{ "exported_at": "2026-11-15 21:35:00", "count": 471, "records": [ { "id": 2, "title": "Explainability of possibilistic and fuzzy rule-based systems", "title_ar": "", "title_en": "", "author": "Ismaïl Baaj", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2022, "language": "fr", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; expert-systems; xai", "keywords": "", "abstract": "Explicabilité des systèmes à base de règles possibilistes et floues Aujourd'hui, les progrès de l'Intelligence Artificielle (IA) ont conduit à l'émergence de systèmes capables d'automatiser des processus complexes, en utilisant des modèles qui peuvent être difficiles à comprendre par les humains. Lorsque les humains utilisent ces systèmes d'IA, il est bien connu qu'ils veulent comprendre leurs comportements et leurs actions, car ils ont davantage confiance dans les systèmes qui peuvent expliquer leurs choix, leurs hypothèses et leurs raisonnements. La capacité d'explication des systèmes d'IA est devenue une exigence des utilisateurs, en particulier dans les environnements à risque pour l'homme, comme les véhicules autonomes ou la médecine. Cette exigence reflète le récent regain d'intérêt pour l'intelligence artificielle eXplicable (abrégée en XAI), un domaine de recherche qui vise à développer des systèmes d'IA capables d'expliquer leurs résultats d'une manière compréhensible pour les humains. Dans ce contexte, nous introduisons des paradigmes explicatifs pour deux types de systèmes d'IA : un système à base de règles possibilistes (où les règles encodent des informations négatives) et un système à base de règles floues composé de règles à possibilité, qui encodent des informations positives. Nous développons une interface possibiliste entre l'apprentissage et le raisonnement basé sur des règles si-alors qui établit des points de rencontre entre Knowledge Representation and Reasoning (KRR) et Machine Learning (ML). Cette interface est construite en généralisant le système d'équations min-max de Henri Farreny et Henri Prade, qui a été développé pour l'explicabilité des systèmes à base de règles possibilistes. Dans le cas d'une cascade, c'est-à-dire lorsqu'un système utilise deux ensembles de règles possibilistes chaînés, nous montrons que nous pouvons représenter son système d'équations associé par un réseau de neurones min-max explicite. Cette interface peut permettre le développement de méthodes d'apprentissage possibilistes qui seraient cohérentes avec le raisonnement à base de règles. Pour XAI, nous introduisons des méthodes pour justifier les résultats d'inférence des systèmes à base de règles possibilistes et floues. Nos méthodes conduisent à former deux types d'explications d'un résultat d'inférence de ces systèmes : sa justification et son caractère inattendu (un ensemble d'énoncés logiques qui ne sont pas impliqués dans la détermination du résultat considéré tout en étant liés à celui-ci). Enfin, nous proposons une représentation graphique de l'explication d'un résultat d'inférence d'un système à base de règles possibilistes ou floues en termes de graphes conceptuels. Pour un résultat d'inférence, nous représentons sa justification, son caractère inattendu et une combinaison de sa justification et de son caractère inattendu.", "source_url": "https:\/\/openalex.org\/W7150879522", "pdf_url": "https:\/\/theses.hal.science\/tel-03647652v1\/file\/BAAJ_Ismail_2022.pdf", "doi": "10.70675\/a0a60f05z4d42z4169z92cfz1b841e53be1a", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7150879522", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 126, "title": "Machine learning in E-learning environments : Optimisation of efficiency and text classification through linguistic feature selection and tokenization", "title_ar": "", "title_en": "", "author": "Daniel Schönle", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2024, "language": "fr", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; nlp; ai-applications; speech-recognition; ai-education", "keywords": "", "abstract": "Apprentissage automatique dans les environnements de formation en ligne : optimisation de l'efficacité et de la classification des textes par la sélection de caractéristiques linguistiques et la tokenisation L'optimisation des capacités de classification de texte en ML trouve des applications majeures dans les environnements d'apprentissage adaptatifs (ALE) et les systèmes de chatbot. Les ALE exploitent le ML pour s'ajuster aux besoins individuels des apprenants, améliorant ainsi l'apprentissage autorégulé tout en fournissant des données utiles aux éducateurs. Les chatbots, quant à eux, renforcent l'expérience éducative à travers des interactions d'apprentissage personnalisées et une évaluation adaptée. Une enquête sur les capacités des ALE explore leurs limites et défis, tout en mettant l’accent sur le traitement de données robustes. Concernant l'intégration des chatbots, une évaluation dans le cadre du projet FiSK démontre leur impact sur la formation des enseignants, en particulier par l'amélioration des simulations d'apprentissage et le perfectionnement des compétences didactiques. Le modèle WUP (Weighted Unimportant Part-of-Speech) permet une sélection améliorée des caractéristiques textuelles en filtrant les données en fonction de l'importance des parties du discours, augmentant la précision des prédictions ML. De plus, la méthode LinPair affine la tokenisation WordPiece en incorporant des informations linguistiques, surmontant les limitations liées au vocabulaire restreint. Une mesure d'efficacité, QCO (Quality-Focused Optimization), est introduite pour évaluer la qualité des modèles ML en fonction des ressources consommées et du temps de calcul. Enfin, des améliorations dans l'automatisation de l'évaluation des réponses des étudiants sont démontrées via l'utilisation de grands modèles linguistiques, grâce à des techniques avancées de traitement du langage naturel.", "source_url": "https:\/\/openalex.org\/W7151732118", "pdf_url": "https:\/\/theses.hal.science\/tel-05295859v1\/document", "doi": "10.70675\/36864c68z31f7z47ecz985dza74020a9b53e", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7151732118", "other_sources": "", "verification_status": "verified", "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": 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": 140, "title": "Anomaly-based network intrusion detection using machine learning", "title_ar": "", "title_en": "", "author": "Maxime Labonne", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2020, "language": "fr", "research_domain": "machine-learning", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; ai-applications; ai-cybersecurity; ai-industry", "keywords": "", "abstract": "Détection d'intrusion réseau par anomalies avec apprentissage automatique Ces dernières années, le piratage est devenu une industrie à part entière, augmentant le nombre et la diversité des cyberattaques. Les menaces qui pèsent sur les réseaux informatiques vont des logiciels malveillants aux attaques par déni de service, en passant par le phishing et l'ingénierie sociale. Un plan de cybersécurité efficace ne peut plus reposer uniquement sur des antivirus et des pare-feux pour contrer ces menaces : il doit inclure plusieurs niveaux de défense. Les systèmes de détection d'intrusion (IDS) réseaux sont un moyen complémentaire de renforcer la sécurité, avec la possibilité de surveiller les paquets de la couche 2 (liaison) à la couche 7 (application) du modèle OSI. Les techniques de détection d'intrusion sont traditionnellement divisées en deux catégories : la détection par signatures et la détection par anomalies. La plupart des IDS utilisés aujourd'hui reposent sur la détection par signatures ; ils ne peuvent cependant détecter que des attaques connues. Les IDS utilisant la détection par anomalies sont capables de détecter des attaques inconnues, mais sont malheureusement moins précis, ce qui génère un grand nombre de fausses alertes. Dans ce contexte, la création d'IDS précis par anomalies est d'un intérêt majeur pour pouvoir identifier des attaques encore inconnues.Dans cette thèse, les modèles d'apprentissage automatique sont étudiés pour créer des IDS qui peuvent être déployés dans de véritables réseaux informatiques. Tout d'abord, une méthode d'optimisation en trois étapes est proposée pour améliorer la qualité de la détection : 1\/ augmentation des données pour rééquilibrer les jeux de données, 2\/ optimisation des paramètres pour améliorer les performances du modèle et 3\/ apprentissage ensembliste pour combiner les résultats des meilleurs modèles. Les flux détectés comme des attaques peuvent être analysés pour générer des signatures afin d'alimenter les bases de données d'IDS basées par signatures. Toutefois, cette méthode présente l'inconvénient d'exiger des jeux de données étiquetés, qui sont rarement disponibles dans des situations réelles. L'apprentissage par transfert est donc étudié afin d'entraîner des modèles d'apprentissage automatique sur de grands ensembles de données étiquetés, puis de les affiner sur le trafic normal du réseau à surveiller. Cette méthode présente également des défauts puisque les modèles apprennent à partir d'attaques déjà connues, et n'effectuent donc pas réellement de détection d'anomalies. C'est pourquoi une nouvelle solution basée sur l'apprentissage non supervisé est proposée. Elle utilise l'analyse de l'en-tête des protocoles réseau pour modéliser le comportement normal du trafic. Les anomalies détectées sont ensuite regroupées en attaques ou ignorées lorsqu'elles sont isolées. Enfin, la détection la congestion réseau est étudiée. Le taux d'utilisation de la bande passante entre les différents liens est prédit afin de corriger les problèmes avant qu'ils ne se produisent.", "source_url": "https:\/\/openalex.org\/W7149556586", "pdf_url": "https:\/\/theses.hal.science\/tel-02988296\/document", "doi": "10.70675\/7f7fbc77zaab9z4f60z9b41z7236bc47d138", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7149556586", "other_sources": "", "verification_status": "verified", "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": 152, "title": "Optimization of intrusion detection systems based on machine learning for cybersecurity by boosting feature engineering and model selection techniques", "title_ar": "", "title_en": "Optimization of intrusion detection systems based on machine learning for cybersecurity by boosting feature engineering and model selection techniques", "author": "Xavier Andrés Larriva Novo", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «PhD»", "university": "Technical University of Madrid", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-cybersecurity", "keywords": "", "abstract": "This PhD Thesis Works was developed in the context of intrusion detection systems based on Machine Learning techniques and contribute to the performance of intrusion detection systems based on feature engineering and Machine Learning model optimization. Machine Learning is in a fortunate position that makes systematic experimentation easy and profitable. In this way, the development of new systems such as intrusion detection systems based on Machine Learning has proven their effectiveness in terms of accuracy and reduced the number of false alarms. However, these systems are constantly evolving and need further research to become even more efficient and accurate. Intrusion detection systems based on Machine Learning are able to extrapolate data and detect anomalies. Nevertheless, these systems need optimal features to detect the possibility of an attack. The main objective of this PhD Thesis work is to design, implement and validate an intrusion detection system capable of reliably detecting cyber attacks. This system will respond to potential attacks by considering the optimization of different phases of a Machine Learning model, such as feature selection, data preprocessing, and model selection. This system will process information quickly and efficiently, considering the current state of the art in the area. In this PhD thesis work, an intrusion detection system has been designed based on feed-forward neural network and recurrent neural networks. Also, a characterization of cybersecurity datasets based on these models has been developed. The result is a model that allows a characterization to be applied to different network scenarios for cybersecurity anomaly detection. Based on this model, a distributed preprocessing model for intrusion detection systems based on Machine Learning was developed for real cybersecurity datasets with current attacks. The result was an intrusion detection system capable of processing real-world, large-scale cybersecurity datasets with high accuracy. In addition, this PhD Thesis proposes the development of a dynamic model selector capable of making the best prediction for individual Machine Learning-based intrusion detection systems, thus increasing the overall accuracy in terms of detection rate for different types of attacks. Finally, all contributions were evaluated against related state-of-the-art studies and are presented through this document as a compendium of articles for this PhD Thesis work. ----------RESUMEN---------- Este trabajo de tesis doctoral se desarrolló en el contexto de los sistemas de detección de intrusos basados en técnicas de aprendizaje automático y contribuye al rendimiento de los sistemas de detección de intrusos basados en la ingeniería de atributos y la optimización del modelo. El aprendizaje automático se encuentra en una posición afortunada que hace que la experimentación sistemática sea fácil y rentable. De esta forma, el desarrollo de nuevos sistemas como los sistemas de detección de intrusos basados en aprendizaje automático han demostrado su eficacia en cuanto a precisión y reducido el número de falsas alarmas. Sin embargo, estos sistemas están en constante evolución y necesitan más investigación para ser aún más eficientes y precisos. Los sistemas de detección de intrusos basados en el aprendizaje automático son capaces de extrapolar datos y detectar anomalías. Sin embargo, estos sistemas necesitan características óptimas para detectar de manera acertada la posibilidad de un ataque. El objetivo principal de esta tesis es diseñar, implementar y validar un sistema de detección de intrusos capaz de detectar ciberataques de manera confiable. Este sistema responderá a posibles ataques considerando la optimización de diferentes fases de un modelo de aprendizaje automático, como la selección de características, el preprocesamiento de datos y la selección del modelo. Este sistema procesará la información de manera rápida y eficiente, considerando el estado del arte actual en el área. En esta tesis doctoral se ha diseñado un sistema de detección de intrusiones basado en una red neuronal feed-forward y una red neuronal recurrente. Asimismo, se ha desarrollado una caracterización de conjuntos de datos de ciberseguridad en base a estos modelos. El resultado es un modelo que permite aplicar una caracterización a diferentes escenarios de red para la detección de anomalías de ciberseguridad. Sobre la base de este modelo, se desarrolló un modelo de preprocesamiento distribuido para sistemas de detección de intrusos para conjuntos de datos reales con ataques reales. El resultado fue un sistema de detección de intrusos capaz de procesar datos a gran escala con alta precisión.", "source_url": "https:\/\/openalex.org\/W4210657881", "pdf_url": "https:\/\/oa.upm.es\/69604\/1\/XAVIER_LARRIVA_NOVO.pdf", "doi": "10.20868\/upm.thesis.69604", "handle": "", "repository_name": "UPM Digital Archive (Technical University of Madrid)", "source_record_id": "W4210657881", "other_sources": "", "verification_status": "verified", "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": 159, "title": "Trustable machine learning for intrusion detection systems", "title_ar": "", "title_en": "", "author": "Robin Duraz", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2024, "language": "fr", "research_domain": "computer-vision", "research_subdomain": "ai-cybersecurity", "categories": "machine-learning; computer-vision; ai-applications; ai-cybersecurity", "keywords": "", "abstract": "Apprentissage automatique de confiance pour les systèmes de détection d'intrusion Les systèmes de détection d'intrusion sont des composants essentiels à la défense de notre écosystème numérique. Récemment, les avancées en apprentissage automatique ont permis de développer de nouveaux types de système de détection d'intrusion, permettant de s'éloigner du besoin de créer des règles de détection de plus en plus complexes. Ces systèmes de détection utilisant l'apprentissage automatique sont capables d'apprendre de façon autonome différents comportements, à la condition d'avoir un jeu de données bien calibré. Le contexte de la cybersécurité amène des besoins spécifiques, des besoins qui sont différents des tâches d'apprentissage automatique les plus courantes : la reconnaissance d'images et le traitement du langage. Cela implique qu'il faut adapter les différents procédés utilisés en apprentissage automatique pour répondre à ces attentes. Étant dans un environnement ayant d'importants enjeux, les systèmes de détection d'intrusion devraient plutôt être utilisés pour aider à la prise de décisions, mais cela reste essentiel de pouvoir leur faire confiance. De ce fait, dans cette thèse, nous avons dans un premier temps développé une nouvelle métrique basée sur les scores CVSS, permettant d'intégrer des connaissances de cybersécurité dans le processus d'évaluation des systèmes de détection d'intrusion. Nous nous sommes ensuite concentrés sur le besoin de comprendre des décisions qui ne sont pas compréhensibles. Même si le domaine de l'explicabilité n'est pas assez avancé pour correctement expliquer ces décisions, cela reste possible de vérifier la confiance en celles-ci d'une façon plus robuste, amenant à examiner ou corriger d'éventuelles erreurs. Finalement, nous nous sommes efforcés de compléter les approches existantes en utilisant des techniques d'apprentissage automatique récentes pour augmenter la capacité à détecter de nouvelles cyberattaques. Toutes ces méthodes contribuent donc à créer des systèmes de détection d'intrusion utilisant l'apprentissage automatique qui sont plus fiables.", "source_url": "https:\/\/openalex.org\/W7152081341", "pdf_url": "https:\/\/theses.hal.science\/tel-04929212v1\/file\/2024IMTA0433_Duraz-Robin_Diffusion.pdf", "doi": "10.70675\/e2fe5f32z19e5z41caz9b55z7b33b874c3c0", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7152081341", "other_sources": "", "verification_status": "verified", "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": 161, "title": "Generative Quantum Machine Learning", "title_ar": "", "title_en": "Generative Quantum Machine Learning", "author": "Christa Zoufal", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «doctoral thesis»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "generative-ai", "research_subdomain": "gan", "categories": "machine-learning; generative-ai; quantum-ml; gan", "keywords": "", "abstract": "The goal of generative machine learning is to model the probability distribution underlying a given data set. This probability distribution helps to characterize the generation process of the data samples. While classical generative machine learning is solely based on classical resources, generative quantum machine learning can also employ quantum resources - such as parameterized quantum channels and quantum operators - to learn and sample from the probability model of interest. Applications of generative (quantum) models are multifaceted. The trained model can generate new samples that are compatible with the given data and extend the data set. Additionally, learning a model for the generation process of a data set may provide interesting information about the corresponding properties. With the help of quantum resources, the respective generative models have access to functions that are difficult to evaluate with a classical computer and may improve the performance or lead to new insights. Furthermore, generative quantum machine learning can be applied to efficient, approximate loading of classical data into a quantum state which may help to avoid - potentially exponentially - expensive, exact quantum data loading. The aim of this doctoral thesis is to develop new generative quantum machine learning algorithms, demonstrate their feasibility, and analyze their performance. Additionally, we outline their potential application to efficient, approximate quantum data loading. More specifically, we introduce a quantum generative adversarial network and a quantum Boltzmann machine implementation, both of which can be realized with parameterized quantum circuits. These algorithms are compatible with first-generation quantum hardware and, thus, enable us to study proof of concept implementations not only with numerical quantum simulations but also real quantum hardware available today.", "source_url": "https:\/\/openalex.org\/W3212842386", "pdf_url": "https:\/\/arxiv.org\/pdf\/2111.12738", "doi": "10.3929\/ethz-b-000514692", "handle": "", "repository_name": "arXiv (Cornell University)", "source_record_id": "W3212842386", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 162, "title": "Quantum Machine Learning for Industrial Applications", "title_ar": "", "title_en": "", "author": "Léo Monbroussou", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2025, "language": "fr", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; ai-applications; quantum-ml; ai-industry", "keywords": "", "abstract": "Machine learning quantique appliqué à des cas d'usage industriels Cette thèse explore le développement de méthodes en Apprentissage Automatique Quantique (Quantum Machine Learning, QML) destinées aux applications industrielles, en mettant l'accent sur la jonction entre les résultats théoriques et les contraintes du matériel quantique actuel. L'apprentissage automatique classique a profondément transformé des secteurs tels que la santé, la finance ou la logistique, mais il se heurte aujourd'hui à plusieurs limites : croissance exponentielle des données, coûts de calcul élevés, enjeux de confidentialité et de sécurité, ainsi que la consommation énergétique massive des grands modèles. L'informatique quantique, initialement proposée pour la simulation des systèmes physiques, est apparue comme une piste prometteuse pour accélérer certaines tâches d'apprentissage, mais de nombreux algorithmes nécessitent des ordinateurs quantiques tolérants aux fautes, encore inaccessibles. Les algorithmes variationnels quantiques, plus adaptés aux machines bruitées, représentent les candidats les plus réalistes, bien qu'ils souffrent encore de problèmes d'entrainement, de ressources théoriques pour évaluer leur utilité, et de la difficulté à garantir que ces modèles quantiques ne peuvent être simulés ou approximer classiquement. La première partie de la thèse étudie ces défis à travers les circuits quantiques préservant des sous-espaces, et en particulier les circuits préservant le poids de Hamming des états. Ces derniers présentent des propriétés favorables à l'entrainement. Toutefois, ces régimes se révèlent souvent classiquement simulables dans un temps polynomial, ce qui illustre la tension entre entraînement et avantage quantique. Est alors proposé de se concentrer sur des algorithmes limités à des avantages polynomiaux, mais possédant des garanties de convergence. Une attention particulière est portée sur les architectures photoniques, qui préservent naturellement le nombre de particules et permettent des cadences de répétition élevées. Leur contrôlabilité est caractérisée et de nouveaux schémas, bien que sous-optimaux, sont proposés afin d'exploiter des avantages polynomiaux susceptibles d'avoir une valeur concrète dans un cadre industriel. Sur ces bases, la thèse introduit une famille d'algorithmes quantiques préservant des sous-espaces et qui mimiquent des briques élémentaires de l'apprentissage classique. Ces méthodes allient garanties théoriques, bonnes propriétés d'entrainement, offrant une voie pragmatique pour l'adoption industrielle de l'apprentissage quantique à court terme, même en l'absence d'avantage exponentiel. La seconde partie propose un cadre théorique fondé sur l'analyse de Fourier des circuits variationnels. Cette approche permet de traiter conjointement l'expressivité et la trainabilité, et de comparer systématiquement les circuits quantiques aux méthodes classiques. Elle met en évidence des conditions de convergence distinctes et précise les cas où des modèles approchés (surrogates) suffisent à simuler le comportement quantique. Ces résultats apportent des outils conceptuels pour concevoir des circuits résistants à une simulation classique efficace et pour mieux cerner les situations où un véritable avantage quantique peut émerger. En combinant l'étude de la trainabilité, la conception d'architectures adaptées et le développement d'outils analytiques, cette thèse contribue à définir les conditions dans lesquelles l'apprentissage quantique peut avoir un impact industriel réel. Elle montre que si l'avantage exponentiel reste difficile à atteindre avec les dispositifs actuels, des gains polynomiaux — notamment dans des architectures photoniques à haut débit — peuvent déjà constituer un atout compétitif. Les résultats offrent ainsi un cadre théorique et algorithmique pour guider la conception de modèles quantiques équilibrant expressivité, trainabilité et résistance à la simulation classique, et ouvrent la voie à des applications industrielles concrètes du QML.", "source_url": "https:\/\/openalex.org\/W7164192938", "pdf_url": "https:\/\/arxiv.org\/pdf\/2606.14822", "doi": "10.70675\/78b65d31z6c74z4ccfz8ce3zdbde6bf748e9", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7164192938", "other_sources": "", "verification_status": "verified", "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": 164, "title": "Quantum Machine Learning Applications and Algorithms", "title_ar": "", "title_en": "", "author": "Marco Russo", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «Ph.D»", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2026, "language": "it", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml", "keywords": "", "abstract": "This dissertation is presented in partial fulfillment of the requirements for Ph.D. degree in the Graduate School of Politecnico di Torino (ScuDo).La scrittura scientifica è notoriamente fredda, austera, priva di emozioni.Qualsiasi risultato ottenuto da un esperimento, sia esso inconcludente o grandioso, viene presentato con la solita formula \"in questa figura e in quella tabella si può vedere che\", immancabilmente seguito da un \"possiamo quindi dedurre che\".Poi un abisso, oltre il quale c'è un altro mondo, quello dell'espressione dell'anima, dei nostri moti interiori e della consapevolezza di essi.Abbiamo deciso appunto di metterci un abisso in mezzo, e in fondo non è che si possa pretendere altrimenti; la scienza è questione di fatti e non di opinioni.Ma dentro colui che scrive con freddezza, nel momento in cui lo fa, il suo sentire non si spegne.Meglio allora non smettere mai di avere la testa nel secondo mondo anche se ci tocca lavorare nel primo.Il sentire di chi scrive si nasconde tra ogni riga.E se dal punto di vista dell'utile conta quello che nelle prossime pagine sarà nero su bianco, quel che importa a me sono in realtà le righe invisibili.Ognuna di queste racchiude chi sono mentre scrivo, e le persone e le cose che in qualche modo, in fasi diverse di questo percorso, hanno avuto un impatto di cui magari non si rendono conto.È per loro che quelle righe bianche sono scritte.Per mio fratello, in cui vedo molta più forza di quanta ne veda lui in sé stesso, e che, anche se ogni tanto la marea glielo nasconde e lui se lo dimentica, è così in gamba che alla fine si guarderà indietro ridendo.Per mia madre, che non ha mai potuto fare a meno di essere preoccupata per qualsiasi cosa, che ha sempre saputo ascoltarmi in ogni momento mettendo da parte ogni impegno, e che quando sono andato in posti incredibili sembrava vivere quelle cose attraverso i miei occhi.Una vita passata a sentire più di noi le nostre stesse emozioni, sognando con noi.", "source_url": "https:\/\/openalex.org\/W7217833618", "pdf_url": "https:\/\/iris.polito.it\/bitstream\/11583\/3012489\/2\/phd_thesis.pdf", "doi": "", "handle": "", "repository_name": "PORTO@Iris", "source_record_id": "W7217833618", "other_sources": "", "verification_status": "verified", "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": 166, "title": "Variational algorithms for quantum machine learning", "title_ar": "", "title_en": "Variational algorithms for quantum machine learning", "author": "Filippo Orazi", "supervisor": "", "degree_type": "phd", "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna)", "university": "University of Bologna", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; quantum-ml; predictive-analytics; llm", "keywords": "", "abstract": "In recent years, we have observed a new revolution in computer science, driven by the increasing availability of computational power and data. This historic event has seen the capillary diffusion of what is commonly known as artificial intelligence (AI). In particular, we have seen a rapid improvement of large language models that now can interact with people on a level that appeared impossible just a few years ago. Despite this newly found focus on AI and computer science, the technical difficulties that limit the increase in computational power are increasing, and the prediction described by Moore's law no longer holds. For this reason physicists, computer scientists, mathematicians, and engineers are studying another computational paradigm called quantum computing. Similar to the rise of large language models, quantum computing has gained significant attention in the last ten years due to the development of initial quantum hardware and its rapid advancements. The quantum computing paradigm offers a fundamentally different set of tools with respect to what is called classical computation. Quantum properties and effects like superposition, entanglement, and interference give us the ability to solve problems that we could not approach classically. In this thesis, we explore the intersection between artificial intelligence and quantum computing from an experimental point of view. We researched quantum AI models, utilizing both the classical and quantum paradigms using variational architectures. This thesis is organized as follows: part 1 contains an introduction to quantum computation, starting from the postulate of quantum mechanics, through an introduction to QML and finally the state of the art. Part 2 contains the description of the research we performed. It's divided into two main chapters containing fundamentally different approaches to quantum computing. Each chapter contains section illustrating our research on that approach.", "source_url": "https:\/\/openalex.org\/W7204115278", "pdf_url": "https:\/\/amsdottorato.unibo.it\/id\/eprint\/13067\/1\/orazi_filippo_tesi.pdf", "doi": "10.48676\/unibo\/amsdottorato\/13067", "handle": "", "repository_name": "AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna)", "source_record_id": "W7204115278", "other_sources": "", "verification_status": "verified", "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": 176, "title": "A Novel Framework for Quantum Machine Learning", "title_ar": "", "title_en": "A Novel Framework for Quantum Machine Learning", "author": "Antonio Macaluso", "supervisor": "", "degree_type": "phd", "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna)", "university": "University of Bologna", "university_source": "repository_host", "college": "", "department": "", "country": "Italy", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "deep-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; deep-learning; quantum-ml", "keywords": "", "abstract": "Quantum computation is an emerging computing paradigm with the potential to revolutionise the world of information technology. It leverages the laws of quantum mechanics to endow quantum machines with tremendous computing power, thus enabling the solution of problems impossible to address with classical devices. For this reason, the field is attracting ever-increasing attention from both academic and private sectors, and its full potential is still to be understood. This dissertation investigates how classical machine learning can benefit from quantum computing and provides several contributions to the emerging field of Quantum Machine Learning. The idea is to provide a universal and efficient framework that can reproduce the output of a plethora of classical machine learning algorithms exploiting quantum computation’s advantages. The proposed framework is named Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine multiple functions to solve typical supervised learning tasks. Thanks to this property, in its general formulation MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions. The theoretical design of the quantum algorithm and the corresponding circuit’s implementation are presented. As a second meaningful addition, two practical applications are illustrated: the quantum version of ensemble methods and neural networks. The final contribution addresses the restriction to linear operations imposed by quantum mechanics. The idea is to exploit a quantum transposition of classical Splines to approximate non-linear functions, thus overcoming this limitation and introducing significant advantages in terms of computational complexity theory.", "source_url": "https:\/\/openalex.org\/W3205058257", "pdf_url": "http:\/\/amsdottorato.unibo.it\/9791\/2\/Antonio_Macaluso_tesi.pdf", "doi": "10.48676\/unibo\/amsdottorato\/9791", "handle": "", "repository_name": "AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna)", "source_record_id": "W3205058257", "other_sources": "", "verification_status": "verified", "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": 182, "title": "Noise and trainability in quantum machine learning", "title_ar": "", "title_en": "", "author": "Valentin Heyraud", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2023, "language": "fr", "research_domain": "machine-learning", "research_subdomain": "quantum-ml", "categories": "machine-learning; quantum-ml", "keywords": "", "abstract": "Bruit et capacité d'apprentissage en apprentissage automatique quantique Cette thèse est consacrée à l'exploration de l'interface entre l'informatique quantique et l'apprentissage automatique, en mettant l'accent sur les effets du bruit et de la décohérence. Dans la première partie, nous étudions l'utilisation de systèmes quantiques ouverts pour la réalisation de tâches classiques de reconnaissance des formes. En particulier, nous nous intéressons à l'impact du bruit sur les machines à noyau quantique dans une configuration de calcul à réservoir. Les modèles que nous considérons sont basés sur l'utilisation d'un système quantique de grande taille et non contrôlé, un réservoir, qui est excité par un signal d'entrée à traiter. Des mesures sont ensuite effectuées sur le système et une combinaison linéaire des résultats est optimisée afin de réaliser la tâche souhaitée. Dans le cadre théorique associé aux méthodes à noyau, nous analysons l'effet de la dissipation sur l'expressivité de ces modèles. Nous montrons que le bruit affectant le réservoir peut agir comme une régularisation implicite qui aide à prévenir l'ajustement excessif. Ces résultats sont accompagnés d'une étude numérique d'un ensemble de machines à noyaux bruitées basées sur des chaînes de spins quantiques sujets à de la décohérence, et dont l'évolution markovienne est décrite par une équation maîtresse de type Lindblad. Dans la deuxième partie de la thèse, nous nous concentrons sur les algorithmes quantiques variationnels. Nous y présentons un schéma de simulation classique efficace pour estimer des quantités moyennées à la sortie d'un circuit quantique paramétré dont les paramètres de rotation sont choisis aléatoirement. Nous étudions d'abord les canaux quantiques associés aux moyennes de rotations aléatoires d'un et de deux qubits autour d'un axe Z. Sous certaines hypothèses, nous montrons que ces canaux peuvent être décomposés en sommes convexes de canaux associés à des transformations unitaires de Clifford. Ce résultat peut être interprété comme un effet de décohérence artificiel induit par le choix aléatoire des angles de rotation. Il nous permet construire un schéma de simulation efficace basé sur le célèbre théorème de Gottesman-Knill. Cette méthode permet notamment d'estimer efficacement l'amplitude moyenne du gradient de la fonction de coût par des simulations classiques avec une complexité polynomiale en le nombre de qubits. Elle peut donc être utilisée pour explorer des stratégies de conception de circuits variationnels quantiques permettant d'éviter les difficultés d'entrainement due à une disparition du gradient.", "source_url": "https:\/\/openalex.org\/W4402623683", "pdf_url": "https:\/\/theses.hal.science\/tel-04702049\/document", "doi": "10.70675\/bff70fa6zf210z4833zb75cze3a22be94ef6", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W4402623683", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 209, "title": "Trustful AI through fair and privacy-preserving federated machine learning", "title_ar": "", "title_en": "", "author": "Nawel Benarba", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2026, "language": "fr", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; fairness", "keywords": "", "abstract": "Intelligence artificielle digne de confiance grâce à l'apprentissage fédéré équitable et respectueux de la vie privée L'augmentation massive du volume de données disponibles a transformé l'informatique moderne, contribuant à l'essor de l'apprentissage automatique dans plusieurs domaines tels que la santé, la finance et les systèmes autonomes. Cette dépendance croissante aux modèles d'apprentissage pour la prise de décision dans ces domaines soulève des préoccupations majeures en matière d'équité et de confidentialité, étant donné que les décisions qu'ils produisent peuvent influencer de manière significative les individus et les organisations dans des contextes d'application réels. Motivés par la nécessité de mieux comprendre et d'atténuer ces enjeux, la présente recherche étudie les défis associés à ces problématiques et propose des contributions notables en faveur de systèmes d'apprentissage plus responsables et plus fiables. La première partie de cette thèse porte sur l'équité, devenue une préoccupation centrale en apprentissage automatique en raison de son potentiel à amplifier les disparités et à renforcer les biais existants. Ce constat, associé à l'émergence de régulations telles que l'AI Act de l'Union européenne et l'Executive Order américain, souligne la nécessité d'intégrer l'équité comme principe fondamental dans la conception des systèmes d'apprentissage automatique. Compte tenu de cela, nous avons examiné comment les méthodes de sélection automatique de données, souvent utilisées pour améliorer l'efficacité en termes de temps en utilisant des sous-ensembles plus petits d'entraînement plutôt que le jeu de données complet, influencent l'équité et la qualité des modèles. Nous avons mené la première analyse à grande échelle révélant l'impact souvent ignoré de la sélection sur ces deux aspects, nous avons identifié les facteurs clés qui en sont à l'origine et nous avons avancé des pistes pertinentes pour favoriser un apprentissage plus équitable et efficace. Parallèlement aux régulations promouvant un apprentissage plus équitable, d'autres insistent sur la protection des données utilisées, tel que le règlement général sur la protection des données (RGPD), qui a favorisé l'adoption de l'apprentissage fédéré, permettant à plusieurs parties d'entraîner un modèle global sans partager leurs données. Cependant, la nature distribuée et collaborative de ce paradigme d'apprentissage amplifie les biais hérités de l'apprentissage centralisé et introduit également de nouveaux types de biais. Pour éclairer ces enjeux, nous nous avons proposé une revue approfondie de l'équité en apprentissage fédéré, identifiant les principales sources de biais, couvrant l'ensemble des stratégies de mitigation existantes, et mettant en évidence des pistes de recherche encore ouvertes pour faire progresser l'équité dans ce cadre. Bien que l'apprentissage fédéré permette à plusieurs parties d'entraîner un modèle sans partager leurs données locales, de nombreuses études montrent qu'il reste vulnérable à diverses menaces portant atteinte à la vie privée des utilisateurs. Parmi ces menaces figurent les attaques d'inférence d'appartenance, qui permettent à un adversaire de déterminer si un point de données faisait partie de l'ensemble d'entraînement, exposant potentiellement des informations confidentielles. Dans ce contexte, la deuxième partie de cette thèse se concentre sur les atteintes à la vie privée en apprentissage fédéré, en accordant une attention particulière aux attaques d'inférence d'appartenance. Pour contrer ces attaques, nous avons introduit GAPER, une méthode granulaire et adaptative fondée sur la confidentialité différentielle, offrant une protection efficace contre ce type d'attaque tout en préservant la qualité du modèle, et soutenue à la fois par des garanties théoriques et des évaluations empiriques. De manière plus générale, ces travaux font progresser le développement de systèmes d'apprentissage non seulement performants, mais également fondamentalement alignés avec les principes d'équité et de confidentialité.", "source_url": "https:\/\/openalex.org\/W7170059833", "pdf_url": "https:\/\/theses.fr\/2026ISAL0013\/document", "doi": "10.70675\/17b0463eza470z4230za6edze526eaf22206", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7170059833", "other_sources": "", "verification_status": "verified", "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": 225, "title": "Sensitivity analysis and algorithmic fairness for machine learning and artificial intelligence", "title_ar": "", "title_en": "", "author": "Clément Bénesse", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2022, "language": "fr", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; fairness", "keywords": "", "abstract": "Analyse de sensibilité et équité algorithmique pour le machine learning et l'intelligence artificielle Au cours des dernières années, le déploiement d'algorithmes utilisant du Machine Learning a explosé. En permanence à la recherche de performances améliorées au prix de toujours plus de données, ces algorithmes ont révolutionné de nombreux domaines. Néanmoins, de plus en plus de problèmes transparaissent lors de l'utilisation de ces modèles. Dans cette thèse, nous nous intéressons à deux écueils particuliers que sont le manque d'explicabilité, ainsi que le défaut d'équité pouvant mener à des discriminations qui peut être présent dans ces algorithmes. Nous montrons comment ces deux sujets sont liés en présentant un cadre probabiliste unificateur permettant de transposer des techniques de l'un des domaines vers l'autre.", "source_url": "https:\/\/openalex.org\/W7151539384", "pdf_url": "https:\/\/utheme.utoulouse.fr\/files\/original\/dbbe0ed98fc2d0f31e153387c81e933fa6b09906.pdf", "doi": "10.70675\/0445eb0az81f4z4ea8z8a87z1a0fef986a20", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7151539384", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 228, "title": "Algorithmic fairness analyses of supervised machine learning in education", "title_ar": "", "title_en": "", "author": "Mélina Verger", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2024, "language": "fr", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "machine-learning; responsible-ai; ai-applications; fairness; ai-education", "keywords": "", "abstract": "Analyses de l'équité algorithmique pour l'apprentissage automatique en éducation Cette thèse vise à évaluer et réduire l'iniquité algorithmique des modèles d'apprentissage automatique largement utilisés en éducation. En effet, ces modèles prédictifs, fondés sur des données éducatives et des traces d'apprentissage de plus en plus abondantes, ont pour but d'améliorer l'expérience d'apprentissage humain. Ils permettent, par exemple, de prédire le décrochage scolaire ou de personnaliser l'expérience d'apprentissage en adaptant les contenus éducatifs selon les besoins de chaque apprenant et apprenante. Cependant, il a été démontré à plusieurs reprises que ces modèles peuvent produire des prédictions biaisées et discriminantes, le plus souvent du fait de prédictions systématiquement moins bonnes observées pour les personnes noires comparées aux personnes blanches, et pour les femmes comparées aux hommes. Il est donc devenu crucial d'évaluer l'équité des résultats des modèles prédictifs, vis-à-vis des différents groupes présents dans les données. Les travaux de l'état de l'art se sont concentrés sur la comparaison entre groupes des performances prédictives des modèles considérés. Par exemple, pour un classifieur binaire et pour les groupes hommes\/femmes, le taux de bonnes prédictions est calculé pour chaque groupe, et la différence entre ces taux indiquerait une iniquité. Bien que cette approche soit prédominante dans la littérature, elle ne capture l'iniquité qu'en termes de performance prédictive, alors que celle-ci peut se manifester sous d'autres formes et de manière plus nuancée qu'une différence de taux, ce qui doit être approfondi. L'objectif principal de cette thèse est donc d'approfondir la compréhension et l'évaluation de l'iniquité algorithmique, pour ensuite identifier son éventuelle présence dans des contextes sous-étudiés. Ces contextes incluent à la fois des attributs sensibles et des populations d'apprenants et apprenantes peu voire pas considérés. Pour ce faire, nous avons conçu une nouvelle mesure d'équité algorithmique, abrégée MADD, qui s'appuie sur les distributions des résultats des modèles d'apprentissage supervisé. Cette approche via les distributions permet en plus d'exploiter des analyses graphiques pour comprendre l'iniquité quantifiée par la MADD. Nous avons démontré théoriquement et expérimentalement la validité de cette mesure, et avons ainsi découvert que les potentielles iniquités observées dans les données ne sont pas toujours reflétées dans les résultats des modèles, comme par exemple cela a été le cas pour le biais de genre de nos expériences. De plus, nous avons développé une technique de réduction de l'iniquité utilisant la MADD, ainsi que de nouvelles méthodes d'évaluation pour plusieurs attributs sensibles simultanément. En effet, la littérature considère chaque attribut séparément, alors que la théorie de l'intersectionnalité de Crenshaw (1989, 1991) défend l'idée que leurs influences combinées produisent des discriminations uniques et différentes pour certains groupes. Nos résultats expérimentaux montrent que certaines combinaisons augmentent, réduisent, ou maintiennent le niveau d'iniquité observé initialement. Enfin, nous avons mené des analyses d'équité pour de nouveaux attributs sensibles, démographiques ou liés au contexte d'apprentissage, et avec de nouvelles populations d'apprenants et apprenantes, de pays d'Afrique, de Philippines, d'Haïti et de France, grâce à la collecte de données issues d'un MOOC (ou massive open online course) et d'une plateforme en ligne (Canvas LMS). Ces expériences ont révélé des iniquités qui n'ont pas été découvertes auparavant, apportant ainsi un éclairage sur les potentielles iniquités réelles présentes dans ces contextes d'étude. Afin de faciliter les réplications de nos travaux ainsi que l'application de nos méthodes dans d'autres contextes, nous avons créé une librairie Python open source, nommée maddlib. Les données (sauf celles des Philippines) et nos codes documentés sont également disponibles en ligne.", "source_url": "https:\/\/openalex.org\/W7151984572", "pdf_url": "https:\/\/theses.hal.science\/tel-05097943v1\/file\/145482_VERGER_2024_archivage.pdf", "doi": "10.70675\/cd7cfce4zdadaz4145zb07czdcb9669230ea", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7151984572", "other_sources": "", "verification_status": "verified", "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": 232, "title": "[MASTER THESIS] Machine learning for experimental bifurcation analysis", "title_ar": "", "title_en": "[MASTER THESIS] Machine learning for experimental bifurcation analysis", "author": "Grégoire Bourdouch", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «MASTER»", "university": "University of Liège", "university_source": "repository_host", "college": "", "department": "", "country": "Belgium", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "computer-vision", "research_subdomain": "cnn", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn", "keywords": "", "abstract": "Understanding and predicting the dynamic vibrational response of mechanical systems remains a core challenge in structural dynamics. Real-world structures frequently exhibit nonlinear behaviour, resulting in complex phenomena such as primary and secondary resonances, multistability, and the emergence of unstable response branches. These features lie beyond the reach of standard experimental modal analysis (EMA), which, based on linear assumptions and typically relying on swept-sine excitation, can only capture the stable portions of the frequency response. As a result, crucial information about bifurcations and unstable dynamics remains experimentally inaccessible. Although control-based continuation methods have been developed to overcome this limitation, their practical application is hindered by the need for precise controller tuning and complex feedback architectures. This thesis proposes a novel data-driven methodology that bridges this gap by combining standard EMA techniques with deep learning. Specifically, it investigates whether unstable branches of nonlinear frequency response curves (FRCs) can be predicted solely from experimental swept-sine measurements, which contain only the stable portions of the response. To the best of the author's knowledge, this is the first approach that enables the prediction of unstable branches of nonlinear frequency response curves from experimental swept-sine data alone. To this end, a supervised learning framework is developed and applied to the Duffing oscillator, a one-degree-of-freedom nonlinear system with cubic stiffness. A convolutional neural network is trained on synthetic datasets, with input data generated from binary image representations of swept-sine envelopes. The model successfully predicts not only the fundamental resonance but also subharmonic and superharmonic resonances, effectively reconstructing the full FRC. Crucially, the methodology is validated on an experimental electronic Duffing oscillator, confirming that unstable branches can indeed be predicted from swept-sine tests alone. The approach is then extended to other oscillator types and nonlinearity forms, demonstrating promising generalization capabilities. Overall, this work presents a practical and effective alternative to control-based methods for the analysis of nonlinear experimental data. It demonstrates the feasibility of integrating standard EMA techniques with machine learning to predict otherwise inaccessible dynamic features.", "source_url": "https:\/\/openalex.org\/W7154803031", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Open Repository and Bibliography (University of Liège)", "source_record_id": "W7154803031", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 233, "title": "Machine Learning Models for Industrial Process Monitoring : A study at IPercept", "title_ar": "", "title_en": "Machine Learning Models for Industrial Process Monitoring : A study at IPercept", "author": "Wijesundara Ranasinghe Appuhamilage, Iresha Subhashini", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "Dalarna University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-industry", "keywords": "", "abstract": "This study was conducted as part of a master’s thesis at KTH Royal Institute of Technology in collaboration with IPercept in Sweden. The study focuses on the development of an intelligent process monitoring system for a pressing machine of a washer manufacturing, integrating vibration signal analysis with machine learning techniques to enhance quality control, reduce downtime, and enable predictive maintenance, thereby optimizing process efficiency. The data analysis for a specific washer type with labeled data focuses on the Z-axis data, which aligns with the expected vertical pressing motion of the pressing machine. The CRISP-DM method is followed using Python programming with the Pandas Library to develop the model for process monitoring. The most significant features were selected for classification, and the boundary values for each feature were determined for the sub-signals corresponding to individual punches in the pressing machine. The monitoring process enables predictive maintenance, fault detection, and process optimization throughout the manufacturing operation.", "source_url": "https:\/\/openalex.org\/W7112572315", "pdf_url": "https:\/\/kth.diva-portal.org\/smash\/get\/diva2:2012630\/FULLTEXT01", "doi": "", "handle": "", "repository_name": "Diva portal (Dalarna University Library)", "source_record_id": "W7112572315", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 234, "title": "Optimizing Operating Costs at EMX Using Machine Learning-Based Forecasting And Capacity Planning Models", "title_ar": "", "title_en": "Optimizing Operating Costs at EMX Using Machine Learning-Based Forecasting And Capacity Planning Models", "author": "Sara Hussein Abu Iktish", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "American University of Sharjah", "university_source": "repository_host", "college": "", "department": "", "country": "United Arab Emirates", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "A Master of Science thesis in Engineering Systems Management by Sara Hussein Abu Iktish entitled, “Optimizing Operating Costs at EMX Using Machine Learning-Based Forecasting and Capacity Planning Models”, submitted in July 2025. Thesis advisor is Dr. Hussam AlShraideh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).", "source_url": "https:\/\/openalex.org\/W7162362814", "pdf_url": "", "doi": "10.82366\/411", "handle": "", "repository_name": "DSpace at AUS (American University of Sharjah)", "source_record_id": "W7162362814", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 235, "title": "Forecasting the Next Winning Stock: A Comparative Analysis of Machine Learning Models", "title_ar": "", "title_en": "Forecasting the Next Winning Stock: A Comparative Analysis of Machine Learning Models", "author": "Fernandez Mendez, Blanca Elvira", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "York University", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; predictive-analytics; transformers", "keywords": "", "abstract": "Stock price prediction is a common and complex problem due to the high volatility of financial markets. This master’s thesis presents a new approach to stock price forecasting by reformulating the problem as a multiclass classification task. The main objective is to predict which stock will yield the highest return the next day within a given set of features. To this end, various statistical and machine learning models are analyzed, with special emphasis on the Transformer model due to its relevance and alignment with the structure of this work. The present study proposes a novel idea to address the problem. Its contributions stand out in an initial exploratory analysis of model performance, as well as in risk minimization in investments, enabling portfolio diversification thanks to the Transformer model.", "source_url": "https:\/\/openalex.org\/W7112810442", "pdf_url": "", "doi": "", "handle": "", "repository_name": "York University Digital Library (York University)", "source_record_id": "W7112810442", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 236, "title": "Efficiency Advances in Moment Tensor Potentials for Machine-Learning Atomistic Modelling", "title_ar": "", "title_en": "Efficiency Advances in Moment Tensor Potentials for Machine-Learning Atomistic Modelling", "author": "Zijian Meng", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "Queen's University Library", "university_source": "repository_host", "college": "", "department": "", "country": "Canada", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "machine-learning", "research_subdomain": "ai-education", "categories": "machine-learning; ai-applications; ai-education; ai-smart-cities", "keywords": "", "abstract": "The study of nuclear materials is strongly complemented by computational materials science (CMS), which provides an alternative means of investigating radiation-induced phenomena, defect evolution, and thermophysical properties—all of which can be hazardous, technically challenging, or impractical to study using traditional experimental methods. Within CMS, a central focus is the accurate atomic-level description of potential energy surfaces and atomic forces, which enables a wide range of simulations. These descriptions, known as interatomic potentials, have recently seen significant advancement through machine learning models. Co-supervised by the Departments of Mechanical and Materials Engineering, and Electrical and Computer Engineering, this Master’s thesis applies the principles of CMS, high-performance computing, and machine learning to advance the computational efficiency of Machine Learning Interatomic Potentials (MLIPs). While emphasis is placed on a specific MLIP, the Moment Tensor Potential (MTP), the methodologies developed herein can be extended for more general use. This thesis presents three contributions, each associated with a manuscript that has been published in, submitted to, or is under internal review before submission to a peer-reviewed journal. The first contribution introduces an efficient small-cell active learning protocol, achieving an order-of-magnitude reduction in training costs while maintaining high fidelity. The second contribution enhances the scalability of the MTP through an improved CPU implementation and a new GPU implementation, enabling higher simulation rates and the modeling of million-atom systems. The third contribution proposes a novel post-training pruning algorithm that is both data-driven and cost-aware, optimizing the MTP’s algorithmic structure and achieving up to sevenfold speedups. Collectively, these contributions establish a more efficient and scalable MTP framework, expanding the scale of accurate atomistic simulations for nuclear materials and for computational materials research more broadly.", "source_url": "https:\/\/openalex.org\/W7206226763", "pdf_url": "", "doi": "", "handle": "", "repository_name": "QSpace (Queen's University Library)", "source_record_id": "W7206226763", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 237, "title": "Machine learning for smart building heating optimization", "title_ar": "", "title_en": "Machine learning for smart building heating optimization", "author": "Matěj VACHUTA", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master's»", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "deep-learning", "research_subdomain": "rnn", "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-smart-cities", "keywords": "", "abstract": "This master's thesis deals with the optimization of building heating control using machine learning methods. Traditional control systems often fail to efficiently respond to the thermal inertia of buildings and variable external influences. Model Predictive Control (MPC) addresses these shortcomings but faces challenges in identifying and maintaining accurate physical models. Therefore, this thesis proposes and evaluates a hybrid approach in a simulation environment, combining a nominal model predictive controller with a residual correction layer provided by a recurrent neural network (LSTM). An experimental comparison with three reference strategies (On\/Off, PID, and pure MPC) across three room types and six climate scenarios showed that the pro- posed hybridcontroller statisticallysignificantly reducesthe timeof thermalcomfort violation. The greatest benefit was observed in spaces with variable occupancy and high nominal model inaccuracy (e.g., meeting rooms), where the time outside the comfort zone decreased by tens of percent while maintaining comparable energy consumption. Conversely, in thermally stable zones with a well-identified model, the differencewasminimal. Theresultsdemonstratethatcombiningphysics-basedMPC with data-driven residual correction represents an effective solution that compensates for model inaccuracies without compromising the stability of the optimization process.", "source_url": "https:\/\/openalex.org\/W7163834794", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7163834794", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 238, "title": "Practical aspects of developing and validating corporate probability of default model and the use of machine learning : master's thesis", "title_ar": "", "title_en": "Practical aspects of developing and validating corporate probability of default model and the use of machine learning : master's thesis", "author": "Aljoša Ortl", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master's»", "university": "University of Ljubljana", "university_source": "repository_host", "college": "", "department": "", "country": "Slovenia", "governorate": "", "publication_year": 2016, "language": "en", "research_domain": "machine-learning", "research_subdomain": "ai-industry", "categories": "machine-learning; ai-applications; ai-industry", "keywords": "", "abstract": "", "source_url": "https:\/\/openalex.org\/W2623923887", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Repository of the University of Ljubljana (University of Ljubljana)", "source_record_id": "W2623923887", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 239, "title": "Energy efficient system for detection of elephants with Machine Learning : master's thesis", "title_ar": "", "title_en": "Energy efficient system for detection of elephants with Machine Learning : master's thesis", "author": "Marko Sagadin", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master's»", "university": "University of Maribor", "university_source": "repository_host", "college": "", "department": "", "country": "Slovenia", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "computer-vision", "research_subdomain": "image-classification", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; cnn; image-classification; ai-smart-cities", "keywords": "", "abstract": "Human-Elephant Conflicts are a major problem in terms of elephant conservation. According to WILDLABS, an average of 400 people and 100 elephants are killed every year in India alone because of them. Early warning systems replace the role of human watchers and warn local communities of nearby, potentially life threatening, elephants, thus minimising the Human-Elephant Conflicts. In this Master's thesis we present the structure of an early warning system, which consists of several low-power embedded systems equipped with thermal cameras and a single gateway. To detect elephants from captured thermal images we used Machine Learning methods, specifically Convolutional Neural Networks. The main focus of this thesis was the design, implementation and evaluation of Machine Learning models running on microcontrollers under low-power conditions. We designed and trained several accurate image classification models, optimised them for on-device deployment and compared them against models trained with commercial software in terms of accuracy, inference speed and size. While writing firmware, we ported a part of the TensorFlow library and created our own build system, suitable for the libopencm3 platform. We also implemented reporting of inference results over the LoRaWAN network and described a possible server-size solution. We finally a constructed fully functional embedded system from various development and evaluation boards, and evaluated its performance in terms of power consumption. We show that embedded systems with Machine Learning capabilities are a viable solution to many real life problems.", "source_url": "https:\/\/openalex.org\/W3134983987", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Maribor digital library (University of Maribor)", "source_record_id": "W3134983987", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 240, "title": "A Machine learning model for task allocation", "title_ar": "", "title_en": "A Machine learning model for task allocation", "author": "Simon Kipkemboi Kibiwot", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "Strathmore University", "university_source": "repository_host", "college": "", "department": "", "country": "Kenya", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "", "categories": "machine-learning", "keywords": "", "abstract": "Thesis Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in Information Technology", "source_url": "https:\/\/openalex.org\/W3187528674", "pdf_url": "https:\/\/su-plus.strathmore.edu\/bitstreams\/09625ec1-a206-47bd-930f-dc3d0d9698ec\/download", "doi": "", "handle": "", "repository_name": "SU+ Digital Repository (Strathmore University)", "source_record_id": "W3187528674", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 241, "title": "Watermarking machine learning models", "title_ar": "", "title_en": "", "author": "Sofiane Lounici", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2022, "language": "fr", "research_domain": "computer-vision", "research_subdomain": "", "categories": "machine-learning; computer-vision", "keywords": "", "abstract": "Tatouage numérique des modèles d’apprentissage automatique La protection de la propriété intellectuelle des modèles d’apprentissage automatique apparaît de plus en plus nécessaire, au vu des investissements et de leur impact sur la société. Dans cette thèse, nous proposons d’étudier le tatouage de modèles d’apprentissage automatique. Nous fournissons un état de l’art sur les techniques de tatouage actuelles, puis nous le complétons en considérant le tatouage de modèles au-delà des tâches de classification d’images. Nous définissons ensuite les attaques de contrefaçon contre le tatouage pour les plateformes d’hébergement de modèles, et nous présentons une nouvelle technique de tatouages par biais algorithmique. De plus, nous proposons une implémentation des techniques présentées.", "source_url": "https:\/\/openalex.org\/W7151373744", "pdf_url": "", "doi": "10.70675\/2182b7a8zb67dz4845z9cb4z0f38acf7b0d1", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7151373744", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:47" }, { "id": 242, "title": "Machine Learning Methods for Credit Risk Modelling", "title_ar": "", "title_en": "Machine Learning Methods for Credit Risk Modelling", "author": "Matěj Drábek", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master's»", "university": "", "university_source": "", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "This master's thesis is divided into three parts. In the first part I described P2P lending, its characteristics, basic concepts and practical implications. I also compared P2P market in the Czech Republic, UK and USA. The second part consists of theoretical basics for chosen methods of machine learning, which are naive bayes classifier, classification tree, random forest and logistic regression. I also described methods to evaluate the quality of classification models listed above. The third part is a practical one and shows the complete workflow of creating classification model, from data preparation to evaluation of model.", "source_url": "https:\/\/openalex.org\/W7135437631", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Digital Repository (National Repository of Grey Literature)", "source_record_id": "W7135437631", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:47", "updated_at": "2026-11-15 16:52:48" }, { "id": 243, "title": "Machine learning-powered liability determination in motor vehicle accidents", "title_ar": "", "title_en": "Machine learning-powered liability determination in motor vehicle accidents", "author": "Michael Machatschek", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "The determination of liability in motor vehicle accidents is a complex problem influenced by various dynamic factors. Frequently an initial assessment of liability must be made early in the claims settlement process. However, at this stage, decision-makers regularly find themselves in a situation with incomplete information, which is why a decision must be made under uncertainty. Machine learning can facilitate and improve these decision situations by learning from historical data and thereby generating decision criteria. This master thesis explores how the decision of liability determination in motor vehicle accidents can be improved by machine learning. To achieve this goal, this thesis analyzes to what extent can a machine learning model predict the liability constellation in motor vehicle accidents. An adapted variant of the CRISP-DM methodology was applied to achieve the thesis goal. In the first step, the underlying business problem was examined, and the business goals of the desired solution were defined. In the second step, the data relevant to the solution was collected, processed, and analyzed. In the third step, concrete models were developed. Finally, the third step results were discussed and evaluated with the goals from the first step. The outcomes of this work are two artifacts. On the one hand, a model that is trained to predict the liability constellation. On the other hand, an analysis of how the problem can be solved using different machine learning techniques.", "source_url": "https:\/\/openalex.org\/W3165875476", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W3165875476", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "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": 245, "title": "Pump diagnostics using machine learning", "title_ar": "", "title_en": "Pump diagnostics using machine learning", "author": "C.J. van Ekris", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "transfer-learning", "categories": "machine-learning; medical-ai; ai-applications; transfer-learning; ai-education; ai-industry", "keywords": "", "abstract": "The master thesis shows how transfer learning can be used to train machine-learning models to automatically recognize faults in a UHPLC pump. With the developed machine-learning models it is possible to recognize faults in real-time, estimate the seriousness of a fault and to differentiate between several faults. With the help of the 20-Sim model the UHPLC pump, all kind of faults can be replayed and log data can be generated. With this generated log data, machine-learning models can be trained. Using the trained machine-learning models it should be possible for untrained users of the pump to recognize several faults and to solve them.", "source_url": "https:\/\/openalex.org\/W3108210491", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3108210491", "other_sources": "", "verification_status": "verified", "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": 247, "title": "Bayesian Optimization in Machine Learning", "title_ar": "", "title_en": "Bayesian Optimization in Machine Learning", "author": "José Jiménez-Luna", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "machine-learning", "research_subdomain": "ai-education", "categories": "machine-learning; ai-applications; ai-education", "keywords": "", "abstract": "Bayesian optimization has risen over the last few years as a very attractive approach to find the optimum of noisy, expensive to evaluate, and possibly black-box functions. One of the fields where these functions are common is in machine-learning, where one typically has to fit a particular model by minimizing a specified form of loss. In this Master s thesis we first focus on reviewing the most recent literature on Gaussian Processes as well as Bayesian optimiza- tion methods, then we benchmark said methods against several real case machine-learning scenarios and lastly we provide open source software that will allow researchers to apply these strategies in other problems.", "source_url": "https:\/\/openalex.org\/W2724199550", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W2724199550", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 248, "title": "Machine learning for fuzzing: State of art", "title_ar": "", "title_en": "Machine learning for fuzzing: State of art", "author": "Pablo Barranca Fenollar", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "machine-learning", "research_subdomain": "", "categories": "machine-learning", "keywords": "", "abstract": "Machine learning has become more and more popular in recent years. This popularization has been stimulated by multiple factors: large and affordable computational power, new powerful algorithms, new tools that make it easy to use machine learning algorithms, availability of big data to train the models, etc. Many disciplines have experienced significant changes thanks to its adoption. The fuzzing field has not been an exception. Many researchers have proposed applying machine learning algorithms to the various stages of the fuzzing process. Most studies seem to have brought improvements to the task, however it is not always clear at what cost. Moreover, the reasons behind the selection of one algorithm instead of another are not clear in much of the published literature. This master thesis not only presents the benefits and disadvantages of using various machine learning algorithms in each fuzzing stage, but also identifies new promising paths that researchers should take.", "source_url": "https:\/\/openalex.org\/W3098677050", "pdf_url": "", "doi": "", "handle": "", "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "source_record_id": "W3098677050", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 249, "title": "How machine learning drive the development of autonomous cars - A study of emerging technologies and the evolving value chain Master’s Thesis in the Master’s Programme", "title_ar": "", "title_en": "How machine learning drive the development of autonomous cars - A study of emerging technologies and the evolving value chain Master’s Thesis in the Master’s Programme", "author": "Filip Lundqvist ; Robin de Richelieu", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "Chalmers University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "deep-learning", "research_subdomain": "ai-industry", "categories": "machine-learning; deep-learning; robotics; ai-applications; ai-industry", "keywords": "", "abstract": "Autonomous cars have gained much attention in the past few years. Partially automated features are sold to consumers, and advance autonomous test vehicles are driving on public roads. These features and vehicles have caused a debate about their safety as they have been involved in a couple of fatal crashes in recent years. To simultaneously develop autonomous cars and features without compromising safety is a big challenge for the automotive industry, which is investing heavily in technology. This thesis aims to describe constraints in advanced driver-assistance systems (ADAS) and autonomous driving (AD) developed with machine learning techniques, how companies are adapting to this new technology demand, and current development paths towards autonomous cars. A model of the development of ADAS\/AD was created by interviewing industry experts and a literature review. Companies efforts in the area were identified by searching databases and reading news articles and press releases. Data is a valuable asset but requires extensive work to be useful when developing ADAS\/AD. Collecting data per se is not considered as a challenging task. Instead, the difficulty is to collect data about edge-cases, that is, rare situations occurring maybe once in a human lifetime of driving. Another key takeaway is the inherent difficulty of creating consistent datasets to train neural networks with, as it requires humans to interpret subjective situations the same way. Incumbent firms in the automotive industry are accompanied by startups and established technology companies from outside the industry in trying to develop and capture value from ADAS\/AD. Collaborations and acquisitions are prominent ways to get desired know-how and secure supply of critical components. A schematic overview of the relationships in the industry is mapped to give the reader an idea of the sophisticated ecosystem companies are part of through investments, acquisitions, spin-offs and collaborations. Efforts to capture value from ADAS\/AD can be divided into two branches. One being OEMs developing and offering ADAS in consumer products aiming to develop autonomous driving gradually. The second is characterized by companies targeting autonomous robo-taxi solutions in geo-fenced areas. Although both approaches are facing many similar challenges, they also differ in specific areas such as operational design domain.", "source_url": "https:\/\/openalex.org\/W2952549142", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Chalmers Publication Library (Chalmers University of Technology)", "source_record_id": "W2952549142", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 250, "title": "Machine Learning Models for Network Intrusion Detection and Authentication of Smart Phone Users", "title_ar": "", "title_en": "Machine Learning Models for Network Intrusion Detection and Authentication of Smart Phone Users", "author": "Samad Ahmadi", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "Morehead State University", "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": "A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Science by S. Sareh Ahmadi on November 18, 2019.", "source_url": "https:\/\/openalex.org\/W2995442373", "pdf_url": "https:\/\/scholarworks.moreheadstate.edu\/cgi\/viewcontent.cgi?article=1356&context=msu_theses_dissertations", "doi": "", "handle": "", "repository_name": "ScholarWorks - MoreheadState (Morehead State University)", "source_record_id": "W2995442373", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 251, "title": "Machine learning with adversarial perturbations and noisy labels", "title_ar": "", "title_en": "Machine learning with adversarial perturbations and noisy labels", "author": "Xingjun Ma", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «Doctor of Philosophy»", "university": "University of Melbourne", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; robotics; predictive-analytics; image-classification; face-recognition; speech-recognition", "keywords": "", "abstract": "Machine learning models such as traditional random forests (RFs) and modern deep neural networks (DNNs) have been successfully used to solve complex learning problems in many applications such as speech recognition, image classification, face recognition, gaming agents and self-driving cars.For example, DNNs have demonstrated near or even surpassing human-level performance in image classification tasks.Despite their current success, these models are still vulnerable to noisy real-world situations where illegitimate or noisy data may exist to corrupt learning.Studies have shown that by adding small, human imperceptible (in the case of images) adversarial perturbations, normal samples can be perturbed into \"adversarial examples\", and DNNs can be made to misclassify adversarial examples with a high level of confidence.This arouses security concerns when employing DNNs in security-sensitive applications such as fingerprint recognition, face verification and autonomous cars.Studies have also found that DNNs can overfit to noisy (incorrect) labels and as a result, generalize poorly.This has been one of the key challenges when applying DNNs in noisy real-world scenarios where even high-quality datasets tend to contain noisy labels.Another open question in machine learning is whether actionable knowledge (or feedback) can be generated from prediction models to support decision making towards some long-term learning goals (for example, mastering a certain type of skills in a simulation-based learning (SBL) environment).We view the feedback generation problem from a new perspective of adversarial perturbation, and explore the possibility of using adversarial techniques to generate feedback.In this thesis, we investigate machine learning models including DNNs and RFs, and their learning behavior through the lens of adversarial perturbations iii Declaration This is to certify that 1. the thesis comprises only my original work towards the degree of Doctor of Philosophy except where indicated in the Preface, 2. due acknowledgment has been made in the text to all other material used, 3. the thesis is fewer than 80,000 words in length, exclusive of tables, maps, bibliographies and appendices.", "source_url": "https:\/\/openalex.org\/W2910507727", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Minerva Access (University of Melbourne)", "source_record_id": "W2910507727", "other_sources": "", "verification_status": "verified", "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": 253, "title": "Predicting Fashion using Machine Learning techniques", "title_ar": "", "title_en": "Predicting Fashion using Machine Learning techniques", "author": "Mona Dadoun", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "KTH Royal Institute of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Sweden", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "On a high-level perspective, fashion is an art defined by fash- ion stylists and designers to express their thoughts and opinions. Lately, fashion have also been defined by digital publishers such as bloggers and online magazines. These digital publishers create fashion by curating and publishing content that is hopefully rel- evant and of high quality for their readers. Within this master’s thesis, fashion forecasting was investigated by applying supervised machine learning techniques. The problem was investigated by training classification learning models on a real world historical fashion dataset. The investigation has shown promising results, where fashion forecasting has been achieved with an average ac- curacy above 65 % .", "source_url": "https:\/\/openalex.org\/W2767392160", "pdf_url": "", "doi": "", "handle": "", "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "source_record_id": "W2767392160", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 254, "title": "Machine learning framework for petrochemical process industry applications", "title_ar": "", "title_en": "Machine learning framework for petrochemical process industry applications", "author": "Toni Oleander", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master's»", "university": "Aalto University", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "machine-learning", "research_subdomain": "ai-industry", "categories": "machine-learning; expert-systems; ai-applications; ai-industry", "keywords": "", "abstract": "Machine learning has many potentially useful applications in process industry, for example in process monitoring and control. Continuously accumulating process data and the recent development in software and hardware that enable more advanced machine learning, are fulfilling the prerequisites of developing and deploying process automation integrated machine learning applications which improve existing functionalities or even implement artificial intelligence. In this master's thesis, a framework is designed and implemented on a proof-of-concept level, to enable easy acquisition of process data to be used with modern machine learning libraries, and to also enable scalable online deployment of the trained models. The literature part of the thesis concentrates on studying the current state and approaches for digital advisory systems for process operators, as a potential application to be developed on the machine learning framework. The literature study shows that the approaches for process operators' decision support tools have shifted from rule-based and knowledge-based methods to machine learning. However, no standard methods can be concluded, and most of the use cases are quite application-specific. In the developed machine learning framework, both commercial software and open source components with permissive licenses are used. Data is acquired over OPC UA and then processed in Python, which is currently almost the de facto standard language in data analytics. Microservice architecture with containerization is used in the online deployment, and in a qualitative evaluation, it proved to be a versatile and functional solution.", "source_url": "https:\/\/openalex.org\/W2951849673", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Aaltodoc (Aalto University)", "source_record_id": "W2951849673", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 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": 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": 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": 285, "title": "Automatic Detection and Verification System for Arabic Rumor News on Twitter", "title_ar": "", "title_en": "Automatic Detection and Verification System for Arabic Rumor News on Twitter", "author": "Sami Karali", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «doctoral research»", "university": "University of Technology Sydney", "university_source": "repository_host", "college": "", "department": "", "country": "Australia", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; ai-applications; cnn; rnn; llm; arabic-nlp; ai-education", "keywords": "", "abstract": "anguage models have been extensively studied and applied in various fields in recent years.However, the majority of the language use models are designed for and perform significantly better in English compared to other languages, such as Arabic.The differences between English and Arabic in terms of grammar, writing, and word-forming structures pose significant challenges in applying English-based language models to Arabic content.Therefore, there is a critical need to develop and refine models and methodologies that can effectively process Arabic content.This research aims to address the gaps in Arabic language models by developing innovative machine learning (ML) and natural language processing (NLP) methodologies.We apply the developed model to Arabic rumor detection on Twitter to test its effectiveness.To achieve this, the research is divided into three fundamental phases: 1) Efficiently collecting and pre-processing a comprehensive dataset of Arabic news tweets; 2) The refinement of ML models through an enhanced Convolutional Neural Network (ECNN) equipped with N-gram feature maps for accurate rumor identification; 3) The augmentation of decision-making precision in rumor verification via sophisticated ensemble learning techniques.In the first phase, the research meticulously develops a methodology for the collection and pre-processing of Arabic news tweets, aiming to establish a dataset optimized for rumor detection analysis.Leveraging a blend of automated and manual processes, the research navigates the intricacies of the Arabic language, enhancing the dataset's quality for ML applications.This foundational phase ensures removing irrelevant data and normalizing text, setting a precedent for accuracy in subsequent detection tasks.The second phase is to develop an Enhanced Convolutional Neural Network (ECNN) model, which incorporates N-gram feature maps for a deeper linguistic analysis of tweets.This innovative ECNN model, designed specifically for the Arabic language, marks a significant departure from traditional rumor detection models by harnessing the power of spatial feature extraction alongside the contextual insights provided by N-gram analysis.Empirical results underscore the ECNN model's superior performance, demonstrating a marked improvement in detecting and classifying rumors with heightened accuracy and efficiency.The culmination of the study explores the efficacy of ensemble learning methods in enhancing the robustness and accuracy of rumor detection systems.By synergizing the ECNN model with Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU) networks within a stacked ensemble framework, the iii research pioneers a composite approach that significantly outstrips the capabilities of singular models.This innovation results in a state-of-the-art system for rumor verification that outperforms accuracy in identifying rumors, as demonstrated by empirical testing and analysis.This research contributes to bridging the gap between English-centric language models and Arabic language processing, demonstrating the importance of tailored approaches for different languages in the field of ML and NLP.These contributions signify a monumental step forward in the field of Arabic NLP and ML and offer practical solutions for the real-world challenge of rumor proliferation on social media platforms, ultimately fostering a more reliable digital environment for Arabic-speaking communities.n the journey that has been my doctoral research, I have been fortunate to be guided by individuals whose impact has been profound and far-reaching.At the forefront of these cherished relationships is Prof. CT Lin, whose guidance and support have been the beacon that navigated me through the complexities and challenges inherent in this rigorous academic endeavor.Prof. Lin's readiness to engage, discuss, and explore solutions with me, irrespective of the time or day, has been a testament to his dedication to academia and the growth of his students.His wisdom, coupled with an unwavering belief in my potential, provided me with the confidence to pursue this project with vigor and determination.To Prof.", "source_url": "https:\/\/openalex.org\/W7225354344", "pdf_url": "https:\/\/opus.lib.uts.edu.au\/bitstream\/10453\/187339\/1\/thesis.pdf", "doi": "", "handle": "", "repository_name": "OPUS - Open Publications of UTS Scholars (University of Technology Sydney)", "source_record_id": "W7225354344", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 300, "title": "Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components", "title_ar": "", "title_en": "Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components", "author": "Ditsuhi Iskandaryan ; Iskandaryan, Ditsuhi", "supervisor": "", "degree_type": "phd", "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "predictive-analytics", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; ai-smart-cities", "keywords": "", "abstract": "Air quality is considered one of the top concerns. Information and knowledge about air quality can assist in effectively monitoring and controlling concentrations, reducing or preventing its harmful impacts and consequences. The complexity of air quality dependence on various components in spatiotemporal dimensions creates additional challenges to acquire this information. The current dissertation proposes machine learning and deep learning technologies that are capable of capturing and processing multidimensional information and complex dependencies controlling air quality. The following components come together to formulate the novelty of the current work: spatiotemporal forecast of the defined prediction target (nitrogen dioxide); incorporation and integration of air quality, meteorological and traffic data with their features\/variables in spatiotemporal dimensions within a certain spatial extent and temporal interval; the consideration of coronavirus disease 2019 as an external key factor impacting air quality level; and provision of the code and data implemented to incentivise and guarantee reproducibility.", "source_url": "https:\/\/openalex.org\/W4353069857", "pdf_url": "https:\/\/www.tdx.cat\/bitstream\/10803\/687959\/1\/2023_Tesis_Iskandaryan_Ditsuhi.pdf", "doi": "10.6035\/14101.2023.726676", "handle": "", "repository_name": "Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)", "source_record_id": "W4353069857", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 302, "title": "Towards synthetic sensing for smart cities : a machine\/deep learning-based approach", "title_ar": "", "title_en": "", "author": "Faraz Malik Awan", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2022, "language": "fr", "research_domain": "computer-vision", "research_subdomain": "ai-smart-cities", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; ai-smart-cities", "keywords": "", "abstract": "Vers une détection synthétique pour les villes intelligentes : une approche basée sur la machine et l'apprentissage profond Nous avons travaillé sur l'un des axes de recherche les plus importants de la ville intelligente, à savoir les systèmes de transport intelligents (STI). Les ITS englobent plusieurs domaines, tels que les systèmes de notification électronique des véhicules, les informations sur le trafic, le stationnement intelligent et l'environnement. Cependant, dans cette thèse, nous ciblons deux de ses domaines importants : i) le stationnement intelligent et ii) le trafic routier. Nous avons commencé notre recherche par le cas d'utilisation du stationnement intelligent. En effectuant une revue de la littérature, nous avons réalisé que différentes approches de Machine Learning (ML) et de Deep Learning (DL) ont été utilisées pour des solutions de stationnement intelligent. Dans la plupart de ces approches proposées, les zones de stationnement fermées ont été ciblées avec différents ensembles de caractéristiques pour prédire le \"taux d'occupation\" dans les zones de stationnement. Cela nous a incités à effectuer une analyse comparative pour répondre aux questions suivantes : compte tenu du cas d'utilisation de la prédiction de stationnement, comment les modèles ML traditionnels se comportent-ils par rapport aux modèles DL complexes ? Avec des données volumineuses, les modèles ML traditionnels moins complexes peuvent-ils surpasser les modèles DL complexes ? Quelle est la performance de ces modèles pour prédire la disponibilité des places de stationnement individuelles dans la rue plutôt que de prédire le taux d'occupation global d'une zone de stationnement fermée. Pour répondre à ces questions, nous avons choisi cinq algorithmes ML classiques bien connus (K-Nearest Neighbours, Random Forest, Decision Tree) et un algorithme DL (Multilayer Perceptron). Pour approfondir notre étude, nous formons un modèle d'apprentissage d'ensemble, dans lequel nous combinons tous les modèles ML et DL susmentionnés. Nous avons utilisé un énorme ensemble de données sur les pa rkings de la ville de Santander, en Espagne, qui comprend environ 25 millions d'enregistrements. Nous proposons également de recommander des places de stationnement disponibles en fonction de l'emplacement actuel du conducteur. En poursuivant nos objectifs de recherche, nous avons effectué une revue de la littérature sur le trafic routier et avons constaté que le trafic routier est souvent associé à la pollution atmosphérique et à la pollution sonore. Cependant, à notre connaissance, la pollution atmosphérique et la pollution sonore n'ont jamais été utilisées dans le problème de la prédiction du trafic. Dans cette partie de notre recherche, nous avons d'abord utilisé la pollution de l'air (CO, NO, NO2, NOx, et O3) avec les variables atmosphériques, telles que la vitesse et la direction du vent, la température et la pression pour améliorer la prévision du trafic dans la ville de Madrid. Cette expérience réussie nous a incités à étendre notre étude à un autre facteur, qui est égalemen t fortement corrélé au trafic routier, à savoir la pollution sonore. Ainsi, dans le prolongement de nos travaux précédents, nous utilisons dans cette partie de notre recherche la pollution sonore pour améliorer la prévision du trafic dans la ville de Madrid.", "source_url": "https:\/\/openalex.org\/W7151321554", "pdf_url": "https:\/\/theses.hal.science\/tel-03722891v1\/document", "doi": "10.70675\/c01d4efazc638z4123z8020z74dfc82b5875", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7151321554", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 310, "title": "Multimodal machine learning methods for pattern analysis in smart cities and transportation", "title_ar": "", "title_en": "", "author": "Ifigeneia Drosouli", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2024, "language": "fr", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; rnn; transformers; multimodal; ai-smart-cities", "keywords": "", "abstract": "Méthodes d'apprentissage automatique multimodales pour l'analyse de modèles dans les villes intelligentes et les transports Dans le contexte des environnements urbains modernes et densément peuplés, la gestion efficace des transports et la structure des Systèmes de Transport Intelligents (STI) sont primordiales. Le secteur des transports publics connaît actuellement une expansion et une transformation significatives dans le but d'améliorer l'accessibilité, d'accommoder des volumes de passagers plus importants sans compromettre la qualité des déplacements, et d'adopter des pratiques respectueuses de l'environnement et durables. Les avancées technologiques, notamment dans l'Intelligence Artificielle (IA), l'Analyse de Données Massives (BDA), et les Capteurs Avancés (CA), ont joué un rôle essentiel dans la réalisation de ces objectifs et ont contribué au développement, à l'amélioration et à l'expansion des Systèmes de Transport Intelligents. Cette thèse aborde deux défis critiques dans le domaine des villes intelligentes, se concentrant spécifiquement sur l'identification des modes de transport utilisés par les citoyens à un moment donné et sur l'estimation et la prédiction du flux de transport au sein de divers systèmes de transport. Dans le contexte du premier défi, deux approches distinctes ont été développées pour la Détection des Modes de Transport. Tout d'abord, une approche d'apprentissage approfondi pour l'identification de huit médias de transport est proposée, utilisant des données de capteurs multimodaux collectées à partir des smartphones des utilisateurs. Cette approche est basée sur un réseau Long Short-Term Memory (LSTM) et une optimisation bayésienne des paramètres du modèle. À travers une évaluation expérimentale approfondie, l'approche proposée démontre des taux de reconnaissance remarquablement élevés par rapport à diverses approches d'apprentissage automatique, y compris des méthodes de pointe. La thèse aborde également des problèmes liés à la corrélation des caractéristiques et à l'impact de la réduction de la dimensionnalité. La deuxième approche implique un modèle basé sur un transformateur pour la détection des modes de transport appelé TMD-BERT. Ce modèle traite l'ensemble de la séquence de données, comprend l'importance de chaque partie de la séquence d'entrée, et attribue des poids en conséquence en utilisant des mécanismes d'attention pour saisir les dépendances globales dans la séquence. Les évaluations expérimentales mettent en évidence les performances exceptionnelles du modèle par rapport aux méthodes de pointe, soulignant sa haute précision de prédiction. Pour relever le défi de l'estimation du flux de transport, un Réseau Convolutif Temporel et Spatial (ST-GCN) est proposé. Ce réseau apprend à la fois des données spatiales du réseau de stations et des séries temporelles des changements de mobilité historiques pour prédire le flux de métro urbain et le partage de vélos à un moment futur. Le modèle combine des Réseaux Convolutifs Graphiques (GCN) et des Réseaux Long Short-Term Memory (LSTM) pour améliorer la précision de l'estimation. Des expériences approfondies menées sur des ensembles de données du monde réel du système de métro de Hangzhou et du système de partage de vélos de la ville de New York valident l'efficacité du modèle proposé, démontrant sa capacité à identifier des corrélations spatiales dynamiques entre les stations et à faire des prévisions précises à long terme.", "source_url": "https:\/\/openalex.org\/W4404827876", "pdf_url": "https:\/\/theses.hal.science\/tel-04709700\/document", "doi": "10.70675\/879cee13z5ed6z4382z8e6az1a8fe3cfbd48", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W4404827876", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 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": 358, "title": "Computational biology in plant tissue culture: Systematic evaluation of machine learning approaches for callus induction in Solanum L.: A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University", "title_ar": "", "title_en": "Computational biology in plant tissue culture: Systematic evaluation of machine learning approaches for callus induction in Solanum L.: A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University", "author": "Sadegh Mohajer", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «Doctor of Philosophy»", "university": "Lincoln University", "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; transfer-learning; predictive-analytics; optimization; xai; ai-agriculture", "keywords": "", "abstract": "Optimization of plant tissue culture systems remains a major biotechnological challenge; yet successful callus induction, the formation of unorganized, proliferating cell masses from differentiated tissues, enables transformative applications in agriculture, medicine, and fundamental research. Callus serves as a foundation for genetic transformation and genome editing, mass propagation of elite varieties, production of pharmaceutically valuable secondary metabolites, and breeding programs. However, achieving consistent callus induction is hindered by the multifactorial nature of the process, governed by intricate dependencies among environmental conditions, genetic background, hormonal composition, and explant source. Conventional trial-and-error methodologies are further constrained by the difficulty of systematically capturing and optimizing these complex interactions, resulting in protocols with poor transferability and requiring extensive empirical refinement for each new species or genotype. This study established comprehensive machine learning (ML) frameworks to model and predict callus induction rates in the Solanum genus, one of the most diverse angiosperm genera containing approximately 1,500–2,000 species. This genus includes economically significant crop species such as potato, tomato, and eggplant that sustain global food security, as well as wild relatives possessing valuable traits for disease resistance and abiotic stress tolerance. The genus also includes species of pharmaceutical interest, owing to their diverse alkaloids and secondary metabolites, which exhibit antimicrobial, antioxidant, and anticancer activities. Despite their agricultural, nutritional, and medicinal importance, many Solanum species remain recalcitrant in tissue culture, as standard protocols developed for model cultivars often perform poorly when applied to wild species and new genotypes. The ML methods developed here systematically evaluate modeling strategies ranging from global to local approaches, with the goal of addressing the protocol optimization bottleneck that constrains biotechnological applications across this economically and scientifically important genus. Five machine learning algorithms were evaluated: Random Forest and XGBoost as tree-based ensemble models, Support Vector Regression, and Gaussian Process Regression as kernel-based methods, and an artificial neural network (multilayer perceptron) with genetic algorithm optimization for hyperparameter tuning and protocol development. Model interpretability was achieved through SHAP analysis and permutation importance assessment. Genetic algorithm optimization of neural networks identified optimal protocols achieving remarkable callus induction rates, representing substantial improvements over conventional approaches. Feature optimization revealed specific strategies ranging from minimal hormone supplementation to comprehensive growth regulator networks. Sensitivity analysis confirmed that environmental precision provides primary control mechanisms, with hormonal supplementation serving as fine tuning parameters rather than primary drivers. The thesis advances both methodology and biological understanding. It establishes a rigorous methodological framework integrating systematic preprocessing, cross-validation with independent holdout data, and comprehensive interpretability analysis. The approach delivers predictive models with quantified uncertainty and clear performance boundaries. Biologically, the work demonstrates that machine learning can successfully capture complex tissue culture interactions. These findings represent a paradigm shift from empirical protocol development to data-driven optimization, with applications across plant biotechnology, crop improvement, pharmaceutical compound production, and conservation. The work may also motivate future steps that include transfer learning across species, imaging-based phenotyping with deep learning, classification of callus quality tiers, and time-resolved modeling of induction dynamics.", "source_url": "https:\/\/openalex.org\/W7131545409", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Lincoln University Research Archive (Lincoln University)", "source_record_id": "W7131545409", "other_sources": "", "verification_status": "verified", "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": 362, "title": "Machine learning and audio processing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, Auckland, New Zealand", "title_ar": "", "title_en": "Machine learning and audio processing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, Auckland, New Zealand", "author": "Junbo Ma", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «Doctor of Philosophy»", "university": "Massey University", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; data-mining; rnn; speech-recognition", "keywords": "", "abstract": "In this thesis, we addressed two important theoretical issues in deep neural networks and clustering, respectively. Also, we developed a new approach for polyphonic sound event detection, which is one of the most important applications in the audio processing area. The developed three novel approaches are: (i) The Large Margin Recurrent Neural Network (LMRNN), which improves the discriminative ability of original Recurrent Neural Networks by introducing a large margin term into the widely used cross-entropy loss function. The developed large margin term utilises the large margin discriminative principle as a heuristic term to navigate the convergence process during training, which fully exploits the information from data labels by considering both target category and competing categories. (ii) The Robust Multi-View Continuous Subspace Clustering (RMVCSC) approach, which performs clustering on a common view-invariant subspace learned from all views. The clustering result and the common representation subspace are simultaneously optimised by a single continuous objective function. In the objective function, a robust estimator is used to automatically clip specious inter-cluster connections while maintaining convincing intra-cluster correspondences. Thus, the developed RMVCSC can untangle heavily mixed clusters without pre-setting the number of clusters. (iii) The novel polyphonic sound event detection approach based on Relational Recurrent Neural Network (RRNN), which utilises the relational reasoning ability of RRNNs to untangle the overlapping sound events across audio recordings. Different from previous works, which mixed and packed all historical information into a single common hidden memory vector, the developed approach allows historical information to interact with each other across an audio recording, which is effective and efficient in untangling the overlapping sound events. All three approaches are tested on widely used datasets and compared with recently published works. The experimental results have demonstrated the effectiveness and efficiency of the developed approaches.", "source_url": "https:\/\/openalex.org\/W3082433222", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Massey Research Online (Massey University)", "source_record_id": "W3082433222", "other_sources": "", "verification_status": "verified", "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": 370, "title": "Predicting the Role of Gamification Techniques and Machine Learning in Improving Students' Learning", "title_ar": "", "title_en": "Predicting the Role of Gamification Techniques and Machine Learning in Improving Students' Learning", "author": "Mahmood Ahmed Abdulhafedh Al_rashed", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master's»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; ai-applications; predictive-analytics; ai-education", "keywords": "", "abstract": "This master's thesis investigates the role of gamification techniques and machine learning in improving students' learning. The study explores how gamification can enhance students' engagement, motivation, and learning outcomes, and examines the potential of machine learning techniques for predicting students' learning performance.", "source_url": "https:\/\/openalex.org\/W7172345182", "pdf_url": "", "doi": "10.5281\/zenodo.21764154", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7172345182", "other_sources": "", "verification_status": "verified", "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": 374, "title": "Real-time GDP nowcasting in New Zealand : an ensemble machine learning approach : a thesis presented for the degree of Master of Philosophy, School of Natural and Computational Sciences, Massey University, New Zealand", "title_ar": "", "title_en": "Real-time GDP nowcasting in New Zealand : an ensemble machine learning approach : a thesis presented for the degree of Master of Philosophy, School of Natural and Computational Sciences, Massey University, New Zealand", "author": "Jinjin Fan", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "Massey University", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "Gross Domestic Product (GDP) measures the monetary value of all final goods and services that are produced in a region during a period of time. For most countries, GDP is released a limited number of times a year and often with a lag. Understanding the current economic situation, instead of figures quarters ago, is of vital importance for both policy and private entrepreneurs. It is crucial to create a live GDP predictor that could Nowcast current GDP growth rate in the period of government statistics release delay. The Econometric approach for GDP Nowcasting has dominated the forecasting area for many years. However, most of the traditional econometric models could only incorporate a small handful of variables with a linear model structure, which could not meet the requirement of the “big data” era for a better model prediction ability with a large amount of unbalanced variables. With the improvement of computation ability and the increment of high frequency variables, data-driven approaches like Machine Learning Methods have been applied into Nowcasting area. It does not only show a stronger forecasting ability in handling large number of predictors but also present a superior robustness for non-linear data structure. In this research, an Ensemble Method constructed by several Machine Learning Methods have been generated to provide more timely available GDP figures in the period of government statistics release delay. Having integrated an input dataset with data from multiple data sources such as public statistical websites, Reserve Bank of NZ and Stats NZ, our cooperators New Zealand Transport Agency (NZTA) and PayMark, this study is conducted by first applying different Machine Learning methods such as Lightgbm, Xgboost, Support Vector Machine, K- Nearest Neighbors, Ridge Regression, Lasso, Adaboost models. Then these algorithms are combined to generate an Ensemble Model with the assistance of an averaging method, which weights each model individually based on its historical prediction accuracy. The result of the final Ensemble Model is compared with the most commonly used benchmark ARIMA model and Random Walk model in terms of Mean Square Error (MSE) and Median Absolute Error(MAE) value. Statistical tests, such as Friedman Test and Wilcoxon Signed-Rank Test, are employed to check the significance of model superiority. The results indicate that the Ensemble Model significantly outperforms individual Machine Learning algorithm and Random Walk model in forecasting accuracy. When compared with the ARIMA model, it shows slightly better prediction ability with more fore-sights especially in a fluctuating environment.", "source_url": "https:\/\/openalex.org\/W3102280279", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Massey Research Online (Massey University)", "source_record_id": "W3102280279", "other_sources": "", "verification_status": "verified", "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": 400, "title": "Feature Selection and Transfer Learning in Network Data: Enhancing Anomaly Detection with Zero-Shot and Few-Shot Learning", "title_ar": "", "title_en": "Feature Selection and Transfer Learning in Network Data: Enhancing Anomaly Detection with Zero-Shot and Few-Shot Learning", "author": "Joideep Banerjee", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «PhD»", "university": "Staffordshire University", "university_source": "repository_host", "college": "", "department": "", "country": "United Kingdom", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; few-zero-shot; predictive-analytics; xai", "keywords": "", "abstract": "The exponential growth of high-dimensional data across domains such as bioinformatics, healthcare, finance, and image processing has heightened the need for effective feature selection (FS) methods. These techniques improve model performance by identifying relevant features, reducing computational complexity, and mitigating overfitting. This PhD thesis introduces Radian, a novel feature selection method that leverages the statistical properties of range and median to identify the most influential features. Radian effectively distinguishes between relevant and redundant attributes while also detecting anomalies, enhancing both model interpretability and data quality. Radian was rigorously evaluated on multiple benchmark datasets of varying size and complexity. The results show that it consistently outperforms conventional methods such as the Pearson correlation coefficient in three key areas: classification accuracy, feature reduction, and computational efficiency. Its ability to balance performance and simplicity enables the creation of compact, interpretable models that retain or improve predictive accuracy. Beyond feature selection, this research advances transfer learning for tabular data, an area often underexplored in existing literature. Three innovative models TabLoRA, TabLoRA-ZS (zero-shot), and TabLoRA-FS (few-shot) are introduced by integrating TabNet, a deep learning architecture for tabular data, with Low-Rank Adaptation (LoRA) modules. The TabLoRA-ZS model enables generalisation to unseen tasks without prior data, while TabLoRA-FS fine-tunes efficiently with minimal data, addressing the challenges of data scarcity. A major innovation lies in integrating Radian with TabNet and LoRA, allowing dynamic feature selection during transfer learning. This integration improves model adaptability, robustness, and scalability, particularly in environments with limited labelled data. Comprehensive experiments demonstrate that these Radian-enhanced transfer learning models perform competitively with state-of-the-art approaches while maintaining interpretability and efficiency. In conclusion, this thesis contributes to machine learning by (1) proposing Radian, a statistically driven, efficient feature selection method, and (2) developing Radian-integrated TabLoRA models for few-shot and zero-shot transfer learning. Together, they provide scalable, adaptable, and high-performing solutions for data-scarce domains, bridging the gap between feature selection and transfer learning in tabular data analysis.", "source_url": "https:\/\/openalex.org\/W7126002433", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Staffordshire Online Repository (Staffordshire University)", "source_record_id": "W7126002433", "other_sources": "", "verification_status": "verified", "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": 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": 417, "title": "Zero-shot Learning with Shared Embedding Spaces", "title_ar": "", "title_en": "Zero-shot Learning with Shared Embedding Spaces", "author": "Yifan Deng", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "", "university_source": "", "college": "", "department": "", "country": "Japan", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "few-zero-shot", "categories": "machine-learning; deep-learning; nlp; ai-applications; few-zero-shot; ai-industry", "keywords": "", "abstract": "Recently, with the development of deep learning technology, deep learning has achieved great progress, and deep neural network models have even outperformed humans in many tasks, especially in the field of natural language processing (NLP).However, the performance of deep models is still far from human performance in zero-shot learning (ZSL), where the models are asked to deal with targets belonging to unseen classes.ZSL is becoming increasingly important, as it can resolve the lack of labeled data which is a common problem in real-world applications.One approach to ZSL is to acquire shared embedding spaces of the seen and unseen classes.With such shared embedding spaces, the deep models can transfer the knowledge from seen classes to unseen classes, allowing the models to perform the ZSL tasks.This thesis takes bilingual lexicon induction (BLI) and zero-shot relation extraction (RE) in knowledge base question answering (KBQA) as two examples of research subjects in ZSL and investigates various methods utilizing the shared embedding spaces.BLI, generating word translations, is a basic application of bilingual word embedding (BWE) mapping.Research on the BLI can help to improve the understanding of embedding mapping which can be applied to zero-shot RE in KBQA.KBQA is a special type of question answering (QA) tasks, where the questions are based on knowledge bases.KBQA is closely related to real-world applications, as it provides a feasible and practical way to deal with vast real-world knowledge.RE is a key component of KBQA, identifying the relation in the question in order to understand the meaning of the question.Zero-shot RE is the RE where the test relations do not appear in the training data.The research in this thesis pushes the understanding of embedding spaces in zero-shot learning forward and improves the performance of BLI and zero-shot RE.I could not enjoy the meaningful and joyful years in doing my master research without the help of many kind seniors and my lovely friends.First and foremost, I want to thank my advisor, Professor Yoshimasa Tsuruoka.He has encouraged me when I apply for the master's project in Tsuruoka Laboratory, which bred the very beginning of my master's career.He is a successful teacher, providing the most interesting and challenging orientation tutorials.He is also an insightful scholar that he can always point out the covert but fatal problems in my work.He has been caring and supportive about my academic activities and my life and has provided many valuable chances such as a part-time job at the National Institute of Advanced Industrial Science and Technology (AIST).He has been giving suggestions but never giving compulsive orders, which I think has been the reason for the friendly and vigorous atmosphere of our laboratory.I really feel lucky that he is my advisor.I would like to thank our laboratory members.When I came to the laboratory, I could hardly understand Japanese, and I had little practical knowledge of machine learning.My Chinese fellow, Li Tong, has been my \"Japanese interpreter\" and \"technical encyclopedia\" in the first semester.Ri Ryoukan, my deskmate, has always been caring about the research of laboratory members including me.We have discussed over many fields, and some discussions were very inspiring.Matiss Ritkers, a researcher, has been warm-hearted and always ready for help.And other members have been sharing their ideas, comments, suggestions, both in academics and in life experience, which are invaluable to me.I want to thank AIST members, especially my direct leader, Hiroshi Noji.He is firm and persevering, leading me to a series of interesting topics, including the KBQA.I have encountered failure in some difficult topics, but he has never been frustrated and has been always encouraging me to look forward.Other AIST members are also kind and helpful.I really enjoy the open and friendly culture of AIST.Besides, I want to thank Lyu Zhaoyang, my former roommate and best friend.We worked together on the BWE mapping, and we have discussed many latest topics in the machine learning field some of which are very inspiring.I also would like to thank other friends who concern my research and gives useful advice .Thanks to all the members I have mentioned or not mentioned.", "source_url": "https:\/\/openalex.org\/W3217166324", "pdf_url": "https:\/\/repository.dl.itc.u-tokyo.ac.jp\/record\/54828\/files\/48186469.pdf", "doi": "", "handle": "", "repository_name": "Institutional Repositories DataBase (IRDB)", "source_record_id": "W3217166324", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 759, "title": "Explainable Machine Learning for Knowledge Discovery in Environmental Science. PhD thesis, Dublin City University.", "title_ar": "", "title_en": "Explainable Machine Learning for Knowledge Discovery in Environmental Science. PhD thesis, Dublin City University.", "author": "Adam Stapleton", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «PhD»", "university": "Dublin City University", "university_source": "repository_host", "college": "", "department": "", "country": "Ireland", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "xai", "categories": "machine-learning; responsible-ai; ai-applications; data-mining; predictive-analytics; xai; ai-agriculture", "keywords": "", "abstract": "With climate change increasingly impacting communities and around the globe, understanding the Earth system has never been more vital. The many petabytes of data that are generated from remote sensing efforts such as satellite observations and ground-based measurement networks have made the fields of environmental science and Earth system science ripe for the application of machine learning. The fundamental question that drives the research presented in this thesis is how ML models can be meaningfully integrated into environmental science workflows to enhance our understanding and modelling of complex Earth system processes. This thesis demonstrates through three applications the utility of machine learning for the modelling of complex systems in environmental science. In addition these works demonstrate that explanations of the models enable the discovery new relationships within these systems purely from data with little or no prior knowledge. The first application is to the partitioning of evapotranspiration into its components, evaporation and transpiration by predicting the evaporation component from data where only the total evapotranspiration is measured. The data used are from four wetlands in California and served as a preliminary study that introduces the framework for the methodology that is used for the other studies. A key finding uncovered by simple model explanations is that methane flux, a feature whose relationship with evapotranspiration is not generally examined, may contribute to further biophysical process understanding. The second application is to the local-scale modeling of the atmospheric boundary layer height in central Amazonia. This study found gradient boosted ensemble models using all available features to perform best. A modified recursive feature elimination algorithm identified minimal sets of 5–7 surface measurements sufficient for accurate boundary layer height prediction, demonstrating potential for wider spatial monitoring using cost-effective sensors. The study revealed previously unrecognized variables that strongly contributed to boundary layer height predictions, such as deeper soil temperature measurements (40 cm). The final application is to the large-scale modelling of gross primary productivity in primary forest the Amazon basin utilising clustering methods to separate similar regions and understand the complex relationship between climatic variables with gross primary productivity in those regions. Shapely explanations enabled deeper understanding of the relationships discovered by the machine learning models, in particular identifying the vulnerable peripheral regions with increased variability in gross primary productivity under the influence of deforestation and degradation pressures.", "source_url": "https:\/\/openalex.org\/W7154798969", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Dublin City University Open Access Institutional Repository (Dublin City University)", "source_record_id": "W7154798969", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 760, "title": "Bladder Cancer Epidemiology in Latvia and Machine Learning-Based Evaluation of Urinary Exosomal microRNAs as Diagnostic Biomarkers. Doctoral Thesis", "title_ar": "", "title_en": "Bladder Cancer Epidemiology in Latvia and Machine Learning-Based Evaluation of Urinary Exosomal microRNAs as Diagnostic Biomarkers. Doctoral Thesis", "author": "Ērika Bitiņa-Barlote", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «Doctoral Thesis»", "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; responsible-ai; predictive-analytics", "keywords": "", "abstract": "Bladder cancer (BC) is the most prevalent malignancy of the urinary system and ranks as the tenth most common cancer worldwide. Recent data indicate a significant increase in incidence, with hundreds of thousands of new cases diagnosed annually. Based on GLOBOCAN data, bladder cancer ranked 7th among cancers in Latvia in 2022, with 521 new cases. BC was the seventh most prevalent cancer in the world. The global burden of bladder cancer is considerable, with millions of individuals living with the disease. The overall global five-year prevalence was estimated at 1 700 000 in 2020. There is a substantial number of deaths attributed to it each year, making it the 13th leading cause of cancer mortality. Due to the main risk factors – smoking and chemical exposures – associated with BC, it is considered a largely preventable and avoidable cancer. BC is a heterogeneous disease with highly variable clinical, anatomic, and molecular pathology features, along with a high mutation burden. In recent years, microRNAs (miRNAs) have emerged as crucial regulators of cellular activities, and their dysregulation has been associated with the onset and progression of cancer. Although there are many studies on BC around the world, there is still no comprehensive analysis of the epidemiological trends and long-term survival rates of bladder cancer in Latvia, nor has there been an examination of the potential of exosomal microRNAs (miRNAs) as biomarkers for the diagnosis of BC. The aim of my PhD thesis was to carry out a population-based study on the epidemiological trends of bladder cancer in Latvia from 1990 to 2017, and to evaluate the diagnostic potential of urinary exosomal miRNAs in combination with clinical and laboratory data using machine learning approaches. The study also aimed at improving the understanding of cell-to-cell communication between bladder cells in patients with low-grade (LG) and high-grade (HG) BC, and the possible impact of the urinary exosomal miRNAs on the oncotransformation of normal cells. The results of the study showed that bladder cancer incidence, prevalence, and mortality increased in Latvia between 1990 and 2017. Findings revealed that the age-standardised incidence rate of bladder cancer increased from 6.8 per 100 000 in 1990 to 10.8 per 100 000 in 2017, with a statistically significant rise (APC 2.6 %) from 1990 to 2014, followed by a minor and statistically insignificant decline through 2017 (APC −3.6 %). The prevalence rates increased from 25.2 in 1990 to 69.7 in 2017, with an AAPC of 3.8 % ( 95 % CI 3.1 to 4.6) during the whole period. A statistically significant reduction was observed only in the ≥ 80-year age group, with an APC of −7.6% from 2013 to 2017. Mortality rates also increased from 3.9 to 4.4 per 100,000 during the same period, although a notable decrease was observed in the 40-59 age group, with an annual percentage change of −1.1%. Additionally, relative survival rates improved from 55.0% during 1990-2000 to 59.0% in the years 2013–2017, suggesting advancements in treatment or early detection strategies. These epidemiological findings – particularly the rising incidence and the persistent burden in older age groups – underscored the need for more sensitive, non-invasive diagnostic tools, motivating the subsequent investigation of urinary exosomal miRNAs as candidate biomarkers. Striking differences in the miRNA cargo of exosomes from the BC group as compared to the control group were identified. Among the up-regulated exosomal miRNAs, 7 miRNAs in the LG group and 3 miRNAs in the HG group were identified as directly targeting TP53. Twenty-two and 17 PTEN-targeting miRNAs were observed among the up-regulated miRNAs of the LG and HG groups, respectively. The target genes of these exosomal miRNAs and their interaction network predicted that TP53 is the strongest hub gene in both BC exosomal miRNA networks. Further, the possible role of exosomal miRNAs in synchronous BC development was discussed. Building on these differentially expressed candidates, 11 miRNAs were selected based on statistical significance and fold regulation to test whether combining these miRNAs with routine clinical and laboratory data improves diagnostic accuracy compared with using either data type alone. Machine learning was performed using the 11 specially selected miRNAs in 152 samples (89 bladder cancer patients and 63 controls), selected from an initial 210-participant cohort by applying the criterion of complete clinical data availability. Statistical analysis identified notable miRNAs, such as mir-455-3p, mir-30b-3p, and mir-429, as potential biomarkers. This study highlighted the potential of combining miRNA expression levels with laboratory tests and clinical data for improved BC diagnostics.", "source_url": "https:\/\/openalex.org\/W7225651265", "pdf_url": "", "doi": "10.25143\/prom-rsu_2026-30_dt", "handle": "", "repository_name": "", "source_record_id": "W7225651265", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 761, "title": "Bladder Cancer Epidemiology in Latvia and Machine Learning-Based Evaluation of Urinary Exosomal microRNAs as Diagnostic Biomarkers. Summary of the Doctoral Thesis", "title_ar": "", "title_en": "Bladder Cancer Epidemiology in Latvia and Machine Learning-Based Evaluation of Urinary Exosomal microRNAs as Diagnostic Biomarkers. Summary of the Doctoral Thesis", "author": "Ērika Bitiņa-Barlote", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «Doctoral Thesis»", "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; responsible-ai; predictive-analytics", "keywords": "", "abstract": "Bladder cancer (BC) is the most prevalent malignancy of the urinary system and ranks as the tenth most common cancer worldwide. Recent data indicate a significant increase in incidence, with hundreds of thousands of new cases diagnosed annually. Based on GLOBOCAN data, bladder cancer ranked 7th among cancers in Latvia in 2022, with 521 new cases. BC was the seventh most prevalent cancer in the world. The global burden of bladder cancer is considerable, with millions of individuals living with the disease. The overall global five-year prevalence was estimated at 1 700 000 in 2020. There is a substantial number of deaths attributed to it each year, making it the 13th leading cause of cancer mortality. Due to the main risk factors – smoking and chemical exposures – associated with BC, it is considered a largely preventable and avoidable cancer. BC is a heterogeneous disease with highly variable clinical, anatomic, and molecular pathology features, along with a high mutation burden. In recent years, microRNAs (miRNAs) have emerged as crucial regulators of cellular activities, and their dysregulation has been associated with the onset and progression of cancer. Although there are many studies on BC around the world, there is still no comprehensive analysis of the epidemiological trends and long-term survival rates of bladder cancer in Latvia, nor has there been an examination of the potential of exosomal microRNAs (miRNAs) as biomarkers for the diagnosis of BC. The aim of my PhD thesis was to carry out a population-based study on the epidemiological trends of bladder cancer in Latvia from 1990 to 2017, and to evaluate the diagnostic potential of urinary exosomal miRNAs in combination with clinical and laboratory data using machine learning approaches. The study also aimed at improving the understanding of cell-to-cell communication between bladder cells in patients with low-grade (LG) and high-grade (HG) BC, and the possible impact of the urinary exosomal miRNAs on the oncotransformation of normal cells. The results of the study showed that bladder cancer incidence, prevalence, and mortality increased in Latvia between 1990 and 2017. Findings revealed that the age-standardised incidence rate of bladder cancer increased from 6.8 per 100 000 in 1990 to 10.8 per 100 000 in 2017, with a statistically significant rise (APC 2.6 %) from 1990 to 2014, followed by a minor and statistically insignificant decline through 2017 (APC −3.6 %). The prevalence rates increased from 25.2 in 1990 to 69.7 in 2017, with an AAPC of 3.8 % ( 95 % CI 3.1 to 4.6) during the whole period. A statistically significant reduction was observed only in the ≥ 80-year age group, with an APC of −7.6% from 2013 to 2017. Mortality rates also increased from 3.9 to 4.4 per 100,000 during the same period, although a notable decrease was observed in the 40-59 age group, with an annual percentage change of −1.1%. Additionally, relative survival rates improved from 55.0% during 1990-2000 to 59.0% in the years 2013–2017, suggesting advancements in treatment or early detection strategies. These epidemiological findings – particularly the rising incidence and the persistent burden in older age groups – underscored the need for more sensitive, non-invasive diagnostic tools, motivating the subsequent investigation of urinary exosomal miRNAs as candidate biomarkers. Striking differences in the miRNA cargo of exosomes from the BC group as compared to the control group were identified. Among the up-regulated exosomal miRNAs, 7 miRNAs in the LG group and 3 miRNAs in the HG group were identified as directly targeting TP53. Twenty-two and 17 PTEN-targeting miRNAs were observed among the up-regulated miRNAs of the LG and HG groups, respectively. The target genes of these exosomal miRNAs and their interaction network predicted that TP53 is the strongest hub gene in both BC exosomal miRNA networks. Further, the possible role of exosomal miRNAs in synchronous BC development was discussed. Building on these differentially expressed candidates, 11 miRNAs were selected based on statistical significance and fold regulation to test whether combining these miRNAs with routine clinical and laboratory data improves diagnostic accuracy compared with using either data type alone. Machine learning was performed using the 11 specially selected miRNAs in 152 samples (89 bladder cancer patients and 63 controls), selected from an initial 210-participant cohort by applying the criterion of complete clinical data availability. Statistical analysis identified notable miRNAs, such as mir-455-3p, mir-30b-3p, and mir-429, as potential biomarkers. This study highlighted the potential of combining miRNA expression levels with laboratory tests and clinical data for improved BC diagnostics.", "source_url": "https:\/\/openalex.org\/W7225633027", "pdf_url": "", "doi": "10.25143\/prom-rsu_2026-30_dts", "handle": "", "repository_name": "", "source_record_id": "W7225633027", "other_sources": "", "verification_status": "verified", "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": 767, "title": "Master thesis «Research and Software Implementation of Ranking Methods Based on Machine Learning»", "title_ar": "", "title_en": "", "author": "Сергій Володимирович Анурєєв", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "Zaporizhzhya National Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Ukraine", "governorate": "", "publication_year": 2020, "language": "uk", "research_domain": "machine-learning", "research_subdomain": "", "categories": "machine-learning", "keywords": "", "abstract": "Анурєєв С.В. Дослідження та програмна реалізація методів ранжирування на основі машинного навчання \/ С.В. Анурєєв– Запоріжжя: НУ \"Запорізька політехніка\", 2020. –124 с.", "source_url": "https:\/\/openalex.org\/W7217213235", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Electronic Institutional Repository of Zaporizhzhya National Technical University (Zaporizhzhya National Technical University)", "source_record_id": "W7217213235", "other_sources": "", "verification_status": "verified", "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": 773, "title": "weka_cereinae_diver_sp.for_figshare.xlsx Supplementary data for PhD thesis - Chapter 3 - diversification analysis using machine learning", "title_ar": "", "title_en": "weka_cereinae_diver_sp.for_figshare.xlsx Supplementary data for PhD thesis - Chapter 3 - diversification analysis using machine learning", "author": "MONIQUE BRITO", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «PhD»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "The present dataset relates to machine learning analysis aiming to predict diversification rates and abiotic and biotic traits of South American cactus group (subtribe Cereinae).", "source_url": "https:\/\/openalex.org\/W4394283703", "pdf_url": "", "doi": "10.6084\/m9.figshare.22722349", "handle": "", "repository_name": "Figshare", "source_record_id": "W4394283703", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:53" }, { "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": 776, "title": "Machine Learning-based Quality Prediction in the Froth Flotation Process of Mining : Master’s Degree Thesis in Microdata Analysis", "title_ar": "", "title_en": "Machine Learning-based Quality Prediction in the Froth Flotation Process of Mining : Master’s Degree Thesis in Microdata Analysis", "author": "Eric Kwame Osei", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "machine-learning", "research_subdomain": "predictive-analytics", "categories": "machine-learning; predictive-analytics", "keywords": "", "abstract": "In the iron ore mining fraternity, in order to achieve the desired quality in the froth flotation processing plant, stakeholders rely on conventional laboratory test technique which usually takes m ...", "source_url": "https:\/\/openalex.org\/W3043622356", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W3043622356", "other_sources": "", "verification_status": "verified", "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": 778, "title": "Contrastive Temporal Explanations: A Novel Explanation Method for Machine Learning-Based Fault Prediction. Master Thesis University Utrecht", "title_ar": "", "title_en": "Contrastive Temporal Explanations: A Novel Explanation Method for Machine Learning-Based Fault Prediction. Master Thesis University Utrecht", "author": "Joris Graff", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "", "university_source": "", "college": "", "department": "", "country": "Netherlands", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "medical-ai", "research_subdomain": "xai", "categories": "machine-learning; medical-ai; responsible-ai; ai-applications; self-semi-supervised; predictive-analytics; xai; fairness; ai-industry", "keywords": "", "abstract": "Digital twinning has enabled the real-time usage of sensor readings from a variety of systems, such as industrial machines and medical patients.These time-series data offer opportunities to accurately predict the system's future behaviour, e.g. by predicting whether faults are likely to occur within the near future on the basis of the most recent sensor data.Machine learning (ML) algorithms have shown an impressive accuracy in such fault prediction tasks, but suffer from a lack of explainability.This situation problematises the application of such algorithms to real-life problems, where user insight into the prediction model is generally a desideratum.This thesis aims to improve the explainability, and thereby trustworthiness, of fault prediction ML algorithms.In order to have a solution that addresses explainability, the main criteria for successful explanation algorithms have to be identified.A first challenge of this thesis is therefore the formulation of these citeria in a way that answers to theoretical and practical aspects of explainability.A further challenge is to meet these criteria within the domain of fault prediction.The result is a novel solution, called Contrastive Temporal Explanations (CTE), which is shown to outperform state-of-the-art methods on most of the key criteria.First of all, I wish to thank both of my supervisors, Paolo Pileggi from TNO and Hakim Qahtan from the University of Utrecht.Paolo has tirelessly assisted me during the duration of the internship of which this thesis is a product, even when the intervention of a pandemic made it impossible to meet in person.His many phone calls were filled with both encouragement and useful comments.Without his enthusiasm and insights this thesis would not have been possible in the current form.Hakim has helped give structure to the initial literature review stage of this project, and has given me many invaluable suggestions on the content, structure and stylistic aspects of the end product.Without his incidental adjustments I would have repeatedly steered decidedly off course.I also wish to thank Ron Snijders of TNO Groningen for commenting on earlier versions of parts of this thesis, and Sietse Drost and Lars-Uno Axelsson for providing the problem and the data that initially inspired this project, as well as for receiving me and other TNO interns and employees for a tour of the OPRA headquarters.Finally, I am indebted to the researchers, coordinators and administrative and HR officers at the ICT department of TNO The Hague, who have all been crucial in the success of my internship, both in their helpfulness in administrative issues and in creating a welcoming and intellectually stimulating environment that has helped shape many of the ideas presented in this thesis.", "source_url": "https:\/\/openalex.org\/W7132381722", "pdf_url": "https:\/\/publications.tno.nl\/publication\/34637482\/Dhw4Ki\/graff-2020-contrastive.pdf", "doi": "", "handle": "", "repository_name": "TNO Repository", "source_record_id": "W7132381722", "other_sources": "", "verification_status": "verified", "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": 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": 789, "title": "Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System", "title_ar": "", "title_en": "Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System", "author": "Ali Raza", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "federated-learning", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; ai-applications; xai; federated-learning; fairness; ai-cybersecurity", "keywords": "", "abstract": "The growing population around the globe has a significant impact on various sectors including the labor force, healthcare, and the global economy. The healthcare sector is among the most affected sectors by the growing population due to the increasing demand for resources including doctors, nurses, equipment, and healthcare facilities. Intelligent systems have been incorporated to enhance decision-making, management, prognosis, and diagnosis in order to tackle such issues and offer improved healthcare to patients. Among such systems, those based on deep learning (DL), a subclass of machine learning (ML) have outperformed many traditional statistical and ML systems owing to their capability of automatically discovering and learning related features for a given task and robustness. Therefore, the use of DL has seen a steady increase in many applications. Nevertheless, usually, the training of DL models relies on a single centralized server, which brings many challenges: (1) except for some big enterprises most of the small enterprises have limited quality data, which is insufficient to support the training of data-hungry DL models, (2) access to data, which is vital for these systems, often raises privacy concerns. The collection and analysis of sensitive patient information must be done in a secure and ethical manner to ensure the protection of individual privacy rights, (3) high communication cost and computation resources required, (4) a large number of trainable parameters make the outcome of DL hard to explain, which is required in some applications, such as healthcare. Compared to centralized ML, federated learning (FL) improves both privacy and communication costs, where clients collaboratively train a joint model without sharing the raw data directly. FL minimizes privacy breaches and safeguards sensitive data by keeping it distributed locally. This enables collaborative model training while reducing the risk of unauthorized access and data breaches. Additionally, it promotes data diversity and scalability by involving multiple sources in joint model training and decreases communication costs by sharing only model updates instead of the entire dataset. However, FL brings its own challenges. For example, heterogeneous local data among the clients makes it challenging to train a high-performing and robust global model. Sharing updates (hundreds of thousands of parameters) still has high communication costs. Additionally, the distributed nature and access control of local data in FL make it more vulnerable to malicious attacks. Moreover, the challenge of explaining the results of DL still remains challenging, and methods are needed to be developed to bring trust accountability, and transparency in sensitive applications, such as healthcare. Therefore, the aim of this thesis is to create robust frameworks that are secure, high-performing, and privacy-friendly within federated settings. These frameworks will be specifically designed for end-to-end (we train our frameworks using raw data without any manual feature extraction) healthcare applications, considering the presence of non-identically distributed data among clients in FL to bring robustness. By addressing these challenges, the objective is to enhance the overall system's resilience and effectiveness. We also propose a methodology for detecting anomalies within federated settings, particularly in applications with limited available data for the abnormal class. Furthermore, clients in FL are usually resource-constrained with limited computation and communication resources available. Therefore, to support efficient computation and communication in a federated setting we propose a lightweight framework (in terms of the trainable number of parameters). Additionally, to provide explanations of the DL models' outcomes, which are usually hard to explain because of the large number of parameters, we propose model-agnostic explainable AI modules to help explain the results of DL models. Moreover, in order to protect the proposed frameworks against cyber attacks, such as poisoning attacks, we propose a framework in federated settings, which makes the proposed healthcare frameworks more secure and trustworthy. Finally, with experimental analysis using baseline datasets for one of the most common health conditions i.e., cardiovascular diseases (arrhythmia detection, ECG anomaly detection) and human activity recognition (used for supplementing cardiovascular diseases detection), we show the superiority of the proposed frameworks over state-of-the-art work.", "source_url": "https:\/\/openalex.org\/W4399027046", "pdf_url": "http:\/\/www.theses.fr\/2023ULILB019\/document", "doi": "10.22024\/unikent\/01.02.102717", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W4399027046", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 1045, "title": "Classification of ulcer images using convolutional neural networks", "title_ar": "", "title_en": "Classification of ulcer images using convolutional neural networks", "author": "Charlotta Aguirre Nilsson ; Medina Velic", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "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": "medical-imaging", "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; image-classification; medical-imaging", "keywords": "", "abstract": "The use of artificial intelligence has increased within a variety of different fields the last decade, including the area of health care.Machine learning algorithms have already been successfully used in e.g.skin cancer detection in images, indicating its potential for being applied to wound treatment by automatically classifying wound images.In addition to reducing patient discomfort, a machine learning based solution for wound image classification could lower the costs for treatment of wounds.Today, this cost corresponds to the third biggest health care cost for Västra Götalandsregionen, the health care sector in Västra Götaland.This master thesis is a collaboration between the electronics and software development company QRTECH and Västra Götalandsregionen with the aim of creating an image-based aid to be used in the diagnosis of venous leg ulcers.A total of 300 manually annotated images depicting various ulcers for training a network have been provided by Alexandra Forssgren, specialist in dermatology and head of the dermatology department at Skaraborg Hospital.A deep learning based algorithm, using a pre-trained version of the VGG19 convolutional neural network, has been implemented to perform automated binary classification in order to separate venous leg ulcers from other ulcers.Since the annotated ulcer data set was limited, transfer learning was used by pre-training on ImageNet as well as on a set of dermoscopic skin images.The network was implemented in Python using the deep learning library Keras with TensorFlow as backend.To improve performance and reduce overfitting, methods such as dropout, L1\/L2 regularisation and data augmentation were applied.Augmentation included rotation, flipping, contrast enhancement as well as added noise and modified brightness.The best set-up was selected based on the performance metrics accuracy, precision and recall.For the test set, the final network reached an accuracy, precision and recall of approximately 85 %, 82 % and 75 %, respectively.Most certainly, the limited size of the data set affected the results negatively and a larger data set would increase the variety of the training examples, possibly leading to better performance.However, the performance metrics are still promising considering the small data set which proves the potential of a machine learning based solution for ulcer image classification.", "source_url": "https:\/\/openalex.org\/W2889535768", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Chalmers Publication Library (Chalmers University of Technology)", "source_record_id": "W2889535768", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 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": 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": 1069, "title": "Training Methods for Deep Neural Network-Based Acoustic Models in Speech Recognition", "title_ar": "", "title_en": "Training Methods for Deep Neural Network-Based Acoustic Models in Speech Recognition", "author": "Tamás Grósz", "supervisor": "", "degree_type": "phd", "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: SZTE Repository of Dissertations (University of Szeged)", "university": "University of Szeged", "university_source": "repository_host", "college": "", "department": "", "country": "Hungary", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; nlp; speech-recognition", "keywords": "", "abstract": "Nowadays, speech recognition technology is built on Deep Neural Networks. These networks represents the latest direction of machine learning. They are based on the theory of artificial neural networks, which have been used for decades. However, unlike traditional Neural Networks, all deep networks contain many processing layers, which allow the hierarchical processing of the input data. While the concept of deep networks is not totally new, their efficient training required several new achievements. These new networks managed to completely replace the Gaussian Mixture Models in the state-of-the-art speech recognition systems. In this study, we decided to focus on Deep Neural Network-based recognition systems. First, we compared the performance of several new training algorithms with each other, in order to determine the best one for later use. Then, we turned my attention to the algorithms that the new speech recognition systems have inherited from the previous Gaussian Mixture Model-based approaches, as the algorithms might not be optimal for Deep Neural Networks. we proposed new algorithms for obtaining the initial alignment of the frame-level state labels and the creation of context-dependent states, and found that they are better suited for the new acoustic models. Lastly, we also experimented with a data re-sampling method to improve the accuracy of the models.", "source_url": "https:\/\/openalex.org\/W2900836049", "pdf_url": "http:\/\/doktori.bibl.u-szeged.hu\/4225\/1\/GT_dissertation_final.pdf", "doi": "10.14232\/phd.4225", "handle": "", "repository_name": "SZTE Repository of Dissertations (University of Szeged)", "source_record_id": "W2900836049", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 1166, "title": "Application of explainable artificial intelligence to precision oncology", "title_ar": "", "title_en": "Application of explainable artificial intelligence to precision oncology", "author": "Sada-del-Real, K. (Katyna)", "supervisor": "", "degree_type": "phd", "degree_evidence": "نص السجل: «doctoral dissertation»", "university": "University of Navarra", "university_source": "repository_host", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; predictive-analytics; medical-imaging; xai", "keywords": "", "abstract": "This doctoral dissertation, entitled \"Application of Explainable Artificial Intelligence to Precision Oncology,\" explores the development and evaluation of interpretable machine learning models for drug response prediction in personalized cancer therapy. It highlights the critical importance of model interpretability in facilitating the clinical translation of AI-driven solutions in oncology. First, it introduces Optimal Decision Trees (ODT), a novel tree-based model that enhances transparency in treatment guidance through intuitive graphical decision trees. The creation of the ODT R CRAN package ensures its accessibility for researchers and clinicians alike. The model was evaluated against established methods such as Random Forest and XGBoost, enhanced by the SEATS (Systematic Efficacy Assignment with Treatment Seats) method, specifically designed to optimize drug assignment solutions. Through a state-of-the-art review, the dissertation establishes a framework for developing Drug Response Prediction (DRP) models, emphasizing high-quality data, precise normalization, and rigorous validation methods, such as cell-blind and drug-blind cross-validations. These methodologies ensure that DRP models remain accurate, clinically relevant, and adaptable across various datasets. Central to this research is the introduction of SparseGO, a Visible Neural Network (VNN) that leverages the Gene Ontology (GO) hierarchy to enhance computational efficiency and facilitate the discovery of drug mechanisms of action (MoA). The Explainable AI (XAI) method, DeepMoA, employs DeepLIFT and Support Vector Machines (SVMs) to predict drug MoAs, supported by strong computational validation. The research extends to Patient-Derived Cell-lines (PDCs), a crucial step in aligning DRP modeling more closely with patient-specific tumor characteristics. SparseGO and ODT models were trained on this data. This work presents a comprehensive exploration of explainable AI in precision oncology, emphasizing the critical role of interpretability in advancing personalized cancer treatment. Future research will focus on validating these models across diverse datasets, verifying predictions experimentally, and exploring advanced techniques to improve model eneralization and interpretability. This work not only introduces novel methodologies and accessible tools but also paves the way for AI-driven insights to seamlessly integrate into personalized cancer care.", "source_url": "https:\/\/openalex.org\/W4415350853", "pdf_url": "", "doi": "10.15581\/10171\/117527", "handle": "", "repository_name": "Deposito Adademico Digital Universidad De Navarra (University of Navarra)", "source_record_id": "W4415350853", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 1185, "title": "Machine Learning for Predictive Maintenance in Aviation", "title_ar": "", "title_en": "", "author": "Panagiotis Korvesis", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2017, "language": "fr", "research_domain": "nlp", "research_subdomain": "speech-recognition", "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; predictive-analytics; speech-recognition; ai-industry", "keywords": "", "abstract": "Apprentissage Automatique pour la Maintenance Predictive dans le Domaine de l’Aviation L'augmentation des données disponibles dans presque tous les domaines soulève la nécessité d'utiliser des algorithmes pour l'analyse automatisée des données. Cette nécessité est mise en évidence dans la maintenance prédictive, où l'objectif est de prédire les pannes des systèmes en observant continuellement leur état, afin de planifier les actions de maintenance à l'avance. Ces observations sont générées par des systèmes de surveillance habituellement sous la forme de séries temporelles et de journaux d'événements et couvrent la durée de vie des composants correspondants. Le principal défi de la maintenance prédictive est l'analyse de l'historique d'observation afin de développer des modèles prédictifs.Dans ce sens, l'apprentissage automatique est devenu omniprésent puisqu'il fournit les moyens d'extraire les connaissances d'une grande variété de sources de données avec une intervention humaine minimale. L'objectif de cette thèse est d'étudier et de résoudre les problèmes dans l'aviation liés à la prévision des pannes de composants à bord. La quantité de données liées à l'exploitation des avions est énorme et, par conséquent, l'évolutivité est une condition essentielle dans chaque approche proposée.Cette thèse est divisée en trois parties qui correspondent aux différentes sources de données que nous avons rencontrées au cours de notre travail. Dans la première partie, nous avons ciblé le problème de la prédiction des pannes des systèmes, compte tenu de l'historique des Post Flight Reports. Nous avons proposé une approche statistique basée sur la régression précédée d'une formulation méticuleuse et d'un prétraitement \/ transformation de données. Notre méthode estime le risque d'échec avec une solution évolutive, déployée dans un environnement de cluster en apprentissage et en déploiement. À notre connaissance, il n'y a pas de méthode disponible pour résoudre ce problème jusqu'au moment où cette thèse a été écrite.La deuxième partie consiste à analyser les données du livre de bord, qui consistent en un texte décrivant les problèmes d'avions et les actions de maintenance correspondantes. Le livre de bord contient des informations qui ne sont pas présentes dans les Post Flight Reports bien qu'elles soient essentielles dans plusieurs applications, comme la prédiction de l'échec. Cependant, le journal de bord contient du texte écrit par des humains, il contient beaucoup de bruit qui doit être supprimé afin d'extraire les informations utiles. Nous avons abordé ce problème en proposant une approche basée sur des représentations vectorielles de mots. Notre approche exploite des similitudes sémantiques, apprises par des neural networks qui ont généré les représentations vectorielles, afin d'identifier et de corriger les fautes d'orthographe et les abréviations. Enfin, des mots-clés importants sont extraits à l'aide du Part of Speech Tagging.Dans la troisième partie, nous avons abordé le problème de l'évaluation de l'état des composants à bord en utilisant les mesures des capteurs. Dans les cas considérés, l'état du composant est évalué par l'ampleur de la fluctuation du capteur et une tendance à l'augmentation monotone. Dans notre approche, nous avons formulé un problème de décomposition des séries temporelles afin de séparer les fluctuations de la tendance en résolvant un problème convexe. Pour quantifier l'état du composant, nous calculons à l'aide de Gaussian Mixture Models une fonction de risque qui mesure l'écart du capteur par rapport à son comportement normal.", "source_url": "https:\/\/openalex.org\/W7148425078", "pdf_url": "https:\/\/pastel.hal.science\/tel-02003508v1\/file\/65181_KORVESIS_2017_archivage.pdf", "doi": "10.70675\/5c156250zffd4z4dc8z8067zd9361665aa02", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7148425078", "other_sources": "", "verification_status": "verified", "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": 1191, "title": "Online machine learning-based predictive maintenance for the railway industry", "title_ar": "", "title_en": "", "author": "Minh Huong Le Nguyen", "supervisor": "", "degree_type": "phd", "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه", "university": "", "university_source": "", "college": "", "department": "", "country": "France", "governorate": "", "publication_year": 2023, "language": "fr", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; computer-vision; ai-applications; data-mining; predictive-analytics; transformers; ai-industry", "keywords": "", "abstract": "Maintenance prévisionnelle basée sur l'apprentissage automatique en ligne dans le secteur ferroviaire En tant que moyen de transport en commun efficace sur de longues distances, le chemin de fer continuera de prospérer pour son empreinte carbone limitée dans l'environnement. Assurer la fiabilité des équipements et la sécurité des passagers fait ressortir la nécessité d'une maintenance efficace. Outre la maintenance corrective et périodique courante, la maintenance prédictive a pris de l'importance ces derniers temps. Les progrès récents de l'apprentissage automatique et l'abondance de données poussent les praticiens à la maintenance prédictive basée sur les données. La pratique courante consiste à collecter des données pour former un modèle d'apprentissage automatique, puis à déployer le modèle pour la production et à le conserver inchangé par la suite. Nous soutenons qu'une telle pratique est sous-optimale sur un flux de données. Le caractère illimité du flux rend le modèle sujet à un apprentissage incomplet. Les changements dynamiques sur le flux introduisent de nouveaux concepts invisibles pour le modèle et diminuent sa précision. La vitesse du flux rend l'étiquetage manuel impossible et désactive les algorithmes d'apprentissage supervisé. Par conséquent, il est nécessaire de passer d'un paradigme d'apprentissage statique et hors ligne à un paradigme adaptatif en ligne, en particulier lorsque de nouvelles générations de trains connectés générant en continu des données de capteurs sont déjà une réalité. Nous étudions l'applicabilité de l'apprentissage automatique en ligne pour la maintenance prédictive sur des systèmes complexes typiques du secteur ferroviaire. Tout d'abord, nous développons InterCE en tant que framework basé sur l'apprentissage actif pour extraire des cycles d'un flux non étiqueté en interagissant avec un expert humain. Ensuite, nous implémentons un auto-encodeur à mémoire longue et courte durée pour transformer les cycles extraits en vecteurs de caractéristiques plus compacts tout en restant représentatifs. Enfin, nous concevons CheMoc comme un framework pour surveiller en permanence l'état des systèmes en utilisant le clustering adaptatif en ligne. Nos méthodes sont évaluées sur les systèmes d'accès voyageurs sur deux flottes de trains gérés par la société nationale des chemins de fer SNCF de la France.", "source_url": "https:\/\/openalex.org\/W7151281587", "pdf_url": "", "doi": "10.70675\/173d7e4fz2dcdz49d1z846cz5fe2ba50424a", "handle": "", "repository_name": "theses.fr (ABES)", "source_record_id": "W7151281587", "other_sources": "", "verification_status": "verified", "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": 1215, "title": "Pengembangan media pembelajaran fikih e-comic berbasis artificial intelligence untuk meningkatkan problem solving skill siswa MTS Miftahul Mubtadiin Muncar", "title_ar": "", "title_en": "", "author": "Salsa Syafa Rizkyna", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "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": "deep-learning; computer-vision; nlp; ai-applications; transformers; arabic-nlp; ai-education", "keywords": "", "abstract": "INDONESIA: Pada abad 21 ini, sekolah dituntut untuk menguasai keterampilan 4C (creative thinking, critical thinking and problem solving, communication, dan collaboration). Kemampuan ini penting dalam kehidupan sehari-hari maupun dalam proses pembelajaran, termasuk dalam memahami pelajaran Fikih. Namun, ketersediaan media pembelajaran fikih masih terbatas dan belum banyak dimanfaatkan di sekolah. Dengan demikian pada mata pelajaran fikih diperlukan media. Penelitian ini bertujuan mengembangkan media pembelajaran e-comic berbasis Artificial Intelligence untuk meningkatkan kemampuan pemecahan masalah siswa pada mata pelajaran Fikih di MTs Miftahul Mubtadiin Muncar. Pengembangan dilakukan menggunakan model ADDIE yang meliputi tahapan analisis kebutuhan, perancangan media, pengembangan produk, implementasi kepada siswa, serta evaluasi efektivitas media. Hasil validasi para ahli, baik ahli materi, ahli media, maupun ahli pembelajaran, menunjukkan bahwa e-comic berada pada kategori sangat layak dari aspek kualitas visual, akurasi materi, kejelasan penyajian, dan kesesuaian pedagogis. Implementasi kepada siswa kelas VIII memperoleh respon positif dengan skor keseluruhan 85%, yang termasuk kategori sangat baik. Media ini terbukti membantu siswa lebih mudah memahami materi zakat, meningkatkan motivasi belajar, serta memberikan pengalaman pembelajaran yang lebih menarik dan interaktif dibandingkan metode konvensional. ENGLISH: In the 21st century, schools are required to master the 4C skills (creative thinking, critical thinking and problem solving, communication, and collaboration). These skills are important in everyday life and in the learning process, including in understanding Fiqh lessons. However, the availability of Fiqh learning media is still limited and has not been widely used in schools. Therefore, media is needed for Fiqh lessons. This study aims to develop Artificial Intelligence-based e-comic learning media to improve students' problem-solving skills in Fikih at MTs Miftahul Mubtadiin Muncar. The development was carried out using the ADDIE model, which includes the stages of needs analysis, media design, product development, implementation to students, and evaluation of media effectiveness. The results of validation by experts, including subject matter experts, media experts, and learning experts, showed that the e-comic was very feasible in terms of visual quality, material accuracy, clarity of presentation, and pedagogical suitability. Implementation among eighth-grade students received a positive response with an overall score of 85%, which is considered very good. This media has been proven to help students understand zakat material more easily, increase their motivation to learn, and provide a more interesting and interactive learning experience compared to conventional methods. ARABIC: في القرن الحادي والعشرين، تُطالَب المدارس بإتقان مهارات القرن الحادي والعشرين المعروفة بمهارات 4C، وهي التفكير الإبداعي، والتفكير النقدي وحلّ المشكلات، والتواصل، والتعاون. وتُعدّ هذه المهارات ضرورية في الحياة اليومية وفي العملية التعليمية، بما في ذلك فهم مادة الفقه. غير أن توافر الوسائل التعليمية لمادة الفقه لا يزال محدودًا ولم يُستفد منها على نحوٍ أمثل في المدارس، مما يجعل الحاجة إلى تطوير وسائل تعليمية للفقه أمرًا ملحًّا. تهدف هذه الدراسة إلى تطوير وسيلة تعليمية على شكل قصص مصوّرة إلكترونية (E-Comic) قائمة على الذكاء الاصطناعي؛ من أجل تنمية مهارات حلّ المشكلات لدى الطلاب في مادة الفقه بمدرسة MTs مِفتاح المُبتدِئين مونجار. تم تنفيذ عملية التطوير باستخدام نموذج ADDIE الذي يشتمل على مراحل تحليل الاحتياجات، وتصميم الوسيلة التعليمية، وتطوير المنتج، وتطبيقه على الطلاب، ثم تقويم فاعلية الوسيلة. وأظهرت نتائج تحكيم الخبراء، سواء خبراء المادة، أو خبراء الوسائل التعليمية، أو خبراء التعلم، أن الوسيلة التعليمية المصوّرة الإلكترونية جاءت ضمن فئة «صالحة جدًا» من حيث جودة العرض البصري، ودقة المحتوى العلمي، ووضوح العرض، وملاءمتها للجوانب التربوية. كما أسفر تطبيق الوسيلة على طلاب الصف الثامن عن استجابات إيجابية بنسبة إجمالية بلغت 85%، وهي نسبة تندرج ضمن فئة «جيد جدًا». وأثبتت هذه الوسيلة فاعليتها في تسهيل فهم الطلاب لمادة الزكاة، وزيادة دافعيتهم للتعلم، وتوفير تجربة تعليمية أكثر جاذبية وتفاعلية مقارنة بالأساليب التقليدية.", "source_url": "https:\/\/openalex.org\/W7119318770", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eTheses of Maulana Malik Ibrahim State Islamic University (Maulana Malik Ibrahim State Islamic University)", "source_record_id": "W7119318770", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 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": 1302, "title": "Machine learning and natural language processing for sentiment analysis on the internet", "title_ar": "", "title_en": "", "author": "Matija Jerin", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Magistrsk»", "university": "University of Maribor", "university_source": "repository_host", "college": "", "department": "", "country": "Slovenia", "governorate": "", "publication_year": 2025, "language": "sl", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; nlp; rnn; transformers; sentiment-analysis", "keywords": "", "abstract": "Magistrsko delo obravnava področje analize sentimenta z uporabo strojnega učenja in obdelave naravnega jezika. Namen dela je razviti model, ki bi bil sposoben analizirati sentiment besedil, pridobljenih s spletnih platform, zlasti z družbenega omrežja X (prej Twitter). V delu smo uporabili različne metode strojnega učenja in obdelave naravnega jezika. Najprej smo podatke pridobili iz odprtih virov, jih očistili in normalizirali z metodami, kot sta lemmatizacija in tokenizacija. Pri obdelavi podatkov smo uporabili več tehnik, vključno z Bag of Words, s pozitivno\/z negativno frekvenco in s TF-IDF, za kar smo uporabili Python knjižnice, kot sta NLTK in scikit-learn. Model smo učili z metodo logistične regresije, naivnega bayesa in z metodo podpornih vektorjev ter testirali njihovo natančnost s pomočjo ločenih testnih podatkov. Rezultati kažejo, da je logistična regresija v kombinaciji z značilkami TF-IDF dosegla najvišjo natančnost pri predvidevanju sentimenta, in sicer 88,65 %, kar pomeni, da je model sposoben zanesljivo prepoznati sentiment besedil kot pozitiven ali negativen. Kljub uspehu modela obstaja potencial za nadaljnje izboljšave. Uporaba večjih in bolj raznovrstnih podatkovnih zbirk ter naprednejših tehnik globokega učenja, kot so nevronske mreže (LSTM ali BERT), bi lahko še povečala natančnost in zmogljivost modela. Zaključki magistrskega dela potrjujejo, da je analiza sentimenta z uporabo strojnega učenja izvedljiva in uporabna v različnih okoljih. V prihodnje priporočamo implementacijo API-ja za omrežje X, kar bi omogočilo sprotno pridobivanje podatkov in avtomatizirano analizo sentimenta v realnem času. Prav tako bi lahko nadgradnja modela z globokim učenjem pripomogla k obvladovanju kompleksnejših jezikovnih struktur in kontekstov, kot sta sarkazem in večpomenskost.", "source_url": "https:\/\/openalex.org\/W7219248249", "pdf_url": "https:\/\/dk.um.si\/Dokument.php?id=187636&dn=", "doi": "", "handle": "", "repository_name": "University of Maribor digital library (University of Maribor)", "source_record_id": "W7219248249", "other_sources": "", "verification_status": "verified", "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": 1318, "title": "Sentiment analysis on social networks using machine learning and audio processing", "title_ar": "", "title_en": "Sentiment analysis on social networks using machine learning and audio processing", "author": "Mihail Duşçu", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «M.A.»", "university": "", "university_source": "", "college": "", "department": "", "country": "Turkey", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "nlp", "research_subdomain": "sentiment-analysis", "categories": "machine-learning; nlp; ai-applications; sentiment-analysis; speech-recognition; ai-industry", "keywords": "", "abstract": "Thesis (M.A.)--Özyeğin University, Graduate School of Sciences and Engineering, Department of Industrial Engineering, August 2019.", "source_url": "https:\/\/openalex.org\/W3007463049", "pdf_url": "", "doi": "", "handle": "", "repository_name": "eResearch@Ozyegin", "source_record_id": "W3007463049", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:53:01", "updated_at": "2026-11-15 16:53:01" }, { "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": 1345, "title": "Short term forecasting of solar power with machine learning and time series techniques", "title_ar": "", "title_en": "Short term forecasting of solar power with machine learning and time series techniques", "author": "Kesh Pun", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «M.S.»", "university": "", "university_source": "", "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": "Thesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science", "source_url": "https:\/\/openalex.org\/W3126322738", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Holmes Museum Of Anthropology (Wichita State University)", "source_record_id": "W3126322738", "other_sources": "", "verification_status": "verified", "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": 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": 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": 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" } ] }