{ "exported_at": "2026-11-15 17:39:20", "count": 47, "records": [ { "id": 32, "title": "Convolutional Neural Networks based Breast Cancer Detection Using Feature Fusion", "title_ar": "", "title_en": "Convolutional Neural Networks based Breast Cancer Detection Using Feature Fusion", "author": "Doğu Manalı ; Hasan Demırel", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "Eastern Mediterranean University", "university_source": "repository_host", "college": "", "department": "", "country": "Cyprus", "governorate": "", "publication_year": 2023, "language": "en", "research_domain": "medical-ai", "research_subdomain": "cnn", "categories": "deep-learning; medical-ai; cnn", "keywords": "", "abstract": "Master of Science in Electrical and Electronic Engineering. Thesis (M.S.) - Eastern Mediterranean University, Faculty of Engineering, Dept. of Electrical and Electronic Engineering, 2023. Supervisor: Prof. Dr. Hasan Demirel.", "source_url": "https:\/\/openalex.org\/W7201918567", "pdf_url": "https:\/\/i-rep.emu.edu.tr\/bitstreams\/85d3c7ee-6f66-4e11-b0c7-2e6c190e5acd\/download", "doi": "", "handle": "", "repository_name": "Eastern Mediterranean University Institutional Repository (Eastern Mediterranean University)", "source_record_id": "W7201918567", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 47, "title": "Tracking of Humans in Video Stream Using LSTM Recurrent Neural Network", "title_ar": "", "title_en": "Tracking of Humans in Video Stream Using LSTM Recurrent Neural Network", "author": "Masoumeh P. Ghaemmaghami", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2017, "language": "en", "research_domain": "computer-vision", "research_subdomain": "video-analysis", "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; rnn; video-analysis", "keywords": "", "abstract": "In this master thesis, the problem of tracking humans in video streams by using Deep Learning is examined. We use spatially supervised recurrent convolutional neural networks for visual human tracking. In this method, the recurrent convolutional network uses both the history of locations and the visual features from the deep neural networks. This method is used for tracking, based on the detection results. We concatenate the location of detected bounding boxes with high-level visual features produced by convolutional networks and then predict the tracking bounding box for next frames. Because a video contain continuous frames, we decide to have a method which uses the information from history of frames to have a robust tracking in different visually challenging cases such as occlusion, motion blur, fast movement, etc. Long Short-Term Memory (LSTM) is a kind of recurrent convolutional neural network and useful for our purpose. Instead of using binary classification which is commonly used in deep learning based tracking methods, we use a regression for direct prediction of the tracking locations. Our purpose is to test our method on real videos which is recorded by head-mounted camera. So our test videos are very challenging and contain different cases of fast movements, motion blur, occlusions, etc. Considering the limitation of the training data-set which is spatially imbalanced, we have a problem for tracking the humans who are in the corners of the image but in other challenging cases, the proposed tracking method worked well.", "source_url": "https:\/\/openalex.org\/W2769769732", "pdf_url": "", "doi": "", "handle": "", "repository_name": "", "source_record_id": "W2769769732", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "id": 81, "title": "Deep learning based segmentation for multi-modal medical MRI Images", "title_ar": "", "title_en": "", "author": "Mustafa Arikan", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Masterarbeit»", "university": "University of Vienna", "university_source": "repository_host", "college": "", "department": "", "country": "Austria", "governorate": "", "publication_year": 2018, "language": "de", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; multimodal; image-segmentation; medical-imaging", "keywords": "", "abstract": "In dieser Masterarbeit stellen wir einen automatischen Algorithmus für Segmentierung von multi-modalen Datensätzen mittels Deep Learning vor. Unser Algorithmus basiert auf der SegNet Architektur. Wir haben SegNet erwietert, sodass dieses nicht nur die drei Kanäle eines RGB-Bildes, sondern eine beliebige Anzahl von Kanälen von multi-modalen Bildern verarbeiten kann. Weiters haben wir Visualisierungen für das Untersuchen von genauen und robusten Modellen entwickelt. Wir evaluieren unseren Algorithmus auf manuell segmentierten MRI Datensätzen und vergleichen mit einem State-of-the-Art Algorithmus.", "source_url": "https:\/\/openalex.org\/W3157823582", "pdf_url": "", "doi": "10.25365\/thesis.55604", "handle": "", "repository_name": "University of Vienna", "source_record_id": "W3157823582", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:46", "updated_at": "2026-11-15 16:52:46" }, { "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": 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": 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": 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": 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": 276, "title": "Brain semantic segmentation: a deep learning approach in human and Rat MRI studies", "title_ar": "", "title_en": "Brain semantic segmentation: a deep learning approach in human and Rat MRI studies", "author": "Mariana Fontainhas Rodrigues", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Dissertação de mestrado»", "university": "", "university_source": "", "college": "", "department": "", "country": "Portugal", "governorate": "", "publication_year": 2018, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging", "keywords": "", "abstract": "Dissertação de mestrado em Biomedical Engineering Dissertation (área de especialização em Field of Medical informatics)", "source_url": "https:\/\/openalex.org\/W3015538660", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "source_record_id": "W3015538660", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "id": 324, "title": "Promoting Artificial Intelligence Education in K – 12 through Pre-service Teachers’ Development", "title_ar": "", "title_en": "Promoting Artificial Intelligence Education in K – 12 through Pre-service Teachers’ Development", "author": "Olumide Ojo", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master's»", "university": "University of Eastern Finland", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2024, "language": "en", "research_domain": "responsible-ai", "research_subdomain": "fairness", "categories": "responsible-ai; ai-applications; fairness; ai-education", "keywords": "", "abstract": "This study was conducted to develop pre-service CS teachers' artificial intelligence (AI) knowledge through a professional development (PD) program.This research further examined how 45 teacher candidates learned AI concepts, their perspectives as well as their concerns and suggestions regarding implementing AI lessons in classroom.Both quantitative and qualitative methodology were employed to answer the two research questions that guided the study.A duly validated instrument was used to collect data for the quantitative part while qualitatively, semistructured interviews were conducted after the PD program in a public university in Nigeria.The analytical approach used to analyze the data retrieved includes paired sample t-test and thematic analysis.Findings revealed some influence of the PD program on the pre-service teachers' perceptions towards AI.Based on the measured variables, teachers' competence, and skills (Ability) significantly increased post PD, while teachers' cognitive readiness (cognition), which includes knowledge about how AI functions does not increase.Perceived threats about AI decreased post-training, indicating a positive impact on teachers' confidence in learning AI.This study emphasizes the necessity of AI literacy beyond short courses for boosting cognition.Concerns related to ethical issues, infrastructure challenges, and a lack of technical expertise were uncovered as significant barriers to implementing AI lessons in K-12 classrooms, prompting thoughtful suggestions from pre-service teachers.The findings from this study have implications for the development of AI education in the K-12 context and teacher education.iv Acknowledgement Embarking on this master's degree has truly been a journey.As I approach its completion, I am indebted to appreciate individuals who have played a vital role in this intellectual odyssey.I want to give glory to the Almighty God, my helper and sustainer who has been there for me through thick and thin, and for His grace has seen me throughout this program.My unflinching appreciation also goes to my able and amiable supervisor, Dr. Ismaila Sanusi whose unwavering guidance, constructive critiques, and relentless commitment to pushing the boundaries of knowledge have been the foundation of this endeavour.His guidance shaped the rigour of this thesis but also inspired my passion to pursue excellence.To Dr. Solomon Oyelere, Bar.Kola Abiri, Mr. Amuni Olayinka and Mr. Lewis Akinwunmi, I want to say a big thank you for the mentorship and concern to see that I am making progress.The resources and support provided by the University of Eastern Finland, especially the School of Computing have played a role in achieving this academic feat.The enabling academic environment and the timeless assistance from my lecturers and the also program coordinator -Oili Kohonen have paved the way for growth and innovative thinking.I would like to express sincere gratitude to my precious jewel Bukola Ojo and my lovely children Joanna and Jedidah for their prayers, inspiration and understanding.Their endless support throughout the challenging phases of my academic pursuit has been my anchor.Their confidence in my strength has served as a propelling force and a constant reminder of the importance of perseverance.To everyone who has supported me in this course, I extend my thanks for the fellowship that has helped in the actualisation of this academic goal.Your support has been of immense benefit.In summary, have concluded that in unity, we can achieve a lot.I am really grateful to each person mentioned and those I could not mention for their endless support throughout this journey.", "source_url": "https:\/\/openalex.org\/W7215644623", "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/38c3bac6-e930-4fa4-a300-17eb97d4dcc7\/download", "doi": "", "handle": "", "repository_name": "UEF eRepo (University of Eastern Finland)", "source_record_id": "W7215644623", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:48", "updated_at": "2026-11-15 16:52:48" }, { "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": 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": 382, "title": "Attention Mechanisms in Transformers: A Comparative Survey and Structural Enhancements to Linear Attention", "title_ar": "", "title_en": "Attention Mechanisms in Transformers: A Comparative Survey and Structural Enhancements to Linear Attention", "author": "Nizar Bennani", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "", "university_source": "", "college": "", "department": "", "country": "", "governorate": "", "publication_year": 2025, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "deep-learning; nlp; transformers; llm", "keywords": "", "abstract": "This master’s thesis explores the trade-off between computational efficiency and modeling performance in Transformer architectures by studying and improving attention mechanisms. The work consists of two parts: (1) a comparative empirical study evaluating five attention variants—standard Multi-Head Attention, FlashAttention, Sparse Attention, Sliding-Window Attention, and Linear Attention—across multiple Transformer model families, including encoder-only (BERT-style), decoder-only (GPT-style), and full encoder–decoder architectures. This broader evaluation highlights how each mechanism behaves under different structural constraints and reveals the consistent underperformance of linear attention in decoder-only setups due to weaker long-range modeling. (2) A technical contribution proposing two new hybrid mechanisms, Linear SparseAttention and Linear Sliding-Window Attention, which enhance the expressiveness of linear attention while preserving its linear-time complexity. Experiments show that both hybrids significantly outperform standard linear attention and narrow the performance gap with full attention, offering a promising path toward efficient and scalable Transformer models deployable in resource-constrained settings.", "source_url": "https:\/\/openalex.org\/W7114772223", "pdf_url": "", "doi": "10.5281\/zenodo.17879303", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7114772223", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:49", "updated_at": "2026-11-15 16:52:49" }, { "id": 407, "title": "Layout-aware zero-shot learning for visual document matching", "title_ar": "", "title_en": "", "author": "Lucas De Almeida Bandeira Macedo", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «mestrado»", "university": "University of Brasília", "university_source": "repository_host", "college": "", "department": "", "country": "Brazil", "governorate": "", "publication_year": 2026, "language": "pt", "research_domain": "computer-vision", "research_subdomain": "few-zero-shot", "categories": "machine-learning; computer-vision; few-zero-shot", "keywords": "", "abstract": "Dissertação (mestrado) — Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, Programa de Pós-Graduação em Informática, 2026.", "source_url": "https:\/\/openalex.org\/W7166410379", "pdf_url": "http:\/\/repositorio.unb.br\/bitstream\/10482\/55086\/1\/LucasDeAlmeidaBandeiraMacedo_DISSERT.pdf", "doi": "", "handle": "", "repository_name": "RIUnB Institutional Repository (University of Brasília)", "source_record_id": "W7166410379", "other_sources": "", "verification_status": "verified", "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": 604, "title": "Bildgenerierung mittels Generative Adversarial Networks", "title_ar": "", "title_en": "", "author": "Meline A. Ayvazyan", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Masterarbeit»", "university": "Saxon State and University Library Dresden", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2023, "language": "de", "research_domain": "generative-ai", "research_subdomain": "gan", "categories": "generative-ai; gan", "keywords": "", "abstract": "Das Hauptziel der Masterarbeit ist es, die Funktionsweise und das Verhalten von GANs in der Bildgenerierung zu untersuchen und dies anhand eines praktischen Beispiels zu verdeutlichen. Die Arbeit soll einen aktuellen Forschungsüberblick über die generative Modellierung, insbesondere im Bereich der Bildgenerierung, liefern und gleichzeitig die fachlichen Inhalte im Hinblick auf die Funktionsweise von GANs umfassend behandeln. Dabei werden sowohl theoretische Grundlagen als auch die praktische Umsetzung von GANs beleuchtet, um ein ganzheitliches Verständnis für dieses spannende Forschungsfeld zu vermitteln.", "source_url": "https:\/\/openalex.org\/W7139216419", "pdf_url": "https:\/\/tubaf.qucosa.de\/api\/qucosa%3A103059\/attachment\/ATT-0\/", "doi": "", "handle": "", "repository_name": "Qucosa (Saxon State and University Library Dresden)", "source_record_id": "W7139216419", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:51", "updated_at": "2026-11-15 16:52:51" }, { "id": 722, "title": "Artificial Intelligence to Accelerate COVID-19 Identification from Chest X-rays", "title_ar": "", "title_en": "Artificial Intelligence to Accelerate COVID-19 Identification from Chest X-rays", "author": "Dyah Adila", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «M.S.»", "university": "University of Minnesota", "university_source": "repository_host", "college": "", "department": "", "country": "United States", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "medical-ai; medical-imaging", "keywords": "", "abstract": "University of Minnesota M.S. thesis. May 2021. Major: Computer Science. Advisor: Ju Sun. 1 computer file (PDF); vii, 36 pages.", "source_url": "https:\/\/openalex.org\/W3200627206", "pdf_url": "", "doi": "", "handle": "", "repository_name": "University of Minnesota Digital Conservancy (University of Minnesota)", "source_record_id": "W3200627206", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 728, "title": "Classification of X-rays images using Deep Convolutional Neural Network: COVID-19", "title_ar": "", "title_en": "Classification of X-rays images using Deep Convolutional Neural Network: COVID-19", "author": "Laban Kipchirchir Bore", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "university": "Strathmore University", "university_source": "repository_host", "college": "", "department": "", "country": "Kenya", "governorate": "", "publication_year": 2021, "language": "en", "research_domain": "medical-ai", "research_subdomain": "medical-imaging", "categories": "deep-learning; computer-vision; medical-ai; cnn; medical-imaging", "keywords": "", "abstract": "A Research Thesis Submitted to the Graduate School in partial fulfillment of the requirements for the Award of Master of Science Degree in Statistical Sciences at Strathmore University", "source_url": "https:\/\/openalex.org\/W7214869272", "pdf_url": "https:\/\/su-plus.strathmore.edu\/bitstreams\/0e003823-0aad-4fbc-90fb-6d3b97da6001\/download", "doi": "", "handle": "", "repository_name": "SU+ Digital Repository (Strathmore University)", "source_record_id": "W7214869272", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:52", "updated_at": "2026-11-15 16:52:52" }, { "id": 733, "title": "Performance Enhancement of Convolution Neural Network for COVID-19 Classification", "title_ar": "", "title_en": "Performance Enhancement of Convolution Neural Network for COVID-19 Classification", "author": "Bikash Ghimirey ; Roshan Chitrakar", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Master»", "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; image-classification; medical-imaging", "keywords": "", "abstract": "This Master’s thesis investigates a deep learning–based approach for accurate detection of COVID-19 from chest X-ray (CXR) images using Convolutional Neural Networks (CNNs). Accurate and timely identification of COVID-19 cases from radiographic images is essential for effective clinical decision-making, particularly in situations where laboratory testing resources are limited. The study focuses on improving CNN performance through systematic hyperparameter optimization and transfer learning techniques. A VGG16 architecture pretrained on ImageNet was used as the base model, enabling effective feature extraction from medical images while reducing training complexity. A large-scale dataset of approximately 15,000 chest X-ray images was utilized. Comprehensive preprocessing steps including normalization, resizing, and data augmentation were applied to improve model robustness and generalization. Key hyperparameters — including batch size, learning rate, dropout rate, and dataset size — were systematically tuned through extensive experimentation. Model performance was evaluated using standard classification metrics such as accuracy, precision, recall (sensitivity), F1-score, ROC-AUC, and confusion matrices. The models were implemented using Python with TensorFlow\/Keras and trained on Google Colaboratory to leverage high-performance computational resources. The optimized VGG16 model achieved a classification accuracy of 98.5% and sensitivity of 97.8%, significantly reducing false negatives — a critical factor in medical diagnosis. Experimental results demonstrate that appropriate hyperparameter selection, particularly a learning rate of 0.001 and batch size of 64, provides the best balance between performance and stability. This research confirms that systematic hyperparameter tuning can substantially enhance CNN performance for medical image classification and supports the development of reliable AI-assisted diagnostic systems for real-time COVID-19 screening. The methodology can be extended to other medical imaging applications and disease detection tasks.", "source_url": "https:\/\/openalex.org\/W7131246595", "pdf_url": "", "doi": "10.5281\/zenodo.18758362", "handle": "", "repository_name": "Zenodo (CERN European Organization for Nuclear Research)", "source_record_id": "W7131246595", "other_sources": "", "verification_status": "verified", "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": 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": 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": 872, "title": "Semantic segmentation of aerial images for mapping terrain passability", "title_ar": "", "title_en": "", "author": "Jiří Pazdera", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «diplomová práce»", "university": "Brno University of Technology", "university_source": "repository_host", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2026, "language": "cs", "research_domain": "computer-vision", "research_subdomain": "image-segmentation", "categories": "computer-vision; robotics; image-segmentation", "keywords": "", "abstract": "Tato diplomová práce se zabývá využitím metod hlubokého učení pro sémantickou segmentaci leteckých snímků za účelem mapování průjezdnosti terénu pro mobilní roboty. Práce analyzuje současný stav poznání v oblasti hlubokých neuronových sítí a souvisejících datových sad. Pro účely trénování segmentačního modelu neuronové sítě byla sestavena vlastní datová sada obsahující letecké snímky České republiky a vytvořené pixelové anotace. Na základě těchto dat bylo otestováno několik postupů trénování a byly porovnány modely architektur U-Net, DeepLabV3+ a SegFormer. V rámci experimentů byl rovněž vyhodnocen vliv datové augmentace a předtrénování na proces trénování. Nejvyšší kvality segmentace dosáhl model odvozený od architektury DeepLabV3+, jehož výstup byl transformován do mapy náročnosti terénu. Nad touto mapou byl úspěšně implementován algoritmus A* pro demonstraci plánování trasy mezi zvolenými body.", "source_url": "https:\/\/openalex.org\/W7165506220", "pdf_url": "https:\/\/dspace.vut.cz\/bitstreams\/84b01f16-6b47-4878-93d4-d8587c2a05e6\/download", "doi": "", "handle": "", "repository_name": "Brno University of Technology Digital Library (Brno University of Technology)", "source_record_id": "W7165506220", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 896, "title": "Domain-Specific Adaptation of General-Purpose Embeddings for Email Similarity Detection: A Transfer Learning Approach", "title_ar": "", "title_en": "", "author": "Michal Bělohlávek", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «Diplomová práce»", "university": "Czech Technical University", "university_source": "repository_host", "college": "", "department": "", "country": "Czech Republic", "governorate": "", "publication_year": 2026, "language": "cs", "research_domain": "machine-learning", "research_subdomain": "transfer-learning", "categories": "machine-learning; transfer-learning", "keywords": "", "abstract": "Diplomová práce zkoumá dodatečnou adaptaci detektoru e-mailů postaveného na fixním encoderu vět a klasifikaci podle nejbližších sousedů v referenční databázi. Možné modifikace se dělí na úpravy reprezentace a úpravy rozhodovacího pravidla a posuzují se ve třech rodinách metod: geometrické transformace embeddingů, využití metadat zpráv a komprese referenční databáze. Smysluplná adaptace je dosažitelná jen výběrově -- klíčové je místo zásahu, nikoli složitost zvolené metody. Z transformací reprezentace vychází nejlépe centrování, metadata jsou užitečná především v rozhodovacím pravidle a výraznou kompresi databáze lze provést teprve po geometrické stabilizaci reprezentace. Práce přispívá rozkladem dodatečné adaptace na transformační a rozhodovací složku, použitelným v jakémkoli vyhledávacím systému s neměnným encoderem, a návodem ukazujícím, kde jednotlivé modifikace pomáhají a kde nikoli.", "source_url": "https:\/\/openalex.org\/W7200525814", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Cvut DSpace (Czech Technical University)", "source_record_id": "W7200525814", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:54", "updated_at": "2026-11-15 16:52:54" }, { "id": 948, "title": "Can Large Language Models become Innovation Experts? : Emulating Expert Evaluation of Innovation in Startups with Large Language Models", "title_ar": "", "title_en": "Can Large Language Models become Innovation Experts? : Emulating Expert Evaluation of Innovation in Startups with Large Language Models", "author": "Ricarda Nettinger", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "University of Turku", "university_source": "repository_host", "college": "", "department": "", "country": "Finland", "governorate": "", "publication_year": 2026, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; nlp; transfer-learning; llm", "keywords": "", "abstract": "Startups are drivers of innovation and economic growth, but the uncertainty associated with startups makes the evaluation of their innovativeness difficult. Currently, startups are mostly evaluated by experts (e.g., domain experts, potential investors, researchers). However, experts are scarce and can only evaluate a limited number of startups due to their limited cognitive resources as well as time and cost constraints. Large language models (LLMs) might be a suitable and scalable alternative for expert evaluation of innovation in startups due to their emergent abilities. Once LLMs are trained, they can be applied to diverse tasks and contexts without additional training. This master’s thesis aims to answer the question of how LLMs can be used to evaluate the innovativeness of technologies and strategies presented in startup pitches by applying prompt engineering techniques and fine-tuning LLMs.", "source_url": "https:\/\/openalex.org\/W7220762836", "pdf_url": "", "doi": "", "handle": "", "repository_name": "UTUPub (University of Turku)", "source_record_id": "W7220762836", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:55", "updated_at": "2026-11-15 16:52:55" }, { "id": 984, "title": "Deep learning models for face detection and recognition in videos and images", "title_ar": "", "title_en": "", "author": "Stefani Bojanić", "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": "computer-vision", "research_subdomain": "face-recognition", "categories": "deep-learning; computer-vision; face-recognition; video-analysis", "keywords": "", "abstract": "Človeški obraz predstavlja eno od najpomembnejših biometričnih značilnosti, saj združuje informacijo o identiteti, spolu, starosti in čustvenem izrazu. V tem okviru se detekcija in prepoznavanje obrazov kažeta kot dva neločljivo povezana procesa. V magistrski nalogi so predstavljeni ključni izzivi tega področja ter stanje razvoja, ki zajema vse od klasičnih metod do sodobnih pristopov z globokim učenjem, s poudarkom na konvolucijskih nevronskih mrežah. Razvoj in eksperimenti so bili izvedeni s programskim jezikom Python. V okolju Visual Studio Code smo tako razvili sistem za prepoznavanje obrazov z uporabo algoritma ArcFace.", "source_url": "https:\/\/openalex.org\/W7219441107", "pdf_url": "https:\/\/dk.um.si\/Dokument.php?id=199766&dn=", "doi": "", "handle": "", "repository_name": "University of Maribor digital library (University of Maribor)", "source_record_id": "W7219441107", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:56", "updated_at": "2026-11-15 16:52:56" }, { "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": 1064, "title": "Classification of echocardiography images using Convolutional Neural Network to assist Kawasaki disease diagnosis", "title_ar": "", "title_en": "Classification of echocardiography images using Convolutional Neural Network to assist Kawasaki disease diagnosis", "author": "Capucine Bertrand", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "", "university_source": "", "college": "", "department": "", "country": "Spain", "governorate": "", "publication_year": 2019, "language": "en", "research_domain": "medical-ai", "research_subdomain": "video-analysis", "categories": "deep-learning; computer-vision; medical-ai; cnn; video-analysis", "keywords": "", "abstract": "The Kawasaki disease is the most common heart condition affecting young children usually under five years old in developed countries and especially in Asia [1]. It damages blood vessels all over the body and results in vasculitis, myocarditis and coronary dilation causing long term heart complications and making it is essential to be able to detect the disease at an early state. One of the methods used to detect Kawasaki disease is to perform a 2D echocardiography to monitor the inflammation of heart muscles and the swelling of coronary arteries. The improvement of this technique is a cornerstone of a good treatment for these children. Based on the success of Convolutional Neural Networks to solve computer vision problems such as images classification, this master thesis aims to develop a system to ease the diagnosis of Kawasaki disease using echocardiographies focusing more specifically on coronary arteries. To do so, we will use deep learning classification techniques such as convolutional neural networks to extract the frames containing images of a coronary artery in a video of 2D echocardiography. These images can later be used to monitor the state of the coronary arteries.", "source_url": "https:\/\/openalex.org\/W2979291034", "pdf_url": "", "doi": "", "handle": "", "repository_name": "Hispana", "source_record_id": "W2979291034", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:52:58", "updated_at": "2026-11-15 16:52:58" }, { "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": 1262, "title": "Exploration of fine-tuning and inference time of large pre-trained language models in NLP", "title_ar": "", "title_en": "Exploration of fine-tuning and inference time of large pre-trained language models in NLP", "author": "Анна Юрьевна Короткова", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master's»", "university": "Ludwid Maxmilian's Universitat Munchen", "university_source": "repository_host", "college": "", "department": "", "country": "Germany", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "nlp", "research_subdomain": "llm", "categories": "machine-learning; deep-learning; nlp; transfer-learning; transformers; llm", "keywords": "", "abstract": "In this master's thesis, we perform an analysis based on the language models BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019), DistilBERT (Sanh et al., 2020), ALBERT (Lan et al., 2019) and XLNET (Yang et al., 2019) whose architectures are based on the Transformer encoder which relies solely on an attention mechanism.We incorporate a measuring of the fine-tuning and inference time in the Python module transformers which was implemented by huggingface (Wolf et al., 2019).In the scope of this master's thesis, the models are fine-tuned on the General Language Understanding Evaluation tasks (Wang et al., 2019), a collection of nine sentenceor sentence-pair language understanding tasks.To monitor our analyses, we used the tool \"Weights & Biases\" (wandb) (Biewald, 2020) where a logging of hyperparameters and output metrics is possible.The goal of this master's thesis is to develop a diverse benchmark setting and evaluate the respective fine-tuning as well as inference times across different hyperparameters, language models and tasks.More precisely, a hyperparameter sweep with the maximum sequence length and the batch size are regarded, taking on the values {128, 256, 512} and {8, 16, 32}, respectively.We show that the maximum sequence length, the task size as well as the model have an effect on the fine-tuning time, whereas the batch size seems to be of secondary importance.Moreover, the maximum sequence length, the batch size and the choice of the model affect the inference time, whereas the task itself does not show such results.", "source_url": "https:\/\/openalex.org\/W3134720031", "pdf_url": "https:\/\/epub.ub.uni-muenchen.de\/75187\/", "doi": "10.5282\/ubm\/epub.75187", "handle": "", "repository_name": "Open access LMU (Ludwid Maxmilian's Universitat Munchen)", "source_record_id": "W3134720031", "other_sources": "", "verification_status": "verified", "created_at": "2026-11-15 16:53:00", "updated_at": "2026-11-15 16:53:00" }, { "id": 1274, "title": "Cue word guided question generation with BERT model fine-tuned on natural question dataset", "title_ar": "", "title_en": "Cue word guided question generation with BERT model fine-tuned on natural question dataset", "author": "Zijing Zhang", "supervisor": "", "degree_type": "master", "degree_evidence": "نص السجل: «master»", "university": "University of Waikato", "university_source": "repository_host", "college": "", "department": "", "country": "New Zealand", "governorate": "", "publication_year": 2020, "language": "en", "research_domain": "deep-learning", "research_subdomain": "transformers", "categories": "machine-learning; deep-learning; transfer-learning; rnn; transformers", "keywords": "", "abstract": "This thesis aims to develop an efficient question generator for an automated tutoring system. Given a context passage with an answer, the question generator asks questions to help the reader learn new material. By utilizing the BERT model, this thesis experiments on generating type-specific questions with a cue word. This thesis also uses an RNN encoder-decoder architecture for question generation on SQuAD as a comparative baseline and fine-tune the BERT question generation model on Google's Natural Question dataset. Ultimately, I deliver a RESTful API by the end of this year-long master program.", "source_url": "https:\/\/openalex.org\/W3103135561", "pdf_url": "https:\/\/researchcommons.waikato.ac.nz\/bitstreams\/0fe1d698-83bb-4b71-b49f-48611628ecc7\/download", "doi": "", "handle": "", "repository_name": "Research Commons (University of Waikato)", "source_record_id": "W3103135561", "other_sources": "", "verification_status": "verified", "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": 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": 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" } ] }