتم الاعتماد في جميع البيانات الأولية لهذه الدراسة على عينة النهر (عينة الإنترنت الاعتراضية) وبلغ عددها (412) مفردة من عملاء المتاجر الالكترونية، وتم تحليل البيانات باستخدام مصفوفة بيرسون وثبت وجود علاقة ارتباط (متوسطة القوة) ذات معنوية إيجابية بين أبعاد الدراسة ( روبوتات الدردشة، التسعير الديناميكي، العروض الموجهة، تحليل البيانات، فهم العملاء، توصية المحتوى، الرضا، الثقة، الالتزام، الولاء)، حيث جاءت دالة إحصائيًا عند مستوى دلالة (0.01)، وأيضاً تم استخدام معامل الانحدار الخطى المتعدد وثبت وجود تأثير معنوي جزئي بين تقنيات الذكاء الاصطناعي (روبوتات الدردشة، التسعير الديناميكي، العروض الموجهة، تحليل البيانات، فهم العملاء، توصية المحتوى) وجودة علاقات العملاء (الرضا، الثقة، الالتزام، الولاء)، وذلك باستخدام البرنامج الاحصائي IBM SPSS V.29، كما قدمت الدراسة مجموعة من التوصيات التي تلقى الضوء على أهمية تقنيات الذكاء الاصطناعي. This study aimed to determine The Effect of Artificial Intelligence Techniques on Customers' Relationship Quality, Based on a review of previous studies, a conceptual framework was extracted for the dimensions of this study that included two main variables: The independent variable, Represented by Artificial intelligence techniques (Chatbots, dynamic pricing, Promotional Personalization, data analysis, understanding customers, content recommendation) and the dependent variable, customer relationship quality (satisfaction, commitment, trust, loyalty). The primary data for this study was collected through a river sample (Accidental sample) of 412 E-Stores customers, The researcher also used a set of secondary data, obtained from books, articles, and periodicals and scientific dissertations. The results by using the Pearson correlation matrix showed that there is a statistically significant positive correlation (Medium-strength) between the dimensions of the study (Chatbots, dynamic pricing, Promotional Personalization, data analysis, understanding customers, content recommendation, satisfaction, commitment, trust, loyalty), The statistical significance was at a level of (0.01). The study also used the multiple linear regression coefficient and found that there is a partial significant effect between artificial intelligence technologies (Chatbots, dynamic pricing, Promotional Personalization, data analysis, understanding customers, content recommendation) and Customers' Relationship Quality (satisfaction, trust, commitment, loyalty). The study was conducted using the IBM SPSS statistical program V29. The study also presented a set of recommendations that highlight the importance of artificial intelligence Techniques.", "source_url": "https:\/\/openalex.org\/W7171508316", "pdf_url": "", "doi": "10.5281\/zenodo.21593077", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7171508316", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1371, "title": "تصميم وبرمجة خوارزميات الذكاء الاصطناعي لتصنيف وتشخيص أمراض العيون الرقمية", "title_ar": "تصميم وبرمجة خوارزميات الذكاء الاصطناعي لتصنيف وتشخيص أمراض العيون الرقمية", "title_en": "", "author": "الخولاني، محمد يحيى", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Yemen", "governorate": "", "publication_year": 2016, "language": "ar", "research_domain": "medical-ai", "research_subdomain": "", "categories": "medical-ai", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W7203850860", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Science and Technology Institutional Repository (University of Science and Technology, Sana’a, Yemen)", "source_record_id": "W7203850860", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1372, "title": "استخدام تطبیقات الذكاء الاصطناعي في تحسین عملیة اتخاذ القرار في المؤسسة الاقتصادیة", "title_ar": "استخدام تطبیقات الذكاء الاصطناعي في تحسین عملیة اتخاذ القرار في المؤسسة الاقتصادیة", "title_en": "", "author": "سعاد حيدة ; سليمة كادي ; أحمد بن العارية", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "ar", "research_domain": "artificial-intelligence", "research_subdomain": "", "categories": "artificial-intelligence", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W3153891852", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3153891852", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1373, "title": "إسهامات الذكاء الإصطناعي في الترجمة الآلية", "title_ar": "إسهامات الذكاء الإصطناعي في الترجمة الآلية", "title_en": "", "author": "محمد جزول", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "Algeria", "governorate": "", "publication_year": 2019, "language": "ar", "research_domain": "nlp", "research_subdomain": "machine-translation", "categories": "machine-learning; nlp; machine-translation", "keywords": "", "abstract": "ينبع هذا العمل من الرغبة في استكشاف مدى التقدم الذي حصل مؤخرا في مجال تقنيات التعلم الآلي في الترجمة الآلية. والغرض من هذه الدراسة هو اظهار التحسينات المستمرة في الذكاء الاصطناعي والذي اظهر نتائج مذهلة في مجال الترجمة", "source_url": "https:\/\/openalex.org\/W7135300530", "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/c239a08b-cabb-4cac-a8de-ad6e4051aa68\/download", "doi": "", "handle": "", "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen", "source_record_id": "W7135300530", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "id": 1374, "title": "Ad Campaigns Generation Using Deep Learning Approach", "title_ar": "Ad Campaigns Generation Using Deep Learning Approach", "title_en": "", "author": "Atef Ahmed", "supervisor": "", "degree_type": "unspecified", "degree_evidence": "", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "ar", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "deep-learning; rnn", "keywords": "", "abstract": "الحملة الإعلانية عبارة عن مجموعة من الرسائل الإعلانية التي تشارك نفس الفكرة والتي تحدد الرسالة الترويجية للعلامات التجارية والمنتجات والخدمات. الحملات عادة تستخدم عدة وسائط لترويج المنتجات والخدمات مثل النصوص أو الصور والفيديو. الحملات النصية تستخدم في محركات البحث مثل جوجل وبينج (Bing) , بينما حملات الصور والفيديو تستخدم في منصات الشبكات الاجتماعية واليوتيوب. يعد إنشاء إعلان نصي يدويا تحديا كبيرا ويحتاج إلى خبير لإنشاء هذا الإعلان الذي يجب أن يستهدف العملاء. وهذا يتطلب الكثير من الوقت والمال والجهد لأنه يتعين على الخبير إنشاء واختبار حملات نصية عديدة تتطابق مع اهتمام واحتياجات العميل. بالإضافة إلى ذلك، فإن بعض الشركات لديها فهم محدود لعملائها بسبب أن بيانات العملاء مبعثرة في أماكن متعددة مثل رسائل البريد الإلكتروني وأوامر الشراء، لذلك، تحتاج الشركات إلى فهم شامل لاتخاذ الإجراءات والقرارات في الوقت المناسب. في هذا البحث، قمنا بتطوير نظام قادر على توليد حملات إعلانية نصية لمحركات البحث مثل Google وBing استنادا إلى الكلمات الرئيسية التي يبحث عنها العميل. التقنية الخاصة بنا تتكون من أربع مراحل رئيسية. المرحلة الأولى هي جمع بيانات عالية الجودة، أما المرحلة الثانية تتضمن معالجة البيانات، والمرحلة الثالثة هي تطوير نظام إنشاء حملات الإعلانات باستخدام الشبكة العصبية المتكررة (RNN) والتعلم العميق. هذا العمل يختبر معماريات مختلفة مثل LSTM وGRU. المرحلة الأخيرة في هذا البحث هي التقييم. قمنا بإجراء العديد من التجارب لتحديد أفضل نموذج وذلك بالتحقق لأقل قيمة للخطأ أثناء عميلة التعلم والـ Perplexity (PPL) للنص الذي تم إنشاؤه. يتم تقييم النتائج باستخدام مقياسين وهما قابلية القراءة للنص المولد مدى علاقته بالكلمات المفتاحية. تم تقييم مقياس قابلية القراءة بواسطة مختصين بشريين ومكتبة برمجية، في حين تم تقييم علاقة النص المولد بالكلمات المفتاحية فقط بواسطة تقييم بشري. يظهر نتائج التقييم أن النصوص التي تم إنشاؤها بواسطة الشبكة العصبية العميقة (DNN) على الأغلب سهلة القراءة ولها علاقة بالكلمات المفتاحية.", "source_url": "https:\/\/openalex.org\/W2980533915", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2980533915", "other_sources": "", "verification_status": "needs_review", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" } ] }