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    "exported_at": "2026-11-15 22:54:39",
    "count": 459,
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        {
            "id": 25,
            "title": "Deep Learning-Based Breast Cancer Detection with IoMT",
            "title_ar": "",
            "title_en": "Deep Learning-Based Breast Cancer Detection with IoMT",
            "author": "Bo Li",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Auckland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; generative-ai; medical-ai; cnn; transformers; gan; medical-imaging",
            "keywords": "",
            "abstract": "Breast cancer remains a leading cause of cancer-related mortality among women worldwide, which demands innovations in early detection and diagnostic efficiency. Existing approaches often neglect peri-tumoral contextual features, rely on limited or unrealistic synthetic data, and impose latency bottlenecks in distributed healthcare settings. We propose a reserved-space strategy for Region of Interest (ROI) extraction that achieves 97.5% True Positive Rate (TPR) while preserving contextual features, thereby capturing diagnostically critical tumour margins that rigid bounding boxes overlook. To address data scarcity, we develop an enhanced Generative Adversarial Network (GAN) model incorporating Efficient Multi-scale Attention (EMA) modules and Hinge loss, which generates high-fidelity (15.2 Fréchet Inception Distance (FID)) ROI images that maintain anatomical plausibility in lesions and microcalcification diversity, resolving the over smoothing and pathological irrelevance of conventional GAN architectures. This GAN-augmented dataset improves classification AUC by 10% compared to traditional augmentation methods. Through systematic comparison, we demonstrate that the Swin Transformer outperforms the Convolutional Neural Network (CNN) and standard Vision Transformers (ViT) models in mammography classification with 86.8% Area Under the Curve (AUC). Finally, we design a distributed edge-fog-cloud Internet of Medical Things (IoMT) framework that implements our workflow to mitigate computational bottlenecks in clinical deployment.",
            "source_url": "https:\/\/openalex.org\/W7135345431",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ResearchSpace (University of Auckland)",
            "source_record_id": "W7135345431",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 26,
            "title": "Driven segmentation of synthetic microwave images for breast cancer detection",
            "title_ar": "",
            "title_en": "Driven segmentation of synthetic microwave images for breast cancer detection",
            "author": "Bahar, Özlem",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Istanbul Technical University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, continuing to be the leading cause of cancer-related mortality among women. Current global health statistics reveal that millions of new breast cancer cases are diagnosed annually, with a disproportionate concentration in low- and middle-income countries (LMICs). In these regions, disparities in healthcare infrastructure, socioeconomic challenges, and limited access to advanced diagnostic technologies substantially hinder early detection efforts. Early diagnosis of breast cancer is of paramount importance, as timely intervention significantly improves patient survival rates, reduces the need for aggressive treatments, mitigates treatment-related side effects, and ultimately decreases the economic burden on healthcare systems. Despite technological advancements, conventional breast imaging modalities such as mammography, ultrasound, and magnetic resonance imaging (MRI) still face notable limitations globally in terms of accessibility, cost, and diagnostic efficacy. Mammography, regarded as the clinical gold standard for breast cancer screening, involves ionizing radiation and poses potential health risks with frequent use. Moreover, its diagnostic sensitivity is considerably reduced in women with dense breast tissue, leading to false-negative results and delayed treatment. Radiation-free ultrasound imaging requires highly skilled operators, resulting in variability in diagnostic accuracy and limiting its widespread application. Nuclear imaging techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) provide valuable dynamic and functional imaging data; however, their high costs, radiation exposure, and limited availability restrict their broader clinical use. MRI offers superior soft tissue contrast and spatial resolution but is associated with high operational costs, infrastructure demands, and limited accessibility, especially in LMICs. Collectively, these challenges underscore the urgent need for safe, cost-effective, scalable, and reliable novel imaging technologies for early tumor detection. Microwave Imaging (MWI) has recently emerged as a promising alternative modality that enables non-ionizing electromagnetic wave-based examination of the internal structural and functional properties of breast tissue. The fundamental principle of MWI relies on the dielectric property differences—specifically dielectric permittivity (∈_r) and electrical conductivity (σ)—between malignant tumors and normal breast tissue. This contrast provides functional imaging information complementary to the anatomical data obtained from conventional methods. Microwave imaging systems primarily consist of transmitting and receiving antennas, with the collected data computationally reconstructed into images. Additionally, MWI systems are inherently more affordable, portable, and easier to operate, making them highly suitable for resource-constrained clinical environments. Nevertheless, the clinical translation of MWI has been impeded by intrinsic limitations such as relatively low spatial resolution, sensitivity to noise and artifacts, and challenges in accurately localizing tumors within reconstructed images. In response to these challenges, the present thesis proposes a comprehensive deep learning framework aimed at enhancing the diagnostic performance and tumor localization accuracy of microwave breast imaging. Within this scope, synthetic breast tissue models were developed using a high-fidelity simulation platform based on the Reverse Time Migration (RTM) algorithm, which realistically models the propagation and scattering of electromagnetic waves in heterogeneous and homogeneous biological tissues. The RTM algorithm processes source and receiver wavefields bidirectionally and establishes correlations between them to generate an image. It consists of two steps involving the backpropagation of complex conjugate data into the imaging domain and computing the cross-correlation norm of the forward and backward propagated fields. Synthetic breast models were generated employing microwave difference imaging based on the total electric field, necessitating knowledge of the healthy breast state during imaging. The total electric field data required for microwave imaging were synthetically produced via numerical solutions of electromagnetic scattering problems. This study investigates deep learning-based segmentation of low-resolution microwave images generated from total electric field data. The dataset comprises 1000 synthetic transverse breast images, each containing a single tumor, initially created at a high resolution of 400×400 pixels.",
            "source_url": "https:\/\/openalex.org\/W7112466934",
            "pdf_url": "https:\/\/polen.itu.edu.tr\/bitstreams\/45f1e73e-aeb2-438f-96e9-0efeb0835bfb\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Istanbul Technical University Academic Open Archive (Istanbul Technical University)",
            "source_record_id": "W7112466934",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 27,
            "title": "Breast cancer prediction from mammography images using deep learning techniques",
            "title_ar": "",
            "title_en": "",
            "author": "Şeyma Doğru",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2025,
            "language": "tr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging",
            "keywords": "",
            "abstract": "Teknolojik ilerlemeler, birçok sektörde olduğu gibi sağlık alanında da akıllı makinelerin karar verme süreçlerine dahil edilmesine olanak tanımıştır. İnsanlardan daha hızlı analitik düşünebilme ve ilişkileri saptama yetenekleri sayesinde bu makineler, uzmanlara doğru kararların alınmasında önemli ölçüde destek sağlamaktadır. Özellikle kanserin erken teşhisinin hayati öneme sahip olması, bu alanda yapılan yapay zeka çalışmalarını hızlandırmıştır. Kanserin erken tanısı, hastaların sağkalım oranlarının artırılması, organ kaybının önlenmesi ve radyoterapi, kemoterapi gibi tedavilerin en az düzeyde uygulanması bakımından kritik önem taşımaktadır. Memede oluşan küçük lezyonların tespiti genellikle mümkün olmamaktadır. Bu da hastalığın ilerlemesine ve ileri safhalarda fark edilmesine sebep olmaktadır. Kontrastlı Spektral Mamografi (CESM), bu tespit zorluğunu gidermek için geliştirilmiş yeni bir görüntüleme tekniğidir. Meme hücrelerine enjekte edilen kontrast madde ile lezyonları görünür kılan Kontrastlı Spektral Mamografi, dijital mamografide gözden kaçabilen lezyonları yakalamaya yardımcı olur. Doktorların daha güvenilir tanı koymasına ve hastalığa erken fark edilmesine olanak tanır. Bu tez çalışmasında, meme kanserinin teşhis sürecine odaklanılarak Kontrastlı Spektral Mamografi (CESM) görüntüleri kullanılmıştır. Çalışmada iki farklı veri seti üzerinde çeşitli derin öğrenme modelleri eğitilerek analizler gerçekleştirilmiştir. Kullanılan bu veri setleri CDD-CESM veri seti ve CESM@UCBM veri setleridir. CDD-CESM veri setinde toplamda 2006 görüntü bulunmaktadır. Bu görüntülerin 1003 tanesi düşük enerjili görüntü, 1003 tanesi ise bileşik görüntüdür. CESM@UCBM veri setinde ise 1138 adet görüntü bulunmaktadır. Bu görüntülerden 569 tanesi düşük enerjili, 569 tanesi ise bileşik görüntüdür. Bu çalışma kapsamında yapılan eğitimlerde veri setlerine 20 farklı açıdan yaklaşılmıştır. Veri seti 3 sınıflı (Normal, İyi Huylu, Kötü Huylu) ve 2 sınıflı (İyi Huylu, Kötü Huylu) olarak, ayrı ayrı ve birlikte olacak şekilde eğitilmiştir. Aynı zamanda bu ele alım şekillerine veri artırma yöntemleri uygulanarak veriler tekrar eğitilmiştir. Bu kapsamda, ResNet50, DenseNet121, EfficientNetB0, VGG16 ve VGG19 mimarileri uygulanmış ve beşli çapraz doğrulama sonucunda DenseNet121 modeliyle ,46 kesinlik (precision), ,35 duyarlılık (recall) ve ,81 F1-Skor değerleri elde edilmiştir. Bu sonuçlar, önerilen yöntemin meme kanseri tanısında etkin bir destek aracı olabileceğini göstermektedir.",
            "source_url": "https:\/\/openalex.org\/W7216756002",
            "pdf_url": "https:\/\/acikerisim.btu.edu.tr\/bitstreams\/18957f3e-ea89-423c-a8a0-261f29685e20\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Bursa Technical University Institutional Repository",
            "source_record_id": "W7216756002",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 30,
            "title": "Computer aided detection for breast lesion in ultrasound and mammography",
            "title_ar": "",
            "title_en": "Computer aided detection for breast lesion in ultrasound and mammography",
            "author": "Richa Agarwal",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM). Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis. Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging. In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used. The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screening",
            "source_url": "https:\/\/openalex.org\/W3154566879",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W3154566879",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 31,
            "title": "Transformer-based Transfer Learning for Breast Cancer Detection via Digital Breast Tomosynthesis",
            "title_ar": "",
            "title_en": "Transformer-based Transfer Learning for Breast Cancer Detection via Digital Breast Tomosynthesis",
            "author": "Mikea Dimech",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; transfer-learning; cnn; transformers; medical-imaging",
            "keywords": "",
            "abstract": "Breast cancer has emerged as the most prevalent form of cancer worldwide and the leading cause of mortality from any cancer in women, emphasising the critical need for its early detection. Traditional 2D mammography has long been the cornerstone of breast cancer screening, given its proven efficacy in early prognosis and reduction of mortality. However, the limitations of this modality have precipitated the development and adoption of more advanced imaging techniques, such as Digital Breast Tomosynthesis (DBT), which yields a 3D view of the breast. The application of deep learning to classify these scans is paramount to increasing the efficiency and efficacy of screening. However, this task presents arduous challenges such as high computational demands, scarcity of diverse data, and generalisation across varying demographics and imaging equipment. Transfer learning has proven to be effective in overcoming these challenges by enabling models to leverage pre-existing knowledge and adapt to new tasks. However, its application to DBT has been confined to Convolutional Neural Network (CNN) architectures. Meanwhile, the Transformer paradigm, which has revolutionised various deep learning domains and shown promise in traditional mammography, has yet to be explored in the context of DBT via transfer learning, highlighting a notable research gap. This study thus investigates the performance of a pre-trained Transformer model, fine-tuned on a public DBT dataset, comparing it with a fine-tuned state-of-the-art CNN. While the Transformer outperforms the convolutional architecture, the results reveal severe challenges posed by the imbalanced dataset, with models developing biases towards the majority class. Data augmentation, while beneficial, could not fully address the issue, and the need for more diverse datasets became clear. Moreover, the results underscore the importance of preserving the high resolution of DBT images, with fine-grained details, such as subtle changes in breast tissue captured at higher resolutions, proving to be pivotal for accurate diagnosis. Finally, a comparative analysis between CNNs and transformer-based models revealed that Transformers excel in recognising broader patterns across the entire image, which aids to mitigate overfitting. By using pre-trained transformers and fine-tuning them on this dataset, their effectiveness was highlighted, suggesting they can match or even surpass CNNs in breast cancer detection via DBT.",
            "source_url": "https:\/\/openalex.org\/W4385850049",
            "pdf_url": "",
            "doi": "10.5281\/zenodo.8243430",
            "handle": "",
            "repository_name": "Zenodo (CERN European Organization for Nuclear Research)",
            "source_record_id": "W4385850049",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 34,
            "title": "Mammogram and Tomosynthesis Classification Using Convolutional Neural Networks",
            "title_ar": "",
            "title_en": "Mammogram and Tomosynthesis Classification Using Convolutional Neural Networks",
            "author": "Xiaofei Zhang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Kentucky",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; cnn; image-classification; medical-imaging",
            "keywords": "",
            "abstract": "Mammography is the most widely used method of screening for breast cancer. Traditional mammography produces two-dimensional X-ray images, while advanced tomosynthesis mammography produces reconstructed three-dimensional images. Due to high variability in tumor size and shape, and the low signal-to-noise ratio inherent to mammography, manual classification yields a significant number of false positives, thereby contributing to an unnecessarily large number of biopsies performed to reduce the risk of misdiagnosis. Achieving high diagnostic accuracy requires expertise acquired over many years of experience as a radiologist. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. The convolutional process involves simplifying an image containing millions of pixels to a set of small feature maps, thereby reducing the input dimension while retaining the features that distinguish different classes of images. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, high-quality original mammograms and tomosynthesis were obtained with approval from an institutional review board. Different classifiers based on convolutional neural networks were built to classify the 2-D mammograms and 3-D tomosynthesis, and each classifier was evaluated based on its performance relative to truth values generated by a board of expert radiologists. The results show that CNNs have great potential for automatic breast cancer detection using mammograms and tomosynthesis.",
            "source_url": "https:\/\/openalex.org\/W2747822775",
            "pdf_url": "https:\/\/uknowledge.uky.edu\/cgi\/viewcontent.cgi?article=1063&context=cs_etds",
            "doi": "10.13023\/etd.2017.364",
            "handle": "",
            "repository_name": "UKnowledge (University of Kentucky)",
            "source_record_id": "W2747822775",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 35,
            "title": "Mammography Classification and Nodule Detection using Deep Neural Networks",
            "title_ar": "",
            "title_en": "Mammography Classification and Nodule Detection using Deep Neural Networks",
            "author": "Fabian Leander Sinzinger",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; image-classification; medical-imaging",
            "keywords": "",
            "abstract": "Mammographic screenings are the most common modality for an early detection of breast cancer, but a robust annotation of the depicted breast tissue presents an ongoing challenge, even for well-experienced radiologists. Computer-aided diagnosis systems can support the human classification. Modern systems therefore often rely on deep-learning based methods. This thesis investigates the fully automatic on-image classification of mammograms into one of the classes benign, malignant (cancerous) or normal. In this context, we compare two different design paradigms, one straightforward end-to-end model with a more complex decomposition hierarchy. While the end-to-end model consists mainly of the deep-learning based classifier, the decomposition pipeline incorporates multiple stages i.e. a region of interest detection (realized as a fully convolutional architecture) followed by a classification stage. Contrary to initial expectations, the end-to-end classifier turned out to obtain a superior performance in terms of accuracy (end-to-end: 76.57 %, decomposition: 65.66 %, computed as mean over all three classes in a one vs. all evaluation) and an improved area under receiver operating characteristic-score. All discussed parametric models were trained from scratch without using pre-trained network weights. Therefore we discuss the choice of hyper-parameters, initialization, and choice of a feasible cost function. For a successful feature extraction, in the region of interest detection stage, the negative dice coefficient proved itself to be a more robust cost function than the also investigated sensitivity-specificity loss.",
            "source_url": "https:\/\/openalex.org\/W2977003717",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W2977003717",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 39,
            "title": "Breast Cancer Detection fromHistopathological Images using DeepLearning and Transfer Learning",
            "title_ar": "",
            "title_en": "Breast Cancer Detection fromHistopathological Images using DeepLearning and Transfer Learning",
            "author": "Mansi Chowkkar",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging",
            "keywords": "",
            "abstract": "Breast Cancer is the most common cancer in women and it's harming women's mental and physical health. Due to complexities present in Breast Cancer images, image processing technique is required in the detection of cancer. Early detection of Breast cancer required new deep learning and transfer learning techniques. In this paper, histopathological images are used as a dataset from Kaggle. Images are processed using histogram normalization techniques. This research project implements the Convolutional Neural Network(CNN) model based on deep learning and DenseNet-121 based on transfer-learning. Transfer learning uses the Imagenet pre-trained model for training. Hyper-parameter tuning is done for increasing accuracy and precision value. Research achieved 90.9 % test accuracy using the CNN model and 88.03 % accuracy by the transfer learning model.",
            "source_url": "https:\/\/openalex.org\/W3041001745",
            "pdf_url": "https:\/\/norma.ncirl.ie\/4282\/2\/mansichowkkarconfigurationmanual.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "NORMA",
            "source_record_id": "W3041001745",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 40,
            "title": "Development of model observers for quantitative assessment of mammography image quality",
            "title_ar": "",
            "title_en": "Development of model observers for quantitative assessment of mammography image quality",
            "author": "Kretz, Tobias",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging",
            "keywords": "",
            "abstract": "Assurance of image quality has become a basic need in our society as images play a crucial role in this era of social media and digitization. Applications range from surveillance to medical imaging. Image quality is defined as the degree of clarity of its elements. Images undergo a chain of processes before being perceived by the viewer, starting from acquisition to digitization, compression, pre-processing and final presentation. Image quality expresses the extent of distortion of the original image information by various degradation processes. In mammography, image quality specifically defines the perceptibility of clinically relevant structures, like abnormal masses and suspicious micro-calcifications, that allows one to reliably detect breast cancer at an early stage. The image quality in mammography can be assessed by analyzing recordings of technical phantoms that contain artificial lesions with various diameters and contrast levels. This way, a contrast-detail information can be determined, which expresses the ability of an observer to detect the individual lesions with prescribed accuracy. Automatic procedures are favored for objectively determining this contrast-detail information. In this thesis, several data analysis methods for assessing image quality in mammography are developed, tested and compared to the current state-of-the-art procedure which is recommended by a European organization. The first contribution of this thesis is the implementation of a virtual mammography for the simulation of realistic images of a technical phantom. Simulated images of a known virtual specimen allow different image quality assessment procedures to be assessed and compared. In addition, virtual mammography makes it possible to set up large data sets at low cost that are required for the application of data-intensive techniques, such as deep learning. Automatic procedures for image quality assessment in mammography usually comprise expert pre-processing and the utilization of mathematical model observers. Our second contribution is the development of a method that utilizes a recently developed parametric model observer. This way, the contrast-detail information is based on established measures of image quality. Furthermore, an uncertainty of the prediction can be estimated. Compared to the current practice, our proposed approach reduces the workload for quality assurance measurements. Even though our method needs fewer images, it still requires expert pre-processing. Deep learning is able to avoid such expert pre-processing and to derive representative features automatically from the images. Our third contribution is the exploration of the applicability of deep learning methods for image quality assessment in mammography. Specifically, a deep learning observer is developed that estimates the contrast-detail information directly from a single image. No cumbersome pre-processing of the images is required. Our last contribution is the explanation of predictions of the developed deep learning observer as well as the estimation of the uncertainty of its prediction. The former gives interesting insights into the learned strategy, whereas the latter expresses the confidence about our proposed model.",
            "source_url": "https:\/\/openalex.org\/W3092046275",
            "pdf_url": "",
            "doi": "10.14279\/depositonce-10552",
            "handle": "",
            "repository_name": "DepositOnce",
            "source_record_id": "W3092046275",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 41,
            "title": "Methodology for the integration of different deep learning-based models for the detection of lesions in screening mammography.",
            "title_ar": "",
            "title_en": "",
            "author": "Erika Yesenia Suárez Bonilla ; David Esteban Ortega Figueroa",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universidad Industrial de Santander",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Colombia",
            "governorate": "",
            "publication_year": 2023,
            "language": "es",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; medical-ai; predictive-analytics; medical-imaging",
            "keywords": "",
            "abstract": "Los modelos de aprendizaje profundo han demostrado su potencial para la detección del cáncer de mama en mamografías de cribado. Existen muchas arquitecturas de aprendizaje y métodos de entrenamiento que realizar tareas de detección, clasificación y\/o localización. Sin embargo, la implementación y validación de estos plantea serias dudas sobre la reproducibilidad de los resultados. Este trabajo, se intereza en la integración de diferentes modelos para la detección de cáncer de mama. Para ello, construimos un conjunto de datos independiente, para analizar la reproducibilidad de los modelos, y realizar diferentes experimentos para su integración. Este trabajo incluye tres etapas. Primero, se realiza una evaluación comparativa de 5 modelos de cribado para la detección. Segundo, se realiza un análisis de saliencia para estudiar la relación entre las capacidades de detección y localización de lesiones de estos. Y por último, se prueban métodos de integración. Los resultados sugieren que el desempeño en saliencia no es un predictor preciso del rendimiento de un algoritmo en la detección de cáncer de mama. Por esto, la integración se basó únicamente en el procesamiento de las predicciones individuales de cada modelo. Así, el mejor de los resultados se obtuvo con regresión logística mediante validación cruzada, con un rendimiento de AUC = 0.841 (IC 95\\%: 0.759-0.923) a nivel de mama.",
            "source_url": "https:\/\/openalex.org\/W7133378455",
            "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/ce709672-5480-4297-a125-2f95f4bbfcc3\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Universidad Industrial de Santander",
            "source_record_id": "W7133378455",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 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": 52,
            "title": "Predicting U.S. business cycles with recurrent neural networks : An extensive multivariate time-series analysis for comparing LSTM and GRU networks",
            "title_ar": "",
            "title_en": "Predicting U.S. business cycles with recurrent neural networks : An extensive multivariate time-series analysis for comparing LSTM and GRU networks",
            "author": "Markus M. Mäkelä",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Turku",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; predictive-analytics; rnn",
            "keywords": "",
            "abstract": "This study examines how 22 different long short-term memory (LSTM) and gated recurrent unit (GRU) network architectures suit predicting U.S. business cycles. The networks create 91-day forecasts for the dependent variable by using multivariate time-series data comprising 26 leading indicators’ values for the previous 400 days. The proposed models are evaluated by using a train-test split, where the proposed models are trained with data from 1980 to 2005, and the out-of-sample set consists of data between 2005 and 2015. The performance is evaluated by using mean squared error (MSE) and mean absolute error (MAE), and early warning signs are also considered beneficial. The training algorithm consists of typical deep learning methods. MSE and L1 regularization are used for determining the cost, and minibatches of 32 examples are applied together with Nesterov accelerated momentum (NAG) learning algorithm. Early stopping is introduced to halt the training process when strong signs of overfitting are detected. Each proposed recurrent neural network (RNN) architecture is trained three times, and these three networks’ averaged predictions are examined when comparing the architectures. Performance-wise, a few LSTM networks stand out from the other proposed networks. Although the performance results favor the proposed LSTM networks slightly over their GRU equivalents, the difference is not substantial and, in turn, the proposed GRU networks offer less deviation in MSE and MAE between each architecture. However, these steadier performance results do not generate less volatile forecasts. Instead, the best performing networks and architectures differentiate by offering less volatile predictions that also vary less from the real values. Most of the models generate a considerable amount of early warning signs before the 2007 recession, which indicates their suitability for detecting turning points in business cycles. Moreover, a wide range of the proposed LSTM and GRU network architectures learn the general pattern, also the smaller architectures comprising only one hidden layer and less than 500 optimizable parameters. This suggests that these methods offer noteworthy solutions for business cycle forecasting and, more widely, supports applying nonlinear machine learning methods with multivariate data for macroeconomic forecasting tasks where prevalent methods have been found unable to deliver adequate accuracy.",
            "source_url": "https:\/\/openalex.org\/W3093480170",
            "pdf_url": "https:\/\/www.utupub.fi\/bitstreams\/0a464deb-b9ae-47f7-a531-c78d105d607e\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UTUPub (University of Turku)",
            "source_record_id": "W3093480170",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 54,
            "title": "Prediction of Blood Glucose Levels for Diabetic Patients from LSTM-Type Recurrent Neural Networks",
            "title_ar": "",
            "title_en": "",
            "author": "Ismael Vega Rojas",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universidad Industrial de Santander",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Colombia",
            "governorate": "",
            "publication_year": 2023,
            "language": "es",
            "research_domain": "medical-ai",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; rnn; ai-industry",
            "keywords": "",
            "abstract": "En este trabajo de grado se presenta un modelo de estimación de glucosa para pacientes con diabetes mellitus tipo 1, donde se usa el simulador T1DMS (Visentin et al., 2018) con el que se obtiene un conjunto de mediciones de insulina y glucosa para entrenar el modelo de predicción. Para realizar este entrenamiento del modelo se utilizan series de tiempo, tomando como entrada valores pasados de glucosa en sangre e insulina inyectada y generando a la salida instantes de tiempo futuro de glucosa en sangre. Las secuencias de entrada constan de un conjunto de 15 muestras (timesteps) medidas cada minuto, mientras que la secuencia de salida, 210 muestras de glucosa futuras predichas mediante el modelo de estimación que corresponde a la curva completa de un episodio de glucosa después de una ingesta de glucosa por parte del paciente. Con estas secuencias de entradas y salidas se entrenan los modelos de Recurrent Neural Network (RNN) tipo LSTM, para 10 pacientes cada uno con un modelo personalizado, usando solo los episodios o curvas de glucosa de cada paciente en cada modelo. Y otro modelo generalizado, es decir usando los episodios o curvas de glucosa de todos los pacientes en un solo modelo. Para estimar el rendimiento del modelo, se usan las métricas de error MAE y RMSE, se evalúa el ajuste del modelo, se define la arquitectura final utilizando el número de capas y celdas que permitan una mejor precisión, al igual que el optimizador que mejor funcione, como los inicializadores adecuados, y las funciones de activación en cada capa del modelo, para que se obtengan las predicciones más exactas. Donde se toma como base lo aprendido en el curso electivo de Deep learning, dictado en el 2022-2, en la Universidad Industrial de Santander.",
            "source_url": "https:\/\/openalex.org\/W7133451770",
            "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/ead67cab-a089-4477-bdda-d989ffd8f2e2\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Universidad Industrial de Santander",
            "source_record_id": "W7133451770",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 59,
            "title": "Dynamic Analysis of Recurrent Neural Networks",
            "title_ar": "",
            "title_en": "Dynamic Analysis of Recurrent Neural Networks",
            "author": "Han Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Texas A&M University Libraries",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "deep-learning; nlp; rnn; speech-recognition",
            "keywords": "",
            "abstract": "With the advancement in deep learning research, neural networks have become one of the most powerful tools for artificial intelligence tasks. More specifically, recurrent neural networks (RNNs) have achieved state-of-the-art in tasks such as hand-writing recognition and speech recognition. Despite the success of recurrent neural networks, how and why do neural nets work is still not sufficiently investigated. My work on the dynamical analysis of recurrent neural networks can help understand how the input features are extracted in the recurrent layer, how the RNNs make decisions, and how the chaotic dynamics of RNNs affects its behaviors. Firstly, I investigated the dynamics of recurrent neural networks as autonomous dynamical system in the experiment of a two-joint limb controlling task and compared the empirical result and the theoretical analysis. Secondly, I investigated the dynamics of non-autonomous recurrent neural networks on two benchmark tasks: sequential MNIST recognition task and DNA splice junction classification task. How the hidden states of long-short term memory (LSTM) and gated recurrent unit (GRU) cells learn new features and how the input sequence is extracted are demonstrated with experiments. Finally, based on the understanding of the external and internal dynamics of recurrent units, I proposed several algorithms for recurrent neural network compression. The algorithms demonstrate reasonable performance in compression ratio and are able to sustain the performance of the original models.",
            "source_url": "https:\/\/openalex.org\/W3120491280",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "OakTrust (Texas A&M University Libraries)",
            "source_record_id": "W3120491280",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 66,
            "title": "Deep Learning for Medical Image Segmentation",
            "title_ar": "",
            "title_en": "Deep Learning for Medical Image Segmentation",
            "author": "Binyan Hu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; self-semi-supervised; image-segmentation; medical-imaging; federated-learning; fairness; ai-cybersecurity",
            "keywords": "",
            "abstract": "Medical image segmentation (MIS) plays a fundamental role in a wide range of medical image analysis applications. Over the past decade, MIS has been significantly advanced by deep learning (DL). However, the effectiveness of DL-based methods typically depends on large-scale, precisely annotated datasets, which are often unavailable in the medical domain due to the high cost and expertise required for medical data acquisition and annotation. When trained on limited data, deep neural networks (DNNs) are prone to over-fitting, resulting in poor generalisation performance when deployed on previously unseen data. In MIS, this generalisation gap is exacerbated by two key challenges: 1) data scarcity: due to the limited quantity of annotated images available for training, over-fitting the training data causes poor generalisation performance on images unseen during training; 2) expert-specific annotation: variability in annotations across experts arises from the inherent complexity of medical images and differences in expert knowledge. Consequently, a model trained on certain experts' annotations may under-perform when deployed in clinical contexts with new images and new experts. This thesis aims to address these domain-specific limitations, with an overarching goal of generating enhanced supervision signals to improve the generalisation capability of DNNs for MIS across both images and user experts under constrained data settings. Toward achieving this goal, our contributions are threefold.First, we tackle the expert-specific annotation problem by leveraging the supervision from multiple experts via multi-expert annotation, aiming to achieve adaptive MIS that generalises to new experts' annotations. Through extensive empirical analysis, we investigate when and how multi-expert annotation improves a DNN model's generalisation capability to new experts' annotations and what DNN training method can best achieve such generalisation. Our findings provide detailed evidence for the efficacy of multi-expert annotation and practical insights into developing generalisable models that can efficiently adapt to new experts in the presence of expert-specific annotations.Second, we intend to mitigate the impact of data scarcity and improve the model's generalisation to new images by leveraging self-supervision.We develop an adaptive training method, which generates diverse self-supervised tasks from the existing annotated dataset, dynamically selects the most beneficial combination of tasks, and adaptively transfers the knowledge learnt from them to enrich the segmentation model's knowledge.Compared to existing approaches, our method integrates more diverse self-supervised tasks and offers superior effectiveness and efficiency in exploiting their supervision signals to benefit segmentation performance.The third study moves beyond the single-site setting and generates enhanced supervision via multi-site collaboration, where the goal is to improve generalisation performance for participating sites while navigating strict privacy constraints and potential security threats arising from non-transparent local processes. We propose a new federated learning method, tailored for the multi-site MIS scenario, to enable privacy-preserving defence against poisoning attacks by sustaining robust performance and localising the compromised site. Compared to previous approaches, our method provides utility-based defence and diagnosis, does not depend on public datasets, and works for the setting with few participating sites. These features enable more practical and effective federated learning with enhanced privacy preservation and security for multi-site MIS.Extensive experiments across diverse datasets and clinical settings demonstrate the effectiveness of the proposed methods in improving DNN model generalisation for MIS tasks. Collectively, this thesis contributes practical methodologies to address domain-specific challenges in MIS and advances the practical deployment of DNNs in real-world clinical environments.",
            "source_url": "https:\/\/openalex.org\/W7161281791",
            "pdf_url": "",
            "doi": "10.25916\/sut.32150593",
            "handle": "",
            "repository_name": "Swinburne figshare (Swinburne University of Technology)",
            "source_record_id": "W7161281791",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 67,
            "title": "Deep-Learning-Based 3D Medical Image Segmentation and Registration",
            "title_ar": "",
            "title_en": "Deep-Learning-Based 3D Medical Image Segmentation and Registration",
            "author": "Hao Xu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "The University of Sydney",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; few-zero-shot; self-semi-supervised; transformers; multimodal; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Understanding the importance of image segmentation and registration in clinical diagnosis and treatment is critical for advancing precision medicine. Medical image analysis involves complex data processing tasks, particularly in scenarios with limited annotated data and multi-modality feature inconsistencies. Recent deep learning-based methods have provided breakthroughs in image segmentation and registration, especially in modalities like MRI and CT. However, challenges such as one-shot learning, incremental learning, and efficient deformable registration still persist. This thesis introduces a series of innovative deep learning frameworks, focusing on three one-shot segmentation settings and three Segment Anything Model (SAM) -assisted registration methods, to address the issues of annotation scarcity and feature misalignment in medical imaging. For segmentation, we develop three methods for one-shot, class-incremental, and novel-class tract segmentation. These incorporate uncertainty estimation, knowledge distillation, and voxel-level contrastive learning to enhance performance and generalization in one-shot settings. For registration, we propose three SAM-guided frameworks that leverage segmentation masks and anatomical prompts, as well as attention mechanisms, prototype learning, and contour-aware losses. These approaches achieve state-of-the-art accuracy in unsupervised deformable registration. Overall, our methods yield substantial improvements in segmentation and registration accuracy across diverse datasets and demonstrate strong potential for clinical application.",
            "source_url": "https:\/\/openalex.org\/W7126877936",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "The Sydney eScholarship Repository (The University of Sydney)",
            "source_record_id": "W7126877936",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 68,
            "title": "Efficient and Generalised Deep Learning Models for Medical Image Segmentation",
            "title_ar": "",
            "title_en": "Efficient and Generalised Deep Learning Models for Medical Image Segmentation",
            "author": "Zhendi Gong",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Nottingham",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; few-zero-shot; self-semi-supervised; cnn; transformers; image-segmentation; medical-imaging; ai-industry",
            "keywords": "",
            "abstract": "Medical image segmentation is a critical task in computer-assisted diagnosis and therapy planning. Accurate and efficient fully automated segmentation methods are essential for accelerating decision-making processes. Compared with natural image segmentation methods, current deep learning–based segmentation models for medical imaging face challenges related to data scarcity, inconsistent annotations, and high computational cost. Consequently, this thesis aims to develop efficiency and generalised medical image segmentation models, which are fundamental to the clinical adoption and industrial deployment of this technology in healthcare settings. This thesis systematically addresses these challenges through a series of progressively advanced deep learning models. The foundational work, CTranS, forms a baseline medical image segmentation model that combines a convolutional neural network (CNN) based encoder with a Transformer-based decoder. This initial contribution leverages the strengths of CNNs for local feature extraction and Transformers for global contextual understanding, achieving state-of-the-art (SOTA) performance. Subsequently, to enhance the generalisability, MO-CTranS is introduced, which is capable of learning from a mixture of multiple partially labelled datasets. MO-CTranS employs a CNN encoder and Transformer decoder with task-specific tokens to handle label inconsistencies and class imbalances, tackling the challenges of training a single unified model from fragmented and heterogeneously annotated medical data. This innovation moves beyond single-task, fully-annotated model training towards a more scalable and clinically realistic approach. Despite the superior performance of CTranS and MO-CTranS, the computational efficiency of the Transformer blocks hinders their clinical application. Thus, CRFTrans is proposed to enhance the model efficiency. As a novel recursive Vision Transformer layer designed to replace the commonly used cascaded Transformer layers, this approach represents a fundamental shift from simply stacking layers to a recursive refinement process, demonstrating that comparable performance can be achieved with significantly reduced computational complexity, thus facilitating deployment in resource-constrained clinical environments. However, these methods still depend on extensive manual annotation, which remains challenging and time-consuming in clinical practice. Thus, to further enhance both efficiency and generality, a novel semi-supervised medical image segmentation framework, referred to as SSL-MedSAM2, has been developed. SSL-MedSAM2 integrates a training-free few-shot segmentation branch for pseudo labelling and a fully supervised learning branch for label refinement. This design allows the model to leverage a small fraction of labelled images together with a large unlabelled set, dramatically decreasing the annotation effort. Together, these contributions result in deep learning models that are significantly more data-efficient and broadly applicable than conventional approaches. The overall impact of this work is to facilitate the reliable and scalable deployment of segmentation models in real-world clinical settings by bridging the gap among accuracy, efficiency, and generalisability.",
            "source_url": "https:\/\/openalex.org\/W7140609462",
            "pdf_url": "https:\/\/repository.nottingham.ac.uk\/bitstreams\/1a58d983-6aaa-4bbb-a40b-688e6d3af90c\/download",
            "doi": "10.17639\/7969",
            "handle": "",
            "repository_name": "Nottingham ePrints (University of Nottingham)",
            "source_record_id": "W7140609462",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 69,
            "title": "Efficient Deep Learning for Medical Imaging: Precision Segmentation and Beyond",
            "title_ar": "",
            "title_en": "Efficient Deep Learning for Medical Imaging: Precision Segmentation and Beyond",
            "author": "Lucas Gago",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "[eng] This thesis advances automated medical image analysis by introducing four deep learning frameworks that systematically address core technical barriers to clinical deployment, including computational efficiency, variability in image quality, and the robust integration of imaging with clinical data. Through a compendium of research articles, we develop state-of-the-art solutions spanning ultrasound and MRI. First, we present an end-to-end framework for carotid intima-media thickness (CIMT) measurement in ultrasound images, achieving state-of-the-art atherosclerotic plaque characterization while delivering a 20x speed improvement (0.79 to 0.04 seconds per image). The system provides comprehensive outputs, including segmentation masks, automated measurements, and binary plaque detection, eliminating domain-specific post-processing requirements. Secondly, leveraging the features extracted by our end-to-end model, we pioneer their integration into clinical survival models, demonstrating that learned imaging biomarkers significantly enhance cardiovascular risk stratification with a 20% improvement in patient risk reclassification beyond traditional clinical variables. Third, we develop a multilevel EfficientNet-UNet++ architecture for 3D carotid vessel wall segmentation in black-blood MRI that achieves state-of-the-art performance through contextual slice concatenation and resolution optimization. The framework demonstrates optimal performance at 256 x 256 input resolution (6x original size) while maintaining computational efficiency through targeted multilevel processing. Finally, we introduce a quality-aware segmentation framework with custom loss functions for explicit quality modeling during training. When applied to ultrasound colon wall segmentation, this approach achieves a 20% improvement on medium-quality images and a 31% improvement on low-quality images, directly addressing ultrasound's fundamental challenge of variable image quality. Collectively, these contributions establish the technical foundations for robust clinical imaging through efficient segmentation architectures, the integration of imaging with clinical data, explicit quality modeling, and comprehensive clinical validation across two medical imaging modalities.",
            "source_url": "https:\/\/openalex.org\/W7146545742",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)",
            "source_record_id": "W7146545742",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 70,
            "title": "GENERALIZING MEDICAL IMAGE SEGMENTATION TASK WITH EFFICIENT DEEP LEARNING MODELS",
            "title_ar": "",
            "title_en": "GENERALIZING MEDICAL IMAGE SEGMENTATION TASK WITH EFFICIENT DEEP LEARNING MODELS",
            "author": "Abel A. Reyes-Angulo",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Michigan Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; transformers; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Medical Image Segmentation is a critical task in the field of medical imaging, playing a crucial role in diagnostics, treatment planning, and disease monitoring. The emergence of Deep Learning (DL) has ushered in a new era in Artificial Intelligence (AI), propelling remarkable advancements in key domains like language translation, object recognition, and recommendation systems. This evolution has been accompanied by continuous enhancements in computational efficiency and improvements in predictive accuracy. The introduction of sophisticated algorithms, such as convolutional neural networks (CNNs) and transformers, exemplifies these advancements. DL algorithms have demonstrated exceptional efficacy in medical image segmentation tasks, showcasing the potential for AI-driven early diagnostics. However, the deployment of AI systems in clinical environments is often hindered by the substantial computational demands and complexity of cutting-edge DL models. In this research proposal, we explore various methodologies to enrich the visual feature representation for medical images. We focus on integrating global context-oriented techniques, such as attention mechanisms, into the development of parameter-efficient deep learning models. Our goal is to create a generalized, end-to-end medical image segmentation framework that can accurately and efficiently segment medical images across different modalities and conditions. By leveraging advanced deep learning techniques and optimizing model architectures, we aim to enhance the performance and generalization capabilities of medical image segmentation models, ultimately contributing to improved clinical outcomes",
            "source_url": "https:\/\/openalex.org\/W4410497124",
            "pdf_url": "",
            "doi": "10.37099\/mtu.dc.etdr\/1941",
            "handle": "",
            "repository_name": "Digital Commons - Michigan Tech (Michigan Technological University)",
            "source_record_id": "W4410497124",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 71,
            "title": "Brain lesion segmentation in medical images using classical machine learning and deep learning",
            "title_ar": "",
            "title_en": "Brain lesion segmentation in medical images using classical machine learning and deep learning",
            "author": "M.N. Karakolev",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Netherlands",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7225767627",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Theses database, Tilburg University",
            "source_record_id": "W7225767627",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 72,
            "title": "Study on diagnostic-assisted deep learning models in medical image segmentation and translation",
            "title_ar": "",
            "title_en": "Study on diagnostic-assisted deep learning models in medical image segmentation and translation",
            "author": "Tao Hu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7146384150",
            "pdf_url": "https:\/\/nagoya.repo.nii.ac.jp\/record\/2013022\/files\/k15505_review.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Nagoya University Repository (NAGOYA Repository)",
            "source_record_id": "W7146384150",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 73,
            "title": "Active deep learning for medical imaging segmentation",
            "title_ar": "",
            "title_en": "Active deep learning for medical imaging segmentation",
            "author": "Marc Górriz Blanch",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; image-classification; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "This thesis proposes a novel active learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our approach tries to apply in segmentation existing active learning techniques, which is becoming an important topic today because of the many problems caused by the lack of large amounts of data. We explore different strategies to study the image information and introduce a previously used cost-effective active learning method based on the selection of high confidence predictions to assign automatically pseudo-labels with the aim of reducing the manual annotations. First, we made a simple application for handwritten digit classification to get started to the methodology and then we test the system with a medical image database for the treatment of melanoma skin cancer. Finally, we compared the traditional training methods with our active learning proposals, specifying the conditions and parameters required for it to be optimal.",
            "source_url": "https:\/\/openalex.org\/W2768586247",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)",
            "source_record_id": "W2768586247",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 74,
            "title": "Medical image segmentation using deep learning",
            "title_ar": "",
            "title_en": "",
            "author": "Ruya Kaymak",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Fırat University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2019,
            "language": "tr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "Derin öğrenme, Konvolüsyonel Sinir Ağı, Tam Konvolüsyonel Ağ, Tıbbi görüntü bölütleme.",
            "abstract": "Bu tezde, ilk olarak son yıllarda makine öğrenmesi alanında çok fazla ilgi duyulan derin öğrenme yaklaşımı hakkında detaylı bilgilere yer verilmiştir. Daha sonra, en tehlikeli cilt kanseri türü olan melanomun tespitine yardımcı olabilmek adına otomatik bir cilt lezyonu bölütleme uygulaması gerçekleştirilmiştir. Bu tıbbi görüntü bölütleme uygulaması, derin öğrenme tabanlı olan Konvolüsyonel Sinir Ağı (KSA) mimarilerinde değişiklik yapılarak elde edilen Tam Konvolüsyonel Ağ (TKA) mimarileriyle gerçekleştirilmiştir. Uygulama için gerçekleştirilen deneysel çalışmalarda veri seti olarak ISIC 2017 kullanılmış olup FCN-AlexNet, FCN-8s, FCN-16s ve FCN-32s isimli dört farklı TKA mimarisinden yararlanılmıştır. Bu mimariler ve veri seti dikkate alındığında, bu çalışma bilimsel yazında ilk kez gerçekleştirilmiştir. Deneysel çalışmalar için öncelikle TKA'lar ayrı ayrı eğitilmiş ve eğitilen bu ağ modellerinin geçerlilik veri seti üzerindeki doğrulukları ve Dice katsayıları karşılaştırılmıştır. Ayrıca, lezyon bölütleme çıkarımları görselleştirilerek kullanılan TKA mimarilerinin lezyonları ne kadar keskinlikte bölütleyebildiği gözlemlenmiştir. Elde edilen deneysel sonuçlar, cilt lezyonu bölütleme uygulamalarında TKA'ların elverişli olduğunu göstermiştir. Bununla beraber, deneysel sonuçların bilimsel yazına ve tıbbi görüntü bölütleme ile ilgili çalışmalar yapan araştırmacılara katkı sağlayacağı düşünülmektedir. Anahtar Kelimeler: Derin öğrenme, Konvolüsyonel Sinir Ağı, Tam Konvolüsyonel Ağ, Tıbbi görüntü bölütleme.",
            "source_url": "https:\/\/openalex.org\/W7217362636",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DSpace - FIRAT (Fırat University)",
            "source_record_id": "W7217362636",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 75,
            "title": "DEEP LEARNING FOR VOLUMETRIC MEDICAL IMAGE SEGMENTATION",
            "title_ar": "",
            "title_en": "DEEP LEARNING FOR VOLUMETRIC MEDICAL IMAGE SEGMENTATION",
            "author": "Zhuotun Zhu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W3201052870",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3201052870",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 76,
            "title": "Quantifying Uncertainty in Deep Learning-based medical image segmentation",
            "title_ar": "",
            "title_en": "Quantifying Uncertainty in Deep Learning-based medical image segmentation",
            "author": "Benjamin Lambert",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "نص السجل: «Ph.D»",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; image-segmentation; medical-imaging; ai-industry",
            "keywords": "",
            "abstract": "Quantifying the inherent uncertainty in an automated medical image analysis is crucial to guarantee the safe deployment of deep learning models. However, these models, often referred to as black boxes, are known to produce errors with high confidence, potentially leading to misinformed conclusions. The operative goal of Pixyl, Grenoble Institute of Neurosciences, and Inria for this Ph.D. is to develop flexible uncertainty quantification tools that would address the opacity of deep learning models. In medical image analysis, uncertainty estimates are useful at different levels: the voxel, the lesion, the subject (input image or output segmentation), and the estimated volumes. At the voxel level, there is a multitude of solutions proposed in the literature. Through three different brain lesion segmentation tasks, we show the adequacy of the Deep Ensemble framework to detect incorrectly classified voxels. The resulting uncertainty map can be overlayed on the input image to identify the ambiguous regions. Second, specifically tailored for diseases involving the detection of multiple lesions such as Multiple Sclerosis, we propose a lesion-level uncertainty module that associates each identified brain lesion with a confidence score. This allows to draw the clinician's attention to these uncertain lesion instances, which may correspond to false positive detections. Then, as deep learning models lack robustness when the test image is not represented in the training dataset, we build two different subject-level quality control tools. Non-conform inputs are detected using a compressed latent representation, allowing to efficiently compute its distance to the training distribution in a low-dimensional manifold. Yet, this approach focuses on the conformity of the input image and thus is unhelpful in detecting poor-quality segmentations. To alleviate this, a second quality check focusing on segmentation quality is implemented, allowing to enrich the informativeness of the case-level uncertainty quantification. Lastly, we leverage the conformal prediction framework to equip lesional volume estimations with robust predictive intervals. The proposed framework, TriadNet, computes the segmentation and associated predictive intervals in a second, thus being ideal for industrial software. The tools developed during this PhD will allow to enhance medical image analysis software with useful uncertainty estimates, allowing increased trust in the automated results and enabling informed decision-making.",
            "source_url": "https:\/\/openalex.org\/W4406884857",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "HAL",
            "source_record_id": "W4406884857",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 77,
            "title": "Deep learning for medical image super resolution and segmentation",
            "title_ar": "",
            "title_en": "",
            "author": "Chi-Hieu Pham",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2018,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Apprentisage profond pour la super-résolution et la segmentation d'images médicales L'objectif de cette thèse est d'étudier le comportement de différentes représentations d'images, notamment apprentissage profond, dans le contexte d'application en imagerie médicale. Le but est de développer une méthode unifiée efficace pour les applications visées que sont la super résolution, la segmentation et la synthèse. La super-résolution est un procès d'estimation d'une image haute-résolution à partir d'une ou plusieurs images basses résolutions. Dans cette thèse, nous nous concentrons sur la super résolutionunique, c'est-à-dire que l'image haute résolution (HR) est estimée par une image basse-résolution (LR) correspondante. Augmenter la résolution de l'image grâce à la super-résolution est la clé d'une compréhension plus précise de l'anatomie. L'application de la super résolution permet d'obtenir des cartes de segmentation plus précises. Étant donné que deux bases de données qui contiennent les images différentes (par exemple, les images d'IRM et les images de CT), la synthèse est un procès d'estimation d'une image qui est approximative aux images dans la base de données de cible à partir d'une image de la base de données de source. Parfois, certains contrastes tissulaires ne peuvent pas être acquis pendant la séance d'imagerie en raison du temps et des coûts élevés ou de l'absence d'appareils. Une solution possible est à utiliser des méthodes de synthèse d'images médicales pour générer les images avec le contraste différent qui est manquée dans le domaine à cible à partir de l'image du domaine donnée. L'objectif des images synthétiques est d'améliorer d'autres étapes du traitement automatique des images médicales telles que la segmentation, la super-résolution ou l'enregistrement. Dans cette thèse, nous proposons les réseaux neurones pour la super résolutionet la synthèse d'image médicale. Les résultats démontrent le potentiel de la méthode que nous proposons en ce qui concerne les applications médicales pratiques.",
            "source_url": "https:\/\/openalex.org\/W7148632417",
            "pdf_url": "",
            "doi": "10.70675\/1a88776bz8fc5z4ac4z921cze85a14188056",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7148632417",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 78,
            "title": "Privacy-preserving and data-efficient deep learning for medical image segmentation",
            "title_ar": "",
            "title_en": "Privacy-preserving and data-efficient deep learning for medical image segmentation",
            "author": "Zhou ZHENG",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; responsible-ai; image-segmentation; medical-imaging; fairness",
            "keywords": "",
            "abstract": "Medical image segmentation that aims to delineate regions of interest (ROIs) has been a long-standing problem in medical image analysis.It plays an essential role in computer-aided diagnosis (CAD) and computer-aided surgery (CAS).The development of Deep learning (DL) techniques has significantly advanced these areas by providing accurate and automatic segmentation.Despite such remarkable progress and achievements, DL in medical image segmentation still faces several challenges, typically privacy preservation and data efficiency.On one aspect, DL typically requires numerous samples for training.However, sharing medical images among hospitals or medical institutes is usually impeded due to privacy regulations, making it hard to collect large-scale datasets.Moreover, the performance of DL models greatly depends on fully and densely annotated labels, but obtaining such ideal annotations is a bottleneck, particularly in the medical field.By contrast, medical image datasets usually come with imperfect annotations in practice.These limitations emphasize the importance of developing privacy-preserving and data-efficient DL techniques for medical image segmentation with highly distributed datasets accompanying imperfect annotations.Driven by these perspectives, this thesis reevaluates the application of DL in medical image segmentation, particularly the 3-dimensional (3D) abdominal multi-organ segmentation from computed tomography (CT) images and the surgical instrument segi LIST OF FIGURES 4.5 Ablation study on different methods adopted by local clients for handling various imperfect annotations.IL: ideal annotation type.SL: scarce annotation type.NL: noisy annotation type.",
            "source_url": "https:\/\/openalex.org\/W7145109004",
            "pdf_url": "https:\/\/nagoya.repo.nii.ac.jp\/record\/2011569\/files\/k15072_thesis.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7145109004",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 79,
            "title": "Semantic segmentation of 3D medical images with deep learning",
            "title_ar": "",
            "title_en": "",
            "author": "Olivier Petit",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2021,
            "language": "fr",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; medical-ai; transformers; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Segmentation sémantique d'images médicales 3D par deep learning L’apprentissage profond a récemment montré des résultats impressionnants en vision par ordinateur. En particulier avec les réseaux de neurones convolutifs (ConvNets) qui ont redéfini l’état-de-l’art dans de nombreuses applications telles que la segmentation d’images médicales. Dans cette thèse nous abordons des problèmes en segmentation d’organes de l’abdomen en utilisant ces modèles. Premièrement, nous nous sommes intéressés à l’entrainement de ConvNets avec des bases de données partiellement étiquetées. Les professionnels se concentrant, souvent sur des régions anatomiques précises, les bases de données sont de ce fait hétérogènes et partiellement étiquetées. Entraîner un modèle de segmentation directement donne de tr`es mauvais résultats. Nous proposons donc un schéma d’entrainement qui utilise toutes les étiquettes disponibles sans être affect´e par les mauvaises. De plus, un schéma itératif permet de progressivement ré-étiqueter les organes manquants ce qui permet d’améliorer encore notre modèle. La seconde partie étudie l’utilisation d’un a priori spatial sur la position absolue des organes afin d’améliorer la détection des structures et réduire les erreurs aberrantes. Les ConvNets sont par construction incapables de capturer l’information de position spatiale absolue. Cependant, les images médicales sont très structurées et les positions des organes sont connues. Dans ces travaux nous proposons d’utiliser un a priori spatial 3Dqui capture la position des organes et qui va explicitement biaiser le modèle grâce à une fonction d’activation «prior-driven ». Pour finir, nous étudions les Transformers qui permettent de mod´eliser des interactions `a long terme entre les structures anatomiques dans un modèle de segmentation. Les ConvNets ne permettent pas de capturer ces interactions globales principalement `a cause de leur champ réceptif limité. Utiliser le mécanisme d’attention propose dans les Transformers permet de connecter tous les pixels entre eux, ayant pour effet de modéliser des interactions complexes. Nous proposons le modèle U-Transformer et montrons qu’il améliore la qualité de la segmentation sur plusieurs bases de données.",
            "source_url": "https:\/\/openalex.org\/W4392220284",
            "pdf_url": "",
            "doi": "10.70675\/0c16f3d3z8e39z4f4az97f2ze8a6f2043cc3",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W4392220284",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 80,
            "title": "Deep Learning for Medical Image Segmentation using Prior Knowledge and Topology",
            "title_ar": "",
            "title_en": "Deep Learning for Medical Image Segmentation using Prior Knowledge and Topology",
            "author": "Chen Zhao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Michigan Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; gnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Image segmentation refers to the division of a digital image into distinct segments or groups of pixels\/voxels. However, most of the existing deep learning approaches lack the utilization of prior knowledge, such as shape information, which could improve segmentation accuracy. In addition, conventional image segmentation frequently falls short in preserving intricate spatial details, motivating the innovation of strategies for multi-scaled feature integration. Furthermore, traditional image segmentation methods primarily concentrate on pixel-level or region-level analysis. However, given the inherent morphological similarities among various image objects, the significance of topology information surpasses that of pixel-level data in the realm of medical image semantic segmentation, and the incorporation of topology information for image segmentation is important. The first aim of this dissertation is to incorporate shape priors into medical image segmentation. A shape-prior-V-Net (SP-V-Net) is proposed, which contains a shape transformation module to refine the segmentation results according to the shape prior. SP-V-Net has been applied to lung segmentation and proximal femur segmentation. The second aim aims to improve image segmentation by leveraging hierarchical features. Two approaches are proposed: the feature pyramid U-Net++ (FP-U-Net++), which dynamically aggregates the feature pyramid in the decoder of U-Net ++, and the multi-input multi-scale U-Net (MIMS U-Net), which integrates the features in the encoder of the U-Net. The third aim explores topology-based image semantic segmentation using graph neural networks. Three graph-matching networks have been developed, including association graph-based, edge attention graph matching, and hyper-association graph matching networks. The proposed graph-matching networks convert the graph-matching problems into a vertex classification problem using an association graph, where the positive vertex indicates the nodes from two individual graphs are matched. These models were applied to coronary artery semantic labeling on invasive coronary angiograms. Moreover, this study presents a pioneering approach for topology-based image semantic labeling using graph matching. The successful completion of these aims contributes technically accurate and clinically applicable algorithms and techniques for medical image segmentation. The outcomes of this dissertation provide valuable tools for the medical imaging and computer vision communities, advancing the field and improving patient care through accurate and efficient medical image segmentation.",
            "source_url": "https:\/\/openalex.org\/W4390970363",
            "pdf_url": "https:\/\/digitalcommons.mtu.edu\/cgi\/viewcontent.cgi?article=2771&context=etdr",
            "doi": "10.37099\/mtu.dc.etdr\/1691",
            "handle": "",
            "repository_name": "Digital Commons - Michigan Tech (Michigan Technological University)",
            "source_record_id": "W4390970363",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 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": 82,
            "title": "Research on Medical Image Segmentation Based on Deep Learning Methods",
            "title_ar": "",
            "title_en": "Research on Medical Image Segmentation Based on Deep Learning Methods",
            "author": "Yuxiang Zhou",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; transformers; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Medical imaging involves the technique and process of generating visual representations of a patient's body for clinical analysis and medical intervention.Healthcare professionals heavily depend on medical images for accurate diagnosis and treatment.In clinical practice, segmentation is typically performed manually.However, when processing a vast number of medical images, the quality of segmentation can vary based on the expertise of the medical professional.This variability underscores the need for a more consistent layers.MDSU-Net uses DSC layer to reduce model complexity without degrading model performance, which is suitable for different segmentation tasks.MDSU-Net registers a Dice score of 0.7055 on ATLAS, 0.9760 on CHAOS, and 0.8883 on NERVE, respectively.Notably, these scores outperform the state-of-the-art (SoTA) benchmarks.Additionally, in terms of 3D medical image segmentation, we propose Dual Encoder U-Net (DEU-Net), which uses Transformer and CNN respectively to extract 3D medical image features in the encoder.Transformer is a pre-trained model in BTCV, which improves its ability to capture contextual features of medical images and increases the learning speed.Besides, we introduce CBAM with each convolutional layer in the encoder part to enhance CNN's feature extraction capabilities for 3D medical images.To fuse the two kinds of features, we proposed a Dual Feature Fusion Module (DFFM) to fuse the features extracted from the Transformer and CNN in the encoder, making full use of the feature extraction capabilities of the two extractors for datasets of different sizes.We compare DEU-Net with other SoTA methods on the BraTS 2020 and BraTS 2021 datasets, and the results show that the performance of DEU-Net has improved in 3D medical image segmentation task.",
            "source_url": "https:\/\/openalex.org\/W7145787761",
            "pdf_url": "https:\/\/tokushima-u.repo.nii.ac.jp\/record\/2012049\/files\/k3814_fulltext.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7145787761",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 83,
            "title": "Volumetric medical image segmentation with deep learning pipelines",
            "title_ar": "",
            "title_en": "Volumetric medical image segmentation with deep learning pipelines",
            "author": "Imad Eddine Toubal",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Missouri",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Automated semantic segmentation in the domain of medical imaging can enable a faster, more reliable, and more affordable clinical workflow. Fully convolutional networks (FCNs) have been heavily used in this area due to the level of success that they have achieved. In this work, we first leverage recent architectural innovations to make an initial segmentation: (i) spatial and channel-wise squeeze and excitation mechanism; (ii) a 3D U-Net++ network and deep supervision. Second, we use classical methods for refining the initial segmentation: (i) spatial normalization and (ii) local 3D refinement network applied to patches. Finally, we put our methods together in a novel segmentation pipeline. We train and evaluate our models and pipelines on a dataset of a 120 abdominal magnetic resonance - volumetric - images (MRIs). The goal is to segment five different organs of interest (ORI): liver, kidneys, stomach, duodenum, and large bowel. Our experiments show that we can generate full resolution segmentation of comparable quality to the state-of-the-art methods without adding computational cost.",
            "source_url": "https:\/\/openalex.org\/W4321613625",
            "pdf_url": "https:\/\/mospace.umsystem.edu\/xmlui\/bitstream\/10355\/94345\/1\/ToubalImadEddineResearch.pdf",
            "doi": "10.32469\/10355\/94345",
            "handle": "",
            "repository_name": "MOspace Institutional Repository (University of Missouri)",
            "source_record_id": "W4321613625",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 84,
            "title": "Impact of Rater Style on Deep Learning Segmentation in Medical Imaging",
            "title_ar": "",
            "title_en": "",
            "author": "Olivier Vincent",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "École Polytechnique de Montréal",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2021,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; responsible-ai; image-segmentation; medical-imaging; fairness",
            "keywords": "",
            "abstract": "La sclerose en plaques est la maladie auto-immune la plus courante du systeme nerveux central. Elle se caracterise par la presence de lesions dans le cerveau et la moelle epiniere, visibles en imagerie par resonance magnetique (IRM). Cependant, pour extraire des infor-mations utiles des images, il est necessaire de segmenter les lesions sur ces images, ce qui est un processus long et couteux lorsqu’il est realise manuellement par un expert tel qu’un radiologue. L’objectif de ce projet est d’utiliser des methodes innovantes d’apprentissage profond pour ameliorer la segmentation des images medicales. Premierement, pour aborder la generalisation a travers di˙erents contrastes dans la segmen-tation de la sclerose en plaques de la moelle epiniere, nous mettons en œuvre la modula-tion lineaire par caracteristique (FiLM) pour tirer parti de la grande variete de parametres d’acquisitions IRM dans le modele de segmentation, en apprenant les caracteristiques de chaque contraste. Fait interessant, un U-Net bien optimise a atteint les memes performances que notre FiLMed-Unet sur un ensemble de donnees a contrastes multiples (0,72 de score Dice), ce qui suggere qu’il existe un goulot d’etranglement dans cette tâche, qui n’est pas la generalisation a travers di˙erents contrastes. Ce goulot d’etranglement provient probable-ment de la variabilite interexperts, qui est estimee a 0,61 de score Dice dans notre ensemble de donnees. Deuxiemement, afin de s’attaquer a ce goulot d’etranglement, nous quantifions le style des experts qui annotent les donnees sous forme de biais et de consistance. Cela nous permet ensuite d’explorer l’impact des styles d’annotations sur les modeles d’apprentissage profond. Deux ensembles de donnees publics multievaluateurs et multicentriques sont utilises, un de lesions de sclerose en plaques cerebrales et un de segmentation de la matiere grise de la moelle epiniere. Sur les deux ensembles de donnees, les resultats montrent une correlation (R2 = 0, 60 et 0, 93) entre le biais de l’expert et l’incertitude du modele d’apprentissage profond. L’impact de la fusion d’annotations des experts sur cette relation est egalement etudie, et nous montrons que les consensus multicentriques sont plus eycaces que les con-sensus monocentriques pour reduire l’incertitude, car le style de l’expert n’est pas specifique a l’individu, mais principalement au centre.----------ABSTRACT Multiple sclerosis is the most common autoimmune disease of the central nervous system. It is characterized by the presence of lesions in the brain and spinal cord, which are visible in magnetic resonance imaging (MRI). However, to extract useful information from the images, it is necessary to segment the lesions on these images, which is a long and expensive process when performed manually by an expert such as a radiologist. The goal of this project is to use innovative deep learning methods to improve segmentation of medical images. First, to tackle generalization across imaging contrasts in spinal cord multiple sclerosis seg-mentation we implement Feature-wise Linear Modulation (FiLM) to leverage physics knowl-edge within the segmentation model and learn the characteristics of each contrast. Inter-estingly, a well-optimized U-Net reached the same performance as our FiLMed-Unet on a multi-contrast dataset (0.72 of Dice score), which suggests that there is a bottleneck in spinal MS lesion segmentation di˙erent from the generalization across varying contrasts. This bot-tleneck likely stems from inter-rater variability, which is estimated at 0.61 of Dice score in our dataset. Second, as a follow-up we quantify rater style in the form of bias and consistency and explore the impacts on deep learning models. Two multi-rater and multi-center public datasets are used, consisting of brain multiple sclerosis lesion and spinal cord grey matter segmentation. On both datasets, results show a correlation (R2 = 0.60 and 0.93) between rater bias and deep learning uncertainty. The impact of label fusion between raters’ annotations on this relationship is also explored, and we show that multi-center consensuses are more e˙ective than single-center consensuses to reduce uncertainty, since rater style is not individual-specific but mostly center-specific.",
            "source_url": "https:\/\/openalex.org\/W3211467155",
            "pdf_url": "https:\/\/publications.polymtl.ca\/6283\/1\/2021_OlivierVincent.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "PolyPublie (École Polytechnique de Montréal)",
            "source_record_id": "W3211467155",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 85,
            "title": "Quantifying and understanding uncertainty in deep-learning-based medical image segmentation",
            "title_ar": "",
            "title_en": "",
            "author": "Benjamin Lambert",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2024,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Quantification et caractérisation de l'incertitude de segmentation d'images médicales pardes réseaux profonds Ces dernières années, les algorithmes d'intelligence artificielle ont démontré des performances exceptionnelles dans de nombreuses tâches, incluant la segmentation et classification d'images médicales. La segmentation automatique des lésions dans des IRMs du cerveau permet une quantification rapide de la progression de la maladie : un compte des nouvelles lésions, une mesure du volume lésionnel total et une description de la forme des lésions. Cette analyse peut ensuite être exploitée par le neuro-radiologue qui peut s'en servir pour adapter le traitement thérapeutique si nécessaire. Cela permet de rendre la décision médicale plus rapide et plus précise.Actuellement ces algorithmes, que l'on considère souvent comme des boîtes noires, produisent des prédictions sans aucune information concernant leur certitude. Cela empêche la pleine adoption des algorithmes d'intelligence artificielle dans les domaines sensibles, car ils ont en effet tendance à produire des erreurs avec une grande confiance, pouvant tromper les décideurs humains. Identifier et comprendre les causes de ces échecs est un élément clé pour maximiser l'utilité des algorithmes d'intelligence artificielle et permettre leur acceptation au sein du corps médical. Pour atteindre ce but, il est important de pouvoir distinguer entre les deux principales sources d'incertitude. Tout d'abord, l'incertitude aléatoire, qui correspond à l'incertitude liée au bruit intrinsèque de l'image, et les artefacts d'acquisition. Ensuite, l'incertitude épistémique, qui a attrait au manque de connaissance du modèle.L'objectif conjoint de Pixyl et du GIN est d'atteindre une meilleure identification des sources d'incertitude dans les réseaux de neurones profonds, et par conséquent développer de nouvelles méthodes pour estimer cette incertitude dans le cadre d'une utilisation clinique routinière et en temps réel.Dans le cadre de la segmentation d’images médicales, l’estimation de l’incertitude est pertinente à plusieurs échelles. Tout d’abord, à l’échelle du voxel, l’incertitude peut être quantifiée par le biais de cartes d’incertitude. Cela permet de superposer l’image, sa segmentation et la carte d’incertitude pour visualiser les régions incertaines. Ensuite, pour les pathologies comme la Sclérose-en-Plaques, l’attention du radiologue se situe à l’échelle de la lésion plutôt que du voxel. L’estimation d’incertitude structurelle, c’est-à-dire à l’échelle de la lésion, permet au radiologue de rapidement contrôler les lésions incertaines qui peuvent être des faux positifs. Troisièmement, des métriques haut-niveau comme le volume ou le nombre de lésions sont couramment extraites des segmentations. Être capable d’associer à ces métriques des intervalles prédictifs est important pour que le clinicien puisse prendre en compte cette incertitude dans son analyse. Pour finir, l’incertain peut être quantifiée à l’échelle de l’image entière, par exemple pour détecter les images hors-distributions qui présentent une anomalie importante pouvant biaiser leur analyse.Dans cette thèse, des outils de quantification d’incertitude opérant à chacun de ces niveaux sont développés. Plus généralement, les méthodes désirées et attendues devront permettre à Pixyl d'améliorer ces modèles, services et produits actuels. Pour l'application clinique, le temps d'inférence est particulièrement critique : l'assistance à la prise de décision est utile seulement si elle est suffisamment rapide pour être appliquée pendant la consultation du patient (c'est-à-dire en moins de 5 minutes). De plus, les solutions innovantes devront maintenir un haut niveau de performance même appliquée sur de petites bases de données d'image, ce qui est généralement le cas dans le domaine médical.",
            "source_url": "https:\/\/openalex.org\/W7151869013",
            "pdf_url": "",
            "doi": "10.70675\/a043da9aze69dz4ea2z978dzcd9a570ce0a7",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7151869013",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 86,
            "title": "Medical image segmentation with belief function theory and deep learning",
            "title_ar": "",
            "title_en": "",
            "author": "Ling Huang",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2023,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; multimodal; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Segmentation d'images médicales avec la théorie de la fonction de croyance et l'apprentissage en profondeur L'apprentissage profond a montré des contributions prometteuses dans la segmentation d'images médicales avec de puissantes capacités d'apprentissage et de représentation de caractéristiques. Cependant, il présente des limites pour le raisonnement avec et la combinaison d'informations imparfaites (imprécises, incertaines et partielles). Dans cette thèse, nous étudions les approches de segmentation d'images médicales avec la théorie des fonctions de croyance et l'apprentissage profond, en nous concentrant spécifiquement sur la modélisation et la fusion d'informations basées sur des preuves incertaines. Tout d'abord, nous passons en revue les méthodes de segmentation d'images médicales existantes basées sur la théorie de la fonction de croyance et discutons de leurs avantages et défis. Deuxièmement, nous présentons un cadre de segmentation d'images médicales semi-supervisé pour diminuer l'incertitude causée par le manque d'annotations avec la segmentation évidentielle et la fusion de preuves. Troisièmement, nous comparons deux classificateurs d'évidence, le réseau neuronal d'évidence et le réseau de fonction de base radiale, et nous montrons l'efficacité de la théorie de la fonction de croyance dans la quantification de l'incertitude ; nous utilisons les deux classificateurs d'évidence avec des réseaux neuronaux profonds pour construire des modèles d'évidence profonds pour la segmentation du lymphome. Quatrièmement, nous présentons un cadre de fusion d'images médicales multimodales prenant en compte la fiabilité de chaque source d'image RM lors de l'exécution de différentes tâches de segmentation à l'aide de fonctions de masse et d'actualisation contextuelle.",
            "source_url": "https:\/\/openalex.org\/W7151868992",
            "pdf_url": "",
            "doi": "10.70675\/8a8a9e2bz03f7z4d78z879cz9b80ab963e96",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7151868992",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 87,
            "title": "Deep learning methods for localization, segmentation and robustness in medical imaging",
            "title_ar": "",
            "title_en": "",
            "author": "Othmane Laousy",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2024,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; reinforcement-learning; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Méthodes d'apprentissage profond pour la localisation, la segmentation et la robustesse en imagerie médicale Ces dernières années, nous avons assisté à un essor remarquable des avancées à la croisée de l'apprentissage profond et de la médecine, en particulier dans le domaine de l'imagerie médicale. Cette évolution rapide a engendré une multitude de défis nécessitant une attention particulière.L'un des principaux défis découlant de l'évolution des techniques d'apprentissage profond est la demande de données étendues et diversifiées pour l'entraînement des modèles. A mesure que les modèles deviennent plus sophistiqués, leur besoin en données s'intensifie, ce qui constitue un obstacle à l'acquisition d'ensembles de données non seulement volumineux, mais aussi véritablement représentatifs des divers scénarios médicaux rencontrés en pratique.Une autre préoccupation majeure concerne la vulnérabilité des modèles d'intelligence artificielle aux attaques adversariales, où de petites perturbations des données d'entrée peuvent entraîner des répercussions significatives sur la fiabilité du modèle. La sensibilité de ces modèles aux manipulations adverses constitue une menace potentielle pour leur déploiement dans des scénarios médicaux réels, où les conséquences de ces erreurs peuvent être désastreuses. Il est impératif de protéger les modèles d'IA contre les attaques adverses pour inspirer confiance aux professionnels de la santé et aux patients qui comptent sur ces technologies pour obtenir des diagnostics précis et des recommandations de traitement.Cette thèse explore ces défis pressants, offrant des perspectives sur les directions potentielles pour atténuer l'impact de la rareté des données et améliorer la fiabilité des modèles d'IA face aux attaques adversariales.Tout d'abord, nous explorons l'apprentissage par renforcement profond (Deep Reinforcement Learning, DRL) pour aborder les problèmes liés à l'apprentissage dans des contextes où les données sont rares. Cette thèse présente une application novatrice du DRL dans l'analyse d'images médicales, en se concentrant particulièrement sur les défis de localisation et de segmentation pour la quantification de la sarcopénie à partir de scans CT. En optimisant la prise de décision d'un agent par essais et erreurs, nous avons développé un réseau Deep Q-Network qui automatise l'identification de la coupe de la troisième vertèbre lombaire. Pour la segmentation, nous avons employé un algorithme d'optimisation de politique proximale pour tracer les contours des muscles psoas et parietaux, automatisant le calcul de l'indice de masse musculaire squelettique. Le pipeline présenté a ensuite été utilisé pour évaluer la sarcopénie pour le pronostic des patients atteints de cancers solides en état critique, validant ainsi la viabilité pratique de notre approche DRL dans des contextes cliniques réels.La deuxième partie de cette thèse se concentre sur le renforcement de la sécurité et de la fiabilité des modèles d'IA en incorporant une robustesse certifiée contre les attaques adversariales. Les attaques adversariales ont mis en évidence la vulnérabilité des modèles d'apprentissage profond, en particulier dans des domaines à haut risque comme le diagnostic médical. Pour atténuer cette vulnérabilité, nous explorons et développons des approches garantissant un niveau de robustesse contre les manipulations adversariales en tirant parti de la puissance des modèles de diffusion et du lissage aléatoire. Ces méthodes fournissent non seulement une protection contre les éventuelles violations de sécurité, mais instaurent aussi une plus grande confiance dans le déploiement des systèmes de santé pilotés par l'IA.À travers ces orientations de recherche, cette thèse contribue à l'évolution continue de l'apprentissage profond en médecine en abordant les défis de la rareté des données et des attaques adversariales, traçant ainsi une voie potentielle vers un avenir plus résilient et plus fiable pour l'IA dans l'imagerie médicale.",
            "source_url": "https:\/\/openalex.org\/W7152083672",
            "pdf_url": "",
            "doi": "10.70675\/4ffc4285z667dz430czb4d9za731fb5f4d27",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7152083672",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 88,
            "title": "Semi-Supervised Deep Learning using Consistency-Based Methods for Segmentation of Medical Images",
            "title_ar": "",
            "title_en": "Semi-Supervised Deep Learning using Consistency-Based Methods for Segmentation of Medical Images",
            "author": "Axel Rönnberg",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; self-semi-supervised; predictive-analytics; image-segmentation; medical-imaging; ai-education",
            "keywords": "",
            "abstract": "In radiation therapy, a form of cancer treatment, accurately locating the anatomical structures is required in order to limit the impact on healthy cells. The automatic task of delineating these structures and organs is called segmentation, where each pixel in an image is classified and assigned a label. Recently, deep neural networks have proven to be efficient at automatic medical segmentation. However, deep learning requires large amounts of training data. This is a restricting feature, especially in the medical field due to factors such as patient confidentiality. Nonetheless, the main challenge is not the image data itself but the lack of high-quality annotations. It is thus interesting to investigate methods for semi-supervised learning, where only a subset of the images re- quires annotations. This raises the question if these methods can be acceptable for organ segmentation, and if they will result in an increased performance in comparison to supervised models. A category of semi-supervised methods applies the strategy of encouraging consistency between predictions. Consistency Training and Mean Teacher are two methods in which the network weights are updated in order to minimize the impact of input perturbations such as data augmentations. In addition, the Mean Teacher method trains two models, a Teacher and a Student. The Teacher is updated as an average of consecutive Student models, using Temporal Ensembling. To resolve the question whether semi-supervised learning could be beneficial, the two mentioned techniques are investigated. They are used in training deep neural networks with an U-net architecture to segment the bladder and anorectum in 3D CT images. The results showed signs of promise for Consistency Training and Mean Teacher, with nearly all model configurations having improved segmentations. Results also showed that the methods caused a reduction in performance variance, primarily by limiting poor delineations. With these results in hand, the use of semi-supervised learning should definitely be considered. However, since the segmentation improvement was not repeated in all experiment configurations, more research needs to be done.",
            "source_url": "https:\/\/openalex.org\/W3111652972",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W3111652972",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 90,
            "title": "Detection-aided medical image segmentation using deep learning",
            "title_ar": "",
            "title_en": "Detection-aided medical image segmentation using deep learning",
            "author": "Míriam Bellver Bueno",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; object-detection; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and also to assess the response to the according treatments. In this thesis we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans, as well as other anatomical structures and organs of the human body. We have used Convolutional Neural Networks (CNNs), that have proven good results in a variety of tasks, including medical imaging. The network to segment the lesions consists of a cascaded architecture, which first focuses on the liver region in order to segment the lesion. Moreover, we train a detector to localize the lesions and just keep those pixels from the output of the segmentation network where a lesion is detected. The segmentation architecture is based on DRIU (Maninis, 2016), a Fully Convolutional Network (FCN) with side outputs that work at feature maps of different resolutions, to finally benefit from the multi-scale information learned by different stages of the network. Our pipeline is 2.5D, as the input of the network is a stack of consecutive slices of the CT scans. We also study different methods to benefit from the liver segmentation in order to delineate the lesion. The main focus of this work is to use the detector to localize the lesions, as we demonstrate that it helps to remove false positives triggered by the segmentation network. The benefits of using a detector on top of the segmentation is that the detector acquires a more global insight of the healthiness of a liver tissue compared to the segmentation network, whose final output is pixel-wise and is not forced to take a global decision over a whole liver patch. We show experiments with the LiTS dataset for the lesion and liver segmentation. In order to prove the generality of the segmentation network, we also segment several anatomical structures from the Visceral dataset.",
            "source_url": "https:\/\/openalex.org\/W2760887678",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W2760887678",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 92,
            "title": "Explainable Artificial Intelligence in the Image Domain and its Applications to the Medical Field",
            "title_ar": "",
            "title_en": "Explainable Artificial Intelligence in the Image Domain and its Applications to the Medical Field",
            "author": "Mirtha Lucas",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "DePaul University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; responsible-ai; self-semi-supervised; predictive-analytics; cnn; gan; medical-imaging; xai; fairness",
            "keywords": "",
            "abstract": "This dissertation investigates the development of Explainable Artificial Intelligence (XAI) methods for deep learning models in the image domain, with a particular focus on medical imaging applications. Although neural networks achieve high predictive performance, their lack of interpretability limits their adoption in critical domains such as healthcare, where transparency and trust are essential. This work addresses this challenge by proposing novel approaches that improve the interpretability and reliability of model predictions. A primary contribution is the introduction of Riemann–Stieltjes Integrated Grad-CAM (RSI Grad-CAM), a gradient-based attribution method that generates more relevant and spatially localized saliency maps. The method is evaluated using quantitative metrics and statistical analysis, demonstrating improved performance over established techniques such as Grad-CAM and Integrated Grad-CAM. A secondary contribution is the analysis of the interpretability of intermediate layers in convolutional neural networks, demonstrating that clinically meaningful features, such as spiculation in lung nodules, can be identified within specific layers of the model. A third contribution is the development of a human-centered framework based on a Variational Autoencoder-Auxiliary Classifier Wasserstein Generative Adversarial Network (VAE-AC-WGAN), which generates contrastive explanations aligned with clinical reasoning. Experimental results demonstrate that the proposed method produces higher-quality reconstructions and more informative anomaly maps than baseline approaches (e.g., medXGAN), as reflected in improved structural similarity (SSIM) and classifier-based evaluation metrics (AUROC). Furthermore, human evaluation indicates that the generated explanations better capture clinically relevant features, such as spiculation, supporting their interpretability in medical imaging contexts. Overall, this work advances XAI by enhancing model transparency and supporting the trustworthy integration of AI systems into medical decision-making.",
            "source_url": "https:\/\/openalex.org\/W7172068913",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Digital Commons-DePaul (DePaul University)",
            "source_record_id": "W7172068913",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 93,
            "title": "ARTIFICIAL INTELLIGENCE AND THE DUTY TO GIVE REASONS: Beyond the Black Box and the Limits of Explainability",
            "title_ar": "",
            "title_en": "ARTIFICIAL INTELLIGENCE AND THE DUTY TO GIVE REASONS: Beyond the Black Box and the Limits of Explainability",
            "author": "Lucena, Rafael Nogueira de",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "xai",
            "categories": "deep-learning; responsible-ai; xai; fairness",
            "keywords": "",
            "abstract": "The growing integration of Artificial Intelligence (AI) into public administration challenges a fundamental pillar of the rule of law: the duty to give reasons for decisions. The opacity of advanced models, such as Deep Learning (\"black boxes\"), creates a direct conflict with the need for transparency, justification, and oversight of administrative acts. In response, \"Explainable Artificial Intelligence\" (XAI) has been proposed as the solution to align technology with legal guarantees. This dissertation investigates whether XAI, in its current conception, is an adequate and sufficient tool to fulfill the duty to give reasons required by Administrative Law. Through a methodology that combines a systematic literature review, a conceptual analysis of the technology, and a legal analysis of legal reasoning, the study argues that XAI is fundamentally incompatible with normative requirements. The research demonstrates that a technical \"explanation,\" focused on post-hoc computational processes, differs structurally from a legal \"justification,\" which demands an ex ante rationale based on facts and legal norms. The conclusion is that XAI is an insufficient solution, as it fails to address the core issues of the legality and fairness of a decision. Instead of attempting to explain opaque systems, this work highlights the need to adopt inherently interpretable models for high-stakes decisions and to develop an algorithmic governance framework that subordinates technology to the principles of law, ensuring that automated decisions remain transparent, contestable, and legitimate.",
            "source_url": "https:\/\/openalex.org\/W7115034453",
            "pdf_url": "https:\/\/run.unl.pt\/bitstreams\/6f9c6509-594b-47b6-a73d-1700c32ea3ac\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W7115034453",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 94,
            "title": "A Novel Explainable Artificial Intelligence Framework with Improved Learning Mechanisms",
            "title_ar": "",
            "title_en": "A Novel Explainable Artificial Intelligence Framework with Improved Learning Mechanisms",
            "author": "Kabir, Sami",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "نص السجل: «doctoral thesis»",
            "university": "Luleå University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; expert-systems; ai-applications; transfer-learning; self-semi-supervised; predictive-analytics; optimization; cnn; multimodal; xai; fairness; ai-smart-cities",
            "keywords": "",
            "abstract": "Artificial Intelligence (AI) models are increasingly being deployed in various critical domains, such as healthcare, finance, law, and autonomous systems, resulting in remarkable benefits for the society. Due to black-box (sub-symbolic) nature, these AI models do not provide explanation of the predictive output. This lack of explanation results in lack of transparency between human and machine. Such opacity of black-box AI models, particularly deep learning architectures, is a significant concern. To address this concern, Explainable Artificial Intelligence (XAI) has emerged as a vital research area. The aim of XAI is to enhance transparency, trust, and accountability of AI models. Various post-hoc XAI tools explain the outputs of black-box AI models based on training datasets. However, the use of training datasets, rather than domain knowledge, makes such explanations proxy. A biased training dataset may lead to a misleading post-hoc explanation as well. In contrast, an explanation based on domain knowledge will be more trustworthy to an end user. Motivated by this, we propose a novel XAI framework, consisting of Belief Rule Based Expert System (BRBES), to predict output and explain it with reference to domain knowledge. BRBES represents domain knowledge with its rule base. It outperforms other knowledge-driven AI models to handle uncertainty due to ignorance. To improve the accuracy of BRBES, we fine-tune its parameters and structure through evolutionary learning. Moreover, to overcome the scarcity of labeled dataset in this learning mechanism, we integrate semi-supervised and self-supervised learning with BRBES. We explain the output of BRBES with respect to the most influential rule of the rule base of BRBES. Thus, the output of our proposed XAI framework becomes not only accurate, but also explainable. This doctoral thesis delves into the challenges and opportunities surrounding XAI in order to provide a comprehensive understanding of the AI output. It presents a novel XAI framework to provide domain knowledge based energy consumption prediction with improved accuracy, and explain this predictive output in non-technical human language. Then it deals with multi-modal air quality data by integrating deep learning model with BRBES. Moreover, to reduce dependence on labeled data for evolutionary learning, this thesis integrates semi-supervised and self-supervised learning with BRBES. This thesis presents six significant contributions. First, we conduct a Systematic Literature Review (SLR) on XAI using the PRISMA guidelines, delving into the numerous challenges and opportunities of XAI. Extensive research is conducted to explore the definition, terminologies, taxonomy, and application domains of XAI. It highlights various challenges of XAI, such as, no universal definition, trade-off between accuracy and explainability, and lack of standardized evaluation metrics. To address this lack of standardized evaluation metrics, we also propose a unified framework to evaluate XAI. Secondly, we present an innovative explainable BRBES (eBRBES) framework, which offers accurate prediction of building energy consumption phenomenon, while providing insightful explanation and counterfactual based on domain knowledge. As part of eBRBES framework, we also present a novel Belief Rule Based adaptive Balance Determination (BRBaBD) algorithm to assess the optimal balance of the proposed framework between explainability and accuracy. Thirdly, we propose a mathematical model to integrate BRBES with the Convolutional Neural Network (CNN). We leverage the domain knowledge of BRBES, and image data patterns discovered by CNN with this integrated approach. We predict air quality with this integrated model using outdoor ground images and numerical sensor data. Fourth, we integrate two-layer BRBES with CNN to monitor air quality using satellite images, and relevant environmental parameters, such as, cloud, relative humidity, temperature, and wind speed. The two-layer BRBES showcases the strength of BRBES in conducting reasoning across multiple layers. Fifth, we enhance the learning mechanism of BRBES by utilizing the extensive unlabeled energy data, along with limited labeled data. For this purpose, we synthetically generate unlabeled data through weak and strong augmentation. We then pseudo-label these unlabeled data by integrating semi-supervised self-training model with BRBES. By learning from both labeled and pseudo-labeled data, initial BRBES transitions to semi-supervised BRBES. Sixth, to conduct the learning mechanism of BRBES without relying on any labeled data, we integrate self-supervised learning with it. To this effect, we pseudo-label the synthetically generated unlabeled energy data with BRBES in the pretext tasks of self-supervised learning. Then, the initial BRBES learns deeper representation from these pseudo-labeled data, and transitions to self-supervised BRBES.",
            "source_url": "https:\/\/openalex.org\/W7112031890",
            "pdf_url": "https:\/\/ltu.diva-portal.org\/smash\/get\/diva2:2007851\/FULLTEXT01",
            "doi": "",
            "handle": "",
            "repository_name": "DiVA (Luleå University of Technology)",
            "source_record_id": "W7112031890",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 98,
            "title": "Human-in-the-loop and Storytelling-based Explainable Frameworks for the Next Generation of Explainable Ensemble Methods and Artificial Intelligence Techniques",
            "title_ar": "",
            "title_en": "Human-in-the-loop and Storytelling-based Explainable Frameworks for the Next Generation of Explainable Ensemble Methods and Artificial Intelligence Techniques",
            "author": "Akshat Dubey",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; predictive-analytics; llm; medical-imaging; xai; fairness",
            "keywords": "",
            "abstract": "Artificial intelligence (AI) is being adopted in diverse fields, including medical diagnosis and scientific research, to assist with decision-making thanks to its ability to learn underlying patterns from large amounts of data. However, these advances present a challenge. Modern AI systems, particularly deep learning (DL) models and large ensembles, are often difficult for audiences, such as domain experts, to understand due to their inherent black-box nature. This lack of transparency can reduce trust, impede adoption in critical fields, and cause problems with new regulations, such as the European Union’s (EU) AI Act, which impose strict transparency, accountability, and human oversight requirements. This thesis makes the following primary contributions: First, the thesis introduces a five-layer nested model for AI design and validation (Regulation, Domain, Data, Model, Prediction). This model systematically translates regulatory requirements into specific design and validation methodologies, surfacing threats to validity and compliance at each stage. Second, the thesis develops human-centered explainability frameworks, including Storytelling explainable AI (XAI) for medical imaging, which integrates multi-task distillation with visual and textual narratives. Third, the thesis introduces UbiQTree & UbiQVision, frameworks for uncertainty quantification in explainable AI (XAI). UbiQTree is instantiated for ensemble tree models, and UbiQVision is instantiated for deep vision models. They decompose shapley additive explanations (SHAP) based attributions into aleatoric, epistemic, and entangled components using Dirichlet processes and Dempster–Shafer theory (DST). This transformation shifts the focus from point-estimate explanations to distributions of explanations, allowing practitioners to calibrate explanations and facilitate meaningful human intervention. Finally, the thesis demonstrates domain-centric explainability through PepTriX, a framework for explainable peptide analysis. PepTriX integrates protein language models (PLM), predicted 3D structures, graph attention networks (GAT), and cross-modal co-attention to yield mechanistically interpretable representations. Together, these contributions reflect the thesis title, \"Human-in-the-Loop and Storytelling-Based Explainable Frameworks for the Next Generation of Explainable Ensemble Methods and Artificial Intelligence Techniques\". The work argues that explainability should be a core design principle throughout the AI lifecycle, not just an afterthought. Treating explanations as a means of communication and storytelling — i.e., narratives emphasizing the human reasoning process — brings AI systems closer to becoming not only good at achieving enhanced performance metrics, but also accountable, understandable, and better suited to users’ needs. This thesis is based on peer-reviewed publications and submissions covering AI regulations, human-centered explainability, uncertainty quantification, and domain-specific applications. The thesis allows for interpretive\/interpretative human-in-the-loop (HITL) interaction, enabling domain users to inspect and calibrate model behavior as necessary. Together, these works demonstrate that developing trustworthy AI in critical fields requires a comprehensive approach encompassing regulatory compliance, human involvement, technical methodologies, and domain-specific relevance.",
            "source_url": "https:\/\/openalex.org\/W7172247662",
            "pdf_url": "",
            "doi": "10.17169\/refubium-53170",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W7172247662",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:46",
            "updated_at": "2026-11-15 16:52:46"
        },
        {
            "id": 124,
            "title": "Aspect-based Sentiment Analysis for Arabic Reviews Using Deep Learning",
            "title_ar": "",
            "title_en": "Aspect-based Sentiment Analysis for Arabic Reviews Using Deep Learning",
            "author": "Nada Almani",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "The University of Surrey",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; responsible-ai; transfer-learning; sentiment-analysis; arabic-nlp; xai",
            "keywords": "",
            "abstract": "Sentiment analysis is a branch of machine learning that concerns about finding and classifying the polarity for given text. Because of the availability of huge amount of opinionated data that need to be analyzed and interpreted, a lot of recent machine learning research is focused on sentiment analysis applications. it gained a lot of interest due to the. Many sentiment analysis systems are modeled by using different machine learning techniques, but recently, deep learning, by using Artificial Neural Network (ANN) architecture, has showed significant improvements with high tendency to reveal the underlying semantic meaning in the input text. However, the output of these models could not be explained and the efficiency could not be analyzed because ANN models are considered as a black box and the success of these models comes at the cost of interpretability. The main objective of the presented work is developing Arabic sentiment analysis system that understands semantics in input reviews without using any linguistic resources. The first proposed model is Deep Attention-based Review Level Sentiment Analysis model (DARLSA) that use binary classifier to detect reviews’ polarities. Different scenarios and architectures were examined to test the ability of the proposed model to extract salient words out of the input. The results proved the ability of the proposed model to understand a given review by highlighting the most informative words to the class label. The model detected Arabic natural language linguistic features, such as intensification and negation styles, efficiently. Also, the effect of applying transfer learning technique on the problem of Arabic sentiment analysis is experimented on review level model. The second proposed architecture is Deep Attention-based Aspect Level Sentiment Analysis model (DAALSA) for classifying reviews polarity with respect to an aspect into three classes, positive, negative and neutral. Different models were proposed to test the effect of using different attention scoring functions on the classification performance. The results distinguished one model with superior performance compared to other proposed models. To obtain intuitive explanation of the trained models, both models are enhanced with visualization option. The final review representation is a distributed dense vector generated after passing through multi-layers neural network. Heatmap representation is used to visualize the final review representation. In addition, the attention layer’s scoring vector is visualized as well.",
            "source_url": "https:\/\/openalex.org\/W2996475580",
            "pdf_url": "",
            "doi": "10.15126\/thesis.00852989",
            "handle": "",
            "repository_name": "Surrey Research Insight Open Access (The University of Surrey)",
            "source_record_id": "W2996475580",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:47",
            "updated_at": "2026-11-15 16:52:47"
        },
        {
            "id": 128,
            "title": "Classification of medical documents and Opinions using machine learning",
            "title_ar": "",
            "title_en": "Classification of medical documents and Opinions using machine learning",
            "author": "Fatiha Youbi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Algeria",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "machine-learning; deep-learning; nlp; medical-ai; data-mining; sentiment-analysis",
            "keywords": "",
            "abstract": "In recent years, there has been increasing interest in the use of text mining (TM) and machine learning in healthcare. Document classi cation has been a common application, with many studies focusing on classifying medical reports from unstructured text data. However, there is also a need to utilize TM and machine learning for sentiment analysis of medical textual data in social networks and medical forums. In this thesis, the focus was on two TM applications in the medical domain: classifying autopsy reports to detect the manner of death in Wilaya of Tlemcen and analyzing patient and public opinions on healthcare and the COVID-19 pandemic using machine learning techniques. The experiments conducted in both studies showed that automated models for opinion analysis are task-speci c and that feature extraction and deep learning classi er architecture play important roles in the success of these models. The ndings could be useful for improving strategies related to drugs monitoring and COVID-19 surveillance. Future directions include exploring other types of deep learning techniques, using clinical documents for sentiment analysis, and analyzing Algerian health status based on machine learning and deep learning classi ers.",
            "source_url": "https:\/\/openalex.org\/W7135334549",
            "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/c81c9cdb-90b6-4887-a5e6-fb6fcf10f7ad\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen",
            "source_record_id": "W7135334549",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:47",
            "updated_at": "2026-11-15 16:52:47"
        },
        {
            "id": 134,
            "title": "A Support System for Diacritic-aware Classical Arabic Language Processing: Integration of Speech, Text, and Vision Modalities",
            "title_ar": "",
            "title_en": "A Support System for Diacritic-aware Classical Arabic Language Processing: Integration of Speech, Text, and Vision Modalities",
            "author": "Adel AbdelSabour Ahmad Aly",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Washington",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; multimodal; image-segmentation; llm; machine-translation; speech-recognition; arabic-nlp; ai-education",
            "keywords": "",
            "abstract": "Artificial intelligence (AI) impresses us daily, outperforming humans in complex games and tasks. Yet, AI and Large Language Models (LLMs) stumble to grasp a language that is thousands of years old. It's Arabic, where subtle diacritical marks can completely alter a word's meaning. Top language models like ChatGPT and Google's Gemini face challenges with Arabic's unique features, potentially leading to critical misunderstandings. The main obstacle is adapting successful NLP systems from other languages to Arabic without understanding its distinct nuances. This dissertation presents a support system—a Multimodal Integration System—for diacritic-aware Classical Arabic language processing. The system integrates speech, text, and vision modalities to address the unique challenges of Arabic's rich linguistic features, such as diacritics and linguistic styles. Arabic has multiple correct linguistic styles, each preserving the same text but with different diacritics. These variations reflect regional dialects, adding meaning, and altering grammar and rhetoric. The Holy Quran, with its 20 linguistic styles based on a single core text, serves as our ideal dataset. We developed innovative databases and models that push the boundaries of Arabic language processing. Our scalable databases store texts in 7 different Arabic linguistic styles. QR-Vision excels at recognizing precise diacritics in images. QRDiaRec adds diacritics to un-diacritical text in various styles. QRSR and DASAM specialize in speech processing and alignment for Arabic diacritical text and voice. SemSim stands out as a dual-space similarity explorer, analyzing numeric and semantic data. Our methodology involves advanced techniques in data modeling, data quality validation, deep learning, computer vision, signal processing, and interactive data visualization.Our findings demonstrate improvements in addressing key challenges in Arabic Natural Language Processing (NLP). For text processing, we created an Automated Diacritization Deep Learning model. The model supports multiple Arabic diacritical styles, a unique feature in the field. Our best-performing model achieved a 94.2% accuracy rate in adding correct diacritics to Arabic text. In image processing, we built a specialized Optical Character Recognition (OCR) model for diacritic-aware Arabic text. Our OCR model reached an accuracy rate of 91.67% showing an improvement over the existing models. For speech processing, we developed two key systems. The first system, operating at the sentence level, combines our novel FuzTPI algorithm with machine learning models. This hybrid approach achieved up to 96% accuracy in audio segmentation and text-audio classification. Our second system focuses on word-level segmentation and alignment for Arabic diacritic-based speech. It achieved R² values of 0.959 for word start times and 0.957 for end times. These results show an improvement over existing Arabic speech recognition technologies. The dissertation is structured around these three modalities, with each section detailing the challenges, methodologies, and results achieved in processing Arabic with diacritics. They have far-reaching implications for applications such as machine translation, information retrieval, speech recognition, natural language understanding, educational technology, and the preservation of linguistic heritage. By addressing the unique challenges of Arabic diacritics across multiple modalities, this research paves the way for more nuanced and culturally sensitive AI applications in Arabic-speaking contexts.",
            "source_url": "https:\/\/openalex.org\/W7130276073",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ResearchWorks at the University of Washington (University of Washington)",
            "source_record_id": "W7130276073",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:47",
            "updated_at": "2026-11-15 16:52:47"
        },
        {
            "id": 188,
            "title": "Causal Inference Explanations for Graph Neural Networks",
            "title_ar": "",
            "title_en": "Causal Inference Explanations for Graph Neural Networks",
            "author": "Sahil Satish Kumar",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "gnn",
            "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; predictive-analytics; gnn; xai; fairness",
            "keywords": "",
            "abstract": "The increasing popularity of Artificial Intelligence, notably Deep Learning, has prompted its application across diverse real-world scenarios. Graph Neural Networks, a type of Deep Learning model developed for graph-structured data, have gained significant popu- larity owing to their versatility and efficacy. Despite their high accuracy and performance, contemporary Deep Learning models often function as black box systems, lacking trans- parency and trustworthiness crucial for deployment in critical decision-making domains. In response, Explainable Artificial Intelligence has emerged, aiming to enhance the trustworthiness of black box models by devising explanation methods that clarify their inner workings. However, prevalent explanation techniques predominantly leverage correlation and association rather than employing causality, a significant aspect of human comprehension. In this thesis, we propose a novel explanation method using causal inference developed specifically for Graph Neural Networks. Our approach seeks to illuminate the decision- making process of Graph Neural Networks, thereby augmenting their transparency and trustworthiness. To show the generality of our approach we do a benchmark evaluation of our method against state-of-the-art explanation methods on a variety of datasets, tasks and Graph Neural Networks architectures. Furthermore, we apply and evaluate our explanation method in two real-world use cases: medical referral recommendation and space object conjunction prediction. In both cases, we demonstrate the applicability of our method and discuss its limitations.",
            "source_url": "https:\/\/openalex.org\/W7197425670",
            "pdf_url": "https:\/\/run.unl.pt\/bitstreams\/811956c8-4867-4ac0-a47e-557471695537\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W7197425670",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:47",
            "updated_at": "2026-11-15 16:52:47"
        },
        {
            "id": 213,
            "title": "Trustworthy AI Under Distribution Shifts: Enhancing Generalization and Fairness in Deep Learning",
            "title_ar": "",
            "title_en": "Trustworthy AI Under Distribution Shifts: Enhancing Generalization and Fairness in Deep Learning",
            "author": "Gezheng Xu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Western University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "fairness",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; responsible-ai; transfer-learning; predictive-analytics; optimization; fairness",
            "keywords": "",
            "abstract": "Recent advances in machine learning and deep learning have achieved remarkable success across a wide range of applications, yet they largely rely on data-driven paradigms that assume the training and test data share similar distributions. In real-world scenarios, however, this assumption often fails. Distribution shifts between training and deployment environments or among population subgroups can significantly compromise the performance and reliability of AI systems. Moreover, privacy constraints frequently prevent direct access to source data, and ethical concerns surrounding fairness raise additional challenges for the trustworthy use of AI in sensitive domains such as healthcare and finance. These factors together highlight the urgent need for a deeper and more principled understanding of trustworthy AI under distribution shifts. This thesis examines the influence of distribution shifts along two complementary dimensions of trustworthy AI: generalizability and fairness. On one hand, we focus on the noisy and unreliable signals caused by distribution shift, which place learning in a weak-supervision regime and thereby limit AI systems’ generalizability. On the other hand, we focus on the distribution shifts that arise from structural data imbalance or scarcity rooted in historical and societal factors, which give rise to socially unfavorable model behaviors and, in turn, exacerbate fairness issues in the decision-making process. From the generalizability perspective, the thesis studies the domain adaptation problem under data privacy constraints, formulated as the Source-Free Domain Adaptation (SFDA) problem. SFDA is examined in both classification and regression tasks, with a focus on pseudo-label noise mitigation and uncertainty modeling, respectively. For the classification problem, a noise-robust loss–based noise-and-variance control method (NVC-LLN) is proposed to mitigate the unbounded label noise inherent in SFDA. For the regression task, a histogram-based uncertainty reconstruction framework, MERCI, is proposed to refine continuous supervision signals and align feature presentations under distribution shift. For the fairness problem, this thesis focuses on the multiple-subgroup situation, ranging from a single sensitive attribute with multiple subgroups to multiple sensitive attributes with intersectional bias, covering both unfairness discovery and mitigation. For subgroup discovery, a Bias-Guided Generative Network (BGGN) is proposed to identify underrepresented subgroups instead of relying on traditional search methods. For mitigation, a generalization error bound is derived under the PAC-Bayes framework, motivating a probabilistic-predictor-based bilevel optimization algorithm, FAMS, which achieves both fairness and accuracy across multiple subgroups under limited-data conditions. Collectively, these studies advance the development of responsible, adaptive, and human-centric AI systems. These systems are capable of performing reliably across diverse and dynamic environments.",
            "source_url": "https:\/\/openalex.org\/W7205536362",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Scholarship@Western (Western University)",
            "source_record_id": "W7205536362",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:47",
            "updated_at": "2026-11-15 16:52:47"
        },
        {
            "id": 223,
            "title": "Enhancing Fairness in Supervised Machine Learning",
            "title_ar": "",
            "title_en": "Enhancing Fairness in Supervised Machine Learning",
            "author": "Bita Omidi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Ottawa",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "fairness",
            "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; predictive-analytics; fairness",
            "keywords": "",
            "abstract": "The increasing influence of machine learning algorithms and artificial intelligence on the high-impact domains of decision-making has led to an increasing concern for the ethical and legal challenges posed by sensitive data-driven systems. Machine learning can identify the statistical patterns in the historically collected big data generated by a huge number of instances that might be affected by human and structural biases. ML algorithms have the potential to amplify these inequities. Lately, there have been several attempts to reduce bias in artificial intelligence in order to maintain fairness in machine learning projects. These methods fall under three categories of pre-processing, in-processing, and post-processing techniques. There are at least 21 notations of fairness in the recent literature, which not only provide different measurement methods of fairness but also lead to completely different concepts. It is worth mentioning that, it is impossible to satisfy all of the definitions of fairness at the same time and some of them are incompatible with each other. As a result, it is important to choose a fairness definition that need to be satisfied according to the context that we are working on. The current study investigates some of the most common definitions and metrics for fairness introduced by researchers to compare three of the proposed de-biasing techniques regarding their effects on the performance and fairness measures through empirical experiments on four different datasets. The de-biasing methods include the “Reweighing Algorithm”, “Adversarial De-biasing Method”, the “Reject Option Classification Method” performed on the classification tasks of “Survival of patients with heart failure”(Heart Failure Dataset), “Prediction of hospital readmission among diabetes patients” (Diabetes Dataset), “Credit classification of bank account holders” (German Credit Dataset), and “The COVID19 related anxiety level classification of Canadians” (CAMH Dataset). Findings show that the adversarial de-biasing in-processing method can be the best technique for mitigating bias working with the deep learning classifiers when we are capable of changing the classification process. This method has not led to a considerable reduction of accuracy except for the CAMH dataset. The “Reject Option Classification” which is a post-processing method, causes the most deterioration of prediction accuracy in all datasets. On the other hand, this method has the best performance in alleviating the bias generated through the classification process. The “Reweighing Algorithm” as a pre-processing technique does not cause a considerable reduction in the accuracy and is capable of reducing bias in classification tasks, although its performance is not as strong as the Reject Option classifier.",
            "source_url": "https:\/\/openalex.org\/W3196886932",
            "pdf_url": "https:\/\/ruor.uottawa.ca\/bitstreams\/a217da9e-f2fa-421a-b062-ba76c25339ab\/download",
            "doi": "10.20381\/ruor-26258",
            "handle": "",
            "repository_name": "uO Research (University of Ottawa)",
            "source_record_id": "W3196886932",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:47",
            "updated_at": "2026-11-15 16:52:47"
        },
        {
            "id": 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": 244,
            "title": "Steganographic Capacity of Selected Machine Learning and Deep Learning Models",
            "title_ar": "",
            "title_en": "Steganographic Capacity of Selected Machine Learning and Deep Learning Models",
            "author": "Lei Zhang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "San Jose State University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "ai-cybersecurity",
            "categories": "machine-learning; deep-learning; ai-applications; ai-cybersecurity",
            "keywords": "",
            "abstract": "As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic based attack, information would be hidden in a learning model, which might then be used to gain unauthorized access to a computer, or for other malicious purposes. In this research, we determine the steganographic capacity of various classic machine learning and deep learning models. Specifically, we determine the number of low-order bits of the trained parameters of a given model that can be altered without significantly affecting the performance of the model. We find that the steganographic capacity of learning models is surprisingly high, and that there tends to be a clear threshold after which model performance rapidly degrades.",
            "source_url": "https:\/\/openalex.org\/W4380786307",
            "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=2271&context=etd_projects",
            "doi": "10.31979\/etd.q6u5-n9x8",
            "handle": "",
            "repository_name": "San José State University ScholarWorks (San Jose State University)",
            "source_record_id": "W4380786307",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 255,
            "title": "Ensemble Deep Learning Architectures for Brain Tumor Classification and Segmentation in MRI Images",
            "title_ar": "",
            "title_en": "Ensemble Deep Learning Architectures for Brain Tumor Classification and Segmentation in MRI Images",
            "author": "Mallie, Dagmawi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Turku",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Brain tumors (BTs) are the abnormal growth of brain cells. Depending on aggressiveness, location, and impact on brain function, they are classified as either benign or malignant, originating within the brain or metastasizing from other areas of the body. Traditionally, BTs are detected through medical imaging, combined with clinical assessment and expert interpretation. The emergence of machine learning (ML) and advanced artificial intelligence (AI) techniques, particularly deep learning (DL), has changed the reliance that traditional procedures have on radiologists in early tumor detection. Diagnosing BTs involves various imaging techniques and tests to confirm the presence, type, and characteristics of the tumor. Magnetic Resonance Imaging (MRI) is a non-invasive technique that uses magnetic fields and radio waves to paint detailed images of the brain’s soft tissues, which is essential for identifying the tumor’s nature. MRI is more sensitive than computed tomography (CT); thus, it is more efficient in detecting smaller tumors. This thesis investigates the use of ensemble techniques based on deep convolutional neural network (DCNN) architectures for accurate BT classification and segmen- tation. For classification, the ensemble combines ResNet-50, DenseNet-169, and EfficientNet-vS, resulting in a performance improvement of over 0.2% by achieving an accuracy of 99.437% . For tumor segmentation, a similar ensemble strategy is employed using U-Net, U-Net++, and Attention U-Net. This approach also yields consistent, strong results across all evaluation metrics, with improvements of around 1%. All data used in this thesis were sourced from Kaggle, a widely used public platform for researchers. For the classification task, approximately 20,000 MRI scans were collected from three Kaggle datasets. For the segmentation task, a total of 4,293 MRI scans were obtained from the same platform. The results demonstrate that even general-purpose, publicly available DCNN models can detect, classify, and segment BTs with high accuracy, underscoring their potential for real-world clinical applications.",
            "source_url": "https:\/\/openalex.org\/W7112128316",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UTUPub (University of Turku)",
            "source_record_id": "W7112128316",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 256,
            "title": "Comparative Performance Analysis of Deep Learning Models for Brain Tumor Classification from MRI Images",
            "title_ar": "",
            "title_en": "Comparative Performance Analysis of Deep Learning Models for Brain Tumor Classification from MRI Images",
            "author": "Hassan Khan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; transformers; medical-imaging",
            "keywords": "",
            "abstract": "Brain tumors are among the most serious categories of neurological disease, with accurate classification from magnetic resonance imaging (MRI) playing a critical role in treatment planning and patient outcomes.Manual interpretation of MRI scans by radiologists is timeconsuming, subject to inter-observer variability, and constrained by the availability of specialist expertise, particularly in resource-limited settings.These factors have driven growing interest in automated deep learning solutions for brain tumor MRI classification.While convolutional neural networks (CNNs) have demonstrated strong performance on this task through transfer learning, the emergence of Vision Transformers and hybrid CNN-Transformer architectures has created a diverse model landscape whose relative merits remain poorly understood.Existing comparative studies benchmark either multiple CNN variants in isolation or transformer-based models separately, without unified head-tohead evaluation across all three architectural families under identical experimental conditions.Furthermore, deployment-relevant efficiency metrics -inference latency, model size, and GPU memory -are rarely reported alongside accuracy, limiting practical model selection guidance for clinical settings with varying hardware constraints.This thesis addresses these gaps by conducting a systematic benchmark of nine deep learning architectures -four CNN-based (VGG16, ResNet50, DenseNet121, EfficientNet-B0), three Transformer-based (ViT-B\/16, Swin Transformer, DeiT-B), and two Hybrid\/Lightweight (MobileNetV2, MaxViT-Tiny) -evaluated on the Brain Tumor MRI Dataset comprising 7,200 T1-weighted MRI images across four classes (glioma, meningioma, pituitary tumor, no tumor).All models were trained under identical conditions using ImageNet transfer learning and assessed on both classification performance (accuracy, precision, recall, F1-score, AUC-ROC) and computational efficiency (parameter count, model size, inference latency, peak GPU memory).The Swin Transformer achieves the highest test accuracy (98.52%,F1 = 0.9852), demonstrating that hierarchical transformer architectures can outperform CNNs when appropriately designed for the task.MobileNetV2 leads all models on efficiency metrics with 2.2M parameters and 7.55 ms inference latency.Pareto analysis identifies three deployment-optimal models: Swin-T for high-resource GPU environments, DenseNet121 (98.15%, 7.0M parameters) for standard workstations, and MobileNetV2 (97.04%) for edge devices.Efficiency varies by a factor of 61× in parameter count and 25× in GPU memory across the nine models, while accuracy spans only ~2 percentage points, underscoring that architectural design matters far more than raw model size.This study provides the first unified benchmark comparing CNN, Transformer, and Hybrid architectures for brain tumor MRI classification under identical conditions with both accuracy and efficiency metrics reported, yielding evidence-based model selection guidelines for clinical deployment across diverse computational environments.",
            "source_url": "https:\/\/openalex.org\/W7215551487",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/57ebe372-c179-491f-bae8-4ad920908485\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215551487",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 257,
            "title": "Tumor Classification İn Brain Mri İmages Using An Optimized Hybrid Deep Learning Model",
            "title_ar": "",
            "title_en": "",
            "author": "Veysel Tileği ; Danışman: Doç. Dr. Ali Yıldız",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2025,
            "language": "tr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; medical-imaging",
            "keywords": "",
            "abstract": "Son yıllarda derin öğrenme tabanlı yöntemler, tıbbi görüntü analizinde manyetik rezonans (MR) görüntülerinden beyin tümörlerinin otomatik tespiti ve sınıflandırılmasında dikkat çekici başarılar göstermiştir. Ancak MR görüntülerinin manuel olarak yorumlanması, zaman alıcı, uzmanlara bağımlı ve öznel bir süreç olduğundan, otomatik karar destek sistemlerine duyulan gereksinim giderek artmaktadır. Bu tez çalışmasında, literatürdeki önde gelen Konvolüsyonel sinir ağı (Convolutional Neural Networks – CNN) mimarilerinin EfficientNet, ResNet ve DenseNet yapısal üstünlükleri birleştirilerek NeuroTriNet adı verilen yeni bir hibrit CNN modeli önerilmiştir. Önerilen mimari, EfficientNet'in parametre verimliliğini, ResNet'in gradyan kararlılığını ve DenseNet'in yoğun öznitelik paylaşımını tek bir modelde bütünleştirmektedir. Modelin hiperparametreleri, Optuna tabanlı Hiperparametre Optimizasyonu (HPO) yöntemi ile Tree-Structured Parzen Estimator (TPE) örnekleme stratejisi ve MedianPruner erken durdurma algoritması kullanılarak optimize edilmiştir. Eğitim sürecinde, sınıf dengesizliklerini azaltmak amacıyla Focal Loss fonksiyonu tercih edilmiştir. Deneysel tez çalışmaları, birleştirilmiş beyin MR veri kümesi üzerinde yürütülmüştür. Veri kümesi, açık kaynak olarak sunulan IEEE ve Kaggle kaynaklarından elde edilen glioma, meningioma ve sağlıklı beyin sınıflarından oluşmaktadır. Eğitim verileri için çeşitli veri artırma teknikleri uygulanmış, görüntüler 256×256x3 boyutunda normalize edilmiştir. Performans değerlendirmesi doğruluk, makro F1-skoru ve ROC-AUC metrikleri üzerinden gerçekleştirilmiştir. Önerilen NeuroTriNet modeli, doğrulama kümesi üzerinde ,95 doğruluk ve ,95 makro F1-skoru elde ederek yüksek ve dengeli bir sınıflandırma başarımı sergilemiştir. Bağımsız test kümesi sonuçları incelendiğinde ise modelin ,81 doğruluk ve ,71 makro F1-skoru ile güçlü bir genelleme yeteneğine sahip olduğu görülmüştür. Bu tez çalışmasında, beyin tümörü sınıflandırması alanında optimize edilmiş, açıklanabilir ve hesaplama açısından verimli bir derin öğrenme modeli sunmaktadır. Önerilen NeuroTriNet, medikal görüntü analizinde hibrit CNN mimarilerinin etkinliğini ortaya koyarak, klinik karar destek sistemlerinin geliştirilmesine katkı sağlamayı hedeflemektedir.",
            "source_url": "https:\/\/openalex.org\/W7165072612",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Mersin Üniversitesi Açık Erişim Sistemi\/Mersin University Open Access System",
            "source_record_id": "W7165072612",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 258,
            "title": "Brain Tumor Detection and Classification from MRI Images",
            "title_ar": "",
            "title_en": "Brain Tumor Detection and Classification from MRI Images",
            "author": "Anjaneya Teja Sarma Kalvakolanu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "California State Polytechnic University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "A brain tumor is detected and classified by biopsy that is conducted after the brain surgery. Advancement in technology and machine learning techniques could help radiologists in the diagnosis of tumors without any invasive measures. We utilized a deep learning-based approach to detect and classify the tumor into Meningioma, Glioma, Pituitary tumors. We used registration and segmentation-based skull stripping mechanism to remove the skull from the MRI images and the grab cut method to verify whether the skull stripped MRI masks retained the features of the tumor for accurate classification. In this research, we proposed a transfer learning based approach in conjunction with discriminative learning rates to perform the classification of brain tumors. The data set used is a 3064 T MRI images dataset that contains T1 flair MRI images. We achieved a classification accuracy of 98.83%, 96.26%, and 95.18% for training, validation, and test sets and an F1 score of 0.96 on the T1 Flair MRI dataset.",
            "source_url": "https:\/\/openalex.org\/W3158119991",
            "pdf_url": "https:\/\/digitalcommons.calpoly.edu\/cgi\/viewcontent.cgi?article=3799&context=theses",
            "doi": "10.15368\/theses.2021.16",
            "handle": "",
            "repository_name": "DigitalCommons - CalPoly (California State Polytechnic University)",
            "source_record_id": "W3158119991",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 259,
            "title": "Advancements in Automated Brain Tumor Detection Using Deep Learning on MRI Imagery",
            "title_ar": "",
            "title_en": "Advancements in Automated Brain Tumor Detection Using Deep Learning on MRI Imagery",
            "author": "Md. Sohan Biswas",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging",
            "keywords": "Brain tumor, Convolutional Neural Networks, Deep Learning, Transfer Learning",
            "abstract": "The goal of this thesis is to solve the significant problem of inter-observer variability-induced diagnostic delays by presenting a sophisticated deep learning system for automated brain tumor diagnosis utilizing MRI imagery. Pituitary adenoma, extra-axial meningioma, infiltrative glioma, and no-tumor controls are the four classes of brain tumors that are represented in the 7,143 T1 weighted MRI images taken from Kaggle Brain Tumor Dataset and various hospitals in Bangladesh, including Ibn Sina Medical College, Dhaka Medical College, and Comilla Medical College. Using three pretrained convolutional neural network (CNN) architectures; MobileNetV2, EfficientNetB0, and VGG16 optimized through selective layer unfreezing and on-the-fly data augmentation, including random brightness and contrast adjustments to mitigate scanner artifacts, the study applies transfer learning to improve diagnostic accuracy. With a test accuracy of 98.5%, a macro F1-score of 0.98, and an area under the curve (AUC) ≥0.99, MobileNetV2 outperformed the other models after being trained on 5,832 photos. Particularly noteworthy was the MobileNetV2 model's real-time inference ability (0.97) and quick processing times, especially in low-resource settings where prompt diagnosis can have a major impact on survival outcomes. The study highlights the clinical viability of applying AI-assisted diagnostics in various contexts, enhancing access to superior radiological evaluations and possibly cutting down on diagnostic delays in practical applications. Keywords: Brain tumor, Convolutional Neural Networks, Deep Learning, Transfer Learning, MRI Classification Source Code:GitHub: Advancements in Automated Brain Tumor Detection Using Deep Learning on MRI Imagery Author: Md. Sohan BiswasORCID: 0009-0000-5888-373X",
            "source_url": "https:\/\/openalex.org\/W7201959393",
            "pdf_url": "",
            "doi": "10.5281\/zenodo.21845324",
            "handle": "",
            "repository_name": "Zenodo (CERN European Organization for Nuclear Research)",
            "source_record_id": "W7201959393",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 260,
            "title": "Artificial Intelligence for Medical Diagnosis Using Machine Learning for Brain Tumor MRI Classification : магистерская диссертация",
            "title_ar": "",
            "title_en": "Artificial Intelligence for Medical Diagnosis Using Machine Learning for Brain Tumor MRI Classification : магистерская диссертация",
            "author": "М. Ш. С. Мако ; M. S. S. Mako",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Ural Federal University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Russia",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; cnn; medical-imaging; xai",
            "keywords": "",
            "abstract": "The integration of Artificial Intelligence (AI) into the domain of medical diagnostics has ushered in a new era of precision, speed, and reliability, particularly in the analysis of medical imaging. Among the most promising applications is the use of Machine Learning (ML) techniques for the classification and diagnosis of brain tumors from Magnetic Resonance Imaging (MRI) scans. Brain tumors, whether malignant or benign, pose significant health threats and require early, accurate detection to improve prognosis and treatment outcomes. Traditional methods for diagnosing brain tumors are often time-consuming, subject to inter-observer variability, and reliant on the expertise of radiologists. This thesis explores the use of AI-driven machine learning models to automate and enhance the diagnostic process by classifying brain tumor types from MRI images. This research presents a comprehensive study on the implementation of various machine learning algorithms—including Convolutional Neural Networks (CNNs), Support Vector Machines (SVM), and ensemble models—for the classification of different types of brain tumors, such as gliomas, meningiomas, and pituitary tumors. The dataset employed comprises a substantial number of pre-processed and labeled MRI images sourced from publicly available medical imaging repositories. The study begins with a detailed examination of MRI imaging modalities, the clinical characteristics of common brain tumors, and the importance of accurate classification. A robust preprocessing pipeline was designed to handle data augmentation, normalization, and artifact removal, which are critical for enhancing image quality and improving model generalization. Feature extraction techniques, both handcrafted and automated via deep learning, were analyzed to assess their effectiveness in capturing the unique spatial and textural patterns of different tumor types. Subsequently, multiple supervised learning algorithms were trained and tested using cross-validation to compare their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve. CNN-based architectures, particularly those leveraging transfer learning (e.g., ResNet, VGGNet, and EfficientNet), demonstrated superior performance compared to traditional classifiers, achieving classification accuracies exceeding 90% in test scenarios. In addition, model interpretability techniques such as Grad-CAM and SHAP were applied to visualize and explain the decision-making process of the neural networks, addressing one of the key challenges in deploying AI systems in clinical environments: trust and transparency.",
            "source_url": "https:\/\/openalex.org\/W7117637577",
            "pdf_url": "https:\/\/elar.urfu.ru\/bitstream\/10995\/147597\/1\/m_th_m.s.s.mako_2025.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Electronic scientific archive of UrFU (Ural Federal University)",
            "source_record_id": "W7117637577",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 261,
            "title": "Brain Tumor Classification using Deep Learning- Poster",
            "title_ar": "",
            "title_en": "Brain Tumor Classification using Deep Learning- Poster",
            "author": "Rayen Bentemessek",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; cnn; medical-imaging; xai",
            "keywords": "",
            "abstract": "The project presents deep learning solutions to classify brain tumors through MRI images. Two Convolutional Neural Network (CNN) models were developed, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were implemented following CRISP-DM methodology from data understanding to deployment, and they were evaluated using different metrics such as accuracy, precision, recall and F1-score. Key Highlights: •The custom CNN model achieved higher accuracy but failed to locate tumors. •ResNet50 provided a good performance while balancing explainability through Grad-CAM. •Model was deployed through Gradio to demonstrate a real-world use of the solution.",
            "source_url": "https:\/\/openalex.org\/W4417120498",
            "pdf_url": "",
            "doi": "10.63227\/652.199.43",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4417120498",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 262,
            "title": "Image analysis and machine learning in patients with brain tumors",
            "title_ar": "",
            "title_en": "Image analysis and machine learning in patients with brain tumors",
            "author": "Alice Neimantaite",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Gothenburg University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; multimodal; medical-imaging",
            "keywords": "",
            "abstract": "Diffuse lower-grade gliomas (dLGG) are slowly growing primary brain tumors. Patients with dLGG represent a heterogeneous group with various symptoms, diverse progression of the disease as well as survival length. Collecting multidimensional patient data gives the opportunity for application of advanced analysis methods. These methods have shown a great promise with the ability to recognize complex patterns in the data and the potential to improve patient treatment and knowledge on the disease. However, the methods should be applied with consideration to the clinical context. This thesis aims to apply advanced analysis methods for the patients with dLGG in a clinically meaningful manner. Depending on the research question, patients with suspected or histomolecularly confirmed dLGG diagnosis from Sahlgrenska University hospital and external sites were included in the studies of the thesis. The analysed data included multi-modal MRI, clinical data, neuropsychological data and health-related quality of life (HRQoL) assessments. Radiomics was applied for survival prediction (Study I), deep learning for tumor classification (Study II), correlation analysis for investigation of symptom interconnection (Study III) and lesion mapping to find brain structures at risk in isocitrate dehydrogenase (IDH)- mutated gliomas (Study IV). The results showed that radiomics can identify clinically relevant information and a model combining radiomics features with clinical variables improved survival prediction (Study I). Deep learning showed similar performance as the clinical model in classification of IDH-mutation status in non-enhancing gliomas (Study II). Correlation networks revealed that self-reported and observer-assessed variables on mental fatigue and cognitive functioning were not connected (Study III). Lesion mapping quantified brain structures at risk in IDH-mutated gliomas, which were often related to advanced brain functions (Study IV). Advanced analysis methods have aided in the understanding of dLGG in several perspectives from symptoms to prognostic factors for patient survival. The machine learning model showed better performance than clinical models in survival prediction, but not in clinically relevant tumor classification. Our findings strengthen the notion of the importance to apply these methods in a relevant context to gain insight where the methods are clinically useful.",
            "source_url": "https:\/\/openalex.org\/W7217647342",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Gothenburg University Publications Electronic Archive (Gothenburg University)",
            "source_record_id": "W7217647342",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 263,
            "title": "Hybrid Approach for the Classification and Detection of Brain Tumor",
            "title_ar": "",
            "title_en": "Hybrid Approach for the Classification and Detection of Brain Tumor",
            "author": "Venkateshwar Rao K ; Sunny Sowmith Uppala ; Srikar Jakkena",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; predictive-analytics; cnn; object-detection; image-segmentation; medical-imaging; xai",
            "keywords": "",
            "abstract": "Brain tumors represent one of the most severeneurological disorders and require early detection for effectivetreatment. MRI imaging is widely used because it providesdetailed visualization of soft tissues and tumor structures withoutradiation exposure. Despite its advantages, interpreting MRIscans manually is complex and prone to inter-observer variability.Recent advancements in Artificial Intelligence (AI) and DeepLearning have enabled automated medical image analysis withhigh accuracy. Convolutional Neural Networks (CNNs) havedemonstrated strong performance in feature extraction and classification tasks. However, standalone CNN models often requirelarge datasets and may suffer from overfitting when applied tolimited medical datasets. In recent years, Artificial Intelligence(AI) and Deep Learning have significantly transformed medicalimage analysis. Convolutional Neural Networks (CNNs) havedemonstrated remarkable success in various computer visiontasks, including object detection, segmentation, and classification.In medical imaging, CNN-based architectures have been appliedto detect tumors, segment lesions, and classify disease typeswith high accuracy. Despite these advancements, standalone deeplearning models often face challenges such as overfitting, highcomputational complexity, and limited interpretability. Hybridmodels that combine deep learning feature extraction with classical machine learning classifiers have shown promising resultsin improving generalization performance. Additionally, segmentation of tumor regions before classification can significantlyimprove prediction accuracy by eliminating irrelevant background information. This work proposes a comprehensive hybridarchitecture that integrates: • U-Net for tumor segmentation• MobileNetV2 for transfer learning-based feature extraction• SVM and Random Forest for classification • Grad-CAM forexplainability The proposed approach aims to balance accuracy,computational efficiency, and interpretability in a unified framework.",
            "source_url": "https:\/\/openalex.org\/W7154615453",
            "pdf_url": "",
            "doi": "10.5281\/zenodo.19601617",
            "handle": "",
            "repository_name": "Zenodo (CERN European Organization for Nuclear Research)",
            "source_record_id": "W7154615453",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 264,
            "title": "Brain Tumor Classification using Deep Learning Custom CNN vs. ResNet50.",
            "title_ar": "",
            "title_en": "Brain Tumor Classification using Deep Learning Custom CNN vs. ResNet50.",
            "author": "Rayen Bentemessek ; Muhammad Iqbal",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; predictive-analytics; cnn; medical-imaging; xai",
            "keywords": "",
            "abstract": "The project presents a deep learning solution to classify brain tumors through MRI images. Following the CRISP-DM framework, two Convolutional Neural Network (CNN) models were developed and evaluated, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were assessed using standard performance metrics such as accuracy, precision, recall and F1-score. Despite the higher test accuracy achieved by the custom CNN, further interpretability indicated inconsistent attention to the actual tumor regions also known as shortcut learning. On the other hand, ResNet50 showed more reliable and clinically relevant focus which supported its selection as the final model. The selected model was deployed using Gradio, to demonstrate a real-world application with real-time predictions and visual explanation to reduce the black box nature of AI. The results showcase that despite the good performance of custom and tailored CNN models on specific datasets, generalization and real-world relevance are equally important for reliable deployment.",
            "source_url": "https:\/\/openalex.org\/W4417156896",
            "pdf_url": "",
            "doi": "10.63227\/652.299.65",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4417156896",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 265,
            "title": "Human brain tumor classification using images of magnetic resonance and deep learning architectures",
            "title_ar": "",
            "title_en": "Human brain tumor classification using images of magnetic resonance and deep learning architectures",
            "author": "Pedro Lavarda Zorel",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Federal University of Technology – Paraná",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Brazil",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; medical-imaging",
            "keywords": "",
            "abstract": "As the population and life expectancy around the world increase, diseases like cancer are becoming increasingly common. Research conducted by the National Cancer Institute indicates that in 2022 alone, more than 625,000 cancer cases were registered in Brazil, 11,000 of which were Central Nervous System (CNS) cancer. Early detection plays a fundamental role in treating this disease, and the most common way to diagnose brain cancer is through magnetic resonance imaging (MRI); however, the analysis of these images requires trained professionals. As a way to contribute to diagnosis, recent studies explore the use of image processing and Machine Learning algorithms to assist in the detection of human brain tumors. Considering this context, this work presents the creation of models based on convolutional neural networks (CNNs) and pre-trained architectures, ResNet and EfficientNet, to perform the automatic classification of different types of human brain tumors. Different configurations and architectures were evaluated, and the model that showed the best performance in the early detection of brain cancer was EfficientNet-B3, with an accuracy of 98.56%. However, regarding the balance of performance and accuracy, ResNet-50V2 proved superior, using less computational power while achieving an accuracy of 97.88%. For training the models, image pre-processing techniques were used in order to improve the model’s results and contribute to its final accuracy.",
            "source_url": "https:\/\/openalex.org\/W7155671230",
            "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/40220\/1\/classificacaotumorescerebraisaruitetura.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)",
            "source_record_id": "W7155671230",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 266,
            "title": "Brain Tumor Segmentation using Deep Learning: A Hybrid Model.",
            "title_ar": "",
            "title_en": "Brain Tumor Segmentation using Deep Learning: A Hybrid Model.",
            "author": "Gishene, Tigabu Yaya",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Helsinki",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Accurate brain tumor segmentation from MRI scans is crucial for diagnosis, treatment planning, and prognosis. However, manual segmentation is labor-intensive, prone to variability, and susceptible to errors, emphasizing the need for automated solutions. This study proposes the development and evaluation of deep learning architectures for brain tumor segmentation using the BraTS 2020 dataset. We propose and compare two approaches: (1) a standalone 3D U-Net, which processes 3D MRI volumes, and (2) a hybrid 2D model that combines a pre-trained VGG19 encoder with a U-Net decoder for 2D slice-based segmentation. The hybrid model leverages VGG19’s powerful feature extraction capabilities while maintaining the structural advantages of U-Net. To optimize the hybrid model, we applied a data transformation strategy that transforms volumetric MRI data into 2D slices, aligning with the inherent architecture of VGG19. The segmentation models were trained and evaluated using three out of four MRI modalities from the BraTS 2020 dataset, T1Gd, T2, and FLAIR, which provide complementary information for accurate tumor structure identification. Performance evaluation showed that the 3D U-Net exhibits limitations in segmenting tumor subregions, achieving Dice Similarity Coefficients (DSC) of 0.7870 (Necrotic and Non-Enhancing Tumor Core, NCR\/NET), 0.8609 (Edema, ED), and 0.8058 (Enhancing Tumor, ET), with corresponding Intersection over Union (IoU) scores of 0.6489, 0.7558, and 0.6747, respectively. While the model demonstrates high specificity across all tumor classes, its lower sensitivity suggests the need for improvements to detect all instances of pathological tissue more effectively. On the other hand, the hybrid U-Net + VGG19 model demonstrates clearly superior segmentation results, outperforming many existing methods. It achieved DSC scores of 0.9078 (NCR\/NET), 0.9245 (ED), and 0.9175 (ET), consistently exceeding 0.91 across key tumor subregions. Additionally, the hybrid model achieves superior segmentation performance across the three tumor structures, whole tumor (WT), tumor core (TC), and enhancing tumor (ET), achieving DSC scores of 0.9620, 0.9550, and 0.9175, respectively. As observed from the performance evaluation results of the models, the hybrid U-Net with VGG19 provides a computationally efficient and highly accurate approach for brain tumor segmentation.",
            "source_url": "https:\/\/openalex.org\/W7115224021",
            "pdf_url": "https:\/\/www.doria.fi\/bitstream\/10024\/192488\/2\/gishene_tigabu_yaya.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Doria (University of Helsinki)",
            "source_record_id": "W7115224021",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 267,
            "title": "MRI Brain Tumor Segmentation and Classification with Deep Learning",
            "title_ar": "",
            "title_en": "MRI Brain Tumor Segmentation and Classification with Deep Learning",
            "author": "区文伟 ; Wen-Wei Chu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Taiwan",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "學位別：碩士 語文別：中文 指導教授：邱泓文 口試委員：陳俊璋 Chun-Chang Chen;徐建業 Chien-Yeh Hsu 電子論文連結：https:\/\/handle.ncl.edu.tw\/11296\/z99eks",
            "source_url": "https:\/\/openalex.org\/W7111894610",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Taipei Medical University Repository",
            "source_record_id": "W7111894610",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 268,
            "title": "Automatic segmentation and classification of brain tumors based on multisequence MRI images with deep learning methods",
            "title_ar": "",
            "title_en": "Automatic segmentation and classification of brain tumors based on multisequence MRI images with deep learning methods",
            "author": "Sérgio Rafael Mano Pereira",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Programa de Doutoramento em Informatica (MAP-i) das Universidades do Minho, de Aveiro e do Porto",
            "source_url": "https:\/\/openalex.org\/W3165025355",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W3165025355",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 269,
            "title": "Deep learning methods for MRI brain image analysis: 3D convolutional neural networks for Alzheimer's disease detection and brain tumor classification",
            "title_ar": "",
            "title_en": "Deep learning methods for MRI brain image analysis: 3D convolutional neural networks for Alzheimer's disease detection and brain tumor classification",
            "author": "Mahmood Nazari ; Karl Bäckström",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Chalmers University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; cnn; medical-imaging",
            "keywords": "",
            "abstract": "In this thesis, we investigate deep learning methods for medical image analysis, in particular brain disease detection and classification.We attempt to solve two problems, namely (1) Alzheimer's disease (AD) detection and (2) Brain tumor (BT) classification into high-grade and low-grade gliomas.Efficient and accurate diagnosis of AD is essential for initiating treatment at an early stage, increasing the chances of postponing the irreversible neurodegenerative effects of the disease.To this end, many studies have investigated image processing and machine learning methods for computer-aided-diagnosis (CAD) of AD.In the first part of our work, we continue along this line, and propose a simple 3D CNN architecture for feature extraction and classification of pre-processed T1-weighted MRI brain images for AD detection.The dataset used for training and evaluation consists of 340 subjects, and was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI).The resulting test performance on unseen data is a classification accuracy of 98.74%, with a sensitivity (AD detection rate) of 100% which is comparable with the current state of the art.In this thesis, we thoroughly investigate the effect of some parameters involved in the CNN training process, such as dropout probability and learning rate.We also show the impact of data pre-processing on the end performance, by comparing several different pre-processing pipelines.For the second part of our work, namely brain tumor classification, we employ a similar method using CNNs, but using a different architecture.The dataset was obtained from the MICCAI BRaTS competition 2017 and consists of 285 subjects.Our preliminary study on brain tumor classification has shown promising results with a classification accuracy of 85.96%, but further work is required for a more in-depth study of the topic.",
            "source_url": "https:\/\/openalex.org\/W2762624069",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Chalmers Publication Library (Chalmers University of Technology)",
            "source_record_id": "W2762624069",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 271,
            "title": "Brain MRI Image segmentation using deep learning techniques",
            "title_ar": "",
            "title_en": "Brain MRI Image segmentation using deep learning techniques",
            "author": "Niladri Sekhar Bandyopadhyay",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; rnn; image-classification; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "It is the aim of this paper to find out how brain MRI image classification accuracy can be improved through a new hybrid deep learning model that incorporates Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), U-Net, ResNet, and GoogleNet. Given the importance of early detection of brain tumors for effective treatment, the new model was trained on a large dataset of brain MRI images resulting in a high validation accuracy of 97 percent. Despite this high performance, there were 20 misclassified images out of 600 indicating areas that could be tweaked. The research shows that integrating several advanced deep learning techniques greatly enhances tumor detection precision; it also reveals hybrid models’ potential in medical image analysis.",
            "source_url": "https:\/\/openalex.org\/W7127238775",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "NORMA",
            "source_record_id": "W7127238775",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 272,
            "title": "A Deep Learning Web Application for MRI Brain Tumor Images Analysis",
            "title_ar": "",
            "title_en": "",
            "author": "Bouchra Goumidi ; Abir Fettah",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Algeria",
            "governorate": "",
            "publication_year": 2020,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "La possibilité d’améliorer la détection des maladies et la planification des traitements s’accompagne d’algorithmes précis et entièrement automatiques de classification et de segmentation des tumeurs cérébrales à partir de l’imagerie volumétrique 3D par résonance magnétique (IRM). Le gliome est l’un des types les plus courants des tumeurs cérébrales primaires et est divisé en deux grades selon l’Organisation mondiale de la santé (OMS). L’interprétation humaine de la grande quantité de données d’images IRM est non seulement non reproductible mais dépend également de l’expertise, du temps et des efforts. Dans ce travail, nous essayons de résoudre ce problème en développant une application web pour classer les tumeurs cérébrales en deux classes : Gliome de haut grade (GHG) et Gliome de bas grade (GBG). La solution proposée comprend deux parties. Tout d’abord, l’application d’un modèle pré-entraîné basé sur un réseau de neurones convolutif utilisant VGG-16, ResNet-50 et Inception V3 pour transférer les connaissances acquises à quatre ensembles de données, un pour chaque modalité d’IRM (T1, T1ce ,T2 et FLAIR), afin de sélectionner la modalité qui présente des caractéristiques et des informations plus spécifiques, en passant par le prétraitement des images ainsi l’augmentation des données.Pour améliorer encore la classification des tumeurs, les coupes non tumorales ont été supprimées de la classe HGG de l’ensemble de données sélectionné, puis ont été utilisées séparément pour entraîner les trois modèles.Deuxièmement, nous présentons notre application web Med3csEG qui est l’une des systèmes d’aide à la décision en mentionnant toutes les étapes de la modélisation et de la réalisation de chaque fonctionnalité qui sont introduites dans la page d’accueil, les pages d’authentification et d’enregistrement et la page principale pour compléter la tâche de classification des tumeurs cérébrales. Les évaluations sur BraTS 2019, attestent que la modalité T1 présente les caractéristiques les plus discriminantes pour distinguer les gliomes LG et HG avec 0,9513, 0,907 et 0,9487 pour la précision, la sensibilité et la spécificité respectivement. Le modèle Inception V3 entraîné sur l’ensemble de données T1 surpasse les autres modèles avec 0,9975, 0,9894 et 1 pour la précision, la sensibilité et la spécificité respectivement. Les résultats expérimentaux montrent que l’utilisation du modèle Inception V3 avec la modalité IRM T1 peut permettre d’obtenir de bons résultats dans la classification des tumeurs cérébrales.",
            "source_url": "https:\/\/openalex.org\/W7135251127",
            "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/0b6438f2-41df-4d08-a6b0-d459081a5f94\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen",
            "source_record_id": "W7135251127",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 273,
            "title": "Characterization of Pseudo CT From MRI With Deep Learning : Application to Brain Tumors Treated With Radiotherapy",
            "title_ar": "",
            "title_en": "",
            "author": "Émilie Alvarez Andres",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2021,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Caractérisation de pseudo CT générés à partir d'images IRM à l'aide de méthodes deep learning : application aux tumeurs cérébrales traitées par radiothérapie De nos jours, les traitements de tumeurs cérébrales par radiothérapie nécessitent l’acquisition d’un scanner utilisé pendant les étapes de segmentation et de dosimétrie, ainsi que d’une Imagerie par Résonance Magnétique (IRM) jouant un rôle important durant l’étape de segmentation des volumes cibles en particulier. Cependant, cette double modalité implique de recaler spatialement les images, processus qui induit des erreurs de 2mm, actuellement prises en compte par une augmentation de marges. Ainsi, générer des pseudo scanners (pCT) à partir d’images IRM apparaît comme étant une solution attractive pour diminuer les marges et réduire l’irradiation des tissus sains périphériques. La première étape de cette thèse avait pour but de caractériser les paramètres jouant un rôle clé dans la qualité de pCT générés par Deep Learning (DL), à savoir la taille de la cohorte d’entrainement, la séquence IRM utilisée en entrée du réseau, la technique de standardisation des images IRM, le filtre de correction d’inhomogénéités de champ et l’architecture du réseau. Pour ce faire, une large cohorte composée de plus de 400 patients a été constituée, rassemblant des images de multiples appareils d’IRM et localisations tumorales, afin d’assurer la robustesse du modèle. Les pCT obtenus ont tout d’abord été évalués à l’aide de l’erreur absolue moyenne, basée sur les intensités. Des analyses dosimétriques ont ensuite été menées. Toutes les approches étudiées ont atteint des performances dosimétriques équivalentes, excepté pour la taille du jeu d’entrainement. Pour introduire une dosimétrie basée sur les pCT en pratique clinique et déployer une méthodologie de validation de la non-infériorité de la planification de traitement dans le cas d’une radiothérapie basée seulement sur IRM comparée à celle conventionnellement basée sur scanner, une deuxième étude visant à définir les métriques dosimétriques les plus adaptées à une évaluation de pCT sans biais a été réalisée. Leurs corrélations avec des métriques basées sur les intensités ont été calculées. Enfin, l’impact de scenarios simulant des erreurs extrêmes de pCT a été quantifié, basé sur les métriques optimales préalablement définies. Un nouveau jeu de test de 71 patients a été constitué, reflétant les localisations tumorales rencontrées en clinique et les modalités de traitement propres à notre centre. Les gamma index globaux et locaux pour le critère 1%\/1mm associés à des seuils de dose non-extrêmes ont montré leur pertinence pour la tâche d’évaluation de pCT cérébraux. De plus, les différences de métriques issues des histogrammes dose\/volume des volumes cibles et organes à risque doivent aussi être calculées car elles reflètent la performance dosimétrique du pCT pour chaque structure segmentée. Enfin, la génération de pCT a été appliquée à la radiothérapie basée sur protons, grâce à une collaboration avec le Centre de Protonthérapie d’Orsay. Le modèle de DL préalablement développé sur des patients adultes a été testé sur des patients pédiatriques, afin d’évaluer sa généralisabilité. Des performances cliniques satisfaisantes ont été atteintes, excepté pour quelques patients, pouvant potentiellement prouver la transférabilité du modèle évalué. Les travaux futurs comprennent une évaluation dosimétrique à plus grande échelle, avec la composition d’une cohorte de 198 enfants représentant 4 histologies différentes. Le but est de déterminer l’approche d’entrainement et de validation du réseau la plus efficace à l’aide de cartes de pouvoirs d’arrêt pour s’affranchir de l’influence des paramètres des scanners sur les images. Ainsi, une solide compréhension des points clés de la génération de pCT ainsi qu’une méthodologie de leur caractérisation ont été réalisées. Les recommandations en découlant ont le rôle clé de faciliter la quantification et l’interprétation de critères d’évaluation de la qualité de pCT dans le contexte de mise en place d’essais cliniques, tel que l’essai observationnel en cours GliopCT.",
            "source_url": "https:\/\/openalex.org\/W4392422560",
            "pdf_url": "https:\/\/theses.hal.science\/tel-03816279v1\/document",
            "doi": "10.70675\/82d46582z5167z4aaeza3e3z4bce727d5a2f",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W4392422560",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 274,
            "title": "Skull stripping MRI images of the brain using deep learning",
            "title_ar": "",
            "title_en": "Skull stripping MRI images of the brain using deep learning",
            "author": "Øystein Aas Eide",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Skull stripping is the task of finding pixels or voxels that establishes where the brain is in amedical image. It is an important step for many medical applications that analysis the brain.Today manual skull stripping is still the best way to get good and accurate results. Manual skullstripping is a laborious process since it can take between 6 and 8 hours (Eskildsen et al., 2012).There exist many tools for performing skull stripping automatically. Some of these tools arevery good, but they can struggle on Magnetic Resonance Imaging (MRI) scans with differentmodalities (Kleesiek et al., 2016). In this decade deep learning has become very popular invarious fields including medical image analysis, which includes tasks such as medical imagesegmentation. According to Litjens et al. (2017) the amount of published papers on this subjecthave gone from close to 0 in 2012 to over 200 papers published in 2017. In this thesis threedifferent deep learning architectures will be used to do skull stripping. Also, an experiment onliver segmentation will be done to see if the methods will work on other medical imaging tasks.The deep learning architectures will be tested on three different data sets containing MRI scansof the brain. One of these data sets contains only patients with tumors. The architectures weretrained on different data sets to test how well they will perform when they are faced with MRIscans from different sources. Various experiments will be done to see how training and testingon different compositions of these data sets will make the architectures perform.Deep learning methods take a long time to train. In this thesis, the deep learning architectureswill be tested on different hardware configurations to see how long they take to train on thedifferent set ups. In total four different configurations will be tested with different CentralProcessing Units (CPUs) and different Graphics Processing Units (GPUs).",
            "source_url": "https:\/\/openalex.org\/W2901941464",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "NORA - Norwegian Open Research Archives",
            "source_record_id": "W2901941464",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 275,
            "title": "Deep learning for brain tumor segmentation and survival prediction.",
            "title_ar": "",
            "title_en": "Deep learning for brain tumor segmentation and survival prediction.",
            "author": "Tirivangani Magadza",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of KwaZulu-Natal",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "South Africa",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; multimodal; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "A brain tumor is an abnormal growth of cells in the brain that multiplies uncontrolled. The death of people due to brain tumors has increased over the past few decades. Early diagnosis of brain tumors is essential in improving treatment possibilities and increasing the survival rate of patients. The life expectancy of patients with glioblastoma multiforme (GBM), the most malignant glioma, using the current standard of care is, on average, 14 months after diagnosis despite aggressive surgery, radiation, and chemotherapies. Despite considerable efforts in brain tumor segmentation research, patient diagnosis remains poor. Accurate segmentation of pathological regions may significantly impact treatment decisions, planning, and outcome monitoring. However, the large spatial and structural variability among brain tumors makes automatic segmentation a challenging problem, leaving brain tumor segmentation an open challenge that warrants further research endeavors. While several methods automatically segment brain tumors, deep learning methods are becoming widespread in medical imaging due to their resounding performance. However, the boost in performance comes at the cost of high computational complexity. Therefore, to improve the adoption rate of computer-assisted diagnosis in clinical setups, especially in developing countries, there is a need for more computational and memoryefficient models. In this research, using a few computational resources, we explore various techniques to develop deep learning models accurately for segmenting the different glioma sub-regions, namely the enhancing tumor, the tumor core, and the whole tumor. We quantitatively evaluate the performance of our proposed models against the state-of-the-art methods using magnetic resolution imaging (MRI) datasets provided by the Brain Tumor Segmentation (BraTS) Challenge. Lastly, we use segmentation labels produced by the segmentation task and MRI multimodal data to extract appropriate imaging\/radiomic features to train a deep learning model for overall patient survival prediction.",
            "source_url": "https:\/\/openalex.org\/W4399612389",
            "pdf_url": "https:\/\/researchspace.ukzn.ac.za\/bitstreams\/179a4807-2828-41c0-8d3f-771f55ce5b41\/download",
            "doi": "10.29086\/10413\/23075",
            "handle": "",
            "repository_name": "ResearchSpace (University of KwaZulu-Natal)",
            "source_record_id": "W4399612389",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 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": 278,
            "title": "Automatic Arabic Check Processing Using Deep Learning Techniques",
            "title_ar": "",
            "title_en": "Automatic Arabic Check Processing Using Deep Learning Techniques",
            "author": "Suzan Elmajali",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "King Fahd University of Petroleum and Minerals",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Saudi Arabia",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "arabic-nlp",
            "categories": "deep-learning; nlp; arabic-nlp",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7167189961",
            "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/143737\/1\/Suzan_Elmajali_Thesis.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)",
            "source_record_id": "W7167189961",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 279,
            "title": "Gender bias in natural language processing",
            "title_ar": "",
            "title_en": "Gender bias in natural language processing",
            "author": "Christine Basta",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universitat Politècnica de Catalunya",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "machine-translation",
            "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; machine-translation; speech-recognition; fairness",
            "keywords": "",
            "abstract": "(English) Gender bias is a dangerous form of social bias impacting an essential group of people. The effect of gender bias is propagated to our data, causing the accuracy of the predictions in models to be different depending on gender. In the deep learning era, our models are highly impacted by the training data transferring the negative biases in the data to the models. Natural Language Processing models encounter this amplification of bias in the data. Our thesis is devoted to studying the issue of gender bias in NLP applications from different points of view. To understand and manage the effect of bias amplification, evaluation and mitigation approaches have to be explored. The scientific society has exerted significant efforts in these two directions to enable proposing solutions to the problem. Our thesis is devoted to these two main directions; proposing evaluation schemes, whether as datasets or mechanisms, besides suggesting mitigation techniques. For evaluation, we proposed techniques for evaluating bias in contextualized embeddings and multilingual translation models. Besides, we presented benchmarks for evaluating bias for speech translation and multilingual machine translation models. For mitigation direction, we proposed different approaches in machine translation models by adding contextual text, contextual embeddings, or relaxing the architecture’s constraints. Our evaluation studies concluded that gender bias is encoded strongly in contextual embeddings representing professions and stereotypical nouns. We also unveiled that algorithms amplify the bias and that the system’s architecture impacts the behavior. For the evaluation purposes, we contributed to creating several benchmarks for the evaluation purpose; we introduced a benchmark that evaluates gender bias in speech translation systems. This research suggests that the current state of speech translation systems does not enable us to evaluate gender bias accurately because of the low quality of speech translation systems. Additionally, we proposed a toolkit for building multilingual balanced datasets for training and evaluating NMT models. These datasets are balanced within the gender occupation-wise. We found out that high-resource languages usually tend to predict more precise male translations. Our mitigation studies in NMT suggest that the nature of datasets and languages needs to be considered to apply the right approach. Mitigating bias can rely on adding contextual information. However, in other cases, we need to rethink the model and relax some influencing conditions to the bias that do not affect the general performance but reduce the effect of bias amplification. (Español) El prejuicio de género es una forma peligrosa de sesgo social que afecta a un grupo esencial de personas. El efecto del prejuicio de género se propaga a nuestros datos, lo que hace quela precisión de las predicciones en los modelos sea diferente según el género. En la era del aprendizaje profundo, nuestros modelos se ven afectados por los datos de entrenamiento que transfieren los prejuicios de los datos a los modelos. Los modelos de procesamiento del lenguaje natural pueden además amplificar este sesgo en los datos. Para comprender el efecto de la amplificación del prejuicio de género, se deben explorar enfoques de evaluación y mitigación. La sociedad científica ha visto la importancía de estas dos direcciones para posibilitar la propuesta de soluciones al problema. Nuestra tesis está dedicada a estas dos direcciones principales; proponiendo esquemas de evaluación, ya sea como conjuntos de datos y mecanismos de evaluación, además de sugerir técnicas de mitigación. Para la evaluación, propusimos técnicas para evaluar el prejuicio en representaciones vectoriales contextualizadas y modelos de traducción multilingüe. Además, presentamos puntos de referencia para evaluar el prejuicio de la traducción de voz y los modelos de traducción automática multilingüe. Para la dirección de mitigación, propusimos diferentes enfoques en los modelos de traducción automática agregando texto contextual, incrustaciones contextuales o relajando las restricciones de la arquitectura. Nuestros estudios de evaluación concluyeron que el prejuicio de género está fuertemente codificado en representaciones vectoriales contextuales que representan profesiones y sustantivos estereotipados. También revelamos que los algoritmos amplifican el sesgo y que la arquitectura del sistema afecta el comportamiento. Para efectos de evaluación, contribuimos a la creación de varios datos de referencia para fines de evaluación; presentamos un conjunto de datos que evalúa el sesgo de género en los sistemas de traducción de voz. Esta investigación sugiere que el estado actual de los sistemas de traducción del habla no nos permite evaluar con precisión el sesgo de género debido a la baja calidad de los sistemas de traducción del habla.",
            "source_url": "https:\/\/openalex.org\/W4387558641",
            "pdf_url": "https:\/\/upcommons.upc.edu\/bitstream\/2117\/379361\/1\/TCRSB1de1.pdf",
            "doi": "10.5821\/dissertation-2117-379361",
            "handle": "",
            "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)",
            "source_record_id": "W4387558641",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 281,
            "title": "A deep learning odyssey on structured natural language processing",
            "title_ar": "",
            "title_en": "A deep learning odyssey on structured natural language processing",
            "author": "Amulya Gupta",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; nlp; transformers",
            "keywords": "",
            "abstract": "Availability of immense amount of unstructured text has surged the demand for intelligent natural language processing (NLP) systems. Specifically, these systems are expected to represent, extract, transfer and utilize knowledge in order to be successful on language related problems. In this work, we have directed our efforts to explore various forms of knowledge, especially linguistic and latent structures, available for a NLP system. Additionally, we investigate the significance of aforementioned structures in various language relevant tasks ranging from highly researched semantic matching to less explored abstract text summarization, and many more. In chapter 2, we study the interaction between explicit linguistic structures and implicit structure revealed by attention mechanisms. This investigation on various semantic matching datasets, especially question-answering, display significant patterns of substitutability between the two types of structures (explicit and implicit), and may affect other modalities. As a result, we develop a multi-view progressive attention mechanism which is general enough to operate on various linguistic structures of text. Moving further, to represent and extract latent structure from a collection of documents, we propose a family of vector-quantization-based topic models (VQ-TMs) in Chapter 3. Specifically, VQ-TMs consider dense and global topic embeddings which are homomorphic to word embeddings. Moreover, the family exploits vector-quantization (VQ) technique to promote discreteness in the overall architecture. Lastly, the learned topics are successfully transferred to a different task of code-generation. VQ-TMs exploit VQ technique while learning topics representation. Although VQ method aids in learning discrete topic embeddings, yet, the method is built upon a geometric intuition. Moreover, VQ-TMs utilizes the learned topic representations as reference knowledge in a downstream task. Some natural questions arise. Is it possible to exploit probabilistic intuition to infuse discreteness while learning topics representation? Additionally, can the learned topics be leveraged to control sentence generation and augment the performance in aspect-aware item recommendation? In order to push the envelope further, we explore these ideas in Chapter 4.",
            "source_url": "https:\/\/openalex.org\/W4393304434",
            "pdf_url": "https:\/\/dr.lib.iastate.edu\/bitstreams\/aaac0de0-3b7a-4bdf-9328-aee7958dd5de\/download",
            "doi": "10.31274\/td-20240329-4",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4393304434",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 282,
            "title": "Deep Learning Methods for Natural Language Processing Considering Writers and Background World Knowledge",
            "title_ar": "",
            "title_en": "Deep Learning Methods for Natural Language Processing Considering Writers and Background World Knowledge",
            "author": "大輔 大葉",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "",
            "categories": "deep-learning; nlp",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7207841427",
            "pdf_url": "https:\/\/repository.dl.itc.u-tokyo.ac.jp\/record\/2011970\/files\/A40698.pdf",
            "doi": "10.15083\/0002011970",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7207841427",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 283,
            "title": "Arabic Handwriting Recognition Using Deep Learning Neural Networks",
            "title_ar": "",
            "title_en": "Arabic Handwriting Recognition Using Deep Learning Neural Networks",
            "author": "Mohamed Eltay",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "King Fahd University of Petroleum and Minerals",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Saudi Arabia",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "arabic-nlp",
            "categories": "deep-learning; computer-vision; nlp; image-classification; arabic-nlp",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7167164100",
            "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/141562\/1\/MS_Thesis_08-7-2020.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)",
            "source_record_id": "W7167164100",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 284,
            "title": "Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music",
            "title_ar": "",
            "title_en": "Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music",
            "author": "Deepthi Peri",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; nlp; predictive-analytics; rnn; speech-recognition",
            "keywords": "",
            "abstract": "In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. It encompasses the characteristics of a scale, a mode, and a tune, with none of them fully describing it, rendering the Raga a unique concept in ICM. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Identifying and categorizing the Raga is challenging due to its dynamism and complex structure as well as the polyphonic nature of ICM. Hence, Raga recognition—identify the constituent Raga in an audio file—has become an important problem in music informatics with several known prior approaches. Advancing the state of the art in Raga recognition paves the way to improving other Music Information Retrieval tasks in ICM, including transcribing notes automatically, recommending music, and organizing large databases. This thesis presents a novel melodic pattern-based approach to recognizing Ragas by representing this task as a document classification problem, solved by applying a deep learning technique. A digital audio excerpt is hierarchically processed and split into subsequences and gamaka sequences to mimic a textual document structure, so our model can learn the resulting tonal and temporal sequence patterns using a Recurrent Neural Network. Although training and testing on these smaller sequences, we predict the Raga for the entire audio excerpt, with the accuracy of 90.3% for the Carnatic Music Dataset and 95.6% for the Hindustani Music Dataset, thus outperforming prior approaches in Raga recognition.",
            "source_url": "https:\/\/openalex.org\/W3086226352",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "VTechWorks (Virginia Tech)",
            "source_record_id": "W3086226352",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 288,
            "title": "Hand Gesture Segmentation and Sign Language Recognition Using Deep Learning",
            "title_ar": "",
            "title_en": "Hand Gesture Segmentation and Sign Language Recognition Using Deep Learning",
            "author": "M Faisal Nurnoby",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "King Fahd University of Petroleum and Minerals",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Saudi Arabia",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "deep-learning; computer-vision; image-segmentation",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7167381339",
            "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/141918\/1\/MS_THESIS_G201706690_M_FAISAL.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)",
            "source_record_id": "W7167381339",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 289,
            "title": "Natural Language Generation with Reinforcement Learning",
            "title_ar": "",
            "title_en": "Natural Language Generation with Reinforcement Learning",
            "author": "Thomas Scialom",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "gan",
            "categories": "deep-learning; nlp; generative-ai; reinforcement-learning; responsible-ai; gan; fairness",
            "keywords": "",
            "abstract": "Natural Language Generation (NLG) is the subfield of Natural Language Processing, where the task is to produce natural language outputs. Despite the important progress fostered by the application of Deep Learning, generated texts are still inconsistent and contain factual inconsistencies. At the root cause, we argue in this thesis that deep learning models in NLG suffers from inherent flaws in algorithms, which limits their efficiency. At training time, the standard training strategy, Teacher Forcing, induces the so called exposure bias, a mismatch with inference time, where the errors accumulate. Moreover, NLG suffers from a second flaw: its the automatic evaluation does not reflect well human judgement. In this thesis, we explore how to improve both evaluation and training in NLG toward more reliable systems. In particular, we propose a Question Answering based metric. We show how this metric can be used as a reward in a Reinforcement Learning setup to improve NLG models. Toward this objective, we also explore learned rewards that are the discriminators, and introduce several new algorithms that benefit NLG during training and decoding times. In particular, we propose to combine Monte Carlo Tree Search with Generative Adversarial Networks, resulting in state-of-the-art models.",
            "source_url": "https:\/\/openalex.org\/W4392267362",
            "pdf_url": "https:\/\/hal.sorbonne-universite.fr\/tel-03951598v1\/document",
            "doi": "",
            "handle": "",
            "repository_name": "HAL",
            "source_record_id": "W4392267362",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 291,
            "title": "Deep learning based dynamic turkish sign language recognition with leap motion",
            "title_ar": "",
            "title_en": "Deep learning based dynamic turkish sign language recognition with leap motion",
            "author": "Burçak Demircioğlu Kam",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Istanbul Technical University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W3132949820",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Istanbul Technical University Academic Open Archive (Istanbul Technical University)",
            "source_record_id": "W3132949820",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 292,
            "title": "Resources and Applications for Dialectal Arabic: the Case of Levantine",
            "title_ar": "",
            "title_en": "Resources and Applications for Dialectal Arabic: the Case of Levantine",
            "author": "Chatrine Qwaider ; Kathrein Abu Kwaik",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Gothenburg University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; transfer-learning; self-semi-supervised; transformers; llm; sentiment-analysis; arabic-nlp; ai-industry",
            "keywords": "",
            "abstract": "This is a thesis about the computational study of Dialectal Arabic (DA). In particular, the thesis studies DA, with a special emphasis on Levantine Arabic, and develops tools and resources for the computational study of Dialectal Arabic Natural Language Processing (DANLP). It investigates the creation of fine-grained resources that can be used for a variety of computational tasks, and a number of effective models that can deal with the complexity of fine-grained dialectal data. Dialect Identification (DI), as well as Sentiment Analysis (SA) are the Natural Language Processing (NLP) tasks investigated in this thesis. In the first part (Study 1 and Study 2), I study the DI task on both coarse-grained and fine-grained levels. For this reason, I build the first annotated Levantine (SHAMI) Dialect Corpus (SDC). Furthermore, I explore the ability of n-gram language models, Machine Learning (ML) algorithms and ensemble learning techniques to classify and detect 26 Arabic varieties. In the second part, I conduct a linguistic study to measure the lexical distance between MSA and DA, and between the dialects themselves. This is done to check whether transferring knowledge from one variety to another is possible. In the third part, studies 4,5 and 6, I explore Arabic Sentiment Analysis (SA). I investigate the idea of knowledge transfer between MSA and the dialects using SA as a case study. Furthermore, I implement various models such as the pre-trained language model BERT, Deep Learning (DL), ML and feature engineering approaches to detect the sentimental polarity of DA data. I introduce two valuable resources for this task, one focusing on Levantine sentiment (Shami-Senti), and the other for DA in general (ATSAD). I exploit different ways of annotation, e.g. human, lexicon-based and automatic distant supervision annotation. The last study is about choosing the best model for DI and SA. I exploit well-known models and approaches using various kinds of DA resources. The thesis contributes to the field of DANLP in a number of ways. The introduced valuable resources can be seen as a stepping stone for a deeper investigation and understanding of issues in DANLP. They are also reliable and can be used by researchers to address different NLP tasks. The cross-dialectal linguistic studies will open up prospects for researchers to fine-tune models and transfer knowledge among Arabic varieties. A big part of the contribution lies in designing DI and SA models. I implement several ML models that use feature engineering approaches and N-gram language models to identify the dialect or detect the sentiment. For DI, I design and implement an ensemble learning model that is able to handle fine-grained dialects. Additionally, I exploit the usage of DL models on different SA dialectal datasets and achieve competitive results. For both tasks, I exploit the recent pre-trained language models and perform a comparison to choose the best model. I also implement a semi-supervised approach for automatic labelling and annotating data with the help of self-training techniques to improve the performance of the dataset. These models will help researchers dive deeper into DANLP and create practical and industrial systems.",
            "source_url": "https:\/\/openalex.org\/W7216829323",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Gothenburg University Publications Electronic Archive (Gothenburg University)",
            "source_record_id": "W7216829323",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 293,
            "title": "Modularity in deep learning",
            "title_ar": "",
            "title_en": "",
            "author": "Haozhe Sun",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2023,
            "language": "fr",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "Modularité dans l'apprentissage profond L'objectif de cette thèse est de rendre l'apprentissage profond plus efficace en termes de ressources en appliquant le principe de modularité. La thèse comporte plusieurs contributions principales : une étude de la littérature sur la modularité dans l'apprentissage profond; la conception d'OmniPrint et de Meta-Album, des outils qui facilitent l'étude de la modularité des données; des études de cas examinant les effets de l'apprentissage épisodique, un exemple de modularité des données; un mécanisme d'évaluation modulaire appelé LTU pour évaluer les risques en matière de protection de la vie privée; et la méthode RRR pour réutiliser des modèles modulaires pré-entraînés afin d'en construire des versions plus compactes. La modularité, qui implique la décomposition d'une entité en sous-entités, est un concept répandu dans diverses disciplines. Cette thèse examine la modularité sur trois axes de l'apprentissage profond : les données, la tâche et le modèle. OmniPrint et Meta-Album facilitent de comparer les modèles modulaires et d'explorer les impacts de la modularité des données. LTU garantit la fiabilité de l'évaluation de la protection de la vie privée. RRR améliore l'efficacité de l'utilisation des modèles modulaires pré-entraînés. Collectivement, cette thèse fait le lien entre le principe de modularité et l'apprentissage profond et souligne ses avantages dans certains domaines de l'apprentissage profond, contribuant ainsi à une intelligence artificielle plus efficace en termes de ressources.",
            "source_url": "https:\/\/openalex.org\/W7152089629",
            "pdf_url": "https:\/\/theses.hal.science\/tel-04418605\/document",
            "doi": "10.70675\/9f57d770z3466z4d39z8cfez9826045d155d",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7152089629",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 294,
            "title": "Sentiment Analysis for the Low-Resourced Latinised Arabic \"Arabizi\"",
            "title_ar": "",
            "title_en": "Sentiment Analysis for the Low-Resourced Latinised Arabic \"Arabizi\"",
            "author": "Taha Tobaili",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "The Open University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "deep-learning; nlp; ai-applications; sentiment-analysis; arabic-nlp; ai-industry",
            "keywords": "",
            "abstract": "The expansion of digital communication mediums from private mobile messaging into the public through social media presented an opportunity for the data science research and industry to mine the generated big data for artificial information extraction. A popular information extraction task is sentiment analysis, which aims at extracting polarity opinions, positive, negative, or neutral, from the written natural language. This science helped organisations better understand the public’s opinion towards events, news, public figures, and products. However, sentiment analysis has advanced for the English language ahead of Arabic. While sentiment analysis for Arabic is developing in the literature of Natural Language Processing (NLP), a popular variety of Arabic, Arabizi, has been overlooked for sentiment analysis advancements. Arabizi is an informal transcription of the spoken dialectal Arabic in Latin script used for social texting. It is known to be common among the Arab youth, yet it is overlooked in efforts on Arabic sentiment analysis for its linguistic complexities. As to Arabic, Arabizi is rich in inflectional morphology, but also codeswitched with English or French, and distinctively transcribed without adhering to a standard orthography. The rich morphology, inconsistent orthography, and codeswitching challenges are compounded together to have a multiplied effect on the lexical sparsity of the language, where each Arabizi word becomes eligible to be spelled in many ways, that, in addition to the mixing of other languages within the same textual context. The resulting high degree of lexical sparsity defies the very basics of sentiment analysis, classification of positive and negative words. Arabizi is even faced with a severe shortage of data resources that are required to set out any sentiment analysis approach. In this thesis, we tackle this gap by conducting research on sentiment analysis for Arabizi. We addressed the sparsity challenge by harvesting Arabizi data from multi-lingual social media text using deep learning to build Arabizi resources for sentiment analysis. We developed six new morphologically and orthographically rich Arabizi sentiment lexicons and set the baseline for Arabizi sentiment analysis on social media.",
            "source_url": "https:\/\/openalex.org\/W3127469975",
            "pdf_url": "",
            "doi": "10.21954\/ou.ro.00011f21",
            "handle": "",
            "repository_name": "Open Research Online (The Open University)",
            "source_record_id": "W3127469975",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 295,
            "title": "Traffic flow forecasting with deep learning",
            "title_ar": "",
            "title_en": "Traffic flow forecasting with deep learning",
            "author": "Per Øyvind Kanestrøm",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; rnn; object-detection; ai-smart-cities",
            "keywords": "",
            "abstract": "In recent years there has been a vast increase in available data with the ad- vancement of smart cities. In the domain of Intelligent Transportation Systems (ITS) this modernisation can positively effect transportation networks, thus cut- ting down travel time, increase efficacy, and reduce environmental impact from vehicles. Norwegian Public Roads Administration (NPRA) is currently deploying a new vehicle detector system named Datainn on all public roads in Norway. Datainn sends metadata on all detected vehicles in real time. This includes information about speed, gap between vehicles, weight, and classification of vehicle type. Many machine learning approaches has been researched in literature on how to forecast traffic flow information. One such approach is that of using Artificial Neural Networks (ANNs). In this research ANN based methods have been explored. This was done by first performing a state-of-the-art Structured Literature Review (SLR) on ANN methods in literature. From the review, Stacked Sparse Autoencoder (SSAE) model was compared with recent advances of Long Short-Term Memory (LSTM) and Deep Neural Network (DNN) on four different prediction horizons. The data foundation was the new Datainn system using traffic data from a highway around Norway s capitol, Oslo. Further, the model performance was assessed with extended feature vectors including more metadata from Datainn. The results found that the LSTM model always outperformed DNN and SSAE, although in general the performance characteristics was somewhat similar. Ex- tending the feature vector with more variables had a negative effect on DNN, while resulting in better performance for Recurrent Neural Network (RNN) on long-term (60 minute) forecasting horizons. For SSAE it had a slight positive effect, but not enough get better results than RNN or DNN.",
            "source_url": "https:\/\/openalex.org\/W2727387027",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "BIBSYS Brage (BIBSYS (Norway))",
            "source_record_id": "W2727387027",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 296,
            "title": "Modeling and Analysis of Data-Driven and Knowledge-Enhanced Traffic Prediction",
            "title_ar": "",
            "title_en": "Modeling and Analysis of Data-Driven and Knowledge-Enhanced Traffic Prediction",
            "author": "Lilan Peng",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; cnn; gnn; ai-smart-cities",
            "keywords": "",
            "abstract": "The rapid growth of urban populations has put unprecedented pressure on transportation systems, causing widespread problems such as congestion, accidents, and environmental pollution. Accurate traffic prediction is therefore essential for building smart cities and intelligent transportation systems, as it enables evidence-based policymaking, risk assessment, and efficient traffic management. Early research on traffic prediction relied mainly on statistical models and machine learning techniques, which struggled to capture complex nonlinear spatio-temporal dependencies in large-scale networks. The advent of traffic big data has prompted a paradigm shift towards deep learning approaches. Recent advances in traffic prediction have increasingly integrated advanced temporal models with Graph Neural Networks to model complex temporal dynamics and localized spatial patterns, which yield incremental yet meaningful performance gains. However, these existing approaches still face four key limitations: (1) Traffic patterns exhibit cross-scale temporal evolution (ranging from minutes to months). (2) Spatial correlations are dynamic and multi-scale among different time scales. (3) Traffic data involves not only pairwise spatial relationships but also high-order functional dependencies among multiple regions and short or long-term temporal dependencies across different time periods. (4) Current data-driven models often lack the constraint of domain knowledge, which may lead the models to learn certain false correlations arising from data noise or randomness, thereby limiting the predictive performance. In response to these challenges, this dissertation investigates four core research questions: (1) How to learn cross-scale temporal dependencies? (2) How to capture multi-scale spatial dependencies? (3) How to model hierarchical spatio-temporal semantic associations? (4) How to integrate domain knowledge into data-driven traffic prediction models? Specifically, four traffic prediction models are developed: (1) Cross-Scale Temporal Relationship Representation Method. Dynamic temporal cross-scale representation model (DSTMR) adopts a hierarchical residual-like layer with gating mechanisms to capture multi-scale temporal dependencies. A dynamic graph generation module is designed to adaptively learn dynamic spatial correlations and generate a dynamic spatial graph structure. It further integrates a graph neural network to model spatial correlations, and is applied to bus station passenger flow forecasting. (2) Multi-Scale Spatial Relationship Representation Method. A multi-scale graph structure learning model (DMGL) is developed. This model optimizes graph structure learning strategies and constructs multi-scale graphs to capture spatial information at different time scales. Specifically, a dynamic multi-scale graph generation module is first designed to construct multi-scale dynamic graph structures through time decomposition and aggregation. Experimental results on real taxi regional passenger flow datasets demonstrate that DMGL achieves significant improvements in prediction performance compared to baseline methods. (3) Hierarchical Spatio-Temporal Semantic Associations Representation Method. A spatio-temporal mixed-order relationship representation model (MixSTRL) is designed. It introduces a spatial mixed-order representation that combines a graph neural network for pairwise spatial dependencies and hypergraph neural networks for high-order spatial relationships, integrated through a squeeze-and-excitation mechanism. Furthermore, it constructs a temporal mixed-order representation module that fuses a temporal convolutional network for short-term dynamics with temporal hypergraph neural networks for long-range periodic dependencies, employing a gated fusion strategy. Experimental evaluations on three taxi regional passenger flow datasets verify the effectiveness. (4) Knowledge-Enhanced and Hierarchical Spatial Pattern Learning Method (KA-HSPL). This method first designs a spatio-temporal embedding module that integrates knowledge-augmented spatial embedding and period-aware temporal embedding. Then, it develops a hierarchical spatial pattern learning module, which combines an adaptive graph convolutional network, a customized hypergraph convolutional network, and a spatio-temporal fusion module to model both pairwise and high-order spatial relationships. Finally, it employs a time-aware graph convolutional network to capture dynamic temporal correlations and devises a spatio-temporal joint graph convolutional network for integrated spatio-temporal dependency learning. The experiments on six highway section speed datasets demonstrate the model's effectiveness. (5) Intelligent Traffic Early Warning Demo System. To integrate and implement the designed four urban traffic prediction models, this dissertation develops an intelligent traffic early warning demo system.",
            "source_url": "https:\/\/openalex.org\/W7222948398",
            "pdf_url": "",
            "doi": "10.18445\/20261006-080745-0",
            "handle": "",
            "repository_name": "University Library Hagen (UB Hagen)",
            "source_record_id": "W7222948398",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 297,
            "title": "Prediction of Deep Learning Methods using Wavelet Transform",
            "title_ar": "",
            "title_en": "",
            "author": "Gökçe Nur Taşağıl Arslan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2025,
            "language": "tr",
            "research_domain": "deep-learning",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; predictive-analytics; cnn; rnn",
            "keywords": "",
            "abstract": "Hava kirliliği, PM10 gibi partikül madde içeren yanma gazlarının salınması sonucu ortaya çıkmaktadır. Doğru ve güvenilir teknikler ile PM10 konsantrasyonlarının tahmini, sağlık risklerini düşürmek için göz önüne alınmalıdır. Ortam hava kalitesi ve halk sağlığı üzerindeki zararlı etkileri en aza indirmek için önleyici tedbirleri yürütmek üzere erken uyarı sistemlerinin potansiyelinin olması, PM10'u tahmin etme sayesinde mümkün hale gelmektedir. Bu çalışma, saatlik geçmiş hava kirletici konsantrasyonları ve meteorolojik verileri kullanarak, Ayrık Dalgacık Dönüşümü (DWT), Çok Katmanlı Algılayıcılar (MLP), Yinelemeli Sinir Ağları (RNN), Uzun Kısa Süreli Bellek (LSTM), Çift Yönlü LSTM (BiLSTM), Kapılı Yinelemeli Birim (GRU) ve Evrişimli Sinir Ağı (CNN) kullanarak 24 saat sonraki PM10 konsantrasyonunu tahmin etmek için bir yöntem önermektedir. İlk olarak, yüksek performanslı modelleri belirlemek için dalgacık tipleri kullanılır ve bu modeller arasında derin öğrenme tekniklerinde iyi performans gösterenler belirlenir. Modeller karşılaştırılırken RMSE, MAE ve R2 değerleri gibi performans parametreleri dikkate alınmıştır. En iyi model, en düşük RMSE (1,908), MAE (0,994), ve en yüksek R2 ()'ye sahip Dmey dalgacık dönüşümü kullanılan DWT-GRU-BiLSTM modeli olmuştur. Bu çalışmada kullanılan model, gelecekteki araştırmalarda bölgesel PM konsantrasyon seviyelerini doğru bir şekilde tahmin etmek için kullanılabilir. Sürdürülebilir akıllı çevre sistemleri kurmak ve önleyici eylemlerde bulunabilen kurum ve kuruluşların doğru zamanda harekete geçmesine yardımcı olmak için, uygulanan bu hibrit derin öğrenme algoritmalarının doğru tahminleme yeteneğinden yararlanılabilir. Hava kirliliğinin arttığı ve insan sağlığı için tehdit oluşturduğu mevcut ortamda, alınabilecek her türlü önleyici tedbir, tüm canlılar için yaşam kalitesini iyileştirecek, hava kirliliğinden kaynaklanan sağlık sorunlarını ve ölümleri azaltacak ve nihayetinde refah derecesini yükseltecektir. Ayrıca, bu bulgular İstanbul gibi yoğun nüfuslu, endüstriyel ve yoğun trafiğe sahip şehirlerin hava kalitesiyle ilgili ikna edici ampirik kanıtlar sunabilir.",
            "source_url": "https:\/\/openalex.org\/W7219471140",
            "pdf_url": "https:\/\/avesis.yildiz.edu.tr\/file?id=4447b2e1-fa87-4912-a948-b76c5deb0c71",
            "doi": "",
            "handle": "",
            "repository_name": "Yildiz Technical University - AVESIS",
            "source_record_id": "W7219471140",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 298,
            "title": "Large-Scale Traffic Flow Prediction Using Deep Learning in the Context of Smart Mobility",
            "title_ar": "",
            "title_en": "Large-Scale Traffic Flow Prediction Using Deep Learning in the Context of Smart Mobility",
            "author": "Arief Barkah Koesdwiady",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Waterloo",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "generative-ai",
            "research_subdomain": "gan",
            "categories": "machine-learning; deep-learning; generative-ai; ai-applications; predictive-analytics; gan; ai-smart-cities",
            "keywords": "",
            "abstract": "Designing and developing a new generation of cities around the world (termed as smart cities) is fast becoming one of the ultimate solutions to overcome cities' problems such as population growth, pollution, energy crisis, and pressure demand on existing transportation infrastructure. One of the major aspects of a smart city is smart mobility. Smart mobility aims at improving transportation systems in several aspects: city logistics, info-mobility, and people-mobility. The emergence of the Internet of Car (IoC) phenomenon alongside with the development of Intelligent Transportation Systems (ITSs) opens some opportunities in improving the tra c management systems and assisting the travelers and authorities in their decision-making process. However, this has given rise to the generation of huge amount of data originated from human-device and device-device interaction. This is an opportunity and a challenge, and smart mobility will not meet its full potential unless valuable insights are extracted from these big data. Although the smart city environment and IoC allow for the generation and exchange of large amounts of data, there have not been yet well de ned and mature approaches for mining this wealth of information to bene t the drivers and traffic authorities. The main reason is most likely related to fundamental challenges in dealing with big data of various types and uncertain frequency coming from diverse sources. Mainly, the issues of types of data and uncertainty analysis in the predictions are indicated as the most challenging areas of study that have not been tackled yet. Important issues such as the nature of the data, i.e., stationary or non-stationary, and the prediction tasks, i.e., short-term or long-term, should also be taken into consideration. Based on this observation, a data-driven traffic flow prediction framework within the context of big data environment is proposed in this thesis. The main goal of this framework is to enhance the quality of traffic flow predictions, which can be used to assist travelers and traffic authorities in the decision-making process (whether for travel or management purposes). The proposed framework is focused around four main aspects that tackle major data-driven traffic flow prediction problems: the fusion of hard data for traffic flow prediction; the fusion of soft data for traffic flow prediction; prediction of non-stationary traffic flow; and prediction of multi-step traffic flow. All these aspects are investigated and formulated as computational based tools\/algorithms\/approaches adequately tailored to the nature of the data at hand. The first tool tackles the inherent big data problems and deals with the uncertainty in the prediction. It relies on the ability of deep learning approaches in handling huge amounts of data generated by a large-scale and complex transportation system with limited prior knowledge. Furthermore, motivated by the close correlation between road traffic and weather conditions, a novel deep-learning-based approach that predicts traffic flow by fusing the traffic history and weather data is proposed. The second tool fuses the streams of data (hard data) and event-based data (soft data) using Dempster Shafer Evidence Theory (DSET). One of the main features of the DSET is its ability to capture uncertainties in probabilities. Subsequently, an extension of DSET, namely Dempsters conditional rules for updating belief, is used to fuse traffic prediction beliefs coming from streams of data and event-based data sources. The third tool consists of a method to detect non-stationarities in the traffic flow and an algorithm to perform online adaptations of the tra c prediction model. The proposed detection approach is developed by monitoring the evolution of the spectral contents of the traffic flow. Furthermore, the approach is specfi cally developed to work in conjunction with state-of-the-art machine learning methods such as Deep Neural Network (DNN). By combining the power of frequency domain features and the known generalization capability and scalability of DNN in handling real-world data, it is expected that high prediction performances can be achieved. The last tool is developed to improve multi-step traffic flow prediction in the recursive and multi-output settings. In the recursive setting, an algorithm that augments the information about the current time-step is proposed. This algorithm is called Conditional Data as Demonstrator (C-DaD) and is an extension of an algorithm called Data as Demonstrator (DaD). Furthermore, in the multi-output setting, a novel approach of generating new history-future pairs of data that are aggregated with the original training data using Conditional Generative Adversarial Network (C-GAN) is developed. To demonstrate the capabilities of the proposed approaches, a series of experiments using arti cial and real-world data are conducted.",
            "source_url": "https:\/\/openalex.org\/W2802252725",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UWSpace (University of Waterloo)",
            "source_record_id": "W2802252725",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 299,
            "title": "Deep Learning for Computer Vision in Smart Cities",
            "title_ar": "",
            "title_en": "Deep Learning for Computer Vision in Smart Cities",
            "author": "David García-Retuerta",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Salamanca",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; video-analysis; ai-industry; ai-smart-cities",
            "keywords": "",
            "abstract": "The Digital Age has caused a rapid shift from traditional industry to an economy mainly based upon information technology. According to recent studies, 74 zettabytes (ZB) of data have been generated, captured and replicated in the world in 2021, with video accounting for 82% of internet traffic. This figure has been amplified due to the coronavirus pandemic, and it is expected to keep increasing, reaching 149 ZB by 2024. Processing this impressive amount of information is one of the main scientific challenges of our time. Against this backdrop, Machine Learning (ML) and two related paradigms have emerged: big data and deep learning. These disciplines take advantage of mathematical optimization methods, bioinspiration and modern Graphics Processing Units (GPUs) to manage large datasets efficiently and effectively.",
            "source_url": "https:\/\/openalex.org\/W4380486366",
            "pdf_url": "https:\/\/gredos.usal.es\/bitstream\/10366\/150948\/1\/PDII_Garcia%20RetuertaD_SmartCities.pdf",
            "doi": "10.14201\/gredos.150948",
            "handle": "",
            "repository_name": "Gredos (University of Salamanca)",
            "source_record_id": "W4380486366",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 300,
            "title": "Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components",
            "title_ar": "",
            "title_en": "Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components",
            "author": "Ditsuhi Iskandaryan ; Iskandaryan, Ditsuhi",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; ai-smart-cities",
            "keywords": "",
            "abstract": "Air quality is considered one of the top concerns. Information and knowledge about air quality can assist in effectively monitoring and controlling concentrations, reducing or preventing its harmful impacts and consequences. The complexity of air quality dependence on various components in spatiotemporal dimensions creates additional challenges to acquire this information. The current dissertation proposes machine learning and deep learning technologies that are capable of capturing and processing multidimensional information and complex dependencies controlling air quality. The following components come together to formulate the novelty of the current work: spatiotemporal forecast of the defined prediction target (nitrogen dioxide); incorporation and integration of air quality, meteorological and traffic data with their features\/variables in spatiotemporal dimensions within a certain spatial extent and temporal interval; the consideration of coronavirus disease 2019 as an external key factor impacting air quality level; and provision of the code and data implemented to incentivise and guarantee reproducibility.",
            "source_url": "https:\/\/openalex.org\/W4353069857",
            "pdf_url": "https:\/\/www.tdx.cat\/bitstream\/10803\/687959\/1\/2023_Tesis_Iskandaryan_Ditsuhi.pdf",
            "doi": "10.6035\/14101.2023.726676",
            "handle": "",
            "repository_name": "Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)",
            "source_record_id": "W4353069857",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 301,
            "title": "Short Term Traffic Flow Prediction Using Deep Learning Approach",
            "title_ar": "",
            "title_en": "Short Term Traffic Flow Prediction Using Deep Learning Approach",
            "author": "Sivabalaji Manoharan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "National College of Ireland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Ireland",
            "governorate": "",
            "publication_year": 2016,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; ai-smart-cities",
            "keywords": "",
            "abstract": "Intelligent transportation systems helps travellers reach their destination at an estimated time. Smart cities have deployed latest technologies to satisfy the needs of travellers through efficient navigation. Adverse weather conditions may lead to increase in traffic congestion as well as accidents. Rainfall, Snow and wet pavements are a major cause for the traffic flow during winter across all areas. Several methods have been researched at a deep level for predicting traffic flow and still an efficient method to satisfy real world traffic problems have not been identified. The accident and weather data are investigated in this paper to predict the flow of traffic in Leeds city. Three models were developed through deep learning neural network algorithm for predicting traffic flow. The predicted Deep neural network model is compared with the superior support vector machine model and the accuracy is higher for the Deep neural networks.",
            "source_url": "https:\/\/openalex.org\/W2593566730",
            "pdf_url": "http:\/\/trap.ncirl.ie\/2520\/1\/sivabalajimanoharan.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "TRAP@NCI (National College of Ireland)",
            "source_record_id": "W2593566730",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 302,
            "title": "Towards synthetic sensing for smart cities : a machine\/deep learning-based approach",
            "title_ar": "",
            "title_en": "",
            "author": "Faraz Malik Awan",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2022,
            "language": "fr",
            "research_domain": "computer-vision",
            "research_subdomain": "ai-smart-cities",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; ai-smart-cities",
            "keywords": "",
            "abstract": "Vers une détection synthétique pour les villes intelligentes : une approche basée sur la machine et l'apprentissage profond Nous avons travaillé sur l'un des axes de recherche les plus importants de la ville intelligente, à savoir les systèmes de transport intelligents (STI). Les ITS englobent plusieurs domaines, tels que les systèmes de notification électronique des véhicules, les informations sur le trafic, le stationnement intelligent et l'environnement. Cependant, dans cette thèse, nous ciblons deux de ses domaines importants : i) le stationnement intelligent et ii) le trafic routier. Nous avons commencé notre recherche par le cas d'utilisation du stationnement intelligent. En effectuant une revue de la littérature, nous avons réalisé que différentes approches de Machine Learning (ML) et de Deep Learning (DL) ont été utilisées pour des solutions de stationnement intelligent. Dans la plupart de ces approches proposées, les zones de stationnement fermées ont été ciblées avec différents ensembles de caractéristiques pour prédire le \"taux d'occupation\" dans les zones de stationnement. Cela nous a incités à effectuer une analyse comparative pour répondre aux questions suivantes : compte tenu du cas d'utilisation de la prédiction de stationnement, comment les modèles ML traditionnels se comportent-ils par rapport aux modèles DL complexes ? Avec des données volumineuses, les modèles ML traditionnels moins complexes peuvent-ils surpasser les modèles DL complexes ? Quelle est la performance de ces modèles pour prédire la disponibilité des places de stationnement individuelles dans la rue plutôt que de prédire le taux d'occupation global d'une zone de stationnement fermée. Pour répondre à ces questions, nous avons choisi cinq algorithmes ML classiques bien connus (K-Nearest Neighbours, Random Forest, Decision Tree) et un algorithme DL (Multilayer Perceptron). Pour approfondir notre étude, nous formons un modèle d'apprentissage d'ensemble, dans lequel nous combinons tous les modèles ML et DL susmentionnés. Nous avons utilisé un énorme ensemble de données sur les pa rkings de la ville de Santander, en Espagne, qui comprend environ 25 millions d'enregistrements. Nous proposons également de recommander des places de stationnement disponibles en fonction de l'emplacement actuel du conducteur. En poursuivant nos objectifs de recherche, nous avons effectué une revue de la littérature sur le trafic routier et avons constaté que le trafic routier est souvent associé à la pollution atmosphérique et à la pollution sonore. Cependant, à notre connaissance, la pollution atmosphérique et la pollution sonore n'ont jamais été utilisées dans le problème de la prédiction du trafic. Dans cette partie de notre recherche, nous avons d'abord utilisé la pollution de l'air (CO, NO, NO2, NOx, et O3) avec les variables atmosphériques, telles que la vitesse et la direction du vent, la température et la pression pour améliorer la prévision du trafic dans la ville de Madrid. Cette expérience réussie nous a incités à étendre notre étude à un autre facteur, qui est égalemen t fortement corrélé au trafic routier, à savoir la pollution sonore. Ainsi, dans le prolongement de nos travaux précédents, nous utilisons dans cette partie de notre recherche la pollution sonore pour améliorer la prévision du trafic dans la ville de Madrid.",
            "source_url": "https:\/\/openalex.org\/W7151321554",
            "pdf_url": "https:\/\/theses.hal.science\/tel-03722891v1\/document",
            "doi": "10.70675\/c01d4efazc638z4123z8020z74dfc82b5875",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7151321554",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 303,
            "title": "Deep learning-based traffic flow prediction and traffic management system for urban transportation networks",
            "title_ar": "",
            "title_en": "Deep learning-based traffic flow prediction and traffic management system for urban transportation networks",
            "author": "Han Zhao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; reinforcement-learning; ai-applications; predictive-analytics; cnn; ai-smart-cities",
            "keywords": "",
            "abstract": "Due to the growth of the population and improved living standards, a lot of urbanization policies are implemented by the governments in many countries.As a result, the total amount of vehicles on the streets is growing rapidly, and traffic congestion occurs frequently.The recurring and non-recurring congestion has resulted in high economic costs, environmental pollution and severe noise pollution.Hence, how to alleviate traffic congestion and delays have become an essential problem in this urbanized world.In this thesis, we proposed a deep learning-based traffic prediction and management system to tackle the traffic congestion problem.First, we developed a domain-adversarial-based graph network for short-term traffic flow prediction.Contrast experiments have been conducted on several opensource traffic datasets, which proved that our proposed method had made some improvements compared to other baselines.Secondly, we proposed an optimized vehicle route guidance and navigation system.The employed deep reinforcement learning method and graph convolutional network can extract several features from traffic data and topological data.The proposed route guidance system can assign the best route and re-route the vehicles in a complex urban transportation network with dynamic traffic conditions.Last but not least, we proposed a deep reinforcement learning-based traffic signal control strategy to reduce the travel delay time and queue length by adjusting phase splits.TD3 algorithm is employed to generate optimal phase splits for the next cycle of traffic signals.The performance of traffic signal control by this strategy is evaluated through several urban road networks with true traffic demand or manually generated demand via VISSIM.",
            "source_url": "https:\/\/openalex.org\/W4389209251",
            "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/171885\/4\/zhaohan_final_thesis.pdf",
            "doi": "10.32657\/10356\/171885",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4389209251",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 304,
            "title": "Traffic Forecasting Applications Using Crowdsourced Traffic Reports and Deep Learning",
            "title_ar": "",
            "title_en": "Traffic Forecasting Applications Using Crowdsourced Traffic Reports and Deep Learning",
            "author": "Ali Alammari",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; cnn; rnn; ai-smart-cities",
            "keywords": "",
            "abstract": "Intelligent transportation systems (ITS) are essential tools for traffic planning, analysis, and forecasting that can utilize the huge amount of traffic data available nowadays. In this work, we aggregated detailed traffic flow sensor data, Waze reports, OpenStreetMap (OSM) features, and weather data, from California Bay Area for 6 months. Using that data, we studied three novel ITS applications using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first experiment is an analysis of the relation between roadway shapes and accident occurrence, where results show that the speed limit and number of lanes are significant predictors for major accidents on highways. The second experiment presents a novel method for forecasting congestion severity using crowdsourced data only (Waze, OSM, and weather), without the need for traffic sensor data. The third experiment studies the improvement of traffic flow forecasting using accidents, number of lanes, weather, and time-related features, where results show significant performance improvements when the additional features where used.",
            "source_url": "https:\/\/openalex.org\/W4407267545",
            "pdf_url": "https:\/\/digital.library.unt.edu\/ark:\/67531\/metadc1703305\/m2\/1\/high_res_d\/ALAMMARI-DISSERTATION-2020.pdf",
            "doi": "10.12794\/metadc1703305",
            "handle": "",
            "repository_name": "University of North Texas Digital Library (University of North Texas)",
            "source_record_id": "W4407267545",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 305,
            "title": "Deep learning models in urban logistics for air quality prediction in the city of Bucaramanga",
            "title_ar": "",
            "title_en": "",
            "author": "Paula Andrea Abril Ortiz ; Édgar Leonardo Porras Ojeda",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universidad Industrial de Santander",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Colombia",
            "governorate": "",
            "publication_year": 2019,
            "language": "es",
            "research_domain": "deep-learning",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-smart-cities",
            "keywords": "",
            "abstract": "El aumento de la población de las últimas décadas y el flujo de personas de las zonas rurales a las grandes ciudades ha ocasionado un incremento del volumen de desplazamientos de pasajeros y mercancías, llevando a la sobresaturación de la circulación vehicular y al aumento de las necesidades de movilidad para satisfacer la competitividad comercial y la calidad de vida de sus habitantes, convirtiendo el tráfico en un factor relevante en la contaminación del aire y un importante elemento de riesgo medioambiental para la salud de grupos vulnerables como mujeres, niños y adultos mayores. Por ello, la gestión óptima de la movilidad urbana se enfoca un modelo capaz de cubrir tanto el aumento de la urbanización como su impacto en la calidad del aire, lo cual representa un reto para los gobiernos a nivel mundial en cuanto el diseño y ejecución de metodologías y estrategias orientadas la reducción de los niveles de contaminación del aire en pro de mejorar la salud cardiovascular y respiratoria de la población. Teniendo en cuenta lo anterior, este proyecto se orienta a la aplicación, valoración y comparación de modelos Deep Learning (DL) entre ellos el LSTM, analizando los datos meteorológicos y de material particulado de tres estaciones de monitoreo ambiental del Área -Metropolitana de Bucaramanga AMB, con el objetivo de obtener un modelo predictivo que apoye la planeación de estrategias orientadas a mejorar la calidad del aire.",
            "source_url": "https:\/\/openalex.org\/W7133456949",
            "pdf_url": "https:\/\/noesis.uis.edu.co\/bitstreams\/d0df09f4-c674-4b89-a85e-1ecdb2d1ec18\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Universidad Industrial de Santander",
            "source_record_id": "W7133456949",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 306,
            "title": "Optimization of video surveillance networks in smart cities using video compressive sensing and deep learning techniques",
            "title_ar": "",
            "title_en": "",
            "author": "Wael Saideni",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2022,
            "language": "fr",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; ai-applications; transformers; video-analysis; ai-smart-cities",
            "keywords": "",
            "abstract": "Optimisation d'un système de video surveillance dans les villes intelligentes en utilisant le Deep Learning et la video compressive sensing La technique de compressive sensing joue un rôle important dans le traitement des données vu que l’acquisition et la compression se font simultanément grâce à un processus de prise de mesures. Cette technique optimise les capacités de stockage des systèmes ainsi que la vitesse et le coût d’acquisition. Récemment, cette technique est devenue de plus en plus utilisée grâce à l’optimisation des algorithmes de reconstruction en utilisant les architectures du Deep Learning. L’objectif principal de cette thèse est de tirer profit des architectures de Deep Learning pour optimiser la technique de compressive sensing en l’appliquant sur des signaux vidéo et par la suite optimiser l’acquisition, la transmission et la reconstruction des vidéos dans les systèmes numériques modernes. Ainsi, la stratégie adoptée au cours de ces travaux de recherche consiste à commencer par établir une étude comparative sur les approches de vidéo compressive sensing (VCS) basées sur le Deep Learning en évaluant la qualité et la vitesse de reconstruction ainsi que les différentes architectures. Puis, deux environnements de VCS ont été conçus : le premier se base sur la prédiction des frames vidéo en implémentant une approche basée sur un nouveau réseau récurrent et le deuxième exploite les dernières performances réalisées avec les Transformers et le mécanisme d’attention. Alors, la démarche adoptée dans ces deux approches repose sur une analyse de l’état de l’art suivie d’une explication de chaque architecture et une validation expérimentale. Les différentes contraintes rencontrées au cours de ces travaux sont discutées et des solutions appropriées sont proposées.",
            "source_url": "https:\/\/openalex.org\/W7151367475",
            "pdf_url": "https:\/\/theses.hal.science\/tel-03850837\/document",
            "doi": "10.70675\/ad16c2f9zf249z4f36z80ddz17f162cfe1a5",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7151367475",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 307,
            "title": "Encrypted Web Traffic Classification Using Deep Learning",
            "title_ar": "",
            "title_en": "Encrypted Web Traffic Classification Using Deep Learning",
            "author": "Iman Akbari",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Waterloo",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "cnn",
            "categories": "deep-learning; computer-vision; ai-applications; cnn; rnn; ai-smart-cities",
            "keywords": "",
            "abstract": "Traffic classification is essential in network management for operations ranging from capacity planning, performance monitoring, volumetry, and resource provisioning, to anomaly detection and security. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, e.g., as a de-facto in HTTP\/2 and QUIC protocols. In the current state of encrypted traffic classification using Deep Learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations exist that are commonly ignored. In this thesis, we design a novel feature engineering approach that generalizes well for encrypted web protocols, and develop a neural network architecture based on Stacked Long Short-Term Memory (LSTM) layers and Convolutional Neural Networks (CNN) that works very well with our feature design. We evaluate our approach on a real-world traffic dataset from a major ISP and Mobile Network Operator. We achieve an accuracy of 95% in service-level classification with less raw traffic and smaller number of parameters, out-performing a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted web protocols. We also evaluate our approach on a public QUIC dataset with finer and application-level granularity in labeling, achieving an overall accuracy of 99%.",
            "source_url": "https:\/\/openalex.org\/W3162766776",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UWSpace (University of Waterloo)",
            "source_record_id": "W3162766776",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 308,
            "title": "Deep learning with applications for spatiotemporal prediction",
            "title_ar": "",
            "title_en": "Deep learning with applications for spatiotemporal prediction",
            "author": "Jiachuan Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; predictive-analytics",
            "keywords": "",
            "abstract": "HKUST Electronic Theses Deep learning with applications for spatiotemporal prediction by Jiachuan Wang thesis 2024 1 online resource (1 unnumbered page, xv, 115 pages) : illustrations (some color) Spatiotemporal prediction has garnered significant attention for many years. In recent years,",
            "source_url": "https:\/\/openalex.org\/W4405880469",
            "pdf_url": "",
            "doi": "10.14711\/thesis-991013270958103412",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4405880469",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 309,
            "title": "Security of Smart Cyber-Physical Grids: A Deep Learning Approach",
            "title_ar": "",
            "title_en": "Security of Smart Cyber-Physical Grids: A Deep Learning Approach",
            "author": "Jacob Sakhnini",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Guelph",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "ai-cybersecurity",
            "categories": "deep-learning; ai-applications; ai-cybersecurity; ai-smart-cities",
            "keywords": "",
            "abstract": "Cyber physical systems are widely used in critical infrastructure; among the most notable applications is the smart cyber-physical grid. The smart grid technologies are accompanied with various advantages including more efficient power generation and increased integration of green energy sources. As such, many cities around the world are investing in smart cyber-physical grid technologies. The use of this technology, however, comes with great risk to cyber threats. Furthermore, current state of the art defense methods lack in robustness, scalability,and computational efficiency. This thesis presents a deep learning based solution for attack detection in cyber-physical systems, particularly in the case of the smart cyber-physical grid.The research methods implemented in this thesis focus on improving robustness, scalability,and computational efficiency of intelligent attack detection algorithms by presenting heuristic methods for feature extraction and a novel deep learning approach that proved robust to varying attack sparsity and data imbalance.",
            "source_url": "https:\/\/openalex.org\/W3040743421",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "The Atrium (University of Guelph)",
            "source_record_id": "W3040743421",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 311,
            "title": "Modeling Vehicular Traffic Shock Wave with Machine Learning Approaches",
            "title_ar": "",
            "title_en": "Modeling Vehicular Traffic Shock Wave with Machine Learning Approaches",
            "author": "Ji‐Hyoung Kim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2016,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; ai-smart-cities",
            "keywords": "",
            "abstract": "The current trend of transforming old cities into smart cities has revealed many issues of the modern cities. One of the issues is the prevailing traffic jam on highways of the modern cities. The vehicular shock wave has been a problem on highways since it is one of the main causes of the traffic jam. The combination of heavy traffic and small traffic perturbations or unexpected driver actions are the main causes of shock waves. In order to alleviate road traffic caused by shock waves, it is crucial to have a system that predicts shock waves and informs them to the drivers. In this dissertation, we analyzed 6 months of freeway traffic data of Los Angeles, CA, provided by CalTrans PeMS (Performance Measurement System) and obtained the vehicular shock wave propagation speed of each freeway. Based on this information, we propose a machine learning approaches to predict shock waves. We utilize Hidden Markov Model (HMM) to predict if the shock wave will occur and propagate based on neighboring lanes' traffic information. Addtionally, HMM is used to estimate the probability of lane change from one lane to other lanes based on the occupancy of a lane. Baum-Welch algorithm is used to predict the parameters (occupancy and state).We also utilized Deep Learning (DL) in order to predict the shock wave occurrence and propagation. We compared Stacked AutoEncoder (SAE), Deep Belief Networks (DBN), and HMM for the accuracy of the prediction of shock wave occurrences and propagation. These approaches have been tested on the same PeMS data sets and achieved good accuracy. In the future, our models will be used to include modern collision prevention techniques (e.g., anti-shock wave strategies) to test their efficacy and help to reduce the number of potential accidents and save energy in the process. Also, our models can be used to improve traffic simulators to provide driving patterns that are close to real human's.",
            "source_url": "https:\/\/openalex.org\/W2561644561",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "eScholarship (California Digital Library)",
            "source_record_id": "W2561644561",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 314,
            "title": "Deep Learning-Based Time Series Prediction Techniques",
            "title_ar": "",
            "title_en": "Deep Learning-Based Time Series Prediction Techniques",
            "author": "Faria Ferdowsy",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; predictive-analytics",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7215320880",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/cd92228b-c11b-44ad-abc6-03c56b6166f5\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215320880",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 332,
            "title": "Deep learning methods for sequential decision-making in dynamic systems",
            "title_ar": "",
            "title_en": "Deep learning methods for sequential decision-making in dynamic systems",
            "author": "Vasileios Kochliaridis",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "نص السجل: «doctoral dissertation»",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; reinforcement-learning; robotics; predictive-analytics; multimodal; speech-recognition",
            "keywords": "",
            "abstract": "This doctoral dissertation investigates the development, improvement, and evaluation of Deep Learning methods for sequential decision-making in complex and dynamic systems. Such systems are characterized by temporal dependencies, uncertainty, noisy observations, partial observability, non-stationary conditions, and continuously changing environments. The main objective of the dissertation is to design intelligent models that can extract informative representations from sequential data, adapt their decisions over time, and operate reliably under real-world constraints. The proposed methods combine Deep Learning, Reinforcement Learning, imitation learning, uncertainty estimation, and physics-informed learning, with applications in algorithmic trading, autonomous driving, robotic manipulation, and dynamic-system modeling. In algorithmic trading, the dissertation introduces a series of learning-based methods, including TraderNet-CR, UNSURE, VPM, and ViT², for processing financial time series and supporting cryptocurrency trading decisions. These approaches investigate the integration of market representation learning, risk-sensitive decision-making, uncertainty estimation, and transferable predictive models. Particular emphasis is placed on improving robustness under volatile market conditions and reducing the risks associated with unreliable or overconfident predictions. In autonomous driving, several architectures are developed for perception, driving-policy learning, and adaptive vehicle control. The proposed CIL++, CILRL++, and CILRLv3 methods combine Conditional Imitation Learning with Reinforcement Learning to improve driving performance, generalization, and adaptation to previously unseen situations. Additional approaches investigate multimodal perception and temporal modeling, while NADAS addresses adaptive driving and Advanced Driver-Assistance Systems under computational and real-time constraints. The dissertation also examines the control of a human-sized InMoov robotic hand. Deep Learning models are developed for translating visual information and natural-language commands into robotic actuator movements, enabling human-to-robot motion transfer and speech-driven manipulation. These methods demonstrate how heterogeneous sensory inputs can be transformed into coordinated robotic actions. Finally, the dissertation studies Physics-Informed Neural Networks for modeling dynamic systems governed by differential equations. Methods such as GPS, GENESIS, REPS, and GENERAL introduce adaptive and stochastic sampling strategies that focus training on informative regions of the physical domain. These approaches improve convergence and predictive accuracy, particularly in problems involving complex dynamics and steep solution gradients. Overall, the dissertation demonstrates that robust sequential decision-making requires models that combine data-driven representation learning with uncertainty awareness, adaptability, domain knowledge, and application-specific constraints. The results contribute toward the development of intelligent systems that are more accurate, efficient, scalable, and reliable when deployed in complex real-world environments.",
            "source_url": "https:\/\/openalex.org\/W7169305037",
            "pdf_url": "",
            "doi": "10.12681\/eadd\/62340",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W7169305037",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 333,
            "title": "Novel Deep Learning methods for optical recognition",
            "title_ar": "",
            "title_en": "Novel Deep Learning methods for optical recognition",
            "author": "Richárd Bence Rádli",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: REAL-PhD (Hungarian Academy of Sciences)",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Hungary",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7225757392",
            "pdf_url": "https:\/\/real-phd.mtak.hu\/2699\/1\/radli_richard_bence_dissertation.pdf",
            "doi": "10.18136\/pe.2026.1002",
            "handle": "",
            "repository_name": "REAL-PhD (Hungarian Academy of Sciences)",
            "source_record_id": "W7225757392",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 334,
            "title": "Predictive Scheduling of Incoming Goods Using Machine Learning and Deep Learning Methods",
            "title_ar": "",
            "title_en": "",
            "author": "Johannes Simon Michael Rechenauer",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "TU Wien",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Austria",
            "governorate": "",
            "publication_year": 2026,
            "language": "de",
            "research_domain": "deep-learning",
            "research_subdomain": "data-mining",
            "categories": "machine-learning; deep-learning; ai-applications; data-mining; predictive-analytics; ai-education",
            "keywords": "",
            "abstract": "Die vorliegende Arbeit untersucht den Einsatz von Machine-Learning- und Deep-Learning-Verfahren zur prädiktiven Terminierung von Wareneingängen. Ausgangspunkt bilden betriebliche Herausforderungen, welche durch Lieferabweichungen entstehen und eine zuverlässige Materialversorgung erschweren. Die zentrale Zielsetzung besteht somit darin, Wareneingänge mithilfe datengetriebener Prognosemodelle so zu terminieren, dass Materialbedarfe gedeckt und gleichzeitig Lagerbestände möglichst gering gehalten werden. Methodisch orientiert sich diese Arbeit am CRISP-DM-Prozess, welcher als Rahmen dient, um die betriebliche Problemstellung systematisch in ein Data-Mining-Problem zu überführen. Dafür wird die Problemstellung zunächst konzeptionell in einem Unternehmen verortet. Gleichzeitig werden Grundlagen in den Bereichen logistische Planung, Data Mining, Deep Learning und Machine Learning gelegt. Darauf aufbauend wird ein Data-Mining-Projekt in einem realen Unternehmensumfeld ausgearbeitet. Dabei wird die Wareneingangsterminierung als betriebliche Problemstellung eingeordnet und als Klassifikationsproblem formuliert. Im Rahmen der Modellierungsphase werden daraufhin verschiedene Boosting-Modelle und Deep-Learning-Ansätze verglichen. Der Fokus liegt in diesem Zusammenhang nicht auf der isolierten Leistung einzelner Algorithmen, sondern auf der Leistungsfähigkeit der gesamten Modell-Pipeline. Diese umfasst die modellspezifische Datenvorverarbeitung, das Modelltraining, die Hyperparameter-Optimierung sowie die anschließende Evaluation. Dadurch werden die Verfahren unter möglichst praxisnahen Bedingungen hinsichtlich ihrer Vorhersageleistung und praktischen Anwendbarkeit miteinander verglichen. Die Ergebnisse zeigen, dass Boosting-Verfahren im untersuchten Anwendungsfall am besten abschneiden. Insbesondere die auf LightGBM basierende Pipeline erzielt die beste Kombination aus Vorhersageleistung und Effizienz, während die getesteten Deep-Learning-Modelle trotz längerer Trainingszeiten schwächere Ergebnisse liefern. Aufbauend auf den Ergebnissen des Modellvergleichs wird dargestellt, wie prognostizierte Verzögerungsklassen in konkrete Vorschläge zur Wareneingangsterminierung überführt und im Rahmen des CRISP-DM-Prozesses in einem Unternehmen implementiert werden können. Somit leistet die Arbeit einerseits einen Beitrag zur praxisnahen Ausarbeitung eines Data-Mining-Projekts und verdeutlicht andererseits, dass moderne Boosting-Verfahren insbesondere bei kleinen tabellarischen Datensätzen Deep-Learning-Architekturen überlegen sein können.",
            "source_url": "https:\/\/openalex.org\/W7223559876",
            "pdf_url": "https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/230957\/1\/Rechenauer%20Johannes%20Simon%20Michael%20-%202026%20-%20Praediktive%20Terminierung%20von...pdf",
            "doi": "10.34726\/hss.2026.144705",
            "handle": "",
            "repository_name": "reposiTUm (TU Wien)",
            "source_record_id": "W7223559876",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 335,
            "title": "Landslide Risk Assessment and Bridge Disaster Management Using Machine Learning and Deep Learning Models",
            "title_ar": "",
            "title_en": "Landslide Risk Assessment and Bridge Disaster Management Using Machine Learning and Deep Learning Models",
            "author": "Fudong Ren",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "machine-learning; deep-learning",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7143658765",
            "pdf_url": "https:\/\/eprints.lib.hokudai.ac.jp\/repo\/huscap\/all\/95010\/Ren_Fudong.pdf",
            "doi": "10.14943\/doctoral.k16343",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7143658765",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 336,
            "title": "Text-to-point cloud image synthesis using deep learning",
            "title_ar": "",
            "title_en": "Text-to-point cloud image synthesis using deep learning",
            "author": "Βότσαρης, Εμμανουήλ Ε. ; Βότσαρης, Εμμανουήλ Ε.",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Thessaly",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Greece",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "",
            "categories": "deep-learning; computer-vision; generative-ai",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7112122528",
            "pdf_url": "http:\/\/ir.lib.uth.gr\/xmlui\/bitstream\/11615\/85698\/4\/31078.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "University of Thessaly Institutional Repository (University of Thessaly)",
            "source_record_id": "W7112122528",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 337,
            "title": "Incorporation of deep learning strategies to improve QOS in VXLAN-based networks",
            "title_ar": "",
            "title_en": "Incorporation of deep learning strategies to improve QOS in VXLAN-based networks",
            "author": "Kumarawadu KR Siriniwansa",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Tampere University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; reinforcement-learning; responsible-ai; ai-applications; predictive-analytics; cnn; rnn; xai; ai-smart-cities",
            "keywords": "",
            "abstract": "This study examines how to improve Quality of Service (QoS) management in VXLAN-based overlay networks by integrating deep learning (DL) techniques. Due to issues like fluctuating workloads, complicated virtualized settings, and increased data traffic, traditional QoS mechanisms frequently fail to provide optimal performance metrics like packet loss, latency, and jitter in modern networks. Examining how DL models, such as CNNs, RNNs, autoencoders, and reinforcement learning, can forecast traffic patterns, identify abnormalities, and optimize resource allocation, the study reviews current QoS strategies in VXLAN. With a focus on situations like SDN, data centers, and NFV, it examines the possible advantages and intrinsic drawbacks of using DL for real-time QoS enhancement. The report also suggests future directions for real-world application while identifying present research limitations in areas like data availability, model explainability, and secure deployment. In order to improve efficiency, security, and user experience, work ultimately promotes intelligent, adaptive QoS frameworks driven by DL that can handle the complexity of large-scale, virtualized network infrastructures.",
            "source_url": "https:\/\/openalex.org\/W7132872346",
            "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/231186\/2\/SiriniwansaKumarawaduKumuthuRandima.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Tampere University Institutional Repository (Tampere University)",
            "source_record_id": "W7132872346",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 338,
            "title": "Deep Learning and Ideological Rhetoric",
            "title_ar": "",
            "title_en": "Deep Learning and Ideological Rhetoric",
            "author": "Brice D. L. Acree",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of North Carolina at Chapel Hill",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2016,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "deep-learning; nlp; cnn; speech-recognition",
            "keywords": "",
            "abstract": "Political conflict unfolds in language. To understand the quest for, and exercise of, power, we must understand political speech. For more than a decade, political methodologists have sought to develop methods for using language to study how politicians speak to the public, to the media, and to each other. This dissertation advances that line of research. Chapters 2 and 3 address `deep learning' methods for analyzing political texts. These computational models, built on artificial neural network architectures, seek to automatically learn complex patterns in data. Chapter 2 introduces basic deep learning models, and Chapter 3 introduces distributional word representations and convolutional neural network models. The final chapter points these methods toward a substantive problem. As a discipline, we have studied, and debated, the nature of political ideology. We have often found that, for citizens and elites alike, ideology is oriented around a single primary dimension: liberalism to conservatism. In Chapter 4, I offer a different view. Using the language from political ideologues and presidential candidates, I show that ideology can be much richer and varied than a single dimension will capture, but that political debate compresses ideological expression into a single dimension.",
            "source_url": "https:\/\/openalex.org\/W2966973115",
            "pdf_url": "",
            "doi": "10.17615\/mm0p-jk38",
            "handle": "",
            "repository_name": "Carolina Digital Repository (University of North Carolina at Chapel Hill)",
            "source_record_id": "W2966973115",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 339,
            "title": "DEEP LEARNING FOR DATA-DRIVEN DECISION-MAKING",
            "title_ar": "",
            "title_en": "DEEP LEARNING FOR DATA-DRIVEN DECISION-MAKING",
            "author": "Zhiqiang Wan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Rhode Island",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "ai-smart-cities",
            "categories": "deep-learning; medical-ai; reinforcement-learning; robotics; responsible-ai; ai-applications; ai-smart-cities",
            "keywords": "",
            "abstract": "As we are entering the big data era, data are becoming high-volume, complex, and heterogeneous. Nowadays, almost every field and sector of the modern society is being impacted by big data, ranging from energy system to health care, and from business to government. The excessive amount of data contains potential and highly useful values. Extracting values from these data and utilize it to support the decision-making process is of paramount importance to improve productivity in business and enhance the well-being of our society. However, data-driven decision-making also arises with many challenges, such as feature selection, data fusion, and real-time decision-making. To overcome these challenges, this dissertation will develop deep learning algorithms and frameworks for data-driven decision-making. This work is composed of three major parts: automatic feature extraction, heterogeneous data fusion, and deep reinforcement learning (DRL) based data-driven frameworks. This dissertation will first investigate the automatic feature extraction to avoid the burden of the “feature engineering” process. To make sure the feature extractor can be deployed to different scenarios, domain adaptive feature extraction will be studied. This dissertation will then examine the heterogeneous data fusion problem where feature refinement, normalization, transformation, and fusion will be investigated. With the automatic feature extraction and heterogeneous data fusion, this dissertation will develop data-driven decision-making frameworks for real-world applications, including robot-assisted pedestrian regulation and real-time electric vehicle (EV) charging management. Numerous experiments will be conducted to verify the effectiveness of the proposed frameworks.",
            "source_url": "https:\/\/openalex.org\/W3095226734",
            "pdf_url": "https:\/\/digitalcommons.uri.edu\/cgi\/viewcontent.cgi?article=2224&context=oa_diss",
            "doi": "10.23860\/wan-zhiqiang-2020",
            "handle": "",
            "repository_name": "Digital Commons - URI (University of Rhode Island)",
            "source_record_id": "W3095226734",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 340,
            "title": "Variational and Deep Learning Methods in Computer Vision",
            "title_ar": "",
            "title_en": "Variational and Deep Learning Methods in Computer Vision",
            "author": "Ivan Díaz",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "نص السجل: «Tesis Doctoral»",
            "university": "King Juan Carlos University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "",
            "categories": "deep-learning; computer-vision",
            "keywords": "",
            "abstract": "Tesis Doctoral le?da en la Universidad Rey Juan Carlos de Madrid en 2019. Directores de la Tesis: Juan Jos? Pantrigo Fern?ndez y Emanuele Schiavi",
            "source_url": "https:\/\/openalex.org\/W2992684445",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "BURJC Digital (King Juan Carlos University)",
            "source_record_id": "W2992684445",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 341,
            "title": "Deep Learning for Unsupervised Relation Extraction",
            "title_ar": "",
            "title_en": "",
            "author": "Etienne Simon",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2022,
            "language": "fr",
            "research_domain": "computer-vision",
            "research_subdomain": "",
            "categories": "deep-learning; computer-vision",
            "keywords": "",
            "abstract": "Apprentissage profond pour l'extraction non supervisée de relations Détecter les relations exprimées dans un texte est un problème fondamental de la compréhension du langage naturel. Il constitue un pont entre deux approches historiquement distinctes de l'intelligence artificielle, celles à base de représentations symboliques et distribuées. Cependant, aborder ce problème sans supervision humaine pose plusieurs problèmes et les modèles non supervisés ont des difficultés à faire écho aux avancées des modèles supervisés. Cette thèse aborde deux lacunes des approches non supervisées : le problème de la régularisation des modèles discriminatifs et le problème d'exploitation des informations relationnelles à partir des structures des jeux de données. La première lacune découle de l'utilisation de réseaux neuronaux profonds. Ces modèles ont tendance à s'effondrer sans supervision. Pour éviter ce problème, nous introduisons deux fonctions de coût sur la distribution des relations pour contraindre le classifieur dans un état entraînable. La deuxième lacune découle du développement des approches au niveau des jeux de données. Nous montrons que les modèles non supervisés peuvent tirer parti d'informations issues de la structure des jeux de données, de manière encore plus décisive que les modèles supervisés. Nous exploitons ces structures en adaptant les méthodes non supervisées existantes pour capturer les informations topologiques à l'aide de réseaux convolutifs pour graphes. De plus, nous montrons que nous pouvons exploiter l'information mutuelle entre les données topologiques et linguistiques pour concevoir un nouveau paradigme d'entraînement pour l'extraction non supervisée de relations.",
            "source_url": "https:\/\/openalex.org\/W7151633375",
            "pdf_url": "https:\/\/theses.hal.science\/tel-03828226\/document",
            "doi": "10.70675\/54e335dezf72bz49b7zb1e3z04b661fda611",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7151633375",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 342,
            "title": "Exploration of Deep Learning Models on Streamflow Simulations",
            "title_ar": "",
            "title_en": "Exploration of Deep Learning Models on Streamflow Simulations",
            "author": "Jinyang Li",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; rnn; image-segmentation; ai-agriculture",
            "keywords": "",
            "abstract": "Streamflow simulation and forecasting is an important approach for water resources management and flood mitigation, and both physically-based and data-driven models have been used to predict the streamflow at daily and hourly scale. Although physically-based models can help understand the underlying mechanisms of the hydrological process based on mathematical and physical equations, the performance of the models largely depends on the availability and quality of the spatial information. The computation cost also limits its application in the fast, reliable and accurate streamflow simulation and forecasting. In contrast, data-driven models, especially deep learning models, are capable of learning the nonlinear relationships between the inputs and outputs without explicitly referring to the physical process. In addition, deep learning techniques, powered by graphics processing units (GPUs) can allow researchers to train models 10 or 20 times faster, which is more promising for inputs-outputs simulation. Currently, the deep learning models rely heavily on prior streamflow information to make predictions, while the hydrological observations are limited in the real world applications. In this Thesis, the effectiveness of deep learning models on streamflow simulation without using the previous streamflow information was explored. In specific, the three deep learning models the ANN, RNN and LSTM models were used to simulate the streamflow at multiple lead time and different sites using the precipitation and soil moisture. In addition, the effectiveness of deep learning models on learning from a physically-based model to generate pixel-to-pixel streamflow mapping has also been tested.All three deep learning models can achieve reliable and accurate streamflow simulations at the lead time of 1-5 h at the Ninnekah site with Pearson correlation coefficient (r) over 0.9 for all three models. Based on the prediction of the peak and low flow on different sites, the LSTM model shows more consistent and reliable streamflow simulation results and can work better in different basin scales. For the U-net model, it can learn the patterns of ParFlow-CLM model well and achieves average r of 0.770 and 0.820 in the whole domain for validation and calibration, respectively. However, the models have limitations in predicting the peak due to the feature of the imbalanced data.",
            "source_url": "https:\/\/openalex.org\/W3188379214",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "eScholarship (California Digital Library)",
            "source_record_id": "W3188379214",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 343,
            "title": "ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM",
            "title_ar": "",
            "title_en": "",
            "author": "Gian Maria Campedelli",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "it",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "Il terrorismo di stampo jihadista rappresenta una minaccia per la società e una sfida per gli scienziati interessati a comprenderne la complessità. Questa complessità richiede costantemente nuovi sviluppi in termini di ricerca sul terrorismo. Migliorare la conoscenza empirica rispetto a tale fenomeno può potenzialmente contribuire a sviluppare applicazioni concrete e, in ultima istanza, a prevenire danni all’uomo. In considerazione di tali aspetti, questa tesi presenta un nuovo quadro metodologico che integra scienza delle reti, modelli stocastici e apprendimento profondo per far luce sul terrorismo jihadista sia a livello esplicativo che predittivo. In particolare, questo lavoro compara e analizza le organizzazioni jihadiste più attive a livello mondiale (ovvero lo Stato Islamico, i Talebani, Al Qaeda, Boko Haram e Al Shabaab) per studiarne i pattern comportamentali e predirne le future azioni. Attraverso un impianto teorico che si poggia sulla concentrazione spaziale del crimine e sulle prospettive strategiche del comportamento terroristico, questa tesi persegue tre obiettivi collegati utilizzando altrettante tecniche ibride. In primo luogo, verrà esplorata la complessità operativa delle organizzazioni jihadiste attraverso l’analisi di matrici stocastiche di transizione e verrà presentato un nuovo coefficiente, denominato “Normalized Transition Similarity”, che misura la somiglianza fra paia di gruppi in termini di dinamiche operative. In secondo luogo, i processi stocastici di Hawkes aiuteranno a testare la presenza di meccanismi di dipendenza temporale all’interno delle più comuni sotto-sequenze strategiche di ciascun gruppo. Infine, il framework integrerà la meta-reti complesse e l’apprendimento profondo per classificare e prevedere i target a maggiore rischio di essere colpiti dalle organizzazioni jihadiste durante i loro futuri attacchi. Per quanto riguarda i risultati, le matrici stocastiche di transizione mostrano che i gruppi terroristici possiedono un ricco e complesso repertorio di combinazioni in termini di armi e obiettivi. Inoltre, i processi di Hawkes indicano la presenza di diffusa self-excitability nelle sequenze di eventi. Infine, i modelli predittivi che sfruttano la flessibilità delle serie temporali derivanti da grafi dinamici e le reti neurali Long Short-Term Memory forniscono risultati promettenti rispetto ai target più a rischio. Nel complesso, questo lavoro ambisce a dimostrare come connessioni astratte e nascoste fra eventi possano essere fondamentali nel rivelare le meccaniche del comportamento jihadista e come processi memory-like (ovvero molteplici comportamenti ricorrenti, interconnessi e non randomici) possano risultare estremamente utili nel comprendere le modalità attraverso cui tali organizzazioni operano.",
            "source_url": "https:\/\/openalex.org\/W3013089364",
            "pdf_url": "",
            "doi": "10.83049\/unicatt\/publicatt\/10280_70552",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3013089364",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 344,
            "title": "Deep Learning Approaches for Condition Monitoring and Prognostic Analysis",
            "title_ar": "",
            "title_en": "Deep Learning Approaches for Condition Monitoring and Prognostic Analysis",
            "author": "Zunya Shi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Michigan",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; ai-industry",
            "keywords": "",
            "abstract": "Predictive analytics is a technique to make predictions about future unknown events by analyzing current and historical data. The market for predictive analysis is expected to reach $12.4 billion in 2022. Prognostic analysis is one dimension of predictive analytics in engineering discipline and focuses on predicting the time when a system or a component will no longer perform its intended function. A robust and accurate prognostic analysis makes for the increment of production reliability and safety as well as maintenance cost reduction, thus becoming one key task of condition-based maintenance (CBM). Given multiple sensors to simultaneously monitor the health condition during the age of Internet of Things (IoT) and Industry 4.0, traditional prognostic technologies have the limited ability of handling large-scale dataset embedded with complex structures. Therefore, this dissertation mainly focuses on developing high-performance Deep Learning (DL) technologies for condition monitoring and prognostic analysis under the data-rich environment. Besides the CBM, the proposed DL technologies can be also successfully extended and applied into other fields, like battery power estimation, Alzheimer’s Disease (AD) prediction, etc., which have been validated in the dissertation with real case studies. In addition, this dissertation also studied and answered research questions like how to evaluate the quality of features for prognostic analysis, how to select and extract high-level features from original dataset, how to capture the interactions between different features, and so forth. The answers for these questions play important roles in model development for prognostic analysis.",
            "source_url": "https:\/\/openalex.org\/W4287024228",
            "pdf_url": "",
            "doi": "10.7302\/2533",
            "handle": "",
            "repository_name": "Deep Blue (University of Michigan)",
            "source_record_id": "W4287024228",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 345,
            "title": "Modeling and Optimization of Active Distribution Network Operation Based on Deep Learning",
            "title_ar": "",
            "title_en": "Modeling and Optimization of Active Distribution Network Operation Based on Deep Learning",
            "author": "Wenlong Liao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Aalborg Universitet",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Denmark",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "During his doctoral studies, he aims to bridge the gap",
            "source_url": "https:\/\/openalex.org\/W4384202958",
            "pdf_url": "https:\/\/vbn.aau.dk\/files\/539422562\/PHD_WL.pdf",
            "doi": "10.54337\/aau539422562",
            "handle": "",
            "repository_name": "VBN Forskningsportal (Aalborg Universitet)",
            "source_record_id": "W4384202958",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 346,
            "title": "Deep Learning for 3D Information Extraction from Indoor and Outdoor Point Clouds",
            "title_ar": "",
            "title_en": "Deep Learning for 3D Information Extraction from Indoor and Outdoor Point Clouds",
            "author": "Ying Li",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "نص السجل: «doctoral dissertation»",
            "university": "University of Waterloo",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; object-detection; image-segmentation",
            "keywords": "",
            "abstract": "This thesis focuses on the challenges and opportunities that come with deep learning in the extraction of 3D information from point clouds. To achieve this, 3D information such as point-based or object-based attributes needs to be extracted from highly-accurate and information-rich 3D data, which are commonly collected by LiDAR or RGB-D cameras from real-world environments. Driven by the breakthroughs brought by deep learning techniques and the accessibility of reliable 3D datasets, 3D deep learning frameworks have been investigated with a string of empirical successes. However, two main challenges lead to the complexity of deep learning based per-point labeling and object detection in real scenes. First, the variation of sensing conditions and unconstrained environments result in unevenly distributed point clouds with various geometric patterns and incomplete shapes. Second, the irregular data format and the requirements for both accurate and efficient algorithms pose problems for deep learning models. To deal with the above two challenges, this doctoral dissertation mainly considers the following four features when constructing 3D deep models for point-based or object-based information extraction: (1) the exploration of geometric correlations between local points when defining convolution kernels, (2) the hierarchical local and global feature learning within an end-to-end trainable framework, (3) the relation feature learning from nearby objects, and (4) 2D image leveraging for 3D object detection from point clouds. Correspondingly, this doctoral thesis proposes a set of deep learning frameworks to deal with the 3D information extraction specific for scene segmentation and object detection from indoor and outdoor point clouds. Firstly, an end-to-end geometric graph convolution architecture on the graph representation of a point cloud is proposed for semantic scene segmentation. Secondly, a 3D proposal-based object detection framework is constructed to extract the geometric information of objects and relation features among proposals for bounding box reasoning. Thirdly, a 2D-driven approach is proposed to detect 3D objects from point clouds in indoor and outdoor scenes. Both semantic features from 2D images and the context information in 3D space are explicitly exploited to enhance the 3D detection performance. Qualitative and quantitative experiments compared with existing state-of-the-art models on indoor and outdoor datasets demonstrate the effectiveness of the proposed frameworks. A list of remaining challenges and future research issues that help to advance the development of deep learning approaches for the extraction of 3D information from point clouds are addressed at the end of this thesis.",
            "source_url": "https:\/\/openalex.org\/W3140945473",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UWSpace (University of Waterloo)",
            "source_record_id": "W3140945473",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 347,
            "title": "Deep learning-based damage detection on cherry leaves",
            "title_ar": "",
            "title_en": "",
            "author": "Hazel Bozcu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2024,
            "language": "tr",
            "research_domain": "deep-learning",
            "research_subdomain": "cnn",
            "categories": "deep-learning; cnn; rnn",
            "keywords": "",
            "abstract": "Bu çalışmada tarımsal üretimde verim artışını sağlamak amacıyla kiraz yapraklarındaki hastalıkların tespiti için derin öğrenme yöntemlerinin kullanılması amaçlanmaktadır. Günümüzde yaprak hastalıkların tespiti uzman personel tarafından yapılabilmekte ancak uzun sürmektedir. Ayrıca, bu uzmanların sayısı yetersiz olabilir, bu da doğru tespitin zorluğuna işaret etmektedir. Bu nedenle, kiraz üretimini artırmak ve hastalıkları erken teşhis etmek için derin öğrenme temelli hastalık tespit uygulamalarının kullanımı bu çalışmanın ana amacıdır. Çalışmada iki farklı veri seti üzerinde kiraz yaprak hastalığı tespiti için farklı derin öğrenme modellerinin performansı incelenmiştir. Ortalama sonuçlara göre, MobileNet-V2 modeli .68 doğruluk ve .83 hassasiyet ile yüksek bir performans sergilemiş, Inception-V3 modeli .17 F1-Skor ile tatmin edici sonuçlar vermiştir. Önerilen CNN modeli .83 F1- Skor ile dikkat çekmiş, önerilen CNN + LSTM modeli .33 geri çağırma değeri ile tatmin edici sonuçlar sunmuş ve önerilen CNN + BiLSTM modeli .33 F1-Skor ile yüksek bir performans göstermiştir. En iyi sonuçlar kategorisinde, MobileNet-V2 modeli .03 doğruluk oranı ve .33 F1- Skor ile en iyi sonuçları vermiş, Inception-V3 modeli .00 doğruluk ve .33 hassasiyet ile tatmin edici bir performans sergilemiştir. Önerilen CNN modeli .66 F1-Skor ile iyi bir performans sergilemiş, önerilen CNN + LSTM modeli .67 doğruluk ve .67 F1-Skor ile en iyi sonuçları vermiş ve önerilen CNN + BiLSTM modeli .66 F1-Skor ile yüksek bir performans göstermiştir. Bu sonuçlar, kiraz yaprak hastalığı tespiti için derin öğrenme modellerinin umut veren sonuçlar elde ettiğini göstermektedir. Çalışma kapsamında en iyi sonuçlar MobileNet-V2 ve önerilen CNN + LSTM modelleri tarafından elde edilmiştir. Bu çalışmanın, çeşitli veri setleri kullanılarak güvenirliliği arttırılabilir, farklı derin öğrenme yöntemleri kullanılarak hastalık tespit başarım oranları yükseltilebilir ve hastalık tespit süreleri düşürülebilir.",
            "source_url": "https:\/\/openalex.org\/W7218050026",
            "pdf_url": "https:\/\/acikkaynak.bilecik.edu.tr\/bitstreams\/629b0f54-7406-4380-bfcc-442022f926d5\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Bilecik Şeyh Edebali University Institutional Repository",
            "source_record_id": "W7218050026",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 348,
            "title": "Evaluation and validation of deep learning strategies for bioimage analyses",
            "title_ar": "",
            "title_en": "Evaluation and validation of deep learning strategies for bioimage analyses",
            "author": "Dennis Segebarth",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Würzburg University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "fairness",
            "categories": "deep-learning; computer-vision; responsible-ai; fairness",
            "keywords": "",
            "abstract": "Significant advances in fluorescence imaging techniques enable life scientists today to gain insights into biological systems at an unprecedented scale. The interpretation of image features in such bioimage datasets and their subsequent quantitative analysis is referred to as bioimage analysis. A substantial proportion of bioimage analyses is still performed manually by a human expert - a tedious process that is long known to be subjective. Particularly in tasks that require the annotation of image features with a low signal-to-noise ratio, like in fluorescence images of tissue samples, the inter-rater agreement drops. However, like any other scientific analysis, also bioimage analysis has to meet the general quality criteria of quantitative research, which are objectivity, reliability, and validity. Thus, the automation of bioimage analysis with computer-aided approaches is highly desirable. Albeit conventional hard-coded algorithms are fully unbiased, a human user has to set its respective feature extraction parameters. Thus, also these approaches can be considered subjective. Recently, deep learning (DL) has enabled impressive advances in computer vision research. The predominant difference between DL and conventional algorithms is the capability of DL models to learn the respective task on base of an annotated training dataset, instead of following user-defined rules for feature extraction. This thesis hypothesized that DL can be used to increase the objectivity, reliability, and validity of bioimage analyses, thus going beyond mere automation. However, in absence of ground truth annotations, DL models have to be trained on manual and thus subjective annotations, which could cause the model to incorporate such a bias. Moreover, model training is stochastic and even training on the same data could result in models with divergent outputs. Consequently, both the training on subjective annotations and the model-to-model variability could impair the quality of DL-based bioimage analyses. This thesis systematically assessed the impacts of these two limitations experimentally by analyzing fluorescence signals of a protein called cFOS in mouse brain sections. Since the abundance of cFOS correlates with mouse behavior, behavioral analyses could be used for cross-validation of the bioimage analysis results. Furthermore, this thesis showed that pooling the input of multiple human experts during model training and integration of multiple trained models in a model ensemble can mitigate the impact of these limitations. In summary, the present study establishes guidelines for how DL can be used to increase the general quality of bioimage analyses.",
            "source_url": "https:\/\/openalex.org\/W3199015547",
            "pdf_url": "https:\/\/nbn-resolving.org\/urn:nbn:de:bvb:20-opus-243728",
            "doi": "10.25972\/opus-24372",
            "handle": "",
            "repository_name": "Online Publication Service of Würzburg University (Würzburg University)",
            "source_record_id": "W3199015547",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 349,
            "title": "Deep Learning and Reward Design for Reinforcement Learning",
            "title_ar": "",
            "title_en": "Deep Learning and Reward Design for Reinforcement Learning",
            "author": "Xiaoxiao Guo",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Michigan",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "cnn",
            "categories": "deep-learning; computer-vision; nlp; reinforcement-learning; cnn",
            "keywords": "",
            "abstract": "One of the fundamental problems in Artificial Intelligence is sequential decision making in a flexible environment. Reinforcement Learning (RL) gives a set of tools for solving sequential decision problems. Although the theory of RL addresses a general class of learning problems with a constructive mathematical formulation, the challenges posed by the interaction of rich perception and delayed rewards in many domains remain a significant barrier to the widespread applicability of RL methods. The rich perception problem itself has two components: 1) the sensors at any time step do not capture all the information in the history of observations, leading to partial observability, and 2) the sensors provide very high-dimensional observations, such as images and natural languages, that introduce computational and sample-complexity challenges for the representation and generalization problems in policy selection. The delayed reward problem—that the effect of actions in terms of future rewards is delayed in time—makes it hard to determine how to credit action sequences for reward outcomes. This dissertation offers a set of contributions that adapt the hierarchical representation learning power of deep learning to address rich perception in vision and text domains, and develop new reward design algorithms to address delayed rewards. The first contribution is a new learning method for deep neural networks in vision-based real-time control. The learning method distills slow policies of the Monte Carlo Tree Search (MCTS) into fast convolutional neural networks, which outperforms the conventional Deep Q-Network. The second contribution is a new end-to-end reward design algorithm to mitigate the delayed rewards for the state-of-the-art MCTS method. The reward design algorithm converts visual perceptions into reward bonuses via deep neural networks, and optimizes the network weights to improve the performance of MCTS end-to-end via policy gradient. The third contribution is to extend existing policy gradient reward design method from single task to multiple tasks. Reward bonuses learned from old tasks are transferred to new tasks to facilitate learning. The final contribution is an application of deep reinforcement learning to another type of rich perception, ambiguous texts. A synthetic data set is proposed to evaluate the querying, reasoning and question-answering abilities of RL agents, and a deep memory network architecture is applied to solve these challenging problems to substantial degrees.",
            "source_url": "https:\/\/openalex.org\/W2751395930",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Deep Blue (University of Michigan)",
            "source_record_id": "W2751395930",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 350,
            "title": "Exploring deep learning techniques to tackle the sparsity problem in recommender systems",
            "title_ar": "",
            "title_en": "Exploring deep learning techniques to tackle the sparsity problem in recommender systems",
            "author": "Meshal Alfarhood",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Missouri",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; predictive-analytics; transformers",
            "keywords": "",
            "abstract": "With the inception of e-commerce in the early twenty-first century, people's lifestyles have drastically changed. People today tend to do many of their daily routines online, such as shopping, reading the news, and watching movies. Nevertheless, consumers often face diffculties while exploring related items such as new fashion trends because they are not aware of their existence due to the overwhelming amount of information available online. This phenomenon is widely known as \"information overload\". Therefore, recommender systems (RSs) are a critical solution for helping users make decisions when there are lots of choices. RSs have been integrated into and have become an essential part of every website due to their effectiveness in increasing customer interactions, attracting new customers, and growing business revenue. Machine learning, and deep learning (DL) in particular, have achieved a great success in resolving various computer science problems. Generally, DL-based approaches have enhanced performance remarkably compared with traditional approaches. Specifically, DL-based approaches have become the state-of-the-art techniques in RSs. Therefore, in this dissertation, three DL-based contributions are presented to address the natural data sparsity problem in RSs: (1) DeepHCF, a deep-hybrid, collaborative-filtering model that trains two deep models via joint training for rating prediction tasks; (2) CATA, a collaborative attentive autoencoder that integrates the attention mechanism to enhance the recommendation quality for ranking prediction tasks; and (3) CATA++, an extended version of CATA that employs a dual attentive autoencoder to leverage more of the item's content. All proposed models have gone through comprehensive experiments to evaluate their performance against state-of-the-art models using real-world datasets. Our experimental results show the superiority of our models over state-of-the-art models according to various evaluation metrics.",
            "source_url": "https:\/\/openalex.org\/W4389989985",
            "pdf_url": "https:\/\/mospace.umsystem.edu\/xmlui\/bitstream\/10355\/88863\/1\/AlfarhoodMeshal.pdf",
            "doi": "10.32469\/10355\/88863",
            "handle": "",
            "repository_name": "MOspace Institutional Repository (University of Missouri)",
            "source_record_id": "W4389989985",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 351,
            "title": "Community College Students' Deep Learning Approaches in OER Courses",
            "title_ar": "",
            "title_en": "Community College Students' Deep Learning Approaches in OER Courses",
            "author": "Kim Grewe",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Old Dominion University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "ai-education",
            "categories": "deep-learning; medical-ai; ai-applications; ai-education",
            "keywords": "",
            "abstract": "Open Educational Resources (OER) have the potential to bridge the gap for community college students not only because they are more affordable or provide access but also because they have the potential to make learning more meaningful for these same students. Although issues related to access and affordability have been extensively researched, less is known about the ways in which OER use may impact community college students’ deep approaches to learning. More qualitative research around OER efficacy from the student perspective is needed. The purpose of this study was to describe the ways students use OER and how students’ OER use may impact their deep approaches to learning. This study employed qualitative research methods, collecting data from focus groups composed of community college students. The major findings indicated that students use OER to relieve some of the financial stress associated with being a community college student. Students often go outside of the class environment to find OER to help them address learning preferences, diagnose and accommodate learning disabilities, remediate weak skills areas, and learn more about topics of interest. These students benefit from and sometimes participate in Open Pedagogical practices and demonstrate deep approaches to learning when they access openly licensed and freely shared OER and use OER to collaborate, peer validate, and publish work outside the class environment. Students also benefit from strong OER design. OER are often well-sequenced, using techniques like scaffolding and chunking to move learners through material at a manageable pace for optimal learning. OER are often interactive and make use of adaptive technologies to personalize instruction and engage learners. Presenting the students’ perspective through qualitative research is a critical component to better understanding the efficacy of OER for student learning in community college. Faculty should continue to explore OER use in their courses and should widen their traditional circles of collaboration to design their courses using OER. Community college administrators should begin to explore OER as part of their strategic plans for decreasing the financial burden of attending college for students and for increasing student learning and success at their colleges.",
            "source_url": "https:\/\/openalex.org\/W2963789226",
            "pdf_url": "",
            "doi": "10.25777\/w9a9-d072",
            "handle": "",
            "repository_name": "ODU Digital Commons (Old Dominion University)",
            "source_record_id": "W2963789226",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 352,
            "title": "Computer aided diagnosis system for breast cancer using deep learning.",
            "title_ar": "",
            "title_en": "Computer aided diagnosis system for breast cancer using deep learning.",
            "author": "Asma Baccouche",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Louisville",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; medical-imaging; ai-cybersecurity",
            "keywords": "",
            "abstract": "The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that",
            "source_url": "https:\/\/openalex.org\/W4306250372",
            "pdf_url": "https:\/\/ir.library.louisville.edu\/cgi\/viewcontent.cgi?article=5203&context=etd",
            "doi": "10.18297\/etd\/3931",
            "handle": "",
            "repository_name": "ThinkIR: The University of Louisville's Institutional Repository (University of Louisville)",
            "source_record_id": "W4306250372",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 353,
            "title": "Cyberthreat discovery in open source intelligence using deep learning techniques",
            "title_ar": "",
            "title_en": "",
            "author": "Eunice Picareta Branco",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2017,
            "language": "pt",
            "research_domain": "computer-vision",
            "research_subdomain": "ai-cybersecurity",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; ai-cybersecurity",
            "keywords": "",
            "abstract": "Face à necessidade crescente de se processar grandes quantidades de dados relativos a ameaças de segurança, fomos cativados pelo desafio da descoberta de ameaças cibernéticas em fontes abertas através do uso de técnicas de aprendizagem automática. Em termos de dados, isto significa que trabalhámos com informação recolhida de fontes abertas como o Twitter. O que distingue o nosso trabalho encontra-se no modo como escolhemos abordar este desafio. A nossa hipótese é a de que processar tais quantidades de dados através de métodos de aprendizagem automática representa uma vantagem significativa em termos de eficiência e adequação, pelo que recorremos a redes neuronais. Escolhemos esta abordagem uma vez que as abordagens de aprendizagem automática têm vindo a ganhar destaque merecido uma vez que asseguram uma maneira robusta de resolver um número de tarefas extremamente complexas no contexto de problemas de big data. Esta dissertação introduz conceitos e noções gerais em que o nosso trabalho se baseia, apresenta o trabalho relacionado consultado por forma a ser eventualmente útil em trabalhos futuros, apresenta também o trabalho que realizámos, os resultados obtidos, e elenca sugestões sobre linhas de progresso promissoras e trabalho futuro. Antes de discutir resultados, é necessário começar por introduzir conceitos centrais, o primeiro dos quais sendo o de aprendizagem automática. Aprendizagem automática (machine learning) pode ser definida como a área ou abordagem da inteligência artificial de forma a que o sistema tenha a aptidão de aprender e melhorar com a experiência. Isto significa que não é necessária programação explícita para resolver o problema de partida pois o sistema de aprendizagem procura por regularidades nos dados e adquire a capacidade de tomar melhores decisões com base nos dados de exemplo que recebe. Aprofundando esta abordagem, uma rede neuronal é um paradigma de processamento inspirado no modo como processos biológicos nervosos, como os que ocorrem no cérebro humano, processam informação. A chave deste paradigma é a conexão entre os elementos básicos do sistema. Este é composto por um grande número de elementos de processamento, os neurónios, organizados em rede que entregam as suas saídas uns aos outros para resolverem problemas específicos, cabendo notar que uma rede neuronal é tipicamente condicionada no seu desenho pelo problema que se pretende que resolva, ou seja, é configurada para uma única aplicação (e.g. reconhecimento de padrões, classificação de dados, etcetera). De entre as técnicas de aprendizagem automática, a aprendizagem profunda (deep learning) tem adquirido grande relevância e vários projectos têm procurado explorar as suas vantagens. Trata-se de uma subárea da aprendizagem automática, e em particular das redes neuronais, sendo que o que distingue esta abordagem consiste no facto de os dados de entrada passarem por várias camadas funcionais de neurónios, usualmente não lineares, até serem totalmente processados. No nosso projecto, a rede neuronal foi aplicada na resolução do problema que consiste na classificação de tweets em itens que se referem a uma ameaça de segurança, ou itens não relevantes a esse respeito. Com essa finalidade, foi implementada uma rede neuronal convolucional, que comparativamente necessita de pouca intervenção humana para ser posta a funcionar. A vantagem de se aliviar a necessidade de tal intervenção também se prende com o tipo da rede, que pode ser supervisionada ou não supervisionada. Em aprendizagem supervisionada, um conjunto de dados de treino injectado na rede é composto por pares de entrada\/saída, sendo que a entrada é tipicamente composta por um vector e a saída é o resultado pretendido para a entrada respetiva. A rede é treinada sobre todo o conjunto de dados para depois ser aplicada a novas situações ou dados de entrada desconhecidos. É assim necessário que o algoritmo de processamento generalize a partir dos dados de treino. No caso da aprendizagem não supervisada, os dados injectados na rede são apenas de entrada, o que obriga a rede a inferir funções que descrevem a possível estrutura subjacente aos dados, pois a sua classificação explícita não é fornecida à rede. Como os dados não estão associados à sua classificação, não é trivial avaliar a adequação do resultado obtido pela rede neste caso. Outro conceito importante é o de redes profundas (deep) vs. rasas (shallow). As redes neuronais são organizadas por camadas. Estas camadas são compostas por nós inter-conectados que contêm funções de activação, compreendendo a camada de entrada, as camadas escondidas, que pode englobar várias camadas para processamento de dados, e a camada de saída. O termo redes rasas é usado para descrever as redes que contêm apenas uma ou duas camadas escondidas, que são funcionalmente idênticas. No caso de redes profundas, estas tendem a ter mais camadas escondidas, com grupos de camadas com funcionalidades distintas.",
            "source_url": "https:\/\/openalex.org\/W2782794488",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W2782794488",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:48",
            "updated_at": "2026-11-15 16:52:48"
        },
        {
            "id": 358,
            "title": "Computational biology in plant tissue culture: Systematic evaluation of machine learning approaches for callus induction in Solanum L.: A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University",
            "title_ar": "",
            "title_en": "Computational biology in plant tissue culture: Systematic evaluation of machine learning approaches for callus induction in Solanum L.: A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University",
            "author": "Sadegh Mohajer",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "نص السجل: «Doctor of Philosophy»",
            "university": "Lincoln University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "xai",
            "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; ai-applications; transfer-learning; predictive-analytics; optimization; xai; ai-agriculture",
            "keywords": "",
            "abstract": "Optimization of plant tissue culture systems remains a major biotechnological challenge; yet successful callus induction, the formation of unorganized, proliferating cell masses from differentiated tissues, enables transformative applications in agriculture, medicine, and fundamental research. Callus serves as a foundation for genetic transformation and genome editing, mass propagation of elite varieties, production of pharmaceutically valuable secondary metabolites, and breeding programs. However, achieving consistent callus induction is hindered by the multifactorial nature of the process, governed by intricate dependencies among environmental conditions, genetic background, hormonal composition, and explant source. Conventional trial-and-error methodologies are further constrained by the difficulty of systematically capturing and optimizing these complex interactions, resulting in protocols with poor transferability and requiring extensive empirical refinement for each new species or genotype. This study established comprehensive machine learning (ML) frameworks to model and predict callus induction rates in the Solanum genus, one of the most diverse angiosperm genera containing approximately 1,500–2,000 species. This genus includes economically significant crop species such as potato, tomato, and eggplant that sustain global food security, as well as wild relatives possessing valuable traits for disease resistance and abiotic stress tolerance. The genus also includes species of pharmaceutical interest, owing to their diverse alkaloids and secondary metabolites, which exhibit antimicrobial, antioxidant, and anticancer activities. Despite their agricultural, nutritional, and medicinal importance, many Solanum species remain recalcitrant in tissue culture, as standard protocols developed for model cultivars often perform poorly when applied to wild species and new genotypes. The ML methods developed here systematically evaluate modeling strategies ranging from global to local approaches, with the goal of addressing the protocol optimization bottleneck that constrains biotechnological applications across this economically and scientifically important genus. Five machine learning algorithms were evaluated: Random Forest and XGBoost as tree-based ensemble models, Support Vector Regression, and Gaussian Process Regression as kernel-based methods, and an artificial neural network (multilayer perceptron) with genetic algorithm optimization for hyperparameter tuning and protocol development. Model interpretability was achieved through SHAP analysis and permutation importance assessment. Genetic algorithm optimization of neural networks identified optimal protocols achieving remarkable callus induction rates, representing substantial improvements over conventional approaches. Feature optimization revealed specific strategies ranging from minimal hormone supplementation to comprehensive growth regulator networks. Sensitivity analysis confirmed that environmental precision provides primary control mechanisms, with hormonal supplementation serving as fine tuning parameters rather than primary drivers. The thesis advances both methodology and biological understanding. It establishes a rigorous methodological framework integrating systematic preprocessing, cross-validation with independent holdout data, and comprehensive interpretability analysis. The approach delivers predictive models with quantified uncertainty and clear performance boundaries. Biologically, the work demonstrates that machine learning can successfully capture complex tissue culture interactions. These findings represent a paradigm shift from empirical protocol development to data-driven optimization, with applications across plant biotechnology, crop improvement, pharmaceutical compound production, and conservation. The work may also motivate future steps that include transfer learning across species, imaging-based phenotyping with deep learning, classification of callus quality tiers, and time-resolved modeling of induction dynamics.",
            "source_url": "https:\/\/openalex.org\/W7131545409",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Lincoln University Research Archive (Lincoln University)",
            "source_record_id": "W7131545409",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 361,
            "title": "Comparative analysis of the efficacy of machine learning models in detecting cancer from medical imagery: a thesis in Computer Science",
            "title_ar": "",
            "title_en": "Comparative analysis of the efficacy of machine learning models in detecting cancer from medical imagery: a thesis in Computer Science",
            "author": "Kyle Andrew Furtado",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging",
            "keywords": "",
            "abstract": "According to a study performed by John Hopkins Medicine in 2023, misdiagnoses make up approximately 11.1% of total diagnoses in the United States, which result in anything from minor to fatal error for the patient. One of the main contributors to this unfortunate statistic is the heavy reliance on human-read medical scans. As a result, this study investigates the effectiveness of various Machine Learning (ML) models in classifying cancer from histopathological medical images. More specifically, Deep Learning (DL) Convolutional Neural Networks (CNNs) leveraging Transfer Learning (TL) are under analysis. For each experiment, one generic CNN is trained from scratch followed by four prominent pretrained models (i.e., DenseNet, VGG16, ResNet, and InceptionNet). The primary disease used for experimentation was colorectal cancer, while brief research was also done with lung cancer, leukemia, and breast cancer. Each model was trained across various levels of data augmentation and shuffles in data to provide a more verifiable set of findings when compared amongst each other. Results indicate optimal model selections for cancer detection under varying scenarios, such as training constraints (e.g., time and computational resources) along with dataset conditions (e.g., sizes and class balances). Overall, this research provides valuable insights on ideal CNNs for histopathological cancer detection that can be applied to developing future cancer detection software, reducing the heavy reliance on human-interpreted medical imagery diagnosis.",
            "source_url": "https:\/\/openalex.org\/W4409785731",
            "pdf_url": "",
            "doi": "10.62791\/20332",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4409785731",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 375,
            "title": "Leveraging Self-Attention in Transformer Models for Improved Retail Demand Forecasting",
            "title_ar": "",
            "title_en": "Leveraging Self-Attention in Transformer Models for Improved Retail Demand Forecasting",
            "author": "Rohith Sardena",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "National College of Ireland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Ireland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; predictive-analytics; rnn; transformers",
            "keywords": "",
            "abstract": "Inventory management and operation planning in retail sector involve accurate demand forecasting as a key element of the process. Linear models and tree-based models are typical examples of traditional forecasting models that may not work well in capturing complex, non-linear, and time-evolving demands that retail demand exhibits especially when it is affected by promotions, seasonal trends, and other external market factors. The study examines the possibility of deep learning models based on Transformers, which will use self-attention mechanisms, to improve the accuracy and scalability of retail demand forecasting systems. Model development was done using a detailed retail dataset with historical sales, inventory, pricing, discounts, promotions, weather conditions, and other contextual characteristics. A number of models were applied and tested in the study: Linear Regression, Decision Tree, Random Forest, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Median Absolute Error (MedianAE) were used to evaluate performance. The classical models were moderate in their performance, and Linear Regression and Random Forest injected MAEs of 7.47 and 7.55 respectively, and RMSEs of more than 8.6. Deep learning models, on the contrary, showed a dramatic increase. Transformer model performed the best and its MAE was 0.81, RMSE was 0.99, and MedianAE was 0.74, which are better than LSTM (MAE: 0.83) and GRU (MAE: 0.83). The results verify that self-attention mechanism of Transformer is capable of learning long-range dependencies and complex temporal interactions, giving significant improvement on the baseline models. This study shows the ability of Transformers to be an efficient and scalable method of real-time retail demand forecasting. Further development will consider multi-step forecasting, probabilistic additions and deployment in other retail areas.",
            "source_url": "https:\/\/openalex.org\/W7204618203",
            "pdf_url": "https:\/\/norma.ncirl.ie\/9664\/2\/rohithsardenaconfigurationmanual.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "TRAP@NCI (National College of Ireland)",
            "source_record_id": "W7204618203",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 377,
            "title": "Hybrid Deep Learning Model: LLM-Enhanced Linear Self-Attention Transformer-CNN for Stock Price Prediction",
            "title_ar": "",
            "title_en": "Hybrid Deep Learning Model: LLM-Enhanced Linear Self-Attention Transformer-CNN for Stock Price Prediction",
            "author": "Yilong Chen",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Auckland University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; cnn; transformers; multimodal; llm",
            "keywords": "",
            "abstract": "The unpredictability and inherent complexities of financial markets pose persistent challenges in accurately forecasting stock prices. Recent innovations in deep learning methodologies, specifically the integration of technical analysis with linguistic processing through large language models (LLMs), have demonstrated significant potential in financial forecasting applications. Despite substantial advances in deep learning for stock price prediction, current approaches face critical limitations in effectively integrating multi-modal data while lacking sophisticated technical analysis capabilities, particularly when simultaneously modeling both temporal dependencies in financial time series and spatial features from stock chart visualizations across diverse market regimes and volatility conditions. This study proposes an innovative hybrid deep learning framework aimed at addressing these multi-modal integration challenges, technical analysis limitations, and cross-market adaptability issues through a structured three-phase methodology. In the initial phase, we leverage an LLM to conduct thorough technical analysis of historical financial data, transforming raw market information into rich textual representations. These representations encapsulate intricate financial patterns using well-established technical indicators such as Bollinger Bands (BBs). By interpreting these financial metrics, the LLM generates structured textual summaries that enhance the downstream predictive modeling process. The second phase involves dual processing pipelines. One pipeline utilizes an optimized attention-based Transformer to process the LLM-generated embeddings, capturing long-range market dependencies. Concurrently, the second pipeline deploys a Convolutional Neural Network (CNN) to analyze visual stock chart patterns, extracting spatially relevant features that complement the temporal insights derived from the Transformer model. In the concluding phase, the outputs from both pipelines—textual, sequential, and visual data—are aggregated to generate holistic stock price predictions. The architecture employs Transformers for extracting extended temporal relationships within market sequential information, whereas Convolutional Networks detect regional spatial configurations from graphical stock representations. Large language models enhance our dataset by generating sophisticated technical metrics, subsequently embedded into numerical vectors through FinBERT encoding techniques. The fusion of these multimodal representations enables a more robust and accurate forecasting model, capitalizing on the complementary strengths of each component to enhance predictive performance.",
            "source_url": "https:\/\/openalex.org\/W7131750035",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Tuwhera (Auckland University of Technology)",
            "source_record_id": "W7131750035",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 380,
            "title": "A Comparative Forecasting Performance Analysis of Symbolic Representation Techniques for Time Series Using Transformer Models and Multi-Head Attention Mechanisms",
            "title_ar": "",
            "title_en": "A Comparative Forecasting Performance Analysis of Symbolic Representation Techniques for Time Series Using Transformer Models and Multi-Head Attention Mechanisms",
            "author": "Rami Alalwan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Klagenfurt",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Austria",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; responsible-ai; ai-applications; predictive-analytics; rnn; transformers; xai; ai-industry",
            "keywords": "",
            "abstract": "The prediction of future values from historical time series data is widely applied infields such as industrial monitoring, finance, and decision support systems. Whilemost forecasting approaches operate directly on numerical time series, symbolicrepresentations have emerged as an alternative that can reduce dimensionality, fil-ter noise, and improve interpretability. However, their effectiveness for forecastingtasks particularly when combined with modern deep learning architectures—remainsinsufficiently explored. This thesis conducts a structured comparison between nu-merical and symbolic approaches to time series forecasting, employing deep learn-ing techniques with particular emphasis on Long Short-Term Memory (LSTM) mod-els and Transformer-based architectures. Numerical time series are transformedinto symbolic sequences using Symbolic Aggregate approXimation (SAX) and Com-pressed adaptive Brownian bridge-based symbolic aggregation (ABBA)-inspired rep-resentation, and both representations are evaluated under identical experimentalconditions. Experiments are conducted on datasets of increasing complexity, includ-ing simple synthetic signals, medium complexity averaged time series, and complexreal-world datasets. The results show that symbolic representations can outperformnumerical representations on simple datasets and achieve comparable performanceon medium complexity datasets, while no clear advantage is observed for highlycomplex and volatile time series. Symbolic inputs demonstrate robustness to noiseand strong pattern-learning capabilities, particularly for smooth or quasi-periodicsignals commonly found in industrial and IoT monitoring applications. However,for complex datasets, symbolic abstraction may lead to temporal misalignment andloss of fine-grained information. Additionally, the study highlights limitations ofthe Mean Absolute Percentage Error (MAPE) metric for complex and non-stationarydata, emphasizing the need for complementary evaluation measures. Overall, thiswork provides a structured evaluation of symbolic time series forecasting in the con-text of modern deep learning, clarifying when symbolic representations are benefi-cial and identifying their limitations as data complexity increases.",
            "source_url": "https:\/\/openalex.org\/W7139666984",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "University of Klagenfurt",
            "source_record_id": "W7139666984",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 385,
            "title": "Feedforward Neural Networks and the Transformer model",
            "title_ar": "",
            "title_en": "Feedforward Neural Networks and the Transformer model",
            "author": "Ebba Brunvall",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; transformers",
            "keywords": "",
            "abstract": "This thesis presents the mathematical framework underlying two central architectures in modern deep learning: the feedforward neural network and the transformer model. Beginning from first principles, the report introduces the perceptron model and builds toward the multi-layer perceptron by incorporating activation functions, layer structure, and matrix notation. The learning process is examined through parameter initialization, the cost function, the backpropagation algorithm, stochastic gradient descent, and regularization techniques including early stopping and dropout. Hyperparameter tuning via grid search is also discussed. The thesis then transitions to the transformer model, introduced in the 2017 paper \"Attention Is All You Need,\" covering the mathematical formulation of scaled dot-product self-attention, multi-head attention, tokenization and positional encoding. The encoder and decoder modules are derived in full matrix form, detailing their respective sublayers, residual connections and layer normalization. Finally, the optimization and regularization choices of the original transformer are discussed. The aim throughout is to bridge the gap between intuitive descriptions and mathematical exposition.",
            "source_url": "https:\/\/openalex.org\/W7205320669",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Högskolan Kristianstad Publikationer",
            "source_record_id": "W7205320669",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 388,
            "title": "Transformer Block Coupling and Self-Attention Flows for Token Evolution",
            "title_ar": "",
            "title_en": "Transformer Block Coupling and Self-Attention Flows for Token Evolution",
            "author": "Anton Pavlovych Sugolov",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Toronto",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "deep-learning; nlp; transformers; llm",
            "keywords": "",
            "abstract": "Transformers and Large Language Models have achieved incredible performance in deep learning, especially in natural language processing, and a precise understanding of the internal mechanisms driving their success is essential. We first provide an empirical analysis of structural characteristics of transformer blocks, by analyzing the trajectories of LLM token embeddings as they traverse the model depth. We linearize the system along the trajectories through their Jacobian matrices and uncover transformer block coupling in a multitude of LLMs, characterized by the coupling of their top singular vectors across tokens and depth. Our findings reveal that coupling positively with model performance, and that this relationship is stronger than with other hyperparameters such as parameter count, model depth, and embedding dimension. Further, we investigate recent theoretical perspectives that cast transformers as iterative dynamical systems which modify embeddings. We provide an analysis of self-attention as a vector field that acts on Gaussian mixtures, building on recent works that construct interaction functionals on measures with similar properties to self-attention. Collectively, these insights offer a novel perspective on token interactions in transformers, opening new directions for studying their mechanisms as well as improving training and generalization.",
            "source_url": "https:\/\/openalex.org\/W7132879793",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "TSpace (University of Toronto)",
            "source_record_id": "W7132879793",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 394,
            "title": "Sparse Attention Mechanisms in Vision Transformers: from Spatiotemporal Transformers to Instance-Dependent Attention Patterns",
            "title_ar": "",
            "title_en": "Sparse Attention Mechanisms in Vision Transformers: from Spatiotemporal Transformers to Instance-Dependent Attention Patterns",
            "author": "Brendan William Duke",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Toronto",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; cnn; transformers; image-segmentation; video-analysis; speech-recognition",
            "keywords": "",
            "abstract": "Deep learning (DL) algorithms have produced superior results for a variety of vision, language, and speech processing tasks, but have proven to be power and compute-hungry. In the past, hardware advancements have met the growing need for compute. Yet in recent years the end of Dennard scaling and Moore's law makes hardware advancements unlikely to continue at the same fast pace. Hence innovative algorithmic solutions are needed to meet DL algorithms' ever-growing demand for compute. In this dissertation we investigated methods using sparsity and conditional computation to meet this compute demand. The two main components of this contribution involve applying structure and dynamic prediction of sparsity patterns by machine learning algorithms. The hypothesis was that structured and dynamic sparse computation improves latency and memory footprint compared to existing approaches with equal algorithm effectiveness. To show this, we built on the state of the art in sparse computations. This dissertation fills in previously unexplored areas of applying structured sparsity to videos, and of predicting per-instance sparsity that is unstructured. The purpose was to investigate using structured and per-example sparsity to design performant and effective deep learning algorithms. Existing literature had investigated sparsity in computer vision, but only in the context of either convolutional neural networks (CNNs) or Transformers applied to image processing. Given the increased computational complexity, this raised the question of how sparsity fares when applied to video processing. We investigated this question in work presented at the CVPR 2021 conference called Sparse Spatiotemporal Transformers for Video Object Segmentation (SSTVOS). SSTVOS looks at the highly computationally demanding problem of applying Transformers to video by using sparse attention blocks. The sparse attention blocks in SSTVOS use a fixed sparsity pattern that takes advantage of a priori knowledge of which visual features to attend to. However, using fixed sparsity patterns limits us to problems where we have this a priori knowledge. Therefore we further executed a research direction to learn sparsity patterns that are performant (low latency and memory usage) and effective (superior predictive power) in a method called Sparsifiner. Sparsifiner predicts per-example sparsity patterns as a more flexible generalization of the fixed sparsity patterns in SSTVOS. With the expectation that a machine learning model learns to predict sparsity patterns superior to any fixed pattern designed by hand, the idea for Sparsifiner was born. Sparsifiner improves over existing sparsity methods in Vision Transformers~(ViTs) by predicting per-instance sparsity patterns with a computationally-efficient connectivity predictor module. The method serves as empirical evidence of the effectiveness of per-instance sparsity patterns compared to fixed sparsity patterns. Sparsifiner shows a Pareto-optimal trade-off between FLOPs and top-1 accuracy on ImageNet when compared to contemporary structured-sparse methods. Together, both SSTVOS and Sparsifiner constitute a basis for future work in the direction of structured and dynamic sparsity in deep learning.",
            "source_url": "https:\/\/openalex.org\/W7132914290",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "TSpace (University of Toronto)",
            "source_record_id": "W7132914290",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 396,
            "title": "Arbitrary-order Sampling and Hand Motion Modeling with Transformers",
            "title_ar": "",
            "title_en": "Arbitrary-order Sampling and Hand Motion Modeling with Transformers",
            "author": "Maxwell Clarke",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; responsible-ai; self-semi-supervised; transformers; fairness",
            "keywords": "",
            "abstract": "Transformer models have been quickly taking over many tasks in the field of deep learning, due to their versatility while maintaining high performance. This thesis provides an introduction to transformer models, presents experiments with new ways of sampling data with them, and then applies them to the domain of hand motion modeling. Firstly, a comprehensive introduction to transformer models is given, including the attention operation, masking, architecture variants, and different pre-training tasks. Secondly, an experiment is presented using a probabilistic transformer model on the MNIST dataset, which was trained so that it is capable of arbitrary-order sampling. The experiment compares different sampling orders, including some dynamic sampling order heuristics based on the entropy. The experiments find that such sampling orders introduce a statistical bias into the samples. Lastly, the problem domain of hand motion modeling is introduced, and transformer models are trained to generate hand-motion sequences, via self-supervised learning on a motion-capture dataset. Both deterministic and probabilistic models are trained. The deterministic models can generate realistic-looking hand motions, but cannot be directed to generate specific motions. The probabilistic model performs poorly.",
            "source_url": "https:\/\/openalex.org\/W4389570359",
            "pdf_url": "https:\/\/openaccess.wgtn.ac.nz\/articles\/thesis\/Arbitrary-order_Sampling_and_Hand_Motion_Modeling_with_Transformers\/24785205\/1\/files\/43587534.pdf",
            "doi": "10.26686\/wgtn.24785205",
            "handle": "",
            "repository_name": "Open Access Victoria University of Wellington | Te Herenga Waka (Figshare)",
            "source_record_id": "W4389570359",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 397,
            "title": "Improving end-to-end transformer model architecture in ASR",
            "title_ar": "",
            "title_en": "Improving end-to-end transformer model architecture in ASR",
            "author": "Yingzhu Zhao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; nlp; transformers; speech-recognition",
            "keywords": "",
            "abstract": "As a result of advancement in deep learning and neural network technology, end-to-end models have been introduced into automatic speech recognition (ASR) successfully and achieved superior performance compared to conventional hybrid systems. End-to-end models simplify the traditional GMM-HMM models by transcribing speech to text directly with fast computation speed and fast development time. Transformer model, the latest end-to-end model, has achieved a huge success not only in ASR but also in natural language processing and computer vision. In spite of its great performance, transformer model architecture can be further improved to better suit the characteristics of ASR. To be more specific, ASR performance is greatly affected by the speaker mismatch between training and test data, and speaker adaptation is a long-standing problem in ASR. Additionally, ASR has the characteristic to have monotonic alignment between text output and speech input. Different phonemes along an utterance may require different level of computation due to varying complexity and noise level. How to better deploy the transformer model for ASR remains a challenge. In this thesis, three novel architectural changes to the transformer model are proposed for the above ASR characteristics. For each of the proposed methods, detailed experimental evaluation is carried out to compare the performance against the transformer baseline with analysis. In the first study, to alleviate the performance drop in ASR due to speaker mismatch between training and test data, we present a unified framework for speaker adaptation, which consists of feature adaptation and model adaptation. A speaker-aware persistent memory model is presented to capture speaker knowledge through the persistent memory in order to generalize better to unseen test speakers, which belongs to the feature adaptation method. Furthermore, a model-based gradual pruning approach is deployed to free up partial model encoder parameters for target speaker adaptation without sacrificing the original model performance. Finally, instead of only adapting to general speakers or specific target speaker, we design a multi-speaker adaptation model capable of adapting to multiple speakers simultaneously, which is practical in ubiquitous environment with multiple users. Our proposed approach brings relative 2.74-6.52% word error rate (WER) reduction on general speaker adaptation. On target speaker adaptation, our method outperforms the baseline with up to 20.58% relative WER reduction, and surpasses the finetuning method by up to relative 2.54%. In the second study, we then address the text output speech input misalignment problem in transformer model which greatly affects the ASR accuracy during training and inference. An effective cross attention biasing technique in transformer is proposed that takes monotonic alignment between text output and speech input into consideration by making use of cross attention weights. Specifically, a Gaussian mask is applied on cross attention weights to limit the input speech context range locally given alignment information. A regularizer is further introduced for alignment regularization. Experiments on LibriSpeech dataset find that our proposed model can obtain improved output-input alignment for ASR, and yields 14.5%-25.0% relative WER reductions. In the final study, to resolve the problem of static number of layers in transformer model, we present universal speech transformer. It generalizes the speech transformer with dynamic numbers of encoder\/decoder layers, which can relieve the burden of tuning depth related hyperparameters. Universal transformer adds the depth and positional embeddings repeatedly for each layer, which dilutes the acoustic information carried by hidden representation, and it also performs a partial update of hidden vectors between layers, which is less efficient especially on the very deep models. For better use of universal transformer, we modify its processing framework by removing the depth embedding and only adding the positional embedding once at transformer encoder frontend. Furthermore, to update the hidden vectors efficiently, especially on the very deep models, we adopt a full update. Experiments on LibriSpeech, Switchboard and AISHELL-1 datasets show that our model outperforms a baseline by 3.88%-13.7%, and surpasses other model with less computation cost.",
            "source_url": "https:\/\/openalex.org\/W4367678477",
            "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/166407\/2\/thesis_ZhaoYingzhu.pdf",
            "doi": "10.32657\/10356\/166407",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4367678477",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 400,
            "title": "Feature Selection and Transfer Learning in Network Data: Enhancing Anomaly Detection with Zero-Shot and Few-Shot Learning",
            "title_ar": "",
            "title_en": "Feature Selection and Transfer Learning in Network Data: Enhancing Anomaly Detection with Zero-Shot and Few-Shot Learning",
            "author": "Joideep Banerjee",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "نص السجل: «PhD»",
            "university": "Staffordshire University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "xai",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; few-zero-shot; predictive-analytics; xai",
            "keywords": "",
            "abstract": "The exponential growth of high-dimensional data across domains such as bioinformatics, healthcare, finance, and image processing has heightened the need for effective feature selection (FS) methods. These techniques improve model performance by identifying relevant features, reducing computational complexity, and mitigating overfitting. This PhD thesis introduces Radian, a novel feature selection method that leverages the statistical properties of range and median to identify the most influential features. Radian effectively distinguishes between relevant and redundant attributes while also detecting anomalies, enhancing both model interpretability and data quality. Radian was rigorously evaluated on multiple benchmark datasets of varying size and complexity. The results show that it consistently outperforms conventional methods such as the Pearson correlation coefficient in three key areas: classification accuracy, feature reduction, and computational efficiency. Its ability to balance performance and simplicity enables the creation of compact, interpretable models that retain or improve predictive accuracy. Beyond feature selection, this research advances transfer learning for tabular data, an area often underexplored in existing literature. Three innovative models TabLoRA, TabLoRA-ZS (zero-shot), and TabLoRA-FS (few-shot) are introduced by integrating TabNet, a deep learning architecture for tabular data, with Low-Rank Adaptation (LoRA) modules. The TabLoRA-ZS model enables generalisation to unseen tasks without prior data, while TabLoRA-FS fine-tunes efficiently with minimal data, addressing the challenges of data scarcity. A major innovation lies in integrating Radian with TabNet and LoRA, allowing dynamic feature selection during transfer learning. This integration improves model adaptability, robustness, and scalability, particularly in environments with limited labelled data. Comprehensive experiments demonstrate that these Radian-enhanced transfer learning models perform competitively with state-of-the-art approaches while maintaining interpretability and efficiency. In conclusion, this thesis contributes to machine learning by (1) proposing Radian, a statistically driven, efficient feature selection method, and (2) developing Radian-integrated TabLoRA models for few-shot and zero-shot transfer learning. Together, they provide scalable, adaptable, and high-performing solutions for data-scarce domains, bridging the gap between feature selection and transfer learning in tabular data analysis.",
            "source_url": "https:\/\/openalex.org\/W7126002433",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Staffordshire Online Repository (Staffordshire University)",
            "source_record_id": "W7126002433",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 403,
            "title": "Vision–Language Models for Zero- and Few-Shot Anomaly Detection in Chest X-Rays",
            "title_ar": "",
            "title_en": "Vision–Language Models for Zero- and Few-Shot Anomaly Detection in Chest X-Rays",
            "author": "Md Ashrafuzzaman",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Jyväskylä",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; ai-applications; few-zero-shot; predictive-analytics; multimodal; llm; medical-imaging; ai-industry",
            "keywords": "Vision–Language Models, Anomaly Detection, Chest X-Ray, Zero-Shot Learning, Few-Shot Learning, Medical Imaging.",
            "abstract": "Deep Learning has achieved huge success in different fields lately, especially in the medical field; however, today’s traditional models are based on supervised learning and rely heavily on large amounts of expert-labeled data, which makes training time-consuming and a lot more expensive. This presents a significant challenge, especially for anomaly detection in medical images, where rare pathologies or unexpected findings are hard to find. For the workflow of Chest X-Ray (CXR) interpretation, the ability to find “out-of-distribution” anomalies without prior examples or a very limited number of ex-amples is critical for patient safety and efficiency in patient diagnosis. In this thesis I investigated the application of Vision-Language Models (VLMs) to per-form Zero-Shot and Few-Shot anomaly detection in Chest X-Rays. By using the joint em-bedding space of image and text representations (such as those found in CLIP or Bio-medCLIP), I propose a framework that identifies anomalies by measuring the semantic similarity between CXR images and the descriptive text prompts. I used different Prompt Engineering strategies ranging from simple labels to complex clinical descriptions to op-timize zero-shot performance. Subsequently, for few-shot adaptation, I used prototypical networks to compute class prototypes from the support embeddings without updating the model weights. Our result was inflated by patient-level data leakage, but after enforcing a strict patient-disjoint split, few-shot performance plateaued at 50-60% which indicates that prototypes captured patient identity rather than generalized pathology. In zero shot, BiomedCLIP achieved 55% balanced accuracy versus 41% for CLIP which demonstrates the benefits of medical pretraining while Qwen2-VL defaulted to predicting Normal, showing that just following instructions alone is not sufficient for medical anomaly detec-tion. This research shows that Vision-Language Models (VLMs) can be used for data-efficient and flexible medical AI systems. By reducing the reliance on large-scale, labeled datasets, these models can be more easily deployed for rare disease detection, and can assist radiol-ogists in further investigation and the identification of critical findings in resource con-strained environments. Keywords: Vision–Language Models, Anomaly Detection, Chest X-Ray, Zero-Shot Learning, Few-Shot Learning, Medical Imaging.",
            "source_url": "https:\/\/openalex.org\/W7198111354",
            "pdf_url": "https:\/\/jyx.jyu.fi\/bitstreams\/dce3f054-4d7e-4496-bafc-a9230d78c155\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Jyväskylä University Digital Archive (University of Jyväskylä)",
            "source_record_id": "W7198111354",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 406,
            "title": "LLMs for Structured Educational Data: A Zero and Few-Shot Approach to Student Performance Prediction",
            "title_ar": "",
            "title_en": "LLMs for Structured Educational Data: A Zero and Few-Shot Approach to Student Performance Prediction",
            "author": "Karandeep Singh Mann",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "National College of Ireland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Ireland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; nlp; responsible-ai; ai-applications; transfer-learning; few-zero-shot; predictive-analytics; llm; xai; ai-education",
            "keywords": "",
            "abstract": "Early and accurate prediction of student performance is crucial for enabling timely academic support to the at-risk students. The past work have used machine learning and deep learning approaches on educational datasets such as OULAD and achieved strong results for prediction of student outcomes. But they relied on feature engineering, required retraining and did not offered any interpretability. This study tries to check whether large language models (LLMs) can be used to overcome these limitations by using zero and few-shot prompting strategies on structured tabular datasets. Evaluation has been done on three open-source LLMs across binary, multiclass and per-class settings. The models reached up to accuracies of 89.6% and 75.9% on binary and multi-class predictions respectively, all without the need for fine-tuning. They even produced short natural language explanations to describe why a particular prediction was made. The findings show that LLMs can not only become an alternative to the traditional prediction models but can also guide educators to make informed decisions through their explanation based insights.",
            "source_url": "https:\/\/openalex.org\/W7163607681",
            "pdf_url": "https:\/\/norma.ncirl.ie\/9331\/2\/karandeepsinghmannconfigurationmanual.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "TRAP@NCI (National College of Ireland)",
            "source_record_id": "W7163607681",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 409,
            "title": "Zero\/Few-Shot Dark Kinase-Phosphosite Associatıon Predictıon With Biologically Grounded Data Augmentation",
            "title_ar": "",
            "title_en": "Zero\/Few-Shot Dark Kinase-Phosphosite Associatıon Predictıon With Biologically Grounded Data Augmentation",
            "author": "Mert Pekey",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "transfer-learning",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; transfer-learning; few-zero-shot; predictive-analytics",
            "keywords": "",
            "abstract": "Protein phosphorylation, a fundamental cellular process mediated by kinases, iscrucial for signaling, and its dysregulation is implicated in numerous human diseases.A significant challenge persists in identifying substrate phosphosites for the vastnumber of understudied ’dark’ kinases, for which conventional supervised machinelearning methods are ineffective due to data scarcity. To address this gap, thisthesis develops a zero- and few-shot learning framework and introduces biologicallygrounded data augmentation strategies, all evaluated on the DARKIN benchmark.We introduce two novel deep learning architectures: DARKIN-FT, a compatibilitybasedmodel that enhances performance through end-to-end fine-tuning of phosphositeencoder, and DARKIN-Interact, a binary classification model that directlycaptures kinase–substrate interactions via joint attention over sequence pairs. Thecentral contribution is a systematic investigation into biologically grounded dataaugmentation, evaluating three distinct strategies: (i) kinase-conditional phosphositegeneration via a fine-tuned ProGen2 model, (ii) weak supervision using predictionsfrom the Kinase Substrate Specificity Atlas (KSSA), and (iii) augmentationwith homologous sequences. Our results demonstrate that DARKIN-FT and DARKIN-Interact significantly outperformexisting baselines on the DARKIN benchmark. The investigation intodata augmentation yielded mixed results: while kinase conditional generation withProGen2 and weak labeling with KSSA degraded the performance, augmentationwith homologous sequences improved the Macro Average Precision of the DARKINInteractmodel. While the results are promising, challenges persist in disambiguatingkinases with high sequence similarity.Overall, this thesis establishes a framework for kinase–phosphosite interaction predictionin low-data regimes and provides valuable insights into the strengths andlimitations of data augmentation in the dark kinase-phosphosite association task.",
            "source_url": "https:\/\/openalex.org\/W7124522292",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Sabanci University",
            "source_record_id": "W7124522292",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 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": 418,
            "title": "Low-shot Learning based on Pre-trained Model",
            "title_ar": "",
            "title_en": "Low-shot Learning based on Pre-trained Model",
            "author": "Ziqin Zhou",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Adelaide",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; transfer-learning; few-zero-shot; self-semi-supervised; cnn; transformers; multimodal; image-classification; image-segmentation; fairness",
            "keywords": "",
            "abstract": "In the field of computer vision, deep learning has demonstrated remarkable accomplishments. However, the dependence on extensive annotated datasets poses significant challenges, particularly when such data is scarce, costly, or ethically sensitive. This thesis addresses the crucial issue of low-shot learning by aiming to equip learning models with the capability to generalize effectively from limited examples. It explores various low-supervision methodologies, including self-supervised, unsupervised, semi-supervised, and active learning, with the goal of reducing the reliance on labeled data. One potential solution to this challenge is leveraging pre-trained models trained on large-scale datasets and transferring them to downstream tasks and unknown classes. Nevertheless, directly applying pre-trained models in a frozen state often fails to meet the requirements of specific tasks. Fine-tuning these models on base classes with limited training samples and then transferring them to novel domains presents obstacles, as it may disrupt the general knowledge encoded in the model, leading to overfitting on the base classes and suboptimal performance on unseen categories. The central focus of this research is to fully exploit the knowledge encoded in large pre-trained models and enhance their capacity to transfer to novel classes. This thesis introduces a systematic approach to balance generalization and task-specific adaptation, ensuring that fine-tuning preserves pre-trained knowledge while effectively adapting to new data. These models encapsulate extensive knowledge from diverse data sources, enabling swift adaptation to new tasks and classes with minimal additional training. The downstream tasks investigated in this doctoral work range from the low-shot problem for few-shot image classification to more intricate endeavors such as zero-shot and few-shot pixel-level semantic segmentation. The exploration encompasses various pre-trained model architectures, including convolutional and transformer-based models, spanning both pure vision and cross-modal vision-language paradigms. By harnessing the representational capabilities of state-of-the-art pre-trained models, this research aims to push the boundaries of what is achievable with limited data. The novel methodologies proposed strive to maximize performance, maintain generalization across domains, and improve inference efficiency, paving the way for broader adoption in low-data scenarios.",
            "source_url": "https:\/\/openalex.org\/W7216837125",
            "pdf_url": "https:\/\/digital.library.adelaide.edu.au\/server\/api\/core\/bitstreams\/c8d7f0de-908a-4f17-b259-cc6fb0be75e4\/content",
            "doi": "",
            "handle": "",
            "repository_name": "Adelaide Research & Scholarship (AR&S) (University of Adelaide)",
            "source_record_id": "W7216837125",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 420,
            "title": "Zero Shot Learning for Visual Object Recognition with Generative Models",
            "title_ar": "",
            "title_en": "Zero Shot Learning for Visual Object Recognition with Generative Models",
            "author": "Maunil R. Vyas",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Arizona State University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "few-zero-shot",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; ai-applications; few-zero-shot; ai-education",
            "keywords": "",
            "abstract": "abstract: Visual object recognition has achieved great success with advancements in deep learning technologies. Notably, the existing recognition models have gained human-level performance on many of the recognition tasks. However, these models are data hungry, and their performance is constrained by the amount of training data. Inspired by the human ability to recognize object categories based on textual descriptions of objects and previous visual knowledge, the research community has extensively pursued the area of zero-shot learning. In this area of research, machine vision models are trained to recognize object categories that are not observed during the training process. Zero-shot learning models leverage textual information to transfer visual knowledge from seen object categories in order to recognize unseen object categories. Generative models have recently gained popularity as they synthesize unseen visual features and convert zero-shot learning into a classical supervised learning problem. These generative models are trained using seen classes and are expected to implicitly transfer the knowledge from seen to unseen classes. However, their performance is stymied by overfitting towards seen classes, which leads to substandard performance in generalized zero-shot learning. To address this concern, this dissertation proposes a novel generative model that leverages the semantic relationship between seen and unseen categories and explicitly performs knowledge transfer from seen categories to unseen categories. Experiments were conducted on several benchmark datasets to demonstrate the efficacy of the proposed model for both zero-shot learning and generalized zero-shot learning. The dissertation also provides a unique Student-Teacher based generative model for zero-shot learning and concludes with future research directions in this area.",
            "source_url": "https:\/\/openalex.org\/W3033566688",
            "pdf_url": "https:\/\/d1rbsgppyrdqq4.cloudfront.net\/s3fs-public\/c7\/224428\/Vyas_asu_0010N_19723.pdf?versionId=9.mNojbapvXyDdAkYDVdACBOPX.DAQKJ&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ477LUGVF2%2F20260320%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20260320T113737Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=047adb58111dabff5588f6b4baa44d70b2fb05ab9cab0058f3017db023743c2c",
            "doi": "",
            "handle": "",
            "repository_name": "Arizona State University Library Digital Repository (Arizona State University)",
            "source_record_id": "W3033566688",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 435,
            "title": "A generative chatbot with natural language processing",
            "title_ar": "",
            "title_en": "A generative chatbot with natural language processing",
            "author": "David Liebman",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "deep-learning; nlp; generative-ai; rnn; transformers; llm; speech-recognition",
            "keywords": "",
            "abstract": "The goal in this thesis is to create a chatbot, a computer program that can respond verbally to a human in the course of simple day-to-day conversations. A deep learning neural network model called the Transformer is used to develop the chatbot. A full description of a Transformer is provided. The use of a few different Transformer-based Natural Language Processing models to develop the chatbot, including Generative Pre-Training 2 (GPT2), are shown. For comparison a Gated Recurrent Unit (GRU) based model is included. Each of these are explained below. The chatbot code is installed on a small device such as the Raspberry Pi with speech recognition and speech-to-text software. In this way a device that can carry out a verbal conversation with a human might be created. For the GRU-based model a Raspberry Pi 3B with 1GB RAM can be used. A Raspberry Pi 4B with 4GB of RAM is needed to run a chatbot with the GPT2.",
            "source_url": "https:\/\/openalex.org\/W3155268491",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "SUNY Digital Repository Support (State University of New York System)",
            "source_record_id": "W3155268491",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 451,
            "title": "Computer vision for non-rigid object assembly automation: With applications in automotive wire harness assembly",
            "title_ar": "",
            "title_en": "Computer vision for non-rigid object assembly automation: With applications in automotive wire harness assembly",
            "author": "Hao Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Chalmers University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "ai-industry",
            "categories": "deep-learning; computer-vision; robotics; ai-applications; multimodal; ai-industry",
            "keywords": "",
            "abstract": "This thesis examines the role of computer vision in facilitating robotic automation for non-rigid object assembly, with a particular focus on wire harness assembly tasks during the automotive final assembly stage. Despite extensive research in this field, industrial adoption has remained limited. Accordingly, the thesis analyzes the challenges associated with applying computer vision to wire harness assembly automation and explores strategies to enable practical deployment in industrial environments.Employing both qualitative and quantitative methods, the research progresses from problem identification to artifact design, demonstration, and evaluation in laboratory and industrially relevant environments. The studies identify challenges at the object, scene, data, and operational levels. To address these challenges, three primary artifacts were developed and evaluated. First, a learning-based perception pipeline enables markerless detection of wire harness components. This demonstrates the feasibility of deep learning-based component recognition and highlights limitations when components possess highly similar or occluded visual features. Second, a robot-assisted pipeline for automated multi-view data acquisition and multimodal annotation substantially accelerates the preparation of computer vision datasets. This pipeline also supports the training and evaluation of learning-based perception methods for industrial applications. Third, a vision-based human–robot collaboration framework for wire harness installation significantly reduces localized physical discomfort while maintaining task success. However, this approach increases mental demand and cycle time, with the majority of the additional time attributable to robot execution.In summary, this thesis provides deployable methods and practical guidance for data-centric development, interaction design, and takt-time-oriented workflow optimization in non-rigid object assembly automation. It also demonstrates that, given current technological constraints, computer vision is most effective as a human-centered enabler of robot-assisted assembly rather than as a direct pathway to fully autonomous robotic assembly of non-rigid objects.",
            "source_url": "https:\/\/openalex.org\/W7160860637",
            "pdf_url": "https:\/\/research.chalmers.se\/publication\/551870\/file\/551870_Fulltext.pdf",
            "doi": "10.63959\/chalmers.dt\/5880",
            "handle": "",
            "repository_name": "Chalmers Research (Chalmers University of Technology)",
            "source_record_id": "W7160860637",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 452,
            "title": "Computer Vision for Aircraft Fuselage Defect Detection: A YOLO11-Based Approach",
            "title_ar": "",
            "title_en": "Computer Vision for Aircraft Fuselage Defect Detection: A YOLO11-Based Approach",
            "author": "Pedro Afonso Bruno Arroteia",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; transfer-learning; self-semi-supervised; predictive-analytics; object-detection; image-segmentation; ai-industry",
            "keywords": "",
            "abstract": "Aircraft fuselages are exposed to operational and environmental stresses that generate defects such as scratches, paint peeling, rust, and rivet damage. Conventional inspection techniques, though widely used, are labor-intensive, time-consuming, and dependent on inspector expertise, which can lead to variability and overlooked anomalies. These chal- lenges highlight the need for automated solutions that ensure consistency and efficiency in quality inspection processes. This thesis presents the development of a computer vision-based system for auto- mated aircraft fuselage defect detection, combining deep learning with a user-oriented interface. The system employs the YOLO11 object detection architecture, fine-tuned on a specialized dataset to detect and classify four defect types. To mitigate data scarcity and class imbalance, data augmentation and transfer learning were applied. The model was evaluated on training, validation, and testing sets, achieving 0.814 precision and 0.735 recall on the training set, and on the validation set 0.722 precision, 0.787 recall, 0.659 accuracy, mAP@0.5 of 0.82, and mAP@0.5:0.95 of 0.524. Beyond model training, a RESTful API and web-based Human–Machine Interface were developed, enabling inspection images to be uploaded and processed, with results presented as bounding boxes, defect types, and confidence scores. This integration reflects Industry 5.0 principles of human–AI collaboration by allowing technicians to interpret, adjust, and validate predictions. The results demonstrate the feasibility of an intelligent inspection system that enhances accuracy, efficiency, and transparency in aircraft maintenance workflows. While challenges such as dataset scarcity and background variability remain, the framework establishes a solid foundation for future work in semi-supervised learning, segmentation-based approaches, and real-time deployment on edge devices. By contributing both a validated detection model and an operational platform, this study advances the application of AI in aviation quality inspection and supports safer, more efficient maintenance practices.",
            "source_url": "https:\/\/openalex.org\/W7224044969",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W7224044969",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 459,
            "title": "Shoulder Keypoint-Detection from Object Detection",
            "title_ar": "",
            "title_en": "Shoulder Keypoint-Detection from Object Detection",
            "author": "Prince Kapoor",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Ottawa",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; cnn; rnn; object-detection; image-segmentation; ai-agriculture",
            "keywords": "",
            "abstract": "This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image analysis challenges. Due to the advent of deep learning, CNN had been used in almost all the computer vision applications and that is why there is utter need to understand the miniature details of these feature extractors and find out their pros and cons of each feature extractor meticulously. In order to perform our experimentation, we decided to explore an object detection task using a particular model architecture which maintains a sweet spot between computational cost and accuracy. The model architecture which we had used is LSTM-Decoder. The model had been experimented with different CNN feature extractor and found their pros and cons in variant scenarios. The results which we had obtained on different datasets elucidates that CNN plays a major role in obtaining higher accuracy and we had also achieved a comparable state-of-the-art accuracy on Pedestrian Detection Dataset. In extension to object detection, we also implemented two different model architectures which find shoulder keypoints. So, One of our idea can be explicated as follows: using the detected annotation from object detection, a small cropped image is generated which would be feed into a small cascade network which was trained for detection of shoulder keypoints. The second strategy is to use the same object detection model and fine tune their weights to predict shoulder keypoints. Currently, we had generated our results for shoulder keypoint detection. However, this idea could be extended to full-body pose Estimation by modifying the cascaded network for pose estimation purpose and this had become an important topic of discussion for the future work of this thesis.",
            "source_url": "https:\/\/openalex.org\/W2888395965",
            "pdf_url": "",
            "doi": "10.20381\/ruor-22271",
            "handle": "",
            "repository_name": "uO Research (University of Ottawa)",
            "source_record_id": "W2888395965",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 460,
            "title": "Advancing heat stress detection in dairy cows through machine learning and computer vision",
            "title_ar": "",
            "title_en": "Advancing heat stress detection in dairy cows through machine learning and computer vision",
            "author": "Shu, Hang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Liège",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Belgium",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; predictive-analytics; cnn; object-detection; image-segmentation; face-recognition; video-analysis; xai; ai-agriculture",
            "keywords": "",
            "abstract": "Heat stress detection in dairy cows has long been connected with production loss. However, the reduction in milk yield lags behind the exposure to heat stress events for about two days. Other stress responses, such as physiological and behavioural changes, are well documented to be activated by dairy cows in the earlier stage of heat stress compared with production loss. Among all candidate indicators, body surface temperatures (BST), respiration rate (RR), and relevant behaviours have been concluded to be the most appropriate indicators due to their high feasibility of acquisition and early response. Vision-based methods are promising for accurate measurements while adhering to animal welfare principles. Meanwhile, predictive models show a non-invasive alternative to obtain these data and can provide useful insights with their interpretations. Thus, this thesis aimed to provide non-invasive solutions to the detection of heat stress in dairy cows by using artificial intelligence techniques. The detailed research content and relevant conclusions are as follows: An automated tool based on improved UNet was proposed to collect facial BST from five facial landmarks (i.e., eyes, muzzle, nostrils, ears, and horns) on cattle infrared images. The baseline UNet model was improved by replacing the traditional convolutional layers in the decoder with Ghost modules and adding efficient channel attention modules. The improved UNet outperformed other comparable models with the highest mean Intersection of Union of 80.76% and a slightly slower but still good inference speed of 32.7 frames per second (FPS). Agreement analysis reveals small to negligible differences between the temperatures obtained automatically in the area of eyes and ears and the ground truth. A vision-based method was proposed to measure RR for multiple dairy cows lying on free stalls. The proposed method involved various computer vision tasks (i.e., instance segmentation, object detection, object tracking, video stabilisation, and optical flow) to obtain respiration-related signals and finally utilised Fast Fourier Transform to extract RR. The results show that the measured RR had a Pearson correlation coefficient of 0.945, a root mean square error (RMSE) of 5.24 breaths per minute (bpm), and an intraclass correlation coefficient of 0.98 compared with visual observation. The average processing time and FPS on 55 test video clips (mean ± standard deviation duration of 16 ± 4 s) was 8.2 s and 64, respectively. A deep learning-based model was proposed to recognise cow behaviours (i.e., drinking, eating, lying, standing-in, and standing-out) that are known to be influenced by heat stress. The YOLOv5s model was selected due to its ability to compress the weight size while maintaining accuracy. It had a mean average precision of 0.985 and an inference speed of 73 FPS. Further validation demonstrates the excellent capacity of the proposed model in measuring herd-level behavioural indicators, with an intraclass correlation coefficient of 0.97 compared with manual observation. Critical thresholds were determined by using piecewise regression models with environmental indicators as the predictors and animal-based indicators as the outcomes. An ambient temperature (Ta) threshold was determined at 26.1 °C when the automated measured mean eye temperature reached 35.3 °C. A Ta threshold of 23.6 °C and a temperature-humidity index (THI) threshold of 72 were determined when the automated measured RR reached 61.1 and 60.4 bpm, respectively. In addition, the test dairy herd began to change their standing and lying behaviour at the earliest Ta of 23.8 ℃ or THI of 68.5. Four machine learning algorithms were used to predict RR, vaginal temperature (VT), and eye temperature (ET) from 13 predictor variables from three dimensions: production, cow-related, and environmental factors. The artificial neural networks yielded the lowest RMSE for predicting RR (13.24 bpm), VT (0.30 ℃), and ET (0.29 ℃). The results interpreted with partial dependence plots and Local Interpretable Model-agnostic Explanations show that P.M. measurements and winter calving contributed most to high RR and VT predictions, whereas lying posture, high Ta, and low wind speed contributed most to high ET predictions. Based on these results, an integrative application of all the proposed measurement, prediction, and assessment methods has been suggested, wherein RGB and infrared cameras are used to measure animal-based indicators, and critical thresholds, along with model interpretation, are used to assess the heat stress state of dairy cows. This strategy ensures timely and thorough cooling of cows in all areas of the dairy farm, thereby minimising the negative impact of heat stress to the greatest extent.",
            "source_url": "https:\/\/openalex.org\/W7111984551",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Open Repository and Bibliography (University of Liège)",
            "source_record_id": "W7111984551",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 461,
            "title": "Advances in Decision Forests and Ferns with Applications in Deep Representation Learning for Computer Vision",
            "title_ar": "",
            "title_en": "Advances in Decision Forests and Ferns with Applications in Deep Representation Learning for Computer Vision",
            "author": "Zuo, Yan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; image-classification; object-detection; image-segmentation",
            "keywords": "",
            "abstract": "In recent years, deep learning has largely embedded itself within computer vision as an extremely useful tool for accomplishing a range of vision-based tasks such as image classification, object detection and semantic segmentation. With this surge in popularity in deep learning, more traditional computer vision methods such as decision forests have somewhat fallen out of favour in the community despite their benefits. This thesis hopes to bridge this gap; it focuses on advancing the ensemble methods of decision forests and ferns and incorporating them within deep learning frameworks for computer vision applications.",
            "source_url": "https:\/\/openalex.org\/W3165855881",
            "pdf_url": "",
            "doi": "10.26180\/5cec9c40848a3",
            "handle": "",
            "repository_name": "Figshare",
            "source_record_id": "W3165855881",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 462,
            "title": "Deep learning for small object detection",
            "title_ar": "",
            "title_en": "Deep learning for small object detection",
            "author": "Brais Bosquet",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; object-detection",
            "keywords": "",
            "abstract": "Small object detection has become increasingly relevant due to the fact that the performance of common object detectors falls significantly as objects become smaller. Many computer vision applications require the analysis of the entire set of objects in the image, including extremely small objects. Moreover, the detection of small objects allows to perceive objects at a greater distance, thus giving more time to adapt to any situation or unforeseen event.",
            "source_url": "https:\/\/openalex.org\/W3142604139",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W3142604139",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 465,
            "title": "Computer vision Applied to horticultural Multifunctional Harvest Automation - lettuce",
            "title_ar": "",
            "title_en": "Computer vision Applied to horticultural Multifunctional Harvest Automation - lettuce",
            "author": "Flávio Roberto de Freitas Gonçalves",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; robotics; ai-applications; object-detection; ai-agriculture",
            "keywords": "",
            "abstract": "Agricultural automation has become significant in our country due to the need for domestic companies to compete properly with foreign companies and increased productivity and reduced losses. Using advanced technology features such as embedded electronic devices and precision farming techniques and process control, increased production is now possible. Horticultural crops are of great economic, social and food importance to the world's population. The current major challenge in the horticultural agricultural mechanization sector is harvesting. To this end, the development of a computer vision system employing image processing and artificial neural networks that allow the identification of vegetables and provide positioning parameters for a robotic actuator set for harvesting operations would meet this challenge. The objective of this work was to develop a detection system that through these resources enables the detection of vegetables and their positioning. For this purpose a capture apparatus was defined using model OV2640 serial cameras with ArduCam \/ Arduino control interface set in stereo vision configuration. The programming of the system was done in C # using the EMGU library using deep learning algorithms (YOLO) and depth metric algorithm by means of distance estimation map. As a result we have the definition of the apparatus where operating conditions were established and the performance of the image capture algorithms were analyzed, an image bank was collected, an artificial neural network was defined and training was performed with these images to identify the lettuce horticulture. , the object detection system was generated, the distance detection module was calibrated and lettuce detection tests were performed. Evaluating the results we concluded that it is possible to detect vegetables using the implemented system.",
            "source_url": "https:\/\/openalex.org\/W7120869170",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W7120869170",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 467,
            "title": "SYNTHETIC SCENE BASED OBJECT DETECTION: CAN YOU TRAIN NEURAL NETWORKS WITH SYNTHETICALLY GENERATED DATA TO RECOGNIZE REAL OBJECTS FOR MANUFACTURING?",
            "title_ar": "",
            "title_en": "SYNTHETIC SCENE BASED OBJECT DETECTION: CAN YOU TRAIN NEURAL NETWORKS WITH SYNTHETICALLY GENERATED DATA TO RECOGNIZE REAL OBJECTS FOR MANUFACTURING?",
            "author": "Gabriella Aiello",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Rhode Island",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; ai-applications; cnn; object-detection; image-segmentation; video-analysis; ai-industry",
            "keywords": "",
            "abstract": "Within the manufacturing industry, Artificial Intelligence (AI) has revolutionized the way manufacturers mass produce goods and has supported a more consumer driven focus. The ability of computers to understand digital images or videos is a key sensing capability that enables connected enterprise systems. Deep learning (DL) techniques used for computer vision tasks such as object detection reliably learn features without explicitly defining said features in the training process. Evaluating deep learning techniques for object detection applications can be broken into three steps: gathering\/processing data, model training\/evaluation and model deployment. This research focuses on the validity of using synthetically generated data to train convolutional neural networks (CNN) to recognize objects. An object detection pipeline was created to provide an infrastructure for new models to be created using 3D computer aided design (CAD) models of objects. The pipeline was built for research purposes but can be used for application purposes beyond testing models performance. A design of experiments was created to explore significant dataset parameters as well as hyperparameters used to train deep neural networks for object detection. Some significant factors explored were dataset size, inter-class variation, learning rate, number of epochs, and type of ma- chine learning model. Detectron2 was used to train was used to implement most object detection algorithms. Using optimized synthetically generated datasets and optimal hyperparameters, the object detection pipeline was capable of detecting objects bounding box and segmentation with average precision greater than 80%.",
            "source_url": "https:\/\/openalex.org\/W3134322164",
            "pdf_url": "https:\/\/digitalcommons.uri.edu\/cgi\/viewcontent.cgi?article=2894&context=theses",
            "doi": "10.23860\/thesis-aiello-gabriella-2020",
            "handle": "",
            "repository_name": "Digital Commons - URI (University of Rhode Island)",
            "source_record_id": "W3134322164",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 470,
            "title": "Deep Learning Object Detection Models in Robotic Process Automation",
            "title_ar": "",
            "title_en": "Deep Learning Object Detection Models in Robotic Process Automation",
            "author": "Waltteri Vuorimaa",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Aalto University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; object-detection",
            "keywords": "",
            "abstract": "In recent years, businesses have become increasingly aware of their need to automate previously manual business processes. Adoption rates of robotic process automation (RPA) tools have increased a lot, and this has generated a great deal of value for companies. But, alas, especially some older legacy applications are still extremely problematic from the point of view of RPA platforms, and they cannot often be interfaced in a same fashion as some more modern applications. This is especially lamentable, as it is often the older applications, which tend to be used in the more manual business processes. This thesis aims to build a foundation for building and evaluating commercializable computer vision (CV) object detection models, that resolve the issues faced by RPA applications that arise from the lack of operating system (OS) level user interface (UI) visual element data. This kind of models could be used as a basis for building completely new RPA tools, or for augmenting existing solutions. The research questions discussed by this study are as follows: 1. Is using machine learning models for the detection of user interface elements viable? 2. What are the implications to existing RPA applications? 3. Can we build a model that is able to generalize from data from another domain? This thesis discusses the theoretical background of object detection and machine learning, and presents three potential families of machine learning model architectures that could be used for the aforementioned tasks. These models were then trained and compared to the CV capabilities of a commercial RPA platform, UiPath. The results show that the most simple object detection models cannot compete with the current commercial offering. There is, however, a clear path to further research, and it is clear that building a object detection system for UI elements is viable. In addition, this thesis presents the first publicly available dataset of (manually) annotated desktop UI screenshot images, and a tool for generating artificial user interface data.",
            "source_url": "https:\/\/openalex.org\/W3154899117",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Aaltodoc (Aalto University)",
            "source_record_id": "W3154899117",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 471,
            "title": "Enhancing Plant Disease Detection in Smart Agriculture Using Deep Learning Models and Advanced Data Augmentation Techniques",
            "title_ar": "",
            "title_en": "Enhancing Plant Disease Detection in Smart Agriculture Using Deep Learning Models and Advanced Data Augmentation Techniques",
            "author": "Nurbol Mukhametzhan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nazarbayev University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Kazakhstan",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; few-zero-shot; cnn; ai-agriculture",
            "keywords": "",
            "abstract": "Deep learning for automatic detection of plant diseases has emerged as an innova- tive method to address agricultural productivity and food security issues. While deep CNNs have achieved near-ceiling performance on benchmark datasets under controlled conditions, the results do not necessarily translate to real-world agricultural environments. The reason for this is domain shift: models are typically trained on clean laboratory images. Field images include background clutter, illumination variations, occlusions, and inconsistent capture conditions [1, 2, 10, 11]. This thesis investigates cross-domain plant disease classification under a controlled- to-field setting using transfer-learned CNN architectures, namely EfficientNet-B0, MobileNetV3-Large, and ResNet-50 [21, 22, 20]. PlantVillage is used as the source domain for training, while PlantDoc is used as the target domain for evaluation and adaptation. The study evaluates a 12-class controlled-to-field transfer task using a harmonized subset of overlapping classes from both datasets. The implemented framework compares four training pipelines: P1, which uses preprocessing only and no augmentation; P2, which applies basic geometric and photometric augmentation; P3, which applies stronger environmental augmentation together with mixed-image regularization using MixUp and CutMix; and P4, which serves as an ablation study by applying P3’s environmental transforms without image mixing. [24, 26, 27]. The results show that source-domain validation performance does not translate into strong field performance. In the zero-shot cross-domain setting, where models are trained on PlantVillage and evaluated directly on PlantDoc without target-domain training, the best configuration reaches only 24.84% accuracy on the filtered PlantDoc FULL evaluation set of 1405 images, confirming a substantial lab-to-field generalization gap. When target-domain supervision is introduced through fine-tuning on the filtered PlantDoc training split and evaluation on the untouched filtered PlantDoc test split, performance improves substantially, reaching up to 59.32% accuracy and 0.5734 macro-F1. The results further show that basic augmentation is more reliable than the most aggressive augmentation setting for zero-shot transfer, while supervised fine-tuning on target-domain data provides the largest reduction in the deployment gap. Furthermore, a target-domain data efficiency ablation reveals that supervised adaptation scales logarithmically. Models recover the majority of their fine-tuned performance using only a fraction of the target dataset, demonstrating that bridging the lab-to-field gap does not strictly require massive field-collected datasets. Overall, this research studies plant disease recognition using field data. It shows that while domain adaptation improves robustness and makes models more deployable, augmenting the source training data alone is not sufficient to solve the controlled- to-field generalization problem.",
            "source_url": "https:\/\/openalex.org\/W7199181336",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Nazarbayev University Repository (Nazarbayev University)",
            "source_record_id": "W7199181336",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 472,
            "title": "Deep Learning for Plant Disease Detection Using Image Analysis",
            "title_ar": "",
            "title_en": "Deep Learning for Plant Disease Detection Using Image Analysis",
            "author": "Muhammad Butt",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; transfer-learning; cnn; xai; ai-agriculture",
            "keywords": "",
            "abstract": "The study explores the design and execution viability of deep learning algorithms to the automated system of identifying plant diseases using leaf images.The global agricultural sector is experiencing significant losses in crop production due to late or inappropriate diagnosis of diseases caused by diseases especially in areas that lack agronomic knowledge.Four convolutional neural network architectures were tested to reduce such a problem: one of my own baseline CNN, ResNet-50, Inception-v3, and EfficientNet-B0.Training of the models was done using the PlantVillage dataset with the help of transfer learning, data augmentation, and routine preprocessing operations.The model performance was measured based on the accuracy, the precision, the recall, the F1score and ROC-AUC.Robustness testing was done by the introduction of synthetic noise, changes in light, and background complexity to simulate conditions in the realworld field.Grad-CAM visualisations were also added to increase the clarity and transparency of the model decisions.With empirical findings, it has been shown that EfficientNet-B0 gives an optimal trade-off between classification accuracy and computational efficiency, making it suitable to be deployed on a mobile or edge platform.The research offers practical results to the scalable, explainable, and resourceefficient implementation of artificial intelligence systems in the area of disease management in agriculture.",
            "source_url": "https:\/\/openalex.org\/W7215477915",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/7d1433de-a71a-45fc-b741-2ce3d525322e\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215477915",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 473,
            "title": "Plant Disease Detection and Classification Using Deep Learning Techniques",
            "title_ar": "",
            "title_en": "Plant Disease Detection and Classification Using Deep Learning Techniques",
            "author": "Mazhar Iqbal",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; ai-agriculture",
            "keywords": "",
            "abstract": "The early and accurate detection of plant diseases is very important in protecting crop yield and ensuring food security.This thesis is going to explore how deep learning methods, especially Convolutional Neural Networks (CNNs) and ResNet-50, can help identify and classify diseases in Tomato, Potato, and Pepper plants.A dataset of over 38,000 images of both healthy and diseased leaf samples were collected.Different data augmentation techniques like rotation, shearing, zooming, and flipping were used to increase the efficiency of models and balance the number of images in each class.This research looks at how well a custom CNN and a fine-tuned ResNet-50 model perform on the three crops by checking their training, validation, and test results.The result shows that the CNN model's performance was reasonably well, and it achieved test accuracy of 94.12% (Tomato), 92.83% (Potato), and 91.98% (Pepper), while the ResNet-50 model has shown much better results, with an accuracy score of 99.37% and has shown a strong performance across all datasets.Diseases like Late Blight and Yellow Leaf Curl Virus, that are very difficult to visually distinguish but ResNet-50 was very precise at identifying these diseases.This work shows how deep learning is very effective in plant disease detection but also explores that these models could be used in real farming environments to help farmers take quick action and reduce crop losses.By decreasing dependence on manual inspection and helping in early disease detection, the research helps to support greener, technology-driven agriculture.",
            "source_url": "https:\/\/openalex.org\/W7215253019",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/396f0104-78e4-40da-ba0b-543e36d34d00\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215253019",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 474,
            "title": "An efficient deep learning approach to detect plant diseases using image data",
            "title_ar": "",
            "title_en": "An efficient deep learning approach to detect plant diseases using image data",
            "author": "Ahmad Anis ; Babli Saha ; Maisa Tarannum Srizee",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "BRAC University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Bangladesh",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; predictive-analytics; cnn; transformers; ai-agriculture",
            "keywords": "",
            "abstract": "Plant diseases drastically impact on the yield and quality of crops and this is a great challenge to food security and sustainable agriculture in the world and more so in countries such as Bangladesh. There is need to therefore ensure the early and accurate diagnosis of plant diseases to enhance productivity and safeguard food supplies. Plant disease classification based on images has been highly successful with the swift development of deep learning, especially Convolutional Neural Networks (CNNs).In this thesis, a lightweight hybrid CNN-Transformer model is suggested as a classification of rice leaf disease. A number of pretrained CNN models, such as ResNet50, VGG16, InceptionV3, MobileNet V3, EfficientNetB0, and DenseNet121 are tested with transfer learning to determine the baseline performance. The dataset used in the study is the 23, 265 images of rice leaf in 10 different classes including healthy and diseased. To (hopefully) bypass the shortcomings of single-architecture models, the hybrid model proposed replaces local feature extraction with EfficientNetB0 and global contextual learning with TinyViT as well as a lightweight version of MobileNet. The experimental findings indicate that the hybrid method is better than single CNN and independent Transformer models as it has an accuracy of 98.04%with high precision, recall, and F1-score and low costs of computation. The results demonstrate the usefulness of lightweight CNN-Transformer models in real-time and resource-limited agricultural disease prediction and farm health crop management.",
            "source_url": "https:\/\/openalex.org\/W7202234903",
            "pdf_url": "https:\/\/dspace.bracu.ac.bd\/bitstreams\/270bad3b-32c1-4c00-9d85-93d3776dcfb2\/download",
            "doi": "",
            "handle": "",
            "repository_name": "BRAC University Institutional Repository (BRAC University)",
            "source_record_id": "W7202234903",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 475,
            "title": "Deep Learning-Based Early Detection and Classification of Plant Diseases Using Leaf Images",
            "title_ar": "",
            "title_en": "Deep Learning-Based Early Detection and Classification of Plant Diseases Using Leaf Images",
            "author": "Manish Saha",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; object-detection; ai-agriculture",
            "keywords": "",
            "abstract": "Early and accurate detection of plant diseases is essential for improving crop yield and ensuring agricultural sustainability.This study proposes a deep learning-based approach for the automated detection and classification of plant diseases using leaf images.A custom Convolutional Neural Network (CNN) architecture is developed and evaluated against two stateof-the-art object detection models-Faster R-CNN and R-FCN.The proposed CNN model is trained and tested on a labeled dataset of diseased and healthy leaf images under varied lighting and background conditions.Performance is assessed using key evaluation metrics including accuracy, precision, recall, and F1-score.Experimental results reveal that the CNN model outperforms the benchmark models, achieving an accuracy of 94.80%, precision of 94.67%, recall of 94.73%, and an F1-score of 94.70%.These results confirm the model's robustness and generalization capability in realworld conditions.The approach offers a low-cost, scalable, and reliable solution for farmers and agricultural experts to identify plant diseases at an early stage, enabling timely intervention and reducing crop losses.This research contributes to the advancement of intelligent agricultural systems by integrating deep learning techniques for plant health monitoring.",
            "source_url": "https:\/\/openalex.org\/W7215313652",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/ed8770ae-8680-4f5b-9b6f-502ea8048e35\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215313652",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 476,
            "title": "Disease detection and mapping in olive groves using UAVs and deep learning for precision agriculture",
            "title_ar": "",
            "title_en": "Disease detection and mapping in olive groves using UAVs and deep learning for precision agriculture",
            "author": "Maurício Morais",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; medical-ai; robotics; ai-applications; object-detection; ai-agriculture",
            "keywords": "",
            "abstract": "This dissertation presents the implementation and validation of a cost-effective, Unmanned Aerial Vehicle (UAV) based system for automated detection and spatial mapping of olive knot disease in olive groves. Addressing the need for accessible and efficient plant disease monitoring in Precision Agriculture (PA), the proposed methodology leverages existing UAV imagery capabilities with lightweight Deep Learning (DL) models, specifically the You Only Look Once (YOLO) object detection architecture, to enable scalable and accurate detection. The academic contributions presented in this work have resulted in two peer-reviewed publications related to the dissertation topic, as detailed at the end of this document. An annotated dataset of UAV-acquired images was compiled, and several state-of-the-art YOLO object detection models were trained and evaluated under identical conditions. The best-performing model achieved a strong F1-score, demonstrating good results in detecting olive knot disease and accurately mapping its spatial distribution within the plantation. The workflow integrates spatial cross-referencing of detections with UAV flight path data and proximity analysis, enabling the assignment of disease detections to individual trees. An interactive map interface, developed using the Folium Python library, provides visualization of the disease distribution and supports practical grove management. The experimental results indicate that cost-effective UAVs and lightweight DL models can be effectively combined for plant disease detection and spatial analysis, offering a robust and scalable approach for real-world agricultural applications. Limitations regarding early symptom detection and image quality are discussed, and directions for future work are proposed.",
            "source_url": "https:\/\/openalex.org\/W7112960058",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W7112960058",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 477,
            "title": "A Unified Framework for Distributed and Resource-Aware Plant Disease Detection Using Federated and Adaptive Hybrid Deep Learning",
            "title_ar": "",
            "title_en": "A Unified Framework for Distributed and Resource-Aware Plant Disease Detection Using Federated and Adaptive Hybrid Deep Learning",
            "author": "Ticauris S Stokes",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Southern Mississippi",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "federated-learning",
            "categories": "machine-learning; deep-learning; medical-ai; robotics; responsible-ai; ai-applications; predictive-analytics; federated-learning; fairness; ai-agriculture; ai-smart-cities",
            "keywords": "",
            "abstract": "Plant disease detection in precision agriculture must operate under significant computational constraints, particularly in low-resource (LR) environments such as unmanned aerial vehicles (UAVs) and edge devices. While LR models enable efficient real-time inference, they often lack the representational capacity required to accurately detect complex disease patterns. In contrast, high-resource (HR) models achieve superior detection performance due to their deep architectures but are computationally expensive and unsuitable for direct deployment in resource-constrained settings. This fundamental trade-off between efficiency and accuracy presents a major challenge for practical agricultural monitoring systems. To address the distributed nature of agricultural data, federated learning (FL) is adopted to enable collaborative model training across multiple data sources without requiring centralized data aggregation. FL improves generalization and preserves data privacy; however, it does not resolve the inherent computational disparity between LR and HR models. Consequently, models trained under FL still face limitations when deployed in real-time LR environments. To bridge this gap, this work proposes a hybrid adaptive inference framework that integrates LR and HR models within a unified system. The framework dynamically selects between efficient LR inference and more accurate HR processing based on prediction confidence, enabling effective utilization of computational resources while maintaining high detection performance. Furthermore, a Bayesian optimization strategy is employed to determine optimal decision thresholds, ensuring a balanced trade-off between accuracy, processing time, and energy consumption. Experimental results demonstrate that the proposed framework significantly reduces computational cost while preserving high detection accuracy, enabling scalable and efficient plant disease detection in real-world UAV-based agricultural systems.",
            "source_url": "https:\/\/openalex.org\/W7162049655",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Aquila Digital Community (University of Southern Mississippi)",
            "source_record_id": "W7162049655",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 478,
            "title": "Transfer Learning-Based EfficientNetB0 Model for Multi-Crop Plant Disease Classification",
            "title_ar": "",
            "title_en": "Transfer Learning-Based EfficientNetB0 Model for Multi-Crop Plant Disease Classification",
            "author": "Mebrahtu Yohannes",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Ethiopia",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; ai-agriculture",
            "keywords": "",
            "abstract": "globally. Early and accurate diagnosis of diseases is key to reducing losses and improving agricultural practices. Traditional methods of plant disease diagnosis are known for being lengthy, cumbersome, and prone to human error. Recent developments in deep learning have shown promise in the automatic diagnosis and classification of plant diseases. Namely, convolutional neural networks (CNNs) perform quite well for such problems. The purpose of this study is to present an approach to plant disease classification that is based on the use of transfer learning and utilizes the Convolutional Neural Network architecture called EfficientNetB0. Transfer learning with ImageNet pretrained weights and an efficient architecture for feature extraction was chosen because of its ability to significantly lower the computational resources required and improve performance. Moreover, plant leaf images from different datasets were taken into account to provide better training. The model achieved 99.32 % training accuracy, 99.37% validation accuracy, and 99.32% test accuracy. It also had impressive convergence capability and low computational complexity, with around 4.7 million parameters and the size of the saved model at around 34 MB. It can be concluded from the findings that the transfer learning-based EfficientNetB0 model provides a great balance between accurate classification and efficiency, while improving the practicality of the system.",
            "source_url": "https:\/\/openalex.org\/W7212441753",
            "pdf_url": "",
            "doi": "10.82589\/muir-1230",
            "handle": "",
            "repository_name": "Mekelle University Institutional Repository (MU-IR)",
            "source_record_id": "W7212441753",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 479,
            "title": "Efficient deep learning for mobile weed classification",
            "title_ar": "",
            "title_en": "Efficient deep learning for mobile weed classification",
            "author": "Leonardo G. Sanches",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Federal University of Technology – Paraná",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Brazil",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "ai-agriculture",
            "categories": "deep-learning; computer-vision; medical-ai; ai-applications; ai-agriculture",
            "keywords": "",
            "abstract": "Agricultural production across regions faces several challenges in cultivation, including climate variability, pests, diseases, and weeds. Deep neural network models have proven efficient in addressing some of these challenges, for example, in identifying and classifying weeds. A practical way for a farmer to monitor the health of their crop is to take a picture of a plant using their mobile device’s camera and run an application on the device that analyzes the image using a neural network. If it identifies the invasive plant, the application can determine the weed type in the image. The problem with developing this type of application is that many deep learning models require significant computational power, making them unsuitable for mobile devices. To mitigate this problem, we developed a pipeline for evaluating deep learning models suitable for weed classification. Our solution uses a set of metrics to assess both computational efficiency and model performance. Furthermore, we use our pipeline to generate an optimized model that performs inference on mobile devices with performance comparable to that of traditional deep learning models.",
            "source_url": "https:\/\/openalex.org\/W7167994211",
            "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/40711\/1\/deeplearningeficienteparaclassificacao.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)",
            "source_record_id": "W7167994211",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:49",
            "updated_at": "2026-11-15 16:52:49"
        },
        {
            "id": 480,
            "title": "Plant disease classification system using deep learning",
            "title_ar": "",
            "title_en": "Plant disease classification system using deep learning",
            "author": "Mohd Azizul Asmad Latip",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universiti Malaysia Sabah",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Malaysia",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; cnn; ai-cybersecurity; ai-agriculture",
            "keywords": "",
            "abstract": "In agriculture production, the unlimited and no disease plant product from farming become important things due this product is necessities in terms foods and mainly source for highly nutrient to community regardless country in the world. However, an increase in the human population requires an increase in agricultural production. Generally, the most important thing in agriculture that affects the quantity and quality of crops is plant diseases and make crop disease become a major threat to food security. In general, a farmer knows that his plant is attacked by a disease through direct vision. But, this process is sometimes inaccurate. Plant diseases are not only a threat to food security at the global scale, but can also have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops. With the development of machine learning technology, plant disease classification can be done automatically using deep learning. In deep learning, a convolutional neural network (CNN) is a class of deep neural networks, most commonly applied to analysing visual imaginary. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels that scan the hidden layers and translation invariance characteristics.",
            "source_url": "https:\/\/openalex.org\/W7198848850",
            "pdf_url": "https:\/\/eprints.ums.edu.my\/id\/eprint\/33302\/1\/PLANT%20DISEASE%20CLASSIFICATION%20SYSTEM%20USING%20DEEP%20LEARNING.24pages.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "UMS Institutional Repository (Universiti Malaysia Sabah)",
            "source_record_id": "W7198848850",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 481,
            "title": "Deep learning detector for pests and plant disease recognition",
            "title_ar": "",
            "title_en": "Deep learning detector for pests and plant disease recognition",
            "author": "Oluwatimilehin Adekunle Ileladewa",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; image-classification; object-detection; ai-agriculture",
            "keywords": "",
            "abstract": "Pests and diseases in plants have been a major challenge factor in the agricultural field. And developing a quick and accurate model could help in detecting pests and diseases in plants. Meanwhile, evolution in deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. However, in this project, deep learning techniques are used in developing a model for diseases and pest detection in plants, and then train and test the model before eventually integrating the model into a mobile application. The goal of this project is to develop a framework that can classify the class of a plant, and detect areas of the plant leaf already affected by diseases, and eventually deploy the framework on a mobile application. In order to find a suitable meta-architecture for the aim of the project, we use the combination of Single Shot MultiBox Detector and MobileNet (SSD MobileNet) where Single Shot MultiBox Detector (SSD) is the algorithm that takes a single shot to detect multiple objects within an image, and mobilenet is a neural network for recognition and classification. The system is trained using a large dataset containing different classes of both diseased and healthy images of plants from PlantVillage. Final results of the project reveal that our proposed system can recognize and detect various type of pests and diseases that have been trained in the model, with the ability to handle the complexity of a plant’s surrounding area.",
            "source_url": "https:\/\/openalex.org\/W3133549069",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3133549069",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 482,
            "title": "Plant recognition, detection, and counting with deep learning",
            "title_ar": "",
            "title_en": "Plant recognition, detection, and counting with deep learning",
            "author": "Pornntiwa Pawara",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Groningen \/ Centre for Information Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Netherlands",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "image-classification",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; image-classification; ai-education; ai-agriculture",
            "keywords": "",
            "abstract": "In agricultural and farm management, plant recognition, plant detection, and plant counting systems are crucial. We can apply these tasks to several applications, for example, plant disease detection, weed detection, fruit harvest system, and plant species identification. Plants can be identified by looking at their most discriminating parts, such as a leaf, fruit, flower, bark, and the overall plant, by considering attributes as shape, size, or color. However, the identification of plant species from field observation can be complicated, time-consuming, and requires specialized expertise. Computer vision and machine-learning techniques have become ubiquitous and are invaluable to overcome problems with plant recognition in research. Although these techniques have been of great help, image-based plant recognition is still a challenge. There are several obstacles, such as considerable species diversity, intra-class dissimilarity, inter-class similarity, and blurred resource images. Recently, the emerging of deep learning has brought substantial advances in image classification. Deep learning architectures can learn from images and notably increase their predictive accuracy. This thesis provides various techniques, including data augmentation and classification schemes, to improve plant recognition, plant detection, and plant counting system.",
            "source_url": "https:\/\/openalex.org\/W3118912755",
            "pdf_url": "https:\/\/pure.rug.nl\/ws\/files\/156115980\/Complete_thesis.pdf",
            "doi": "10.33612\/diss.156115978",
            "handle": "",
            "repository_name": "University of Groningen research database (University of Groningen \/ Centre for Information Technology)",
            "source_record_id": "W3118912755",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 483,
            "title": "Salient map image for plant disease using deep learning",
            "title_ar": "",
            "title_en": "Salient map image for plant disease using deep learning",
            "author": "Poey Poey Quah",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; image-segmentation; ai-agriculture",
            "keywords": "",
            "abstract": "Plant diseases are a critical factor that impacts the yield and quality of crops and economics in the agricultural sector. This can shown in the incident of a fungal wheat disease in North Texas caused $250 million loss of revenue of the affected country in the year of 2001. Hence, there is essential in protecting the crops from diseases to ensure production quality and quantity. Early detection of the plant diseases is necessary, and it can help to prevent the spreading of the diseases by choosing an appropriate treatment for the plants. However, the process is often trailed by the lack of necessary infrastructure that offers simplicity in performing accurate classifications. Thus, rapid and accurate detection of plant disease through machine learning is essential to minimizing or averting this hardship. On top of that, the existing work does not segment the progression area of the disease on the leaf. In which, this area giving a lot of information on the disease. Especially to the pattern of disease symptoms that are very similar such as in the case of vegetable early and late blight is currently not given much consideration in the machine learning process. Hence, the objective of this project is to construct a salient map image that tracks the disease progression right from inception to manifestation following the pathological disease anatomy. Semantic segmentation with ConvolutionalNeuralNetwork (CNN) is used to construct the salient map image, through transfer learning with SegNet. In this project, 460 images of early blight and late blight diseases plants from PlantVillage dataset is used for the training and testing processes of the CNN. Next, the training parameters are fine-tuned in order to optimize the deep learning model accuracy. At the end of the project, the deep learning model will be able to segment the leaf image into several regions with the overall accuracy of 89.567% and overall IOU of 52.5448%. Also, although the transfer learning on FCN with same data-set and training parameters has slightly better performance with overall accuracy of 89.91% and IOU of 53.92%, its main drawbacks of long model training duration and consumption of huge memory size has made SegNet more preferable in this project. With the gradient map image generated, the pattern of each disease manifestation along the leaf surface can be tracked and quantified for better understanding and characterization based on their anatomy.",
            "source_url": "https:\/\/openalex.org\/W3213994822",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3213994822",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 484,
            "title": "Performance comparison of traditional machine learning and deep learning methods in plant disease detection",
            "title_ar": "",
            "title_en": "",
            "author": "Musa Çetinkaya",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2024,
            "language": "tr",
            "research_domain": "medical-ai",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; cnn; rnn; object-detection; ai-agriculture",
            "keywords": "",
            "abstract": "Geleneksel makine öğrenmesi (GMÖ) ve derin öğrenme (DÖ), yapay zeka alanında iki önemli yaklaşımdır. GMÖ, tahmine dayalı modeller oluşturmak için destek vektör makineleri (DVM), k-en yakın komşular, karar ağaçları gibi çeşitli algoritmalar kullanır. Bu yaklaşımda özellik çıkartımı, algoritmanın başarımını etkileyen en önemli unsurdur. GMÖ küçük veri kümelerinde başarılı sonuçlar elde ederken karmaşık veriler ve büyük ölçekli veri kümelerinde aynı başarıyı gösteremezler. Makine öğrenmesinin bir alt kümesi olan DÖ ise ham veriden hiyerarşik özellik temsillerini otomatik olarak öğrenmek için çok katmanlı sinir ağlarını kullanır. Finans, endüstri, tıp gibi alanlarda ve görüntü sınıflandırma, doğal dil işleme ve ses-tanıma işleme gibi çalışmalarda çok iyi sonuçlar elde ederken büyük miktarda veri ve hesaplama kaynağı gerektirir. Bu tez çalışması GMÖ ve DÖ modellerinin tahmin performansını karşılaştırmak amacıyla bitki hastalıklarının bilgisayar destekli teşhisini sunmaktadır. Bitki hastalıklarının tespiti, modern tarımda ürün kayıplarını en aza indirmek ve gıda güvenliğini sağlamak açısından çok önemlidir. Bitki hastalıklarının erken ve doğru bir şekilde belirlenmesi pestisit kullanımını azaltabilir, mahsul verimini artırabilir ve ürün kalitesini iyileştirebilir. Bu amaçla, çalışmada geleneksel yöntemlerden Lineer Regresyon, DVM ve Rastgele Orman ile DÖ yöntemlerinden Evrişimli Sinir Ağı, LSTM, YOLOv8 ve VGG16 algoritmalarının performansları karşılaştırılmıştır. Elde edilen sonuçlar GMÖ’de DVM için ile en yüksek olmak üzere ortalama ,9 doğruluk elde edilirken DÖ’de CNN için doğruluk en yüksek ve ortalama ,8 olarak elde edilmiştir.",
            "source_url": "https:\/\/openalex.org\/W7217036510",
            "pdf_url": "https:\/\/acikkaynak.bilecik.edu.tr\/bitstreams\/aceb2775-96b9-48c5-8354-231a1e200291\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Bilecik Şeyh Edebali University Institutional Repository",
            "source_record_id": "W7217036510",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 485,
            "title": "Deep Learning for Plant Disease Detection and Classification on Field Images",
            "title_ar": "",
            "title_en": "",
            "author": "Emmanuel Moupojou Matango",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2024,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "image-segmentation",
            "categories": "deep-learning; computer-vision; medical-ai; ai-applications; image-segmentation; ai-agriculture",
            "keywords": "",
            "abstract": "Apprentissage profond pour la détection des maladies de plantes sur des images de terrain Plusieurs modèles d’apprentissage profond ont été proposées pour aider les agriculteurs à détecter le plus efficacement possible les maladies des cultures et éviter les pertes de rendement. Ces modèles sont généralement entrainés sur des bases de données, telles que PlantVillage, composées d'images de plantes prises dans des conditions de laboratoire avec des feuilles uniques et un arrière-plan uniforme. Un réseau de neurones entraîné sur une telle base de données a une très faible précision lorsqu'il est exécuté sur des images de terrain qui ont quant à elles des arrière-plans complexes et plusieurs feuilles par image. Dans cette thèse, nous proposons premièrement la base de données FieldPlant contenant 5 170 images de maladies des plantes collectées dans les plantations et annotées manuellement. Deuxièmement, nous proposons une combinaison de modèles pour identifier avec précision les maladies des plantes sur des images de terrain. Cette solution utilise le modèle de segmentation universel pour extraire efficacement tous les objets identifiables dans une image. Pour distinguer les objets d'arrière-plan identifiés et les objets feuilles, nous utilisons la Description de Données Entièrement Convolutives, un modèle de classification profond à une classe pour la détection d'anomalies. Notre modèle peut détecter plusieurs maladies apparaissant sur différentes feuilles dans la même image et améliore de 15% la précision de classification sur des bases de données publiques de maladies de plantes telles que PlantDoc.",
            "source_url": "https:\/\/openalex.org\/W7152640065",
            "pdf_url": "http:\/\/www.theses.fr\/2024TROY0008\/document",
            "doi": "10.70675\/6c91b081z9504z442dz842fzab0592a5e598",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7152640065",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 486,
            "title": "Deep learning approaches for yield prediction and crop disease recognition",
            "title_ar": "",
            "title_en": "Deep learning approaches for yield prediction and crop disease recognition",
            "author": "Luning Bi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Iowa State University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; ai-applications; predictive-analytics; optimization; cnn; rnn; transformers; gan; ai-agriculture",
            "keywords": "",
            "abstract": "The increase of the world population has brought significant challenges to the agriculture production system. Although mechanization has been realized in agriculture, many tasks (e.g., breeding, field inspection) are still labor-intensive and time-consuming. Therefore an automatic and intelligent solution is needed for the advancement of agricultural production. During this process, the biggest challenge is how to teach computers to understand the concepts in the real world. For example, an experienced expert can easily determine whether a plant is diseased or healthy. However, this may be challenging for the computer. Thus, the motivation of this dissertation study is to tackle these challenges in precision agriculture. This dissertation consists of four papers that propose different deep learning methods for the most challenging problems in agriculture. In the first paper, a genetic algorithm (GA)-assisted deep neural network was built for yield prediction using genetic information and environmental factors. In the global search phase, the GA was introduced to help determine the best initial weights of the neural network. In the local phase, random perturbation was used to avoid the local optimum. By using the proposed method, the root mean square error can be reduced by up to 10%. In the second paper, we proposed a generative adversarial network (GAN)-based approach to generate additional images for the classification of plant species and diseases using limited data. CNN was used as the basic network to classify species and diseases. GAN and label smoothing regularization (LSR) were combined to generate additional training images. Regular data augmentation techniques were also used to expand the dataset. The results showed that compared with using the real dataset only, the proposed method can improve the prediction accuracy by 6%. In the third paper, the potential of using satellite imagery for plant disease detection was explored. A gated recurrent units (GRU)-based model was presented for early detection of soybean sudden death syndrome (SDS) through time-series satellite imagery. The results showed that, compared to XGBoost and fully connected deep neural network (FCDNN), the GRU-based can improve the overall prediction accuracy by 7%. In addition, the proposed method can also be adapted to predict the future development of SDS. In the fourth paper, a transformer-based approach was proposed for soybean yield prediction using time-series camera images and seed treatments information. First, a vision transformer (ViT) base model was designed to extract features from the images. Then another transformer-based model was established to predict the yield using the time-series features. A case study was been conducted using a data set that was collected during the 2020 soybean-growing seasons in Canada. The experiment results show that compared to non-time series prediction and other baseline models, the proposed approach can reduce the mean squared error by 25%-40%. In conclusion, this dissertation aims to apply different state-of-art deep learning methods in agriculture. The study covers different topics, which range from yield prediction, species classification, to plant disease classification and prediction. At the model level, the application of linear models, tree-based methods, fully connected neural networks, convolutional neural networks, time-series models and transformers to different tasks have been investigated. In terms of the learning type, both unsupervised learning and supervised learning have been utilized. The experimental results have shown that appropriate deep learning methods can achieve better performance than traditional methods on specific tasks. Based on our work, more applications of deep learning techniques can be developed in the future.",
            "source_url": "https:\/\/openalex.org\/W7135318077",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Iowa State University Digital Repository (Iowa State University)",
            "source_record_id": "W7135318077",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 487,
            "title": "A deep learning model for early detection of maize diseases in Tanzania",
            "title_ar": "",
            "title_en": "A deep learning model for early detection of maize diseases in Tanzania",
            "author": "Flavia Mayo",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; transformers; ai-agriculture",
            "keywords": "",
            "abstract": "Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of residents depending on it for their livelihood. Maize is the main and dominating food crop in the country, accounting for 45% of all farmland produce. Inevitably, its production is affected by diseases that, if detected early, could be easily treated. Maize Streak Virus (MSV) and Maize Lethal Necrosis (MLN) are commonly reported diseases that are often detected too late by farmers. This raised the need for a sophisticated method for early detection of these diseases to enable timely treatment. This study investigated the potential of developing a deep-learning model for the early detection of maize diseases in Tanzania. Imagery datasets were used for model training, they were collected through physical observation with the help of a plant pathologist from three regions of the country; Arusha, Kilimanjaro, and Manyara. Two models, a Convolutional Neural Network (CNN) and a Vision Transformer (ViT), were developed from scratch and classified into four classes: Healthy, MLN, MSV, and WRONG. The results revealed that the ViT model outperformed the CNN model, achieving validation accuracies of 0.931 and 0.9096 respectively. Despite the superior performance of the ViT model, the CNN model was selected for deployment in a mobile-based application due to its smaller size, lower memory, and small computational requirements. A quantitative research method using a survey questionnaire was employed to gather requirements for system development and validation. Validation of the model's performance through questionnaires administered to farmers and agricultural experts, yielding highly positive feedback. This enthusiastic reception highlights the potential impact of the developed application on improving early disease detection and enhancing maize productivity in Tanzania. Empowering stakeholders of agriculture to identify more effective methods of managing them before serious harm is done. Consequently, custodians of agriculture, such as the Ministry of Agriculture, organizations, and other companies, will have the potential to detect maize diseases early, henceforth adding up to the increase in the Gross Domestic Product (GDP) and guaranteeing the country’s food security.",
            "source_url": "https:\/\/openalex.org\/W4409342614",
            "pdf_url": "",
            "doi": "10.58694\/20.500.12479\/2917",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4409342614",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 488,
            "title": "TOWARDS RELIABLE PLANT PHENOTYPING WITH DEEP LEARNING AND REMOTE SENSING",
            "title_ar": "",
            "title_en": "TOWARDS RELIABLE PLANT PHENOTYPING WITH DEEP LEARNING AND REMOTE SENSING",
            "author": "Umme Fawzia Rahim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "ai-agriculture",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; ai-agriculture",
            "keywords": "",
            "abstract": "Quantitative measurement of plant components (e.g., leaves, inflorescences, flowers, and fruits) is of great interest in precision agriculture since it provides essential information for breeding research and guided agricultural management to enhance crop quality and productivity, as well as for yield estimation and adjusting marketing strategies.The conventional manual phenotyping is time and labor intensive, and often destructive, which remains as the major bottleneck to the advancement of precision and intelligent agriculture.Such restrictions encouraged the growth of high throughput plant phenotyping (HTPP), which is primarily based on sensing technologies and automated data retrieval and processing.These automatic approaches acquire significant amounts of data, primarily in the format of digital images, which need to be thoroughly processed.This is today possible due to the development of powerful image analysis technologies that can automatically identify objects in images using machine learning methods.Due to a range of environmental conditions, field phenotyping still faces significant challenges; therefore, the progress of HTPP capabilities for field conditions can be highly relevant to support digitalization of farm data and precision farming.This thesis introduces an automated image-based phenotyping system using advanced computer vision techniques (i.e., deep learning and multi-object tracking) and appropriate pre-and post-processing methods (i.e., image contrast enhancement, density map-based",
            "source_url": "https:\/\/openalex.org\/W7144386906",
            "pdf_url": "https:\/\/shizuoka.repo.nii.ac.jp\/record\/14937\/files\/K1255.pdf",
            "doi": "10.14945\/00029830",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7144386906",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 489,
            "title": "Deep Learning for autonomous intelligent agents",
            "title_ar": "",
            "title_en": "Deep Learning for autonomous intelligent agents",
            "author": "Fateme Fotouhi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "federated-learning",
            "categories": "deep-learning; computer-vision; medical-ai; robotics; responsible-ai; ai-applications; multimodal; federated-learning; fairness; ai-agriculture",
            "keywords": "",
            "abstract": "Autonomous intelligent agents possess the capability to autonomously perceive their environment and make decisions based on their prior knowledge or learning experiences. The field of Deep Learning (DL) has witnessed significant advancements in recent years, leading to the development of diverse algorithms applicable in various domains. By leveraging these algorithms, intelligent agents can extract and learn higher-level features from data, enabling quick and efficient perception. These algorithms find utility across domains such as visual perception for agents, modular learning frameworks, and collaborative learning scenarios in distributed deep learning. Despite the remarkable progress in DL algorithms, several challenges persist, including limited training data, data imbalance, scalability concerns, and data privacy considerations. The ability of intelligent agents to interpret visual data holds immense importance in applications where human assessment of data is time-consuming and labor-intensive. An illustrative example of such an application is disease detection in agriculture, where manual execution of this task can consume hours or even days for a single field. Consequently, training agents through deep learning algorithms and deploying them on edge devices for inference can provide effective solutions to such challenges. Intelligent agents are not confined to executing visual perception alone; they can also acquire knowledge through modular deep learning frameworks. In certain applications, such as robotics, intelligent agents must make decisions based on diverse visual and linguistic inputs from their environment. This task becomes even more challenging when dealing with cluttered and complex environments or when multiple factors, such as object manipulation, come into play, surpassing simple navigation requirements. Moreover, intelligent agents can collaborate to train a consensus model within a distributed setting. In decentralized deep learning, agents privately update their local deep learning models using their individual data and achieve consensus through peer-to-peer communication. This approach eliminates the need for a central server as in Federated Learning (FL). However, communication overhead remains a challenge in decentralized learning. Recent studies have proposed communication-efficient methods, although the reduction in communication time often comes at the expense of significant accuracy loss, particularly in large-scale networks. Additionally, a pertinent question arises regarding the efficacy of decentralized learning methods in practical scenarios, such as anomaly detection in agriculture. Specifically when the complexity intensifies when we encounter the non-uniform distribution of various plant genotypes within a field. This report aims to address the aforementioned challenges in different facets of employing intelligent agents. Firstly, an autonomous framework for disease detection in agriculture is investigated, targeting issues such as insufficient training data and data imbalance. In this context, a comparative analysis is performed between manually collected Single Nucleotide Polymorphisms (SNPs) and DL-executed SNPs to evaluate the performance of DL models. Secondly, a modular vision-language framework is proposed to tackle the challenges presented by the ALFRED dataset. This modular framework comprises six distinct modules, designed to enhance the agent's task completion abilities within this dataset. Thirdly, a communication-efficient graph-based method is introduced for decentralized deep learning, aiming to reduce communication time while simultaneously preserving high accuracy, even in large-scale networks. Fourthly, an investigation is conducted into detecting anomalies in agricultural fields using decentralized deep learning. This research aims to demonstrate the efficacy and generalizability of decentralized learning compared to local learning approaches, particularly in scenarios involving distributed genotypic differences across the field. Lastly, concluding remarks are provided, and future research directions are discussed to further advance the utilization of intelligent agents.",
            "source_url": "https:\/\/openalex.org\/W4399730369",
            "pdf_url": "",
            "doi": "10.31274\/td-20240617-351",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4399730369",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 490,
            "title": "Android application for crop disease diagnosis using image processing and deep learning (Smart Agriculture).",
            "title_ar": "",
            "title_en": "Android application for crop disease diagnosis using image processing and deep learning (Smart Agriculture).",
            "author": "Munashe Brian Katsande",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Zimbabwe",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Zimbabwe",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; object-detection; ai-agriculture",
            "keywords": "",
            "abstract": "Plant diseases are a major threat to food security worldwide. An accurate and a faster approach to detection and diagnosis of diseases in crops will go a long way to help farmers save their crop and increase yield. Recent developments in smartphone technology and deep neural networks have allowed researchers to develop accurate and ease to use systems to help farmer in this regard. In this dissertation, we developed an android based cotton crop disease detector using deep convolutional networks and image processing. We made use of transfer learning using a pre trained Inceptionv3 model. Additional layers were added to the pretrained model and trained on our dataset. The trained model finally integrated into an android mobile app and experimental results on the developed model were able to achieve an average accuracy of 83%.",
            "source_url": "https:\/\/openalex.org\/W3201423842",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "University of Zimbabwe Institutional Repository (University of Zimbabwe)",
            "source_record_id": "W3201423842",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 491,
            "title": "Plant Species Detection in Aerial and Satellite Images using Deep Learning",
            "title_ar": "",
            "title_en": "Plant Species Detection in Aerial and Satellite Images using Deep Learning",
            "author": "Anastasiia N. Safonova",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Granada",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "",
            "categories": "deep-learning; computer-vision; robotics",
            "keywords": "",
            "abstract": "It is unquestionable that the conservation of plant species is essential for the preservation of nature, climate, and human wellbeing.Classically, the task of conserving threatened plant species was generally done through direct field supervision by natural resources managers.Thanks to the advances in sensor technologies and also in aircrafts such as unmanned aerial vehicles (UAV), high resolution remote sensing (RS) data are becoming an important resource for monitoring threatened plant species in large areas.Such RS data are usually multi-spectral images, with three or more bands, of up to 3 cm\/pixel resolution, providing an orthogonal or quasi-orthogonal view of the considered plant species.This information may not be as complete as the information provided by natural images; however, it might be sufficient to monitor tree species located in very large areas with difficult access.",
            "source_url": "https:\/\/openalex.org\/W3183821534",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repository of the University of Granada (University of Granada)",
            "source_record_id": "W3183821534",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 492,
            "title": "Detection of diseases in rice plant with deep learning methods and artificial network based NDVI values",
            "title_ar": "",
            "title_en": "",
            "author": "İrfan Ökten",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2022,
            "language": "tr",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "deep-learning; medical-ai; cnn",
            "keywords": "",
            "abstract": "Dünya çapında gıda tüketimi göz önüne alındığında pirincin önemli bir yere sahip olduğu görülür. Çeltik bitkisi, buğdaygiller ailesinden mısır ve buğdaydan sonra en fazla ekimi yapılan bitkidir. Çeltik üretiminde çeşitli zararlılardan ve hastalıklardan dolayı üretim kaybı yaşanmaktadır. Genellikle yaşanan bu kayıplara neden olan üç temel hastalık bulunmaktadır: yaprak patlaması (Leaf Blast - Pyricularia grisea), kahverengi lekeler (Brown Spot - Helminthosporium oryzae) ve beyaz noktalar (Hispa - Dicladispa armigera). Tüm bu hastalık belirtileri bitkinin yaprağında bulunmaktadır. Yaprak görüntülerinden bitki hastalığının otomatik bir şekilde teşhisi geliştirme aşamasında olan bir konudur. Bu tez kapsamında çeltik bitkisine ait bu üç hastalığı tespit etmek için derin öğrenme modellerinden Evrişimli Sinir Ağı (CNN: Convolution Neural Networks) kullanılmıştır. Çalışmada kullanılan 3341 adet çeltik bitkisi yaprağına ait RGB veri seti Kaggle sitesinden elde edilmiştir. Çeltik bitkisinin RGB yaprak görüntülerinden derin öğrenme modeli ile çeltik bitkisine ait üç önemli hastalık (Brown Spot, Leaf Blast ve Hispa) tespiti yüksek doğruluk oranlarıyla elde edilmiştir. Çeltik bitkisine ait hastalıkların erken tespiti için CNN modelinin deneyler sonucu bulunan en uygun hiper parametreler ile eğitimi gerçekleştirilmiştir. Ağın eğitimi için çeltik bitkisine ait RGB görüntüler kullanılarak ,78 oranında doğruluk elde edilmiştir. Tarım alanlarında ürünlerin sağlık durumlarının incelenmesinde genellikle multispektral kameralardan ve uydu görüntülerinden elde edilen Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI: Normalized Difference Vegetation Index) değerleri kullanılmaktadır. NDVI verilerinin elde edilmesinde kullanılan uzaktan algılama tekniklerinde yüksek maliyet ve hava durumunun uygunluğu öne çıkmaktadır. Diğer alternatif olan insansız hava araçlarına (İHA) entegre edilmiş multispektral kamera sistemlerinin maliyetlerinin yüksek olması ve uzman gereksinimine ihtiyaç duyulması bir başka sorundur. Bu tez çalışmasında ikinci olarak multispektral kameraların yerine standart RGB görüntü veren kamera sistemlerinden NDVI değerini tahmin etmek (nNDVI: Neural network-based Normalized Difference Vegetation Index) için yeni bir yapay sinir ağı modeli önerilmiştir. Önerilen ağın eğitim ve testlerinde kullanılan veri seti İsviçre'de bir tarım alanından ve Togo'da bir çiftlikten multispektral kamera ile alınan görüntülerden elde edilmiştir. Bu model sayesinde ,013 doğruluk oranı ile RGB görüntülerinden NDVI verileri elde edilmiştir. Son olarak bu tez kapsamında çeltik bitkisinin standart RGB görüntülerinden nNDVI verileri elde edilerek, bu veriler ile derin öğrenme modelinin eğitimi yapılmış ve ,97'lik bir doğruluk oranına ulaşılmıştır. Çeltik bitkisindeki yaprak görüntüleri ile hastalığının tespiti için elde edilen bu başarım oranı, yöntemin uygulanabilirliğini göstermektedir.",
            "source_url": "https:\/\/openalex.org\/W7217327925",
            "pdf_url": "https:\/\/acikkaynak.bilecik.edu.tr\/bitstreams\/7f64b62f-1f4b-408b-8fad-6e1b1f3cf42b\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Bilecik Şeyh Edebali University Institutional Repository",
            "source_record_id": "W7217327925",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 493,
            "title": "Recognition of Plant Diseases Using Convolutional Neural Networks",
            "title_ar": "",
            "title_en": "Recognition of Plant Diseases Using Convolutional Neural Networks",
            "author": "Cyntia D. León-Lemus",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Western Institute of Technology and Higher Education",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Mexico",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; ai-agriculture",
            "keywords": "",
            "abstract": "Early detection of diseases in crops for human consumption is crucial for agricultural production in the world. Nowadays, Deep learning algorithms are used for the analysis and classification of images such as the convolutional neural network used to detect diseases in plants. Unfortunately, these algorithms still need a lot of computation to obtain results that could take hours, days, or maybe weeks for a prediction. This paper aims to provide an analysis of how the Wavelet transform in distributed platforms can provide competitive results with a fraction of the resources used comparatively in time and memory used to detect diseases in tomato leaves.",
            "source_url": "https:\/\/openalex.org\/W3089570666",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ITESO Institutional Repository (ReI) (Western Institute of Technology and Higher Education)",
            "source_record_id": "W3089570666",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 494,
            "title": "Deep Learning for Early Detection, Identification, and Spatiotemporal Monitoring of Plant Diseases Using Multispectral Aerial Imagery",
            "title_ar": "",
            "title_en": "Deep Learning for Early Detection, Identification, and Spatiotemporal Monitoring of Plant Diseases Using Multispectral Aerial Imagery",
            "author": "Joseph Mbugua",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Claremont Colleges",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "image-classification",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; image-classification; ai-agriculture",
            "keywords": "",
            "abstract": "Production of food crops is hampered by the proliferation of crop diseases which cause huge harvest losses. Current crop-health monitoring programs involve the deployment of scouts and experts to detect and identify crop diseases through visual observation. These monitoring schemes are expensive and too slow to offer timely remedial recommendations for preventing the spread of these crop-damaging diseases. There is thus a need for the development of cheaper and faster methods for identifying and monitoring crop diseases. Recent advances in deep learning have enabled the development of automatic and accurate image classification systems. These advances coupled with the widespread availability of multispectral aerial imagery provide a cost-effective method for developing crop-diseases classification tools. However, large datasets are required to train deep learning models, which may be costly and difficult to obtain. Fortunately, models trained on one task can be repurposed for different tasks (with limited data) using transfer learning technique. The purpose of this research was to develop and implement an end-to-end deep learning framework for early detection and continuous monitoring of crop diseases using transfer learning and high resolution, multispectral aerial imagery. In the first study, the technique was used to compare the performance of five pre-trained deep learning convolutional neural networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) in classifying crop diseases for apples, grapes, and tomatoes. The results of the study show that the best performing crop-disease classification models were those trained on the VGG16 network, while those trained on the ResNet50 network had the worst performance. The other studies compared the performance of using transfer learning and different three-band color combinations to train single- and multiple-crop classification models. The results of these studies show that models combining red, near infrared, and blue bands performed better than models trained with the traditional visible spectral band combination of red, green, and blue. The worst performing models were those combining near infrared, green, and blue bands. This research recommends that further studies be undertaken to determine the best band combinations for training single- and multi-label classification models for both crops and plants and diseases that affect them.",
            "source_url": "https:\/\/openalex.org\/W3177001023",
            "pdf_url": "https:\/\/scholarship.claremont.edu\/cgi\/viewcontent.cgi?article=1237&context=cgu_etd",
            "doi": "10.5642\/cguetd\/218",
            "handle": "",
            "repository_name": "Scholarship - Claremont (Claremont Colleges)",
            "source_record_id": "W3177001023",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 497,
            "title": "Efficient Graph Neural Networks for Predicting Chemistry",
            "title_ar": "",
            "title_en": "Efficient Graph Neural Networks for Predicting Chemistry",
            "author": "Teng Jiek See",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "gnn",
            "categories": "machine-learning; deep-learning; predictive-analytics; gnn",
            "keywords": "",
            "abstract": "Graph Neural Networks (GNNs) are powerful deep learning models for predicting molecular properties but they require large datasets and significant computational resources. My research focuses on enhancing their efficiency and accuracy by improving how these models learn and process information internally. By inventing new model architectures and training strategies, I demonstrate that GNNs can achieve superior prediction accuracy while using either the same or even fewer computational resources. My work advances the development of efficient GNNs for chemistry prediction.",
            "source_url": "https:\/\/openalex.org\/W7203887660",
            "pdf_url": "",
            "doi": "10.26180\/33294069",
            "handle": "",
            "repository_name": "Monash University",
            "source_record_id": "W7203887660",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 499,
            "title": "Exploring Graph Neural Networks for Metagenomic Assembly Graphs",
            "title_ar": "",
            "title_en": "Exploring Graph Neural Networks for Metagenomic Assembly Graphs",
            "author": "Tiago Miguel Campos Amaral Gomes",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "gnn",
            "categories": "machine-learning; deep-learning; data-mining; gnn",
            "keywords": "",
            "abstract": "Metagenomics has transformed microbial research by analyzing environmental DNA without the need for culturing. Metagenomic binning focuses on clustering the DNA fragments obtained from these environments based on their shared characteristics, providing insights into microbial community composition. Deep learning approaches offer a promising approach towards this outcome by leveraging graph representations of metagenomic data to improve clustering accuracy. However, binning remains challenging with long-read sequencing technologies. While long reads enable more comprehensive genomic coverage, their high error rates create obstacles that traditional binning algorithms struggle to overcome, limiting accurate genome reconstruction. In this thesis, we develop a framework for metagenomic binning of long-read sequencing data, integrating Graph Neural Networks (GNNs) with graph-based error correction techniques. We leverage the MetaFlye assembler to construct assembly graphs from errorprone long-read data and incorporate a GNN model based on GraphSAGE for unsupervised clustering, aiming to accurately and efficiently group DNA fragments into taxonomic bins. Additionally, we explore and evaluate graph-based error correction methodologies designed to refine the graph structure by reducing erroneous connections and reinforcing biologically meaningful ones, thereby improving the quality of metagenomic binning. Through this research, we seek to develop computational methods that enhance the accuracy and reliability of metagenomic analysis.",
            "source_url": "https:\/\/openalex.org\/W7128567491",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W7128567491",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 507,
            "title": "Graph Neural Networks for Circuit Clustering and Partitioning",
            "title_ar": "",
            "title_en": "Graph Neural Networks for Circuit Clustering and Partitioning",
            "author": "A R Soroush",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Guelph",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "gnn",
            "categories": "machine-learning; deep-learning; data-mining; predictive-analytics; gnn",
            "keywords": "",
            "abstract": "Circuit clustering and circuit partitioning are essential tasks in the Very Large Scale Integration (VLSI) design flow and are usually solved using algorithmic techniques. Graph Neural Networks (GNNs), a branch of artificial neural networks, leverage the predictive power of deep learning on graph-structured data. Given the circuits' graphical nature, GNNs are increasingly applied to various VLSI design tasks. This research investigates applying GNNs to solve circuit clustering and partitioning. Moreover, a hierarchical framework is suggested, linking the clustering GNN to the partitioning GNN, where information gained from clustering GNN enriches the initial dataset before being fed to the partitioning GNN. The proposed method motivates further exploration of preceding a clustering GNN to other GNNs addressing other VLSI layout design problems (e.g. placement), enhancing the node features. Furthermore, the proposed linkage between two distinct GNNs can be extended to other GNNs that solve consecutive tasks in VLSI system design.",
            "source_url": "https:\/\/openalex.org\/W7204615144",
            "pdf_url": "https:\/\/atrium.lib.uoguelph.ca\/bitstreams\/6fc219c1-974a-4906-86a9-059e815dcc21\/download",
            "doi": "",
            "handle": "",
            "repository_name": "The Atrium (University of Guelph)",
            "source_record_id": "W7204615144",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 508,
            "title": "Meta Learning for Graph Neural Networks",
            "title_ar": "",
            "title_en": "Meta Learning for Graph Neural Networks",
            "author": "Rohan Dhamdhere",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Rochester Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; optimization; cnn; gnn; image-classification; image-segmentation; video-analysis",
            "keywords": "",
            "abstract": "Deep learning has enabled incredible advances in pattern recognition such as the fields of computer vision and natural language processing. One of the most successful areas of deep learning is Convolutional Neural Networks (CNNs). CNNs have helped improve performance on many difficult video and image understanding tasks but are restricted to dense gridded structures. Further, designing their architectures can be challenging, even for image classification problems. The recently introduced graph CNNs can work on both dense gridded structures as well as generic graphs. Graph CNNs have been performing at par with traditional CNNs on tasks such as point cloud classification and segmentation, protein classification and image classification, while reducing the complexity of the network. Graph CNNs provide an extra challenge in designing architectures due to more complex weight and filter visualization of generic graphs. Designing neural network architectures, yielding optimal performance, is a laborious and rigorous process. Hyperparameter tuning is essential for achieving state of the art results using specific architectures. Using a rich suite of predefined mutations, evolutionary algorithms have had success in delivering a high-quality population from a low-quality starter population. This thesis research formulates the graph CNN architecture design as an evolutionary search problem to generate a high-quality population of graph CNN model architectures for classification tasks on benchmark datasets.",
            "source_url": "https:\/\/openalex.org\/W2923863837",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "RIT Scholar Works (Rochester Institute of Technology)",
            "source_record_id": "W2923863837",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 515,
            "title": "Graph neural networks and their application to social network analysis",
            "title_ar": "",
            "title_en": "Graph neural networks and their application to social network analysis",
            "author": "Marek Behún",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Czech Republic",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "gnn",
            "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; gnn",
            "keywords": "",
            "abstract": "Recently, the research on Graph Neural Networks (GNNs) made it possi- ble to apply deep learning techniques to graph-structured data. In this thesis, we explore the application of GNNs to Social Network Analysis (SNA). We build and compare deep learning models for the prediction of hotel review ratings, hotel classes, and hotel scores on data scraped from the Tripadvisor website. We consider the resulting models precise enough to be used by rec- ommender systems. A non-trivial part of this thesis is also the description of the theory behind GNNs and visualization techniques for high-dimensional data. We also provide software suitable for further experimentation on this topic.",
            "source_url": "https:\/\/openalex.org\/W7135806338",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Digital Repository (National Repository of Grey Literature)",
            "source_record_id": "W7135806338",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 523,
            "title": "Deep Learning-Based Vision System for Intelligent Object Detection and Robotic Sorting Solution",
            "title_ar": "",
            "title_en": "Deep Learning-Based Vision System for Intelligent Object Detection and Robotic Sorting Solution",
            "author": "Anastasiia Stepanova",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Tampere University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; object-detection; image-segmentation; ai-industry",
            "keywords": "",
            "abstract": "Manufacturing and production enterprises encounter digital development in the scope of Industry 4.0. Car workshops as part of the automotive industry aftermarket are in the transition from manual labour to automation and robotics solutions that aim to facilitate the workspace productivity by decreasing workers’ fatigue and errors. Automotive industry expects efficiency increase by 30% due to the deployment of autonomous robotics in both production and operation. Machine vision serving as the perceptive brain for automation systems allows robots to navigate in the unstructured environments of car workshops. Recent advancements in deep neural networks allow models to learn hierarchical features and patterns extracted from visual data and hence contribute to automated solutions in car workshops’ unstructured environments that present a certain challenge for machine learning. This thesis does not aim to replace human workers, but to provide aid in necessary but secondary task of the post-work tool sorting and arranging in a car service garage. In this project, a deep learning-based vision system was developed and evaluated. The primary task of the project included automatic identification, instance segmentation, and pose estimation of metallic wrenches in automotive workshop environment. Metallic tools represent a certain machine vision challenge due to the high specular reflection that causes significant segmentation mask fragmentation and affects performance of 3D cameras. This issue was addressed by utilizing a deeplearning-based keypoint detection as geometric anchors to interpolate and repair fragmented masks. The vision framework utilizes custom trained Detectron2 segmentation framework. The primary technical contribution of this thesis is the developed keypoint-guided mask reconstruction algorithm. Unlike standard frameworks that encounter fragmented segmentation masks on reflective objects, this system exploits detected semantic keypoints as stable geometric anchors that are further utilized to interpolate the mask and repair incomplete visual data. In this system, Zivid2 M70 industrial 3D camera was used for perception task to ensure high precision of depth values. The developed vision system was experimentally validated through three testing cases: static repeatability, rotational accuracy, and robustness under occlusion. The test results have shown high system stability: angular standard deviation of 0.17° and depth variance of 0.02 mm. The system achieved MAE of 1.33° and RMSE of 1.69° in a 180° rotational sweep. Furthermore, the developed algorithm to reconstruct fragmented masks has shown precise orientation calculation in occluded scenes with angular variance of 0.08°. Therefore, the proposed vision system provides accurate and reliable data for robotic manipulation and automated tool arranging tasks under unstructured conditions of car workshops.",
            "source_url": "https:\/\/openalex.org\/W7164869336",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Tampere University Institutional Repository (Tampere University)",
            "source_record_id": "W7164869336",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 526,
            "title": "Intelligent Fault Diagnosis of Wheeled Mobile Robots",
            "title_ar": "",
            "title_en": "Intelligent Fault Diagnosis of Wheeled Mobile Robots",
            "author": "Xiaoxue Mei",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "fairness",
            "categories": "deep-learning; medical-ai; robotics; responsible-ai; ai-applications; fairness; ai-industry",
            "keywords": "",
            "abstract": "This research focuses on improving the reliability of intelligent robots through artificial intelligence–based fault diagnosis. By analysing multi-sensor data, advanced deep learning methods are developed to enable robots to automatically detect and identify faults under complex and dynamic working conditions. The research further introduces privacy-preserving collaborative learning among multiple robots and efficient AI models suitable for real-world deployment. This work contributes to safer, more reliable, and intelligent robot–human collaboration in industrial and service environments.",
            "source_url": "https:\/\/openalex.org\/W7141181167",
            "pdf_url": "",
            "doi": "10.25916\/sut.31856386",
            "handle": "",
            "repository_name": "Swinburne figshare (Swinburne University of Technology)",
            "source_record_id": "W7141181167",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 538,
            "title": "Intelligent Robotic Recycling of Flat Panel Displays",
            "title_ar": "",
            "title_en": "Intelligent Robotic Recycling of Flat Panel Displays",
            "author": "Adam Sanderson",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Waterloo",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; robotics; ai-applications; cnn; object-detection; image-segmentation; ai-industry",
            "keywords": "",
            "abstract": "As the population and prosperity continue to rise the demand for high-tech products is rapidly increasing. Displays are a huge part of this market with millions being created each year. We have a finite amount of resources on this planet and it is imperative that we properly dispose of our devices. In this thesis an intelligent robotic workcell for dismantling and recycling of flat panel displays is proposed and tested. This system utilizes industrial robots and open source tools to process a flat panel display (FPD) with the goal of removal of cold compact fluorescent tubes (CFLs). These are removed so the FPD can be shredded and properly recycled without the mercury present in the CFLs contaminating the materials or harming workers. This system utilizes many new and innovative techniques including deep learning algorithms for object detection and image segmentation. These deep learning techniques specifically Faster R-CNN for object detection and DeepLab for image segmentation have been shown to be extremely robust and capable in this application. The system was tested both with a real world system and with simulated geometry with a live vision feed. During simulation, the system was capable of processing flat panel FPDs in between 110 and 230 seconds per unit. It has been calculated using the simulation results, that this system can be profitable and has an approximate payback period of between 0.19 and 4.87 years depending on the material being fed into the system. The large range is due to the difference in value between monitor and TV style FPDs. The monitors have far less value than the TVs and are far more difficult to process.",
            "source_url": "https:\/\/openalex.org\/W2947070151",
            "pdf_url": "https:\/\/uwspace.uwaterloo.ca\/bitstreams\/c1b45110-475c-469e-ab35-3d38398d358d\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UWSpace (University of Waterloo)",
            "source_record_id": "W2947070151",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 542,
            "title": "Natural language processing for business documents",
            "title_ar": "",
            "title_en": "",
            "author": "Ελευθέριος-Παναγιώτης Λούκας",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2025,
            "language": "el",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; nlp; few-zero-shot; rnn; transformers; llm",
            "keywords": "",
            "abstract": "Η Επεξεργασία Φυσικής Γλώσσας (NLP) για επιχειρηματικά και χρηματοοικονομικά κείμενα (Hahn et al., 2018; Chen et al., 2022) αποτελεί ένα ταχέως αναπτυσσόμενο ερευνητικό πεδίο που εφαρμόζει υπολογιστικές τεχνικές σε κείμενα όπως εταιρικά έγγραφα, αναφορές αναλυτών και οικονομικές ειδήσεις. Τα έγγραφα αυτά παρουσιάζουν μοναδικές προκλήσεις λόγω του εξειδικευμένου λεξιλογίου (El-Haj et al., 2019), του κρίσιμου ρόλου των αριθμητικών δεδομένων, των ιδιαίτερων συντακτικών δομών και της σημασιολογίας του εκάστοτε πεδίου. Οι δυσκολίες αυτές εντείνονται από ευρύτερα προβλήματα, όπως η επεξεργασία μεγάλου όγκου μη δομημένων δημόσιων δεδομένων και η οικονομικά αποδοτική ανάπτυξη γλωσσικών μοντέλων, ιδίως για οργανισμούς με περιορισμένους πόρους όπως οι small-to-medium enterprises (SMEs). Η αντιμετώπιση αυτών των προκλήσεων είναι κρίσιμη για εφαρμογές που εκτείνονται από την ανίχνευση απάτης (Goel and Gangolly, 2012), τη long-form summarization (Cao et al., 2024) και την εξαγωγή πληροφορίας έως τη χρηματοοικονομική question answering (Maia et al., 2018). Η παρούσα διατριβή στοχεύει στην προώθηση της εφαρμοσμένης NLP και της αξιοποίησης επιχειρηματικών εγγράφων σε πραγματικές συνθήκες, αντιμετωπίζοντας σύγχρονες βιομηχανικές προκλήσεις σε διαφορετικά επίπεδα της τεχνητής νοημοσύνης, και πιο συγκεκριμένα στο επίπεδο των δεδομένων, των εφαρμογών και της ανάπτυξης (deployment), με σταθερή έμφαση σε περιβάλλοντα περιορισμένων πόρων. Διατυπώνουμε τρία βασικά ερευνητικά ερωτήματα: (1) Πώς μπορούν τα ανοικτής πρόσβασης, μη δομημένα επιχειρηματικά έγγραφα να αξιοποιηθούν αποτελεσματικά για NLP; (2) Πώς μπορούν οι σύγχρονες μέθοδοι NLP που βασίζονται σε deep learning (DL) να προσαρμοστούν και να επεκταθούν ώστε να δημιουργούν επιχειρηματική αξία σε εργασίες όπως το automatic document tagging, λαμβάνοντας υπόψη τις ιδιαιτερότητες της χρηματοοικονομικής γλώσσας και την έντονη εξάρτησή της από αριθμητικά δεδομένα; (3) Για τις συνήθεις βιομηχανικές εργασίες text classification, ποιες προσεγγίσεις είναι οι πιο ακριβείς και οικονομικά αποδοτικές σε συνθήκες περιορισμένων πόρων; Το τρίτο ερώτημα μελετάται μέσω μιας πραγματικής εφαρμογής οικονομικά αποδοτικής αναγνώρισης πρόθεσης σε διαλόγους πελατών, με σύγκριση μεταξύ BERT-based models και Large Language Models (LLMs) και με βελτιστοποίηση του κόστους ανάπτυξης των LLMs. Για την αντιμετώπιση των παραπάνω, αρχικά εστιάζουμε στον «εκδημοκρατισμό» της πρόσβασης στα επιχειρηματικά έγγραφα μέσω της ανάπτυξης του EDGAR-CORPUS, του μεγαλύτερου δημόσια διαθέσιμου financial NLP corpus στα Αγγλικά, των domain-specific word embeddings EDGAR-W2V, τα οποία υπερτερούν των εναλλακτικών προσεγγίσεων, καθώς και του EDGAR-CRAWLER, ενός open-source εργαλείου εξαγωγής χρηματοοικονομικών δεδομένων με εκατοντάδες χρήστες από τον ακαδημαϊκό χώρο, τη FinTech κοινότητα και την ανάπτυξη web εφαρμογών. Στη συνέχεια, εισάγουμε το XBRL tagging ως μια πραγματική εργασία NLP, όπου δημιουργούμε το FiNER-139, το πρώτο dataset για το συγκεκριμένο πρόβλημα, και δείχνουμε ότι τα LSTM μοντέλα μπορούν να υπερέχουν έναντι του BERT λόγω περιορισμών της αρχιτεκτονικής των transformers και της τυπικής τεχνικής tokenization σε σχέση με τον κατακερματισμό αριθμητικών tokens. Μετά από εκτενή αξιολόγηση διαφορετικών μεθόδων, προτείνουμε μια νέα τεχνική tokenization με χρήση pseudo-tokens για transformer μοντέλα, η οποία βελτιώνει σημαντικά την απόδοση σε numeric-first tasks, οδηγώντας στην κυκλοφορία νέων state-of-the-art BERT μοντέλων (SEC-BERT) ειδικά σχεδιασμένων για το χρηματοοικονομικό πεδίο. Τέλος, για την οικονομικά αποδοτική αναγνώριση πρόθεσης, πραγματοποιούμε μια εκτενή συγκριτική μελέτη στο Banking77 dataset (Casanueva et al., 2020), δείχνοντας ότι μικρότερα BERT-based models μπορούν να είναι πιο αποδοτικά και οικονομικά από τα LLMs, απαιτώντας μόνο ελαφρώς περισσότερα επισημασμένα δεδομένα εκπαίδευσης. Παράλληλα, παρουσιάζουμε το “Dynamic Few-Shot Prompting”, μια προσέγγιση βασισμένη σε Retrieval-Augmented Generation (RAG) που μειώνει δραστικά το κόστος inference των LLMs διατηρώντας υψηλή ακρίβεια, και διερευνούμε τη χρησιμότητα της synthetic data generation. Η διατριβή καθιστά δημόσια διαθέσιμους βασικούς πόρους: το EDGAR-CORPUS, τα EDGAR-W2V embeddings, το λογισμικό EDGAR-CRAWLER, το dataset FiNER-139, την οικογένεια μοντέλων SEC-BERT, καθώς και ένα επιμελημένο υποσύνολο του Banking77 dataset, το οποίο περιλαμβάνει expert-selected παραδείγματα για κάθε intent class και το οποίο αποδεικνύεται κρίσιμο για την επίτευξη υψηλής απόδοσης σε few-shot learning σενάρια.",
            "source_url": "https:\/\/openalex.org\/W4417302042",
            "pdf_url": "",
            "doi": "10.12681\/eadd\/60507",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4417302042",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 557,
            "title": "Natural language processing as autoregressive generation",
            "title_ar": "",
            "title_en": "Natural language processing as autoregressive generation",
            "author": "Xiang Lin",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; responsible-ai; predictive-analytics; llm; machine-translation; fairness",
            "keywords": "",
            "abstract": "The advances in deep learning have led to great achievements in many Natural Language Processing (NLP) tasks. With the nature of language, i.e., sequential data, most NLP tasks can be framed into the sequence learning framework, such as text generation. As one of the most important foundations for modern NLP techniques, autoregressive generation models have achieved dominant performance in a great deal of NLP tasks. Therefore, this thesis emphasizes improving the autoregressive generation model for different NLP tasks. While many tasks can naturally fit into the sequence learning framework, some of them, e.g., building discourse parsing tree, require sophisticated designs to fit into neural models. Therefore, this thesis firstly emphasizes a novel unified framework for discourse parsing, which builds a discourse tree in a top-down depth-first manner, and it frames the task as an autoregressive generation task with the goal of each step being the prediction of the node position given a piece of text. The proposed approach is proven effective with extensive empirical experiments. In addition, I extend the above framework by proposing a hierarchical decoder, which leverages the information from parents and siblings of the nodes that are currently processed. The proposed decoder utilizes the nature of the tree structure and further improves the experiment performance on both discourse parsing and dependency parsing tasks. On the other hand, the de facto strategies, i.e., cross entropy loss and teacher forcing, for training the autoregressive generation models have been shown problematic in certain aspects. For example, cross entropy loss, which is one of the widely leveraged training objective functions, often leads to text degeneration in text generation, and teacher forcing suffers from the exposure bias problem, where there exists a mismatch between the training and testing setup. For text degeneration, I introduce a class of diminishing attentions, which enforces the submodularity of the coverage calculated by cross attention in the sequence-to-sequence model. The proposed diminishing attentions achieve notable improvement on several neural text generation tasks, including text summarization, machine translation, and image paragraph generation. Further, I propose a novel training objective, ScaleGrad, to replace cross entropy, which significantly reduces the degeneration problem in different text generation tasks. In fact, ScaleGrad can be extended to problems beyond text degeneration. It provides wide flexibility to inject different inductive biases into the text generation model by directly modifying the gradient information in the output layer. Next, for the exposure bias problem, this thesis introduces a novel type of scheduled sampling based on training accuracy, which requires only minimal hyper-parameter tuning compared to existing scheduled sampling methods. Additionally, a novel imitation loss is proposed to further enforce the model’s generative behavior to match the teacher-forced behavior. Moreover, this thesis demonstrates that reducing exposure bias can improve the robustness of language models against repetition and toxic errors.",
            "source_url": "https:\/\/openalex.org\/W4382929605",
            "pdf_url": "",
            "doi": "10.32657\/10356\/168487",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4382929605",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 564,
            "title": "Self- and semi-supervised machine learning techniques for discovering novel ocular and neuroimaging biomarkers of Alzheimer's disease",
            "title_ar": "",
            "title_en": "Self- and semi-supervised machine learning techniques for discovering novel ocular and neuroimaging biomarkers of Alzheimer's disease",
            "author": "Charlene Z. L. Ong",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; responsible-ai; self-semi-supervised; predictive-analytics; cnn; gnn; multimodal; image-segmentation; gan; medical-imaging; xai",
            "keywords": "",
            "abstract": "There is increasing prevalence of Alzheimer’s disease worldwide. To manage the progression of Alzheimer’s disease, biomarkers can be utilized to identify patients with the disease or predict subsequent progression. While neuroimaging biomarkers have been long established to visualize structural changes associated with Alzheimer’s disease, there are other promising biomarkers such as retinal imaging biomarkers which offer the possibility of detecting early pathophysiological changes and are non-invasive and widely available. Nevertheless, the link between retinal imaging biomarkers to Alzheimer’s disease remains unclear and there is much difficulty in procuring datasets of patients for investigation, in particular longitudinal datasets. At the same time, advances in deep learning also offer much promise for furthering our understanding of the link between ophthalmic biomarkers and dementia. Self-supervised learning techniques offer potential for learning new biomarkers which might not have been defined previously. However, there is often a lack of sufficient ground truth annotations, hence hampering the application of deep learning models. In light of this, the work in this thesis aims to address these issues by exploring the use of self or semi-supervised learning approaches and leveraging on both neuroimaging and ophthalmic imaging. By utilizing unlabeled datasets, self or semi-supervised learning could potentially improve the model performance on a range of tasks, such as segmentation, disease classification, and prognosis prediction. More importantly, self-supervised learning techniques could uncover novel biomarkers in a data-driven manner. Some of these methods include generative adversarial networks, graph neural networks, and masked autoencoders, which are explored in this study for biomarker discovery. Firstly, the challenge of lack of sufficient ground truth annotation to develop models for retinal layer segmentation is investigated. Retinal layer segmentation is critical for quantifying the retinal layer thickness, which has been shown to be a useful diagnostic biomarker for age-related macular degeneration and a potential biomarker for Alzheimer’s disease. While there have been existing approaches developed which reduce annotation need, we propose to develop a deep learning approach using generative adversarial learning that utilizes a very limited number of labels for segmentation. We demonstrated that our approach showed comparable or superior performance to other state-of-the-art approaches, particularly with limited labels. In addition, we also evaluated our approach under various noisy conditions and showed it was robust in most noisy conditions. Secondly, current dementia diagnosis is based primarily on clinical assessment. Neuroimaging in the form of magnetic resonance imaging is used to provide additional granularity. It will hence be practical to explore neuroimaging biomarkers as a value add to clinical assessment, especially in a pre-dementia cohort. Our proposed approach uses a graph convolutional neural network to combine both clinical and non-clinical features such as neuroimaging for the classification of cognitive syndromes. It integrates a Cross Attention Module to highlight the salient features in the non-clinical features, attaining the highest accuracy and Matthew’s correlation coefficient compared to existing state-of-the-art methods. In addition, by investigating the feature interpretability via Integrated Gradients, several nonclinical features, such as cerebrospinal fluid volume, right hippocampal fissure volume, right frontal pole thickness, and left pallidum volume were identified as key features for the classification. Thirdly, we explore the utility of ophthalmic biomarkers as prognostic biomarkers for cognitive decline in Alzheimer’s disease patients. In parallel, we explore the possibility of combining both ophthalmic and neuroimaging features to form multimodal features. The application of these multimodal features is investigated in a clinical trial dataset consisting of participants with mild-to-moderate Alzheimer’s disease. To learn key feature representations associated with the ophthalmic images, we utilize self-supervised learning via masked autoencoders to extract feature representations. For neuroimaging, due to the availability of established neuroimaging software, we utilize standard preprocessing pipelines to obtain these neuroimaging features. We investigated the combination of these features using a graph convolutional network model for the prediction of cognitive decline in Alzheimer’s disease and showed initial promising results with the addition of optical coherence tomography data.",
            "source_url": "https:\/\/openalex.org\/W7127051058",
            "pdf_url": "",
            "doi": "10.32657\/10356\/205534",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W7127051058",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 565,
            "title": "Semi-Self-Supervised Learning for Deep Models: Reducing Annotation Costs in Dense-Pattern Agricultural Image Analysis",
            "title_ar": "",
            "title_en": "Semi-Self-Supervised Learning for Deep Models: Reducing Annotation Costs in Dense-Pattern Agricultural Image Analysis",
            "author": "Najafian, Keyhan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Saskatchewan",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; ai-applications; transfer-learning; self-semi-supervised; object-detection; image-segmentation; video-analysis; ai-agriculture",
            "keywords": "",
            "abstract": "Most of the success of deep learning in computer vision depends on supervised learning (SL) and large-scale, manually annotated datasets. Creating such datasets is a major bottleneck due to the substantial cost and labor required, particularly for pixel-level tasks on images with dense and irregular object distributions, as commonly encountered in precision agriculture. While existing semi- and self-supervised methods attempt to reduce this dependency by requiring minimal to no annotation, they still require massive, difficult-to-acquire unlabeled datasets. Self-supervised learning typically involves pretraining on extensive unlabeled data to learn generalizable representations, followed by fine-tuning on task-specific labeled samples, whereas semi-supervised learning combines a small set of labeled data with larger unlabeled collections. To mitigate these challenges, this dissertation introduces a semi-self-supervised learning paradigm that integrates data synthesis, domain adaptation, and spatiotemporal modeling to develop high-performing models across various tasks. Specifically, our paradigm leverages high-frame-rate unannotated video, supplemented with minimal human-annotated static images and video frames, to create large-scale annotated datasets more efficiently than conventional data collection. The produced data includes synthetic training samples, which are rendered through data simulation (synthetic data), and automatically labeled real samples (generated data), where all labels originate from the initial minimal human annotations, thus eliminating the need for further manual labeling and directly addressing the data scarcity problem. We demonstrate the efficacy of our proposed semi-self-supervised paradigm through four key studies on progressively more challenging computer vision tasks within the demanding context of precision agriculture. The framework is first applied to object detection, where a computational synthesis method creates large-scale bounding-box annotated datasets from minimal manually annotated video frames, achieving robust performance of over 80%. Extending this to the pixel-level, the method generates high-quality semantic masks for scenes characterized by dense and overlapping objects, reaching a 91% Dice score with merely 19 annotated video frames and static images. For the more granular task of instance segmentation, our approach enables scalable instance-level object analysis, obtaining over 90% accuracy. Finally, by integrating optical flow and spatiotemporal modeling, our method effectively segments objects in complex video sequences with minimal supervision, outperforming conventional approaches. Together, these studies validate the framework’s ability to deliver high-performing models across diverse and complex tasks while drastically reducing manual labeling effort. These studies establish that our proposed semi-self-supervised paradigm offers a transformative approach for developing DL models. By effectively leveraging video data and supervision derived from minimal human annotations, coupled with domain adaptation, this methodology demonstrates significant scalability and generalizability. It successfully enables instance-level and pixel-level reasoning even with limited annotation, confirming its potential to overcome annotation bottlenecks and advance DL applications in complex, data-constrained real-world scenarios, particularly within the field of precision agriculture.",
            "source_url": "https:\/\/openalex.org\/W7113662752",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "HARVEST (University of Saskatchewan)",
            "source_record_id": "W7113662752",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 568,
            "title": "Improving Human Behavior Understanding with Semi-supervised Learning",
            "title_ar": "",
            "title_en": "Improving Human Behavior Understanding with Semi-supervised Learning",
            "author": "Bruno Manuel Degardin",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; responsible-ai; self-semi-supervised; cnn; transformers; multimodal; video-analysis; gan; diffusion; fairness",
            "keywords": "",
            "abstract": "Recent advancements in Computer Vision (CV) and Machine Learning (ML) have fundamentally revolutionized the field of human behaviour understanding, enabling intelligent systems to move beyond simple motion detection to the interpretation of complex, semantic actions from vast streams of visual data. This paradigm shift has been driven by the exponential growth of Deep Learning algorithms, particularly the evolution from 3D Convolutional Neural Networks (3D CNNs) to modern Vision Transformers (ViTs) , which have achieved remarkable success in modeling long-range spatiotemporal dependencies in RGB videos and multimodal signals. However, despite their impressive performance on controlled benchmarks, the deployment of these pixel-based models in real-world scenarios remains heavily constrained. Their reliance on high-dimensional visual data often incurs prohibitive computational costs that hinder real-time application on edge devices. Furthermore, these methods exhibit significant sensitivity to background clutter and viewpoint variations , and the processing of raw video raises critical privacy concerns by capturing unnecessary appearance details such as identity and clothing. Maximizing the potential of this modality presents a significant set of challenges that hinder widespread deployment. The heavy reliance on massive, high-quality annotated datasets creates a prohibitive bottleneck for supervised learning, while existing generative models used for data augmentation often lack physical plausibility or controllability. Furthermore, self-supervised methods borrowed from video domains frequently fail to capture the fine-grained dynamics of skeletal motion, and current evaluation metrics for synthetic data struggle to distinguish between realistic kinematics and artifacts. Addressing these limitations requires a fundamental shift toward more label-efficient, physically aware, and generative approaches to behaviour analysis. In this thesis, we proposed a comprehensive suite of novel solutions to mitigate these problems by exploiting semi-supervised and self-supervised learning paradigms, as well as advanced generative modeling. First, we addressed the limitations of temporal reasoning in Graph Convolutional Networks (GCNs) by proposing REGINA, a reasoning- augmented architecture. This method injects explicit temporal self-similarity cues into the network through a learnable temporal adjacency matrix, allowing the model to adaptively optimize its receptive fields to capture long-range dependencies that standard convolutions often miss. Addressing data scarcity, we proposed Kinetic-GAN, a generative adversarial network that scales action conditioning by an order of magnitude over prior works. By generating actions directly from a disentangled latent space using a mapping network and stochastic noise injection, it synthesizes diverse, high-fidelity global body movements across more than 100 action classes. We further advanced representation learning without labels through ATOM, a self-supervised strategy that leverages atomic motion representation learning. By decomposing actions into short, fine-grained primitives and training the backbone to recover their temporal order, ATOM learns robust features from the intrinsic structure of motion, transferring effectively to supervised tasks without the computational burden of heavy reconstruction heads. To bridge the gap between generation and recognition, we developed an intrinsic, kinematics-aware quality assessment framework. Recognizing that conventional distribution metrics fail to capture physical validity, this framework utilizes per-sample kinematic indicators within a weaklysupervised discriminator to iteratively filter synthetic samples, ensuring only physically plausible data is used to boost downstream performance. Finally, pushing the boundaries of controllable synthesis, we proposed GLDM, a Spatiotemporal Graph Latent Diffusion Model. GLDM projects skeleton sequences into a compact, graph-aligned latent space and performs diffusion with graph-aware updates. Crucially, by integrating cross-attention mechanisms, GLDM enables precise multi-modal conditioning, allowing generation to be guided not just by class labels but also by natural language prompts, achieving generalization beyond training data, unconstrained temporal flexibility and solving the mode collapse issues inherent to adversarial approaches. Results obtained by the proposed methods show that it is possible to improve HBU systems regarding label efficiency, generative controllability, and physical plausibility, while still obtaining state-of-the-art results. All proposed methods were evaluated on several large-scale benchmarks, demonstrating that robust behaviour understanding can be achieved with significantly reduced reliance on manual annotation.",
            "source_url": "https:\/\/openalex.org\/W7225277943",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W7225277943",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 572,
            "title": "Transformers and contrastive semi-supervised learning for medical image segmentation",
            "title_ar": "",
            "title_en": "Transformers and contrastive semi-supervised learning for medical image segmentation",
            "author": "Qianying Liu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "The University of Glasgow",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; self-semi-supervised; cnn; transformers; image-segmentation; medical-imaging; fairness; ai-education",
            "keywords": "",
            "abstract": "Medical Image Semantic Segmentation (MISS), the process of assigning a semantic label to each pixel in an image, is a foundational task in computational medicine, critical for quantitative diagnostics and treatment planning. However, developing robust MISS models faces two intertwined challenges. First, there is an architectural dilemma: Convolutional Neural Networks (CNNs), like U-Net, excel at learning local features but are limited by their receptive fields, failing to capture global context essential for segmenting organs with large deformations. Conversely, Vision Transformers (ViTs) effectively model long-range dependencies but lack the inductive biases of CNNs, leading to poor generalization on the small datasets typical in medicine without extensive pre-training. Second, the prohibitive cost and expertise required for creating pixel-level annotations create a severe data scarcity bottleneck. While Semi-Supervised Learning (SSL) aims to mitigate this by leveraging unlabeled data, existing methods often fail to learn high-level semantic relations and are susceptible to confirmation bias from noisy pseudo-labels, class imbalance, and suboptimal contrastive sample selection. This thesis presents a comprehensive investigation to systematically address these challenges, delivering a cohesive suite of novel deep learning frameworks. The contributions are four-fold: First, to resolve the architectural trade-off, this work introduces CS-Unet, a pure Transformer network built upon a U-Net-like architecture. Its core innovation is the Convolutional Swin Transformer (CST) block, which integrates convolutions directly within the Multi-Head Self-Attention and Feed-Forward Network modules. This design imbues the Transformer with inherent localized spatial context and strong inductive biases, enabling it to efficiently learn both local and global features. Without pre-training, CS-Unet outperforms existing Transformer and CNN-based models on multi-organ and cardiac datasets, achieving state-of-the-art performance with fewer parameters. Second, to address data scarcity, a novel Multi-Scale Cross Supervised Contrastive Learning (MCSC) framework for SSL is developed. MCSC jointly trains CNN and Transformer models, using a cross-teaching paradigm where each network provides pseudo-labels for the other. Crucially, it moves beyond simple output consistency by applying a contrastive loss to feature maps at multiple scales, enforcing hierarchical semantic consistency. To handle the class imbalance endemic to medical imaging, a class-prevalence-aware loss is used to ensure features for infrequent classes are learned robustly. Third, to fortify SSL against noisy pseudo-labels, a certainty-guided contrastive learning strategy is proposed. This approach mitigates the impact of inaccurate pseudo-labels by using a certainty metric to guide the selection of samples for contrastive learning. The framework’s computational efficiency is enhanced through novel sampling strategies that select a few representative samples for contrasting, and a negative memory bank is used to increase sample diversity and eliminate dependence on batch size. Fourth, this thesis introduces a new paradigm for SSL by leveraging external anatomical priors through the Contrastive Cross-Teaching with Registration (CCT-R) framework. CCT-R is the first method to integrate spatial registration transforms into the learning process. It features two novel modules: a Registration Supervision Loss (RSL), which uses transforms between labeled and unlabeled volumes to generate an additional, highly reliable source of pseudo-labels, and Registration-Enhanced Positive Sampling (REPS), which uses registration to identify anatomically-corresponding positive pairs across volumes for contrastive learning. Overall, these contributions provide a powerful toolkit that significantly alleviates the annotation bottleneck in medical AI. The proposed methods demonstrate state-of-the-art performance on challenging segmentation benchmarks, delivering a pathway to develop accurate, data-efficient models for real-world clinical applications and opening new avenues for research into fusing geometric priors with semantic segmentation.",
            "source_url": "https:\/\/openalex.org\/W7127166515",
            "pdf_url": "",
            "doi": "10.5525\/gla.thesis.85734",
            "handle": "",
            "repository_name": "Enlighten: Theses (The University of Glasgow)",
            "source_record_id": "W7127166515",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 575,
            "title": "Self-Supervised Learning for Geometry",
            "title_ar": "",
            "title_en": "Self-Supervised Learning for Geometry",
            "author": "Huangying Zhan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Adelaide",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; self-semi-supervised; predictive-analytics; video-analysis; ai-smart-cities",
            "keywords": "",
            "abstract": "This thesis focuses on two fundamental problems in robotic vision, scene geometry understanding and camera tracking. While both tasks have been the subject of research in robotic vision, numerous geometric solutions have been proposed in the past decades. In this thesis, we cast the geometric problems as machine learning problems, specifically, deep learning problems. Differ from conventional supervised learning methods that using expensive annotations as the supervisory signal, we advocate for the use of geometry as a supervisory signal to improve the perceptual capabilities in robots, namely Geometry Self-supervision. With the geometry self-supervision, we allow robots to learn and infer the 3D structure of the scene and ego-motion by watching videos, instead of expensive ground-truth annotation in traditional supervised learning problems. Followed by showing the use of geometry for deep learning, we show the possibilities of integrating self-supervised models with traditional geometry-based methods as a hybrid solution for solving the mapping and tracking problem. We focus on an end-to-end mapping problem from stereo data in the first part of this thesis, namely Deep Stereo Matching. Stereo matching is one of the oldest problems in computer vision. Classical approaches to stereo matching typically rely on handcrafted features and a multiple-step solution. Recent deep learning methods utilize deep neural networks to achieve end-to-end trained approaches while significantly outperforming classic methods. We propose a novel data acquisition pipeline using an untethered device (Microsoft HoloLens) with a Time-of-Flight (ToF) depth camera and stereo cameras to collect real-world data. A novel semi-supervised method is proposed to train networks with ground-truth supervision and self-supervision. The large scale real-world stereo dataset with semi-dense annotation and dense self-supervision allow our deep stereo matching network to generalize better when compared to prior arts. Mapping and tracking using a single camera (Monocular) is a harder problem when compared to that using a stereo camera due to varies well-known challenges. In the second part of this thesis, We decouple the problem into single view depth estimation (mapping) and two view visual odometry (tracking) and propose a self-supervised framework, namely SelfTAM, which jointly learns the depth estimator and the odometry estimator. The self-supervised problem is usually formulated as an energy minimization problem consist of an energy of data consistency in multi-view (e.g. photometric) and an energy of prior regularization (e.g. depth smoothness prior). We strengthen the supervision signal with a deep feature consistency energy term and a surface normal regularization term. Though our method trains models with stereo sequence such that a real-world scaling factor is naturally incorporated, only monocular data is required in the inference stage. In the last part of this thesis, we revisit the basics of visual odometry and explore the best practice to integrate deep learning models with geometry-based visual odometry methods. A robust visual odometry system, DF-VO, is proposed. We use deep networks to establish 2D-2D\/3D-2D correspondences and pick the best correspondences from the dense predictions. Feeding the high-quality correspondences into traditional VO methods, e.g. Epipolar Geometry and Prospective-n-Points, we can solve visual odometry problem within a more robust framework. With the proposed self-supervised training, we can even allow the models to perform online adaptation in the run-time and take a step toward a lifelong learning visual odometry system.",
            "source_url": "https:\/\/openalex.org\/W3132493307",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Adelaide Research & Scholarship (AR&S) (University of Adelaide)",
            "source_record_id": "W3132493307",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 581,
            "title": "Semi-Supervised Self-Organizing Maps with Time-Varying Structures for Clustering and Classification",
            "title_ar": "",
            "title_en": "Semi-Supervised Self-Organizing Maps with Time-Varying Structures for Clustering and Classification",
            "author": "Pedro H. M. Braga",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "self-semi-supervised",
            "categories": "machine-learning; deep-learning; self-semi-supervised; data-mining",
            "keywords": "",
            "abstract": "In recent years, the advances in technology have produced datasets of increasing size, not only regarding the number of samples but also the number of features. Unfortunately, despite these advances, creating a sufficiently large amount of properly labeled data with enough examples for each class is not an easy task. Organizing and labeling such data is challenging, expensive, and time-consuming. Also, it is usually done manually, and people can label with different formats and styles, incorporating noise and errors to the dataset. Hence, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Therefore, at the current stage of research, it is of great importance to put forward semi-supervised learning models aiming to combine both types of data, in order to benefit from the distinct information they can provide, to obtain better performances of both clustering and classification tasks, that would expand the range of machine learning applications. Moreover, it is also important to develop methods that are easy to parameterize in a way that become robust to the different characteristics of the data at hand. In this sense, the Self-Organizing Maps (SOM) can be considered as good options to address such objectives. It is a biologically inspired neural model that uses unsupervised and incremental learning to produce prototypes of the input data. However, such an unsupervised characteristic makes it unfeasible for SOM to execute Semi-Supervised Learning. In that way, this Dissertation presents some new proposals based on SOM to perform Semi-Supervised learning tasks for both clustering and classification. It is done by introducing to SOM the standard concepts of Learning Vector Quantization (LVQ), which can be seen as its supervised counterpart, to build hybrid approaches. Such proposals can dynamically switch between the two types of learning at training time, according to the availability of labels and automatically adjust themselves to the local variance observed in each data cluster. In the course of this work, the experimental results show that the proposed models can surpass the performance of other traditional methods not only in terms of classification but also regarding clustering quality. It also enhances the range of possible applications of a SOM and LVQ-based models by combining them with recent and promising techniques from Deep Learning to solve more complex problems commonly found in such field.",
            "source_url": "https:\/\/openalex.org\/W3119985271",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Americanae (AECID Library)",
            "source_record_id": "W3119985271",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 582,
            "title": "Study of Semi-supervised Deep Learning Methods on Human Activity Recognition Tasks",
            "title_ar": "",
            "title_en": "Study of Semi-supervised Deep Learning Methods on Human Activity Recognition Tasks",
            "author": "Shiping Song",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; nlp; self-semi-supervised; predictive-analytics; rnn",
            "keywords": "",
            "abstract": "This project focuses on semi-supervised human activity recognition (HAR) tasks, in which the inputs are partly labeled time series data acquired from sensors such as accelerometer data, and the outputs are predefined human activities. Most state-of-the-art existing work in HAR area is supervised now, which relies on fully labeled datasets. Since the cost to label the collective instances increases fast with the increasing scale of data, semi-supervised methods are now widely required. This report proposed two semi-supervised methods and then investigated how well they perform on a partly labeled dataset, comparing to the state-of-the-art supervised method. One of these methods is designed based on the state-of-the-art supervised method, Deep-ConvLSTM, together with the semi-supervised learning concepts, self-training. Another one is modified based on a semi-supervised deep learning method, LSTM initialized by seq2seq autoencoder, which is firstly introduced for natural language processing. According to the experiments on a published dataset (Opportunity Activity Recognition dataset), both of these semi-supervised methods have better performance than the state-of-the-art supervised methods.",
            "source_url": "https:\/\/openalex.org\/W3036519956",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W3036519956",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 586,
            "title": "Semi-Supervised Image Segmentation of Medical Data",
            "title_ar": "",
            "title_en": "Semi-Supervised Image Segmentation of Medical Data",
            "author": "Ove Nicolai Dalheim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; self-semi-supervised; data-mining; cnn; image-segmentation",
            "keywords": "",
            "abstract": "Bladder cancer is the fourth most common cancer type in Norway, and tenth most common on a global scale. More and more tissue samples are sent to pathologists labs, increasing the workload and affecting the waiting time for patients. The corresponding increase is not seen in number of pathologists. Digitization and scanning of the tissue samples unveil the world of computational pathology, along with the many possibilities within it. Supervised approaches have been proposed within deep learning earlier, however, many express the lack of labeled data as a source for performance degradation. Convolutional neural networks have proven effective on image processing within the field of medicine. Some work based on semi-supervised approaches within computational pathology has been published in the recent years, however, most researchers are exploring supervised methods. This thesis proposes several methods to tile-wise segment histological images of bladder cancer into six different classes background, blood, damaged tissue, muscle tissue, stroma tissue and urothelium. A multiscale model is fed the tile at three different levels of magnification, and inherits capabilities from the VGG16 network through transfer learning. The proposed methods are either based on semi-supervised learning utilizing probability or clustering within a self-training process, or based on a combination of both expert and non-expert annotations. The dataset used in this thesis consists of about 360 whole-slide images, of which only 37 contain regions annotated by a pathologist, allowing for only about 10 % of the dataset to be utilized for supervised methods. Through the course of this thesis, a total of 145 new whole-slide images were introduced during training through semi-supervised learning, utilizing about 50 % of the dataset through various methods. The non-expert approach increased the F1-score for the muscle tissue class with 9.18 % from an initial 79.42 %, and the cluster-baser approach saw an increase of 1.38 % in accuracy from the initial 94.61 %. The method involving non-expert annotations outperformed both semi-supervised techniques with regards to segmentation, as a semi-supervised method will introduce new uncertain features to a deep learning model. This will have a strong impact on sensitive classes that are poorly represented in the training dataset. That said, a significant improvement is seen by using semi-supervised techniques as well, and scored better than non-expert with regards to classification.",
            "source_url": "https:\/\/openalex.org\/W3087951694",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "NORA - Norwegian Open Research Archives",
            "source_record_id": "W3087951694",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:50",
            "updated_at": "2026-11-15 16:52:50"
        },
        {
            "id": 590,
            "title": "Discriminator-driven diffusion generative adversarial network for synthetic medical image generation",
            "title_ar": "",
            "title_en": "Discriminator-driven diffusion generative adversarial network for synthetic medical image generation",
            "author": "Dennis Chia Yin Lim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; generative-ai; medical-ai; gan; medical-imaging",
            "keywords": "",
            "abstract": "This research project addresses the significant challenge of class imbalance in medical deep learning classification models, particularly when the abnormal class is underrepresented. Class imbalance can severely affect the classification performance of deep learning models, potentially leading to patient misdiagnosis and impeding clinician trust in AI adoption. In healthcare, class imbalance arises due to various factors, including the rarity of diseases or medical scenarios and data insufficiency for specific classes within large datasets. Existing solutions, such as traditional data augmentation and Generative Adversarial Networks (GANs) like DCGAN, as well as Conditional GANs like Pix2Pix and Stable Diffusion, have attempted to mitigate this issue, but with limitations.",
            "source_url": "https:\/\/openalex.org\/W7131907591",
            "pdf_url": "",
            "doi": "10.25916\/sut.31368169",
            "handle": "",
            "repository_name": "Swinburne figshare (Swinburne University of Technology)",
            "source_record_id": "W7131907591",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 591,
            "title": "Generative modelling under epistemic uncertainty",
            "title_ar": "",
            "title_en": "Generative modelling under epistemic uncertainty",
            "author": "Muhammad Mubashar",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Oxford Brookes University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; robotics; predictive-analytics; llm; gan; diffusion",
            "keywords": "",
            "abstract": "The deployment of Deep Learning in safety-critical domains is hindered by pathological overconfidence and an inability to distinguish between aleatoric uncertainty (data ambiguity) and epistemic uncertainty (lack of knowledge). This thesis addresses these limitations by establishing a rigorous framework for Random-Set Deep Learning, shifting from point-estimate probabilities to belief functions over the power set of outcomes. Enabled by a novel Budgeting strategy that ensures scalability, this framework allows models to explicitly represent ignorance. While we validate these principles through Random-Set Neural Networks (RS-NN) for classification and a Unified Evaluation Framework, the primary contribution of this work lies in re-imagining Generative AI under epistemic uncertainty. We introduce Random-Set Large Language Models (RS-LLMs), which predict belief functions over token sets to quantify second-order uncertainty, thereby providing a robust mechanism for hallucination detection. Furthermore, we propose Epistemic Generative Adversarial Networks (GANs) and Epistemic Diffusion Models. By modeling the uncertainty of the generation process itself via second-order distributions, these architectures significantly mitigate mode collapse and enhance sample diversity compared to standard baselines. Finally, we demonstrate the practical utility of these generative capabilities in Autonomous Driving. We introduce the ROAD-INTENT dataset and utilize Random-Set Vision Language Models (RS-VLMs) to automate the annotation of actor intent with calibrated uncertainty scores, paving the way for safer, self-aware AI systems.",
            "source_url": "https:\/\/openalex.org\/W7135169854",
            "pdf_url": "",
            "doi": "10.24384\/j47e-p788",
            "handle": "",
            "repository_name": "Radar (Oxford Brookes University)",
            "source_record_id": "W7135169854",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 595,
            "title": "Synthesizing High B-Value Diffusion-Weighted Imaging of the Prostate using Diffusion Models",
            "title_ar": "",
            "title_en": "Synthesizing High B-Value Diffusion-Weighted Imaging of the Prostate using Diffusion Models",
            "author": "Pedro Rodrigues Parracho",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; generative-ai; medical-ai; image-segmentation; gan; diffusion; medical-imaging",
            "keywords": "",
            "abstract": "Prostate cancer (PCa) remains among the most prevalent and deadly cancers among men worldwide, with rising incidence rates placing increasing pressure on healthcare systems. Magnetic Resonance Imaging (MRI), and more specifically, Diffusion-Weighted Imaging (DWI), has proven to be an ideal diagnostic tool in the early detection of PCa. However, acquisition of high b-value DWI, which is critical in detecting diffusion-restricted lesions, is challenging due to the prolonged scan times and susceptibility to noise and motion artifacts. This dissertation explores the clinical practicability of generating high b-value prostate DWI from low b-value prostate DWI inputs using generative artificial intelligence (AI) models with the aim of decreasing acquisition times and artifact susceptibility. Three generative models were tested: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs). Each model was conditioned to take any low b-value DWI and generate a resulting DWI at any requested high b-value. Quantitative evaluation was conducted with image similarity metrics, comparing the generated DWI with the real acquired DWI. Diagnostic performance was also quantified with a deep learning lesion segmentation model in an attempt to determine diagnostic preservation in the generated DWI. The DDPM architecture was the most effective generative architecture, with the best performance in quantitative scores, in addition to high visual realism in maintaining diffusion effects. The generated images also preserved the majority of the diagnostic accuracy of the original high b-value scans, with preservation of lesion detection rates and localisation of lesions. The results verify that diffusion models are the most adequate architecture for low-to-high b-value DWI generation and can retain diagnostic performance. This study proved that generative AI can significantly enhance MRI workflows, reducing scan times, and facilitating scalable PCa screening programs, especially in resource-constrained environments, paving the way for more accessible diagnostic pathways.",
            "source_url": "https:\/\/openalex.org\/W7224044743",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W7224044743",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 598,
            "title": "On large deviations in probabilistic deep learning and generative modeling",
            "title_ar": "",
            "title_en": "On large deviations in probabilistic deep learning and generative modeling",
            "author": "Nilsson, Viktor",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; medical-ai; responsible-ai; predictive-analytics; cnn; image-segmentation; llm; gan; diffusion; xai",
            "keywords": "",
            "abstract": "The topic of this thesis is the use of probabilistic methods in machine learning. These play a foundational role in motivating and defining machine learning algorithms, as well as in explaining why, and how well, the algorithms work. During the years in which this work was conducted (2020-2025), machine learning has gone from delivering a handful of impressive demonstrable results to becoming a staple of modern developed society, with products such as ChatGPT, etc., having capacity and applicability far beyond what anyone in the field expected ten years ago. Explaining why the modern methods work so well, despite their conceptual simplicity and elegance, requires both empirical and theoretical studies. This thesis has both parts, while the emphasis is on theory. The first part of the thesis, Papers A-C, concerns the implementation and analysis of novel methodologies in deep learning, whereas Papers D-F concern purely theoretical large deviations results for machine learning adjacent models. The main thread is the application of mathematical tools from probability theory and statistics, such as the theory of large deviations and empirical process theory, to the understanding and improvement of machine learning methodology. In Paper A, which presents the most applied direction pursued in the thesis, a deep probabilistic network model is applied to a task from the field of clinical radiation therapy, namely dose prediction, where a value of radiation dose target shall be assigned to each pixel\/voxel of a human tissue based on a CT-image. The developed probabilistic model is based on mixture density networks. It is empirically demonstrated that a convolutional U-net can learn a satisfactory mixture distribution in each pixel. To the best of our knowledge, this is the first implementation of mixture density networks working on images with a convolutional architecture. In Paper B, we construct and implement a new method called REMEDI for entropy estimation of continuous distributions using deep neural networks. Further, using empirical process theory, it is shown that such an estimator has a consistency property, ensuring that it has the theoretical capacity to estimate the entropy to arbitrary precision. The method is based on the celebrated Donsker-Varadhan lemma, a well-known fact from the theory of large deviations. The applicability of the method is demonstrated on distributions in moderate dimension, as well as on the task of training model in the information bottleneck framework, with satisfactory performance. In Paper C, we explore the integration of non-parametric model components into the flow-matching framework. This is done by learning a heavily compressed latent representation of images in the training dataset, which are then used as conditioning variables for the vector field network. Effectively, these can be seen as synthetic, continuous labels. The gain is a more efficient learning process, compared to baseline models, and more interpretable sampling. It is demonstrated that with sufficient compression, overfitting can be avoided, and diversity among samples attained, despite conditioning on training samples. Arguably, the most impactful area in machine learning is generative modeling. One part of the thesis deals with applying the theory of large deviations to two of its major methods, generative adversarial networks (GAN), and diffusion models, in particular Schrödinger bridges. The goal here is to prove large deviation principles, for certain sequences of probability measures associated with the models. In both cases, this allows a strong characterization of the convergence of these models, under varying certain model parameters, toward an idealized description of their behavior, often carrying a well-understood mathematical structure. In Schrödinger bridges, this idealized limit model consists of a dynamical optimal transport plan. This tells us that when varying the parameter in question, which is the reference noise level, or, as is often equivalent, the level of entropic regularization, the plans converge rapidly toward optimal transport behavior, justifying the interpretation of weakly regularized Schrödinger bridges as approximate optimal transport plans. Since Schrödinger bridges (or entropically regularized optimal transport plans) have nicer computational properties than optimal transport, they are often used in its place, and it is therefore important to understand how close this connection is. The large deviation principles derived here, applicable to several popular deep generative models, thus contribute to this understanding. Stating and proving such large deviations results are the contents of Papers E and F. For GANs, we consider recently developed particle systems making up cohorts of networks for the generative task, in Paper D.",
            "source_url": "https:\/\/openalex.org\/W7112536257",
            "pdf_url": "https:\/\/kth.diva-portal.org\/smash\/get\/diva2:2015506\/FULLTEXT02",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W7112536257",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 600,
            "title": "High-resolution synthesis across domains: a wavelet-driven approach to generative modeling",
            "title_ar": "",
            "title_en": "High-resolution synthesis across domains: a wavelet-driven approach to generative modeling",
            "author": "SIGILLO, LUIGI",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Sapienza University of Rome",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Italy",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; generative-ai; medical-ai; ai-applications; gan; diffusion; medical-imaging; ai-industry",
            "keywords": "",
            "abstract": "Deep generative modeling is revolutionizing visual data synthesis, from creative industry applications to tools for scientific discovery in fields like medical imaging and remote sensing. In image synthesis, deep learning models like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs) help create novel, realistic, and controllable visual content. In specialized applications, these models can augment scarce data for medical analysis or enhance imagery for remote sensing tasks. A crucial challenge in applying deep learning to image synthesis is overcoming the persistent trade-offs between resolution, fidelity, and computational efficiency. While generative models have advanced significantly, they often struggle with generalization across diverse domains and data modalities. Conventional methods tend to scale poorly to ultra-high resolutions (UHR), leading to artifacts like repeated structures or blurred textures. Furthermore, these models often apply uniform refinement processes across all spatial regions, disregarding local frequency variations and failing to optimally allocate supervision to areas of different visual complexity. Wavelet transforms, particularly Discrete Wavelet Transforms (DWT) and their hypercomplex extension, Quaternion Wavelet Transforms (QWT), have shown promising results in multi-scale signal analysis. These methods operate by decomposing images into a hierarchy of frequency sub-bands, capturing both global structure and fine-grained details. This formulation endows models with the ability to leverage sparse representations and dimensionality reduction. However, their potential to reshape feature representation, inform conditioning, and adapt training objectives in a model-agnostic fashion had not been comprehensively exploited. In this thesis, we explore this concept and exploit the wavelet-driven learning paradigm to overcome the aforementioned shortcomings of traditional generative models. We leverage multi-scale analysis to make models inherently aware of frequency and spatial information. We first design a structure-aware GAN, StawGAN, tailored for cross-domain infrared-to-RGB image translation. Building on this foundation, we develop specialized diffusion models for domain-specific tasks, including high-fidelity maritime image super-resolution and efficient EEG-to-image synthesis. Moving beyond these conventional generative approaches, we introduce a series of wavelet-driven architectures that explicitly incorporate multi-scale signal representations. Among them is QUAVE, a novel framework leveraging quaternion wavelet transforms (QWT) to enhance feature extraction and improve generalization in medical imaging. Expanding on these insights, we also pioneer several wavelet-based super-resolution models: a QWT-conditioned diffusion model, a metadata- and wavelet-aware architecture for satellite imagery, and a highly efficient hybrid framework, Wavelet Diffusion GAN, that combines the strengths of GANs and diffusion processes. Finally, we address a direct extension of these works, focusing on high-fidelity synthesis. We culminate our investigation with a Latent Wavelet Diffusion (LWD) framework, a general and lightweight solution that enables existing latent diffusion and flow matching models to achieve UHR (up to 4K) synthesis without architectural modifications or additional inference costs. Through extensive experiments across various generative tasks involving different domains and data modalities, we have thoroughly explored the wavelet-driven paradigm while addressing scenario-specific challenges in fidelity, efficiency, and generalization, advancing research in this field.",
            "source_url": "https:\/\/openalex.org\/W7113426803",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "IRIS Research product catalog (Sapienza University of Rome)",
            "source_record_id": "W7113426803",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 601,
            "title": "Generative and geometric deep learning for 3D genome organization and biomolecular interaction modeling",
            "title_ar": "",
            "title_en": "Generative and geometric deep learning for 3D genome organization and biomolecular interaction modeling",
            "author": "Yanli Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Missouri",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; nlp; generative-ai; medical-ai; predictive-analytics; optimization; gnn; llm; gan; diffusion",
            "keywords": "",
            "abstract": "The three-dimensional organization of the genome and the interactions between biomolecules are fundamental to understanding cellular function and disease. Recent advances in sequencing technologies and structural biology have generated unprecedented amounts of biological data, creating new opportunities to study these processes. However, these data are often sparse, noisy, and highly complex, making it difficult to accurately model biological systems using conventional computational methods. This dissertation explores how generative and geometric deep learning can address these challenges by learning meaningful biological representations from high-dimensional genomic and structural data. The first part of this dissertation focuses on modeling three-dimensional genome organization from single-cell Hi-C data. Because single-cell Hi-C experiments capture only a small fraction of chromatin interactions, computational methods are essential for recovering missing information and revealing genome architecture. I first developed a deep residual generative adversarial network to enhance sparse single-cell Hi-C contact maps by recovering biologically meaningful chromatin interactions. Building on this work, I proposed HiCDiff, a diffusion model that iteratively denoises chromatin contact maps and produces more accurate representations of genome organization. These improved contact maps provide a stronger foundation for downstream analyses and structural modeling. To further characterize genome architecture, I developed an SO(3)-equivariant graph neural network that reconstructs three-dimensional chromosome structures directly from single-cell Hi-C data. By incorporating geometric equivariance into the learning process, the model captures the spatial relationships among genomic loci and reconstructs chromosome conformations with improved accuracy and biological consistency. The second part of this dissertation extends geometric deep learning to biomolecular interaction modeling. I developed MPBind, a multitask framework for predicting protein binding sites by integrating protein language model embeddings with equivariant graph neural networks. This approach combines evolutionary information encoded in protein sequences with three-dimensional structural features, enabling accurate prediction of diverse protein interactions while achieving strong generalization across previously unseen proteins. Although these studies address different biological problems, they share a common theme: combining generative models with geometry-aware neural networks to learn from complex biological data. Generative deep learning improves the quality of sparse and noisy experimental measurements, while geometric deep learning enables models to capture the structural principles underlying biological systems. Together, the methods presented in this dissertation provide a unified framework for studying genome organization and biomolecular interactions, demonstrating how modern artificial intelligence can advance computational genomics and structural biology and support future discoveries in biology and precision medicine.",
            "source_url": "https:\/\/openalex.org\/W7224088771",
            "pdf_url": "",
            "doi": "10.32469\/10355\/113055",
            "handle": "",
            "repository_name": "MOspace Institutional Repository (University of Missouri)",
            "source_record_id": "W7224088771",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 602,
            "title": "Music Generation with Generative Adversarial Networks",
            "title_ar": "",
            "title_en": "Music Generation with Generative Adversarial Networks",
            "author": "Li, Yupeng ; Linberg, Jonatan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Jönköping University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "generative-ai",
            "research_subdomain": "gan",
            "categories": "deep-learning; generative-ai; gan",
            "keywords": "",
            "abstract": "At present, state-of-the-art deep learning music generation systems require a lot time and hardware resources to develop. This means that they are almost exclusively available to large companies. In order to reduce these requirements, more efficient techniques and methods need to be utilised. This project aims to investigate various approaches by developing a music generation system using generative adversarial networks, comparing different techniques and their effect on the system's performance. Our results show the difficulties in generating music in a more resource-constrained environment. We find that structuring the input space with conditional model constraints improves the systems' ability to conform to musical standards. The results also indicate the importance of a patch-based discriminator for evaluating the texture of the generated music. Finally, we propose a similarity loss as a way of reducing mode collapse in the generator, thus stabilising the training process.",
            "source_url": "https:\/\/openalex.org\/W7112384551",
            "pdf_url": "https:\/\/hj.diva-portal.org\/smash\/get\/diva2:1787759\/FULLTEXT01",
            "doi": "",
            "handle": "",
            "repository_name": "Jönköping University Publications (Jönköping University)",
            "source_record_id": "W7112384551",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 603,
            "title": "Generative Adversarial Networks for Classic Cryptanalysis",
            "title_ar": "",
            "title_en": "Generative Adversarial Networks for Classic Cryptanalysis",
            "author": "Deanne Charan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "San Jose State University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "generative-ai",
            "research_subdomain": "gan",
            "categories": "machine-learning; deep-learning; generative-ai; gan",
            "keywords": "",
            "abstract": "The necessity of protecting critical information has been understood for millennia. Although classic ciphers have inherent weaknesses in comparison to modern ciphers, many classic ciphers are extremely challenging to break in practice. Machine learning techniques, such as hidden Markov models (HMM), have recently been applied with success to various classic cryptanalysis problems. In this research, we consider the effectiveness of the deep learning technique CipherGAN---which is based on the well- established generative adversarial network (GAN) architecture---for classic cipher cryptanalysis. We experiment extensively with CipherGAN on a number of classic ciphers, and we compare our results to those obtained using HMMs.",
            "source_url": "https:\/\/openalex.org\/W4206278187",
            "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=2034&context=etd_projects",
            "doi": "10.31979\/etd.h2mh-uh52",
            "handle": "",
            "repository_name": "San José State University ScholarWorks (San Jose State University)",
            "source_record_id": "W4206278187",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 613,
            "title": "Dual-Attention Generative Adversarial Network and Flame and Smoke Analysis",
            "title_ar": "",
            "title_en": "Dual-Attention Generative Adversarial Network and Flame and Smoke Analysis",
            "author": "Yuchuan Li",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Ottawa",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; expert-systems; predictive-analytics; transformers; image-segmentation; video-analysis; gan",
            "keywords": "",
            "abstract": "Flame and smoke image processing and analysis could improve performance to detect smoke or fire and identify many complicated fire hazards, eventually to help firefighters to fight fires safely. Deep Learning applied to image processing has been prevailing in recent years among image-related research fields. Fire safety researchers also brought it into their studies due to its leading performance in image-related tasks and statistical analysis. From the perspective of input data type, traditional fire research is based on simple mathematical regressions or empirical correlations relying on sensor data, such as temperature. However, data from advanced vision devices or sensors can be analyzed by applying deep learning beyond auxiliary methods in data processing and analysis. Deep Learning has a bigger capacity in non-linear problems, especially in high-dimensional spaces, such as flame and smoke image processing. We propose a video-based real-time smoke and flame analysis system with deep learning networks and fire safety knowledge. It takes videos of fire as input and produces analysis and prediction for flashover of fire. Our system consists of four modules. The Color2IR Conversion module is made by deep neural networks to convert RGB video frames into InfraRed (IR) frames, which could provide important thermal information of fire. Thermal information is critically important for fire hazard detection. For example, 600 °C marks the start of a flashover. As RGB cameras cannot capture thermal information, we propose an image conversion module from RGB to IR images. The core of this conversion is a new network that we innovatively proposed: Dual-Attention Generative Adversarial Network (DAGAN), and it is trained using a pair of RGB and IR images. Next, Video Semantic Segmentation Module helps extract flame and smoke areas from the scene in the RGB video frames. We innovated to use synthetic RGB video data generated and captured from 3D modeling software for data augmentation. After that, a Video Prediction Module takes the RGB video frames and IR frames as input and produces predictions of the subsequent frames of their scenes. Finally, a Fire Knowledge Analysis Module predicts if flashover is coming or not, based on fire knowledge criteria such as thermal information extracted from IR images, temperature increase rate, the flashover occurrence temperature, and increase rate of lowest temperature. For our contributions and innovations, we introduce a novel network, DAGAN, by applying foreground and background attention mechanisms in the image conversion module to help reduce the hardware device requirement for flashover prediction. Besides, we also make use of combination of thermal information from IR images and segmentation information from RGB images in our system for flame and smoke analysis. We also apply a hybrid design of deep neural networks and a knowledge-based system to achieve high accuracy. Moreover, data augmentation is also applied on the Video Semantic Segmentation Module by introducing synthetic video data for training. The test results of flashover prediction show that our system has leading places quantitative and qualitative in terms of various metrics compared with other existing approaches. It can give a flashover prediction as early as 51 seconds with 94.5% accuracy before it happens.",
            "source_url": "https:\/\/openalex.org\/W3204711149",
            "pdf_url": "",
            "doi": "10.20381\/ruor-26991",
            "handle": "",
            "repository_name": "uO Research (University of Ottawa)",
            "source_record_id": "W3204711149",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 627,
            "title": "Reinforcement Learning and Dynamical Systems",
            "title_ar": "",
            "title_en": "Reinforcement Learning and Dynamical Systems",
            "author": "Björn Lindenberg",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "",
            "categories": "deep-learning; medical-ai; reinforcement-learning; robotics",
            "keywords": "",
            "abstract": "This thesis concerns reinforcement learning and dynamical systems in finite discrete problem domains. Artificial intelligence studies through reinforcement learning involves developing models and algorithms for scenarios when there is an agent that is interacting with an environment. By taking actions the agent may induce changes in the observed environment, where a modeled reward system reinforces correct behavior through learning algorithms. Reinforcement learning can be used in a wide variety of different domains, such as finance, robotics, games, routing and health care. However as the subject matures there is an increasing need to more heavily rely on advanced concepts in mathematics and deep learning to further our understanding of existing problems and find new algorithmic insights. Discrete dynamical systems arise in virtually any setting as soon as there is a set of elements subjected to iteration by a defining function. The function may be seen to represent the passing of time or to define the rules for state transitions. If the set of elements is finite but very large then we may find applications in several different fields such as operations research, cryptography and biology, where understanding properties of the structure and long-term behavior without explicit enumeration is key. In Paper I we extend the model of categorical reinforcement learning with a group-aided training procedure involving multiple agents. By having the agents learn through shared distributional information but act independently we argue for an accelerated learning process. We empirically show that the procedure may lead to much more robust learning, stronger individual agent performance and good ensemble efficiency. In Paper II we continue to build upon distributional reinforcement learning for finite Markov processes. A common approach among algorithms is to apply transformations on agent returns for stability and flexibility over a variety of different tasks. We show that one of the most successful methods may not work for a stochastic process. As a solution we introduce a new distributional operator that handles a large class of transformations with guaranteed theoretical convergence. We also propose an approximating single-actor algorithm based on these novel insights, which when tested achieves state-of-the-art performance compared to similar algorithms. In Paper III we focus on the issue of efficient exploration in reinforcement learning by studying the regret that a learning algorithm might have versus an optimal policy. Specifically, in the paper we derive a Bayesian regret bound for Thompson sampling on linear multi-armed bandits with Gaussian reward models when we have environment uncertainty described by a set of multivariate normal-gamma distributions. In Paper IV we derive sharp bounds on the number of iterations needed for any linear finite dynamical system to stabilize on an inner set of cycles. These bounds may be used in cycle analysis and criterion tests to understand the long-term behavior of extremely large systems.",
            "source_url": "https:\/\/openalex.org\/W4376641259",
            "pdf_url": "https:\/\/lnu.diva-portal.org\/smash\/get\/diva2:1756782\/FULLTEXT01",
            "doi": "10.15626\/lud.494.2023",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4376641259",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 630,
            "title": "Reinforcement Learning for Generative Art",
            "title_ar": "",
            "title_en": "Reinforcement Learning for Generative Art",
            "author": "Jieliang Luo",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "generative-ai",
            "research_subdomain": "",
            "categories": "machine-learning; generative-ai; reinforcement-learning; robotics",
            "keywords": "",
            "abstract": "Reinforcement learning (RL) is an efficient class of sequential decision-making algorithms that have achieved remarkable success in a broad range of applications, such as robotic manipulations, strategic games, or autonomous driving. The most well-known example of reinforcement learning is AlphaGo, a computer program that plays the board game Go and outperforms top human Go players. Unlike other two major machine learning categories, supervised learning and unsupervised learning, in which media artists are actively engaged, reinforcement learning has yet to result in many creative applications. Generative art is usually driven, in whole or in part, by autonomous systems that are derived from a set of rules. Interestingly, an RL policy can be seen as an autonomous system where the rules are learned by interacting with its environment. Regardless of its initial purpose, reinforcement learning has the potential to expand the boundary of generative art. However, a formal process of applying reinforcement learning to generative art does not yet exist and the current RL tools require an in-depth understanding of RL concepts. To bridge the gap, the first part of the dissertation introduces a conceptual framework to adapt reinforcement learning for generative art. The framework proposes a term RL-based generative art to denote a novel form of generative art of which the use of RL agents is the key element. The creative process of RL-based generative art and possible emergent behaviors are discussed in the framework. This leads to a discussion of several author's related practices on generative art, deep-learning art, and reinforcement learning. Those practices are critical for understanding the conceptual and technical details of each component in order to construct the framework. The second part introduces RL5, a JavaScript library for rapidly prototyping RL environments and training RL policies in web browsers. The library combines RL algorithms and RL environments into one framework and is fully compatible with p5.js. RL5 is developed with a particular focus on simplicity to favor (re)usability of RL algorithms and development of RL environments. Specifically, the library implemented three RL algorithms, Tabular Q-learning, REINFORCE, and DDPG, to cover all the three families of model-free RL, and nine RL environments that six of them address autonomous agents in steering behaviors, which can be used as building blocks for complex systems. Finally, the author demonstrates four different use cases of how to apply RL5 for pedagogical and creative applications.",
            "source_url": "https:\/\/openalex.org\/W3024023292",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "eScholarship (California Digital Library)",
            "source_record_id": "W3024023292",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 631,
            "title": "Discovery in Reinforcement Learning",
            "title_ar": "",
            "title_en": "Discovery in Reinforcement Learning",
            "author": "Vivek Veeriah",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Michigan",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; computer-vision; reinforcement-learning; robotics; predictive-analytics",
            "keywords": "",
            "abstract": "This thesis focuses on Reinforcement Learning (RL) which considers an agent that makes sequen- tial decisions in an environment to maximize its reward. Recent breakthroughs on domains such as Go, Atari games, and Robotics are a result of combining advances from Deep Learning with RL algorithms. This combination of RL with deep neural networks has moved the field from hand- designing feature representations to learning features directly from raw data (e.g., pixel images). Despite these advancements, RL practitioners still need to define what predictive knowledge an agent needs to learn, instead of the agent autonomously discovering such knowledge. Developing the ability to discover what predictive knowledge is useful to learn about could allow the RL agents to learn more efficiently. In this thesis, we identify some of the challenges towards designing agents with the aforementioned ability, and propose and evaluate methods to address them. In the first part of this thesis, we present an approach for discovering multiple subgoals to learn optimal policies from a single stream of experience. We show that this approach is a useful pre- training procedure and as a source of auxiliary task updates when there is a main task of interest. In the second part of this thesis, we present three approaches to discover useful knowledge of various forms to maximize the agent’s task performance. The first one introduces an architecture and an associated meta-gradient algorithm to discover predictive questions about the agent’s ex- perience to drive representation learning in RL agents. We show that it helps in faster learning when used as auxiliary tasks. The second work introduces another meta-gradient approach to dis- cover temporal-abstractions in the form of options that enables a hierarchical RL agent to learn faster on new, unseen tasks. The third one presents an algorithm to select a small number of affor- dances in the form of actions or options from a continuous space to improve the performance of a model-based planning agent on hierarchical tasks. The final part studies the problem of learning and maintaining accurate knowledge in the form of option-conditional predictions in a partially-observable environment. Specifically, we implement and empirically demonstrate the thought experiment by Ring [2021], which provided a detailed blueprint of how high-level, abstract knowledge could potentially be represented as layered pre- dictions of agent’s sensorimotor stream in navigation environments. In addition to this empirical demonstration, we introduce an approach to discover those option-conditional predictions which were previously hand-defined and demonstrate the feasibility of this approach.",
            "source_url": "https:\/\/openalex.org\/W4294885165",
            "pdf_url": "",
            "doi": "10.7302\/6337",
            "handle": "",
            "repository_name": "Deep Blue (University of Michigan)",
            "source_record_id": "W4294885165",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 636,
            "title": "Multimodal perception for autonomous driving",
            "title_ar": "",
            "title_en": "Multimodal perception for autonomous driving",
            "author": "Beltrán de la Cita, Jorge",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Carlos III University of Madrid",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; robotics; ai-applications; multimodal; object-detection; ai-industry; ai-smart-cities",
            "keywords": "",
            "abstract": "Autonomous driving is set to play an important role among intelligent transportation systems in the coming decades. The advantages of its large-scale implementation –reduced accidents, shorter commuting times, or higher fuel efficiency– have made its development a priority for academia and industry. However, there is still a long way to go to achieve full self-driving vehicles, capable of dealing with any scenario without human intervention. To this end, advances in control, navigation and, especially, environment perception technologies are yet required. In particular, the detection of other road users that may interfere with the vehicle’s trajectory is a key element, since it allows to model the current traffic situation and, thus, to make decisions accordingly. The objective of this thesis is to provide solutions to some of the main challenges of on-board perception systems, such as extrinsic calibration of sensors, object detection, and deployment on real platforms. First, a calibration method for obtaining the relative transformation between pairs of sensors is introduced, eliminating the complex manual adjustment of these parameters. The algorithm makes use of an original calibration pattern and supports LiDARs, and monocular and stereo cameras. Second, different deep learning models for 3D object detection using LiDAR data in its bird’s eye view projection are presented. Through a novel encoding, the use of architectures tailored to image detection is proposed to process the 3D information of point clouds in real time. Furthermore, the effectiveness of using this projection together with image features is analyzed. Finally, a method to mitigate the accuracy drop of LiDARbased detection networks when deployed in ad-hoc configurations is introduced. For this purpose, the simulation of virtual signals mimicking the specifications of the desired real device is used to generate new annotated datasets that can be used to train the models. The performance of the proposed methods is evaluated against other existing alternatives using reference benchmarks in the field of computer vision (KITTI and nuScenes) and through experiments in open traffic with an automated vehicle. The results obtained demonstrate the relevance of the presented work and its suitability for commercial use.",
            "source_url": "https:\/\/openalex.org\/W2964186846",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "e-Archivo (Carlos III University of Madrid)",
            "source_record_id": "W2964186846",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 637,
            "title": "Development of deep learning based perception module for autonomous driving",
            "title_ar": "",
            "title_en": "",
            "author": "Mustafa Alper Balım",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2025,
            "language": "tr",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning; robotics",
            "keywords": "",
            "abstract": "Otonom araçlar, günümüzde güvenli, konforlu ve sürdürülebilir ulaşım çözümleri sunma potansiyeli sayesinde hızla gelişmekte ve geleceğin mobilite vizyonunu şekillendirmektedir. Otonom araçların temelini oluşturan yazılım ve donanım mimarisi; sensör donanımları, veri işleme altyapısı ve karar verme algoritmalarını içeren bütüncül bir yapıdan oluşmaktadır. Bu mimaride LiDAR, kamera ve RADAR gibi sensörlerin entegrasyonu ve yüksek hesaplama kapasiteli işlemciler, çevresel algılamanın doğruluğunu doğrudan etkilemektedir. Otonom sistemlerde en kritik katmanlardan biri olan algılama katmanı, çevredeki nesneleri doğru ve hızlı şekilde tespit ederek sürüş kararlarının temelini oluşturur. Bu katmanın başarımı; kullanılan sensörlerin çözünürlüğü, kanal sayısı, veri işleme teknikleri ve öğrenme algoritmalarının başarısına bağlı olarak şekillenir. Ancak, derin öğrenme tabanlı 3B nesne tespiti modelleri, eğitim ve test veri kümeleri arasındaki farklılıklardan kaynaklanan alan farkı (domain shift) nedeniyle farklı senaryolarda ciddi performans düşüşleri yaşamaktadır. Bu çalışmada, alan farkı problemini çözmek amacıyla altı farklı modern alan adaptasyonu yöntemi (UDA, Bi3D, CLUE, ST3D, SESS ve Uni3D) sistematik olarak değerlendirilmiş ve karşılaştırılmıştır. Çalışmalarda, açık (public) veri kümeleri olan Waymo ve nuScenes'in yanı sıra, KARSAN prototip otonom minibüsü ile toplanan yüksek çözünürlüklü gerçek dünya veri seti ve farklı LiDAR konfigürasyonlarına sahip özel bir simülasyon veri seti kullanılmıştır. Deneysel sonuçlar, SESS ve Uni3D yöntemlerinin, alan farkına bağlı performans kayıplarını önemli ölçüde azalttığını ve yüksek genel doğruluk elde ettiğini ortaya koymuştur. Ayrıca, LiDAR çözünürlüğü ve kanal sayısının performans üzerindeki etkileri detaylı şekilde analiz edilerek sensör konfigürasyonlarının sistem başarımına katkıları gösterilmiştir. Sonuç olarak, bu tez çalışması, otonom araçlarda güvenli ve genellenebilir algılama katmanı geliştirilmesine katkı sağlayacak kapsamlı bir alan adaptasyonu kıyaslaması sunmaktadır. Bulguların, hem akademik araştırmalarda hem de endüstriyel uygulamalarda önemli pratik faydalar sunacağı öngörülmektedir.",
            "source_url": "https:\/\/openalex.org\/W7218252149",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Bursa Technical University Institutional Repository",
            "source_record_id": "W7218252149",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 638,
            "title": "Deep Learning Based Perception on Radar Data for Autonomous Driving",
            "title_ar": "",
            "title_en": "Deep Learning Based Perception on Radar Data for Autonomous Driving",
            "author": "Meyer, Michael",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Christian-Albrechts-Universität zu Kiel",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; robotics; cnn; gnn; object-detection",
            "keywords": "",
            "abstract": "This thesis investigates perception tasks using automotive radar sensors for autonomous driving. Compared to cameras and lidars, radar sensors offer unique advantages including weather robustness and direct velocity measurements. We specifically focus on leveraging range-beam-doppler tensors as input for deep neural networks, a data representation that contains the full radar signal rather than the commonly used sparse radar pointclouds. The thesis investigates how range-beam-doppler tensors can be processed by neural networks through the exemplary task of scene classification and evaluates the effectiveness of complex-valued convolutions in processing radar data. Our findings show that while scene classification is achievable solely based on radar data, complex-valued convolutions do not yield significant benefits in this context, and the magnitude information alone suffices for this perception task. Furthermore, we investigate how polar radar tensors can be efficiently processed in convolutional neural networks. This leads to the development of radar 3D object detection model using graph neural networks, which improved detection performance by more than 8.9% compared to a model without graph convolutions. We demonstrate that ego-motion compensation, while more complex for range-beam-doppler tensors than pointclouds, is essential for accurate object detection, yielding an 8.7% improvement in average precision. We also demonstrate that discrete autoencoders can effectively compress range-beam-doppler data while maintaining sufficient information for multi-sensor fusion tasks. In comparing radar tensor and pointcloud approaches, we find that current pointcloud-based models achieve slightly superior detection performance. However, our research has significantly narrowed the performance gap between these two data representations and suggests that the range-beam-doppler data level holds considerable promise for future improvements.",
            "source_url": "https:\/\/openalex.org\/W7110640169",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Multimedialen Archiv und Publikationsserver der Christian-Albrechts-Universität zu Kiel (Christian-Albrechts-Universität zu Kiel)",
            "source_record_id": "W7110640169",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 639,
            "title": "Deep Learning-Based Perception Systems for Advancing Autonomous Driving and Road Safety",
            "title_ar": "",
            "title_en": "Deep Learning-Based Perception Systems for Advancing Autonomous Driving and Road Safety",
            "author": "Rahul Vijay Soans",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning; robotics",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7207742820",
            "pdf_url": "",
            "doi": "10.34382\/0002005531",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7207742820",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 640,
            "title": "Research on Deep Learning-based Multi-vehicle Cooperative Perception System for Autonomous Driving",
            "title_ar": "",
            "title_en": "Research on Deep Learning-based Multi-vehicle Cooperative Perception System for Autonomous Driving",
            "author": "Yalong Li",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning; robotics",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7207790538",
            "pdf_url": "",
            "doi": "10.18952\/0002000986",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7207790538",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 641,
            "title": "Deep Learning-based Radar Camera Fusion for Perception in Autonomous Driving",
            "title_ar": "",
            "title_en": "Deep Learning-based Radar Camera Fusion for Perception in Autonomous Driving",
            "author": "Huawei Sun ; Lorenzo Servadei ; Technische Universität München",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning; robotics",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7233531946",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W7233531946",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 642,
            "title": "Multi-Scale Deep Learning Frameworks for Trajectory Prediction, Scene Understanding, and Perception in Autonomous Driving",
            "title_ar": "",
            "title_en": "Multi-Scale Deep Learning Frameworks for Trajectory Prediction, Scene Understanding, and Perception in Autonomous Driving",
            "author": "Xingnan Zhou",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Concordia University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; responsible-ai; ai-applications; predictive-analytics; rnn; transformers; xai; fairness; ai-smart-cities",
            "keywords": "",
            "abstract": "Reliable autonomous vehicle operation at urban intersections requires competence across the prediction–perception stack, yet current deep learning models underuse the structural priors that govern real-world driving. This thesis argues that incorporating structural priors at multiple scales improves the accuracy, interpretability, and robustness of autonomous driving models, through five interconnected studies. Chapter 3 introduces a Turn-Aware LSTM that encodes turning intent via cumulative heading change. On UAV footage from a signalised intersection in Châteauguay, Québec, it reduces Final Displacement Error by 15–20% for turning manoeuvres at a 3-second horizon. Chapter 4 establishes lane graph conditioning as an architecture-flexible inductive bias for trajectory prediction. A lightweight module (under 50,000 parameters), evaluated on 89,258 Waymo scenarios, reduces ADE by 9.3% at 3 s and, at 8 s, minADE by 26.6%, minFDE by 32.6%, and miss rate by 42.7%. Chapter 5 recovers real-world locations for anonymised scenarios by matching intersection topologies to OpenStreetMap via WayGraph's 48-dimensional star pattern descriptor. Across 56,797 scenarios it achieves 90% top-1 accuracy under synthetic OSM-to-OSM matching (33–55% under realistic noise), and a geographic audit reveals fourfold under-sampling of signalised intersections (4.3% vs. 17.8% city-wide). Chapter 6 develops a spatial attention visualisation framework that projects Transformer attention onto bird's-eye-view scenes. Applied to MTR-Lite, it identifies a tunnel vision pattern: failed predictions are associated with lower attention entropy (5.72 vs. 5.94 bits) and elevated self-attention; cyclist targets show higher miss rates (88.1% vs. 54.0%). Left-turn manoeuvres trigger a general agent-to-map attention shift (−19.9% vehicle attention, p < 0.001), suggesting possible VRU-related attention vulnerabilities, though the cyclist-specific interaction did not reach significance (p = 0.51, n = 17) and requires validation on larger samples. Chapter 7 shows that viewpoint diversity suppresses false positives via dual-camera LiDAR fusion with vehicle- and drone-mounted cameras. Across 2,600 CARLA frames and ten seeds, symmetric late fusion improves mAP by +0.92 percentage points (p = 0.001) and reduces false positives by 13% on a representative seed. These studies support the thesis argument across five scales—kinematic, road topology, geographic, attentional, and sensor geometry—and outline future research directions.",
            "source_url": "https:\/\/openalex.org\/W7221486811",
            "pdf_url": "https:\/\/spectrum.library.concordia.ca\/id\/eprint\/997687\/6\/Zhou_PhD_F2026.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Spectrum Research Repository (Concordia University)",
            "source_record_id": "W7221486811",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 644,
            "title": "Uncertainty-aware perception, localization and mapping for autonomous driving",
            "title_ar": "",
            "title_en": "Uncertainty-aware perception, localization and mapping for autonomous driving",
            "author": "Kshitij Sirohi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universitätsbibliothek Freiburg",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; predictive-analytics; cnn; image-segmentation",
            "keywords": "",
            "abstract": "Advances in deep learning have revolutionized scene understanding in recent years, achieving unprecedented performance levels. As a result, nearly all autonomous vehicle perception systems now rely on learning-based Deep Neural Networks (DNNs). These methods are trained on curated datasets and evaluated on test sets using task-specific metrics. This approach faces two significant limitations. First, the training and test datasets typically share similar data distributions and conditions, which may differ substantially from those encountered during real-world deployment. Real-world scenarios often present objects and situations absent from the training data. Second, evaluation metrics frequently focus solely on task-specific performance and fail to account for the network's confidence in the evaluation. Consequently, these methods tend to produce deterministic or overly confident outputs without a reliable uncertainty estimation, posing safety risks in critical applications such as autonomous driving. Moreover, the absence of uncertainty estimations in modern perception systems complicates their integration with classical probabilistic methods, that forms the foundation of successful autonomous systems across various applications, particularly in downstream tasks like localization and mapping. In this thesis, we focus on developing perception methods that enable real-time uncertainty estimation and leverage this uncertainty to integrate perception information with probabilistic approaches for localization and mapping. First, we introduce a new perception task that aims to achieve uncertainty-aware holistic scene understanding. To facilitate this, we provide a formal definition of novel metrics designed for holistic, uncertainty-aware perception evaluation, along with a DNN-based architecture tailored to perform this task using camera images. Next, we focus on the segmentation of 3D LiDAR point clouds, given the indispensable role the LiDAR sensor plays for 3D scene understanding in an autonomous vehicle. As the LiDAR point cloud brings challenges like sparse and unstructured data, we develop specialized methods to leverage 3D information from LiDAR point cloud into 2D convolutional neural networks and set the new benchmark for LiDAR panoptic segmentation. Furthermore, we present the first method that combines holistic perception capabilities with uncertainty estimation for LiDAR point clouds. By leveraging both the 3D spatial information of the point cloud and prediction uncertainties, our approach resolves ambiguities in segmenting challenging objects, significantly improving perception performance. Finally, we focus on the downstream tasks by utilizing the perception uncertainty as a bridge between modern perception and probabilistic approaches for localization and mapping. Especially in the final chapter, we showcase that utilizing perception uncertainties can improve mapping and localization performance. Moreover, our approach also propagates the perception uncertainties into the final mapping and localization results, thus providing their respective uncertainties. Our methods demonstrate that high-accuracy, uncertainty-aware perception based on deep learning is achievable, and perception uncertainty is essential for a robust downstream performance.",
            "source_url": "https:\/\/openalex.org\/W7147585983",
            "pdf_url": "",
            "doi": "10.6094\/unifr\/279476",
            "handle": "",
            "repository_name": "FreiDok plus (Universitätsbibliothek Freiburg)",
            "source_record_id": "W7147585983",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 645,
            "title": "Constraint-Guided Multimodal Learning for End-to-End Autonomous Driving",
            "title_ar": "",
            "title_en": "Constraint-Guided Multimodal Learning for End-to-End Autonomous Driving",
            "author": "Maoning Ge ; Maoning GE",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; robotics; ai-applications; predictive-analytics; multimodal; ai-smart-cities",
            "keywords": "",
            "abstract": "Autonomous driving in complex urban environments requires not only accurat e perception and prediction, but also strict adherence to physical constra ints and safety requirements.While recent deep learning approaches and Vi sion-Language-Action (VLA) models have significantly advanced the capabili ties of data-driven systems, they often overlook vehicle dynamics and envi ronmental constraints, which may lead to physically infeasible or unsafe b ehaviors.This thesis investigates constraint-guided multimodal learning f or autonomous driving, with a focus on integrating explicit physical const raints into trajectory prediction and end-to-end control.",
            "source_url": "https:\/\/openalex.org\/W7162977153",
            "pdf_url": "https:\/\/nagoya.repo.nii.ac.jp\/record\/2014422\/files\/k15729_abstract.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7162977153",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 647,
            "title": "Interaction-Aware Maneuver and Lane Change Prediction Using Deep Learning Architectures for Autonomous Driving",
            "title_ar": "",
            "title_en": "Interaction-Aware Maneuver and Lane Change Prediction Using Deep Learning Architectures for Autonomous Driving",
            "author": "Petrit Rama",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Kaiserslautern University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; responsible-ai; ai-applications; predictive-analytics; rnn; transformers; gnn; multimodal; xai; ai-smart-cities",
            "keywords": "",
            "abstract": "Maneuver prediction is a critical component of decision-making in autonomous driving, particularly in complex urban environments where traffic participants continuously interact under uncertainty. Accurate and interpretable prediction of future driving maneuvers, such as lane changes, is essential for safe and anticipatory planning. This thesis investigates interaction-aware maneuver prediction from the ego-vehicle perspective using deep learning-based approaches that integrate spatial reasoning, temporal modeling, and multi-modal perception. The research presents a progression of architectures that address maneuver and lane change prediction at increasing levels of complexity. First, interaction-aware models based on graph neural networks (GNNs) and recurrent neural networks (RNNs) jointly capture spatial interactions between traffic agents and the temporal evolution of driving behavior for scene understanding. Building on this, a cognitively inspired two-layer hierarchical decision-making framework separates planned maneuvers from stimuli and cause reasoning to improve interpretability. Subsequently, a deep multi-modal architecture based on Edge-Featured Graph Attention Networks (EGATs) is developed for lane change prediction, integrating visual perception, spatial interaction graphs, ego-vehicle state signals, and lane-level information. To support this research, a new in-house multi-modal dataset, WylonSet, is introduced and later extended to WylonSet++, providing high-quality annotations for lane change maneuvers and, in its extended version, for maneuver phases in diverse urban traffic scenarios. Finally, a transformer-based multi-modal architecture is proposed for multi-horizon lane change and phase prediction. The model employs attention-based fusion across modalities and incorporates uncertainty estimation at the modality, fusion, and prediction levels, enabling interpretable and future-aware behavior prediction. Experiments on four real-world driving datasets show that interaction-aware, multi-modal, and attention-based architectures consistently improve prediction accuracy and temporal consistency compared with uni-modal and non-interaction-aware variants. The transformer-based architecture predicts lane change maneuvers and their phases across horizons of up to 3 s, achieving an overall macro F1-score above 97% on WylonSet++ and detecting lane change events on average about 1 s before their onset. Overall, this thesis contributes new models, datasets, and evaluation methodology for maneuver and lane change prediction, providing a foundation for interpretable and deployment-oriented decision-making systems in autonomous driving.",
            "source_url": "https:\/\/openalex.org\/W7222983659",
            "pdf_url": "",
            "doi": "10.26204\/kluedo\/13713",
            "handle": "",
            "repository_name": "Publication Server of Kaiserslautern University of Technology (Kaiserslautern University of Technology)",
            "source_record_id": "W7222983659",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 648,
            "title": "Safety of Machine Learning Systems in Autonomous Driving",
            "title_ar": "",
            "title_en": "Safety of Machine Learning Systems in Autonomous Driving",
            "author": "Fadi Al-Khoury",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; robotics; responsible-ai; ai-applications; speech-recognition; fairness; ai-industry",
            "keywords": "",
            "abstract": "Machine Learning, and in particular Deep Learning, are extremely capable tools for solving problems which are difficult, or intractable to tackle analytically. Application areas include pattern recognition, computer vision, speech and natural language processing. With the automotive industry aiming for increasing amount of automation in driving, the problems to solve become increasingly complex, which appeals to the use of supervised learning methods from Machine Learning and Deep Learning. With this approach, solutions to the problems are learned implicitly from training data, and inspecting their correctness is not possible directly. This presents concerns when the resulting systems are used to support safety-critical functions, as is the case with autonomous driving of automotive vehicles. This thesis studies the safety concerns related to learning systems within autonomous driving and applies a safety monitoring approach to a collision avoidance scenario. Experiments are performed using a simulated environment, with a deep learning system supporting perception for vehicle control, and a safety monitor for collision avoidance. The related operational situations and safety constraints are studied for an autonomous driving function, with potential faults in the learning system introduced and examined. Also, an example is considered for a measure that indicates trustworthiness of the learning system during operation.",
            "source_url": "https:\/\/openalex.org\/W2770758792",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W2770758792",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 650,
            "title": "Distributed Intelligence for 3D Object Tracking in Autonomous Vehicles, A Multi-Sensor Approach to Object Tracking and Planning for Self-Driving Cars, Real-Time 3D Perception and Path Planning in Autonomous Systems,,Vision-Based Localization and Distributed Architecture for Driverless Cars,A Hybrid Deep Learning Framework for Object Detection and Trajectory Planning in Autonomous Vehicles",
            "title_ar": "",
            "title_en": "Distributed Intelligence for 3D Object Tracking in Autonomous Vehicles, A Multi-Sensor Approach to Object Tracking and Planning for Self-Driving Cars, Real-Time 3D Perception and Path Planning in Autonomous Systems,,Vision-Based Localization and Distributed Architecture for Driverless Cars,A Hybrid Deep Learning Framework for Object Detection and Trajectory Planning in Autonomous Vehicles",
            "author": "Manas Madine",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Massachusetts Amherst",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; robotics; ai-applications; predictive-analytics; object-detection; medical-imaging; ai-industry; ai-smart-cities",
            "keywords": "",
            "abstract": "Abstract-I: Autonomous Navigating car : To make car move as fast as possible and with absolutely no collisions. And Controlling a car with high dimensional sensory inputs and Driving a car autonomously around a racetrack with obstacles like pedestrians and other vehicles and even traffic rules. We created a deep Q-network (DQN) agent to perform the task of autonomous car driving from raw sensory inputs. We evaluated our agent’s performance against several standard agents in a racing simulation environment (CARLA). Our results indicate that our DQN agent is capable of successfully controlling a car to navigate around a simulation environment(CARLA). Abstract-II: 3D Object Tracking in Autonomous Driving: we developed a stereo-camera based 3D multi Vehicle tracking System. The main objective of this system is to accurately predict location and orientation of vehicles from stereo camera data. It has three modules: a 2D object detection network, 3D position extraction and 3D object correlation. This system utilizes the Kalman filtering to improve the robustness of missed detections Abstract-III: trajectory planning: The planning module in an autonomous navigation can be considered as the system’s brain where the vehicle trajectory for the next seconds is planned. In this module, we make use of the perception data from the 3D object tracking to plan a secure and smooth trajectory for the vehicle. The trajectory planning objective is to provide the vehicle with a secure trajectory constrained by the vehicle dynamics limits, the navigation comfort and safety, and the traffic rules. Once the desired trajectory is generated, the next step is to control the vehicle in order to track this trajectory with the desired speed profile also defined by the planning module. The vehicle control is indeed the control of the vehicle actuators, such as the steering wheel, the accelerator and the braking force, in order to manage the vehicle motions. Abstract-IV: Distributed System Architecture and Development Process: For autonomous driving, complex autonomous driving algorithms, including perception, 3D object tracking, localization, planning, and control, are required with many heterogeneous sensors and computers. To manage the complexity of the driving algorithms and the heterogeneity of the system components, we applied distributed system architecture to the autonomous driving system, and a system platform for the distributed system of an autonomous car. The development process provides the guidelines to design and develop the distributed system of an autonomous vehicle. A time-triggered network protocol, FlexRay, to be applied as the main network of the software platform to improve the network bandwidth, fault tolerance, and system performance.",
            "source_url": "https:\/\/openalex.org\/W7135301869",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ScholarWorks@UMassAmherst (University of Massachusetts Amherst)",
            "source_record_id": "W7135301869",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 651,
            "title": "Deep Structured Multi-Task Learning for Computer Vision in Autonomous Driving",
            "title_ar": "",
            "title_en": "Deep Structured Multi-Task Learning for Computer Vision in Autonomous Driving",
            "author": "Marvin Teichmann",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Cambridge",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; responsible-ai; predictive-analytics; cnn; image-segmentation; xai",
            "keywords": "",
            "abstract": "The field of computer vision is currently dominated by deep learning advances. Convolutional Neural Networks (CNNs) have become the predominant tool for solving almost any computer vision task, so state-of-the-art systems have been built by using the predictive capabilities of Convolutional Neural Networks (CNNs). Many of those systems use simple encoder–decoder based design, where an off-the-shelf CNN architecture is combined with a task-specific decoder and loss function in order to create an end-to-end trainable model. This ultimately raises the question of whether these kinds of models are the future of computer vision. In this thesis we argue that this is not the case. We start off by discussing three limitations of simple end-to-end training. We proceed by showing how it is possible to overcome those limitations by using an approach that we call structured modelling. The idea is to use CNNs to compute a rich semantic intermediate representation which is then used to solve the actual problem by applying a geometric and task-related structure. In this work we solve the localization, segmentation and landmark recognition task using structured modelling, and we show that this approach can improve generalization, interpretability and robustness. We also discuss how this approach is particularly useful for real-time applications such as autonomous driving. Visual perception is a multi-module problem that requires several different computer vision tasks to be solved. We discuss how, by sharing computations, we can improve not only the inference speed but also the prediction performance by using the structural relationship between the tasks. Lastly, we demonstrate that structured modelling is able to achieve state-of-the-art performance, making it a very relevant approach for solving current and future computer vision problems.",
            "source_url": "https:\/\/openalex.org\/W3049041115",
            "pdf_url": "",
            "doi": "10.17863\/cam.56207",
            "handle": "",
            "repository_name": "Apollo (University of Cambridge)",
            "source_record_id": "W3049041115",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 654,
            "title": "Application of deep learning for enhancing simultaneous localization and mapping in autonomous driving",
            "title_ar": "",
            "title_en": "Application of deep learning for enhancing simultaneous localization and mapping in autonomous driving",
            "author": "Jintian Ge",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; transfer-learning; self-semi-supervised; multimodal; image-segmentation",
            "keywords": "",
            "abstract": "Simultaneous Localization and Mapping, commonly referred to as SLAM, represents a class of algorithms that involves constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's position within it. This technique is foundational in robotics and autonomous systems, enabling machines and robots to explore, understand, and navigate their surroundings. Deep learning, characterized by its prowess in handling vast data and intricate patterns, has shown promise in several Computer Vision (CV) tasks, i.e., semantic segmentation, optical flow estimation, and disparity estimation. Deep learning excels at these tasks, delivering dense and accurate results. However, as a downstream work of perception, SLAM pays little attention to the application of these results. Therefore, this paper aims to explore possible applications of deep learning (especially CV tasks) in SLAM tasks. In this thesis, we delve into the confluence of Deep Learning and SLAM tasks, emphasizing the potential for performance enhancement and methodological innovation. Our exploration is structured around four SLAM related tasks, each distinct yet synergistically linked around a center topic \"application of deep learning to enhance performance\": an automatic calibration approach for radar-camera system as a pre-process of multi-modal SLAM, which leverages deep learning models to eliminate the need for special markers; the application of deep learning to stereo 3D reconstruction through disparity estimation, presenting its potential for dense environment local mapping; the utility of semantics in initial pose estimation and feature matching within visual odometry for localization, refining the precision of positioning techniques; and introduction of supervised contrastive learning for image representation, targeting the challenges associated with fine-tuning deep learning models in real-world deployments. Collectively, these tasks underline the potential of deep learning in advancing robotic methodologies and offer a vision for future research directions.",
            "source_url": "https:\/\/openalex.org\/W4396622348",
            "pdf_url": "",
            "doi": "10.32657\/10356\/174794",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4396622348",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 655,
            "title": "Learning autonomous driving policies for dynamic urban and racing environments",
            "title_ar": "",
            "title_en": "Learning autonomous driving policies for dynamic urban and racing environments",
            "author": "Peide Cai",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "xai",
            "categories": "deep-learning; computer-vision; reinforcement-learning; robotics; responsible-ai; ai-applications; multimodal; xai; ai-smart-cities",
            "keywords": "",
            "abstract": "It is believed that self-driving vehicles will play an essential role in reducing road accidents, alleviating traffic congestion, and reducing energy consumption in transportation, thus providing safety, efficiency, and convenience for everyone. This thesis focuses on building autonomous driving systems leveraging deep learning under complex contexts from normal-speed urban to high-speed drifting\/racing scenarios. The first part studies the frameworks of end-to-end driving in dynamic urban scenarios based on imitation learning (IL).We progressively consider all-day and all-weather driving conditions using a combination of multi-modal sensors, i.e., cameras, lidars, and radars. The uncertainty measures are also utilized to indicate dangerous cases and improve the interpretability of the driving system. In the second part, we focus on local motion planning using semantic state abstractions rather than learning the entire navigation stack to achieve more generalizable and strategic driving performance in interaction-intensive environments, e.g., unregulated intersections\/ roundabouts. The interactions among different road users are modeled implicitly through graph attention networks (GAT), where the inter-vehicle influences are adaptively weighted according to specific contexts. By training the whole network through deep reinforcement learning (DRL), the proposed method performs better in balancing driving safety and efficiency than the baselines. The learned policy can also be transferred into a real-world dataset thanks to the decoupled perception-planning framework. Furthermore, the third part highlights highly dynamic drifting\/racing scenarios to facilitate agile autonomous driving near the handling limits under high-speed conditions. We first study the drift cornering control problems based on model-free DRL approaches, then combine IL and model-based DRL to solve a vision-based autonomous racing task. The proposed method is validated both in a realistic simulator and on a physical RC car. Related methodologies also support the 1st HKUST autonomous RC-car racing competition1.",
            "source_url": "https:\/\/openalex.org\/W4307183652",
            "pdf_url": "",
            "doi": "10.14711\/thesis-991013106451703412",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4307183652",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 656,
            "title": "Generating and Exploiting Deep Learning Variants to Increase Utilization of the Heterogeneous Resources in Autonomous Driving Platforms",
            "title_ar": "",
            "title_en": "Generating and Exploiting Deep Learning Variants to Increase Utilization of the Heterogeneous Resources in Autonomous Driving Platforms",
            "author": "Roger Pujol Torramorell",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; robotics; object-detection",
            "keywords": "",
            "abstract": "Nowadays, Deep learning-based solutions and, in particular, deep neural networks (DNNs) are getting into several core functionalities in critical real-time embedded systems (CRTES), like those in planes, cars, and satellites, from vision-based perception (object detection and object tracking) systems to trajectory planning. As a result, several deep learning instances are running simultaneously at any time on the same computing platform. However, while modern computing platforms offer a variety of computing elements (e.g., CPUs, GPUs, and specific accelerators) in which those DNN instances can be executed depending on their computational requirements and temporal constraints. Currently, most DNNs are mainly programmed to exploit one particular computing element, regular cores of the GPUs. This lack of variety causes a resource imbalance and under-utilization of the various computing element resources when executing several DNN instances, causing an increase in DNN tasks' execution time requirements. In this Thesis, (a) we develop different variants (implementation) of well-known DNN libraries used in the Apollo Autonomous Driving software for each of the computing elements of the latest NVIDIA Xavier system-on-chip. Each variant is configured to balance resource requirements and performance: the regular CPU core implementation that can run on 2, 4, and 6 cores (always leaving 2 cores free for other computations); the GPU with regular and Tensor cores variants that can run on 4 or 8 GPU's Stream Multiprocessors (SM); and 1 or 2 NVIDIA's Deep Learning Accelerators (NVDLA); (b) we show that each particular variant\/configuration offers different resource utilization\/performance point. (c) we show how those heterogeneous computing elements can be exploited by a static scheduler to sustain the execution of multiple and diverse DNN variants on the same platform.",
            "source_url": "https:\/\/openalex.org\/W3121071986",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W3121071986",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 657,
            "title": "Design and Implementation of an Autonomous Driving System with a Deep Learning Approach on a Scaled Vehicle Platform",
            "title_ar": "",
            "title_en": "Design and Implementation of an Autonomous Driving System with a Deep Learning Approach on a Scaled Vehicle Platform",
            "author": "Jesudara Omidokum",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Michigan",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "ai-education",
            "categories": "deep-learning; robotics; ai-applications; ai-education",
            "keywords": "",
            "abstract": "The thesis project is developed to create a platform for autonomous driving research and education purposes. The main goal of this study is to present a complete and adequate way for building a platform with a drive-by-wire system and sensor packages that are both comprehensive and appropriate for testing and deploying machine-learning-based algorithms on a scaled vehicle. The vehicle platform addresses reproducibility issues, onboard processing capability restrictions, vehicle scale, and system cost with the existing related vehicle platforms. Our vehicle platform system integrates both drives-by-wire and an autonomous system enabled with sensor packages in an easy-to-implement format. The platform is a generic and multilevel system, with flight controller programs combined with a GPS module handling the mid to high-level motor controls and a laptop powered by a graphics processing unit (GPU) capable of handling the advanced and more complex algorithms. The vehicle platform is validated by employing it in a deep-learning-based behavioral cloning study. The platform's affordability and adaptability would benefit broader research and the education community.",
            "source_url": "https:\/\/openalex.org\/W4311648930",
            "pdf_url": "",
            "doi": "10.7302\/6650",
            "handle": "",
            "repository_name": "Deep Blue (University of Michigan)",
            "source_record_id": "W4311648930",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 658,
            "title": "Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving",
            "title_ar": "",
            "title_en": "Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving",
            "author": "Dominik Jargot",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Delft University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Netherlands",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; robotics; object-detection",
            "keywords": "",
            "abstract": "Nowadays, autonomous driving is a trending topic in the automotive field. One of the most crucial challenges of autonomous driving research is environment perception. Currently, many techniques achieve satisfactory performance in 2D object detection using camera images. Nevertheless, such 2D object detection might be not sufficient for autonomous driving applications as the vehicle is operating in a 3D world where all the dimensions have to be considered. In this thesis a new method for 3D object detection, using deep learning approach is presented. The proposed architecture is able to detect cars using data from images and point clouds. The proposed network does not use any hand-crafted features and is trained in an end-to-end manner. The network is trained and evaluated with the widely used KITTI dataset. The proposed method achieves an average precision of 81.38%, 67.02%, and 65.30% on the easy, moderate, and hard subsets of the KITTI validation dataset, respectively. The average inference time per scene is 0.2 seconds.",
            "source_url": "https:\/\/openalex.org\/W3038194063",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Research Repository (Delft University of Technology)",
            "source_record_id": "W3038194063",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 659,
            "title": "Learning Birds-Eye View Representations for Autonomous Driving",
            "title_ar": "",
            "title_en": "Learning Birds-Eye View Representations for Autonomous Driving",
            "author": "Thomas Roddick",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Cambridge",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; predictive-analytics; cnn; object-detection; ai-smart-cities",
            "keywords": "",
            "abstract": "Over the past few years, progress towards the ambitious goal of widespread fully-autonomous vehicles on our roads has accelerated dramatically. This progress has been spurred largely by the success of highly accurate LiDAR sensors, as well the use of detailed high-resolution maps, which together allow a vehicle to navigate its surroundings effectively. Often, however, one or both of these resources may be unavailable, whether due to cost, sensor failure, or the need to operate in an unmapped environment. The aim of this thesis is therefore to demonstrate that it is possible to build detailed three-dimensional representations of traffic scenes using only 2D monocular camera images as input. Such an approach faces many challenges: most notably that 2D images do not provide explicit 3D structure. We overcome this limitation by applying a combination of deep learning and geometry to transform image-based features into an orthographic birds-eye view representation of the scene, allowing algorithms to reason in a metric, 3D space. This approach is applied to solving two challenging perception tasks central to autonomous driving. The first part of this thesis addresses the problem of monocular 3D object detection, which involves determining the size and location of all objects in the scene. Our solution was based on a novel convolutional network architecture that processed features in both the image and birds-eye view perspective. Results on the KITTI dataset showed that this network outperformed existing works at the time, and although more recent works have improved on these results, we conducted extensive analysis to find that our solution performed well in many difficult edge-case scenarios such as objects close to or distant from the camera. In the second part of the thesis, we consider the related problem of semantic map prediction. This consists of estimating a birds-eye view map of the world visible from a given camera, encoding both static elements of the scene such as pavement and road layout, as well as dynamic objects such as vehicles and pedestrians. This was accomplished using a second network that built on the experience from the previous work and achieved convincing performance on two real-world driving datasets. By formulating the maps as an occupancy grid map (a widely used representation from robotics), we were able to demonstrate how predictions could be accumulated across multiple frames, and that doing so further improved the robustness of maps produced by our system.",
            "source_url": "https:\/\/openalex.org\/W3216727358",
            "pdf_url": "",
            "doi": "10.17863\/cam.77645",
            "handle": "",
            "repository_name": "Apollo (University of Cambridge)",
            "source_record_id": "W3216727358",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 660,
            "title": "Lidar-based scene understanding for autonomous driving using deep learning",
            "title_ar": "",
            "title_en": "Lidar-based scene understanding for autonomous driving using deep learning",
            "author": "Víctor Vaquero Gómez",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "نص السجل: «PhD»",
            "university": "Universitat Politècnica de Catalunya",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; predictive-analytics; image-segmentation; ai-smart-cities",
            "keywords": "",
            "abstract": "With over 1.35 million fatalities related to traffic accidents worldwide, autonomous driving was foreseen at the beginning of this century as a feasible solution to improve security in our roads. Nevertheless, it is meant to disrupt our transportation paradigm, allowing to reduce congestion, pollution, and costs, while increasing the accessibility, efficiency, and reliability of the transportation for both people and goods. Although some advances have gradually been transferred into commercial vehicles in the way of Advanced Driving Assistance Systems (ADAS) such as adaptive cruise control, blind spot detection or automatic parking, however, the technology is far from mature. A full understanding of the scene is actually needed so that allowing the vehicles to be aware of the surroundings, knowing the existing elements of the scene, as well as their motion, intentions and interactions. In this PhD dissertation, we explore new approaches for understanding driving scenes from 3D LiDAR point clouds by using Deep Learning methods. To this end, in Part I we analyze the scene from a static perspective using independent frames to detect the neighboring vehicles. Next, in Part II we develop new ways for understanding the dynamics of the scene. Finally, in Part III we apply all the developed methods to accomplish higher level challenges such as segmenting moving obstacles while obtaining their rigid motion vector over the ground. More specifically, in Chapter 2 we develop a 3D vehicle detection pipeline based on a multi-branch deep-learning architecture and propose a Front (FR-V) and a Bird’s Eye view (BE-V) as 2D representations of the 3D point cloud to serve as input for training our models. Later on, in Chapter 3 we apply and further test this method on two real uses-cases, for pre-filtering moving obstacles while creating maps to better localize ourselves on subsequent days, as well as for vehicle tracking. From the dynamic perspective, in Chapter 4 we learn from the 3D point cloud a novel dynamic feature that resembles optical flow from RGB images. For that, we develop a new approach to leverage RGB optical flow as pseudo ground truth for training purposes but allowing the use of only 3D LiDAR data at inference time. Additionally, in Chapter 5 we explore the benefits of combining classification and regression learning problems to face the optical flow estimation task in a joint coarse-and-fine manner. Lastly, in Chapter 6 we gather the previous methods and demonstrate that with these independent tasks we can guide the learning of higher challenging problems such as segmentation and motion estimation of moving vehicles from our own moving perspective. Con más de 1,35 millones de muertes por accidentes de tráfico en el mundo, a principios de siglo se predijo que la conducción autónoma sería una solución viable para mejorar la seguridad en nuestras carreteras. Además la conducción autónoma está destinada a cambiar nuestros paradigmas de transporte, permitiendo reducir la congestión del tráfico, la contaminación y el coste, a la vez que aumentando la accesibilidad, la eficiencia y confiabilidad del transporte tanto de personas como de mercancías. Aunque algunos avances, como el control de crucero adaptativo, la detección de puntos ciegos o el estacionamiento automático, se han transferido gradualmente a vehículos comerciales en la forma de los Sistemas Avanzados de Asistencia a la Conducción (ADAS), la tecnología aún no ha alcanzado el suficiente grado de madurez. Se necesita una comprensión completa de la escena para que los vehículos puedan entender el entorno, detectando los elementos presentes, así como su movimiento, intenciones e interacciones. En la presente tesis doctoral, exploramos nuevos enfoques para comprender escenarios de conducción utilizando nubes de puntos en 3D capturadas con sensores LiDAR, para lo cual empleamos métodos de aprendizaje profundo. Con este fin, en la Parte I analizamos la escena desde una perspectiva estática para detectar vehículos. A continuación, en la Parte II, desarrollamos nuevas formas de entender las dinámicas del entorno. Finalmente, en la Parte III aplicamos los métodos previamente desarrollados para lograr desafíos de nivel superior, como segmentar obstáculos dinámicos a la vez que estimamos su vector de movimiento sobre el suelo. Específicamente, en el Capítulo 2 detectamos vehículos en 3D creando una arquitectura de aprendizaje profundo de dos ramas y proponemos una vista frontal (FR-V) y una vista de pájaro (BE-V) como representaciones 2D de la nube de puntos 3D que sirven como entrada para entrenar nuestros modelos. Más adelante, en el Capítulo 3 aplicamos y probamos aún más este método en dos casos de uso reales, tanto para filtrar obstáculos en movimiento previamente a la creación de mapas sobre los que poder localizarnos mejor en los días posteriores, como para el seguimiento de vehículos.",
            "source_url": "https:\/\/openalex.org\/W3021063730",
            "pdf_url": "https:\/\/upcommons.upc.edu\/bitstreams\/b8c27eee-0c12-4e12-8f18-1b7e1af52108\/download",
            "doi": "10.5821\/dissertation-2117-341602",
            "handle": "",
            "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)",
            "source_record_id": "W3021063730",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 661,
            "title": "Action recognition in videos using deep learning",
            "title_ar": "",
            "title_en": "Action recognition in videos using deep learning",
            "author": "Shiyu Li",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; few-zero-shot; video-analysis; llm",
            "keywords": "",
            "abstract": "This dissertation systematically studies the two core challenges faced by visual-language pretraining models (such as CLIP) in video action recognition tasks: insufficient temporal modeling and ambiguous semantic space. By constructing a temporal-enhanced model (TC-CLIP) and a semantic-enhanced model (OST) respectively, this dissertation optimizes and evaluates existing methods from two dimensions: “temporal modeling reinforcement” and “semantic label refinement”. TC-CLIP proposes a temporal contextualization mechanism that introduces global temporal interactions during visual encoding through a “filter-aggregate-inject” process, effectively modeling long-range dependencies and dynamic evolution between video frames. OST, on the other hand, leverages large language models to generate rich spatio-temporal descriptors and dynamically aligns fine-grained textual semantics with local video features through an optimal transport-based semantic alignment mechanism, addressing the problems of ambiguous and vague category label semantics. Zero-shot and few-shot experiments were conducted on the HMDB-51 and UCF-101 datasets for ActionCLIP, TC-CLIP, and OST models. The results demonstrate that both improved strategies significantly enhance the performance of the baseline model, ActionCLIP. Experimental results indicate that the temporal-enhancement and semantic-enhancement strategies are complementary: TC-CLIP performs better in scenarios with large motion and prominent temporal features (such as UCF-101), while OST demonstrates stronger robustness in scenarios with complex backgrounds, subtle actions, and ambiguous semantics (such as HMDB-51). Furthermore, few-shot experiments reveal the performance patterns of the two strategies under different amounts of data: semantic enhancement has an advantage when data is extremely scarce due to prior knowledge, whereas temporal enhancement exhibits stronger learning potential as data volume increases. This study not only confirms the necessity of introducing explicit temporal modeling and fine-grained semantic alignment in video tasks but also clarifies their applicable boundaries and complementary relationship, providing theoretical foundations and practical directions for developing general video recognition models that consider both temporal dynamics and semantic understanding.",
            "source_url": "https:\/\/openalex.org\/W7155590036",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W7155590036",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 662,
            "title": "Applied deep learning for human action recognition in video surveillance, healthcare monitoring, and robotics",
            "title_ar": "",
            "title_en": "Applied deep learning for human action recognition in video surveillance, healthcare monitoring, and robotics",
            "author": "Thean Chun Koh",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; robotics; responsible-ai; cnn; video-analysis; fairness",
            "keywords": "",
            "abstract": "Human behavior recognition is a cutting-edge field of artificial intelligence focused on interpreting and understanding human actions, gestures and activities through advanced computational algorithms and machine learning techniques. These algorithms serve as the foundation for AI systems to effectively interact with and assist humans in various aspects of daily life, from personal tasks to complex decision-making processes. However, developing effective human behavior recognition systems faces several challenges. The inherent complexity and variability of human behavior present significant obstacles. Actions vary widely due to cultural backgrounds, individual habits and environmental factors, complicating the creation of universal models. Additionally, issues such as occlusion in crowded environments, variations in lighting conditions and the need for real-time processing further hinder accuracy and reliability. Specifically, traditional methods such as 3D Convolutional Neural Networks (3D CNNs) can be computationally intensive and may struggle with real-time processing requirements, while optical flow techniques often fail to provide robust performance under varying lighting conditions and occlusions. Moreover, ethical considerations surrounding privacy and data security pose additional barriers to widespread adoption. Furthermore, current models often rely on single modalities, which limits their ability to accurately capture human behavior in dynamic environments where multiple individuals are present.",
            "source_url": "https:\/\/openalex.org\/W4412190996",
            "pdf_url": "",
            "doi": "10.32657\/10356\/200238",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4412190996",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 663,
            "title": "Privacy Motivated Models for Deep Learning-based Human Action Recognition in Intelligent Video Surveillance",
            "title_ar": "",
            "title_en": "Privacy Motivated Models for Deep Learning-based Human Action Recognition in Intelligent Video Surveillance",
            "author": "Mickael Willy Jacky Cormier",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Karlsruhe Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "deep-learning; computer-vision; responsible-ai; video-analysis; fairness",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7135042273",
            "pdf_url": "",
            "doi": "10.5445\/ir\/1000191274",
            "handle": "",
            "repository_name": "Repository KITopen (Karlsruhe Institute of Technology)",
            "source_record_id": "W7135042273",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 665,
            "title": "Deep learning for action recognition in videos",
            "title_ar": "",
            "title_en": "",
            "author": "Ahmed Mazari",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2020,
            "language": "fr",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; video-analysis",
            "keywords": "",
            "abstract": "Apprentissage profond pour la reconnaissance d’actions en vidéos De nos jours, les contenus vidéos sont omniprésents grâce à Internet et les smartphones, ainsi que les médias sociaux. De nombreuses applications de la vie quotidienne, telles que la vidéo surveillance et la description de contenus vidéos, ainsi que la compréhension de scènes visuelles, nécessitent des technologies sophistiquées pour traiter les données vidéos. Il devient nécessaire de développer des moyens automatiques pour analyser et interpréter la grande quantité de données vidéo disponibles. Dans cette thèse, nous nous intéressons à la reconnaissance d'actions dans les vidéos, c.a.d au problème de l'attribution de catégories d'actions aux séquences vidéos. Cela peut être considéré comme un ingrédient clé pour construire la prochaine génération de systèmes visuels. Nous l'abordons avec des méthodes d'intelligence artificielle, sous le paradigme de l'apprentissage automatique et de l'apprentissage profond, notamment les réseaux de neurones convolutifs. Les réseaux de neurones convolutifs actuels sont de plus en plus profonds, plus gourmands en données et leur succès est donc tributaire de l'abondance de données d'entraînement étiquetées. Les réseaux de neurones convolutifs s'appuient également sur le pooling qui réduit la dimensionnalité des couches de sortie (et donc atténue leur sensibilité à la disponibilité de données étiquetées).",
            "source_url": "https:\/\/openalex.org\/W7149524092",
            "pdf_url": "",
            "doi": "10.70675\/d7175e36z7156z47c3za09bz49b02a753fd7",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7149524092",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 666,
            "title": "Human Action Recognition in Videos with Shallow and Deep Learning",
            "title_ar": "",
            "title_en": "",
            "author": "Thi hai hong Phan",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2019,
            "language": "fr",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; cnn; video-analysis",
            "keywords": "",
            "abstract": "Reconnaissance d'actions humaines dans des vidéos avec l'apprentissage automatique Ces dernières années, la reconnaissance d’action humaine (HAR) a attiré l’attention de la recherche grâce à ses diverses applications telles que les systèmes de surveillance intelligents, l’indexation vidéo, l’analyse des activités humaines, les interactions homme-machine, et ainsi de suite. Les problèmes typiques que les chercheurs envisagent sont la complexité des mouvements humains, les variations spatio-temporelles, l'encombrement, l'occlusion et le changement des conditions d'éclairage. Cette thèse porte sur la reconnaissance automatique des actions humaines en cours dans une vidéo. Nous abordons ce problème de recherche en utilisant à la fois des approches d'apprentissage traditionnel peu profond et d'apprentissage profond.Premièrement, nous avons commencé les travaux de recherche avec des méthodes d’apprentissage traditionnelles peu profondes, fondées sur des caractéristiques créées manuellement, en introduisant un nouveau fonctionnalité appelée descripteur MOMP (Motion of Oriented Magnitudes Patterns). Nous avons ensuite intégré ce descripteur discriminant aux techniques de représentation simples mais puissantes telles que le sac de mots visuels, le vecteur de descripteurs agrégés localement (VLAD) et le vecteur de Fisher pour mieux représenter les actions. En suite l'PCA (Principal Component Analysis) et la sélection des caractéristiques (la dépendance statistique, l'information mutuelle) sont appliquées pour rechercher le meilleur sous-ensemble des caractéristiques afin d'améliorer les performances et de réduire les coûts de calcul. La méthode proposée a permis d'obtenir les résultats d'état de l'art sur plusieurs bases de données communes.Les approches d'apprentissage profond récentes nécessitent des calculs intensifs et une utilisation importante de la mémoire. Ils sont donc difficiles à utiliser et à déployer sur des systèmes aux ressources limitées. Dans la deuxième partie de cette thèse, nous présentons un nouvel algorithme efficace pour compresser les modèles de réseau de neurones convolutionnels afin de réduire à la fois le coût de calcul et l’empreinte mémoire au moment de l’exécution. Nous mesurons la redondance des paramètres en fonction de leurs relations à l'aide des critères basés sur la théorie de l'information, puis nous éliminons les moins importants. La méthode proposée réduit considérablement la taille des modèles de différents réseaux tels qu'AlexNet, ResNet jusqu'à 70% sans perte de performance pour la tâche de classification des images à grande échelle.L'approche traditionnelle avec le descripteur proposé a permis d'obtenir d'excellentes performances pour la reconnaissance de l'action humaine mais seulement sur de petits bases de données. Afin d'améliorer les performances de la reconnaissance sur les bases de données de grande échelle, dans la dernière partie de cette thèse, nous exploitons des techniques d'apprentissage profond pour classifier les actions. Nous introduisons les concepts de l'image MOMP en tant que couche d'entrée de CNN et incorporons l'image MOMP dans des réseaux de neurones profonds. Nous appliquons ensuite notre algorithme de compression réseau pour accélérer et améliorer les performances du système. La méthode proposée réduit la taille du modèle, diminue le sur-apprentissage et augmente ainsi la performance globale de CNN sur les bases de données d'action à grande échelle.Tout au long de la thèse, nous avons montré que nos algorithmes obtenaient de bonnes performances sur bases de données d'action complexes (Weizmann, KTH, UCF Sports, UCF-101 et HMDB51) avec des ressources limitées.",
            "source_url": "https:\/\/openalex.org\/W7148690778",
            "pdf_url": "",
            "doi": "10.70675\/fa813329z9321z4fc2za918zd4282f6ec3ac",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7148690778",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 667,
            "title": "Video-based similar gesture action recognition using deep learning and GAN-based approaches",
            "title_ar": "",
            "title_en": "Video-based similar gesture action recognition using deep learning and GAN-based approaches",
            "author": "Di Wu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Technology Sydney",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; robotics; responsible-ai; predictive-analytics; cnn; rnn; video-analysis; gan; fairness",
            "keywords": "",
            "abstract": "Human action is not merely a matter of presenting patterns of motion of different parts of the body, in addition, it is also a description of intention, emotion and thoughts of the person. Hence, it has become a crucial component in human behavior analysis and understanding. Human action recognition has a wide variety of applications such as surveillance, robotics, health care, video searching and human-computer interaction. Analysing human actions manually is tedious and easily prone to errors. Therefore, computer scientists have been trying to bring the abilities of cognitive video understanding to human action recognition systems by using computer vision techniques. However, human action recognition is a complex task in computer vision because of the camera motion, occlusion, background cluttering, viewpoint variation, execution rate and similar gestures. These challenges significantly degrade the performance of the human action recognition system. The purpose of this research is to propose solutions based on traditional machine learning methods as well as the state-of-the-art deep learning methods to automatically process video-based human action recognition. This thesis investigates three research areas of video-based human action recognition: traditional human action recognition, similar gesture action recognition, and data augmentation for human action recognition. To start with, the feature-based methods using classic machine learning algorithms have been studied. Recently, deep convolutional neural networks (CNN) have taken their place in the computer vision and human action recognition research areas and have achieved tremendous success in comparison to traditional machine learning techniques. Current state-of-the-art deep convolutional neural networks were used for the human action recognition task. Furthermore, recurrent neural networks (RNN) and its variation of long-short term memory (LSTM) are used to process the time series features which are handcrafted features or extracted from the CNN. However, these methods suffer from similar gestures, which appear in the human action videos. Thus, a hierarchical classification framework is proposed for similar gesture action recognition, and the performance is improved by the multi-stage classification approach. Additionally, the framework has been modified into an end-to-end system, therefore, the similar gestures can be processed by the system automatically. In this study, a novel data augmentation framework for action recognition has been proposed, the objective is to generate well learnt video frames from action videos which can enlarge the dataset size as well as the feature bias. It is very important for a human action recognition system to recognize the actions with similar gestures as accurately as possible. For such a system, a generative adversarial net (GAN) is applied to learn the original video datasets and generate video frames by playing an adversarial game. Furthermore, a framework is developed for classifying the original dataset in the first place to obtain the confusion matrix using a CNN. The similar gesture actions will be paired based on the confusion matrix results. The final classification result will be applied on the fusion dataset which contains both original and generated video frames. This study will provide realtime and practical solutions for autonomous human action recognition system. The analysis of similar gesture actions will improve the performance of the existing CNN-based approaches. In addition, the GAN-based approaches from computer vision have been applied to the graph embedding area, because graph embedding is similar to image embedding but used for different purposes. Unlike the purpose of the GAN in computer vision for generating the images, the GAN in graph embedding can be used to regularize the embedding. So the proposed methods are able to reconstruct both structural characteristics and node features, which naturally possess the interaction between these two sources of information while learning the embedding.",
            "source_url": "https:\/\/openalex.org\/W3020614990",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UTS ePRESS (University of Technology Sydney)",
            "source_record_id": "W3020614990",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 668,
            "title": "A Dual-Stream Deep Learning Architecture for Action Recognition in Salmon from Underwater Video.",
            "title_ar": "",
            "title_en": "A Dual-Stream Deep Learning Architecture for Action Recognition in Salmon from Underwater Video.",
            "author": "Håkon Måløy",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "deep-learning; computer-vision; ai-applications; rnn; video-analysis; ai-agriculture; ai-industry",
            "keywords": "",
            "abstract": "Over half of the costs from breeding salmon in the Norwegian salmon farming industry comes from feed usage. Today the feeding process is largely a manual labor, requiring an operator to monitor the amount of feed sinking to the bottom of a breeding cage. When the amount of feed exceeds a certain threshold, the feeding process is terminated. Automation of this process and using salmon motion behavior instead of sinking feed to determine when to terminate feeding, could greatly reduce costs both in through the labor needed and amount of feed wasted. Resent developments in Human Action Recognition have shown that Deep Learning approaches are well suited to perform Action Recognition. We therefore examine the feasibility of using Deep Learning approaches to automate the feeding process. We use 76 videos of salmon collected from within a breeding cage during the month of November 2016. Using these videos for training, validation and testing, we propose three approaches to automatically classify Feeding and NonFeeding behavior in salmon. The three approaches are a Spatial Architecture, a Spatial Recurrent Architecture and a Dual-Stream Architecture. Our results show that all our proposed architectures are able to separate videos of Feeding and NonFeeding salmon with high accuracy. We also find that the Dual- Stream Architecture is the best performing architecture. It combines spatial and temporal information through the use of a Spatial Stream, a novel Temporal Stream and a Recurrent Neural Network (RNN). Our Dual-Stream Architecture is able to accurately classify 80.0% all of our testing videos, presenting state-of-the-art performance. To the best of our knowledge, both our Temporal Stream and our Dual-Stream Architecture are original and novel architectures, as is the application of Deep Learning inference models for the Salmon Activity Recognition domain in optimization of feeding operation in Norwegian Aquaculture. We hope the results presented in this thesis will contribute to achieve a higher sustainability in Norwegian salmon aquaculture, optimize feeding operations, and consequently reduce potential waste. Future work beyond the results presented in this thesis concerns research on understanding of what our the Deep Learning architecture have learned and visualizing this learning process.",
            "source_url": "https:\/\/openalex.org\/W2809657688",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "BIBSYS Brage (BIBSYS (Norway))",
            "source_record_id": "W2809657688",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 669,
            "title": "Data augmentation of visual rhythms using symmetric extension for deep learning video based human action recognition",
            "title_ar": "",
            "title_en": "",
            "author": "Hemerson Tacon",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "pt",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "deep-learning; computer-vision; video-analysis",
            "keywords": "",
            "abstract": "Nos últimos anos, avanços significativos foram alcançados no problema de classificação de imagens devido ao aprimoramentos dos modelos de Aprendizagem Profunda. Entretanto, no que diz respeito ao Reconhecimento de Ações Humanas, ainda existe muito espaço para melhorias. Uma forma de melhorar o desempenho de tais modelos é através do aumento de dados. Dessa forma propomos, como aumento de dados, o uso de múltiplos recortes do Ritmo Visual, simetricamente estendidos no tempo e separados por uma distância fixa. Propomos ainda utilizar uma nova forma de extração do Ritmo Visual, o Ritmo Visual Ponderado. Este método propõe reforçar os padrões de movimento pesando os aspectos mais próximos de uma posição específica no vídeo na qual julgamos que a ação tenha maior probabilidade de ocorrer. O método final consiste na replicação do Ritmo Visual Ponderado concatenando quantas cópias forem necessárias ao longo da dimensão temporal, tendo as cópias pares invertidas horizontalmente. Esse método torna possível a extração de recortes que correspondam ao tamanho de entrada fixo da Rede Neural Convolucional utilizada, bem como a preservação da taxa de amostragem do vídeo, o que é crucial para não distorcer a velocidade das ações. Não obstante, os vários recortes garantem que toda extensão espacial e temporal do Ritmo Visual seja contemplada. Com o objetivo de avaliar nosso método, empregamos uma estratégia multi-fluxo. Essa estratégia consiste na combinação de informações extraídas a partir dos frames RGB dos vídeos, do Fluxo Ótico, e dos Ritmos Visuais Simetricamente Estendidos horizontal e vertical. Nosso método resultou em taxas de acurácia próximas ao estado da arte nos conjuntos de dados UCF101 e HMDB51.",
            "source_url": "https:\/\/openalex.org\/W3137056519",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Americanae (AECID Library)",
            "source_record_id": "W3137056519",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 670,
            "title": "Deep Learning Methods for Human Action Recognition",
            "title_ar": "",
            "title_en": "Deep Learning Methods for Human Action Recognition",
            "author": "Zeqi Yu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Auckland University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; cnn; rnn; video-analysis",
            "keywords": "",
            "abstract": "Human action recognition from digital videos is a hot topic in the field of computer vision. It has a pretty assortment of applications in a myriad of fields such as video surveillance, human-computer interaction, visual information retrieval, and unmanned driving. With the exponential growth of surveillance data on the Internet in recent years, how to implement effective and efficient analysis of video data is extremely crucial. Traditional machine learning methods that only extract computable features have limitations and do not suit massive visual data, meanwhile, deep learning methods, especially convolutional neural networks, have gained great attainments in this field. The goal of human action recognition is to classify patterns so as to understand human actions from visual data and export corresponding tags. In addition to spatial correlation existing in 2D images, human actions in a video on the correlation in the temporal domain. Due to the complexity of human actions, changes of perspectives, background noises, and lighting conditions will affect the recognition. In order to solve these thorny problems, three algorithms are designed and implemented in this thesis. Based on convolutional neural networks (CNN), Two-Stream CNN, CNN+LSTM, and 3D-CNN are harnessed to identify human actions. Each algorithm is explicated and analyzed in detail. HMDB-51 dataset is employed to test these algorithms and gain the best results. Our experimental results demonstrate that the three methods have effectively identified human actions in given videos, the best algorithm thus is verified.",
            "source_url": "https:\/\/openalex.org\/W3157063314",
            "pdf_url": "https:\/\/openrepository.aut.ac.nz\/bitstreams\/ed753409-6ddd-4042-9f3a-2fff51193d63\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Tuwhera (Auckland University of Technology)",
            "source_record_id": "W3157063314",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 671,
            "title": "Learning Interaction Grammar for Action Recognition",
            "title_ar": "",
            "title_en": "Learning Interaction Grammar for Action Recognition",
            "author": "Tengyu Liu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "deep-learning; computer-vision; video-analysis",
            "keywords": "",
            "abstract": "Video action recognition has been in the center of the stage since its introduction in 2004 [SLC04]. During the past 15 years, countless methods had been proposed to understand what human is doing in a video clip. While some works infer action label directly from pixel information, some other works propose to learn multi-level hierarchical structure that composes actions. In this work, we propose the learning of a two-layer grammar model for action recognition that is based on human object interaction. To evaluate the idea, we proposed an interaction grammar for action recognition on video. The model is evaluated by simulated annealing with MCMC and by deep learning.",
            "source_url": "https:\/\/openalex.org\/W2792899696",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "eScholarship (California Digital Library)",
            "source_record_id": "W2792899696",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 672,
            "title": "Action recognition using deep learning",
            "title_ar": "",
            "title_en": "Action recognition using deep learning",
            "author": "Petar Palasek",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Queen Mary University of London",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "deep-learning; computer-vision; cnn; video-analysis",
            "keywords": "",
            "abstract": "In this thesis we study deep learning architectures for the problem of human action recognition in image sequences, i.e. the problem of automatically recognizing what people are doing in a given video.As unlabeled video data is easily accessible these days, we first explore models that can learn meaningful representations of sequences without actually having to know what is happening in the sequences at hand.More specifically, we first explore the convolutional restricted Boltzmann machine (RBM) and show how a stack of convolutional RBMs can be used to learn and extract features from sequences in an unsupervised way.Using the classical Fisher vector pipeline",
            "source_url": "https:\/\/openalex.org\/W2785072843",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Queen Mary Research Online (Queen Mary University of London)",
            "source_record_id": "W2785072843",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 673,
            "title": "Egocentric action recognition from noisy videos",
            "title_ar": "",
            "title_en": "Egocentric action recognition from noisy videos",
            "author": "Lijin Yang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; transformers; video-analysis",
            "keywords": "",
            "abstract": "Recognizing human actions in videos can make machines better interpret the behavior of human beings.As one of the basic fields of computer vision, action recognition has attracted significant research attention.In particular, with the recent advance of deep learning, there is a huge boost of action recognition performance.Deep learning models that takes as input video clips can reliably recognize the actions compared with traditional methods using hand-crafted features. List of Tables4.1 Action recognition performance comparison with baseline methods. .4.2 Quantitative comparison on cooperating with different backbones. . .4.3 Ablation study for different parts of our model on recognizing interrupted actions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.4 Ablation study for different parts of our model on localizing useful frames in super noisy videos. . . . . . . . . . . . . . . . . . . . . . . .5.1 Quantitative comparison on using different attention mechanisms. . .",
            "source_url": "https:\/\/openalex.org\/W3014414335",
            "pdf_url": "https:\/\/repository.dl.itc.u-tokyo.ac.jp\/record\/54246\/files\/48186461.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W3014414335",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 674,
            "title": "Domain adaptation for video action recognition",
            "title_ar": "",
            "title_en": "Domain adaptation for video action recognition",
            "author": "Xiyu Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; multimodal; video-analysis",
            "keywords": "",
            "abstract": "Humans can effortlessly learn from a specific data distribution and generalize well to various situations without excessive supervision. In contrast, deep learning models often struggle to achieve similar generalization capabilities. This is primarily because deep models are trained with algorithms that aim to minimize empirical risks on training data and assume that test data share the same distribution as train data. However, significant domain shifts between training (source) and testing (target) data can occur, causing deep models to generalize poorly on target domains and necessitating additional supervision for adaptation. To address this, Video-based Unsupervised Domain Adaptation (VUDA) has been proposed as a cost-efficient approach for transferring video action recognition models from the source domain to an unlabeled target domain. Nonetheless, VUDA relies on strong assumptions, such as identical label spaces and fixed target domains, which may not hold true in real-world applications. Consequently, this thesis aims to eliminate these assumptions to broaden the applicability of video adaptation methods, focusing on two major shortcomings of conventional VUDA methods, e.g., partial domain adaptation (adapting from a source domain with many classes to a target domain with fewer classes) and continual domain adaptation (adapting to continuously changing target domains). For partial domain adaptation, this thesis proposes the Multi-modality Cluster-calibrated partial Adversarial Network (MCAN), which constructs a multi-modal network to extract robust features and a novel calibration method to refine target class distribution estimation, effectively filtering out irrelevant source classes. To further address some real challenges in the field of adapting deep video models, the problem of continuous video domain adaptation is defined and this thesis proposes Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART). This method leverages attentive learning and a novel data generalization enhanced self-knowledge distillation to preserve previously learned knowledge on seen target domains while adapting to newly encountered ones, ultimately providing a performative model for multiple seen target domains at a minimal cost. This thesis evaluates the proposed partial and continuous video domain adaptation methods on existing and newly constructed benchmarks in this thesis. Our results demonstrated significant performance improvements for MCAN and CART, with MCAN showing particularly strong gains when domain shifts were substantial and CART demonstrating a superior capability of preserving learned knowledge. In conclusion, our research findings on partial and continuous domain adaptation effectively broadened the applicability of video domain adaptation methods, making them more general and cost-efficient.",
            "source_url": "https:\/\/openalex.org\/W4390585107",
            "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/172273\/2\/wang_xiyu_final_thesis.pdf",
            "doi": "10.32657\/10356\/172273",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4390585107",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:51",
            "updated_at": "2026-11-15 16:52:51"
        },
        {
            "id": 675,
            "title": "Deep Learning for Skeleton-Based Human Action Recognition",
            "title_ar": "",
            "title_en": "Deep Learning for Skeleton-Based Human Action Recognition",
            "author": "Sohaïb Laraba",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Belgium",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; robotics; responsible-ai; cnn; video-analysis; xai",
            "keywords": "",
            "abstract": "Human action recognition from videos has a wide range of applications, including video surveillance and security, human-computer interaction, robotics, health care, etc. Nowadays, 3D skeleton-based action recognition has drawn increasing attention thanks to the availability of low-cost motion capture devices, and accessibility of large-scale 3D skeleton datasets, in addition to real-time skeleton estimation algorithms. In the first part of this thesis, we present a novel representation of motion capture sequences for 3D skeleton-based action recognition. The proposed approach consists of representing the 3D skeleton sequences into RGB image-like data and leveraging recent convolutional neural networks (CNNs) to model the long-term temporal and spatial structural information for action recognition. Extensive experiments have shown the superiority of the proposed approach over the state-of-the-art methods for 3D skeleton-based action recognition. In order to extract skeleton sequences, different devices extract first the depth information using multiple technologies (stereo, time-of-flight, etc.), then 3D skeleton poses are extracted using different algorithms. In the very late years, new researches proposed to extract skeleton sequences directly from RGB videos. The most precise methods extract 2D skeletons in real-time and with high accuracy. In the second part of this thesis, we leverage these tools to extend the use of our proposed approach to RGB videos. We first extract 2D skeleton sequences from RGB videos, and then, following approximately the same process as in the first part, we use CNNs for human action recognition. Different experiments showed that the proposed method outperforms different state-of-the-art methods on a large benchmark dataset. Another contribution of this thesis relates to the interpretability of deep learning models. Deep learning models are still considered alchemy due to the lack of understanding of their internal operations. Interpretability is a crucial task to understand and trust the decisions made by the machine learning model. Thus, we propose in the third part of this thesis to use CNN interpretation methods to understand the behavior of our classifier and extract the most informative joints during the execution of a particular action. This method allows us to see from the CNN point of view the most important joints, and understand why certain actions are confused by the proposed classifier.",
            "source_url": "https:\/\/openalex.org\/W3093549486",
            "pdf_url": "",
            "doi": "10.13140\/rg.2.2.30723.53283",
            "handle": "",
            "repository_name": "ORBi UMONS",
            "source_record_id": "W3093549486",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 678,
            "title": "Deep Learnıng Archıtectures for Collectıve Actıvıty Recognıtıon",
            "title_ar": "",
            "title_en": "Deep Learnıng Archıtectures for Collectıve Actıvıty Recognıtıon",
            "author": "Cemil Zalluhoğlu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; cnn; transformers; video-analysis",
            "keywords": "",
            "abstract": "Collective activity recognition, which analyses the behavior of groups of people in videos, is an essential goal of video surveillance systems. In this thesis, we proposed three new solutions and one novel dataset for the collective activity recognition task. In the first method, we propose a new multi-stream convolutional neural network architecture that utilizes information extracted from multiple regions. The proposed method is the first work that uses a multi-stream network and multiple regions in this problem. Various strategies to fuse multiple spatial and temporal streams are explored. We evaluate the proposed method on two benchmark datasets, the Collective Activity Dataset, and the Volleyball Dataset. Our experimental results show that the proposed method improves collective activity recognition performance when compared to the state-of-the-art approaches. In trying to solve this problem, we realized that the existing datasets are insufficient for deep learning methods and have many limitations. Then we introduce the ”Collective Sports (C-Sports)” dataset, which is a novel benchmark dataset for multi-task recognition of both collective activity and sports categories. Various state-of-the-art techniques are evaluated on this dataset, together with a multi-task variant, which demonstrates increased performance. From the experimental results, we can say that while the sports action category recognition is relatively an easy task, there is still room for improvement for collective activity recognition, especially for the distant view situations. We believe that C-Sports dataset will stir further interest in this research direction. Our second proposed method involves an attention mechanism. We utilize the soft attention-based attention mechanism for action recognition and collective activity recognition tasks. We use attention maps that have high response values to regions that need attention in videos. We describe a method that using this attention mechanism with two distinct 3D-ConvNets architectures which are standard 3D-ConvNets (C3D) and inflated 3D-ConvNets (I3D). We evaluate our method on four benchmark datasets; two of them are about action recognition task, UCF101, and HMDB51. Others are related to collective activity recognition problem, Collective Activity Dataset, and Collective Sports Dataset. Experimental results show that the 3D attention-based ConvNets improves the performance on all datasets when compared to baselines which are 3D-ConvNets architectures without an attention mechanism. Our last proposed method of this thesis involves relation reasoning method and 3D attention mechanism. We propose a 3D Spatio-temporal relation network. We create this architecture by adding new methods step by step on the base Temporal Relation Network (TRN). First, a 2D attention mechanism has been added to TRN architecture. Then the 2D architecture is moved into 3D space. Finally, a 3D attention mechanism has been added on 3D TRN architecture. We evaluate these networks on one activity recognition and three collective activity recognition datasets, Something-Something v1, Collective Activity Recognition, Collective Sports, and Volleyball datasets, respectively. Our results show that the methods with attention mechanism improve the recognition performance. Besides, 3D networks obtain better accuracy when compared to 2D networks.",
            "source_url": "https:\/\/openalex.org\/W3138559006",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Hacettepe University Institutional Repository (hacettepe.edu.tr)",
            "source_record_id": "W3138559006",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 679,
            "title": "Robust and efficient deep learning methods for vision-based action recognition",
            "title_ar": "",
            "title_en": "Robust and efficient deep learning methods for vision-based action recognition",
            "author": "Yuecong Xu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; transfer-learning; rnn; image-classification; video-analysis",
            "keywords": "",
            "abstract": "Vision-based action recognition, which performs action recognition based solely on RGB frames, has received strong research interest thanks to its wide applications in various fields, e.g. surveillance, smart homes, and autonomous driving. Significant progress has been made in vision-based action recognition thanks to the development of recognition technologies, particularly deep learning methods which have proven their effectiveness in visual recognition tasks, such as image classification. Compared to static images, videos contain additional information due to the additional temporal dimension, which includes both temporal and spatiotemporal correlation features. Therefore, the key to robust and efficient action recognition lies in the effective and efficient utilization of the temporal and spatiotemporal correlation features embedded within videos. In this thesis, we first investigate extracting temporal features in a robust and efficient manner. While methods for extracting temporal features were proposed, these methods either require the computation or estimation of optical flow, which demand high computational power and large storage resource; or extracts only linear feature along the temporal dimension which results in inferior performances. To extract temporal features without the utilization of optical flow, we propose Attentive Correlated Temporal Feature (ACTF) which leverages inter-frame correlation feature and exploits both bilinear and linear correlations between successive frames on the regional level. By excluding optical flow estimation or calculation, ACTF can be combined with any spatial feature extraction network under the two-stream structure for end-to-end training. Meanwhile, capturing long-range spatiotemporal dependencies is an effective strategy for extracting spatiotemporal correlation features. Previous works have proposed methods utilizing either hand-crafted features or stacks of convolution or recurrent modules, both of which are computationally inefficient, and cause difficulty in network optimization. While the more recent non-local block inspired by the non-local means could extract long-range dependencies without affecting the networks' optimization, it significantly increases the parameter size and computational cost of the inserted networks. To extract robust long-range dependencies more efficiently, we explore on further improving the non-local neural network by proposing a novel long-range spatiotemporal dependencies extraction module, the Pyramid Non-Local (PNL) module. It extends the original non-local block by incorporating regional feature correlations at multiple scales. PNL upscales the effectiveness of the original non-local block by additionally addressing the spatiotemporal correlations between different regions, while improving the efficiency of the original non-local block with a significant decrease in computation cost. Besides the development of recognition technologies, the progress made in vision-based action recognition could also be attributed to the development of large-scale video datasets, which enable the effective training of deep learning models. However, it could be observed that the majority of current research focuses on videos in normal illumination, partly due to the fact that current benchmark datasets for vision-based action recognition are normally collected from web videos shot mostly under normal illumination. Yet, we argue that vision-based action recognition should not be constrained in normal illuminated videos. Vision-based action recognition in dark videos are also useful in various scenarios, e.g., night surveillance and self-driving at night. Such a task has rarely been researched, partly due to the lack of sufficient datasets for such a task. To this end, this thesis bridges the gap of the lack of data and pioneers vision-based action recognition in dark videos by collecting a novel dataset: the Action Recognition in the Dark (ARID) dataset. In this thesis, the ARID dataset is analyzed thoroughly with a comprehensive benchmark of current deep learning methods. Meanwhile, though the introduction of ARID pioneers vision-based action recognition in dark videos and bridges the gap between the absence of dark video datasets with the need for research, the scale of such a dataset is relatively small compared to current large-scale video datasets. One solution for training robust models for domains with less labeled data would be to transfer models learned in well-labeled domains. However, models trained in one domain would not generalize well in the other domain due to domain shift across domains, which is presented as distribution discrepancy between different domains. Domain adaptation (DA) approaches address domain shifts and enable networks to be applied to different scenarios.",
            "source_url": "https:\/\/openalex.org\/W3214258728",
            "pdf_url": "",
            "doi": "10.32657\/10356\/153169",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W3214258728",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 680,
            "title": "Leveraging deep learning for visual understanding of videos",
            "title_ar": "",
            "title_en": "Leveraging deep learning for visual understanding of videos",
            "author": "Clement Xian Ren Tan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; robotics; self-semi-supervised; predictive-analytics; transformers; multimodal; image-classification; object-detection; image-segmentation; video-analysis; llm",
            "keywords": "",
            "abstract": "In the field of Computer Vision (CV), the pursuit of human-level recognition and reasoning of visual scenes has been a long-standing aspiration. Over the past decade, significant contributions to the progress of CV have been made by deep learning, facilitated by the availability of big data and increased computational power. In CV, visual understanding encompasses not only the fundamental aspect of recognition, which involves identifying and categorizing objects and patterns within visual data, but also reasoning, which involves higher-level cognitive processes such as inferring relationships, predicting outcomes, and drawing meaningful conclusions based on the observed visual information. Human-like recognition capabilities across a variety of visual recognition tasks such as image classification, object detection, semantic segmentation, and instance segmentation, have been achieved by machines. The huge success of deep learning for visual recognition tasks has prompted researchers to tackle visual reasoning tasks, which are more challenging. The recognition and reasoning of objects within a scene is of paramount importance to applications such as human-robot collaborations and autonomous vehicles. This thesis aims at addressing the issues associated with current deep learning models for visual understanding and will focus on three tasks: (1) video object segmentation, (2) abductive action inference, and (3) action-conditioned scene graph prediction. Firstly, video object segmentation is an object tracking task that traditionally relies on supervised learning and necessitates extensive annotated datasets for deep learning model training. It is confronted with issues of repetitiveness and impracticality due to the manual labeling of these vast datasets. In addition, tracking objects within videos presents a unique challenge caused by appearance changes and occlusion across video frames. The work proposed in this thesis explores the potential of self-supervised learning for video object segmentation, aiming to harness freely available internet data such as YouTube videos for model training. Different from existing self-supervised learning approaches for video object segmentation that model pixel-to-pixel correspondence, this research shifts the focus towards modeling superpixel-to-superpixel correspondence. To achieve this, a novel approach which involves the tracking of superpixels between video frames through an attention mechanism trained within an end-to-end self-supervised framework is proposed. This approach aims to enhance the performance of existing self-supervised video object segmentation models by leveraging deep learning techniques and mitigating the limitations associated with supervised learning using large-scale annotated datasets. It also aims to enhance existing self-supervised models with the benefits of superpixels. Secondly, this thesis introduces the complex task of abductive action inference to assess the abductive reasoning abilities of deep learning models in comprehending visual scenes. A set of innovative object-relational models designed specifically for this task is presented and compared with existing state-of-the-art image, video, and vision-language models. These models are tasked with abducting the human-performed action that led to the scene depicted in an image or snapshot. The experiments demonstrate the potential of deep learning models in performing abductive action inference. This research is vital in advancing our understanding of the reasoning capacities of deep learning models, representing a significant stride toward attaining human-level reasoning capabilities in visual comprehension. Lastly, in action-conditioned scene graph prediction, the task involves predicting scene graph relations for a future state based on an initial state and an action. Instead of predicting visual representations for future states, each state is organized into a scene graph comprising human-object relationship triplets, thereby succinctly encapsulating the dynamics within the scene. Notably, this task remains underdeveloped in visual understanding research, with prior video scene graph generation approaches largely overlooking actions as a crucial signal. Actions applied to a preconditioned state generate an effect state embodying their anticipated outcomes. Human cognitive reasoning allows the inference of action consequences based on the initial scene context. To address this challenge, this thesis introduces the Action-conditioned Scene Graph dataset. Furthermore, this thesis proposes the AERT (Action-conditioned Effect Relational Transformer) model, designed to capture scene relations and action context to effectively predict future scene graph relations.",
            "source_url": "https:\/\/openalex.org\/W4402201451",
            "pdf_url": "",
            "doi": "10.32657\/10356\/179521",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4402201451",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 681,
            "title": "A deep learning approach for action classification in American football video sequences",
            "title_ar": "",
            "title_en": "A deep learning approach for action classification in American football video sequences",
            "author": "Jacob A. Westerberg",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; video-analysis",
            "keywords": "",
            "abstract": "The artificial intelligence is a constant topic of conversation with a field of research that is pushed forward by some of the world's largest companies and universities. Deep learning is a branch of machine learning within artificial intelligence based on learning representation of data such as images and texts by processing the data through deep neural networks. Sports are competitive businesses that over the years have become more data driven. Statistics play a big role in the development of the practitioners and the tactics in order to win. Sport organizations have big statistic teams since statistics are manually obtained by these teams. To learn a machine to recognize patterns and actions with deep learning would save a lot of time. In this thesis a deep learning approach is used to examine how well it can perform to classify the actions pass and run in American footbal lgames. A deep learning architecture is first trained and developed on a public video dataset and then trained to classify run and pass plays on a new American football dataset called the All-22 dataset. Results and earlier research show that deep learning has potential to automatize sport statistic but is not yet ready to overtake the role statistic teams have. Further research, bigger and more task specific datasets and more complex architectures are required to enhance the performance of this specific type of deep learning based video recognition.",
            "source_url": "https:\/\/openalex.org\/W2977364195",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W2977364195",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 682,
            "title": "Action Recognition in Multi-view Videos",
            "title_ar": "",
            "title_en": "Action Recognition in Multi-view Videos",
            "author": "Dongang Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "The University of Sydney",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; image-classification; video-analysis",
            "keywords": "",
            "abstract": "A long-lasting goal in the field of artificial intelligence is to develop agents that can perceive and understand the rich visual world around us. With the improvement in deep learning and neural networks, many previous difficulties in the computer vision area have been resolved. For example, the accuracy in image classification has even exceeded human being in the ImageNet challenge. However, some issues are still attractive in the community, like action recognition and its application in multi-view videos. Based on a large number of previous works in the last few years, we propose a new Dividing and Aggregating Network (DA-Net) to address the problem of action recognition in multi-view videos in this thesis. First, the DA-Net can learn view-independent representations shared by all views at lower layers and learn one view-specific representation for each view at higher layers. We then train view-specific action classifiers based on the view-specific representation for each view and a view classifier based on the shared representation at lower layers. The view classifier is used to predict how likely each video belongs to each view. Finally, the predicted view probabilities from multiple views are used as the weights when fusing the prediction scores of view-specific action classifiers. We also propose a new approach based on the conditional random field (CRF) formulation to pass message among view-specific representations from different branches to help each other. Comprehensive experiments are conducted accordingly. The experiments on three benchmark datasets clearly demonstrate the effectiveness of our proposed DA-Net for multi-view action recognition. We also conduct the ablation study, which indicates the three modules we proposed can provide steady improvements to the prediction accuracy.",
            "source_url": "https:\/\/openalex.org\/W3003420344",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "The Sydney eScholarship Repository (The University of Sydney)",
            "source_record_id": "W3003420344",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 683,
            "title": "Deep learning for action recognition and prediction",
            "title_ar": "",
            "title_en": "Deep learning for action recognition and prediction",
            "author": "Qiuhong Ke",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Western Australia",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; rnn; video-analysis",
            "keywords": "",
            "abstract": "This thesis proposes novel deep leaning solutions for real-world applications, including action recognition and prediction from videos. The first part leverages Convolutional Neural Networks (CNN) to learn deep spatial-temporal information of skeleton sequences for action recognition. The second part presents three new methods based on CNN and Recurrent Neural Networks for action prediction. The last part of this thesis presents a new feature extraction method based on CNN for better person re­identification. Extensive experiments have shown the superiority of the proposed methods compared to state of-the-art methods for action recognition, action prediction and person re-identification.",
            "source_url": "https:\/\/openalex.org\/W2903904611",
            "pdf_url": "",
            "doi": "10.26182\/5b74d8e4281d1",
            "handle": "",
            "repository_name": "UWA Profiles and Research Repository (University of Western Australia)",
            "source_record_id": "W2903904611",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 684,
            "title": "A study on Human Emotion Recognition in Video Images using Deep Learning",
            "title_ar": "",
            "title_en": "A study on Human Emotion Recognition in Video Images using Deep Learning",
            "author": "Jargalsaikhan Orgil",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; reinforcement-learning; self-semi-supervised; image-classification; image-segmentation; video-analysis; sentiment-analysis; speech-recognition",
            "keywords": "",
            "abstract": "From the beginning of this century, Artificial Intelligence (AI) has evolved to handle problems in image recognition, classification, segmentation, etc. AI learning is categorized by supervised, semi-supervised, unsupervised or reinforcement learning. Some researchers have said that the future of AI is self­awareness, which is based on reinforcement learning by rewards based on task success. Moreover, it is said that the reward would be harvested from human reactions, specially emotion recognition. On the other hand, emotion recognition is a new inspiring field, but the lack of enough amount of data for training an AI system is the major problem. Fortunately, in the near future, it will be necessary to correctly recognize human emotions because image and video dataset availability is rapidly increasing. Emotions are mental reactions (such as anger, fear, etc.) marked by relatively strong feelings and usually causing physical reactions to previous actions in a short time duration focused on specific objects. In this Work, we are focusing on emotion recognition using face, body part, and intonation. As stated earlier, automatic understanding of human emotion in a wild setting using audiovisual signals is extremely challenging. Latent continuous dimensions can be used to accomplish the analysis of human emotional states, behaviors, and reactions displayed in real-world settings. Moreover, Valence and Arousal combinations constitute well-known and effective representations of emotions. In this thesis, a new Non-inertial loss function is proposed to train emotion recognition deep learning models. It is evaluated in wild settings using four types of candidate networks with different pipelines and sequence lengths. It is then compared to the Concordance Correlation Coefficient (CCC) and Mean Squared Error (MSE) losses commonly used for training. To prove its effectiveness on efficiency and stability in continuous or non-continuous input data, experiments were performed using the Aff-Wild dataset. Encouraging results were obtained. The contributions of the proposed method Non-Inertial loss function are as follows: 1.The new loss function allows for Valence and Arousal to be viewed together. 2.Ability to train on less data. 3.Better results. 4.Faster training times. The rest of this thesis explains our motivation, the proposed methods and finally presents our results.",
            "source_url": "https:\/\/openalex.org\/W7146545736",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7146545736",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 685,
            "title": "Learning temporal variations for action recognition",
            "title_ar": "",
            "title_en": "Learning temporal variations for action recognition",
            "author": "Qili Zeng",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Boston University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "deep-learning; computer-vision; ai-applications; video-analysis; ai-industry",
            "keywords": "",
            "abstract": "As a core problem in video analysis, action recognition is of great significance for many higher-level tasks, both in research and industrial applications. With more and more video data being produced and shared daily, effective automatic action recognition methods are needed. Although, many deep-learning methods have been proposed to solve the problem, recent research reveals that single-stream, RGB-based networks are always outperformed by two-stream networks using both RGB and optical flow as inputs. This dependence on optical flow, which indicates a deficiency in learning motion, is present not only in 2D networks but also in 3D networks. This is somewhat surprising since 3D networks are explicitly designed for spatio-temporal learning. In this thesis, we assume that this deficiency is caused by difficulties associated with learning from videos exhibiting strong temporal variations, such as sudden motion, occlusions, acceleration, or deceleration. Temporal variations occur commonly in real-world videos and force a neural network to account for them, but often are not useful for recognizing actions at coarse granularity. We propose a Dynamic Equilibrium Module (DEM) for spatio-temporal learning through adaptive Eulerian motion manipulation. The proposed module can be inserted into existing networks with separate spatial and temporal convolutions, like the R(2+1)D model, to effectively handle temporal video variations and learn more robust spatio-temporal features. We demonstrate performance gains due to the use of DEM in the R(2+1)D model on miniKinetics, UCF-101, and HMDB-51 datasets.",
            "source_url": "https:\/\/openalex.org\/W3156070674",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "OpenBU (Boston University)",
            "source_record_id": "W3156070674",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 691,
            "title": "Federated Learning for Protecting Medical Data Privacy",
            "title_ar": "",
            "title_en": "Federated Learning for Protecting Medical Data Privacy",
            "author": "Abhishek Reddy Punreddy",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "San Jose State University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "federated-learning",
            "categories": "machine-learning; deep-learning; nlp; medical-ai; responsible-ai; predictive-analytics; federated-learning; fairness",
            "keywords": "",
            "abstract": "Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access to medical data, these Deep learning techniques have yet to completely exploit medical data despite their immense potential benefits. Many data proprietors are unable to benefit from large-scale deep learning due to privacy and confidentiality concerns associated with data sharing. However, without access to sufficient data, Deep Learning will not be able to realize its maximum potential when transitioning from the research phase to clinical practice [2]. This project addresses this problem by implementing Federated Learning and Encrypted Computations on text data, such as Multi Party Computation. SyferText, a Python library for privacy-protected Natural Language Processing that leverages PySyft to conduct Federated Learning, is used in this context.",
            "source_url": "https:\/\/openalex.org\/W4380791856",
            "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=2277&context=etd_projects",
            "doi": "10.31979\/etd.cfgv-t6wa",
            "handle": "",
            "repository_name": "San José State University ScholarWorks (San Jose State University)",
            "source_record_id": "W4380791856",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 707,
            "title": "Studies on biomolecular structure learning and federated privacy protection",
            "title_ar": "",
            "title_en": "Studies on biomolecular structure learning and federated privacy protection",
            "author": "Hanlin Gu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "federated-learning",
            "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; federated-learning; fairness; ai-smart-cities",
            "keywords": "",
            "abstract": "This thesis mainly focuses on two topics: 1) elucidating the structure and dynamics of biological macromolecules, 2) privacy protection of federated learning. Understanding the structure and dynamics of biological molecules is a central pillar of molecular biology. A series of machine learning tools are developed in simulation or experimental data. In particular, one part of the work involves better utilization of Cryo-EM images obtained experimentally. A robust denoising approach is proposed in Cryo-EM images to help structure reconstruction. Then a two-stage classification scheme is presented that could accurately classify the denoised Cryo-EM images from multiple conformations, which allows inferring the underlying free energy landscape. In addition, a kinetic algorithm based on projection operator framework via deep learning is designed to assign numerous microstates into a handful of metastable states. It is helpful to understand the conformational dynamics of complex biomolecules from the simulation perspective. Another work focuses on federated privacy protection. Federated deep learning aims to preserve users' data privacy by decentralizing data from the central server to end devices. However, adversaries may still infer the private training data from the released model and updated gradients. A privacy-preserving Federated Deep Learning with Private Passport framework is proposed without sacrificing the model performance and computation efficiency.",
            "source_url": "https:\/\/openalex.org\/W4386966456",
            "pdf_url": "",
            "doi": "10.14711\/thesis-991013080354203412",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4386966456",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 710,
            "title": "Performance analysis of deep learning approaches for pneumonia diagnosis using chest x-ray images",
            "title_ar": "",
            "title_en": "Performance analysis of deep learning approaches for pneumonia diagnosis using chest x-ray images",
            "author": "Çağlar Çiltaş",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging",
            "keywords": "",
            "abstract": "Diagnosis of pneumonia from Chest X-Ray images holds a significant place in clinical practice due to its low cost and widespread availability. However, achieving high performance with deep learning-based methods generally requires a large amount of labeled data, which constitutes a significant limitation in medical image analysis. In this thesis, the performance of deep learning and traditional machine learning approaches for diagnosing pneumonia from Chest X-Ray images under conditions of low-label data is comprehensively evaluated. For this purpose, a Hybrid Prototype Fusion Network (HPFN) developed based on the ConvNeXt-Tiny architecture is proposed, and deep feature representation and prototype-based similarity calculation are combined in a single decision mechanism. Furthermore, the proposed model is compared with Xception, VGG16, and ResNet50 architectures, and traditional approaches consisting of Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting classifiers using SIFT, KAZE, and HOG features are also analyzed on the same dataset. All experiments were performed using the Chest X-Ray (Pneumonia) dataset, and the models were evaluated using accuracy, precision, sensitivity, and F1-score measures. Experimental results showed that the proposed HPFN model achieved 96.38% accuracy, 96.23% precision, 97.95% sensitivity, and 97.08% F1-score, outperforming both the compared deep learning architectures and traditional feature-based methods. Literature comparisons also revealed that the proposed approach is an effective computer-aided diagnostic method providing reliable, stable, and high-accuracy data under low-label data conditions.",
            "source_url": "https:\/\/openalex.org\/W7168706197",
            "pdf_url": "https:\/\/thesis.rawdatalibrary.net\/thesis\/gogus-rontgeni-goruntulerinden-zaturre-tanisinda-derin-ogrenme-tabanli\/pdf?lang=en",
            "doi": "10.71008\/ebyu.thesis.2026.196",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W7168706197",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 711,
            "title": "CNN-Based Deep Learning Models for Automated Tuberculosis Detection from Chest X-rays",
            "title_ar": "",
            "title_en": "CNN-Based Deep Learning Models for Automated Tuberculosis Detection from Chest X-rays",
            "author": "Pooja Santhosh",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "National College of Ireland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Ireland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging",
            "keywords": "",
            "abstract": "Tuberculosis (TB) remains one of the leading health concerns of the world especially in the low-resource regions where radiologists who can help one with diagnosis are scarce. Although chest X-ray imaging is prevailing examinations used in the screening of TB, it relies strongly on interpretation of experts and may prejudice performance and delay in areas that are under-privileged. Luckily, due to the advent of artificial intelligence, methods of deep learning, and Convolutional Neural Networks (CNNs) have a very high potential of automating the process of analysing medical images. The purpose of this project is to carry out the comparison of the performance of the three most popular and popular pretrained CNN architectures ResNet18, DenseNet121, and EfficientNet-B0 to see which model is more suitable in the problem of detecting TB based on a chest X-ray. We already have a publicly available dataset in Kaggle and will fine-tune the models by using transfer learning. We will use a stratified data split by training, validation and test sets to balance the set classes. All models are going to be tested in PyTorch with standard metrics such as accuracy, precision, accuracy and F1-score. The findings of the given comparison will illuminate on the trade-off between accuracy and computational efficiency of the models.",
            "source_url": "https:\/\/openalex.org\/W7204614449",
            "pdf_url": "https:\/\/norma.ncirl.ie\/9663\/2\/poojasanthoshconfigurationmanual.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "TRAP@NCI (National College of Ireland)",
            "source_record_id": "W7204614449",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 712,
            "title": "Deep Learning-Based Medical Image Analysis for Enhanced Diagnosis and Severity Assessment of Viral Pneumonia",
            "title_ar": "",
            "title_en": "Deep Learning-Based Medical Image Analysis for Enhanced Diagnosis and Severity Assessment of Viral Pneumonia",
            "author": "Enshaei, Nastaran",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Concordia University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Viral pneumonia remains a critical global health concern due to its potential for rapid transmission and widespread outbreaks, as highlighted by the COVID-19 pandemic. Timely and accurate diagnosis, severity assessment, and prognosis are essential for effective patient care and public health management. Chest imaging modalities such as chest X-rays (CXRs) and computed tomography (CT) scans play a central role in diagnosing and managing viral pneumonia. However, interpreting these images is often resource-intensive and subject to inter-observer variability, especially during surges in clinical demand. Deep learning (DL) holds promise for automating image analysis, detecting subtle radiologic features, and enhancing clinical decision-making. Nevertheless, real-world deployment faces persistent challenges, including limited access to large-scale well-annotated datasets, domain shifts, and the heterogeneous presentation of disease. To address these challenges, this thesis advances DL-based medical image analysis for the diagnosis and severity assessment of viral pneumonia, with a specific focus on COVID-19. Key contributions include: (i) Employing game-theoretic data valuation (Data Shapley) to identify mislabeled samples in chest image training datasets; (ii) Introducing a domain-adaptive strategy guided by model prediction confidence to improve the generalization of DL segmentation models; (iii) Developing robust DL frameworks for precise segmentation of COVID-19 lesions in chest CT scans, accounting for their diverse visual characteristics and spatial variability; and (iv) Designing a DL-based diagnostic and severity scoring system for early-stage COVID-19 using baseline CXRs, with performance comparable to expert radiologists. Together, these contributions advance DL-assisted tools for the diagnosis and severity assessment of viral pneumonia in clinical practice.",
            "source_url": "https:\/\/openalex.org\/W7112797530",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Spectrum Research Repository (Concordia University)",
            "source_record_id": "W7112797530",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 713,
            "title": "Analisis Kerentanan Model CNN terhadap Serangan PGD pada Klasifikasi Citra X-ray COVID-19",
            "title_ar": "",
            "title_en": "Analisis Kerentanan Model CNN terhadap Serangan PGD pada Klasifikasi Citra X-ray COVID-19",
            "author": "Elga Putri Tri Farma",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Maine at Machias",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; transfer-learning; cnn; medical-imaging; ai-cybersecurity",
            "keywords": "",
            "abstract": "Advances in deep learning have revolutionized lung disease diagnosis through chest X-ray (CXR) images, particularly since the COVID-19 pandemic. Convolutional Neural Network (CNN) architectures such as DenseNet-121 demonstrate high accuracy in distinguishing viral pneumonia, lung opacity, and normal cases. However, recent studies reveal that these models possess fundamental weaknesses against iteratively designed adversarial attacks. This study aims to analyze the robustness of the DenseNet-121 model against the Projected Gradient Descent (PGD) attack in a four-class classification task on COVID-19 chest X-ray images. The methodology involves training the model using transfer learning on the COVID-19 Radiography Dataset (21,165 images), followed by testing with PGD attacks at various epsilon levels (0.1\/255 to 0.3\/255). The results show that at epsilon 0.1\/255, global model accuracy drops drastically from 95.42% to 25.32%, with an Attack Success Rate (ASR) reaching 74.68%. The most severe degradation occurs in the Lung Opacity class (F1-score drop of 88.90%) and the COVID-19 class (F1-score drop of 82.99%). Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations reveal the phenomenon of semantic decoupling, where the model's attention shifts from diseased lung regions toward background artifacts. In conclusion, the iterative nature of PGD attacks effectively exploits gaps in high-dimensional decision manifolds, rendering unprotected CNN models unsuitable for direct deployment in attack-prone clinical environments.",
            "source_url": "https:\/\/openalex.org\/W7219288436",
            "pdf_url": "https:\/\/eprints.umm.ac.id\/id\/eprint\/34043\/1\/PENDAHULUAN.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "UMM Institutional Repository (University of Maine at Machias)",
            "source_record_id": "W7219288436",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 714,
            "title": "Analisis Kerentanan Model CNN terhadap Serangan FGSM pada Klasifikasi Citra X-ray COVID-19",
            "title_ar": "",
            "title_en": "Analisis Kerentanan Model CNN terhadap Serangan FGSM pada Klasifikasi Citra X-ray COVID-19",
            "author": "Muhammad Hisyam Kamil",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Maine at Machias",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; medical-imaging; ai-cybersecurity",
            "keywords": "",
            "abstract": "The advancement of deep learning, particularly Convolutional Neural Network (CNN) architectures, has brought significant changes in the automated diagnosis of lung diseases through chest X-ray (CXR) images, including pneumonia and COVID-19. However, despite achieving high diagnostic precision, CNN models remain highly susceptible to adversarial attacks, defined as small, visually imperceptible alterations that cause high-confidence mispredictions. This study aims to analyze the vulnerability of the DenseNet-121 model to Fast Gradient Sign Method (FGSM) attacks in classifying COVID-19 chest X-ray images. The research method involves training the DenseNet-121 model using the COVID-19 Radiography Dataset consisting of 21,165 images with four diagnostic categories: COVID-19, Lung Opacity, Normal, and Viral Pneumonia. The model was then tested on clean data and FGSM-perturbed data with varying epsilon values. The results show that at epsilon 0.1\/255, the global model accuracy dropped drastically from 95.42% to 59.28%, with an Attack Success Rate (ASR) of 40.72%. Class-wise analysis shows that the Lung Opacity class experienced the highest F1-Score degradation of 51.13%, followed by COVID-19 at 48.83%. Gradient-weighted Class Activation Mapping (Grad-CAM) visualization confirms that this performance degradation is associated with a shift in the model's attention from clinically relevant pulmonary regions toward background noise (semantic decoupling). This study concludes that high accuracy on clean data does not guarantee model robustness against adversarial attacks, necessitating the integration of defense mechanisms such as adversarial training to improve model safety in clinical deployment.",
            "source_url": "https:\/\/openalex.org\/W7219437683",
            "pdf_url": "https:\/\/eprints.umm.ac.id\/id\/eprint\/34046\/3\/BAB%20II.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "UMM Institutional Repository (University of Maine at Machias)",
            "source_record_id": "W7219437683",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 715,
            "title": "Deep Learning-Based Automated Pneumonia Detection from Chest X-Rays: A Comparative Study of Custom CNN and Transfer Learning Architectures",
            "title_ar": "",
            "title_en": "Deep Learning-Based Automated Pneumonia Detection from Chest X-Rays: A Comparative Study of Custom CNN and Transfer Learning Architectures",
            "author": "Ahmed Sajim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Southern Mississippi",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging",
            "keywords": "",
            "abstract": "Pneumonia is a leading global cause of mortality, claiming approximately 2.5 million lives an-nually and placing exceptional diagnostic pressure on radiologists in resource-limited settings. Manual interpretation of chest X-ray (CXR) images is time-consuming, subject to inter-observer variability, and limited by radiologist availability. This thesis presents a systematic investiga-tion into deep learning-based automated pneumonia detection comparing five convolutional neural network (CNN) architectures: a custom-designed 2D CNN and four pretrained transfer learning models—ResNet, DenseNet, MobileNet, and VGG19. A targeted data augmentation pipeline addresses the severe class imbalance in the Kag-gle Chest X-Ray Pneumonia dataset, expanding the Normal class from 1,583 to 9,495 images using clinically motivated transformations. The proposed Custom CNN incorporates progres-sive multi-scale filters (128→256→512), bottleneck 1×1 convolutions, and calibrated dropout regularization—all tailored for pneumonia-specific feature patterns. Evaluated on 2,066 held-out test images, the Custom CNN achieved the highest test accuracy of 97.5%, with 0.97 precision and recall across both classes and only 52 total misclassifications—outperforming all four transfer learning baselines. These results demonstrate that domain-specific architectural design combined with principled data balancing can surpass generic pretrained models for specialized medical imag-ing tasks on limited datasets.",
            "source_url": "https:\/\/openalex.org\/W7160676225",
            "pdf_url": "https:\/\/aquila.usm.edu\/cgi\/viewcontent.cgi?article=2101&context=honors_theses",
            "doi": "",
            "handle": "",
            "repository_name": "Aquila Digital Community (University of Southern Mississippi)",
            "source_record_id": "W7160676225",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 716,
            "title": "Design and evaluation of an automated pneumonia detection system using chest X-ray images: a multi-model comparative study",
            "title_ar": "",
            "title_en": "Design and evaluation of an automated pneumonia detection system using chest X-ray images: a multi-model comparative study",
            "author": "Mingxu Lyu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; predictive-analytics; cnn; medical-imaging; xai",
            "keywords": "",
            "abstract": "The difference between pneumonia and COVID-19 in chest radiographic images remains a clinical challenge due to their similar radiographic features. Although deep learning has shown potential in automated diagnosis, most existing studies focus on single models and lack systematic architectural comparisons. In this study, multiple deep convolutional neural networks are evaluated on public chest X-ray datasets for binary pneumonia and multi-class COVID-19 classification tasks. The results indicate that different models show different strengths, with some achieving higher sensitivity and others better specificity. To address these limitations, an ensemble method is proposed to combine the advantages of multiple models. In addition, gradient-based visualization is applied to interpret the model predictions, and the highlighted regions are generally consistent with areas of clinical significance. In general, this work provides a practical framework for model comparison and offers an interpretable approach to potential clinical screening applications.",
            "source_url": "https:\/\/openalex.org\/W7154546169",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W7154546169",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 718,
            "title": "Improving Model Generalization of Pneumonia Detection from Chest Xray Images Using Deep Learning and Transfer Learning",
            "title_ar": "",
            "title_en": "Improving Model Generalization of Pneumonia Detection from Chest Xray Images Using Deep Learning and Transfer Learning",
            "author": "Akhil Kochamparampil Anil",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Wales Trinity Saint David",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; medical-imaging",
            "keywords": "",
            "abstract": "Pneumonia continues to be a major global health threat and one of the leading causes of mortality, with chest X-rays serving as the primary diagnostic tool. Despite their promise, deep learning models for pneumonia detection often face limitations in generalization, performing strongly on familiar datasets but losing accuracy when applied to unseen data from different clinical environments. This study addresses that challenge by applying transfer learning with ResNet152 and DenseNet201 to improve generalization in pneumonia detection using chest Xray images. Publicly available datasets, including the COVID-19 Radiography Database and the Chest X-ray (COVID-19 & Pneumonia) dataset, were merged, pre-processed, and balanced prior to training. Model optimization involved freezing and unfreezing layers, adjusting learning rates, and applying various data augmentation strategies. Performance was evaluated with metrics such as accuracy, precision, recall, F1-score, error rate, and AUC-ROC, and further validated using an independent external dataset. Experimental results showed that DenseNet201 consistently outperformed ResNet152, reaching 93.2% accuracy with an AUC of 0.9916 on the main dataset and 88.3% accuracy with an AUC of 0.9742 on the external dataset. By comparison, ResNet152 achieved 86.4% accuracy (AUC 0.9665) on the main dataset and 84.6% accuracy (AUC 0.9573) on the external dataset. Confusion matrix analysis revealed that most errors occurred between Normal and Pneumonia classes, mirroring real world diagnostic challenges. In conclusion, DenseNet201 demonstrated superior robustness and generalization compared to ResNet152, highlighting its suitability for clinical application and underscoring the necessity of external validation in medical AI research.",
            "source_url": "https:\/\/openalex.org\/W7135203014",
            "pdf_url": "https:\/\/repository.uwtsd.ac.uk\/id\/eprint\/4143\/1\/Anil_Kochamparampil_A_MSc_Thesis.pdf",
            "doi": "10.82227\/repository.uwtsd.ac.uk.00004143",
            "handle": "",
            "repository_name": "University Of Wales Trinity Saint David Research Repository (University of Wales Trinity Saint David)",
            "source_record_id": "W7135203014",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 719,
            "title": "Screening Chest X-rays for Covid-19 with Deep Learning",
            "title_ar": "",
            "title_en": "",
            "author": "Eric Robert Gill",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universitat Oberta de Catalunya",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2021,
            "language": "ca",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; object-detection; medical-imaging",
            "keywords": "",
            "abstract": "El nou Coronavirus ha provocat una pandèmia global amb alts costos econòmics i socials. La naturalesa emergent de la malaltia associada del Coronavirus, Covid-19, ha trobat moltes nacions mal preparades per controlar la seva propagació, resultant en altes taxes d'infecció i pressió en els sistemes de salut. La detecció ràpida i la subsegüent quarantena de persones infectades és la mesura més eficaç contra la propagació del virus amb l'excepció de la vacunació. Generalment, el diagnòstic es porta a terme amb tests de Polímer invertit o antígens que poden ser cars i no sempre fàcilment disponibles. Aquests mètodes requereixen personal especialitzat i contacte físic amb el pacient, així com temps per processar els resultats. Les màquines de raigs X estan disponibles en hospitals arreu del món en països de gairebé totes les situacions econòmiques. La radiografia s'ha utilitzat en molts casos d'ús de cribatge i diagnòstic i hi ha una investigació àmplia sobre la seva aplicabilitat a la pandèmia de Coronavirus. Aquest projecte investiga la viabilitat de l'ús de Xarxes Neurals Convolucionals per detector símptomes de Covid-19 en imatges radiogràfiques de pulmons. Els rajos X són massivament disponibles, econòmics i no invasius. Els resultats mostren que les Xarxes Neurals Convolucionals poden classificar les imatges de raigs X en classes de Covid, Normal i Pneumonia Viral amb alts nivells de Precisió i Sensitivitat.",
            "source_url": "https:\/\/openalex.org\/W3173179115",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "The Oberta in Open Access (Universitat Oberta de Catalunya)",
            "source_record_id": "W3173179115",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 720,
            "title": "Machine Learning Algorithms for Computer-aided Diagnosis of Myocardial Infarction from 2D Echocardiography and COVID-19 from Chest X-rays",
            "title_ar": "",
            "title_en": "Machine Learning Algorithms for Computer-aided Diagnosis of Myocardial Infarction from 2D Echocardiography and COVID-19 from Chest X-rays",
            "author": "Aysen Degerli Ahishali",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Tampere University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; image-segmentation; medical-imaging; ai-cybersecurity",
            "keywords": "",
            "abstract": "The importance of e-health has drastically increased as the world has engaged sharply in the digital world, which provoked computer-aided diagnosis (CAD) algorithms to become a necessity for accurate and automatic detection and differential diagnosis of various diseases and medical conditions. Recent advancements in machine learning brought novel solutions for more complex clinical tasks that improved medical diagnostics. Accordingly, machine and deep learning methods are widely utilized in CAD-based techniques, which analyze medical data by performing pattern recognition to reveal any anomalies to guide medical doctors and experts in clinical tasks. However, in the development phase of CAD algorithms, there are several limitations and challenges regarding the size, quality, and availability of medical data for analysis using machine learning. Hence, there is an urgent need for accurate, reliable, and robust CAD algorithms to overcome the limitations and challenges in medical diagnostics. This thesis focuses on providing machine learning algorithms for CAD-based detection and assessment of myocardial infarction (MI) and coronavirus disease 2019 (COVID-19) using apical 4-chamber (A4C) and apical 2-chamber (A2C) view 2D echocardiography recordings and chest X-ray (CXR) images, respectively. The hypothesis defined in this thesis is that the proposed CAD algorithms using machine learning can provide assistive and visualization tools in clinical practice for the detection and assessment of MI and COVID-19. Accordingly, the thesis contributions are categorized into computer-aided diagnosis of MI and COVID-19, where task-specific solutions are developed for the assessments. The most severe manifestation of coronary artery disease is myocardial infarction, which is colloquially known as heart attack that leads to irreversible necrosis of the myocardium and even death. The detection of MI requires the assessment of regional wall motion abnormality of the left ventricle that can be primarily monitored and evaluated with 2D echocardiography. This thesis contributes to a pioneer 2D echocardiography dataset, HMC-QU for the detection of MI over single-view and multi-view echocardiography. Accordingly, another contribution of this thesis is a generic pseudo-labeling annotation approach that accelerates and enhances pixel-level manual annotations by preventing the subjective and time-consuming annotation process. In single-view and multi-view echocardiography assessments, this thesis contributes to a feature engineering phase, where myocardial segment features are extracted and used to train several traditional machine learning models and one-class classification methods reaching the highest sensitivity levels of 88.75% in single-view and 90.91% in multi-view echocardiography. The assessment of pulmonary regions plays a vital role in COVID-19 clinical diagnosis and prevention of contamination to the public. This thesis contributes to the composition of the pioneer and the largest CXR dataset, called QaTa-COV19, for the recognition, early detection, and pneumonia segmentation of COVID-19. Accordingly, another contribution of this thesis is the human-machine collaboration approach for the annotation of COVID-19 pneumonia regions for the segmentation. The thesis contributes to several machine learning models and solutions to perform a reliable detection and localization of COVID-19 pneumonia in the lungs using segmentation neural networks, direct detection from a proposed operational neural network, and representation-based classification models. The findings of this thesis are promising in computer-aided diagnosis of MI and COVID-19, where the proposed solutions reached high sensitivity levels of 90.9% and 98.6%, respectively preventing subjective and expert-dependent assessments and detection in 2D echocardiography and CXR imaging. The last contribution of this thesis is the developed visualization and assistive tools, where color-coded trackings on echocardiography recordings, myocardial infarction displacement curves, COVID-19 infection maps, and machine learning-based detection and annotation tools serve medical doctors in medical diagnostics addressing visual enhancements over 2D echocardiography and CXR imaging.",
            "source_url": "https:\/\/openalex.org\/W7133005963",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Tampere University Institutional Repository (Tampere University)",
            "source_record_id": "W7133005963",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 721,
            "title": "Deep Learning For The Classification of Lung Diseases Using Chest X-Rays",
            "title_ar": "",
            "title_en": "Deep Learning For The Classification of Lung Diseases Using Chest X-Rays",
            "author": "Khamesi, Zahra",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Concordia University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; medical-imaging",
            "keywords": "",
            "abstract": "The discovery of X-rays marked a significant milestone in the field of medicine. One of the most common types of X-rays, the chest X-ray (CXR), allows doctors to examine an individual’s internal structure without surgery. Over the years, deep learning methods and algorithms have been developed to automate lung disease detection and identification. This paper introduces RADIA, a project that combines multiple deep learning techniques to identify abnormal areas and abnormal- ities in chest X-rays. RADIA builds upon previous studies conducted by the Stanford ML group, such as ChexNet and ChexPert. Our team utilized the ConvNeXt-Large, a deep learning convo- lutional model, implemented with a pre-trained ConvNext algorithm on the ImageNet database to classify various pathologies from public datasets like ChestX-ray14, CheXpert, MIMIC-CXR, PadChest, and VinDr-CXR, as well as a private dataset obtained from the Picture Archiving Com- munication System (PACS) at Verdun and Notre Dame Hospitals in Montreal in the collaboration with CIUSSS (Centre Integre Universitaire de Sante et de Services Sociaux du Centre-Sud-de-l’Ile- de-Montreal) and valuable consultants from the radiology team at Notre Dame Hospital contributed to the project’s success. Our team employed image enhancement and augmentation techniques to create various image versions. We used different and novel approaches to address the challenges, and the results were evaluated using metrics such as AUC, F1, and G-Means to analyze performance with imbalanced input data. It is essential to note that the project’s development extends beyond the creation of a web tool based on deep learning techniques. Our future plans involve building a decision helper that combines inference models and web tools to assist healthcare professionals.",
            "source_url": "https:\/\/openalex.org\/W7112380474",
            "pdf_url": "https:\/\/spectrum.library.concordia.ca\/id\/eprint\/993141\/7\/Khamesi_MCompSc_S2024.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Spectrum Research Repository (Concordia University)",
            "source_record_id": "W7112380474",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 724,
            "title": "Detecting Coronavirus Disease 2019 Pneumonia in Chest X-Ray Images Using Deep Learning",
            "title_ar": "",
            "title_en": "Detecting Coronavirus Disease 2019 Pneumonia in Chest X-Ray Images Using Deep Learning",
            "author": "Ziqi Zhu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; predictive-analytics; medical-imaging",
            "keywords": "",
            "abstract": "The coronavirus disease 2019 (COVID-19) pandemic has already become a global threat. To fight against COVID-19, effective and fast screening methods are needed. This study focuses on leveraging deep learning techniques to automatically detect COVID‐19 pneumonia in chest X-ray images. Two models are trained based on transfer learning and residual neural network. The first one is a binary classifier that separates COVID-19 pneumonia and non-COVID-19 cases. It classifies all test cases correctly. The second one is a four-class classifier that distinguishes COVID-19 pneumonia, viral pneumonia, bacterial pneumonia and normal cases. It reaches an average accuracy, precision, sensitivity, specificity, and F1-score of 93\\\\%, 93\\\\%, 93\\\\%, 97\\\\%, and 93\\\\%, respectively. To understand on how the four-class classifier detects COVID-19 pneumonia, we apply Gradient-weighted Class Activation Mapping (Grad-CAM) method and find out that the classifier is able to focus on the patchy areas in chest X-ray images and make accurate predictions.",
            "source_url": "https:\/\/openalex.org\/W3040290756",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "eScholarship (California Digital Library)",
            "source_record_id": "W3040290756",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 725,
            "title": "Classification of chest diseases from x-ray images using deep learning",
            "title_ar": "",
            "title_en": "Classification of chest diseases from x-ray images using deep learning",
            "author": "Arifa Khatun",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; medical-imaging",
            "keywords": "",
            "abstract": "In today's modern age, the field of Artificial Intelligence and Health Care are inseparable.Every now and then, doctors and patients use various technology to diagnose diseases.But still, interpreting or identifying diseases through different medical tests such as X-ray or MRI cannot be fully done using artificial intelligence due to the lack of good output from various models.This paper tried to tackle this problem of classifying three diseases that a professional can identify through a chest X-ray image.Prior to this work, many had done similar works but only a few were able to get prediction accuracy over 90%.So, this paper explored that area to get better prediction result and by fitting ResNet50, DenseNet121, MobileNetV2, InceptionNetV3 models, it was seen that ResNet50 outperformed other three models with over 98% accuracy.The result shows that deep learning models can do well classifying diseases from a test image with a very little margin of error.",
            "source_url": "https:\/\/openalex.org\/W7215672098",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/ff3e94fd-ba08-4c15-94d8-a48c0cc1e702\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215672098",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 726,
            "title": "Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning",
            "title_ar": "",
            "title_en": "Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning",
            "author": "Ahashan Habib Niloy ; Shammi Akhter Shiba ; Sadaf Fahim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "BRAC University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Bangladesh",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; medical-imaging",
            "keywords": "",
            "abstract": "Coronavirus 2019 (in short, COVID-19), originated in the Wuhan province of China in December 2019, has been declared a global pandemic by WHO in March 2020.Since its inception, it's rapid spread among nations had initially collapsed the world economy and the increasing death-pool created a strong fear among people as the virus spread through human contact.Initially doctors struggled to diagnose the increasing number of patients as there was less availability of testing kits and failed to treat people efficiently which ultimately led to the collapse of the health sector of several countries.To help doctors primarily diagnose the virus, researchers around the world have come up with some radiology imaging techniques using the Convolutional Neural Network (CNN).While some of them worked on x-ray images and some others on CT scan images, none worked on both the image types.Thus there's no way to know which image works better for a particular model.This, therefore, insisted us to perform a comparison between x-ray and CT scan images.Thus we came up with a novel CNN model named CoroPy which works for both the image types and shows that in 2 classes (normal and covid), CT scan images show a better accuracy and it is 99.17% whereas it is 95.73% for x-ray images.However, in the case of 3 classes (normal, covid and viral pneumonia), x-ray images show a better accuracy and it is 92.45% whereas it is 68.81% for CT scan images.",
            "source_url": "https:\/\/openalex.org\/W3201978509",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "BRAC University Institutional Repository (BRAC University)",
            "source_record_id": "W3201978509",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 727,
            "title": "Deep Learning-Based Web Application for the Detection of COVID-19 from Chest CT-Scan Images",
            "title_ar": "",
            "title_en": "",
            "author": "Kaouther Nouni",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Algeria",
            "governorate": "",
            "publication_year": 2021,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; medical-imaging",
            "keywords": "",
            "abstract": "La maladie à coronavirus 2019 causée par le coronavirus 2 du syndrome respiratoire aigu sévère (SRAS-CoV-2) apparu pour la première fois en Chine s’est propagée à plus de 200 pays, et plus de 1 700 000 cas confirmés et 111 600 décès ont été enregistrés, avec une augmentation mondiale massive du nombre des cas chaque jour. L’infection pulmonaire ou pneumonie est la complication commune du COVID-19 alors que les techniques d’imagerie, en particulier la tomographie assistée par ordinateur (CT), ont joué un rôle important dans le diagnostic et l’évaluation du traitement de la maladie. Les modèles d’apprentissage profond sont largement utilisés dans l’analyse automatique des images radiologiques, ce qui ouvre la voie à l’utilisation de techniques de classification pour aider à la détection précoce de l’infection. L’objectif de ce travail est de répondre à ce problème en construisant une application web qui catégorise les images de covid-19 et de non covid-19. La première partie du travail consiste à préparer les données en utilisant le prétraitement des images et l’augmentation des données. L’évaluation des données est ensuite passée par deux modèles d’entraînement : VGG16 et ResNet. A la fin, les techniques de visualisation Grad-Cam ont été utilisées pour mettre en évidence les régions infectées dans l’image. La deuxième partie est la présentation de l’application web qui est un sys- tème d’aide à la décision qui consiste à télécharger une image CT-scan et à attendre qu’elle soit classée quelques secondes plus tard. Le VGG16 a montré une grande précision permettant à l’application de donner une meilleure décision lors de la classification des cas infectés tout en ajoutant la visualisation qui explique la prédiction du modèle",
            "source_url": "https:\/\/openalex.org\/W7135312671",
            "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/f17ae859-b899-46f3-8a67-6f0623ebcfcd\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen",
            "source_record_id": "W7135312671",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 731,
            "title": "Use of Swin Transformer to classify chest radiographic images and diagnose COVID-19",
            "title_ar": "",
            "title_en": "Use of Swin Transformer to classify chest radiographic images and diagnose COVID-19",
            "author": "Aroldo Ferraz",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; transformers; medical-imaging",
            "keywords": "",
            "abstract": "According to data from the World Health Organization (WHO), since the beginning of 2020, the COVID-19 pandemic has infected over 770.56 million people worldwide. Of those infected, more than 6.95 million individuals have lost their lives. COVID-19 is an extremely contagious disease and can swiftly overwhelm healthcare systems if infections are not diagnosed, and if isolation measures and treatment for deteriorating conditions are not promptly implemented. The primary screening test used to diagnose COVID-19 has been the Real Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR). Although it is accurate and reliable, it takes a significant amount of time to deliver a definitive diagnosis. Since the onset of the pandemic, several researchers have demonstrated the feasibility of employing various Deep Learning (DL) techniques based on Convolutional Neural Networks (CNN), which have shown promising performance in diagnosing COVID-19 from X-rays and CT scans. However, some authors argue that existing DL methods, based on CNN, are being challenged by newer DL architectures and models. Motivated by this, in this study, we propose the use of the Swin Transformer (instead of CNN) for COVID-19 screening using X-ray images from four original datasets: COVID-QU-Ext Dataset, SARS-COV2-CT Dataset, HUST-19, and HCV-UFPR-COVID-19. These datasets were merged using different strategies, resulting in a total of 9 distinct datasets. We employed transfer learning to address the issue of data scarcity. Moreover, we assessed whether the models have a high generalization capability, based on the learned features, allowing training on one dataset, and testing on another, aiming to evaluate if there are significant losses in the metric values obtained. Upon analyzing the cumulative results from experiments across all datasets and strategies, the Swin Transformer stands out. In terms of sensitivity\/recall, it performed 107% and 80% better than ViT and CNN, respectively. In the HUST-19 dataset, the Swin Transformer achieved the maximum score (1 or 100%) for all metrics and continued to show notable performance in other datasets. Compared to the state of the art, the results reveal a promising and highly competitive performance for the Swin Transformer. Additionally, statistical tests were conducted which showed, in several instances, that there are statistically significant differences in the metrics obtained by the Swin Transformer when compared to CNN and ViT.",
            "source_url": "https:\/\/openalex.org\/W7120831202",
            "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/32649\/1\/swintransformerimagensradiograficas.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W7120831202",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 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": 734,
            "title": "Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning",
            "title_ar": "",
            "title_en": "Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning",
            "author": "Joy Ingrid Arreola Minjarez ; José Díaz ; Boris Jesús Mederos Madrazo",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Mexico",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; medical-imaging",
            "keywords": "",
            "abstract": "The novel coronavirus (COVID-19) is a disease that mainly affects the lung tissue. The detection of lesions caused by this disease can help to provide an adequate treatment and monitoring its evolution. This research focuses on the bi- nary classification of lung lesions caused by COVID-19 in images of computed tomography (CT) using deep learning. The database used in the experiments comes from two independent repositories, which contains tomographic scans of patients with a positive diagnosis of COVID-19. The output layers of four pre-trained convolutional networks were adapted to the proposed task and re-trained using the fine-tuning technique. The models were validated with test images from the two database’s repositories. The model VGG19, considering one of the repositories, showed the best performance with 88% and 90.2% of accuracy and recall, respectively. The model combination using the soft voting technique presented the highest accuracy (84.4%), with a recall of 94.4% employing the data from the other repository. The area under the receiver operating characteristic curve was 0.92 at best. The proposed method based on deep learning represents a valuable tool to automatically classify COVID-19 lesions on CT images and could also be used to assess the extent of lung infection.",
            "source_url": "https:\/\/openalex.org\/W4224050704",
            "pdf_url": "http:\/\/rmib.mx\/index.php\/rmib\/article\/download\/1208\/912",
            "doi": "10.17488\/rmib.43.1.1",
            "handle": "",
            "repository_name": "Revista Mexicana de Ingeniería Biomédica",
            "source_record_id": "W4224050704",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 735,
            "title": "Adaptive Deep Learning for Vision and Multimodal Understanding with Heterogeneous Data",
            "title_ar": "",
            "title_en": "Adaptive Deep Learning for Vision and Multimodal Understanding with Heterogeneous Data",
            "author": "Youwei Liang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; transfer-learning; few-zero-shot; predictive-analytics; transformers; multimodal; image-segmentation; llm",
            "keywords": "",
            "abstract": "Large-scale pretraining has produced powerful deep learning representations, but real-world data rarely match the clean, uniform assumptions of standard transfer learning. This dissertation uses heterogeneous data to describe settings in which information differs in modality, representation, relevance, quality, or frequency across inputs, outputs, tasks, or supervision. Examples include molecules represented as graphs, images, strings, and text; images containing both informative and redundant regions; noisy or incomplete labels; and rare but important classes. Applying pretrained models in these settings requires deciding which information to process, how to connect different forms of information, and how much to trust each supervision signal. This dissertation develops adaptive learning as a unifying approach through three paradigms: adaptive selection focuses computation on useful input components; adaptive alignment maps heterogeneous modalities, tasks, and outputs into a shared predictive space; and adaptive supervision adjusts imperfect or imbalanced training signals. The first contribution, EViT, demonstrates adaptive selection for efficient Vision Transformers. Standard Vision Transformers devote equal computation to all image patches despite substantial redundancy. EViT uses class-token attention to identify important tokens, preserves them in subsequent layers, and fuses inattentive tokens into a compact representation rather than discarding their context. This progressive token reorganization reduces computation while maintaining accurate visual recognition. The second contribution, DrugChat, demonstrates adaptive alignment for multimodal molecular prediction. Molecules can be represented as chemical graphs, rendered images, SMILES strings, and natural-language prompts, while predictions may require labels, probabilities, categorical decisions, or free-form descriptions. DrugChat encodes molecular graphs and images with pretrained encoders, projects them into the latent space of an instruction-following language model, and conditions generation on molecular structure and task prompts. It thereby aligns heterogeneous inputs in a shared representation space and expresses diverse prediction targets through a common language-generation interface. The third contribution, RobPicker, demonstrates adaptive supervision for cryo-electron tomography particle picking, where annotations are expensive, incomplete, noisy, and highly imbalanced across macromolecular species. Its meta-learning framework jointly trains a segmentation network, a label correction network, and a data reweighting network. Guided by cleaner validation data, these components refine imperfect labels and adapt each subtomogram's contribution to the loss, giving greater influence to rare or small particle classes. Together, EViT, DrugChat, and RobPicker apply adaptation at complementary levels of the learning pipeline: selecting what data to process, aligning heterogeneous representations and outputs, and learning from imperfect supervision.",
            "source_url": "https:\/\/openalex.org\/W7222391945",
            "pdf_url": "https:\/\/escholarship.org\/content\/qt3h17286t\/qt3h17286t.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "eScholarship (California Digital Library)",
            "source_record_id": "W7222391945",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 737,
            "title": "Advancing Computational Pathology with Multimodal Deep Learning",
            "title_ar": "",
            "title_en": "Advancing Computational Pathology with Multimodal Deep Learning",
            "author": "Md. Mamunur Rahaman",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; few-zero-shot; self-semi-supervised; predictive-analytics; multimodal; llm; medical-imaging; xai",
            "keywords": "",
            "abstract": "Histopathological assessment of tissue biopsies remains the cornerstone of cancer diagnosis, yet it is limited by diagnostic subjectivity and an incomplete view of the molecular heterogeneity that drives outcomes. This dissertation presents a suite of artificial intelligence (AI) frameworks that integrate deep learning, computer vision, and multimodal data to enhance diagnostic precision, prognostication, and therapeutic guidance across multiple cancer settings. The research first establishes a method for improving classification robustness through HistopathAI, a framework employing supervised contrastive learning and a hybrid deep feature fusion architecture. This approach yields more discriminative and generalizable feature representations, particularly in the context of class imbalance common in medical datasets. Building on this, the work demonstrates the power of integrating visually interpretable features from the tumor microenvironment. An AI-driven biomarker for Head and Neck Squamous Cell Carcinoma (HNSCC), which quantifies the spatial architecture of immune and stromal cells, was developed, and its prognostic value was validated on independent patient cohorts, confirming its strong potential for clinical risk stratification. A central contribution is the development of “virtual spatial transcriptomics” that bridges histology and genomics. The BrSTNet framework predicts spatial gene expression from routine H&E images in breast cancer, establishing a strong benchmark for image-to-transcriptome inference. This capability is translated to a direct clinical application in prostate cancer with ST-DoxPCa, a predictive signature for docetaxel benefit. The validation strategy was explicitly tailored to disease context: for the RTOG0521 trial (localized disease), a prognostic model trained independently in the Cleveland Clinic prostatectomy cohort (CCF) was applied in a fully blinded manner, without refitting to stratify patients and assess treatment effect; for the CHAARTED trial (metastatic disease), a separate treatment-response model was trained exclusively on the standard-of-care (ADT-only) arm. In both settings, ST-DoxPCa stratified patients into subgroups that either derived a significant survival benefit from docetaxel or showed no advantage, supporting treatment de-escalation for likely non-responders. Finally, to address the critical challenge of data scarcity, the thesis introduces MR-PHE, a novel framework for zero-shot learning in histopathology. By leveraging vision-language models with a multi-resolution, prompt-guided approach, MR-PHE enables accurate classification without task-specific training data, often outperforming fully supervised methods and offering a scalable solution for rare diseases. Collectively, these contributions advance the frontier of computational pathology, delivering a suite of validated, AI-driven tools that translate complex tissue-based data into clinically actionable insights. The methodologies developed herein represent a significant step toward a new paradigm of precision oncology, where patient care is personalized based on a deeper, quantitative understanding of tumor biology derived from standard pathology slides.",
            "source_url": "https:\/\/openalex.org\/W7116859814",
            "pdf_url": "",
            "doi": "10.26190\/unsworks\/31906",
            "handle": "",
            "repository_name": "UNSWorks (University of New South Wales, Sydney, Australia)",
            "source_record_id": "W7116859814",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 738,
            "title": "AI-Generated Image Detection Using Multimodal Deep Learning",
            "title_ar": "",
            "title_en": "AI-Generated Image Detection Using Multimodal Deep Learning",
            "author": "Mamadou Keïta",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; responsible-ai; few-zero-shot; transformers; multimodal; llm; gan; diffusion; xai; fairness",
            "keywords": "",
            "abstract": "Generative artificial intelligence (AI) and its applications offer significant benefits but raise potentially critical societal and ethical concerns. This thesis explores the field of synthetic content generation and detection, with a particular focus on synthetic images. These are AI-generated images that can appear indistinguishable from real photographs, creating new challenges for digital forensics, media integrity, and public trust. While generative models such as generative adversarial networks (GANs) and diffusion models have evolved rapidly, most existing detection methods remain limited in terms of generalizability, robustness, and interpretability. To address these challenges, this research investigates new AI-driven frameworks to improve the generalizability, robustness, and interpretability of current image detection methods.The various aspects of synthetic image detection are covered in this thesis by four key contributions. The first contribution, Bi-LORA, presents an efficient vision-language approach that reformulates the detection problem into an image caption task, achieving strong zero-shot generalization across unseen generative models. The second, RAVID, proposes a retrieval-augmented visual detection framework that enhances robustness and interpretability by incorporating external visual context. The third, DeeCLIP, presents a lightweight transformer-based model that fuses shallow and deep features to improve resistance to image degradation and post-processing. Finally, FIDAVL unifies synthetic image detection and source attribution in a single multitasking framework based on the soft prompt tuning of vision-language models.This thesis provides an in-depth analysis of the current state of synthetic image research and its societal impacts. It emphasizes the urgent need for effective and robust detection methods. The approaches introduced advance the scientific community and could benefit practical applications in media authenticity and security. This research marks an important step forward in developing solutions to the ethical challenges posed by GenAI.",
            "source_url": "https:\/\/openalex.org\/W7126004607",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "HAL",
            "source_record_id": "W7126004607",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 739,
            "title": "Multimodal AI for 3D-language understanding",
            "title_ar": "",
            "title_en": "Multimodal AI for 3D-language understanding",
            "author": "Andrea Amaduzzi",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna)",
            "university": "University of Bologna",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Italy",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "deep-learning; computer-vision; nlp; multimodal; llm",
            "keywords": "",
            "abstract": "Understanding the world through natural language is a fundamental challenge in computer vision, requiring models to bridge the gap between geometric representations and semantic understanding. While recent advances in deep learning have enabled impressive progress in multimodal tasks, effectively connecting 3D data with language remains difficult due to the computational complexity of 3D representations and the scarcity of high-quality paired 3D-language datasets. In this Thesis, we focus on developing novel deep learning techniques that can efficiently process and describe 3D data through natural language. First, we address the critical lack of high quality 3D-language datasets and reliable evaluation methods for text-to-3D generation by introducing a benchmark comprising GPT2Shape, a dataset of 3D models with high-quality language annotations, CrossCoherence, a novel quantitative metric for evaluating text-to-3D coherence, and HST, a human-validated test set. This benchmark demonstrates superior performance compared to existing 2D-based evaluation metrics. In the second part of this Thesis, we introduce NeRF-language models — a new paradigm of Multimodal Large Language Models that directly process the weights of Neural Radiance Fields (NeRFs) rather than explicit representations like images or point clouds materialized from them. We show that this approach enables holistic 3D understanding without computationally expensive rendering, achieving state-of-the-art performance on the first NeRF-language datasets. Finally, we propose a method for computing spatially-aware tokens directly from the weights of a NeRF, enabling fine-grained spatial understanding and detailed scene comprehension. Our resulting model demonstrates strong capabilities in spatial reasoning on multi-object scenes, with promising generalization to real-world scenarios.",
            "source_url": "https:\/\/openalex.org\/W7172489949",
            "pdf_url": "",
            "doi": "10.48676\/unibo\/amsdottorato\/12771",
            "handle": "",
            "repository_name": "AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna)",
            "source_record_id": "W7172489949",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 740,
            "title": "Deep learning and natural language processing for computer aided diagnosis",
            "title_ar": "",
            "title_en": "Deep learning and natural language processing for computer aided diagnosis",
            "author": "Sadam Hussain",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Tecnológico de Monterrey",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Mexico",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; predictive-analytics; transformers; multimodal; llm; medical-imaging; xai",
            "keywords": "",
            "abstract": "Multimodal artificial intelligence (AI) is a cutting-edge technique that integrates diverse modalities, such as imaging and textual data, to enhance classification and regression tasks. This dissertation focuses on the integration, comparison, and evaluation of multimodal AI for breast cancer diagnosis and prognosis. To achieve these objectives, we curated a comprehensive multimodal dataset comprising digital mammograms and corresponding radiological reports. Leveraging this dataset, we introduced and assessed various state-of-the-art (SOTA) multimodal techniques for three key tasks: breast cancer classification, reduction of false-positive biopsies with explainable AI (XAI), and short- term (5-year) risk prediction of breast cancer.In this work, we also introduced a benchmark dataset of radiological reports from breast cancer patients and provided baseline performance evaluations using SOTA machine learning (ML), deep learning (DL), and large language models (LLMs) for BI-RADS category classification. Our approach evaluated the performance of diverse SOTA multimodal architectures, including ResNet, VGG, E!cientNet, MobileNet, and Vision Transformers (ViT). For textual data processing, we employed both general-purpose and domain-specific pretrained LLMs such as BERT, bioGPT, ClinicalBERT, and DeBERTa, which were also integrated into multimodal architectures for enhanced lassification.Notably, our proposed multiview multimodal feature fusion (MMFF) architecture, combining SE-ResNet50 with an artificial neural network (ANN), achieved an AUC of 0.965 for breast cancer classification, significantly outperforming both single-modal and multimodal SOTA architectures. For reducing unnecessary breast biopsies, our multimodal approach achieved an AUC of 0.72, showcasing its clinical utility in minimizing patient burden. Moreover, our ViT and bioGPT-based multimodal architecture achieved an AUC of 0.77 for short-term risk prediction, outperforming the SOTA MIRAI model, which achieved an AUC of 0.59 on our in-house dataset. This work highlights the potential of multimodal AI in advancing breast cancer diagnosis and prognosis, demonstrating its superiority over traditional and unimodal approaches across multiple critical tasks.",
            "source_url": "https:\/\/openalex.org\/W7225556007",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Repositorio del Tecnológico de Monterrey (Tecnológico de Monterrey)",
            "source_record_id": "W7225556007",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 741,
            "title": "Exploring Multimodal Deep Learning Approaches for Coronary Artery Disease Diagnosis",
            "title_ar": "",
            "title_en": "Exploring Multimodal Deep Learning Approaches for Coronary Artery Disease Diagnosis",
            "author": "Popov, Maxim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United Arab Emirates",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; self-semi-supervised; multimodal; llm; medical-imaging",
            "keywords": "",
            "abstract": "Coronary Artery Disease (CAD) is a prevalent cause of death in the modern world, with about 20 million victims every year. Limited annotated data, inter-observer variability, and the intrinsic complexity of angiographic imaging challenge its diagnosis. This thesis presents a novel deeplearning framework to enhance coronary artery disease (CAD) diagnosis through multimodal analysis of Xray coronary angiograms. We develop several interrelated contributions to address these issues that combine advanced contrastive learning and vision-language modeling.",
            "source_url": "https:\/\/openalex.org\/W7106339970",
            "pdf_url": "",
            "doi": "10.82419\/166",
            "handle": "",
            "repository_name": "MBZUAI iRep",
            "source_record_id": "W7106339970",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 742,
            "title": "Multimodal and explainable Learning using Fusion Architecture Through Exploration (FATE) approach",
            "title_ar": "",
            "title_en": "Multimodal and explainable Learning using Fusion Architecture Through Exploration (FATE) approach",
            "author": "Otmane Amel",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Belgium",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "deep-learning; computer-vision; nlp; medical-ai; responsible-ai; multimodal; image-segmentation; video-analysis; llm; medical-imaging; xai",
            "keywords": "",
            "abstract": "The way we experience the world is multimodal, each of our five senses perceives the world differently, yet the brain can explore this information at once with ease. This work explores the potential of mimicking this exceptional cognitive capability in machines. This thesis lies in this research area, called Multimodal Deep Learning, where we spotlight the importance of robust fusion methods and effective modality representations in real-world scenarios. This study concludes with a proposed light, easy-to-use, and extendable fusion method called MultConcat, that was evaluated for multiple data source combinations: image and text, RGB and depthmap frames (video flows), as well as an MRI and tabular data in a medical context. We then propose FATE (Fusion Architecture Through Exploration), a methodology that treats a given input multimodal problem by solving real-world research questions related to the fusion method, the appropriate fusion timing, and the best encoder for each modality. Furthermore, we propose a concept-based explainability approach for multimodal settings based on segmentations using Foundation models to ensure human interpretability. Finally, we explore the difference in model design we might have between ready-to-use Vision-Language Models, Multimodal Large Language Models, and configurable models that can be built using our suggested FATE methodology.",
            "source_url": "https:\/\/openalex.org\/W7221428022",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ORBi UMONS",
            "source_record_id": "W7221428022",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 743,
            "title": "A Study on Robustness, Adaptation, and Generalization for Visual and Multimodal Learning",
            "title_ar": "",
            "title_en": "A Study on Robustness, Adaptation, and Generalization for Visual and Multimodal Learning",
            "author": "Xiasi Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; responsible-ai; ai-applications; transfer-learning; self-semi-supervised; multimodal; llm; fairness; ai-cybersecurity",
            "keywords": "",
            "abstract": "Recent advances in visual and multimodal learning have demonstrated impressive performance on diverse tasks. However, their reliability in real-world scenarios remains challenged by issues of robustness, adaptation, and generalization. In this thesis, we tackle these challenges by establishing four integral pillars of reliable deep learning: (1) robustness against adversarial attacks, (2) adaptation to sequential data streams, (3) resilience to domain shift and unseen categories, and (4) enhanced generalization via self-supervised learning. First, we identify a critical vulnerability in vision-language models (VLMs): their susceptibility to adversarial manipulation of inference efficiency. We propose a black-box evaluation framework to assess this efficiency robustness, establishing a new dimension for VLM safety. Second, we find that VLMs exhibit limited adaptability to evolving data streams. We explore long-tailed class incremental learning within the VLM framework and introduce a method to address the dual challenges of catastrophic forgetting and data imbalance. Third, we examine vision models’ failures on out-of-distribution samples. We tackle the open-set domain adaptation challenge, where both distribution shift and unknown classes appear in the target domain, and develop a two-stage framework that adapts the model to classify known classes while detecting unknown ones. Fourth, we revisit self-supervised pretraining in computer vision and design a novel objective to enhance the generalizability of pre-trained models. Collectively, these contributions form a unified framework for advancing robustness, adaptation, and generalization, facilitating more trustworthy visual and multimodal intelligence.",
            "source_url": "https:\/\/openalex.org\/W7135015825",
            "pdf_url": "",
            "doi": "10.14711\/thesis-hdl169527",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W7135015825",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 744,
            "title": "Multimodal Deep Learning for Driver Monitoring: Integrating EEG and Vision for Robust Drowsiness Detection and Safety Enhancement",
            "title_ar": "",
            "title_en": "Multimodal Deep Learning for Driver Monitoring: Integrating EEG and Vision for Robust Drowsiness Detection and Safety Enhancement",
            "author": "Ur Rahman, Shams",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Dublin City University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Ireland",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; transformers; multimodal; face-recognition; video-analysis; ai-education",
            "keywords": "",
            "abstract": "Road accidents remain a major global concern, with driver drowsiness and delayed reaction times recognized as key contributing factors. This thesis advances driver-monitoring research by developing multimodal approaches that integrate electroencephalography (EEG) and vision data to predict reaction times and drowsiness. The investigation first demonstrates that pre-stimulus EEG signals—specifically spectral power in the alpha and theta bands—contain rich information for estimating reaction times to critical road events. Using subject-independent machine-learning pipelines, short EEG windows recorded before event onset effectively differentiate between fast and slow responses. The work then explores the benefits of incorporating vision data as a second modality by fusing EEG signals with camera-based observations of the driver. One branch converts EEG power-spectral-density features into image-like representations for analysis with deep convolutional neural networks and transformer models. Another branch directly integrates raw EEG signals with synchronised video frames through end-toend multimodal transformer architectures. Results indicate that transformers equipped with cross-modal attention capture complex interdependencies between neural and visual cues, yielding significant improvements in driver-drowsiness detection over unimodal approaches. Real-time deployment is addressed by designing and optimising a lightweight pipeline for edge-based processing. This resource-efficient model enables rapid analysis of facial cues under diverse driving conditions, ensuring operation on embedded platforms such as smartphones and automotive edge devices. Extensive evaluations on large-scale simulated datasets confirm the generalisability of the proposed approaches across varied driving scenarios. Experiments reveal that transformer-based fusion significantly enhances predictive performance by effectively combining complementary neural and visual cues. Moreover, the lightweight pipeline maintains high accuracy under stringent computational constraints, enabling real-time, ondevice deployment.",
            "source_url": "https:\/\/openalex.org\/W7112463353",
            "pdf_url": "https:\/\/doras.dcu.ie\/31498\/1\/shams_thesis_final_version.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Dublin City University Open Access Institutional Repository (Dublin City University)",
            "source_record_id": "W7112463353",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 745,
            "title": "Learning semantic representations of language, vision and code for visualization-oriented dialogue",
            "title_ar": "",
            "title_en": "Learning semantic representations of language, vision and code for visualization-oriented dialogue",
            "author": "Henrik Voigt",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; responsible-ai; expert-systems; transfer-learning; self-semi-supervised; transformers; multimodal; llm; fairness",
            "keywords": "",
            "abstract": "Data visualization is essential for navigating the vast volume of data that defines our time, letting humans use perception to make sense of complex datasets. Yet creating visualizations requires mastery of specialized programming languages, producing a gap between the analytical intention in the user's mind and its implementation in executable code. This raises the cognitive load on experts, who cannot concentrate fully on visual exploration while also writing the visualization program, and it creates a barrier for non-experts, hindering widespread access to insights from data. Bridging this gap is a central challenge for Visualization-oriented Natural Language Interfaces (V-NLIs), which translate human intent into executable code. This work decomposes the problem into two interrelated challenges: understanding, which builds rich, contextual representations of user intent, and generation, which reliably synthesizes visualization programs under strict real-time constraints. The thesis addresses both through five interconnected contributions. It begins with a survey of 119 papers, revealing that while the community focuses on sophisticated interactive exploration, the underlying technology relies on classical, rule-based methods, motivating a turn to deep learning. KeywordScape, a visual analytics system, makes the contextualized representations of transformer models visible and interactive, showing they can themselves resolve ambiguity and enable fine-grained exploration. Paparazzi extends this to multimodal representations, revealing that large vision-language models such as CLIP exhibit surprising deficiencies in 3D viewpoint understanding, and introduces a data-efficient contrastive fine-tuning method to improve geometric grounding. Finally, VIST5, a practical V-NLI built on a retrieval-augmented architecture around a small language model, shows that complex visualization code can be synthesized in real time while ensuring data privacy and modular extensibility.",
            "source_url": "https:\/\/openalex.org\/W7170734941",
            "pdf_url": "",
            "doi": "10.22032\/dbt.71264",
            "handle": "",
            "repository_name": "Common Library Network (Der Gemeinsame Bibliotheksverbund)",
            "source_record_id": "W7170734941",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 746,
            "title": "Multimodal Learning for Vision and Language",
            "title_ar": "",
            "title_en": "Multimodal Learning for Vision and Language",
            "author": "Junhua Mao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; few-zero-shot; cnn; rnn; multimodal",
            "keywords": "",
            "abstract": "This thesis focuses on proposing and addressing various tasks in the field of vision and language, a new and challenging area which contains the hottest research topics for both computer vision and natural language processing. We first proposed an effective RNN-CNN framework (Recurrent Neural Network-Convolutional Neural Network) to address the task of image captioning (i.e. describing an image with a sentence). Based on this work, we proposed effective models and constructed large-scale datasets, for various vision and language tasks, such as unambiguous object descriptions (i.e. Referring expressions), image question answering, one-shot novel concept captioning, multimodal word embedding, and multi-label classification. Many of these tasks have not been successfully addressed or even been investigated before. Our work are among the first deep learning effort for these tasks, and achieves the state-of-the-art results. We hope the methods and datasets proposed in this thesis could provide insight for the future development of vision and language.",
            "source_url": "https:\/\/openalex.org\/W2612979518",
            "pdf_url": "https:\/\/escholarship.org\/content\/qt676212wx\/qt676212wx.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "eScholarship (California Digital Library)",
            "source_record_id": "W2612979518",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 747,
            "title": "Towards Natural Language Understanding using Multimodal Deep Learning",
            "title_ar": "",
            "title_en": "Towards Natural Language Understanding using Multimodal Deep Learning",
            "author": "Siegfried Bös",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Delft University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Netherlands",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "deep-learning; computer-vision; nlp; ai-applications; multimodal; object-detection; video-analysis; speech-recognition; ai-agriculture",
            "keywords": "",
            "abstract": "This thesis describes how multimodal sensor data from a 3D sensor and microphone array can be processed with deep neural networks such that its fusion, the trained neural network, is a) more robust to noise, b) outperforms unimodal recognition and c) enhances unimodal recognition in absence of multimodal data. We built a framework for a complete workflow to experiment with multimodal sensor data ranging from recording (with Kinect 3D sensor), labeling, 3D signal processing, analysing and replaying. We also built three custom recognizers (automatic speech recognizer, 3D object recognizer and 3D gesture recognizer) to convert the raw sensor streams to decisions and feed this to the neural network using a late fusion strategy. We recorded 25 particpants performing 27 unique verbal and gestural interactions (intents) with objects and trained the neural network using a supervised strategy. We proved that the framework works by building a deep neural networks assisted speech recognizer that performs approximately 5% better with multimodal data at 20 dB SnR up to 61% better with multimodal data at -5 dB SnR while performing identical to the individual recognizer when fed a unimodal datastream. Analysis shows that performance gain in low acoustic noise is due to true fusion of classifer results while gain at high acoustic noise is due to absence of speech results as it cannot detect speech events anymore, while the gesture recognizer is not affected. The impact of this thesis is significant for computational linguists and computer vision researchers as it describes how practical issues with (real and) real-time data can be solved such as dealing with sensor noise, GPU offloading for computational performance, 3D object and hand tracking. The speech-, object- and gesture recognizers are not state-of-the-art and the small vocabulary with 27 unique phrases and 9 objects can be considered a preliminary experiment. The main contributions of this thesis project are a) validated multimodal fusion framework and workflow for embodied natural language understanding named MASU, b) 600GB, 2,5 hour labelled multimodal database with synchronous multi channel audio and 3D video, c) algorithm for 3D hand-object detection and tracking, d) recipe to train a deep neural network model for multimodal fusion and e) demontrate MASU in practical real-time scenario.",
            "source_url": "https:\/\/openalex.org\/W2758142123",
            "pdf_url": "https:\/\/repository.tudelft.nl\/file\/File_91e1bf2c-74c9-43b0-bba6-3902d2b876f9",
            "doi": "",
            "handle": "",
            "repository_name": "Research Repository (Delft University of Technology)",
            "source_record_id": "W2758142123",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 748,
            "title": "A Real-Time and Automatic Ultrasound-Enhanced Multimodal Second Language Training System: A Deep Learning Approach",
            "title_ar": "",
            "title_en": "A Real-Time and Automatic Ultrasound-Enhanced Multimodal Second Language Training System: A Deep Learning Approach",
            "author": "Mozaffari Maaref",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Ottawa",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; ai-applications; multimodal; video-analysis; speech-recognition; medical-imaging; ai-education",
            "keywords": "",
            "abstract": "The critical role of language pronunciation in communicative competence is significant, especially for second language learners. Despite renewed awareness of the importance of articulation, it remains a challenge for instructors to handle the pronunciation needs of language learners. There are relatively scarce pedagogical tools for pronunciation teaching and learning, such as inefficient, traditional pronunciation instructions like listening and repeating. Recently, electronic visual feedback (EVF) systems (e.g., medical ultrasound imaging) have been exploited in new approaches in such a way that they could be effectively incorporated in a range of teaching and learning contexts. Evaluation of ultrasound-enhanced methods for pronunciation training, such as multimodal methods, has asserted that visualizing articulator’s system as biofeedback to language learners might improve the efficiency of articulation learning. Despite the recent successful usage of multimodal techniques for pronunciation training, manual works and human manipulation are inevitable in many stages of those systems. Furthermore, recognizing tongue shape in noisy and low-contrast ultrasound images is a challenging job, especially for non-expert users in real-time applications. On the other hand, our user study revealed that users could not perceive the placement of their tongue inside the mouth comfortably just by watching pre-recorded videos. Machine learning is a subset of Artificial Intelligence (AI), where machines can learn by experiencing and acquiring skills without human involvement. Inspired by the functionality of the human brain, deep artificial neural networks learn from large amounts of data to perform a task repeatedly. Deep learning-based methods in many computer vision tasks have emerged as the dominant paradigm in recent years. Deep learning methods are powerful in automatic learning of a new job, while unlike traditional image processing methods, they are capable of dealing with many challenges such as object occlusion, transformation variant, and background artifacts. In this dissertation, we implemented a guided language pronunciation training system, benefits from the strengths of deep learning techniques. Our modular system attempts to provide a fully automatic and real-time language pronunciation training tool using ultrasound-enhanced augmented reality. Qualitatively and quantitatively assessments indicate an exceptional performance for our system in terms of flexibility, generalization, robustness, and autonomy outperformed previous techniques. Using our ultrasound-enhanced system, a language learner can observe her\/his tongue movements during real-time speech, superimposed on her\/his face automatically.",
            "source_url": "https:\/\/openalex.org\/W3022567045",
            "pdf_url": "",
            "doi": "10.20381\/ruor-24710",
            "handle": "",
            "repository_name": "uO Research (University of Ottawa)",
            "source_record_id": "W3022567045",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 749,
            "title": "Adaptive Fusion Techniques for Effective Multimodal Deep Learning",
            "title_ar": "",
            "title_en": "Adaptive Fusion Techniques for Effective Multimodal Deep Learning",
            "author": "Gaurav Sahu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Waterloo",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; nlp; generative-ai; transformers; multimodal; video-analysis; sentiment-analysis; machine-translation; speech-recognition; gan",
            "keywords": "",
            "abstract": "Effective fusion of data from multiple modalities, such as video, speech, and text, is a challenging task due to the heterogeneous nature of multimodal data. In this work, we propose fusion techniques that aim to model context from different modalities effectively. Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide “how” to combine given multimodal features more effectively. We propose two networks: 1) Auto-Fusion network, which aims to compress information from different modalities while preserving the context, and 2) GAN-Fusion, which regularizes the learned latent space given context from complementing modalities. A quantitative evaluation on the tasks of multimodal machine translation and emotion recognition suggests that our adaptive networks can better model context from other modalities than all existing methods, many of which employ massive transformer-based networks.",
            "source_url": "https:\/\/openalex.org\/W3084985854",
            "pdf_url": "https:\/\/uwspace.uwaterloo.ca\/bitstreams\/d8ed4daf-ffb2-4594-8566-a7a3085f4b99\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UWSpace (University of Waterloo)",
            "source_record_id": "W3084985854",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 750,
            "title": "Multimodal deep learning for decoding visual and neural representation",
            "title_ar": "",
            "title_en": "Multimodal deep learning for decoding visual and neural representation",
            "author": "Xianglin Zheng",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Adelaide",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "multimodal",
            "categories": "deep-learning; computer-vision; medical-ai; multimodal",
            "keywords": "",
            "abstract": "Multimodal deep learning combines different types of data, like images and brain signals. Traditional models often need lots of labeled examples and focus on one type of data, making them less able to handle real-world challenges. This thesis uses new deep learning and multimodal techniques to better process visual and brain data (Electroencephalogram), with three key projects. First, a new way to create image captions without labeled examples tackles mismatches between images and text. Second, it links images and EEG to classify objects by decoding brain activity from seeing images. Third, a three-part model combines images, words, and EEG data to understand how the brain processes what we see. Together, these methods advance multimodal learning, improving image captioning, EEG-based classification, and our understanding of how the brain works with visual information.",
            "source_url": "https:\/\/openalex.org\/W7222375543",
            "pdf_url": "https:\/\/digital.library.adelaide.edu.au\/server\/api\/core\/bitstreams\/8162363f-9ed4-4346-a556-b25a48693a12\/content",
            "doi": "",
            "handle": "",
            "repository_name": "Adelaide Research & Scholarship (AR&S) (University of Adelaide)",
            "source_record_id": "W7222375543",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 751,
            "title": "Multimodal Deep Learning for Zero-Shot Action Recognition",
            "title_ar": "",
            "title_en": "Multimodal Deep Learning for Zero-Shot Action Recognition",
            "author": "Nan WU",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; few-zero-shot; cnn; multimodal; video-analysis; llm",
            "keywords": "",
            "abstract": "Advancements in machine learning have improved the performance of machine learning (ML) models.However, larger training datasets present challenges, such as prolonged labeling and training times.One potential solution is zero-shot learning, which enables models to identify unfamiliar entities without extensive training data.This not only saves time and effort but also allows easy integration of new categories.However, its efficacy requires enhancement for broad real-world applications.This paper presents three methods to optimize the accuracy of zero-shot action recognition (ZSAR).1. Three-stream graph convolutional networks (TS-GCN):Current models often handle only one data type, limiting their information leverage.Our proposed two-stream network processes both RGB image data and optical flow data, which captures object movement.Each branch calculates video classification separately, and a weighted average determines the final classification.2. Improving ZSAR using human instruction with text description: The video-text matching model (TVMM) model gauges the matching degrees between text features and video content.We annotated text features for each action, and the TVMM model used these for action recognition.The results showed improved action recognition accuracy.3. Zero-shot action recognition with ChatGPT-based instruction: Manual text feature annotation improved accuracy but at a labor cost.To alleviate this, we used ChatGPT for automated text feature generation.This eliminated the need for human annotation.While achieving high recognition accuracy with reduced labor costs, it was slightly lower than previous methods but still suitable for various real-life applications.We propose three innovative methods that optimize zero-shot action recognition.The experiments demonstrate the effectiveness of our approach.",
            "source_url": "https:\/\/openalex.org\/W7207547986",
            "pdf_url": "https:\/\/opac.ll.chiba-u.jp\/da\/curator\/900122990\/WLA_0144.pdf",
            "doi": "10.20776\/900122990",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7207547986",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 752,
            "title": "Multimodal Integration for Natural Language Classification and Generation",
            "title_ar": "",
            "title_en": "Multimodal Integration for Natural Language Classification and Generation",
            "author": "Zhihao Zhang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "The University of Sydney",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; transfer-learning; transformers; multimodal",
            "keywords": "",
            "abstract": "Multimodal integration is a framework for building models that can accept information from different types of modalities. Due to the recent success in the Transformer model and Pre-training Fine-tuning Techniques, Vision-and-Language Pre-training Models have been heavily investigated and they achieved State-of-the-Art in various of Vision-and-Language downstream tasks, such as Visual Question Answering, Image Text Matching and Image Captioning. However, most of the previous studies focus on improving the performance of the models and only provide accessible code for research purposes. There are several existing open-source libraries such as Natural Language Toolkit, OpenCV and HuggingFace, which combine and standardise the available models and tools for easy access, but applying these libraries still requires expertise in both Deep Learning and programming. Moreover, there has been no recent research aimed at establishing user-friendly multimodal question-answering platforms for non-deep-learning users. Therefore, the question of how State-Of-The-Art multimodal models can be easily applied by professionals in other domains remains open. Apart from the first challenge, there exists another challenge in the less-common domain. Since general multimodal domains such as street view, landscape, and indoor scenes have been extensively studied with current VL-PMs, while specific domains like medicine, geography, and esports have garnered less attention. Due to the difficulties in data collection, there aren't many publicly available multimodal datasets, and those that exist tend to be small. This scarcity poses challenges for model training. Consequently, the question of how to collect a comprehensive multimodal dataset in the esports domain and how to improve domain-specific multimodal models remains open. Therefore, the main focus of this thesis is integrating multimodal information for natural language classification and generation tasks by addressing the two challenges.",
            "source_url": "https:\/\/openalex.org\/W7126883915",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "The Sydney eScholarship Repository (The University of Sydney)",
            "source_record_id": "W7126883915",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 753,
            "title": "Aligning vision and language for image captioning using deep learning",
            "title_ar": "",
            "title_en": "Aligning vision and language for image captioning using deep learning",
            "author": "Cai, Chen",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; self-semi-supervised; predictive-analytics; rnn; transformers; multimodal; video-analysis",
            "keywords": "",
            "abstract": "A longstanding objective in the field of multi-modal research uniting computer vision and natural language processing is to develop models that can comprehend the intricate relationship between vision and language. In recent years, we have witnessed notable developments directed towards this objective, enabling computers to interpret visual information and articulate it through captions. Although significant advances have been made, aligning complex visual scenes and language for image captioning tasks using deep learning approaches remains challenging due to the distinct characteristics between the modalities. In this thesis, we propose four new methods that effectively align vision and language for image captioning-based tasks through deep learning techniques. These methods include integrating visual and semantic attributes for controllable image captioning, aligning visual and object semantics for grounded image captioning, learning the relationship between multi-scale visual features with language for image change captioning, and bridging visual and global temporally caption knowledge for temporal sentence grounding. The major contributions of this thesis are summarized as follows: A new Attribute Controlled Image Captioning (ACC) method has been proposed that seamlessly integrates semantic attributes with visual content, enabling automatic modification of the generated captions in the fashion domain. Our approach utilizes semantic attributes as a control signal, giving users the ability to specify particular fashion attributes and styles to incorporate while generating captions. Furthermore, we clean, filter, and assemble a new fashion image caption dataset to facilitate learning and enable us to investigate the effectiveness of our method. A new one-stage Weakly Supervised Grounded Image Captioner (WS-GIC) is proposed that aligns visual and word representation to perform captioning and grounding at the top-down image level. We introduce a Recurrent Grounding Module (RGM) within the decoder to compute Visual Language Attention Maps (VLAMs) for grounding, where VLAMs indicate the spatial region and location of the generated groundable object words in the caption. In addition, we explicitly inject a relation module into our one-stage framework to encourage the relation understanding. The relation semantics aid the prediction of relation words in the caption. A new Interactive Change-aware Transformer Network (ICT-Net) is proposed to extract and incorporate the most critical change of interest in the image, enhancing the generation of change descriptions for complex remote sensing bitemporal scenes. The proposed framework comprises an Interactive Change-aware Encoder (ICE) to capture the crucial difference between bitemporal image features, an Adaptive Fusion Module (AFM) to adaptively aggregate the relevant change-aware features in the encoder layers while minimizing the impact of irrelevant visual features, and a Cross Gated-Attention (CGA) module in the change decoder that enhances the modeling of essential relationships between multi-scale features with word representation, thereby improving change captioning generation. A new Temporal Sentence Grounding (TSG) method is proposed to bridge the domain gap between multi-modal features by leveraging extensive temporally global caption knowledge that is sourced from the relevant video and temporally localized text queries. We introduce the Pseudo-query Intermediary Network (PIN) to contrastively align visual features with temporally global textual knowledge to enhance the similarity between visual and language features. Furthermore, we leverage the pseudo queries prompt to propagate the knowledge, enhancing the learning of feature alignment within the multi-modal fusion module for better temporal grounding. Throughout the thesis, we illustrate how each of the proposed methods aligns vision and language for image captioning-based tasks. Experimental results on public datasets indicate that the proposed methods can achieve better performance. These approaches contribute to enhancing vision and language multi-modalities understanding.",
            "source_url": "https:\/\/openalex.org\/W4405996657",
            "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/181511\/2\/CC_ntu_thesis.pdf",
            "doi": "10.32657\/10356\/181511",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4405996657",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 754,
            "title": "A multimodal deep learning approach for food tray recognition",
            "title_ar": "",
            "title_en": "",
            "author": "Joan Peracaula Prat",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universitat de Barcelona",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2020,
            "language": "ca",
            "research_domain": "deep-learning",
            "research_subdomain": "multimodal",
            "categories": "deep-learning; multimodal",
            "keywords": "",
            "abstract": "Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Marc Bolaños i Petia Radeva",
            "source_url": "https:\/\/openalex.org\/W3158443751",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona)",
            "source_record_id": "W3158443751",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 755,
            "title": "Multimodal Deep Learning for Visual Question Answering: Techniques and Challenges",
            "title_ar": "",
            "title_en": "",
            "author": "Oualid Ilyes Malfi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Algeria",
            "governorate": "",
            "publication_year": 2024,
            "language": "fr",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; nlp; medical-ai; rnn; transformers; multimodal; medical-imaging",
            "keywords": "",
            "abstract": "Cette thèse explore le domaine de la réponse visuelle aux questions (VQA), une application avancée de l'apprentissage profond multimodal qui combine la vision par ordinateur et le traitement du langage naturel pour permettre aux machines de répondre à des questions sur le contenu visuel. L'objectif principal est de développer un modèle VQA robuste capable d'interpréter et de raisonner sur des scènes visuelles complexes de manière similaire à celle des humains. En utilisant divers modèles tels que VGG19, ResNet50, EfficientNet-B5 et ViT pour l'extraction de caractéristiques d'image, et un modèle basé sur BERT ou LSTM pour l'extraction de caractéristiques des questions, l'étude emploie une méthode de fusion de niveau intermédiaire par multiplication élémentaire ou concaténation pour combiner ces caractéristiques. La recherche s'appuie sur des jeux de données comme CLEVR, conçu pour tester les véritables capacités de raisonnement en fournissant des objets rendus en 3D avec des questions détaillées, et VQA-RAD, destiné à des problèmes réels, qui soutient la prise de décision clinique à travers des images de radiologie accompagnées de questions et réponses pertinentes. Les résultats expérimentaux ont montré que la combinaison du modèle ViT, BERT et la technique de multiplication a excellé, atteignant des précisions élevées de 98,70 % sur le jeu de données CLEVR et de 96 % sur le jeu de données VQA-RAD, en faisant le modèle le plus efficace pour la réponse aux questions visuelles générales et médicales.",
            "source_url": "https:\/\/openalex.org\/W7135331956",
            "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/2d865c52-986c-4153-96d4-e60980214c02\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen",
            "source_record_id": "W7135331956",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 756,
            "title": "Bridging images and natural language with deep learning",
            "title_ar": "",
            "title_en": "Bridging images and natural language with deep learning",
            "author": "Jiuxiang Gu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "",
            "categories": "deep-learning; computer-vision; nlp",
            "keywords": "",
            "abstract": "Throughout the thesis, I demonstrate how each of the proposed methods can bridge the gap between images and natural language. Experimental results on public vision and language datasets have shown all these methods are able to obtain significant performance improvement on vision and language tasks such as image captioning and cross-modal retrieval.",
            "source_url": "https:\/\/openalex.org\/W2992651911",
            "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/85399\/1\/PhD_Thesis_JiuxiangGu_Final.pdf",
            "doi": "10.32657\/10356\/85399",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W2992651911",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 758,
            "title": "Multimodal emotion recognition using deep learning techniques",
            "title_ar": "",
            "title_en": "Multimodal emotion recognition using deep learning techniques",
            "author": "Tien Dung Nguyen",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "deep-learning; nlp; robotics; multimodal; sentiment-analysis; speech-recognition",
            "keywords": "",
            "abstract": "This thesis investigates the use of deep learning techniques to address the problem of machine understanding of human affective behaviour and improve the accuracy of both unimodal and multimodal human emotion recognition. The objective was to explore how best to configure deep learning networks to capture individually and jointly, the key features contributing to human emotions from three modalities (speech, face, and bodily movements) to accurately classify the expressed human emotion. The outcome of the research should be useful for several applications including the design of social robots.",
            "source_url": "https:\/\/openalex.org\/W3013449349",
            "pdf_url": "https:\/\/eprints.qut.edu.au\/180753\/1\/Tien%20Dung_Nguyen_Thesis.pdf",
            "doi": "10.5204\/thesis.eprints.180753",
            "handle": "",
            "repository_name": "Queensland University of Technology",
            "source_record_id": "W3013449349",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 762,
            "title": "Author attribution for Reddit data: a comparative study on machine learning models : a thesis in Data Science",
            "title_ar": "",
            "title_en": "Author attribution for Reddit data: a comparative study on machine learning models : a thesis in Data Science",
            "author": "Sairama Amulya Baswa",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; nlp; rnn",
            "keywords": "",
            "abstract": "The proliferation of user-generated content on online platforms like Reddit has created vast datasets that can be used to extract significant knowledge, such as author attribution through various analytical techniques. Author attribution is the task of identifying the writer of a given text, or in this case, comments or posts. This analysis can help us understand how people interact on Reddit, and it could also be useful for detecting issues such as fake accounts, secondary accounts, or just getting a better sense of how individual users behave. Namely, author verification and author attributions help us identify if two or more accounts are from the same person or genuinely from different people. However, the challenge is that figuring out this information from short and casual posts like you often see on Reddit is not straightforward. The brevity usually leads to sparse feature representations, and informal language can obscure consistent stylistic patterns that are readily apparent in longer, more formal writing. Existing author attribution methods may not be optimally suited for capturing the subtle yet distinctive linguistic cues that differentiate authors within the specific context of Reddit’s or similar platforms’ communication style. This research addresses the challenge of author attribution in Reddit comments by employing a comprehensive feature engineering strategy coupled with the evaluation of several machine learning and deep learning models. Our hypothesis is that, by looking at different language details, especially stylometric details that are typical of Reddit posts, we can figure out if two comments were written by the same person. Our literature survey suggested that stylometry analysis is more relevant for author attribution or verification. To assess the effectiveness of these engineered features, we evaluate the performance of three distinct classification models: a decision tree, a random forest, and a bidirectional long short-term memory (LSTM) network. In bidirectional LSTM, we use two different inputs and see the performance. The outcomes of this research hold significant implications for various stakeholders. Researchers in natural language processing (NLP) and computational social science can leverage our feature engineering pipeline and the comparative model analysis as a benchmark and a source of inspiration for future work on author attribution in online contexts. Furthermore, understanding the linguistic markers that characterize individual authors on platforms like Reddit can inform sociolinguistic studies and the evolution of online communication. Ultimately, this work aims to advance the state-of-the-art author attribution for short, informal text data, providing practical tools and valuable insights for a range of applications.",
            "source_url": "https:\/\/openalex.org\/W4416154571",
            "pdf_url": "",
            "doi": "10.62791\/20519",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4416154571",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 764,
            "title": "Improving computational efficiency via tiny machine learning data-centric methods: a thesis in Data Science",
            "title_ar": "",
            "title_en": "Improving computational efficiency via tiny machine learning data-centric methods: a thesis in Data Science",
            "author": "Michael Soricelli",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; transformers; image-classification; ai-smart-cities",
            "keywords": "",
            "abstract": "As the capabilities of Machine Learning models grow, so does the cost of power consumption and time. In the era of deep learning, models have become larger and larger. While the complexity of these models leads to greater performance, they also require more computation to train. According to recent estimates, the state of the art transformer based models require 3-5 months to train, while consuming 50 gigawatt-hours of energy. While not all deep learning models are not this large, higher performing models do follow the trend of larger model size over time. As a consequence, this makes training high performing models on resource constrained devices more challenging. To address this problem, one can try to minimize training costs through a data-centric approach. Moreover, one can attempt to minimize the dataset size with the goal being to reduce training costs by limiting the amount of data needed for training with the goal of maintaining performance. There are two data-centric approaches that are used primarily in this work, dataset pruning and dataset condensation. Dataset pruning is an approach that removes less efficient training samples from a dataset, while data condensation is a technique that aims to condense the knowledge from a large dataset into a smaller, synthetic dataset while preserving the performance of models trained on it. In this work, we propose a couple of different methods that utilize both these ideas to minimize training costs when training a deep neural network. We utilize vector quantization and normalizing flow models to perform variations of distribution learning. Distribution learning is utilized in this work to guide the training process towards more data efficient samples and improve upon existing state of the art dataset pruning methods. Our results show significantly reduced training costs across multiple benchmark image classification datasets, while also maintaining classification performance and outperforming other state of the art dataset pruning methods.",
            "source_url": "https:\/\/openalex.org\/W4416154402",
            "pdf_url": "",
            "doi": "10.62791\/20484",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4416154402",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 766,
            "title": "A machine learning approach to volume tracking in multiphase flow simulations: a thesis in Mechanical Engineering",
            "title_ar": "",
            "title_en": "A machine learning approach to volume tracking in multiphase flow simulations: a thesis in Mechanical Engineering",
            "author": "Aaron Mak",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "ai-industry",
            "categories": "machine-learning; deep-learning; ai-applications; ai-industry",
            "keywords": "",
            "abstract": "Multiphase flow is referred to flow of two or more immiscible phases (liquid, gas and solid). It is encountered in many essential natural phenomena and industrial processes. Computational simulations have emerged as a powerful and reliable tool for multiphase flow research to further current understanding and uncover new insights. They complement and are often strong alternatives to experimental methods, especially in studies where experiments are unfeasible or prohibitively expensive, due to, for example, short length or time scales or complex geometries. An essential component of multiphase flow simulations is capturing the dynamics of the interface separating the immiscible phases and tracking the phase volumes. Various methods have been proposed to achieve this, including the front tracking, level set, and volume-of-fluid (VOF) methods. The VOF method has become one of the most commonly used approaches to volume tracking and is the focus of this thesis. In VOF, the most common solutions are performed in two steps: interface reconstruction followed by flux calculation for volume advection. They represent a significant computational cost in VOF based multiphase flow simulations. In this work, a new approach using machine learning(ML) is used to generate a general advection function in a two-dimensional VOF scheme, which bypasses interface reconstruction and flux calculation. Although ML functions require a larger upfront cost to train, the resulting functions may be less computationally expensive to use when compared to traditional VOF methods. The data set in this work was generated from translation and rotation of a circle under various spatial and temporal resolutions. The ML training was performed using MATLAB’s Deep Learning Toolbox. To find an optimal neural network configuration, a grid search method based on the validation performance was used. Additionally, a rating system was developed to assess the overall performance of each function, as a potential alternative to solely relying on validation performance. This thesis presents results from commonly used advection tests to evaluate performance of volume tracking methods. In ideal conditions, the computation speed up is four times compared to the conventional VOF method, although determining ideal conditions will require further testing. In terms of accuracy, however, the VOF method remains superior.",
            "source_url": "https:\/\/openalex.org\/W4409543070",
            "pdf_url": "",
            "doi": "10.62791\/20258",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4409543070",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 772,
            "title": "An examination of adversarial machine learning on connected and autonomous vehicles: a thesis in Computer Engineering",
            "title_ar": "",
            "title_en": "An examination of adversarial machine learning on connected and autonomous vehicles: a thesis in Computer Engineering",
            "author": "David E. Austin",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; robotics; ai-applications; predictive-analytics; rnn; ai-cybersecurity",
            "keywords": "",
            "abstract": "Connected and Autonomous Vehicle (CAV), a.k.a. driverless car, provides a solution to safety and efficiency in surface transportation. Yet, CAV technology introduces new security risks beyond the standard vehicle vulnerabilities. Machine Learning (ML) promises to secure CAV by utilizing telemetry data to validate legitimacy among CAVs as a method of Misbehavior Detection. However, most ML models base a misassumption of a benign environment, where the data during testing and training keep an equivalent distribution, which makes ML models vulnerable to adversarial perturbations. Furthermore, the lack of knowledge in CAV behavior opens new attack surfaces for adversaries to invalidate ML models. This thesis examines an attackers’ mindset to fool a ML system for Misbehavior Detection in CAV; thereby CAV defense strategies become robust to adversarial ML . Three software packages, OMNeT++, SUMO, and Veins, integrate to simulate CAV. The dataset for ML, comes from the Vehicle Reference Misbehavior Dataset, which this work expands with new attacks. Progressively, the work evaluates the accuracy of K-Nearest Neighbors and Random Forest for ML algorithms and that of Logistic Regression Neural Network and Recurrent Neural Network with Long-Short-Term-Memory for Deep Learning (DL) algorithms, all coded in Python. A Random Walk movement theory generates synthetic data following a normal distribution to further evaluate the robustness of the ML \/DL models, i.e., the effectiveness of spoofed data from the attacker’s point of view. The work reaches a conclusion that DL models perform better than ML models in most cases. This work contributes to ML Misbehavior Detection in CAV, exposing the challenges posed by adversarial ML and recommending the use of DL over ML against adversaries. As a result, it strengthens CAV defense with robust DL models. Future work will solve the limitations of DL such as the need for large quantity of data and balanced classes of data. To correct unbalanced classes, synthetic data generation can use specific python packages such as SMOTE. Undersampling or oversampling the minority or majority classes, respectively, could be another solution. Pairing Feature Analysis, such as Exploratory Data Analysis, to identify the most relevant features would train the models effectively and accurately. Other future work includes engineering the features from the datasets as well as using the Real-World Datasets from CAV pilot cities.",
            "source_url": "https:\/\/openalex.org\/W4409456610",
            "pdf_url": "",
            "doi": "10.62791\/20107",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4409456610",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:52",
            "updated_at": "2026-11-15 16:52:52"
        },
        {
            "id": 777,
            "title": "GenVRAD: dataset generator for machine learning detection of vehicle-roadside attacks : a thesis in Computer Engineering",
            "title_ar": "",
            "title_en": "GenVRAD: dataset generator for machine learning detection of vehicle-roadside attacks : a thesis in Computer Engineering",
            "author": "Devaj Ramsamooj",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "generative-ai",
            "research_subdomain": "fairness",
            "categories": "machine-learning; deep-learning; generative-ai; robotics; responsible-ai; ai-applications; fairness; ai-cybersecurity; ai-smart-cities",
            "keywords": "",
            "abstract": "Autonomous vehicles are gaining popularity rapidly for transportation safety and efficiency. However, to achieve fully automated driving without human intervention, cars need to be aware of traffic and environmental dynamics. One key component of their awareness is through communication. Connected and Automated Vehicles (CAV) leverage both automation and communication technologies to obtain information with nearby devices for decision making. Vehicular Ad-Hoc NETworks (VANETs) provide an architecture to support CAV with cars’ On-Board Units (OBUs) to communicate with each other and other devices like Roadside Units (RSUs). This communication can involve sharing information about traffic conditions, road hazards, and other data. Security of RSUs is crucial to ensure reliability and trustworthiness of its communication. Some security concerns with RSUs are authentication of RSUs, data integrity, etc. Addressing these concerns will require multiple types of mechanisms such as encryption and intrusion detection systems (IDS) to prevent misbehaving RSUs. With any type of communication, security of information is always at the forefront. Conventional security techniques have been tested, such as password protection and biometric security, but they do not meet the needs of the high dynamics of VANET. Machine and Deep have the ability to learn without human intervention, which is highly desirable to CAV. Since CAV is still new, there is little data to train and test Machine and Deep learning algorithms. Some attempts to synthesize attack datasets but only on vehicle-to-vehicle (V2V) communication. To the best of our knowledge, there is no data generator for vehicle-to-infrastructure (V2I). This research fills the gap by providing a dataset generator to inject misbehaving RSUs. Using simulation, data is generated to be able train and test several algorithms. Our work reveals machine learning algorithms are not sufficient to solve VANET security problems. The deep learning algorithm shows promise, but more analysis will be needed to be suitable for VANET security.",
            "source_url": "https:\/\/openalex.org\/W4409785539",
            "pdf_url": "",
            "doi": "10.62791\/20351",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4409785539",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 789,
            "title": "Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System",
            "title_ar": "",
            "title_en": "Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System",
            "author": "Ali Raza",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "federated-learning",
            "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; ai-applications; xai; federated-learning; fairness; ai-cybersecurity",
            "keywords": "",
            "abstract": "The growing population around the globe has a significant impact on various sectors including the labor force, healthcare, and the global economy. The healthcare sector is among the most affected sectors by the growing population due to the increasing demand for resources including doctors, nurses, equipment, and healthcare facilities. Intelligent systems have been incorporated to enhance decision-making, management, prognosis, and diagnosis in order to tackle such issues and offer improved healthcare to patients. Among such systems, those based on deep learning (DL), a subclass of machine learning (ML) have outperformed many traditional statistical and ML systems owing to their capability of automatically discovering and learning related features for a given task and robustness. Therefore, the use of DL has seen a steady increase in many applications. Nevertheless, usually, the training of DL models relies on a single centralized server, which brings many challenges: (1) except for some big enterprises most of the small enterprises have limited quality data, which is insufficient to support the training of data-hungry DL models, (2) access to data, which is vital for these systems, often raises privacy concerns. The collection and analysis of sensitive patient information must be done in a secure and ethical manner to ensure the protection of individual privacy rights, (3) high communication cost and computation resources required, (4) a large number of trainable parameters make the outcome of DL hard to explain, which is required in some applications, such as healthcare. Compared to centralized ML, federated learning (FL) improves both privacy and communication costs, where clients collaboratively train a joint model without sharing the raw data directly. FL minimizes privacy breaches and safeguards sensitive data by keeping it distributed locally. This enables collaborative model training while reducing the risk of unauthorized access and data breaches. Additionally, it promotes data diversity and scalability by involving multiple sources in joint model training and decreases communication costs by sharing only model updates instead of the entire dataset. However, FL brings its own challenges. For example, heterogeneous local data among the clients makes it challenging to train a high-performing and robust global model. Sharing updates (hundreds of thousands of parameters) still has high communication costs. Additionally, the distributed nature and access control of local data in FL make it more vulnerable to malicious attacks. Moreover, the challenge of explaining the results of DL still remains challenging, and methods are needed to be developed to bring trust accountability, and transparency in sensitive applications, such as healthcare. Therefore, the aim of this thesis is to create robust frameworks that are secure, high-performing, and privacy-friendly within federated settings. These frameworks will be specifically designed for end-to-end (we train our frameworks using raw data without any manual feature extraction) healthcare applications, considering the presence of non-identically distributed data among clients in FL to bring robustness. By addressing these challenges, the objective is to enhance the overall system's resilience and effectiveness. We also propose a methodology for detecting anomalies within federated settings, particularly in applications with limited available data for the abnormal class. Furthermore, clients in FL are usually resource-constrained with limited computation and communication resources available. Therefore, to support efficient computation and communication in a federated setting we propose a lightweight framework (in terms of the trainable number of parameters). Additionally, to provide explanations of the DL models' outcomes, which are usually hard to explain because of the large number of parameters, we propose model-agnostic explainable AI modules to help explain the results of DL models. Moreover, in order to protect the proposed frameworks against cyber attacks, such as poisoning attacks, we propose a framework in federated settings, which makes the proposed healthcare frameworks more secure and trustworthy. Finally, with experimental analysis using baseline datasets for one of the most common health conditions i.e., cardiovascular diseases (arrhythmia detection, ECG anomaly detection) and human activity recognition (used for supplementing cardiovascular diseases detection), we show the superiority of the proposed frameworks over state-of-the-art work.",
            "source_url": "https:\/\/openalex.org\/W4399027046",
            "pdf_url": "http:\/\/www.theses.fr\/2023ULILB019\/document",
            "doi": "10.22024\/unikent\/01.02.102717",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W4399027046",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 793,
            "title": "Addressing data heterogeneity, scarcity, and training efficiency in privacy-preserving federated learning",
            "title_ar": "",
            "title_en": "Addressing data heterogeneity, scarcity, and training efficiency in privacy-preserving federated learning",
            "author": "Chen, Haokun",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Ludwig-Maximilians-Universität München",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; medical-ai; responsible-ai; ai-applications; transfer-learning; few-zero-shot; optimization; multimodal; llm; diffusion; federated-learning; fairness; ai-industry",
            "keywords": "",
            "abstract": "Over the past decade, deep learning has achieved significant breakthroughs in various domains, including computer vision and natural language processing, with applications spanning industries such as healthcare and manufacturing. However, the success of deep neural networks (DNNs) relies heavily on access to large-scale datasets, which can be difficult for individual organizations to acquire. The reasons behind are the complexities and costs for data collection and annotation at large scale. A straightforward solution is to centralize data from multiple organizations for training. However, this approach raises significant privacy concerns, as such data often contains sensitive or confidential information. Furthermore, regulations like the General Data Protection Regulation (GDPR) emphasize the importance of protecting user privacy in inter-organizational data exchanges. Moreover, transmitting large volumes of data introduces substantial computational overhead, further complicating the process. These challenges highlight the urgent need for methods that facilitate collaborative data use while ensuring privacy preservation. Federated learning (FL), which enables multiple parties to collaboratively train a DNN with the assistance of a central server, offers an effective solution to the aforementioned problem. Unlike traditional centralized learning, which requires collecting data from each party, FL eliminates the need to upload data for joint training. Instead, locally trained models are exchanged with a central server, which aggregates the knowledge from all of the uploaded models and then distributes a refined global model to each party. This approach allows each party to benefit from the collective contributions, ultimately enhancing the model performance. This thesis makes four key contributions to addressing challenges in federated learning, including data heterogeneity, data scarcity, and system convergence rate, while preserving client data privacy. For each contribution, we propose a novel method and empirically demonstrate its effectiveness within the relevant problem context. In this thesis, we address key challenges in federated learning (FL) related to data heterogeneity, foundation model adaptation, hyperparameter tuning, and communication efficiency. First, we tackle feature space heterogeneity by introducing a generative augmentation method that aligns diverse client distributions, validated on both public and real-world datasets. We then adapt parameter-efficient fine-tuning techniques for vision-language models in FL by designing a dual-stream adapter that captures both client-specific and client-agnostic knowledge. To optimize performance under limited resources and non-IID conditions, we propose an evolutionary hyperparameter tuning framework that enables efficient online optimization. Lastly, we address data scarcity and heterogeneity in One-Shot FL by personalizing pretrained latent diffusion models, enabling privacy-preserving synthetic data generation that improves performance across challenging domains such as medical and satellite imaging.",
            "source_url": "https:\/\/openalex.org\/W7108654185",
            "pdf_url": "",
            "doi": "10.5282\/edoc.36097",
            "handle": "",
            "repository_name": "Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München)",
            "source_record_id": "W7108654185",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 794,
            "title": "Federated Learning-Based Intrusion Detection System for Privacy-Preserving IoT Networks",
            "title_ar": "",
            "title_en": "Federated Learning-Based Intrusion Detection System for Privacy-Preserving IoT Networks",
            "author": "Prasanna Dhanireddy ; Khushboo Kumari",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; medical-ai; responsible-ai; ai-applications; cnn; transformers; federated-learning; fairness; ai-cybersecurity; ai-industry",
            "keywords": "",
            "abstract": "The rapid growth of IoT devices in smart homes, healthcare, and industrial systems has increased vulnerability to cyber threats. Traditional centralized intrusion detection systems (IDS) raise privacy, and scalability concerns due to the need for centralized data collection. This project aims to develop a Federated Learning (FL)-based IDS that enables distributed IoT nodes to collaboratively train the models without sharing raw data. This study proposes a FL-based IDS framework for IoT environments that systematically evaluates the trade-offs between detection performance, privacy guarantees, model efficiency, and synthetic zero-day like attack robustness. The framework investigates and compares multiple deep learning architectures, including Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Networks (1D-CNN), and TabTransformer (TTF) architectures. The integration of differential privacy was also examined to enhance data protection and analyze its impact on model performance. Additionally, quantization techniques were investigated for the models to reduce model size and improve computational efficiency for deployment on resource-constrained IoT devices. The proposed system is evaluated using the benchmark dataset CICIoT2023 basedon standard performance metrics such as accuracy, precision, recall, F1-score, AUC-ROC score along with inference time, and communication overhead. Experimental results demonstrate that TTF achieved the highest detection performance with an accuracy of 93.97%, F1-score of 92.47%, and AUC-ROC score of 98.11%, while MLP provided superior computational efficiency for resource-constrained IoT devices. The integration of differential privacy introduced a privacy-utility trade-off, where model accuracy decreased from 93.97% to 91.40% under stricter privacy guarantees. Furthermore, post-training dynamic quantization reduced TTF and 1D-CNN model size by 67.72% and 65.66%, thereby decreasing inference time with minimal performance degradation. The optimized federated model also demonstrated robustness against synthetic zero-day like attack scenarios generated using CTGAN. It achieved an accuracy of 90.96%, F1-score of 90.16%, and AUC-ROC of 96.55%, suggesting its potential to detect diverse attack behaviors.",
            "source_url": "https:\/\/openalex.org\/W7213389565",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W7213389565",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 806,
            "title": "OPTIMIZING CONVOLUTIONAL NEURAL NETWORKS VIA GENETIC ALGORITHM-BASED PRUNING",
            "title_ar": "",
            "title_en": "",
            "author": "João Silva",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2025,
            "language": "pt",
            "research_domain": "computer-vision",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; optimization; cnn",
            "keywords": "",
            "abstract": "As redes neurais têm experienciado um crescimento significativo no seu tamanho e complexidade nos últimos anos, levando a avanços notáveis em diversas áreas da inteligência artificial. No entanto, este aumento exponencial no seu número de parâmetros e das necessidades computacionais tem trazido desafios consideráveis, especialmente quando se trata de implementar estes modelos em ambientes com recursos limitados. Além disso, esta exigência computacional dos modelos de Deep Learning tem contribuído para um maior consumo de energia, gerando preocupações económicas e ambientais. Como consequência, reduzir o tamanho e a carga computacional das redes neurais tornou-se um objetivo essencial para alcançar soluções de Deep Learning mais eficientes, escaláveis e sustentáveis. Este trabalho propõe uma abordagem de pruning estruturado para lidar com este desafio, tratando o pruning comouma tarefa de optimização multiobjetivo e recorrendo a um Algoritmo Genético(AG)paraidentificar subredes optimizadas na arquitetura VGG-16 aplicada à tarefa de classificação de imagem com o dataset CIFAR10. Os filtros da rede são representados como um genoma que evolui iterativamente através de processos de seleção natural para encontrar o equilíbrio ideal entre desempenho e eficiência computacional. Devido ao elevado custo computacional das avaliações, utilizou-se uma estratégia de mitigação onde o AG foi treinado e avaliado em apenas 25% do dataset CIFAR-10. A aptidão de cada indivíduo é avaliada com base numa função que pondera a preservação da precisão (40%) e a redução do custo computacional total (NumOps, 60%). Resultados quantitativos demonstram que, em cenários de fine-tuning, o AG superou o método tradicional de pruning por norma L1 em todos os níveis de esparsidade testados, alcançando 88,31% de precisão com 50% de esparsidade, o que representa uma redução de 51,42% no custo computacional (7,14 milhões de operações) face ao modelo base. No entanto, em cenários de reinicialização, embora o AG tenha atingido 92,02% de precisão com 25% de esparsidade (superando o modelo base de 91,05% e reduzindo as operações em 20,46%), não igualou a precisão superior obtida pelo método L1-norm(94,70%). Aumnívelextremode75esparsidade, oalgoritmofalhouemevoluirsoluções viáveis devido à estagnação do espaço de busca. As principais observações revelam que o AG desenvolveu uma estratégia adaptativa inteligente por camada, aprendendo a preservar as camadas iniciais cruciais para a extração de características enquanto aplicava um pruning mais agressivo nas camadas finais. O principal contributo deste trabalho é o desenvolvimento de um framework de pruning evolutivo que descobre configurações arquiteturais eficientes, revelando-se particularmente valioso para a optimização de modelos pré-treinados em contextos reais onde o re-treino total do modelo a partir do zero é impraticável.",
            "source_url": "https:\/\/openalex.org\/W7217945665",
            "pdf_url": "https:\/\/ciencipca.ipca.pt\/bitstreams\/9a8cd140-4f78-482f-b126-23e9d295b2ea\/download",
            "doi": "",
            "handle": "",
            "repository_name": "CiencIPCA",
            "source_record_id": "W7217945665",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 826,
            "title": "Optimizing convolutional neural network parameters using genetic algorithm for breast cancer classification",
            "title_ar": "",
            "title_en": "Optimizing convolutional neural network parameters using genetic algorithm for breast cancer classification",
            "author": "Khatereh Davoudi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Manitoba",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; medical-ai; optimization; cnn; medical-imaging",
            "keywords": "",
            "abstract": "Breast cancer, as the most-regularly diagnosed cancer in women, can be controlled effectively by early-stage tumour diagnosis. Clinical specialists use Computer-Aided Diagnosis (CAD) systems to help aid in their diagnosis, as accurate as possible. Deep learning techniques, such as Convolutional Neural Network (CNN), due to their classification capabilities, have been widely adopted in CAD systems. The parameters of the network, including the weights of the convolution filters, and the weights of the fully connected layers play a crucial role in classification accuracy. Back-propagation technique is the most frequently used approach for training CNN. However, this technique has some disadvantages, such as getting stuck in local minima. In this thesis, we propose to optimize the weights of the CNN using Genetic Algorithm (GA). The work consists of: designing a CNN model to facilitate the classification process, training the model using three different optimizer (mini-batch gradient descent, Adam, and GA), and evaluating the model through various experiments on BreakHis dataset. We show that the CNN model trained through GA performs as well as the Adam optimizer with a classification accuracy of 85%.",
            "source_url": "https:\/\/openalex.org\/W3048136822",
            "pdf_url": "https:\/\/mspace.lib.umanitoba.ca\/bitstreams\/050cf8f8-9e09-4432-8764-8ad5bbcae9e1\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Mspace (University of Manitoba)",
            "source_record_id": "W3048136822",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 828,
            "title": "Deep Learning-Based Medical Image Analysis : Segmentation, Synthesis, and Interactive Visualization for Pulmonary and Hepatic Applications",
            "title_ar": "",
            "title_en": "Deep Learning-Based Medical Image Analysis : Segmentation, Synthesis, and Interactive Visualization for Pulmonary and Hepatic Applications",
            "author": "Liang Lyu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; generative-ai; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Computed tomography (CT) is widely used for the diagnosis and assessment of pulmonary and hepatic diseases because it provides detailed anatomical information.With the development of deep learning, CT image analysis has advanced significantly in segmentation, synthesis, and visualization.However, accurate analysis remains challenging due to complex three-dimensional anatomical structures, small",
            "source_url": "https:\/\/openalex.org\/W7221346931",
            "pdf_url": "https:\/\/ritsumei.repo.nii.ac.jp\/record\/2007568\/files\/k_2107_youshi_ej.pdf",
            "doi": "10.34382\/0002007568",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7221346931",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 829,
            "title": "A Novel Hybrid Deep Learning Model using U-net and Vision Transformer for Polyp Segmentation in medical imaging",
            "title_ar": "",
            "title_en": "A Novel Hybrid Deep Learning Model using U-net and Vision Transformer for Polyp Segmentation in medical imaging",
            "author": "Prity Rani Das",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; transformers; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "This research represents a hybrid model for polyp segmentation in medical images, which combines convolutional neural networks (CNN) and vision transformers (ViT). We propose a hybrid architecture that integrates UNet with a Vision Transformer, where UNet is used as an encoder to capture local features and ViT is used as a bottleneck module to extract global contextual information. To improve the robustness and generalization of the model, various data augmentation techniques are applied to the training data set, including horizontal flip, vertical flip, and rotation. The model is trained and evaluated on the multicenter PolypDB dataset, with all images preprocessed to a consistent resolution, and the corresponding masks are appropriately managed. A loss function, combining Dice Loss and Binary Cross-Entropy (BCE) Loss, is applied to optimize segmentation performance.",
            "source_url": "https:\/\/openalex.org\/W7128515784",
            "pdf_url": "",
            "doi": "10.5281\/zenodo.18591130",
            "handle": "",
            "repository_name": "Zenodo (CERN European Organization for Nuclear Research)",
            "source_record_id": "W7128515784",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 830,
            "title": "Deep learning in medical image analysis : a comparative analysis of multi-modal brain-MRI segmentation with 3D deep neural networks",
            "title_ar": "",
            "title_en": "",
            "author": "Νικόλαος Αδάλογλου",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "el",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; multimodal; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Η τμηματοποίηση\/κατάτμηση σe eικόνeς μαγνητικής τομογραφίας eίναι απαραίτητη για τη διάγνωση, την παρακολούθηση και τον προγραμματισμό θeραπeίας του ασθeνούς. Οι πρακτικές χeιρωνακτικής eπισημeίωσης των ογκομeτρικών στοιχeίων (voxels) απαιτούν ανατομικές γνώσeις, eίναι ιδιαίτeρα δαπανηρές και χρονοβόρeς. Eπιπλέον, μπορeί αρκeτές φορές να eίναι ανακριβeίς λόγω του ανθρώπινου συντeλeστή. Η αυτοματοποιημένη κατάτμηση μπορeί να eξοικονομήσeι χρόνο σe γιατρούς και ακτινολόγους και να παράγeι μια ικανοποιητικά ακριβή λύση για πeραιτέρω ανάλυση. Σe αυτή τη μeταπτυχιακή διπλωματική eργασία, μeλeτάται η αυτοματοποιημένη τμηματοποίηση του eγκeφάλου από τις πολυτροπική πληροφορία eικόνων 3D μαγνητικού συντονισμού( Τ1w, Τ2w, T1-IR, T2-FLAIR). Διeξάγeται μια eκτeταμένη συγκριτική ανάλυση των σύγχρονων 3D αρχιτeκτονικών των νeυρωνικών δικτύων βαθιάς μάθησης, για την τμηματοποίηση των πeριοχών του eγκeφάλου. Αρχίζουμe πeριγράφοντας της θeμeλιώδης αρχές της παραγωγής της μαγνητικής τομογραφίας. Σe eφαρμογές βαθιάς μάθησης, eίναι σημαντικό να γνωρίζουμe τα δeδομένα προς eπeξeργασία για να eκπαιδeυτeί ένα σύστημα μe παραμέτρους της τάξης των eκατομμυρίων. Στη συνέχeια, παρέχeται στον αναγνώστη μια eπισκόπηση του τρόπου μe τον οποίο λeιτουργούν τα δίκτυα βαθιά μάθησης, αναλύοντας eκτeνώς κάθe στοιχeίο (στρώμα) eνός τέτοιου δικτύου. Eφόσον τα δυο πeδία έχουν αναπτυχθeί ξeχωριστά - μαγνητική τομογραφική eικόνα και δίκτυα βαθιάς μάθησης- προσπαθούμe να δώσουμe μια eυρύτeρη προοπτική της διασταύρωσης αυτών των δύο πeδίων μe μια σeιρά eφαρμογών, από την ανακατασκeυή της μαγνητικής τομογραφίας έως την τeχνητή παραγωγή από συστήματα νeυρωνικών δικτύων νέων ιατρικών eικόνων. Η eργασία αυτή eστιάζeι στην πολυτροπική τμηματοποίηση του eγκeφάλου. Για τα πeιράματά μας, χρησιμοποιήσαμe δύο eυρέως γνωστά σύνολα δeδομένων από μαγνητικές τομογραφίας. Τα σύνολα δeδομένων που χρησιμοποιήθηκαν στα πλαίσια διαγωνισμών σe κορυφαία συνέδρια ιατρικής eπeξeργασίας eικόνας, αποσκοπούν στην αξιολόγηση των σύγχρονων αρχιτeκτονικών για τον τμηματοποίηση του eγκeφάλου, παρέχοντας eπισημeιωμένα τρισδιάστατα δeδομένα μαγνητικών. Για να αξιολογήσουμe τις σύγχρονeς αρχιτeκτονικές 3D νeυρωνικών δικτύων, αναλύουμe σύντομα τις προσeγγίσeις που ακολούθησαν οι αρχικές δημοσιeύσeις των συγγραφέων. Eπίσης, παρέχουμe στον αναγνώστη μια διαίσθηση πίσω από τις eπιλογές σχeδιασμού των αρχιτeκτονικών αυτών και Διeξάγουμe μια συγκριτική ανάλυση μέσω eκτeταμένων πeιραμάτων. Τέλος, σχολιάζουμe τα αναφeρόμeνα αποτeλέσματα και παρέχουμe μeλλοντικές κατeυθύνσeις για την υλοποίηση μιας ανοιχτού κώδικα βιβλιοθήκης ιατρικής eπeξeργασίας μαγνητικών τομογραφιών μe βαθιά 3D δίκτυα. Eπeιδή πιστeύουμe στην ανοιχτή έρeυνα και προκeιμένου να αναπαραχθούν τα αποτeλέσματά μας, η πρώτη έκδοση του κώδικα μας eίναι διαθέσιμη στη διeύθυνση: https:\/\/github.com\/black0017\/MedicalZooPytorch",
            "source_url": "https:\/\/openalex.org\/W3033235579",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3033235579",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 831,
            "title": "Exploring Attention Mechanisms and Effective Network Architectures in Deep Learning-based Medical Image Segmentation: Adaptability to Diverse Lesions and Modalities",
            "title_ar": "",
            "title_en": "Exploring Attention Mechanisms and Effective Network Architectures in Deep Learning-based Medical Image Segmentation: Adaptability to Diverse Lesions and Modalities",
            "author": "Hussain Tahir",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; medical-ai; transformers; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7143951589",
            "pdf_url": "",
            "doi": "10.18952\/0002000877",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7143951589",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 832,
            "title": "Weakly supervised learning for medical image segmentation",
            "title_ar": "",
            "title_en": "Weakly supervised learning for medical image segmentation",
            "author": "Hateef Alshewaier",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Cardiff University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; predictive-analytics; image-segmentation; medical-imaging; fairness",
            "keywords": "",
            "abstract": "Medical image segmentation plays a critical role in clinical diagnosis and treatment planning; however, the performance of deep learning models typically depends on large quantities of pixel-wise annotated data, which are costly and time-consuming to obtain. This thesis investigates annotation-efficient approaches for medical image segmentation by developing weakly supervised learning frameworks that reduce annotation requirements while maintaining high segmentation accuracy. The first contribution proposes an enhanced scribble-based segmentation framework that extends a modified U-Net architecture with increased feature learning capacity, mix-based data augmentation, and global and local consistency regularisation. These components improve learning from sparse scribble annotations and promote anatomically coherent segmentation masks. Experimental results demonstrate improved segmentation performance compared with existing scribble-supervised methods. The second contribution presents an ensemble learning framework for weakly supervised medical image segmentation based on single bounding-box annotations. The proposed framework integrates U-Net, ResUNet, and DeepLabV3 using bagging and boosting strategies to exploit complementary architectural characteristics. Despite relying solely on coarse single-bounding-box supervision, the ensemble models improve robustness, reduce model-specific bias, and yield more stable segmentation predictions. Experimental results demonstrate that the ensemble methods consistently outperform the individual models, with boosting achieving the highest segmentation performance. The third contribution introduces a novel dual bounding-box supervision strategy that utilises an inner bounding box to represent high-confidence object regions and an outer bounding box to provide contextual anatomical information. A region-aware weighted loss function encourages accurate object localisation while reducing ambiguity in boundary regions. The proposed framework significantly outperforms conventional single-box weakly supervised approaches and narrows the gap between weak and full supervision. Overall, the findings demonstrate that high-quality medical image segmentation can be achieved using sparse scribble annotations, ensemble learning techniques, and coarse bounding-box supervision without requiring exhaustive pixel-wise labels. The proposed methods reduce annotation effort while maintaining competitive segmentation accuracy, providing a practical and scalable pathway for deploying deep learning-based segmentation systems in clinical environments.",
            "source_url": "https:\/\/openalex.org\/W7219256121",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ORCA Online Research @Cardiff (Cardiff University)",
            "source_record_id": "W7219256121",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 833,
            "title": "Deep Learning and Scientific Machine Learning For Medical Imaging Applications",
            "title_ar": "",
            "title_en": "Deep Learning and Scientific Machine Learning For Medical Imaging Applications",
            "author": "Adrian Celaya",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Rice University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; gnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Medical imaging technologies, such as magnetic resonance imaging (MRI) and computed tomography (CT), are invaluable tools in modern medicine - allowing for safe, non-invasive diagnosis and treatment planning. However, leveraging these imaging techniques for scientific modeling often involves a complex process that includes image acquisition, labeling (i.e., segmenting) regions of interest, generating a computational mesh, and numerical simulation. The contributions of this thesis focus on two areas within this pipeline: deep learning-based methods for efficient, scalable, and accurate 3D medical image segmentation and scientific machine learning (SciML) for solving partial differential equations (PDEs). In the realm of medical imaging segmentation, this thesis presents two innovative architectures that are based on geometric multigrid methods: the FMG and W-Nets. These novel architectures improve the segmentation accuracy compared to current state-of-the-art architectures with fewer parameters and converge to lower loss function values in fewer iterations. This thesis goes on to present the Generalized Surface Loss function, a novel, boundary-aware loss function that improves the accuracy of deep learning-based medical imaging segmentation. These innovations are incorporated into a scalable, open-source framework called the Medical Imaging Segmentation Toolkit, created to empower researchers to develop and test new concepts, such as novel architectures and loss functions, in a simple and reproducible pipeline. The presentation of work related to SciML begins by introducing a novel adaptive collocation point selection strategy for Physics-Informed Neural Networks (PINNs) using the QR Discrete Empirical Interpolation Method (QR-DEIM). This method improves both the training efficiency (i.e., convergence to lower loss values in fewer iterations) and predictive accuracy of PINNs by enabling more informed and targeted sampling of collocation points, particularly in regions of high solution complexity. Additionally, this work demonstrates how convolutional neural networks (CNNs) can approximate finite difference and discontinuous Galerkin (DG) solutions to elliptic and parabolic PDEs, both in supervised and unsupervised settings. These results highlight how SciML techniques can enhance traditional numerical solvers. The thesis culminates in the development of graph neural networks (GNNs) for approximating DG solutions over general domains. By representing computational meshes as graphs, these GNNs can generalize across varying resolutions and topologies, offering a flexible tool for scientific computing in irregular domains. Together, the contributions in this dissertation bridge the gap between deep learning, scientific machine learning, and classical numerical methods, offering a cohesive framework for deploying deep learning and SciML in medical imaging and broader computational physics applications.",
            "source_url": "https:\/\/openalex.org\/W7213488376",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Rice Research Repository (Rice University)",
            "source_record_id": "W7213488376",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 834,
            "title": "Deep learning-based brain tumour image segmentation and its extension to stroke lesion segmentation",
            "title_ar": "",
            "title_en": "Deep learning-based brain tumour image segmentation and its extension to stroke lesion segmentation",
            "author": "Salma Al-Qazzaz",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Cardiff University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn; multimodal; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Medical imaging plays a very important role in clinical methods of treating cancer, as well as treatment selection, diagnosis, an evaluating the response to therapy. One of the best-known acquisition modalities is magnetic resonance imaging (MRI), which is used widely in the analysis of brain tumours by means of acquisition protocols (e.g. conventional and advanced). Due to the wide variation in the shape, location and appearance of tumours, automated segmentation in MRI is a difficult task. Although many studies have been conducted, automated segmentation is difficult and work to improve the accuracy of tumour segmentation is still ongoing. This research aims to develop fully automated methods for segmenting the abnormal tissues associated with brain tumours (i.e. those subject to oedema, necrosis and enhanced) from the multimodal MRI images that help radiologists to diagnose conditions and plan treatment. In this thesis the machine-learned features from the deep learning convolutional neural network (CIFAR) are investigated and joined with hand-crafted histogram texture features to encode global information and local dependencies in the representation of features. The combined features are then applied in a decision tree (DT) classifier to group individual pixels into normal brain tissues and the various parts of a tumour. These features are good point view for the clinicians to accurately visualize the texture tissue of tumour and sub-tumour regions. To further improve the segmentation of tumour and sub-tumour tissues, 3D datasets of the four MRI modalities (i.e. FLAIR, T1, T1ce and T2) are used and fully convolutional neural networks, called SegNet, are constructed for each of these four modalities of images. The outputs of these four SegNet models are then fused by choosing the one with the highest scores to construct feature maps, with the pixel intensities as an input to a DT classifier to further classify each pixel as either a normal brain tissue or the component parts of a tumour. To achieve a high-performance accuracy in the segmentation as a whole, deep learning (the IV SegNet network) and the hand-crafted features are combined, particularly in the grey-level co-occurrence matrix (GLCM) in the region of interest (ROI) that is initially detected from FLAIR modality images using the SegNet network. The methods that have been developed in this thesis (i.e. CIFAR _PI_HIS _DT, SegNet_Max_DT and SegNet_GLCM_DT) are evaluated on two datasets: the first is the publicly available Multimodal Brain Tumour Image Segmentation Benchmark (BRATS) 2017 dataset, and the second is a clinical dataset. In brain tumour segmentation methods, the F-measure performance of more than 0.83 is accepted, or at least useful from a clinical point of view, for segmenting the whole tumour structure which represents the brain tumour boundaries. Thanks to it, our proposed methods show promising results in the segmentation of brain tumour structures and they provide a close match to expert delineation across all grades of glioma. To further detect brain injury, these three methods were adopted and exploited for ischemic stroke lesion segmentation. In the steps of training and evaluation, the publicly available Ischemic Stroke Lesion (ISLES 2015) dataset and a clinical dataset were used. The performance accuracies of the three developed methods in ischemic stroke lesion segmentation were assessed. The third segmentation method (SegNet_GLCM_DT) was found to be more accurate than the other two methods (SegNet_Max_DT and SegNet_GLCM_DT) because it exploits GLCM as a set of hand-crafted features with machine features, which increases the accuracy of segmentation with ischemic stroke lesion.",
            "source_url": "https:\/\/openalex.org\/W3096125045",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ORCA Online Research @Cardiff (Cardiff University)",
            "source_record_id": "W3096125045",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 835,
            "title": "Globally optimal surface segmentation for medical images using deep learning: algorithms, robustness and applications",
            "title_ar": "",
            "title_en": "Globally optimal surface segmentation for medical images using deep learning: algorithms, robustness and applications",
            "author": "Leixin Zhou ; Xiaodong Wu ; Mona K. Garvin",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; cnn; image-segmentation; medical-imaging; ai-cybersecurity",
            "keywords": "",
            "abstract": "Automated image segmentation plays a very import role in quantitative image analysis. In recent several years, deep learning (DL) or CNN based method for semantic segmentation has become very popular in computer vision and then in medical image research communities. Most deep learning based semantic segmentation methods are region-based, i.e. each pixel is labeled as target object or background. As there is no explicit constraint among the labeling of different pixels, usually post processing, such as morphological operation, is required to get reasonable prediction. Actually, one can also model the segmentation problem as surface based: each pixel is labeled as surface or non-surface and then target object can be defined based on prior knowledge, i.e. one has to know region on which side of surface corresponds to target object. Apparently these two views are equivalent but modeled in different way. As one prominent surface-based method, Graph-Search (GS) has achieved great success, especially in medical field. This method is capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. However, the cost function design is usually non-trivial. Inspired by the GS, one research focus is to solve the surface segmentation problem with global optimality guarantee for both 3-D and 2-D images using deep learning, so that the cost functions are learned by deep networks utilizing training dataset. In the era of big data, automated deep learning based image processing enables to process a large amount of medical image data efficiently. This is especially helpful for semantic segmentation, as manual contouring is tedious and time consuming. In many applications, the deep learning based segmentation methods can even achieve expert-level accuracy. However, in practice, deep learning methods may fail due to many factors: such as domain shift, adversarial noise, and low image quality. Therefore, another research focus is testing the robustness of deep learning based segmentation methods and even predicting segmentation quality without ground truth. To accomplish the first objective of this thesis work, novel graph based frameworks are proposed to seamlessly integrated with deep neural networks. First, a graph model based on convex quadratic programming is developed, the solving of which is guaranteed globally optimal. The developed model is applied and validated on the intra-retinal layer segmentation of Spectral Domain Optical Coherence Tomography (SD-OCT) images of eye and vessel walls in Intravascular Ultrasound (IVUS) images. Second, to extend this model based deep learning surface segmentation method to 3-D, one shape-aware patch generation approach for Genus-0 surface is proposed to enable normal 3-D CNN to properly work on. The developed method is applied to prostate segmentation of MR images and spleen segmentation of CT images in 3-D. Third, the robustness of the proposed model based surface segmentation method is tested under adversarial attacks and one robust segmentation quality assessment approach based on conditional reconstruction networks is also developed and verified on cardiac left-ventricular myocardium (LVM) segmentation.",
            "source_url": "https:\/\/openalex.org\/W4240475888",
            "pdf_url": "https:\/\/iro.uiowa.edu\/view\/delivery\/01IOWA_INST\/12788572140002771\/13788572130002771",
            "doi": "10.17077\/etd.005508",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4240475888",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 836,
            "title": "Deep Learning in Medical Image Analysis: Alzheimer’s Disease Classification & Whole Fetal Brain Segmentation.",
            "title_ar": "",
            "title_en": "Deep Learning in Medical Image Analysis: Alzheimer’s Disease Classification & Whole Fetal Brain Segmentation.",
            "author": "Jesús Alejandro Valdés Valdés",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Tecnológico de Monterrey",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Mexico",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; medical-ai; transfer-learning; cnn; image-segmentation; gan; medical-imaging",
            "keywords": "",
            "abstract": "During the last couple years there has been a growing interest in Deep Learning (DL), thanks in part to an increasing availability of computational power and of massive labeled datasets. This work presents two different Deep Learning applications for medical image analysis related tasks. The first application is the classification of Alzheimer Disease (AD) using MRI. AD is the most common form of dementia. However, it’s difficult to diagnose. During this work three different DL models are proposed to classify subjects into either AD or Normal Control (NC) groups. The models proposed are a custom CNN model, vgg16 + Transfer Learning and vgg16 + Fine Tuning. The vgg16 based methods make use of pre-learned weights trained on the ImageNet dataset. All of the models used as an input a single 2D MRI slice. This helped reduced model complexity and training times. After performing a 10 times repeated 5-Fold cross validation it was discovered that the best model was vgg16 with Fine Tuning. It reached an accuracy of 80% and an AUC of 0.86. The obtained results proved that the use of weights learned from different domains could be useful in medical image applications. The main contributions of this section were the use of a single MRI slice to classify patients, the model validation technique and the use of transfer learning. The second application is the fully automatization of the Whole Fetal Brain Segmentation in maternal MRI scans. For this U-Net [41] and other segmentation networks were compared. The U-Net model was also modified to include two types of attention components. Squeeze and Excitation module [16] and Attention Gates [39]. To avoid overfitting the models were trained using rigorous regularization (channel wise dropout, weight decay and data augmentation). They were also trained using different loss functions, including a Hybrid (BCE+Dice) loss function. The results of the 10-fold cross validation experiments showed statistically that no model was able to outperform the vanilla U-Net (DSC of 0.93 and HD 7.59mm). The proposed Attention Gated U-Net trained with the Hybrid loss reached a 0.95 DSC and 5.03mm HD (no significant difference). However, results on challenging data show a better performance for the Attention Gated models. This led to the conclusion that the possible advantage of using attention gates outweighed the cost of adding the attention module. The results were also improved by the use of a post processing step based on traditional computer vision techniques. The main contributions to this problem were the use of 10-fold cross validation, the statistical comparison between models, the use of the Hausdorff distance metric, the post processing step and the addition of attention components. This work also provides an overview of other deep learning applications also used within a medical setting. The techniques mentioned include image labeling, autoencoders and GANs for data augmentation. It also provides an overview of the current challenges and limitations DL faces and the most common way of mitigating them. Thanks to the research conducted during this thesis it can be concluded that the use of DL can be used in a medical setting focused on the analysis of images. Particularly on classification and segmentation tasks. However, certain challenges such as having a large enough data set and availability of the required hardware to carry out research must be considered before attempting to solve a problem with DL.",
            "source_url": "https:\/\/openalex.org\/W3014475976",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Repositorio del Tecnológico de Monterrey (Tecnológico de Monterrey)",
            "source_record_id": "W3014475976",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 837,
            "title": "Deep Learning for Three-Dimensional Multi-Modal Medical Image Processing: From Classification to Segmentation",
            "title_ar": "",
            "title_en": "Deep Learning for Three-Dimensional Multi-Modal Medical Image Processing: From Classification to Segmentation",
            "author": "Kh Tohidul Islam",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Melbourne",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; multimodal; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7201472226",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Minerva Access (University of Melbourne)",
            "source_record_id": "W7201472226",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 838,
            "title": "Development of an Algorithm for Object Detection in Medical Images using Image Segmentation and Deep Learning Techniques",
            "title_ar": "",
            "title_en": "Development of an Algorithm for Object Detection in Medical Images using Image Segmentation and Deep Learning Techniques",
            "author": "Rai Shiv Shankar",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; object-detection; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W2916793112",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "University",
            "source_record_id": "W2916793112",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 839,
            "title": "Deployable and interpretable deep learning architectures: navigating clinical challenges in medical image segmentation",
            "title_ar": "",
            "title_en": "Deployable and interpretable deep learning architectures: navigating clinical challenges in medical image segmentation",
            "author": "Wei Dai",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; responsible-ai; image-segmentation; medical-imaging; xai",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W4404365792",
            "pdf_url": "",
            "doi": "10.14264\/98c85ec",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4404365792",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 840,
            "title": "Anatomy-aided deep learning for Medical Image Segmentation : from the anatomy of the epicardial adipose tissue",
            "title_ar": "",
            "title_en": "Anatomy-aided deep learning for Medical Image Segmentation : from the anatomy of the epicardial adipose tissue",
            "author": "Lu Liu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Medical image segmentation plays a pivotal role in modern healthcare by enabling precise diagnosis, treatment planning, and continuous monitoring of diseases. Among various segmentation tasks, the segmentation of epicardial adipose tissue (EAT) in low-dose computed tomography (LDCT) scans has gained attention due to its relevance in cardiovascular health. EAT is a vital fat depot surrounding the heart, closely associated with inflammatory mediators and cytokines, making it a crucial imaging biomarker for various cardiovascular conditions. LDCT is particularly suited for EAT segmentation, offering detailed anatomical information with minimal radiation exposure, thus allowing for repeated scans and large-scale studies. These studies are essential for assessing cardiovascular risk factors, including EAT volume, and identifying multiple biomarkers in a single scan for comprehensive cardiovascular health evaluations. However, segmenting EAT in LDCT scans presents significant challenges. The irregular shape and non-uniform distribution of EAT around the heart make it difficult to distinguish from surrounding tissues. Additionally, LDCT imaging is prone to noise and artifacts, which obscure image clarity and complicate the accurate delineation of EAT boundaries. The lack of specialized annotated datasets further hampers the development of reliable segmentation algorithms. Although advanced deep learning approaches have been applied to EAT segmentation, they often require large annotated datasets, are sensitive to noise, and struggle with interpreting features without explicit anatomical priors. To address these challenges, this thesis emphasizes the integration of anatomical information, particularly the shape of the pericardium, into segmentation methodologies. This approach aims to improve the accuracy, efficiency, and clinical relevance of EAT segmentation, ultimately contributing to better patient care and more informed medical decision-making. The thesis is built on a foundation of thorough literature review, examining existing anatomy-aided deep learning techniques for medical image segmentation. A significant contribution of the research is the creation of a specialized dataset consisting of 154 LDCT scans, annotated with pericardium and pixel-wise EAT labels. This dataset serves as a critical resource for training and evaluating segmentation algorithms. The thesis explores various deep learning models, such as 3D U-net and its variants, and evaluates their performance using traditional metrics like the Dice coefficient and statistical analyses like Pearson correlation and Bland-Altman analysis. A novel shape-based loss function derived from Fourier descriptors is introduced to enhance segmentation accuracy, particularly in challenging scenarios. Additionally, the thesis explores the use of Fourier neural operators to generalize shape encoding in the Fourier space, addressing the limitations of traditional methods and improving the generalization capabilities of models across different medical image segmentation tasks. Through these advancements, the thesis aims to provide state-of-the-art tools for healthcare professionals, driving progress in medical image segmentation and contributing to enhanced clinical outcomes.",
            "source_url": "https:\/\/openalex.org\/W4400468663",
            "pdf_url": "",
            "doi": "10.3990\/1.9789036562072",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4400468663",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 841,
            "title": "DEEP LEARNING IN MEDICAL IMAGE ANALYSIS: BRAIN STRUCTURAL SEGMENTATION, DIFFUSION MRI RECONSTRUCTION AND DOMAIN ADAPTIVE URINARY TRACT SEGMENTATION",
            "title_ar": "",
            "title_en": "DEEP LEARNING IN MEDICAL IMAGE ANALYSIS: BRAIN STRUCTURAL SEGMENTATION, DIFFUSION MRI RECONSTRUCTION AND DOMAIN ADAPTIVE URINARY TRACT SEGMENTATION",
            "author": "Yuemeng LI",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Pennsylvania",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; transformers; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Deep learning based medical imaging analysis has demonstrated remarkable capabilities in enhancing clinical diagnostics, facilitating better decision-making through improved visualization and analysis. Despite these achievements, the complexity of various imaging modalities presents significant challenges, highlighting the need for robust application across different systems. In this thesis, I address these challenges by integrating advanced deep learning models for specific imaging modalities including MRI, diffusion MRI and CT scans. First, I introduced the Anatomy Context-Encoding Network (ACEnet), a novel 2.5D deep learning model that leverages 3D spatial and anatomical contexts in a 2D framework. This model was designed to enhance the segmentation of brain structures from MRI scans. ACEnet’s performance on benchmark datasets demonstrated superior accuracy and speed, significantly outperforming existing state-of-the-art methods and illustrating its effectiveness for real-time clinical applications. Second, I developed DeepADC-Net, a CNN-Transformer based model for producing accurate Apparent Diffusion Coefficient (ADC) maps from accelerated diffusion-weighted MRI data. By efficiently reconstructing accelerated ADC maps to match fully-sampled ADC maps, DeepADC-Net significantly reduced scan time from 25 minutes to just over six minutes while maintaining high-quality output. This reduction is crucial for improving patient comfort and enhancing the diagnostic process for tumor detection. Lastly, I proposed CycleFormer, a novel cross-modality domain adaptation method that enhances urinary tract segmentation. Utilizing CT urograms, CycleFormer effectively improves the segmentation performance on non-contrast CT scans, specifically for ureters, and has shown notable improvement over alternative state-of-the-art 2D CNN models, providing more accurate and reliable diagnostic tools for clinical practice. In conclusion, the deep learning models developed in this thesis significantly advance the field of medical image analysis, providing robust, efficient and more accurate diagnostic tools that enhance the capability of medical imaging technologies in clinical diagnosis. These contributions demonstrate the potential of deep learning approaches across various medical imaging modalities, highlighting their effectiveness in clinical diagnosis and healthcare settings.",
            "source_url": "https:\/\/openalex.org\/W7135311797",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ScholarlyCommons (University of Pennsylvania)",
            "source_record_id": "W7135311797",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 842,
            "title": "Robust liver vessel segmentation in medical images using 3-D deep learning approaches",
            "title_ar": "",
            "title_en": "",
            "author": "Abir Affane",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2022,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Une approche Deep Learning pour la segmentation robuste des vaisseaux hépatiques sur des images médicales 3-D L’évolution de l’informatique dans la recherche sur les réseaux vasculaires a permis d’améliorer la reconstruction et l’interprétation de ces structures complexes.L’extraction robuste de ces structures sur des images biomédicales nécessite une bonne acquisition lors d’un examen d’imagerie.La segmentation des vaisseaux peut être utilisée pour de nombreuses applications telles que la division du foie en sous-régions. Cette définition des segments de Couinaud a été une bonne initialisation pour les chercheurs pour résoudre plu-sieurs problèmes liés aux maladies du foie, faciliter l’interprétation des images parles radiologues et même pour des planifications chirurgicales. L’exploration des vaisseaux hépatiques repose sur la bonne acquisition lors d’un examen. Le deep learning apporte de nouvelles solutions aux problèmes de segmentation des images médicales et devient une méthodologie de pointe pour de futures applications cliniques. L’étude des structures vasculaires est une tâche difficile en raison de la taille extrêmement petite de la structure du vaisseau, du faible SNR (Signal to Noise Ratio) et du contraste variable des données d’imagerie médicale. L’étude des structures curvilignes est une tâche difficile, en particulier compte tenu des applications d’imagerie médicale telles que la reconstruction des vaisseaux. Depuis l’arrivée des méthodes d’apprentissage profond, plusieurs approches basées sur l’intensité des pixels ont été proposées pour réduire les erreurs de segmentation, mais ces informations ne sont toujours pas suffisantes pour préserver la connectivité au sein des branches et peuvent donner de mauvais résultats de segmentation dans les bifurcations. Ceci est principalement dû à la complexité de ces structures et au contraste inhomogène dans tout le réseau vasculaire des images médicales.Cette thèse est consacrée au développement d’une nouvelle méthode d’apprentissage en profondeur 3-D basée sur des informations topologiques pour la segmentation des vaisseaux hépatiques à partir des images médicales, qui peut aider à améliorer les décisions de segmentation à partir des modèles vasculaires. Ces approches intègrent à la fois l’intensité du pixel et la structure topologique des vaisseaux en développant des modèles 3-D avec une fonction de perte topologique spécifique, qui calcule la similarité entre la carte de vraisemblance prédite par un modèle 3-D de segmentation et les vaisseaux de vérité de terrain. Notre modèle d’apprentissage topologique profond 3-D est une technique prometteuse avec une meilleure qualité de segmentation sur les bifurcations notamment.Dans une première partie, des modèles de segmentation U-Net 3-D sont revisités et comparés. Plus précisément, 3-D U-Net, Dense U-Net et MultiRes U-Net sont opposés les uns aux autres dans la tâche de segmentation des vaisseaux sur le jeu de données scanner public de l’IRCAD. Pour chaque modèle, trois configurations alternatives permettant d’adapter les architectures du réseau neuronal convolutif adaptées aux données volumétriques sont testées. À savoir, les configurations 3-Dcomplètes, basées sur des groupes de coupes (slabs) et sur des patches 3-D sont prises en compte. Ces méthodes permettent de mieux détecter les bifurcations.Cette comparaison des modèles 3-D permet de choisir la meilleure approche basée sur les intensités de l’image.Dans une seconde partie, une méthode basée sur la combinaison des modèles d’apprentissage profond avec des filtres de rehaussement est introduite pour segmenter les vaisseaux hépatiques sur des images 3-D. Cette approche améliore les résultats de segmentation. Plusieurs filtres de rehaussement ont été utilisés et comparés. Enfin la troisième partie de la thèse est consacrée à introduire les signatures topo-logiques. Ces informations peuvent aider à améliorer la décision de segmentation.L’étude hiérarchique du squelette (sous forme de graphe) permet de donner une description graphique du réseau vasculaire hépatique. (...)",
            "source_url": "https:\/\/openalex.org\/W7151558747",
            "pdf_url": "",
            "doi": "10.70675\/e29c06baza80dz47ebza090z3fe9d7c42991",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7151558747",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 843,
            "title": "Deep learning for semantic segmentation in multimodal medical images : application on brain tumor segmentation from multimodal magnetic resonance imaging",
            "title_ar": "",
            "title_en": "",
            "author": "Tongxue Zhou",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2022,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; multimodal; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Apprentissage profond pour la segmentation sémantique d'images médicales multimodales : applications à la segmentation des tumeurs cérébrales à partir d'images IRM multimodales Dans cette thèse, nous développons quatre méthodes de segmentation des tumeurs cérébrales à partir d'images IRM multimodales, basées sur l'apprentissage profond. Les deux premières méthodes se concentrent sur la segmentation tumorale avec des modalités complètes. Tandis que les deux dernières méthodes réalisent la segmentation lorsque certaines modalités sont manquantes. Nous présentons d'abord le premier réseau de neurones profond pour la segmentation d'images multimodales de tumeurs cérébrales. Celui-ci est basé à la fois sur un mécanisme d'attention afin de fusionner les images multimodales et sur une contrainte liée au contexte afin de limiter la région de la segmentation. Compte tenu qu'il existe une corrélation entre les modalités, nous proposons une seconde méthode exploitant cette corrélation multimodale dans l'espace des caractéristiques latentes, à l'aide d'un modèle non linéaire et de la divergence de KL, dans le but de fusionner les images de manière efficace. Cette méthode innovante utilise un bloc de fusion à « triple attention », qui comprend un module d'attention basé sur les modalités, un autre basé sur l'espace spatial, et un troisième basé sur la corrélation multimodale. Il est fréquent que certaines modalités soient manquantes en pratique clinique. Nous proposons donc deux méthodes permettant la segmentation de tumeurs cérébrales avec des modalités manquantes. Nous exploitons à nouveau la corrélation entre les modalités afin de conserver les informations des images manquantes. Dans notre première méthode, le réseau est entraîné avec la totalité des modalités à l'aide de la corrélation multimodale. Il est ensuite testé dans les cas où certaines modalités sont manquantes, et celles-ci sont remplacés par les modalités les plus proches. Afin de retrouver plus précisément les informations manquantes, nous proposons une seconde méthode permettant de générer les caractéristiques de la modalité manquante, grâce à une corrélation multimodale. De plus, nous appliquons une divergence KL, pour garantir la similarité entre les caractéristiques générées et celles de la modalité originale. Nous avons démontré l'efficacité et la bonne performance des méthodes proposées en utilisant une base d'images multimodales publique.",
            "source_url": "https:\/\/openalex.org\/W7150750481",
            "pdf_url": "",
            "doi": "10.70675\/c1d97248z146ez42eez9570z2f9373115998",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7150750481",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 844,
            "title": "A scalable and component-based deep learning parallelism platform : an application to convolutional neural networks for medical imaging segmentation.",
            "title_ar": "",
            "title_en": "",
            "author": "Soulaimane Guedria",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2020,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Une plateforme d'apprentissage profond à base de composants qui passe à l'échelle : une application aux réseaux de neurones convolutionnels pour la segmentation en imagerie médicale Les réseaux neuronaux profonds (DNNs), et plus particulièrement les réseaux neuronaux convolutifs (CNN) entraînés sur des grandes quantités de données, rencontrent un vif succès dans une multitude d'applications capitales, et particulièrement en imageries médicales. Cependant, l'entraînement de réseaux de neurones convolutifs (CNN) (1) est une tâche chronophage. De plus, (2) distribuer l'entraînement des CNNs est un défi ardu en pratique car il s'agit d'un processus fastidieux, répétitif et sujet aux erreurs. En outre, (3) il n'y a actuellement aucune étude approfondie sur la généralisation et la reproductibilité des techniques de parallélisation des CNNs particulièrement sur des applications concrètes de segmentation en imagerie médicale.Dans ce contexte, cette thèse vise à relever les défis susmentionnés. Pour cela, nous avons conçu, implémenté et validé une plateforme d'apprentissage profond à base de composants qui passe à l'échelle pour la segmentation en imagerie médicale. Au début, on introduit R2D2, une boîte à outils d'apprentissage profond de bout en bout qui passe à l'échelle. En effet, R2D2 introduit également un ensemble de nouvelles versions distribuées d'architectures d'apprentissage profond populaires afin d'accélérer l'entraînement effectif des modèles CNNs innovants dans des délais raisonnables et réduire l'écart entre les chercheurs et l'apprentissage en profondeur exigeant des compétences accrues. En outre, cette thèse introduit également Auto-CNNp, un nouveau framework basé sur les composants logiciels pour automatiser la parallélisation des CNNs en encapsulant et en cachant les tâches de routine de parallélisation au sein d'une structure de base tout en gardant la solution logicielle suffisamment flexible et extensible pour une personnalisation spécifique à l'utilisateur. Les résultats de l'évaluation de notre approche automatisée basée sur les composants sont prometteurs. Ils montrent qu'une accélération significative de la tâche de parallélisation CNN a été réalisée au détriment d'un temps d'exécution du framework négligeable, par rapport au temps nécessaire à la stratégie de parallélisation manuelle.Le couple de solutions logicielles précédemment introduites (R2D2 et Auto-CNNp) nous ont donné les outils appropriés pour effectuer une analyse expérimentale approfondie afin d'étudier la généralisation des techniques de parallélisation des CNNs vers la tâche de segmentation. Simultanément, nous avons mené une revue de littérature visant à étudier les sources de la reproductibilité dans l'entraînement des modèles d'apprentissage profond pour une configuration d'entraînement particulière de segmentation en imagerie médicale. Nous proposons également quelques recommandations de bonnes pratiques afin d'atténuer ces problèmes précités de reproductibilité d'entraînement des DNNs pour la segmentation en imagerie médicale. Enfin, nous faisons un certain nombre d'observations en nous basant sur une analyse approfondies des résultats de l'étude expérimentale déjà menée sur le parallélisation des CNNs, qui nous ont permis de proposer des directives et des recommandations pour distribuer l'entraînement des CNNs pour une segmentation sans perte de précision.",
            "source_url": "https:\/\/openalex.org\/W3120525886",
            "pdf_url": "https:\/\/theses.hal.science\/tel-03011791v1\/document",
            "doi": "10.70675\/502fe756z044dz4c71z995fz08791ef9e248",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W3120525886",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 845,
            "title": "Exploring the Potential of Deep Learning for Biomedical Image Segmentation",
            "title_ar": "",
            "title_en": "Exploring the Potential of Deep Learning for Biomedical Image Segmentation",
            "author": "Sharif, Muhammad Hamza",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United Arab Emirates",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; transformers; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Medical image segmentation is the fundamental task in biomedical imaging analysis by identifying and isolating regions of interest from images such as MRI or CT scans. This process is vital for precise diagnosis and effective treatment planning for various conditions, including tumors and anomalies. However, medical image segmentation is a challenging task due to the complex and diverse nature of these images. They contain structures with varying sizes, shapes, and intensities, as well as noise, artifacts, and missing data. Traditional image segmentation methods that rely on handcrafted features and heuristic algorithms have limitations in accurately segmenting medical images due to their complexity and variability. Recent advances in deep learning-based techniques have shown promising results in medical image segmentation by automatically learning feature representations. However, these techniques require innovation in network architecture, learning framework, and training procedures to improve the segmentation process. In this thesis, we aim to introduce new deep learning-based methods to improve medical image segmentation, with a focus on frameworks for high-resolution and volumetric medical image segmentation. The proposed techniques aim to segment various structures, including organs, brain tumors, skin lesions, polyps, and retinal vessels, using medical image data from different modalities such as CT, MRI, endoscopy imaging, and fundus imaging. The thesis proposes two novel deep learning-based techniques. The first one is called TransResNet, which combines a transformer and CNN to effectively segment high-resolution medical images. The proposed method introduces a Cross Grafting Module (CGM) that generates grafted features by combining global semantic and low-level spatial details. This approach achieves state-of-the-art or competitive results on several segmentation tasks, including skin lesions, retinal vessels, and polyp segmentation. The second technique proposed in the thesis is a versatile generic architecture called Y-CA-Net, which can segment volumetric medical images of different modalities (CT, MRI). This method is based on any two encoders and a decoder backbone, with one encoder branch that extracts local features using convolution filters and the other encoder branch that learns global interactions using an attention mechanism. The proposed approach aims to fully exploit the strengths of both convolution and attention mechanisms for volumetric segmentation. The model introduces a simple yet effective Cross Feature Mixer Module (CFMM) to learn semantically enriched features by mixing local and global feature representations. The proposed model has shown significant improvement across different benchmark datasets for multi-organ and brain tumor segmentation compared to existing state-of-the-art methods.",
            "source_url": "https:\/\/openalex.org\/W7106329223",
            "pdf_url": "",
            "doi": "10.82419\/162",
            "handle": "",
            "repository_name": "MBZUAI iRep",
            "source_record_id": "W7106329223",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 846,
            "title": "Learning with imperfect datasets in medical image segmentation",
            "title_ar": "",
            "title_en": "Learning with imperfect datasets in medical image segmentation",
            "author": "Yuhang Ding",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Technology Sydney",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; ai-applications; predictive-analytics; image-segmentation; medical-imaging; fairness; ai-industry",
            "keywords": "",
            "abstract": "edical image segmentation aims to partition medical images into distinct physiological regions, such as organs and lesions, which is crucial for disease diagnosis and treatment planning.The advent of deep neural networks has significantly advanced this field.However, the performance in real-world scenarios remains unsatisfactory due to imperfect data and annotations during both training and deployment.First, scaling up training data and annotations is challenging.This is because obtaining medical images is difficult due to privacy concerns.Furthermore, annotating medical images requires substantial expertise, making the process costly and difficult to carry out.Second, the quality of medical images cannot always be guaranteed in real-world scenarios, leading to significant performance drops in outlier cases.Third, real-world medical applications are safety-critical and demand extremely accurate predictions, a requirement that most existing models fail to meet adequately.These challenges hinder the practical deployment of medical image segmentation.Consequently, both academic and industrial communities are striving to develop highly accurate medical image segmentation algorithms that can perform well despite imperfect data.To this end, this thesis proposes deep learning methods to develop a well-performed medical image segmentation model that can be effectively trained with limited and low-quality data\/annotations.Specifically, a comprehensive suite is proposed from three directions: (1) applying image registration to generate realistic and diverse training samples and adopting barely-supervised learning paradigms to enable learning with insufficient annotated data; (2) devising region-aware fusion module to address missing modality problem; (3) incorporating automatic and interactive medical image segmentation into a single model and one training session to achieve sufficient segmentation performance for practical use.Extensive experiments on several medical image segmentation tasks, such as brain tumor segmentation, brain structure segmentation and abdominal organ segmentation, demonstrate the effectiveness and efficiency of the proposed techniques.ix",
            "source_url": "https:\/\/openalex.org\/W7216881435",
            "pdf_url": "https:\/\/opus.lib.uts.edu.au\/bitstream\/10453\/186717\/1\/thesis.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "OPUS - Open Publications of UTS Scholars (University of Technology Sydney)",
            "source_record_id": "W7216881435",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 847,
            "title": "Medical Image Segmentation by Deep Convolutional Neural Networks",
            "title_ar": "",
            "title_en": "Medical Image Segmentation by Deep Convolutional Neural Networks",
            "author": "Qingbo Kang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Concordia University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Medical image segmentation is a fundamental and critical step for medical image analysis. Due to the complexity and diversity of medical images, the segmentation of medical images continues to be a challenging problem. Recently, deep learning techniques, especially Convolution Neural Networks (CNNs) have received extensive research and achieve great success in many vision tasks. Specifically, with the advent of Fully Convolutional Networks (FCNs), automatic medical image segmentation based on FCNs is a promising research field. This thesis focuses on two medical image segmentation tasks: lung segmentation in chest X-ray images and nuclei segmentation in histopathological images. For the lung segmentation task, we investigate several FCNs that have been successful in semantic and medical image segmentation. We evaluate the performance of these different FCNs on three publicly available chest X-ray image datasets. For the nuclei segmentation task, since the challenges of this task are difficulty in segmenting the small, overlapping and touching nuclei, and limited ability of generalization to nuclei in different organs and tissue types, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs.",
            "source_url": "https:\/\/openalex.org\/W3044795341",
            "pdf_url": "https:\/\/spectrum.library.concordia.ca\/985550\/1\/Kang_MASc_F2019.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Spectrum Research Repository (Concordia University)",
            "source_record_id": "W3044795341",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 848,
            "title": "Constrained deep networks for medical image segmentation",
            "title_ar": "",
            "title_en": "Constrained deep networks for medical image segmentation",
            "author": "Hoel Kervadec",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; self-semi-supervised; predictive-analytics; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Weakly supervised image segmentation, in the form of partially labeled images, is attracting significant research attention as it can mitigate the need for laborious pixel annotations required by deep learning models. Enforcing high-order, global inequality constraints on the network outputs can leverage unlabeled data by guiding the training with prior knowledge, restricting the search space during training to anatomically feasible solutions. A range of possible values (such as a lower\/upper bounds on the size of a organ) can be very valuable to guide training. However, in the context of deep neural networks, standard Lagrangian optimization has been largely avoided, mainly due to the instability and computational complexity ensuing from alternating explicit dual updates and stochastic optimization. Interior point methods, despite their popularity in convex optimization, are not applicable neither, as they require a feasible starting point, which is itself a difficult constrained problem for deep neural networks. In this thesis, we investigate hard inequality constraints in the context of deep networks with both quadratic penalties and more principled log-barrier extensions. We also investigate methods to mitigate class-imbalance in segmentation problems, such as in brain lesions dataset, by constraining the boundary of the predicted segmentation to match the ground-truth boundary. This thesis produced five different publications as first author, and four papers as co-author. Our papers received several awards, and we were invited to publish extended versions of our works in two special issues of Medical Image Analysis (MedIA). In our first contribution, we propose to introduce a differentiable penalty, which enforces inequality constraints directly in the loss function, avoiding expensive Lagrangian dual iterates and proposal generation. From constrained-optimization perspective, our simple penalty-based approach is not optimal as there is no guarantee that the constraints are satisfied. However, surprisingly, it yields substantially better results than Lagrangian-based constrained convolutional neural networks, while reducing the computational demand for training. By annotating only a small fraction of the pixels, our approach reaches performances comparable to full supervision, on three separate tasks. While our experiments focused on basic linear constraints such as the target-region size and image tags, our framework can be easily extended to other non-linear constraints, e.g., invariant shape moments and other region statistics. In our second contribution, we propose log-barrier extensions, which approximate Lagrangian optimization of constrained-CNN problems with a sequence of unconstrained losses. Unlike standard interior-point and log-barrier methods, our formulation does not need an initial feasible solution. We report comprehensive weakly supervised segmentation experiments, with various constraints, showing that our formulation outperforms substantially the existing constrained-CNN methods, both in terms of accuracy, constraint satisfaction and training stability. In our third contribution, we enforce constraints on the boundary of predicted segmentation. Widely used loss functions for CNN segmentation, such as Dice or cross entropy, are based on integrals over the segmentation regions. Unfortunately, for highly unbalanced segmentations, such regional summations have values that differ by several orders of magnitude across classes, which affects training performance and stability. We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions. This can mitigate the difficulties of highly unbalanced problems because it uses integrals over the interface between regions instead of unbalanced integrals over the regions. Furthermore, a boundary loss complements regional information. Inspired by graph-based optimization techniques for computing activecontour flows, we express a non-symmetric L2 distance on the space of contours as a regional integral, which avoids completely local differential computations involving contour points. This yields a boundary loss expressed with the regional softmax probability outputs of the network, which can be easily combined with standard regional losses and implemented with any existing deep network architecture for N-D segmentation. We report comprehensive evaluations on different unbalanced problems, showing that our boundary loss can yield significant increases in performances while improving training stability. In a fourth contribution, we investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of the target region. These regressions are used to effectively regularize the segmentation network, constraining the softmax predictions of the unlabeled images to match the inferred label distributions.",
            "source_url": "https:\/\/openalex.org\/W3215133863",
            "pdf_url": "https:\/\/espace.etsmtl.ca\/id\/eprint\/2734\/1\/KERVADEC_Hoel.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Espace ÉTS (ETS)",
            "source_record_id": "W3215133863",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 849,
            "title": "Employing Attention-Based Learning For Medical Image Segmentation",
            "title_ar": "",
            "title_en": "Employing Attention-Based Learning For Medical Image Segmentation",
            "author": "Alexandros Ferles",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Automated medical image analysis is a non-trivial task due to the complexity of medical data. With the advancements made on Computer Vision through the golden era of Deep Learning, many models which rely on Deep Convolutional Networks have emerged in the Medical Imaging domain and offer important contributions in automating the analysis of medical images. Based on recent literature, this work proposes the adaptation of visual attention gates in Fully Convolutional Encoder-Decoder networks in the Medical Image Segmentation task. Appropriate data pre-processing is performed in the cases of 2-dimensional and 3-dimensional data in order to serve them as proper inputs in conventional and attention-gated Deep Convolutional Networks that try to identify classes in pixel and voxel level respectively. Attention gates can be easily integrated in the conventional networks, that would improve their performance. We present the specific mechanics of attention gates, conduct experiments and analyse our derived results. Finally, based on the latter, we provide our opinion and intuition on how this work can be further expanded towards new research directions.",
            "source_url": "https:\/\/openalex.org\/W3014417460",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W3014417460",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 850,
            "title": "Deep learning for coronary artery segmentation in CTA images",
            "title_ar": "",
            "title_en": "Deep learning for coronary artery segmentation in CTA images",
            "author": "Fredrik Ring",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Chalmers University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "image-segmentation",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation",
            "keywords": "",
            "abstract": "The mortality of cardiovascular diseases (CVD) calls for more knowledge of disease mechanisms and prevention strategies.The SCAPIS study, which is a collaboration between Swedish university hospitals and universities, aims to acquire this knowledge and is the worlds largest study focusing on CVD to date.Among other biomarkers, the SCAPIS study is interested in studying atherosclerotic plaque in coronary arteries which is a major risk factor of CVD.Atherosclerotic plaques can be studied from computed tomography angiography (CTA) images.The first step of the analysis is segmentation of the coronary artery tree, which is tedious and time consuming to do manually.Automatic segmentation techniques can speed up this procedure, but unfortunately, current automatic methods lack the accuracy required and have to be manually corrected.Thus, improving the performance of automatic software could be highly valuable to a large scale study such as SCAPIS.This thesis investigates the viability of a deep learning based automatic segmentation approach and compares its performance to the commercial software Medis QAngioCT Re and to the interobserver variability in manual segmentations from two medical professionals.A version of the state-of-the-art network architecture for vessel wall boundary segmentation, I2I-3D, is implemented in PyTorch and applied to the task.I2I-3D is a fully convolutional network allowing for a variable input size and full volume-to-volume segmentations.It has an encoder-decoder architecture with multiple levels of deep supervision integrated into the decoder.Evaluation on manually corrected segmentations on three CTA images shows that the implemented method outperforms the commercial software for two different segmentation similarity indices; Dice index and modified Hausdorff Distance.However, visual inspection still implies some shortcomings.Although very precise in the segmentation border placement, the network segmentations include broken vessels and unconnected islands of labellings.In contrast, the Medis software's downfall is its inclusion of small, clinically irrelevant vessels.When comparing to the interobserver variability, both automatic approaches perform unfavourably.Nevertheless, I2I-3D shows promising performance despite the relatively limited data set of 25 manually corrected segmentations.The results encourage further development with clear room for improvement.Most importantly, in order to reach full potential the method requires a significantly larger data set.In conclusion, this thesis confirms the viability of using deep learning to segment coronary arteries in cardiac CTA and should be interpreted as a proof-of-concept, rather than the presentation of a finished product.",
            "source_url": "https:\/\/openalex.org\/W2888867878",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Chalmers Publication Library (Chalmers University of Technology)",
            "source_record_id": "W2888867878",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 851,
            "title": "Deep Learning for Pneumothorax Segmentation and Quantification",
            "title_ar": "",
            "title_en": "Deep Learning for Pneumothorax Segmentation and Quantification",
            "author": "Wannipa Sae-Lim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Prince of Songkla University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Thailand",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; predictive-analytics; cnn; transformers; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "This thesis investigates the role of deep learning, focusing on convolutional neural networks and encoder-decoder architectures, to enhance medical image segmentation, specifically for pneumothorax identification in chest X-ray images. Pneumothorax, a critical condition characterized by lung collapse, requires precise diagnostics for effective management and treatment. Traditional image segmentation methods sometimes underperform due to the complexity and variability of chest X-ray images. In this study, we proposed PTXSeg-Net, a novel deep learning model for accurate and efficient pneumothorax segmentation. This model incorporates residual blocks and attention mechanisms to improve learning capabilities and is further enhanced by deep supervision, which allows for more effective gradient utilization across all network layers, significantly enhancing its performance over existing segmentation methods. Additionally, transfer learning with pre-trained models on extensive chest X-ray datasets improves feature extraction, addressing imbalanced datasets. Data refinement techniques are also employed to optimize the training outcomes. The performance of PTXSeg-Net outperforms the existing segmentation model in terms of accuracy, dice and Jaccard coefficients, demonstrating significant improvement in the field of pneumothorax segmentation. Moreover, leveraging the predicted masks of pneumothorax, this thesis proposes an algorithm that calculates the pneumothorax ratio. This algorithm provides a quantitative measure of the appearance of pneumothorax relative to the total lung area, offering a metric for radiologists to assess the severity of the condition more accurately. This integration of deep learning models and practical quantification algorithm underscores the significant contribution to the field of medical image analysis.",
            "source_url": "https:\/\/openalex.org\/W7201935903",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "PSU Knowledge Bank (Prince of Songkla University)",
            "source_record_id": "W7201935903",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 852,
            "title": "Relaxation and restriction for medical image segmentation with convolutional neural networks",
            "title_ar": "",
            "title_en": "Relaxation and restriction for medical image segmentation with convolutional neural networks",
            "author": "Pei Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "991012872963603412 HKUST Electronic Theses Relaxation and restriction for medical image segmentation with convolutional neural networks by Pei Wang thesis 2020 xvii, 99 pages : illustrations ; 30 cm Deep learning has achieved great success in different tasks for natural image",
            "source_url": "https:\/\/openalex.org\/W4234859439",
            "pdf_url": "",
            "doi": "10.14711\/thesis-991012872963603412",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4234859439",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 853,
            "title": "A hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for detecting Keratoconus in patients using genetic algorithm",
            "title_ar": "",
            "title_en": "A hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for detecting Keratoconus in patients using genetic algorithm",
            "author": "Somayeh Ghasedi Katak Lahijani",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Laurentian University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; predictive-analytics; optimization; cnn",
            "keywords": "",
            "abstract": "Nowadays, there are still significant challenges encountered in the accurate diagnosis of various eye diseases, such as Keratoconus (KCN) and cataracts. Early detection of Keratoconus is crucial in preventing its progression and ensuring the best treatment outcomes. Artificial intelligence (AI) is being widely applied in ophthalmology through the training of deep learning networks for the early detection of eye diseases. This research presents a novel, integrated machine learning approach for diagnosing Keratoconus disease by combining feature extraction through Convolutional Neural Networks (CNN) with a Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classification. Employing a multi-objective genetic algorithm, the method optimizes feature selection, aiming to minimize both diagnostic error and the number of features. The study utilizes a dataset of 5,152 ophthalmic images (1288 samples) categorized into Normal (476), Suspect (453), and Keratoconus (359) cases, sourced from an eye clinic in Egypt. Combining a Convolutional Neural Network (CNN) for feature selection with a Genetic Algorithm (GA) significantly improved diagnostic accuracy. Consequently, by focusing on the most relevant features of Keratoconus (KC), the model achieved an impressive 98.63% accuracy for ANN classification with a genetic algorithm, and 98.13% for SVM classification with a genetic algorithm. The accuracy of the algorithm exceeded that of when SVM and ANN were used without the genetic algorithm alone, which were 97.53% and 96.9% respectively, underscoring the benefit of combining Artificial Neural Networks (ANNs) with Genetic Algorithms (GAs) in KC diagnosis. Implementing this model can assist physicians in more accurate Keratoconus detection, providing better predictions regarding patients' eye conditions, and offering timely treatment recommendations.",
            "source_url": "https:\/\/openalex.org\/W7205487330",
            "pdf_url": "https:\/\/laurentian.scholaris.ca\/bitstreams\/ebade523-4710-440b-a199-72030adbc718\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Lu Zone Ul (Laurentian University)",
            "source_record_id": "W7205487330",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 856,
            "title": "Development of Automated Extraction Method for Road and Centerline from Aerial Images Using Deep Convolutional Neural Network",
            "title_ar": "",
            "title_en": "Development of Automated Extraction Method for Road and Centerline from Aerial Images Using Deep Convolutional Neural Network",
            "author": "Tamara Alshaikhli",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "deep-learning; computer-vision; ai-applications; cnn; image-segmentation; ai-smart-cities",
            "keywords": "",
            "abstract": "Road extraction from remote sensing imagery plays an important role in modern life society.That contributes in a lot of areas, such as urban planning, disaster mitigation, transportation engineering, and so on.Manual updating was the common methodology to keep the road information up to date, however the hectic process to label these images manually requires a lot of time and efforts, and is also prone to human error.Therefore, this area attracts a lot of researchers' attentions to find a suitable automated process.This research adopted a deep learning technique as an alternative to solve the problem of road extraction from aerial imagery automatically.The research consisted of two parts.The first one was dedicated to extract road only by a deep convolutional neural network (DCNN).The proposed DCNN was built on the U-Net backbone with a residual block (Res-block) on the encoder part and convolution layers (Conv.layer) on the decoder part.The model shows a good result for extracting road automatically from aerial images, and the research has extended to conduct a comprehensive study covering all the important aspects of this area.The second part of this research was assigned to extract road and centerline simultaneously by extending the previous model with multi task learning scheme (MTL).The proposed model is a complete MTL scheme without any further utilization of thinning algorithms or post processing.The proposed model showed a promising results for the single task learning (STL) to extract road only.In addition, the MTL for extracting road and centerline simultaneously also succeeded in extracting the roads and centerlines simultaneously.In order to evaluate the applicability of the proposed model for another dataset, a case study for Baghdad City, Iraq was performed, and the accuracy was examined.",
            "source_url": "https:\/\/openalex.org\/W7143882449",
            "pdf_url": "https:\/\/opac.ll.chiba-u.jp\/da\/curator\/900120235\/WLA_0058.pdf",
            "doi": "10.20776\/900120235",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7143882449",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 866,
            "title": "Applications of images anomalies detection using deep learning in department store",
            "title_ar": "",
            "title_en": "Applications of images anomalies detection using deep learning in department store",
            "author": "Ram Banstola",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Jyväskylä",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; ai-applications; predictive-analytics; ai-cybersecurity",
            "keywords": "",
            "abstract": "Deep learning is a branch of machine learning which itself is a branch of Artificial Intelligence. The use of deep learning to solve domain specific problems is on the rise. Deep learning has been successfully used to assist sales prediction in retail, disease detection in medicine, road infrastructure monitoring by checking cracks on the road, accidents prone zones, detect anomalous activities in the realm of cyber security etc. At present, user and machine generated data is available abundantly and the challenges for enterprises is to infer new information from the available data to increase profit for the enterprise, produce a reliable system and increase customer satisfaction. Deep learning has been successfully used in classification of data with high precision. However, there are bottlenecks when it comes to anomalies in data because building models to detect anomalies is more difficult than classification problems. This thesis aims to study image anomalies detection and their applications department store using design science research methods. This thesis presents a basic prototype application to demonstrate anomalies in product areas in department stores.",
            "source_url": "https:\/\/openalex.org\/W3139869024",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Jyväskylä University Digital Archive (University of Jyväskylä)",
            "source_record_id": "W3139869024",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:53",
            "updated_at": "2026-11-15 16:52:53"
        },
        {
            "id": 870,
            "title": "UnU-Net: Distance-Aware Uncertainty Quantification for Semantic Segmentation",
            "title_ar": "",
            "title_en": "UnU-Net: Distance-Aware Uncertainty Quantification for Semantic Segmentation",
            "author": "Andrew William Linville",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; image-segmentation; ai-industry",
            "keywords": "",
            "abstract": "Automated mechanical inspections increasingly rely on deep learning models for semantic segmentations of faults. However, standard deterministic models, such as U-Net, are prone to overconfident misclassifications on Out of Distribution (OOD) Data, posing severe risk of silent failures from a confident misclassification. Although methods like Deep Ensembles and Monte Carlo (MC) Dropout provide benchmarks for uncertainty quantification, their use during inference requires multiple forward passes increasing the computational overhead linearly making real time applications challenging. To address this gap, we introduce UnU-Net, a distance aware probabilistic architecture that incorporates a Spectral Normalized Neural Gaussian Process (SNGP) with the final pixel-wise prediction. By replacing conventional normalization techniques with Group Normalization and constraining the weights via Spectral Normalization, UnU-Net prevents feature collapse ensuring a preserved latent space distance. Evaluated on the Carvana and DeepCrack segmentation datasets, UnU-Net extracts precise segmentation masks and quantifies epistemic uncertainty flagging anomalies in a single forward pass. This architecture maintains the accuracy of the standard U-Net and outperforms conventional UQ methods while only requiring a single forward pass.",
            "source_url": "https:\/\/openalex.org\/W7161221772",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "VTechWorks (Virginia Tech)",
            "source_record_id": "W7161221772",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 873,
            "title": "Variational methods and deep learning for semantic image segmentation",
            "title_ar": "",
            "title_en": "",
            "author": "Miguel Chicchón",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Pontifical Catholic University of Peru",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Peru",
            "governorate": "",
            "publication_year": 2025,
            "language": "es",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; transformers; image-segmentation",
            "keywords": "",
            "abstract": "Actualmente, las aplicaciones de visión por computadora, que incluyen la conducción autónoma, la detección de fallas, la imagenología médica y la teledetección, son cada vez más frecuentes y necesitan mecanismos de segmentación o clasificación de píxeles más precisos. Recientemente, las técnicas de aprendizaje profundo que utilizan redes neuronales se han convertido en la estrategia dominante para resolver diversos problemas en visión por computadora; sin embargo, existen limitaciones y problemas aún por resolver. Es por ello que una integración adecuada con técnicas tradicionales, como los métodos variacionales, con sus propias ventajas y limitaciones, resulta de interés para mejorar la precisión y robustez de la segmentación semántica. Esta tesis se centra en la implementación y validación de algoritmos de segmentación de imágenes que combinan métodos variacionales y aprendizaje profundo en cuatro escenarios diferentes. En el primer escenario, que implica la segmentación de peces en entornos subacuáticos, se compararon métodos variacionales integrados utilizando enfoques de post-procesamiento, extremo a extremo (end-to-end) y una función de pérdida combinada. En el segundo escenario, que aborda la segmentación de baches y grietas en pavimentos de carreteras, se entrenaron tres arquitecturas de redes neuronales SOTA (del estado del arte) utilizando una función de pérdida de entropía cruzada combinada y contornos activos. En el tercer escenario, enfocado en la segmentación de techos de edificios en imágenes satelitales, se entrenó una red neuronal U-Net con diferentes redes SOTA en la ruta del codificador (encoder path) mediante aprendizaje por transferencia (transfer learning), y el mejor modelo fue seleccionado para ser entrenado utilizando una función combinada con pérdida de entropía cruzada y pérdida de contornos activos. Finalmente, en el cuarto escenario, que se refiere a la segmentación de cobertura terrestre en imágenes aéreas, se emplearon aprendizaje por transferencia y la función de contornos activos en el entrenamiento de redes neuronales convolucionales y transformadores (transformers). Los resultados cuantitativos consolidados demuestran mejoras significativas: la función de pérdida combinada logró aumentos promedio de4-6% en el mIoU (mean Intersection over Union) en comparación con los enfoques que solo utilizaban entropía cruzada o Dice. Específicamente, para la segmentación de peces, el modelo U-Net-scSE con un codificador ResNeSt-269e alcanzó 73.1% de mIoU; para la detección de baches, 87.45% de mIoU con una reducción de HD95 a 23.40 mm; para la segmentación de techos, se logró una mejora del 14% en IoU utilizando aprendizaje por transferencia; y para la cobertura terrestre, el modelo U-Net-scSE con un codificador MiT- B4 obtuvo 80.4% de mIoU. Estas validaciones a través de cuatro dominios heterogéneos demuestran que la integración sistemática de métodos variacionales en redes profundas proporciona mejoras cuantificables y transferibles en la exactitud de la segmentación.",
            "source_url": "https:\/\/openalex.org\/W7138825433",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Communities in DSpace (Pontifical Catholic University of Peru)",
            "source_record_id": "W7138825433",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 874,
            "title": "Psoriasis support system based on semantic segmentation AI models",
            "title_ar": "",
            "title_en": "Psoriasis support system based on semantic segmentation AI models",
            "author": "Lopes, Toni Marcos Silva",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; ai-applications; cnn; object-detection; image-segmentation; medical-imaging; ai-agriculture",
            "keywords": "",
            "abstract": "Psoriasis is a chronic inflammatory skin disease characterized by the presence of lesions that vary in shape, size, and color. Accurate segmentation of these lesions from clinical images is crucial for effective diagnosis and treatment monitoring. However, the variability in image dimensions, lighting conditions, and the presence of noise in real-world datasets complicates the segmentation task. Additionally, available datasets for psoriasis lesion segmentation are often small, making it challenging to train deep learning models effectively without overfitting. The project aims to enhance the segmentation accuracy of psoriasis lesions by employing a range of AI models and optimizing their performance through the application of advanced pre-processing techniques and data augmentation strategies. The AI models tested include Mask R-CNN, U-Net, YOLOv8n, FCN, DeepLabV3+, BiSeNet, HRNet, PSPNet, and SegNet. Pre-processing techniques, such as bilateral smooth filters and adaptive median filters, were applied to enhance image quality and reduce noise. Furthermore, data augmentations, including random adjustments in brightness, contrast, cropping, rotation, flipping, and scaling, were used to simulate real-world variations and increase the robustness of the models. These augmentations were carefully replicated from related works to mimic their experimental setups. The evaluation of multiple AI models for psoriasis lesion segmentation demonstrated that applying effective pre-processing and data augmentation techniques significantly improves model performance. Among the tested models, FCN achieved the highest F1 score of 0.889, using a bilateral smooth filter and translation augmentations. U-Net and SegNet followed closely, with U-Net reaching an F1 score of 0.885 and SegNet 0.880, both benefiting from similar pre-processing and augmentation strategies. Mask R-CNN and BiseNet also showed competitive results, underscoring the importance of carefully selected augmentations that mimic real-world variability. These findings highlight the value of pre-processing and augmentation in enhancing model generalization, particularly in small, diverse datasets. In addition to the AI-based segmentation work, the project also incorporates a mobile application designed to assist patients in tracking their psoriasis lesions over time. This application enables users to monitor whether lesions are improving or worsening, providing valuable insights for deciding when to consult a dermatologist. By helping patients recognize early signs of deterioration, the application plays a crucial role in supporting more proactive and informed healthcare decisions.",
            "source_url": "https:\/\/openalex.org\/W7113660975",
            "pdf_url": "https:\/\/bdigital.ufp.pt\/bitstreams\/d3e9f0b8-cf78-4d94-8dcd-0f04470834fa\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W7113660975",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 875,
            "title": "Semantic segmentation of mammals in forest regions with U-NET",
            "title_ar": "",
            "title_en": "Semantic segmentation of mammals in forest regions with U-NET",
            "author": "Jhonatan Guilherme de Oliveira Cunha",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Federal University of Technology – Paraná",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Brazil",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "image-segmentation",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; video-analysis",
            "keywords": "",
            "abstract": "Environmental monitoring is vital for preserving animal biodiversity, providing researchers with essential metrics on environmental health and aiding efforts to combat extinction. Mapping the fauna of forest regions is, therefore, essential for informed decision-making and effective planning. However, manual mapping is time-consuming and labor-intensive for biologists. To address this, our study utilized Deep Learning techniques to assist in detecting mammal species in the forests of northwestern Paraná, using videos provided by NAPI Biodiversidade. We implemented a semantic segmentation algorithm using the U-NET convolutional neural network to segment forest images. Our experiments demonstrated that after histogram equalization and without forest grouping, the Dice Coefficient was 79.40%, and the F1 Macro Score for animal presence classification was 86%. However, with forest grouping, there was a substantial drop in the model’s performance, highlighting difficulties in generalizing to new locations. This suggests the necessity of training location-specific models. Additionally, applying histogram equalization through contrast stretching improved the quality of the generated masks and classifications. However, the presence of false negatives indicates a need for more training data.",
            "source_url": "https:\/\/openalex.org\/W7155461023",
            "pdf_url": "http:\/\/repositorio.utfpr.edu.br:8080\/jspui\/bitstream\/1\/35300\/1\/segmentacaosematicamamiferosunet.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná)",
            "source_record_id": "W7155461023",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 880,
            "title": "Semantic segmentation of urban airborne oblique images",
            "title_ar": "",
            "title_en": "Semantic segmentation of urban airborne oblique images",
            "author": "Li Liu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; cnn; rnn; image-classification; image-segmentation; ai-smart-cities",
            "keywords": "",
            "abstract": "Computer recognition and classification of remote sensing digital images is an important research direction in remote sensing image processing.A lot of classification methods has been widely used for urban scene understanding,the large availability of airborne images necessitates an image classification approach that automatically learns relevant features from the rawimage and performs semantic segmentation in an end-to-end framework. For this, deep learning methodologies werepreferred, specifically in the form of convolutional neural network (CNN)which is widely used in computer vision. In this research, airborne oblique imagesare used as data source for semantic segmentation in urban areas, we investigate the ability of Deep Convolutional Neural Network(DCNN)structures that is designed based on U-net architectureand adaptitto our dataset.Different from its original architecture based on VGG11,U-net based on VGG16 architecture with deeper layers isappliedin this study.However, the training time ofdeep neural network is too long because of enormous parametersandpresent some challenges for model training. Here, depthwise separable convolution is coupled with convolution block in our architecture to reduce the model parametersand improve model efficiency.Outputs of deep neural network are sometimesnoisy and the boundaryof objects are not very smoothbecause continuous pooling layers reduce the ability of localization.As a result, the mean field approximation to the fully connected conditional random field (CRF) inference which models label and spectral compatibility inthe pixel-neighbourhood is applied to refine the output of other classifiers.Finally, we achieved anend-to-end modelwhich connect the scores of the U-net classification with a fully connected Conditional Random Field (CRF) architectureand make the inference as Recurrent Neural Network (RNN), to consider the local class label dependencies in an end-to-end structure and make full use ofDCNNs and probability random field model. We compare the fully connected CRF optimized segmentation results with those obtained by applying the trained U-net classifier.Segmentation results indicate that our classification U-net modelwith deeper layershave performed favourably, the modified network not only ensures the accuracy but also greatly shortens the time of model training in the large-scale image classification problem,furthermore, this end-to-end model which combines fully connected CRF has effectively improved the segments of U-net.",
            "source_url": "https:\/\/openalex.org\/W3151720536",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3151720536",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 883,
            "title": "Semantic Segmentation of Urban Scene Images Using Recurrent Neural Networks",
            "title_ar": "",
            "title_en": "Semantic Segmentation of Urban Scene Images Using Recurrent Neural Networks",
            "author": "Venkata Satya Sai Ajay Daliparthi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Dalarna University Library",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; robotics; ai-applications; cnn; rnn; image-segmentation; medical-imaging; ai-smart-cities",
            "keywords": "",
            "abstract": "Background: In Autonomous Driving Vehicles, the vehicle receives pixel-wise sensor data from RGB cameras, point-wise depth information from the cameras, and sensors data as input. The computer present inside the Autonomous Driving vehicle processes the input data and provides the desired output, such as steering angle, torque, and brake. To make an accurate decision by the vehicle, the computer inside the vehicle should be completely aware of its surroundings and understand each pixel in the driving scene. Semantic Segmentation is the task of assigning a class label (Such as Car, Road, Pedestrian, or Sky) to each pixel in the given image. So, a better performing Semantic Segmentation algorithm will contribute to the advancement of the Autonomous Driving field. Research Gap: Traditional methods, such as handcrafted features and feature extraction methods, were mainly used to solve Semantic Segmentation. Since the rise of deep learning, most of the works are using deep learning to dealing with Semantic Segmentation. The most commonly used neural network architecture to deal with Semantic Segmentation was the Convolutional Neural Network (CNN). Even though some works made use of Recurrent Neural Network (RNN), the effect of RNN in dealing with Semantic Segmentation was not yet thoroughly studied. Our study addresses this research gap. Idea: After going through the existing literature, we came up with the idea of “Using RNNs as an add-on module, to augment the skip-connections in Semantic Segmentation Networks through residual connections.” Objectives and Method: The main objective of our work is to improve the Semantic Segmentation network’s performance by using RNNs. The Experiment was chosen as a methodology to conduct our study. In our work, We proposed three novel architectures called UR-Net, UAR-Net, and DLR-Net by implementing our idea to the existing networks U-Net, Attention U-Net, and DeepLabV3+ respectively. Results and Findings: We empirically showed that our proposed architectures have shown improvement in efficiently segmenting the edges and boundaries. Through our study, we found that there is a trade-off between using RNNs and Inference time of the model. Suppose we use RNNs to improve the performance of Semantic Segmentation Networks. In that case, we need to trade off some extra seconds during the inference of the model. Conclusion: Our findings will not contribute to the Autonomous driving field, where we need better performance in real-time. But, our findings will contribute to the advancement of Bio-medical Image segmentation, where doctors can trade-off those extra seconds during inference for better performance.",
            "source_url": "https:\/\/openalex.org\/W3209919241",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Diva portal (Dalarna University Library)",
            "source_record_id": "W3209919241",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 884,
            "title": "Fully convolutional neural network for semantic segmentation on CT scans of pigs",
            "title_ar": "",
            "title_en": "Fully convolutional neural network for semantic segmentation on CT scans of pigs",
            "author": "Jarand Hornseth Pollestad",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; cnn; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Identifying the shape and location of structures within medical images is useful for purposes such as diagnosis and research. This is a cumbersome task if done manually. Recent advances in computer vision and in particular deep learning have made it possible to automate this task to such an extent that it is comparable to human level performance. This thesis reviews the components used to construct a fully convolutional neural network for semantic segmentation. It then proposes a modified network architecture based on an existing state-of-the-art fully convolutional neural network called U-net. The architecture is applied to a binary classification problem involving computed tomography scans of pigs provided by Norsvin SA. The goal is to classify each pixel in the scans as either \"a part of the pig which is edible\" or \"background\" which means everything that is not in the edible class. Each computed tomography scan is too large for the network to process at once. Part of the thesis is therefore devoted to investigating approaches for feeding the information in the scans to the proposed network. The network is trained on 238 scans and evaluated on 37 scans. The evaluation is done quantitatively using the index over union metric and qualitatively through manual inspection of segmented images. The results show that the best performing network on average obtains an index over union score of 0.962 when given a scan for segmentation.",
            "source_url": "https:\/\/openalex.org\/W2963251943",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "NORA - Norwegian Open Research Archives",
            "source_record_id": "W2963251943",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 887,
            "title": "Interpretability of a Deep Learning Model for Semantic Segmentation : Example of Remote Sensing Application",
            "title_ar": "",
            "title_en": "Interpretability of a Deep Learning Model for Semantic Segmentation : Example of Remote Sensing Application",
            "author": "Adrianna Janik",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; ai-applications; predictive-analytics; image-segmentation; xai; fairness; ai-smart-cities",
            "keywords": "",
            "abstract": "Understanding a black-box model is a major problem in domains that relies on model predictions in critical tasks. If solved, can help to evaluate the trustworthiness of a model. This thesis proposes a user-centric approach to black-box interpretability. It addresses the problem in semantic segmentation setting with an example of humanitarian remote sensing application for building detection. The question that drives this work was, Can existing methods for explaining black-box classifiers be used for a deep learning semantic segmentation model? We approached this problem with exploratory qualitative research involving a case study and human evaluation. The study showed that it is possible to explain a segmentation model with adapted methods for classifiers but not without a cost. The specificity of the model is likely to be lost in the process. The sole process could include introducing artificial classes or fragmenting image into super-pixels. Other approaches are necessary to mitigate identified drawback. The main contribution of this work is an interactive visualisation approach for exploring learned latent space via a deep segmenter, named U-Net, evaluated with a user study involving 45 respondents. We developed an artefact (accessible online) to evaluate the approach with the survey. It presents an example of this approach with a real-world satellite image dataset. In the evaluation study, the majority of users had a computer science background (80%), including a large percentage of users with machine learning specialisation (44.4% of all respondents). The model distinguishes rurality vs urbanization (58% of users). External quantitative comparison of building densities of each city concerning the location in the latent space confirmed the later. The representation of the model was found faithful to the underlying model (62% of users). Preliminary results show the utility of the pursued approach in the application domain. Limited possibility to present complex model visually requires further investigation.",
            "source_url": "https:\/\/openalex.org\/W3011114404",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W3011114404",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 888,
            "title": "Semantic Segmentation of RGB-Z Aerial Imagery Using Convolutional Neural Networks",
            "title_ar": "",
            "title_en": "Semantic Segmentation of RGB-Z Aerial Imagery Using Convolutional Neural Networks",
            "author": "Amber Mulder",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Delft University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Netherlands",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; object-detection; image-segmentation",
            "keywords": "",
            "abstract": "Semantic segmentation (or pixel-level classification) of remotely sensed imagery has shown to be useful for applications in fields as mapping of land cover, object detection, change detection and land-use analysis. Deep learning algorithms called convolutional neural networks (CNNs) have shown to outperform traditional computer vision and machine learning approaches in tackling semantic segmentation tasks. Furthermore, addition of height information (Z) to aerial imagery (RGB) is believed to improve segmentation results. However, discussion remains on the following: to what extent height information adds value; the best way to combine RGB information with height information; and what type of height information can best be used. This study aims to answer these questions. In this research work, the CNN architectures FCN-8s, SegNet, U-Net and FuseNet-SF5 are trained to semantically segment 10 cm resolution true ortho imagery of Haarlem, potentially augmented with height information. The outputted topographic maps contain the classes building, road, water and other. Experiments are conducted that allow for the comparison of 1) models trained on RGB and on RGB-Z, 2) models combining RGB and height information through data fusion and through data stacking, and 3) models trained using different types of absolute and relative height approaches. Performances are compared based on scores on the performance measure (mean) intersection over union (IoU) and through visual assessment of outputted prediction maps. The results indicated that on average segmentation performance improves by approximately 1 percent when absolute height information is added. The class building showed to benefit the most from the addition of height information. Furthermore, extracting features from height information in a separate encoder and fusing these into RGB feature maps, led to a higher overall segmentation quality than when height information is provided as a stacked extra band and processed in the same encoder as the RGB information. Finally, models using relative height delivered a higher quality segmentation than when absolute height approaches were used, especially for large objects. The best performing model; FuseNet-SF5 trained on RGB imagery and pixel-level, relative height, retrieved a mean IoU of 0.8427 and IoUs of 0.8744, 0.7865, 0.9131 and 0.7966 for the classes building, road, water and other respectively. This model was able to correctly classify over 90% of the pixels of 67% of all the objects present in the ground truth. Overall, this study showed that, when considering semantic segmentation of aerial RGB imagery, 1) height information can improve segmentation results, 2) adding height information through data fusion can result in a higher segmentation quality than when data stacking is used, and 3) providing relative height to a network, rather than absolute height, can improve semantic segmentation quality.",
            "source_url": "https:\/\/openalex.org\/W3167381927",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Research Repository (Delft University of Technology)",
            "source_record_id": "W3167381927",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 889,
            "title": "Improving LULC Map Production via Semantic Segmentation and Unsupervised Domain Adaptation",
            "title_ar": "",
            "title_en": "Improving LULC Map Production via Semantic Segmentation and Unsupervised Domain Adaptation",
            "author": "Rostyslav-Mykola Tsenov",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; transfer-learning; predictive-analytics; cnn; image-segmentation; ai-smart-cities",
            "keywords": "",
            "abstract": "In recent years, a lot of remote sensing problems benefited from the improvements made in deep learning. In particular, deep learning semantic segmentation algorithms have provided improved frameworks for the automated production of land-use and land-cover (LULC) map generation. Automation of LULC map production can significantly increase its production frequency, which provides a great benefit to areas such as natural resource management, wildlife habitat protection, urban expansion, damage delineation, etc. In this thesis, many different convolutional neural networks (CNN) were examined in combination with various state-of-the-art semantic segmentation methods and extensions to improve the accuracy of predicted LULC maps. Most of the experiments were carried out using Landsat 5\/7 and Landsat 8 satellite images. Additionally, unsupervised domain adaption (UDA) architectures were explored to transfer knowledge extracted from a labelled Landsat 8 dataset to unlabelled Sentinel-2 satellite images. The performance of various CNN and extension combinations were carefully assessed, where VGGNet with an output stride of 4, and modified U-Net architecture provided the best results. Additionally, an expanded analysis of the generated LULC maps for various sensors was provided. The contributions of this thesis are accurate automated LULC maps predictions that achieved ~92.4% of accuracy using deep neural networks; production of the model trained on the larger area, which is six times the size from the previous work, for both 8-bit Landsat 5\/7, and 16-bit Landsat 8 sensors; and generation of the network architecture to produce LULC maps for the unlabelled 12-bit Sentinel-2 data with the knowledge extracted from the labelled Landsat 8 data.",
            "source_url": "https:\/\/openalex.org\/W3162967908",
            "pdf_url": "",
            "doi": "10.36939\/ir.202104301247",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3162967908",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 890,
            "title": "Iris segmentation",
            "title_ar": "",
            "title_en": "Iris segmentation",
            "author": "Vishal Ramesh",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Czech Republic",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "image-segmentation",
            "categories": "deep-learning; computer-vision; medical-ai; image-segmentation",
            "keywords": "",
            "abstract": "Accurate iris image segmentation is crucial to a range of proposed medical diagnosis and treatment systems. Previous models have worked well with healthy eye images but do not generalize to diseased eye images. We work with a dataset where many subjects have eye diseases or deformities. We analyse the performance of the U-Net, a deep learning architecture for semantic segmentation. Our model was trained on a hand-annotated dataset and tuned to generalise on unseen images. Our model achieves a pixel accuracy of 0.8913 on the test set with a relatively short training time. 1",
            "source_url": "https:\/\/openalex.org\/W7135536303",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Digital Repository (National Repository of Grey Literature)",
            "source_record_id": "W7135536303",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 893,
            "title": "Visually attended sound source semantic segmentation : Forming a semantic mask using visual and audio cues",
            "title_ar": "",
            "title_en": "Visually attended sound source semantic segmentation : Forming a semantic mask using visual and audio cues",
            "author": "Petteri Nuotiomaa",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Tampere University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; multimodal; image-segmentation; speech-recognition",
            "keywords": "",
            "abstract": "Sound Source Separation from single input audio mixture is a problem that has had many different proposed solutions. Many of the modern solutions involve using machine learning and especially deep learning to achieve good separation results. The goal of this thesis is to develop a solution to the sound source semantic segmentation problem, evaluate the performance of the proposed method, and compare it with similar solutions The thesis goes over the theory behind the problem and explores some of the previous solutions used for this problem. During the background research, it became clear that there aren’t many solutions trying to solve the exact problem of sound source semantic segmentation. This is why the thesis examines solutions for different problems, that have some relation to the sound source semantic segmentation. This thesis proposes a new solution for sound source semantic segmentation. The solution is based on some of the groundbreaking research done in the sound source separation field. The proposed deep learning system has two networks, one for audio and one for visual information. The audio network is a U-Net type network and the visual network is based on the ResNet model of networks. The addition of visual information to the final separation is done by weighing the audio instrument masks with the probabilities of predicted instruments. The network uses common hyperparameters that are used in different works in the field. The loss function is slightly different than any of the previous works as it uses probabilities instead of hard labels for ground truth values. The results from the proposed system indicate that the system can separate simple audio sources from an audio mixture. The solution didn’t achieve wanted results in all test cases and specifically for duet audios the method had problems separating the instruments. The system showed some promise in the multimodality approach, but the better results for the multimodality approach could be accounted for by the different initial randomization of the network. The proposed solution achieved results that are better than the baseline and the most simple approaches to the same problem but failed to meet the standard separating quality that can be expected from a modern deep learning approach.",
            "source_url": "https:\/\/openalex.org\/W7133075142",
            "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/155461\/2\/NuotiomaaPetteri.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Tampere University Institutional Repository (Tampere University)",
            "source_record_id": "W7133075142",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 897,
            "title": "Transfer learning for language model adaptation",
            "title_ar": "",
            "title_en": "Transfer learning for language model adaptation",
            "author": "Bari M. Saiful",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; transfer-learning; self-semi-supervised; predictive-analytics; llm; sentiment-analysis; machine-translation",
            "keywords": "",
            "abstract": "Language is the pathway to democratize the boundary of land and culture. Bridging the gap between languages is one of the biggest challenges of Artificial Intelligent (AI) systems. The current success of AI systems is dominated by the supervised learning paradigm where gradient-based learning algorithms (i.e., SGD, Adam) are designed to optimize complex high-dimensional planes. These algorithms learn from statistical observations that are typically collected with the intention of a specific task (i.e., product review, sentiment analysis). The use of task-dependent samples makes the learning procedure tedious as it requires manually annotated data. On the contrary, without having a sufficient amount of samples to represent the distribution, deep learning models tend to suffer from the lack of robustness. Due to the natural puzzle of randomness, not all sets of observations are observed in the data collection procedure, thus creating Out-of-Distribution (OOD) problem in learning algorithms. In the search of a generic task agnostic distribution, a large collection of text over various domains can be considered as - Standard Natural Text Distribution (SNTD). The generic idea of Transfer Learning for Natural Language Processing (NLP) is to utilize the SNTD knowledge for any other task-dependent training. Learning SNTD, followed by a task-adaptation method with a smaller volume of annotated data has yielded state-of-the-art (SOTA) results in various supervised NLP tasks. However, annotated data for every task in every language is rare. In language models, there are many kinds of distributional variance. One of the most common ways that distributional variance is encoded into a language model is when it is trained with mono-lingual text and learned dis-jointly. Word embeddings produced from these language models are then used as pre-trained embedding vectors to adapt to a downstream task. We propose adversarial training to project two monolingual distributions into the same space and then improve the robustness of the model by augmented fine-tuning with parameter sharing. By projecting mono-lingual language distribution into the same cross-lingual space, the distribution of the language become aware of each other. These projected distribution are semantically aware of each other in the latent space. Thus when we train one distribution the other distribution automatically adapt to the training data, making it easier to transfer (exchange) knowledge. On top of that, the proposed novel self-training architecture improves cross-lingual transfer by a large margin. Then, we focus on the jointly trained multi-lingual language model, where there is no pre-dominant distributional variance. In the multi-lingual language model, we put more focus on downstream task adaptation. We found that semi-supervised learning with pseudo-augmented data from a pre-trained language model can greatly improve the performance of tasks further down the line. In the end, we introduce a new novel data augmentation framework that uses the neighboring (vicinal) samples of the original training data without explicitly using any parallel text corpora or machine translation system. Our proposed method performs simultaneous self-training with data augmentation and unsupervised sample selection. It also proposes curriculum strategies for different domain samples. With extensive experiments on three different cross-lingual tasks spanning many language pairs, we have demonstrated the effectiveness of our proposed method. While all of the work done above focused on improving task adaptation in multiple languages without supervision, we further investigate how adding a few samples affects multi-lingual task adaptation. To this end, we leverage a small number of support samples from each language and propose an inference-time transductive nearest neighbor-based approach which utilizes the entropy of the query samples for predictions. We show that our proposed method outperforms full model\/full head fine-tuning as well as cross-task fine-tuning. We also show impressive performance gain (~37x) in computational costs for the full inference prediction. However, as the language models grow bigger, it becomes increasingly difficult to perform efficient inferences, especially for multiple tasks. The jointly optimized multi-lingual distribution helps transfer knowledge from a high-resource language to a low-resource language. While working on transductive nearest neighbor inference, we observe that language models are significantly prone to task distributions. Unless we use an extremely large language model (>100B), a model used for a specific task adaptation cannot be used for another task. In this dissertation, our final proposed method addresses this issue by multitask prompted learning.",
            "source_url": "https:\/\/openalex.org\/W4386409617",
            "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/169892\/2\/sbmaruf_thesis.pdf",
            "doi": "10.32657\/10356\/169892",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4386409617",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 899,
            "title": "Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans",
            "title_ar": "",
            "title_en": "Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans",
            "author": "Saad Bin Ashraf",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; image-segmentation; medical-imaging",
            "keywords": "",
            "abstract": "Magnetic Resonance Imaging (MRI) is a vital non-invasive tool for high-resolution brain imaging, yet its long acquisition time frequently results in patient movement leading to motion artifacts, particularly among non-compliant patients like newborns. While Deep Learning (DL) models have emerged as powerful retrospective solutions for motion mitigation, their performance is expected to degrade when applied to data from different scanners, protocols, or patient populations. This thesis investigates the generalizability of DL-based motion correction across heterogeneous adult and neonatal brain MRI domains. The research is conducted through three primary aims. First, the study evaluates the domain generalization of a 3D U-Net model across multi-center adult datasets - IXI, Calgary-Campinas, and OASIS-3, comprising 11 imaging centers with diverse scanner vendors and field strengths. Results indicate that models generalize effectively to unseen adult datasets from various centers, while superior performance is achieved when training incorporates broader intensity distributions. Second, the thesis explores cross-domain transfer learning by applying adult-trained models to the neonatal domain, utilizing the dHCP dataset, which contains real motion from neonatal subjects. Results indicate that adult-pretrained models learn domain-agnostic structural priors, allowing them to reconstruct neonatal brain anatomy despite significant shifts in contrast, size, and motion characteristics. Crucially, fine-tuning with as few as three neonatal scans was shown to substantially improve the mitigation of neonatal-specific artifacts, demonstrating an efficient adaptation strategy for data-scarce domains. Third, the study critically assesses the reliability of common image quality metrics (IQMs). The analysis reveals that traditional metrics are often insensitive to subtle motion and can even yield paradoxical results, where motion-corrupted images appear higher in quality than corrected ones. Overall, this work highlights the potential for deploying robust motion-mitigation frameworks in diverse clinical settings. By leveraging transfer learning, this thesis provides a foundation for more reliable and scalable deep learning–based motion correction in pediatric and adult neuroimaging.",
            "source_url": "https:\/\/openalex.org\/W7140225405",
            "pdf_url": "",
            "doi": "10.11575\/prism\/51192",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W7140225405",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 901,
            "title": "Study of transfer learning and domain adaptation for language modeling in end-to-end speech recognition",
            "title_ar": "",
            "title_en": "Study of transfer learning and domain adaptation for language modeling in end-to-end speech recognition",
            "author": "卓 龔",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Japan",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; nlp; transfer-learning; llm; speech-recognition",
            "keywords": "",
            "abstract": "Automatic Speech Recognition (ASR) has become a technical foundation for a natural human-machine interface: speech input.Recently, there have been various daily applications based on ASR: 1) transcription in dictation as smartphone input method, in automatically generated subtitles, as upstream task of speech translation (ST); 2) speech command recognition in a personal assistant like Siri, Alex, Cortana or intent recognition: booking flight, etc; and 3) QA systems like auto attendant phone systems.Deep neural network-based end-to-end (E2E) ASR systems have gained popularity in recent years due to the fast development of deep learning technologies since they can leverage gigantic speech training data with high performance and high efficiency which outperformed human for domains with sufficient data.For conventional ASR systems, independent modules can be trained on audio signals and textual sequences which gives the ASR system the capability to easily be adapted to different scenarios by only changing one of those modules.Compared to conventional ASR systems, E2E ASR systems usually have a single indivisible module for transforming speech to text.If adaptation is necessary, sufficient data from target domain should be prepared or an external language model can be integrated into the system.However, labeled speech data can always be insufficient for a specific domain while text data are much more easily obtained from web pages, books, journals, etc.Because of these reasons, Language models (LM) have been essential in enhancing the performance of end-to-end (E2E) automatic speech recognition (ASR) systems.There are two dimensions of LM integrations: 1) space dimension, how deep LM should be integrated into an E2E ASR model (combining outputs or neural networks); 2) time dimension, when to integrate LM (training stage; decoding stage; or both).This thesis starts from introducing crucial methodologies for modern E2E, then present several contributions considering above dimensions.",
            "source_url": "https:\/\/openalex.org\/W7144031295",
            "pdf_url": "https:\/\/repository.dl.itc.u-tokyo.ac.jp\/record\/2011417\/files\/A40245.pdf",
            "doi": "10.15083\/0002011417",
            "handle": "",
            "repository_name": "Institutional Repositories DataBase (IRDB)",
            "source_record_id": "W7144031295",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 907,
            "title": "Domain Adaptation for Fair and Robust Machine Learning",
            "title_ar": "",
            "title_en": "Domain Adaptation for Fair and Robust Machine Learning",
            "author": "이우진",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Seoul National University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "South Korea",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transfer-learning",
            "categories": "machine-learning; deep-learning; ai-applications; transfer-learning; ai-cybersecurity",
            "keywords": "",
            "abstract": "Deep learning society has grown tremendously in recent years, leading to increasing demand for large amounts of data. However, it is difficult to gather enough labeled data similar to unlabeled real-world test data. We have to apply the domain adaptation to overcome this limitation. Domain adaptation learns by taking into account differences in probability distributions between different domains, so it outcomes a model that can be applied to both domains. In this dissertation, we first focus on developing domain adaptation methods in terms of learning appropriate representation. We propose a class-conditional domain invariant learning method that can learn a feature space in which features in the same class are expected to be mapped nearby. Improving the performance of domain adaptation has an expected impact that the application area of machine learning will be widened because a large number of unsupervised data can be used for machine learning. In addition to these direct expected impacts, domain adaptation techniques can be used in many aspects of deep learning. The key characteristic of domain adaptation is that it builds a machine learning model that can be applied to two different distributions. Therefore, domain adaptation based approaches can be widely used in machine learning areas that handle two different distributions, such as defense against adversarial attacks or fair machine learning. Robustness of deep learning is important since it can induce various security and safety problems. We focus on modeling deep networks that are robust against adversarial attacks by using domain adaptation methods. Considering normal samples as the source domain and adversarial samples as target domain, we suggest novel Wasserstein distance-based domain adaptation method that can enhance model robustness. As the increase in machine learning influences decisions about important aspects of our lives, the demand for a fair machine learning model has increased rapidly in recent years. We investigate a methodology for developing a fair classification model for data with limited or no labels, by transferring knowledge from another data domain where information is fully available, which is done by controlling the Wasserstein distances between relevant distributions. In real-world applications of machine learning, there are many cases where we need to handle data from various distributions. In these cases, domain adaptation can be the right solution because it considers the difference of probabilistic distributions of two domains. We expect, our algorithms can be helpful to overcome these limitations and be applied to build fair and robust models.",
            "source_url": "https:\/\/openalex.org\/W3182159980",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Seoul National University Open Repository (Seoul National University)",
            "source_record_id": "W3182159980",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 908,
            "title": "Domain adaptive learning with disentangled features",
            "title_ar": "",
            "title_en": "Domain adaptive learning with disentangled features",
            "author": "Xingchao Peng",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Boston University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "transfer-learning",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; transfer-learning",
            "keywords": "",
            "abstract": "Recognizing visual information is crucial for many real artificial-intelligence-based applications, ranging from domestic robots to autonomous vehicles. However, the success of deep learning methods on visual recognition tasks is highly dependent on access to large-scale labeled datasets, which are expensive and cumbersome to collect. Transfer learning provides a way to alleviate the burden of annotating data, which transfers the knowledge learned from a rich-labeled source domain to a scarce-labeled target domain. However, the performance of deep learning models degrades significantly when testing on novel domains due to the presence of domain shift. To tackle the domain shift, conventional domain adaptation methods diminish the domain shift between two domains with a distribution matching loss or adversarial loss. These models align the domain-specific feature distribution and the domain-invariant feature distribution simultaneously, which is sub-optimal towards solving deep domain adaptation tasks, given that deep neural networks are known to extract features in which multiple hidden factors are highly entangled. This thesis explores how to learn effective transferable features by disentangling the deep features. The following questions are studied: (1) how to disentangle the deep features into domain-invariant and domain-specific features? (2) how would feature disentanglement help to learn transferable features under a synthetic-to-real domain adaptation scenario? (3) how would feature disentanglement facilitate transfer learning with multiple source or target domains? (4) how to leverage feature disentanglement to boost the performance in a federated system? To address these needs, this thesis proposes deep adversarial feature disentanglement: a class\/domain identifier is trained on the labeled source domain and the disentangler generates features to fool the class\/domain identifier. Extensive experiments and empirical analysis demonstrate the effectiveness of the feature disentanglement method on many real-world domain adaptation tasks. Specifically, the following three unsupervised domain adaptation scenarios are explored: (1) domain agnostic learning with disentangled representations, (2) unsupervised federated domain adaptation, (3) multi-source domain adaptation.",
            "source_url": "https:\/\/openalex.org\/W3137379704",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "OpenBU (Boston University)",
            "source_record_id": "W3137379704",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 913,
            "title": "Domain Adaptation and Domain Generalization with Representation Learning",
            "title_ar": "",
            "title_en": "Domain Adaptation and Domain Generalization with Representation Learning",
            "author": "Muhammad Ghifary",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2016,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "video-analysis",
            "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; transfer-learning; cnn; image-classification; video-analysis; fairness",
            "keywords": "",
            "abstract": "Machine learning has achieved great successes in the area of computer vision, especially in object recognition or classification. One of the core factors of the successes is the availability of massive labeled image or video data for training, collected manually by human. Labeling source training data, however, can be expensive and time consuming. Furthermore, a large amount of labeled source data may not always guarantee traditional machine learning techniques to generalize well; there is a potential bias or mismatch in the data, i.e., the training data do not represent the target environment. To mitigate the above dataset bias\/mismatch, one can consider domain adaptation: utilizing labeled training data and unlabeled target data to develop a well-performing classifier on the target environment. In some cases, however, the unlabeled target data are nonexistent, but multiple labeled sources of data exist. Such situations can be addressed by domain generalization: using multiple source training sets to produce a classifier that generalizes on the unseen target domain. Although several domain adaptation and generalization approaches have been proposed, the domain mismatch in object recognition remains a challenging, open problem – the model performance has yet reached to a satisfactory level in real world applications. The overall goal of this thesis is to progress towards solving dataset bias in visual object recognition through representation learning in the context of domain adaptation and domain generalization. Representation learning is concerned with finding proper data representations or features via learning rather than via engineering by human experts. This thesis proposes several representation learning solutions based on deep learning and kernel methods. This thesis introduces a robust-to-noise deep neural network for handwritten digit classification trained on “clean” images only, which we name Deep Hybrid Network (DHN). DHNs are based on a particular combination of sparse autoencoders and restricted Boltzmann machines. The results show that DHN performs better than the standard deep neural network in recognizing digits with Gaussian and impulse noise, block and border occlusions. This thesis proposes the Domain Adaptive Neural Network (DaNN), a neural network based domain adaptation algorithm that minimizes the classification error and the domain discrepancy between the source and target data representations. The experiments show the competitiveness of DaNN against several state-of-the-art methods on a benchmark object dataset. This thesis develops the Multi-task Autoencoder (MTAE), a domain generalization algorithm based on autoencoders trained via multi-task learning. MTAE learns to transform the original image into its analogs in multiple related domains simultaneously. The results show that the MTAE’s representations provide better classification performance than some alternative autoencoder-based models as well as the current state-of-the-art domain generalization algorithms. This thesis proposes a fast kernel-based representation learning algorithm for both domain adaptation and domain generalization, Scatter Component Analysis (SCA). SCA finds a data representation that trades between maximizing the separability of classes, minimizing the mismatch between domains, and maximizing the separability of the whole data points. The results show that SCA performs much faster than some competitive algorithms, while providing state-of-the-art accuracy in both domain adaptation and domain generalization. Finally, this thesis presents the Deep Reconstruction-Classification Network (DRCN), a deep convolutional network for domain adaptation. DRCN learns to classify labeled source data and also to reconstruct unlabeled target data via a shared encoding representation. The results show that DRCN provides competitive or better performance than the prior state-of-the-art model on several cross-domain object datasets.",
            "source_url": "https:\/\/openalex.org\/W2516289717",
            "pdf_url": "https:\/\/openaccess.wgtn.ac.nz\/articles\/thesis\/Domain_Adaptation_and_Domain_Generalization_with_Representation_Learning\/17014700\/1\/files\/31470596.pdf",
            "doi": "10.26686\/wgtn.17014700",
            "handle": "",
            "repository_name": "Open Access Victoria University of Wellington | Te Herenga Waka (Figshare)",
            "source_record_id": "W2516289717",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 914,
            "title": "Learning to recognize objects by adaptive knowledge transfer",
            "title_ar": "",
            "title_en": "Learning to recognize objects by adaptive knowledge transfer",
            "author": "Qingyi Tao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; object-detection; medical-imaging",
            "keywords": "",
            "abstract": "When humans learn new knowledge and skills, we can naturally transfer them to other domains. Along with the learning procedures, we learn knowledge and skills for certain tasks and transfer them to similar tasks; we also can use the old knowledge to facilitate the learning of new knowledge. While effective knowledge transfer is a congenital and important learning ability of humans, it is not easy for machine learning mechanisms to adopt the ability of knowledge transfer. In recent years, there are plenty of works studying transfer learning in deep learning. There are still some practical challenges that remain undiscovered, especially under different problem settings encountered in real situations. In this thesis, we explore how to adopt knowledge transfer mechanisms in deep learning approaches in several practical scenarios. Four different works are proposed to study knowledge transfer across different domains and tasks via domain adaptation and model transfer. In particular, we study the web knowledge transfer for object detection task by adapting the web data to the real target dataset, which aims at reducing the human annotation effort for training object detector. In the incremental learning scenario, we study the cross-utilization of the old and new knowledge to overcome the catastrophic forgetting during the incremental and progressive learning process. Lastly, we explore transfer learning in the medical imaging domain by transferring the model pre-trained on normal images. Overall, the major contributions are summarized as follows: - A web knowledge transfer method to enhance the learning of weakly supervised object detection. The proposed method includes an effective web data collection pipeline and a curriculum learning scheme to achieve more effective model optimization during multi-instance learning. - An annotation-effective object detection method by adapting web data to the target data for object detection. This work attempts to learn an object detector from web supervision by adversarial domain adaptation. - An incremental learning scheme that adapts an old model to a new model without forgetting the old knowledge. A systematic study is performed to explore different class incremental methods. Furthermore, we propose a graph-based method to mine the old sample forgettability along with the training of the new tasks, and dynamically select samples that are more forgettable to overcome the catastrophic forgetting. - A lesion detection method for 3D CT images by utilizing model weights that are pre-trained on normal 2D RGB images. Furthermore, an attention-based feature aggregation method is proposed to adaptively transfer the information from neighboring slices to the key slices for more discriminative representation. Through this thesis, we demonstrate three different paradigms of knowledge transfer, including (1) the cross-domain knowledge transfer for adapting web data to an application with real unconstrained data, (2) the continual knowledge transfer from old tasks to new tasks without forgetting the old knowledge, and (3) the model transfer from the normal image domain to the medical domain. Under several practical tasks, the experiments are conducted to demonstrate the effectiveness of the proposed knowledge transfer approaches.",
            "source_url": "https:\/\/openalex.org\/W3177101414",
            "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/151507\/4\/thesis_oneside_2021_taoqingyi.pdf",
            "doi": "10.32657\/10356\/151507",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W3177101414",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:54",
            "updated_at": "2026-11-15 16:52:54"
        },
        {
            "id": 958,
            "title": "large language models",
            "title_ar": "",
            "title_en": "large language models",
            "author": "David Thomas Bernhart",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Klagenfurt",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Austria",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; llm; fairness",
            "keywords": "",
            "abstract": "The adoption of business process modelling languages, such as Business Process Model and Notation (BPMN), is critical for organisations aiming to document, analyse, and optimise their business processes. With the emergence of large language models (LLMs), automated BPMN generation from textual process descriptions has become feasible, offering potential gains in efficiency and scalability. However, evaluating the quality of LLM-based system outputs for a specific use case typically relies on expert judgement. This process is resource-intensive, subjective, and difficult to scale, particularly in large enterprise environments.This thesis investigates whether expert ratings of LLM-generated BPMN models can be predicted using automatically measurable input (process description) and numeric output (model comparison) metrics, across varying levels of input anonymisation. The study uses 48 authentic process descriptions from a real-world ERP context, each subjected to three anonymisation protocols to reflect practical privacy requirements. BPMN models were generated for each process description using three different LLM-based systems. All system outputs were standardised for objective comparison and evaluated by domain experts, who rated model quality and usefulness on multiple dimensions.Deep learning models, combined with systematic feature selection, were trained to predict expert ratings from these metrics. Results show that process description metrics alone have limited predictive value. In contrast, the inclusion of output comparison metrics, particularly when combined with input features, improves prediction accuracy. Feature selection revealed that absolute or count-based metrics are more informative than ratio-based scores, and certain process description features only became important when combined with output data. The findings indicate that automated expert rating prediction for BPMN models is feasible to some extent, depending on the modelling scenario.",
            "source_url": "https:\/\/openalex.org\/W7143273742",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "University of Klagenfurt",
            "source_record_id": "W7143273742",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:55",
            "updated_at": "2026-11-15 16:52:55"
        },
        {
            "id": 983,
            "title": "Face recognition using Deep Learning",
            "title_ar": "",
            "title_en": "Face recognition using Deep Learning",
            "author": "Xavier Serra Alza",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; face-recognition",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W2596912164",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "RECERCAT (Consorci de Serveis Universitaris de Catalunya)",
            "source_record_id": "W2596912164",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 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": 985,
            "title": "Develop Deep Learning-Based Face Recognition Model for Masked and Unmasked Faces",
            "title_ar": "",
            "title_en": "Develop Deep Learning-Based Face Recognition Model for Masked and Unmasked Faces",
            "author": "Tirhas Kebede",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Ethiopia",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; medical-ai; face-recognition",
            "keywords": "",
            "abstract": "Traditional facial recognition systems are no longer as effective due to the widespread use of face masks. To overcome this difficulty, the study suggests a facial recognition (FR) system based on a unified framework specifically designed for partial facial occlusion scenarios that can reliably identify people wearing or not wearing face masks. It is built on the Inception ResNet V1 deep learning model and was trained using the Real-World Masked Face Dataset (RMFD) with 525 subjects. The proposed system outperforms existing methods like FaceNet, achieving a test accuracy of 98.3% through rigorous subject-level cross-validation, while maintaining real-time performance at 45 FPS with optimized model size of 96MB. The solution, which is optimized for real-time deployment, can be used for public health monitoring, access control, and security verification. In both masked and unmasked faces, experimental results show strong generalization and consistent accuracy, allowing for a smooth integration into safety-critical applications without sacrificing effectiveness.",
            "source_url": "https:\/\/openalex.org\/W7162192561",
            "pdf_url": "",
            "doi": "10.82589\/muir-1053",
            "handle": "",
            "repository_name": "Mekelle University Institutional Repository (MU-IR)",
            "source_record_id": "W7162192561",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 986,
            "title": "Face recognition by deep learning",
            "title_ar": "",
            "title_en": "Face recognition by deep learning",
            "author": "Yue Chun Wu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Northeastern University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; cnn; face-recognition; fairness",
            "keywords": "",
            "abstract": "This dissertation focuses on real world challenges when applying deep learning for face recognition. Face recognition aims to recognize people using their face images, which is a hot topic in computer vision field. Especially tons of face data exist on social media nowadays and yield tremendous real world applications. We explore five applications along with challenges applying deep learning algorithms. First, we aim to recognize the large number of people, which is the bottleneck for training a deep convolutional neural network of which the output is equal to the number of people. An independent softmax model is introduced to split the single classifier into several small classifiers, which decomposes the large scale training procedure into several medium training procedures which can be solved separately. Second, we study the low-shot face recognition problem, in which there is very limited number of training samples for some people to recognize. A hybrid classifier framework is presented with multiple classifiers to decompose a single classifier into multiple classifiers that each works well for a part of data. Third, feature representation learning with unbalance data is studied for the face verification application. A center-invariant loss is employed to regularize the deep representation learning. Forth, we study the kinship classification given deep representations. A latent adaptive subspace learning framework is proposed to model the family-wise constraint and person-wise constraint in the subspace based on deep representations. Fifth, we study the catastrophic forgetting problem in incremental learning system, especially for face applications with continuously adding more people in the recognition system. A bias correction model is presented together with knowledge distilling, which tackles the catastrophic forgetting problem that has bias towards new coming classes.",
            "source_url": "https:\/\/openalex.org\/W3130771731",
            "pdf_url": "https:\/\/repository.library.northeastern.edu\/files\/neu:m044pk554\/fulltext.pdf",
            "doi": "10.17760\/d20324043",
            "handle": "",
            "repository_name": "Digital Repository Service (Northeastern University)",
            "source_record_id": "W3130771731",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 987,
            "title": "Face recognition using deep learning",
            "title_ar": "",
            "title_en": "Face recognition using deep learning",
            "author": "Pop, Teodor Cristian ; Rodríguez Esteban, Lucas",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universitat Politècnica de Catalunya",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; cnn; face-recognition",
            "keywords": "",
            "abstract": "The main goal of this project is to obtain the knowledge necessary to understand all the process behind a Face Recognition program. Starting by understanding what is an Artificial Neuron and its different parts, the concept of an Artificial Neural Network, specifically a Convolutional Neural Network and its different layers. This kind of Neural Network is a state-of-art in the field of computer vision. Understanding the concept of Machine learning and Deep learning, including the training and testing steps, which are the fundamental steps in all Deep Learning projects. This project was developed during an exchange program in the National Taiwan University of Science and Technologies, specifically in the department of Computer Science and Information Engineering. The methodology that we followed is the next, every two weeks we did a presentation in front of all members of our lab, and those presentations were divided in two parts, theoretical and practice. The theoretical parts contained a summary of papers and interesting information necessary to develop and understand the practical part. The practical part contained the code in Python and the results obtained, for instance, an image with a face detection. The duration of each presentations was around fifteen minutes plus questions. The final presentation was on 25th June, 2018. We are very proud about the results obtained, first of all because we have obtained the knowledge necessary to understand all the basic theory behind a Face Recognition system, secondly, we have learned a new programming language, Python and most importantly, we have done the training and testing of an Artificial Neural Network.",
            "source_url": "https:\/\/openalex.org\/W7111703248",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UPCommons institutional repository (Universitat Politècnica de Catalunya)",
            "source_record_id": "W7111703248",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 988,
            "title": "Face recognition using deep learning",
            "title_ar": "",
            "title_en": "Face recognition using deep learning",
            "author": "Zi Xen Ooi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; cnn; face-recognition; video-analysis",
            "keywords": "",
            "abstract": "Face recognition system is a technology accomplished at verifying or identifying a person from a video frame from a video source or a digital image. Multiple processing layers have been applied by deep learning to learn representations of data with multiple levels of feature extraction, which have achieved high accuracy to the real-world variations. Although the face recognition system has come a long way and its usage is crucial in several applications, it has remained a variety of challenges in face detection and recognition technologies, which including the pose variations, occlusions, facial expression changes, ageing of the face, illumination, etc. In this project, the real-time face recognition system is implemented using pretrained deep learning models with CCTV (Closed-Circuit Television) camera. The traditional CCTV is only good at recording and it is limited for campus safety and security nowadays. The face recognition system with CCTV camera can be used for controlling user access to physical locations of campus. The face recognition system does not require any kind of physical contact between the users and the device, which provide quick and convenient access to the authorized users. The developed face recognition system exploits the pre-trained Multi-task Cascaded Convolutional Network (MTCNN) model for face detection and the standard techniques with FaceNet embeddings as feature vectors for face recognition. The developed face recognition system was tested with numerous experiments to analyze its performance. Empirical results show the face recognition in uncontrolled environments is much more challenging than in controlled conditions.",
            "source_url": "https:\/\/openalex.org\/W3119380891",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3119380891",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 989,
            "title": "Application of deep learning tools for face recognition",
            "title_ar": "",
            "title_en": "",
            "author": "Karmen Santini",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Zagreb Faculty of Humanities and SocialSciences",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Croatia",
            "governorate": "",
            "publication_year": 2021,
            "language": "sl",
            "research_domain": "medical-ai",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; medical-ai; face-recognition; video-analysis",
            "keywords": "",
            "abstract": "Cilj ovog rada bio je prikazati postupak treniranja, prepoznavanja i zamjene lica primjenom alata za duboko učenje, koje predstavljaju najsuvremeniji pristup u području umjetne inteligencije i primjena neuronskih mreža prilikom učenja i definiranja lica, te ukazati na potencijalne stvarne prijetnje korištenja takve tehnologije, ali i pozitivne strane. Rad se sastoji od teorijskog i praktičnog dijela. U teorijskom dijelu definirani su pojmovi strojno učenje i duboko učenje i ključni elementi i razlike. Prikazana je struktura i način rada neuronske mreže, te osnovni principi rada koji se koriste za duboko učenje. Naglasak rada je na primjenama dubokog učenja te su prikazane tehnike koje koriste razne aplikacije u kojima je implementirano duboko učenje za prepoznavanje lica. U praktičnom dijelu rada provedeno je istraživanje primjenom alata za prepoznavanje lica. Računalni program FaceSwap primjenjuje princip dubokog učenja za stvaranje „dubokih lažnjaka“ (eng. deepfakes) u kojima algoritam uči glavne značajke lica te izvorna lica na slikama ili videozapisima zamjenjuje željenim licem neke druge osobe. Nakon prikaza sustava i analize načina rada, prikazan je praktičan dio zamjene lica u videozapisu te su na kraju prikazani rezultati. Posljednje poglavlje odnosi se na etičke principe i ukazuje na opasnosti i moguću zlouporabu te zaštitu od neprimjerene upotrebe.",
            "source_url": "https:\/\/openalex.org\/W7134758069",
            "pdf_url": "https:\/\/repozitorij.ffzg.unizg.hr\/islandora\/object\/ffzg:4829\/datastream\/PDF",
            "doi": "",
            "handle": "",
            "repository_name": "ODRAZ (University of Zagreb Faculty of Humanities and SocialSciences)",
            "source_record_id": "W7134758069",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 990,
            "title": "Deep Learning for Recognition of Objects, Activities, Faces, and Spatio-Temporal Patterns",
            "title_ar": "",
            "title_en": "Deep Learning for Recognition of Objects, Activities, Faces, and Spatio-Temporal Patterns",
            "author": "Amir Ghaderi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Texas Arlington",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "face-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; predictive-analytics; cnn; image-classification; face-recognition; ai-industry",
            "keywords": "",
            "abstract": "The origin of deep learning is in computer vision. However, researchers found that deep learning is a very powerful tool to solve many problems in other areas like forecasting, finance, human pose estimation, NLP, etc. Deep learning based methods showed a wonderful performance relate to other available methods. We have tried to improve deep learning methods and using them for solving problems in different areas. In this thesis, we will try to use the deep learning techniques for solving problems in different areas such as unsupervised learning, object classification, forecasting, cognitive behavior assessment and face recognition. In the computer vision part, a novel method for unsupervised feature learning for image classification was proposed in the thesis. Training a CNN needs huge amount of data. So, finding the methods to train CNN with unlabeled data is very promising. In the second part, we proposed a new deep learning based framework for forecasting. Forecasting is a challenging task and has many applications in finance, meteorology, etc. We have proposed a new framework for forecasting in cases that there are many nodes to generate data. One application of our framework is prediction of the wind speed for multiple stations around the country. Another problem that we have been using DL to solve is face recognition at scale. Face recognition is very demanding both in academic and industry. Also, we used DL to improve the performance of the system for behavioral assessment.",
            "source_url": "https:\/\/openalex.org\/W2897355615",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UTA ResearchCommons (University of Texas Arlington)",
            "source_record_id": "W2897355615",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 991,
            "title": "Face recognition using feature fusion and deep learning",
            "title_ar": "",
            "title_en": "Face recognition using feature fusion and deep learning",
            "author": "Xin Jiang Wei",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Ulster University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; face-recognition",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W3113452238",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Ulster University Research Portal (Ulster University)",
            "source_record_id": "W3113452238",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 992,
            "title": "3 D Face Recognition Using Deep Learning",
            "title_ar": "",
            "title_en": "3 D Face Recognition Using Deep Learning",
            "author": "Siddiqui Mushtaq",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; face-recognition",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W3189736959",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "University",
            "source_record_id": "W3189736959",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 993,
            "title": "Face sketch recognition using deep learning",
            "title_ar": "",
            "title_en": "Face sketch recognition using deep learning",
            "author": "Fan, Liang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Cardiff University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; gnn; face-recognition",
            "keywords": "",
            "abstract": "Face sketch recognition refers to automatically identifying a person from a set of facial photos using a face sketch. This thesis focuses on matching facial images between front face photos and front face hand-drawn sketches, and between front face photos and front face composite sketches by software. Because different visual domains, different image forms, and different collection methods exist between the matching image pairs, face sketch recognition is more difficult than traditional facial recognition. In this thesis, three novel deep learning models are presented to increase recognition accuracy on face photo-sketch datasets. An improved Siamese network combined with features extracted from an encoder-decoder network is proposed to extract more correlated features from facial photos and the corresponding face sketches. After that, attention modules are proposed to extract features from the same location in the photos and the sketches. In the third method, in order to reduce the difference between different visual domains, the images are transferred into a graph to increase the relationship for different face attributes and facial landmarks. Meanwhile, the graph neural network is utilized to learn the weights of neighbors adaptively. The first is to fuse more image features from the Siamese network and encoder-decoder network for increased the recognition results. Moreover, the attention modules can fix the similarity positions from different domain images to extract the correlated features. The visualized feature maps exhibit the correlated features which are extracted from the photo and the corresponding face sketch. In addition, a stable deep learning model based on graph structure is introduced to capture the topology of the graph and the relationship after images have been mapped into the graph structure for reducing the gap between face photos and face sketches. The experimental results show that the recognition accuracy of our proposed deep learning models can achieve the state-of-the-art on composite face sketch datasets. Meanwhile, the recognition results on hand-drawn face sketch datasets exceed other deep learning methods.",
            "source_url": "https:\/\/openalex.org\/W3170082364",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "ORCA Online Research @Cardiff (Cardiff University)",
            "source_record_id": "W3170082364",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:56",
            "updated_at": "2026-11-15 16:52:56"
        },
        {
            "id": 994,
            "title": "Deep learning approach for face recognition with disguise variations",
            "title_ar": "",
            "title_en": "Deep learning approach for face recognition with disguise variations",
            "author": "Mohit Chawla ; Richa Singh ; Mayank Vatsa A",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; face-recognition",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W3104001892",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3104001892",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 995,
            "title": "The development of a face recognition system based on deep learning",
            "title_ar": "",
            "title_en": "",
            "author": "FURKAN AKIN",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Fırat University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2019,
            "language": "tr",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; face-recognition",
            "keywords": "",
            "abstract": "Biyometri, insanların fiziksel ve davranışsal özelliklerini inceleyerek birbirinden ayırt edilebilmesini sağlayan bilim dalıdır. Biyometrik sistemler ise, insanların kimliklerini tespit etmek amacıyla, biyometrik özelliklerini inceleyerek oluşturulmuş sistemlerdir. Yüz tanıma, biyometrik sistemlerin alanlarından biridir ve kişileri belirlemek için yüzdeki göz, burun, ağız gibi ayrıntıları kullanır. Kamu güvenliğiyle ilgili artan endişeler, fiziksel ve mantıksal erişim için kimlik doğrulama gereksinimi ortaya çıkarmıştır. Bunun yanı sıra akıllı cihazların yönetimi ve yüz analizi ile sınıflandırma alanları otomatik yüz tanıma konusuna olan ilgiyi artırmaktadır. Literatürde yüz tanıma sistemlerinin geliştirilmesi hakkında birçok çalışma yapılmıştır. Bu tezde, her geçen gün kullanımı yaygınlaşan derin öğrenme yöntemlerinden bahsedilmiş ve bu yöntemlerle yüz tanıma siteminin geliştirilmesi amaçlanmıştır. Kullanılan veri setinden farklı örnek kümeleri alınmış ve bu örnekler ile hiper-parametreler denenmiştir. Başarımı arttıran hiper-parametreler kullanılarak cinsiyet ayrımı ve yüz tanıma uygulamaları yapılmıştır. Cinsiyet ayrımı uygulamasında başarı ,25, yüz tanıma uygulamasında ise başarı ,25'e kadar iyileştirilmiştir. Anahtar Sözcükler: Biyometrik Sistemler, Derin Öğrenme, Yüz Tanıma Sistemleri.",
            "source_url": "https:\/\/openalex.org\/W7217158709",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DSpace - FIRAT (Fırat University)",
            "source_record_id": "W7217158709",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 996,
            "title": "Deep Learning Architectures for Heterogeneous Face Recognition",
            "title_ar": "",
            "title_en": "Deep Learning Architectures for Heterogeneous Face Recognition",
            "author": "Seyed Mehdi Iranmanesh",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "West Virginia University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; predictive-analytics; face-recognition",
            "keywords": "",
            "abstract": "Face recognition has been one of the most challenging areas of research in biometrics and computer vision. Many face recognition algorithms are designed to address illumination and pose problems for visible face images. In recent years, there has been significant amount of research in Heterogeneous Face Recognition (HFR). The large modality gap between faces captured in different spectrum as well as lack of training data makes heterogeneous face recognition (HFR) quite a challenging problem. In this work, we present different deep learning frameworks to address the problem of matching non-visible face photos against a gallery of visible faces. Algorithms for thermal-to-visible face recognition can be categorized as cross-spectrum feature-based methods, or cross-spectrum image synthesis methods. In cross-spectrum feature-based face recognition a thermal probe is matched against a gallery of visible faces corresponding to the real-world scenario, in a feature subspace. The second category synthesizes a visible-like image from a thermal image which can then be used by any commercial visible spectrum face recognition system. These methods also beneficial in the sense that the synthesized visible face image can be directly utilized by existing face recognition systems which operate only on the visible face imagery. Therefore, using this approach one can leverage the existing commercial-off-the-shelf (COTS) and government-off-the-shelf (GOTS) solutions. In addition, the synthesized images can be used by human examiners for different purposes. There are some informative traits, such as age, gender, ethnicity, race, and hair color, which are not distinctive enough for the sake of recognition, but still can act as complementary information to other primary information, such as face and fingerprint. These traits, which are known as soft biometrics, can improve recognition algorithms while they are much cheaper and faster to acquire. They can be directly used in a unimodal system for some applications. Usually, soft biometric traits have been utilized jointly with hard biometrics (face photo) for different tasks in the sense that they are considered to be available both during the training and testing phases. In our approaches we look at this problem in a different way. We consider the case when soft biometric information does not exist during the testing phase, and our method can predict them directly in a multi-tasking paradigm. There are situations in which training data might come equipped with additional information that can be modeled as an auxiliary view of the data, and that unfortunately is not available during testing. This",
            "source_url": "https:\/\/openalex.org\/W3168980677",
            "pdf_url": "https:\/\/researchrepository.wvu.edu\/cgi\/viewcontent.cgi?article=9045&context=etd",
            "doi": "10.33915\/etd.8108",
            "handle": "",
            "repository_name": "The Research Repository @ WVU (West Virginia University)",
            "source_record_id": "W3168980677",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 997,
            "title": "Color and deep learning features in face recognition",
            "title_ar": "",
            "title_en": "Color and deep learning features in face recognition",
            "author": "Ze Lu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; face-recognition",
            "keywords": "",
            "abstract": "Face recognition (FR) has been one of the most active research topics in computer vision for more than three decades.It has been widely applied in various practical scenarios such as access control system, massive surveillance, human computer interaction, etc.A conventional FR system consists of four stages, face detection, face alignment, face representation and face matching.Compared with the other three, face representation significantly affects the performance of a FR system because it determines whether the FR system is robust to real world variations, such as illumination, pose, occlusion, etc.Most of the early FR works have been limited to using grayscale images.Recently, research efforts have been dedicated to incorporate color information into the feature extraction process for improving FR performance.Specifically, different feature representations are extracted from face images in a certain color space and then fused together for classification.Main challenges of such color FR tasks include how I would like to thank my supervisor, Professor Alex Kot, and my co-supervisor, Professor Xudong Jiang for their invaluable guidance, support and suggestions given to me in the past four years.I am lucky to have both of them as my supervisors.Professor Kot is a man of wise and broad knowledge.His suggestions to my research and to my career plan benefit me a lot.Professor Jiang spends much time discussing ideas, experiments, and paper writing with me.His devotion to research and approachable characteristics greatly inspire me.Their encouragements help me to overcome the difficulties encountered in my research.",
            "source_url": "https:\/\/openalex.org\/W4205852120",
            "pdf_url": "https:\/\/dr.ntu.edu.sg\/bitstream\/10356\/75855\/1\/main_thesis.pdf",
            "doi": "10.32657\/10356\/75855",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W4205852120",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 998,
            "title": "Real-time Face Detection and Recognition Based on Deep Learning",
            "title_ar": "",
            "title_en": "Real-time Face Detection and Recognition Based on Deep Learning",
            "author": "Hui Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Auckland University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; ai-applications; cnn; face-recognition; video-analysis; ai-agriculture",
            "keywords": "",
            "abstract": "Face recognition is one of the most important applications in video surveillance and computer vision. However, the conventional algorithms of face recognition are susceptible to multiple conditions, such as lighting, occlusion, viewing angle or camera rotation. Therefore, face recognition based on deep learning can greatly improve the recognition speed and compatible external interference. In this thesis, we use convolutional neural networks (ConvNets) for face recognition, the neural networks have the merits of end-to-end, sparse connection and weight sharing. The purpose of this thesis project is to identify the name of different people based on location of the detected box of a face. Then, we can obtain recognition results with different confidence under various distances. This thesis presents different methods with comparisons, namely, comparing the training results and the test results of different parameters under the same model, training results of the same test video under different models. We find that the recognition accuracy of this model is mainly affected by face proportion and the number of samples. If we get larger proportion of a face on screen, then we have higher recognition accuracy; if we obtain much greater number of samples, we can get higher recognition accuracy. In this work, we first collect sufficient samples as our dataset and use the suitable model embedded in the platform Google TensorFlow to complete the training and test. We collected five different faces and obtained 500 images on each face as training set, each of which can be cropped and rotated by using 50 different angles of the picture having a human face, of which 40 for training, 10 for verification. The use of neural networks for face recognition improves the speed of recognition. The contributions of this thesis are: (1) The use of elliptical markers can identify a human face including rotation and position. (2) The confidence of human face recognition is mainly affected by the proportion of face occupied on the screen.",
            "source_url": "https:\/\/openalex.org\/W2896927716",
            "pdf_url": "https:\/\/openrepository.aut.ac.nz\/bitstreams\/986c7b85-f1b5-45aa-8023-0ed17772a752\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Tuwhera (Auckland University of Technology)",
            "source_record_id": "W2896927716",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 999,
            "title": "Unraveling representations for face recognition : from handcrafted to deep learning",
            "title_ar": "",
            "title_en": "Unraveling representations for face recognition : from handcrafted to deep learning",
            "author": "Gaurav Goswami ; Richa Singh ; Mayank Vatsa A",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "India",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; face-recognition",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W2931374629",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "INFLIBNET",
            "source_record_id": "W2931374629",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1000,
            "title": "Study of deep learning approaches to face recognition and object detection YOLO",
            "title_ar": "",
            "title_en": "Study of deep learning approaches to face recognition and object detection YOLO",
            "author": "Tanveen Kaur",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Bridgeport",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; cnn; object-detection; face-recognition",
            "keywords": "",
            "abstract": "In this research, I have focused on deep learning approaches to face detection and recognition and object detection and recognition. This research has mainly focused on training the neural networks or other models with enough amounts of data so that it achieves desirable results. Starting with the basics of neural network where a neuron, the smallest unit in deep learning field, is defined and explained, I have elevated the research to the topic where I could recognize the face of a person or an object in an image. First the neural networks have been introduced in this research and then training of the neural networks has been attained with both the CPU and the GPU. In an algorithm called matrix form of back propagation, the use of multiple GPUs has been made along with CUDA kernel and cuBLAS library. Application of face recognition has been implemented using the pre trained model Facenet and Deep Convolutional Neural Networks. After analyzing neural networks and its facial recognition application, other approaches of deep learning have been given force to. I have used the library OpenCV along with deep learning approaches to implement face recognition, Image registration and YOLO Object Detection and Recognition with the tensor flow and keras environment supported by anaconda for python.",
            "source_url": "https:\/\/openalex.org\/W2942485040",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "University of Bridgeport ScholarWorks (University of Bridgeport)",
            "source_record_id": "W2942485040",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1001,
            "title": "Deep Learning Based Face Detection and Recognition in MWIR and Visible Bands",
            "title_ar": "",
            "title_en": "Deep Learning Based Face Detection and Recognition in MWIR and Visible Bands",
            "author": "Suha Reddy Mokalla",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "West Virginia University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; generative-ai; transfer-learning; face-recognition; gan",
            "keywords": "",
            "abstract": "In non-favorable conditions for visible imaging like extreme illumination or nighttime, there is a need to collect images in other spectra, specifically infrared. Mid-Wave infrared (3-5 microm) images can be collected without giving away the location of the sensor in varying illumination conditions. There are many algorithms for face detection, face alignment, face recognition etc. proposed in visible band till date, while the research using MWIR images is highly limited. Face detection is an important pre-processing step for face recognition, which in turn is an important biometric modality. This thesis works towards bridging the gap between MWIR and visible spectrum through three contributions. First, a dual band based deep face detection model that works well in visible and MWIR spectrum is proposed using transfer learning. Different models are trained and tested extensively using visible and MWIR images and the one model that works well for this data is determined. For this model, experiments are conducted to learn the speed\/accuracy trade-off. Following this, the available MWIR dataset is extended through augmentation using traditional methods and generative adversarial networks (GANs). Traditional methods used to augment the data are brightness adjustment, contrast enhancement, applying noise to and de-noising the images. A deep learning based GAN architecture is developed and is used to generate new face identities. The generated images are added to the original dataset and the face detection model developed earlier is once again trained and tested. The third contribution is the proposal of another GAN that converts given thermal ace images into their visible counterparts. A pre-trained model is used as discriminator for this purpose and is trained to classify the images as real and fake and an identity network is used to provide further feedback to the generator. The generated visible images are used as probe images and the original visible images are used as gallery images to perform face recognition experiments using a state-of-the-art visible-to-visible face recognition algorithm.",
            "source_url": "https:\/\/openalex.org\/W3024705230",
            "pdf_url": "https:\/\/researchrepository.wvu.edu\/cgi\/viewcontent.cgi?article=8577&context=etd",
            "doi": "10.33915\/etd.7632",
            "handle": "",
            "repository_name": "The Research Repository @ WVU (West Virginia University)",
            "source_record_id": "W3024705230",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1002,
            "title": "Face recognition enhancement through the use of depth maps and deep learning",
            "title_ar": "",
            "title_en": "Face recognition enhancement through the use of depth maps and deep learning",
            "author": "Yaser Saleh",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Loughborough University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; transfer-learning; cnn; multimodal; object-detection; face-recognition",
            "keywords": "",
            "abstract": "Face recognition, although being a popular area of research for over a decade has still many open research challenges. Some of these challenges include the recognition of poorly illuminated faces, recognition under pose variations and also the challenge of capturing sufficient training data to enable recognition under pose\/viewpoint changes. With the appearance of cheap and effective multimodal image capture hardware, such as the Microsoft Kinect device, new possibilities of research have been uncovered. One opportunity is to explore the potential use of the depth maps generated by the Kinect as an additional data source to recognize human faces under low levels of scene illumination, and to generate new images through creating a 3D model using the depth maps and visible-spectrum \/ RGB images that can then be used to enhance face recognition accuracy by improving the training phase of a classification task.. With the goal of enhancing face recognition, this research first investigated how depth maps, since not affected by illumination, can improve face recognition, if algorithms traditionally used in face recognition were used. To this effect a number of popular benchmark face recognition algorithms are tested. It is proved that algorithms based on LBP and Eigenfaces are able to provide high level of accuracy in face recognition due to the significantly high resolution of the depth map images generated by the latest version of the Kinect device. To complement this work a novel algorithm named the Dense Feature Detector is presented and is proven to be effective in face recognition using depth map images, in particular under wellilluminated conditions. Another technique that was presented for the goal of enhancing face recognition is to be able to reconstruct face images in different angles, through the use of the data of one frontal RGB image and the corresponding depth map captured by the Kinect, using faster and effective 3D object reconstruction technique. Using the Overfeat network based on Convolutional Neural Networks for feature extraction and a SVM for classification it is shown that a technically unlimited number of multiple views can be created from the proposed 3D model that consists features of the face if captured real at similar angles. Thus these images can be used as real training images, thus removing the need to capture many examples of a facial image from different viewpoints for the training of the image classifier. Thus the proposed 3D model will save significant amount of time and effort in capturing sufficient training data that is essential in recognition of the human face under variations of pose\/viewpoint. The thesis argues that the same approach can also be used as a novel approach to face recognition, which promises significantly high levels of face recognition accuracy base on depth images. Finally following the recent trends in replacing traditional face recognition algorithms with the effective use of deep learning networks, the thesis investigates the use of four popular networks, VGG-16, VGG-19, VGG-S and GoogLeNet in depth maps based face recognition and proposes the effective use of Transfer Learning to enhance the performance of such Deep Learning networks.",
            "source_url": "https:\/\/openalex.org\/W2779335121",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Loughborough University Institutional Repository (Loughborough University)",
            "source_record_id": "W2779335121",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1003,
            "title": "A Face Detection and Recognition System for Color Images using Neural Networks with Boosting and Deep Learning",
            "title_ar": "",
            "title_en": "A Face Detection and Recognition System for Color Images using Neural Networks with Boosting and Deep Learning",
            "author": "Mohammadreza Hajiarbabi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "The University of Kansas",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; face-recognition; speech-recognition",
            "keywords": "",
            "abstract": "A face detection and recognition system is a biometric identification mechanism which compared to other methods such as finger print identification, speech, signature, hand written and iris recognition, is shown to be more important both theoretically and practically. In principle, the biometric identification methods use a wide range of techniques such as machine learning, computer vision, image processing, pattern recognition and neural networks. The methods have various applications such as in photo and film processing, control access networks, etc. In recent years, the automatic recognition of a human face has become an important problem in pattern recognition. The main reasons are structural similarity of human faces and great impact of illumination conditions, facial expression and face orientation. Face recognition is considered one of the most challenging problems in pattern recognition. A face recognition system consists of two main components, face detection and recognition. In this dissertation a face detection and recognition system using color images with multiple faces is designed, implemented, and evaluated. In color images, the information of skin color is used in order to distinguish between the skin pixels and non-skin pixels, dividing the image into several components. Neural networks and deep learning methods has been used in order to detect skin pixels in the image. A skin database has been built that contains skin pixels from different human skin colors. Information from different color spaces has been used and applied to neural networks. In order to improve system performance, bootstrapping and parallel neural networks with voting have been used. Deep learning has been used as another method for skin detection and compared to other methods. Experiments have shown that in the case of skin detection, deep learning and neural networks methods produce better results in terms of precision and recall compared to the other methods in this field. The step after skin detection is to decide which of these components belong to human face. A template based method has been modified in order to detect the faces. The template is rotated and rescaled to match the component and then the correlation between the template and component is calculated, to determine if the component belongs to a human face. The designed algorithm also succeeds if there are more than one face in the component. A rule based method has been designed in order to detect the eyes and lips in the detected components. After detecting the location of eyes and lips in the component, the face can be detected. After face detection, the faces which were detected in the previous step are to be recognized. Appearance based methods used in this work are one of the most important methods in face recognition due to the robustness of the algorithms to head rotation in the images, noise, low quality images, and other challenges. Different appearance based methods have been designed, implemented and tested. Canonical correlation analysis has been used in order to increase the recognition rate.",
            "source_url": "https:\/\/openalex.org\/W2799615539",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KU ScholarWorks (The University of Kansas)",
            "source_record_id": "W2799615539",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1004,
            "title": "Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond",
            "title_ar": "",
            "title_en": "Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond",
            "author": "Felix Juefei-Xu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; generative-ai; cnn; image-classification; face-recognition; gan",
            "keywords": "",
            "abstract": "Many real-world face recognition tasks are under unconstrained conditions such as off-angle pose variations, illumination variations, facial occlusion, facial expression, etc. In this work, we are focusing on the real-world scenarios where only the periocular region of a face is visible such as in the ISIS case. In Part I of the dissertation, we will showcase the face recognition capability based on the periocular region, which we call the periocular face recognition. We will demonstrate that face matching using the periocular region directly is more robust than the full face in terms of age-tolerant face recognition, expression-tolerant face recognition, pose-tolerant face recognition, as well as contains more cues for determining the gender information of a subject. In this dissertation, we will study direct periocular matching more comprehensively and systematically using both shallow and deep learning methods. Based on this, in Part II and Part III of the dissertation, we will continue to explore an indirect way of carrying out the periocular face recognition: periocular-based full face hallucination, because we want to capitalize on the powerful commercial face matchers and deep learning-based face recognition engines which are all trained on large-scale full face images. The reproducibility and feasibility of re-training for a proprietary facial region, such as the periocular region, is relatively low, due to the nonopen source nature of commercial face matchers as well as the amount of training data and computation power required by the deep learning based models. We will carry out the periocular-based full face hallucination based on two proposed reconstructive dictionary learning methods, including the dimensionally weighted K-SVD (DW-KSVD) dictionary learning approach and its kernel feature space counterpart using Fastfood kernel expansion approximation to reconstruct high-fidelity full face images from the periocular region, as well as two proposed generative deep learning approaches that build upon deep convolutional generative adversarial networks (DCGAN) to generate the full face from the periocular region observations, including the Gang of GANs (GoGAN) method and the discriminant nonlinear many-to-one generative adversarial networks (DNMM-GAN) for applications such as the generative open-set landmark-free frontalization (Golf) for faces and universal face optimization (UFO), which tackles an even broader set of problems than periocular based full face hallucination. Throughout Parts I-III, we will study how to handle challenging realworld scenarios such as unconstrained pose variations, unconstrained illumination conditions, and unconstrained low resolution of the periocular and facial images. Together, we aim to achieve unconstrained periocular face recognition through both direct periocular face matching and indirect periocular-based full face hallucination. In the final Part IV of the dissertation, we will go beyond and explore several new methods in deep learning that are statistically efficient for generalpurpose image recognition. Methods include the local binary convolutional neural networks (LBCNN), the perturbative neural networks (PNN), and the polynomial convolutional neural networks (PolyCNN).",
            "source_url": "https:\/\/openalex.org\/W2804539676",
            "pdf_url": "",
            "doi": "10.1184\/r1\/6724106",
            "handle": "",
            "repository_name": "KiltHub Repository",
            "source_record_id": "W2804539676",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1005,
            "title": "Development of deep learning-based 3D face fraud prevention system for face recognition systems",
            "title_ar": "",
            "title_en": "",
            "author": "Zeynep Koyun",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Fırat University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2022,
            "language": "tr",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; ai-applications; face-recognition; video-analysis; medical-imaging; ai-cybersecurity",
            "keywords": "",
            "abstract": "Biyometri, kişileri fiziksel veya davranışsal özelliklerine göre doğrulamak, tanımak ve tanımlamak için kullanılan süreçleri ifade etmektedir. Biyometrik tanımlayıcılar için veri alınabilecek fizyolojik özellikler parmak izleri, avuç içi damarları, avuç içi izleri, yüz, DNA, iris, retina ve el geometrisini içermektedir. En popüler biyometrik tanımlayıcı olan yüz tanıma sistemleri temel olarak, görüntülerdeki yüz bölgelerinin makine öğrenmesi algoritmaları kullanılarak tespit edilip daha önce sistemde kayıtlı kişiler ile eşleştirme yapılarak otomatik kimlik doğrulama sistemi olarak görev yapmaktadır. Yüz tanıma teknolojileri, iris ve parmak izi tanıma gibi diğer teknolojilere ve biyometrik tanımlayıcılara göre kullanışlı, temassız ve kolay uygulanabilir olmasından dolayı pek çok avantaj sağlamaktadır. Ne yazık ki, bir biyometrik veri çalındığında veya kopyalandığında, biyometrinin avantajları dezavantaja dönüşmektedir. Kayıtlı bir kullanıcının bir veya daha fazla fotoğrafının\/videosunun internet üzerinden kolayca ve ucuza elde edilebildiği veya kullanıcının rızası veya fiziksel teması olmadan bile bir kamera kullanarak sadece yüzünün yakalandığı yüz tanıma sistemlerinde bu risk diğer biyometrik verilere göre çok daha yüksektir. Canlılık tespit algoritmaları ile destekli yüz tanıma sistemleri güvenlik sektöründe çok önemli ve kritik bir ihtiyaç olarak görülmektedir. Bu tez çalışmasının amacı, yüz tanıma sistemlerinin güvenirlilik ve doğruluk hassasiyetini en üst düzeye çıkaracak yapay zeka teknolojileri ve derin öğrenme algoritmaları ile yüz sahteciliği tespit algoritmaları geliştirilmesidir. Gerçekleştirilen uygulamaların sonuçları incelendiğinde ,5 başarı performansı ile yüz tanıma sistemlerini aldatmaya yönelik saldırılardan etkilenmeyen stabil, yüksek performanslı ve yüksek doğruluklu bir model elde edilmiştir.",
            "source_url": "https:\/\/openalex.org\/W7217920640",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DSpace - FIRAT (Fırat University)",
            "source_record_id": "W7217920640",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1006,
            "title": "Face recognition using traditional machine learning algorithms and deep neural networks with application to face verification",
            "title_ar": "",
            "title_en": "Face recognition using traditional machine learning algorithms and deep neural networks with application to face verification",
            "author": "Manolis Papadakis",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Delft University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Netherlands",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "face-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; cnn; face-recognition",
            "keywords": "",
            "abstract": "Biometrics authentication has been very useful and necessary nowadays due to the great developments in technology and the transaction of huge amounts of sensitive data on a daily basis. Traditionally, access to some data or service is achieved by means of some documents or a password. However, these methods are not very convenient. Alternatively, typical biometric systems can be employed that use fingerprint, iris, voice, face recognition or a combination of them. This project focuses on the task of face recognition from still images and investigates how different algorithms for face verification perform under various adverse conditions modelled by blur, salt-and-pepper noise and changes in illumination. Conventional pattern recognition algorithms are first presented. Pixel intensities, Gabor features, Local Binary Patterns (LBP) and 2D-DCT coefficients are considered as features while for classification the nearest neighbor (NNC), nearest mean (NMC), SVM classifiers, and Likelihood Ratio Tests (LRT) with Gaussian Mixture Models (GMM) are examined. Out of all these methods, Gabor features combined with the linear SVM classifier are shown to produce best results across all degradations giving an average Equal Error Rate (EER) of 0:97% using the ORL face dataset. Then, emphasis is placed on deep learning and Convolutional Neural Networks (CNN). Specically, VGG-Face with triplet loss training for face verification is suggested. VGG-Face achieves an average EER of 2:63% when both test images of a query image pair are drawn from the same degradation conditions and an average EER of 3:80% when only one image in the given pair is degraded and the other one is derived from the clean ORL dataset. We also experimented with the extracted VGG-Face features and NNC, linear SVM and Gaussian SVM and it is seen that a linear SVM gives an average EER of 1:10% by macro-averaging the Detection Error Tradeoff (DET) curves.",
            "source_url": "https:\/\/openalex.org\/W2981731957",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Research Repository (Delft University of Technology)",
            "source_record_id": "W2981731957",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1007,
            "title": "Research of Face Recognition using Deep Learning for Non- Contact Heartbeat Detection",
            "title_ar": "",
            "title_en": "Research of Face Recognition using Deep Learning for Non- Contact Heartbeat Detection",
            "author": "Jiarui Li",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Taiwan",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; object-detection; face-recognition",
            "keywords": "",
            "abstract": "[[abstract]]我們提出一個應用深度學習架構學習人臉辨識並進行即時心跳量測的方法。本方法實驗結果可以得知，OpenCV自帶人臉辨識與YOLOv2實作出來之即時心率測量之差異，可以比較出OpenCV自帶人臉辨識速度及辨識率不佳，使用YOLOv2來作人臉辨識後，能明顯提升運行效果與準確度，因為YOLOv2不同OpenCV自帶人臉辨識的方法，OpenCV自帶人臉重複搜尋整張圖的相關特徵點，而YOLOv2只對整張圖有興趣的特徵點進行人臉搜索，由此我們可以因為加快的運行速度，而達到未來需要追加其他功時，能擁有更高的拓展可能性。 We propose a method of applying deep learning architecture to learn face recognition and perform instant heartbeat measurement. The experimental results show that OpenCV comes with people. The difference between face recognition and real heart rate measurement made by YOLOv2 can compare the speed and recognition rate of OpenCV's own face recognition. After using YOLOv2 for face recognition, it can significantly improve the running effect and accuracy, because YOLOv2 Different OpenCV's own face recognition method, OpenCV comes with a face to repeatedly search for the relevant feature points of the whole picture, and YOLOv2 only performs face search on the feature points that are of interest to the whole picture, so we can run faster. Speed, and when you need to add other work in the future, you can have a higher expansion potential.",
            "source_url": "https:\/\/openalex.org\/W7111442043",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Chinese Culture University Repository",
            "source_record_id": "W7111442043",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1008,
            "title": "Machine learning applications in medical imaging for early diagnosis of cardiovascular diseases",
            "title_ar": "",
            "title_en": "Machine learning applications in medical imaging for early diagnosis of cardiovascular diseases",
            "author": "Marena Soliman",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Boston University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; optimization; cnn; medical-imaging; fairness",
            "keywords": "",
            "abstract": "Since cardiovascular diseases (CVDs) continue to be the primary cause of morbidity and death worldwide, better patient outcomes depend on early and precise diagnosis. Although they are essential for clinical decision-making, traditional cardiovascular imaging methods such as computed tomography (CT), cardiac magnetic resonance (CMR), and echocardiography sometimes rely on subjective evaluations that are prone to inter-observer variability. A revolutionary method for improving diagnostic automation, efficiency, and accuracy is the incorporation of machine learning (ML) into medical imaging. Large amounts of imaging data can be analyzed using ML algorithms, such as supervised and unsupervised learning models, which can also extract complex patterns and accurately detect small cardiovascular abnormalities. Accurate measurement of cardiac structures and functions is made possible by deep learning models and convolutional neural networks (CNNs), which further enhance feature extraction and classification. By enabling early identification, lowering diagnostic errors, and improving patient prognosis, ML-driven diagnostic technologies have the potential to completely transform cardiovascular healthcare, notwithstanding obstacles relating to data privacy, ethical issues, and regulatory compliance. With a focus on its contribution to the advancement of automated diagnostics and precision medicine, this paper examines the present uses, difficulties, and potential of machine learning in cardiovascular imaging.",
            "source_url": "https:\/\/openalex.org\/W7132960968",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "OpenBU (Boston University)",
            "source_record_id": "W7132960968",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1009,
            "title": "Machine learning for pediatric healthcare",
            "title_ar": "",
            "title_en": "Machine learning for pediatric healthcare",
            "author": "Hamed Fayyaz",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Delaware",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "llm",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; responsible-ai; predictive-analytics; transformers; multimodal; object-detection; llm; fairness",
            "keywords": "",
            "abstract": "Recent ML advances have shown significant promise in assisting clinical decision-making in healthcare. In particular, the integration of ML predictive models into clinical settings has the potential to transform patient care through more personalized, timely, and effective interventions. Despite extensive research on ML for health in adults, there is a research gap regarding its application within pediatric populations. Children are not just \"small adults\"; they differ from adults in many ways that could affect the performance of ML models. These differences underscore the importance of tailoring ML approaches to pediatric care. ☐ In this work, we focus on two major pediatric health issues—obesity and sleep apnea—and explore ML applications in these domains. First, we propose a multi-task deep learning model that predicts BMI trajectory and attrition patterns among weight management program participants using EHR data. Second, we target sleep apnea detection using polysomnography (PSG) signals in children. We present a transformer-based architecture to detect apnea using multimodal biosignals. Subsequently, we investigate the contribution of each signal for apnea detection and identify a subset of signals that achieves a performance comparable to the complete set. We also study the effect of missing and noisy signals (a phenomenon commonly observed in pediatric settings) in apnea detection and present a tailored ML architecture and training algorithm that yields a robust apnea detector model. ☐ In this talk, we additionally cover two recent projects in more detail. The first one relates to extending the obesity case study above, where we develop an end-to-end pipeline following FHIR (Fast Healthcare Interoperability Resources) standards to predict patients' risk of obesity at different future ages. We also use a rigorous uncertainty quantification method to measure the uncertainty of our prediction. Our ML pipeline targets maximizing interoperability across various EHR formats and designing a user (provider)-centric UI to enable effective integration of our tools into clinical practice. ☐ Recognizing the growing influence of LLMs in healthcare, in the second project, we aim to address the need for scalable evaluation of fairness in LLMs. We propose a greatly customized RAG-based pipeline to generate synthetic red-teaming patient scenarios (vignettes) to evaluate social biases in medical LLMs. We merge an auxiliary generator LLM with medical knowledge graphs and a post hoc fact checker module in our design to minimize hallucination patterns when using an LLM-based pipeline and to follow an evidence-based scenario generation approach.",
            "source_url": "https:\/\/openalex.org\/W7217430578",
            "pdf_url": "https:\/\/udspace.udel.edu\/bitstreams\/af4fa320-c901-42a7-afdc-da6869e82a27\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Library, Museums and Press - UDSpace (University of Delaware)",
            "source_record_id": "W7217430578",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1013,
            "title": "DEVELOPMENT OF A MACHINE LEARNING MODEL FOR AUTOMATED DIAGNOSIS OF ECG ABNORMALITIES",
            "title_ar": "",
            "title_en": "DEVELOPMENT OF A MACHINE LEARNING MODEL FOR AUTOMATED DIAGNOSIS OF ECG ABNORMALITIES",
            "author": "Raiymbek Nurtay",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nazarbayev University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Kazakhstan",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn",
            "keywords": "",
            "abstract": "Early diagnosis of Cardiovascular diseases (CVD) remains essential because these diseases represent the main global reason for mortality and disease incidents. Traditional ECG analysis methods struggle to identify minor and intricate abnormalities within extensive databases because Electrocardiograms (ECGs) serve as standard tools to inspect heart health conditions. The research creates a hybrid machine learning model for automated ECG abnormality detection which integrates deep learning features of CNN (1D Convolutional Neural Network) for feature extraction with classifier chain ensemble modeling for multi-label classification. The experimental results based on the PTB-XL database demonstrated an overall accuracy rate of 87.79% together with a weighted accuracy of 95.98% through model training and evaluation. This performance exceeded standard methods. The main contribution of this study includes connecting a machine learning algorithm to a web-based interface which enables healthcare professionals especially those in limited resource environments to submit single-lead ECG images that produce instant diagnostic results. This integration facilitates faster, more efficient ECG analysis with a response time of approximately 22 seconds per image. The hybrid system represents progress in medical diagnostic workstreams and expands automatic cardiovascular disease detection through its monitoring platform despite facing difficulties in detecting uncommon abnormalities. Studies prove that machine learning-based ECG diagnostic systems can successfully enter clinical settings where they create a practical and quick method to enhance cardiovascular treatment.",
            "source_url": "https:\/\/openalex.org\/W7200403297",
            "pdf_url": "https:\/\/nur.nu.edu.kz\/bitstreams\/e0f815fa-e076-4a44-a74e-a54347a9642c\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Nazarbayev University Repository (Nazarbayev University)",
            "source_record_id": "W7200403297",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1017,
            "title": "Machine learning applications in healthcare",
            "title_ar": "",
            "title_en": "Machine learning applications in healthcare",
            "author": "Dallora Moraes, Ana Luiza",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Dalarna University Library",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; medical-imaging; xai",
            "keywords": "",
            "abstract": "Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare. The two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. The diagnosis research context regards the age assessment of the young individuals, which aims to address the drawbacks in the bone age assessment research, investigating new age assessment methods. The prognosis research context regards the prognosis of dementia, which aims to investigate prognostic estimates for older individuals who came to develop the dementia disorder, in a time frame of 10 years. Machine learning applications were shown to be useful in both research contexts. In the diagnosis research context, study I summarized the state of the art evidence in the area of bone age assessment with the use of machine learning, identifying both automated and non-automated approaches for age assessment. Study II investigated a non-automated approach based on the radiologists' assessment and study III investigated an automated approach based on deep learning. Both studies used magnetic resonance imaging. The results showed that the radiologists' assessment as input was not precise enough for the estimation of age. However, the deep learning method was able to extract more useful features from the images and provided better diagnostic performance for the age assessment. In the research context of prognosis, study IV conducted a review on the relevant evidence in on the prognosis of dementia with machine learning techniques, identifying a focus on the research on neuroimaging studies dedicated to validating biomarkers for pharmaceutical research. Study V proposed a multifactorial decision tree approach for the prognosis of dementia in older individuals as to their development or not of dementia in 10 years. Achieving consistent performance results, it provided an interpretable prognostic model identifying possible modifiable and non-modifiable risk factors and possible patient subgroups of importance for the dementia research.",
            "source_url": "https:\/\/openalex.org\/W7112733386",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Diva portal (Dalarna University Library)",
            "source_record_id": "W7112733386",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1028,
            "title": "Statistical Machine Learning on Time Series with Applications to Manufacturing and Healthcare",
            "title_ar": "",
            "title_en": "Statistical Machine Learning on Time Series with Applications to Manufacturing and Healthcare",
            "author": "Ying Liao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Texas Tech University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "transfer-learning",
            "categories": "machine-learning; deep-learning; medical-ai; ai-applications; transfer-learning; predictive-analytics; ai-industry",
            "keywords": "",
            "abstract": "Predictive analysis in time series is critical to making informed decision-making and has been extensively studied in a wide range of fields including manufacturing and healthcare. The temporal patterns contained in time series hold the promise of making predictions on future behaviors as those patterns often reflect the health state evolution. However, developing such predictive models is a challenging problem because (1) the actual health state is typically not directly observable and can only be assessed from the health condition monitoring data (e.g., sensor measurements of the engineering system, physiological signals of the patient); (2) the health state transition may depend heavily on historical states due to complicated failure mechanisms or complex disease progression; (3) the health evolution trends generally exist significant variability due to some critical exogenous factors (e.g., operational and environmental conditions of the engineering systems, gender and age of the patients) and heterogeneity; and (4) when making predictions at an early stage, the temporal patterns may be too subtle to be detected by traditional methods (e.g., abnormality in early ECGs associated with future cardiac diseases). This dissertation addresses these challenges by developing novel and flexible statistical models and leveraging machine learning algorithms, and investigating the performance of the proposed methods in simulation studies as well as real-world applications. We first develop a flexible prognostics framework based on a higher-order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective Gibbs sampling algorithm is designed for model inference and is evaluated by a simulation study. We design a decoding algorithm to estimate the hidden health states and use a simulation approach to predict the remaining useful life for a functioning system in operation. A case study on the NASA turbofan engines demonstrates the practical utility of the proposed prognostics framework. To further capture the variability among time series due to some critical factors and patient-level heterogeneity, we propose a novel and flexible Bayesian mixed-effect higher-order HMM (MHOHMM) to predict patients' future events based on their electronic health records. A range of MHOHMMs are designed to capture different data structures and the optimal one is identified by using the $k$-fold cross-validation. An effective two-stage MCMC sampling algorithm is designed for MHOHMM inference. A simulation study is conducted to evaluate the performance of the proposed sampling algorithm and the MHOHMM-based classifier. The results show that considering the impacts of mixed effects and higher-order transition dynamics significantly improves prediction performance when such impacts present in the data. Furthermore, we conduct a case study on acute hypotensive episode prediction using real-world clinical data to demonstrate the effectiveness and advantage of the proposed prediction framework. For problems of making predictions at an early stage, we adapt state-of-the-art deep learning techniques to detect subtle patterns. To investigate whether mutant mice present subtle abnormalities in their early ECGs before severe arrhythmias occur, we propose a deep transfer learning (TL) model, DeepMiceTL, to predict cardiac conduction diseases using early mouse ECGs. We further apply the Bayesian optimization and $k$-fold cross-validation methods to tune the hyperparameters of the DeepMiceTL. Our results show that DeepMiceTL achieves promising performance to identify ECG patterns during the early course of cardiac conduction disease in mice. Our study also suggests the feasibility for early clinical diagnosis of human cardiac conduction diseases and other types of cardiac arrhythmias using deep learning in the future.",
            "source_url": "https:\/\/openalex.org\/W7134143582",
            "pdf_url": "https:\/\/ttu-ir.tdl.org\/bitstreams\/1817fc56-24a6-49bd-b8bd-56b6016bab98\/download",
            "doi": "",
            "handle": "",
            "repository_name": "ThinkTech (Texas Tech University)",
            "source_record_id": "W7134143582",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1029,
            "title": "Vehicle identity recognition using deep learning",
            "title_ar": "",
            "title_en": "",
            "author": "Mohamed Dhia Elhak Besbes",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2021,
            "language": "fr",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "Reconnaissance de l’identité de véhicules par apprentissage profond Le but de cette thèse est de développer un système de reconnaissance de l'identité des véhicules en se basant à la fois sur la reconnaissance du numéro de la plaque d’immatriculation et de la marque (constructeur, modèle) du véhicule. L'objectif principal est de proposer des solutions pour améliorer la mobilité et la sécurité du trafic routier comme le control des excès de vitesse par les radars automatiques, la surveillance des passages aux frontières et la recherche des véhicules suspects.",
            "source_url": "https:\/\/openalex.org\/W7150936785",
            "pdf_url": "https:\/\/theses.hal.science\/tel-03381928v1\/file\/106326_BESBES_2021_archivage.pdf",
            "doi": "10.70675\/e81c8100z5fd6z4491zb6d6z010e872c68e0",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7150936785",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1032,
            "title": "Deep Learning-Based Text Detection and Recognition",
            "title_ar": "",
            "title_en": "Deep Learning-Based Text Detection and Recognition",
            "author": "Qingqing Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Technology Sydney",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; rnn; image-classification; object-detection; image-segmentation",
            "keywords": "",
            "abstract": "Texts play a critical role in our daily life. They are everywhere such as slogans on posters, licence plates on cars, etc., to transmit information and knowledge. With the popularity of mobile devices with cameras, more and more texts are collected, transmitted and stored as text images. Automatically reading texts from images is of high application potentials. Therefore, related researches have been attracting considerable attentions from the computer vision community. Scene texts and handwritten texts are the two most difficult texts to be automatically read because of the challenges posed by the complexity of backgrounds, the uncertainty of capturing conditions, the diversity of text appearances, touching characters and the variety of handwriting styles. Text detection, 𝒾.𝑒., localizing text areas from images, and text recognition, 𝒾.𝑒., transcribing located text areas into character sequences, are two key steps of robust text reading. In recent years, they have entered a deep learning era, where Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) play important roles. Here, we conduct researches on text detection and recognition based on CNN and LSTM, as presented below. 1. To improve the recall rate of small text areas in oriented text detection, we propose an Xception-based multi-ASPP-assembled scene text detector named DeepText. DeepText inserts multiple Atrous Spatial Pyramid Pooling (ASPP) modules into Xception after feature maps with different resolutions to retain richer information for small text areas, and introduces auxiliary connections and auxiliary losses to speed up convergence and boost the discrimination ability of lower encoder layers. 2. To address the issue that Mask R-CNN cannot fully leverage global information when performing predictions, we propose a scene text detector named GMask R-CNN, where a global mask module is designed to perform semantic segmentation by considering global information. 3. To tackle the problem that LSTM neglects the valuable spatial and structural information of 2-D text images, we propose two scene text recognisers named FACLSTM, which exploits convolution LSTM to directly perform sequential transcription in 2-D space, and ReELFA, which utilizes one-hot encoded locations to enhance features with pixels' spatial information. 4. To solve the problem that CNNs with fully connected layers are not suitable for sequential prediction tasks due to their requirements of fixed-size inputs\/outputs, we propose a CNN-based handwritten text recogniser CFSPP. CFSPP embeds a Spatial Pyramid Pooling-based intermediate layer between convolutional layers and fully connected layers to convert arbitrary-size feature maps into feature vectors with specific lengths.",
            "source_url": "https:\/\/openalex.org\/W3154876829",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UTS ePRESS (University of Technology Sydney)",
            "source_record_id": "W3154876829",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1033,
            "title": "Detection and Recognition for Multiple Flames Using Deep Learning",
            "title_ar": "",
            "title_en": "Detection and Recognition for Multiple Flames Using Deep Learning",
            "author": "Xin Chen",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Auckland University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; cnn; object-detection",
            "keywords": "",
            "abstract": "Object recognition is one of the critical fields of intelligent surveillance which can be applied to tackle multiple security issues. The recognition of a flame is an extended research direction which has significant practical value for multiple applications. The main contribution of this thesis, with the starting point of recognizing flames, is to introduce how Convolution Neural Networks (CNNs) deal with surveillance images. To achieve this, we constructed different neural network models, e.g., Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO). We enhanced the capacity of transformation of CNNs in part of convolution and RoI pooling. In this thesis, we mainly present assorted methods in deep leaning to achieve detection and identification of various flames. First, we introduce the fundamental theories of object detection and recognition using deep learning. After that, we detail the algorithms of pattern classification and compare those algorithms for image feature extraction. Based on the theory of neural networks, we also depict these learning methods and analyze the accuracy of experimental results. The focus of this thesis is on flame recognition using deep learning. For achieving this goal, a significant amount of measured data was used as the input data to ensure the validation of our experiments. We employ TensorFlow as the development environment with an NVIDIA GPU to train the image dataset and construct a detection model for the test dataset. Ultimately, experimental results show that the verification accuracy of our proposed algorithm is up to 98.6%.",
            "source_url": "https:\/\/openalex.org\/W2898907605",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Tuwhera (Auckland University of Technology)",
            "source_record_id": "W2898907605",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1034,
            "title": "Deep Learning for Amharic Text-Image Recognition: Algorithm, Dataset and Application",
            "title_ar": "",
            "title_en": "Deep Learning for Amharic Text-Image Recognition: Algorithm, Dataset and Application",
            "author": "Birhanu Hailu Belay",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Kaiserslautern University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; ai-applications; cnn; rnn; transformers; image-classification; machine-translation; ai-industry",
            "keywords": "",
            "abstract": "Optical Character Recognition (OCR) is one of the central problems in pattern recognition. Its applications have played a great role in the digitization of document images collected from het- erogeneous sources. Many of the well-known scripts have OCR systems with sufficiently high performance that enables OCR applications in industrial\/commercial settings. However, OCR sys- tems yield very-good results only on a narrow domain and very-specific use cases. Thus, it is still a challenging task, and there are other exotic languages with indigenous scripts, such as Amharic, for which no well-developed OCR systems exist. As many as 100 million people speak Amharic, and it is an official working language of Ethiopia. Amharic script contains about 317 different alphabets derived from 34 consonants with small changes. The change involves shortening or elongating one of its main legs or adding small diacritics to the right, left, top, or bottom of the consonant character. Such modifications lead the characters to have similar shapes and make the recognition task complex, but this is particularly interesting for charac- ter recognition research. So far, Amharic script recognition models are developed based on classical machine learning techniques, and they are very limited in addressing the issues for Amharic OCR. The motivation of this thesis is, therefore, to explore and tailor contemporary deep learning tech- niques for the OCR of Amharic. This thesis addresses the challenges in Amharic OCR through two main contributions. The first contribution is an algorithmic contribution in which we investigate deep learning approaches that suit the demand for Amharic OCR. The second is a technical contribution that comprises several works towards the OCR model development; thus, it introduces a new Amharic database consisting of collections of images annotated at a character and text-line level. It also presents a novel CNN- based framework designed by leveraging the grapheme of characters in Fidel-Gebeta (where Fidel- Gebeta consists of the full set of Amharic characters in matrix structure) and achieves 94.97% overall character recognition accuracy. In addition to character level methods, text-line level methods are also investigated and devel- oped based on sequence-to-sequence learning. These models avoid several of the pre-processing stages used in prior works by eliminating the need to segment individual characters. In this design, we use a stack of CNNs, before the Bi-LSTM layers and train from end-to-end. This model out- performs the LSTM-CTC based network, on average, by a CER of 3.75% with the ADOCR test set. Motivated by the success of attention, in addressing the problems’ of long sequences in Neural Machine Translation (NMT), we proposed a novel attention-based methodology by blending the attention mechanism into CTC objective function. This model performs far better than the existing techniques with a CER of 1.04% and 0.93% on printed and synthetic text-line images respectively. Finally, this thesis provides details on various tasks that have been performed for the development of Amharic OCR. As per our empirical analysis, the majority of the errors are due to poor annotation of the dataset. As future work, the methods proposed in this thesis should be further investigated and extended to deal with handwritten and historical Amharic documents.",
            "source_url": "https:\/\/openalex.org\/W3163416829",
            "pdf_url": "https:\/\/kluedo.ub.uni-kl.de\/frontdoor\/index\/index\/docId\/6291",
            "doi": "10.26204\/kluedo\/6291",
            "handle": "",
            "repository_name": "Publication Server of Kaiserslautern University of Technology (Kaiserslautern University of Technology)",
            "source_record_id": "W3163416829",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1035,
            "title": "Contributions on Deep Transfer Learning",
            "title_ar": "",
            "title_en": "Contributions on Deep Transfer Learning",
            "author": "Chetak Kandaswamy",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2016,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn",
            "keywords": "",
            "abstract": "Machine Learning (ML) is reshaping our world by building machines that can see, read, listen, talk, and write (Marr, 2015).For example, imagine you want to eat your favorite food and ask a mobile app what are the nearby restaurants that serve your favorite food.The app uses a ML algorithm to analyze the information of your location and time to provide better service.Imagine a startup that wants to use ML algorithm optimize their resources and requirements.Envision a scenario where a breast cancer patient and a surgeon want to plan a surgery.Generally, it takes a highly trained surgeon's subjective evaluation of the breast cancer surgery to make treatment decisions.Employing ML algorithms allows an objective evaluation of the surgery with a sense of femininity (aesthetic metric by (Beadle et al., 1984)), using 3D imaging that helps the patient and the doctor to visualize and collectively decide what they are seeing, and then make a informed decision about the surgery (Oliveira, 2013).The ability of ML algorithms to automatically understand the contents of images opened new ways to tackle prominent computer vision challenges like view point variation, illumination, occlusion, scale, deformation, background clutter, and intra-class variation.Especially, Deep learning algorithms like Stacked Denoising Autoencoders, Convolutional Neural Networks, and Deep Belief Nets have addressed these challenges with state-of-the-art results and have surpassed human performance.The salient attribute of Deep learning algorithms is that they automatically learn features from a large number of data samples without overfitting the model.Deep learning provides a partial solution to the research statement how to get computer programs to self-learn patterns from data.We consider machine learning as a broadening field which integrates interdisciplinary knowledge of learning processes from other fields such as psychology, biology, neuroscience, and economy.From this we attempt to answer questions such as: what kind of process can lead to learning, under what conditions, and for what kind of data?(Mitchell, 2006) In this thesis we integrated knowledge of deep learning with transfer learning where the learning is inspired from human-like learning processes to build our Deep Transfer Learning framework: \"how to get computer programs to self-learn patterns in data-efficient and domaingeneral way?\"Our survey on the state-of-the-art machine learning methods that use self-learn patterns lead to deep learning models.The subsequent question is about how learning algorithms can be inspired from human-like learning processes.For the sake of simplicity, we narrowed our scope down to human like knowledge transfer from one scenario to another.It is interesting to study the various possible cases of transfer learning settings based on the distributions, posterior probabilities, learning function and the classification tasks.We have designed three main mechanisms that harness the advantages of our proposed Deep Transfer Learning (DTL) framework: 1) Layerwise Transfer Learning (LTL), 2) Source-Target-Source (STS) and 3) Deep Transfer Learning Ensemble.First, we developed two approaches based on LTL mechanism : a) Transfer Learning unsupervised (TLu) and b) Transfer Learning supervised (TLs).On de conhecimento do ser-humano.Como simplificação, reduzimos o espectro de interesse na transferência de conhecimento entre duas tarefas de modo a utilizar conhecimento adquirido numa para resolver outra.É interessante estudar uma série de possíveis variações no processo de transferência de aprendizagem, quer ao nível das distribuições, probabilidades posteriores, funções de aprendizagem e também tarefas de classificação.Foram criados três mecanismos base da framework de deep transfer learning: 1) Layerwise Transfer Learning (LTL), 2) Source-Target-Source (STS) e 3) Deep Transfer Learning Ensemble.Relativamente à primeira metodologia, duas abordagens foram tomadas: a) Transferência de conhecimento não-supervisionada (TLu) e b) Transferência de conhecimento supervisionada (TLs).Por outro lado, os mecanismos de STS desenvolvidos foram testados tanto em problemas com uma fonte de informação como em problemas de múltiplas fontes.Finalmente, foram também desenvolvidas e testadas metodologias ensemble de várias abordagens LTL.De modo a analisar a eficácia dos métodos desenvolvidos, testes práticos foram levados a cabo ao nível de descoberta de fármacos e também de biometria inter-sensor.Um software de DTL baseado em interface interativa para algoritmos de AC baseados em GPU foi também implementado.Este software fornece uma interface para problemas baseline e outras metodologias de transferência de conhecimento.viii A special word of thanks to my friends without whose encouragement and unconditional support it would be a challenge: Saravanan Kandasamy, Kumaresa Vanji, Abhishek Chatterjee, Balamurugan Varadharajan, Herlina Jayadianti and Merle Planten.I would like to…",
            "source_url": "https:\/\/openalex.org\/W2592277968",
            "pdf_url": "https:\/\/repositorio-aberto.up.pt\/bitstream\/10216\/85509\/2\/145323.pdf",
            "doi": "10.34626\/bjwn-t886",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W2592277968",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1037,
            "title": "Deep Learning Methods for Industry and Healthcare",
            "title_ar": "",
            "title_en": "Deep Learning Methods for Industry and Healthcare",
            "author": "Maria Amodeo",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "ai-industry",
            "categories": "deep-learning; medical-ai; ai-applications; ai-industry",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7219179326",
            "pdf_url": "https:\/\/iris.polito.it\/bitstream\/11583\/2963952\/2\/Tesi.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "PORTO@Iris",
            "source_record_id": "W7219179326",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1039,
            "title": "Enhanced facial behavior recognition and rehabilitation using 3D biomechanical features and deep learning approaches",
            "title_ar": "",
            "title_en": "",
            "author": "Duc-Phong Nguyen",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "theses.fr: الفهرس الوطني الفرنسي لأطاريح الدكتوراه",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "France",
            "governorate": "",
            "publication_year": 2022,
            "language": "fr",
            "research_domain": "medical-ai",
            "research_subdomain": "face-recognition",
            "categories": "deep-learning; computer-vision; medical-ai; face-recognition",
            "keywords": "",
            "abstract": "Reconnaissance des comportements et rééducation fonctionnelle du visage à partir des descripteurs biomécaniques 3D et l'apprentissage profond Les patients atteints de paralysie faciale ou les patients sous transplantation faciale présentent des dysfonctionnements faciaux et des mouvements faciaux anormaux. Cela est dû à l'altération du nerf facial et des systèmes musculaires faciaux. La rééducation faciale traditionnelle actuelle est principalement basée sur une approche miroir pour surveiller le retour visuel qualitatif de l'exercice de rééducation. Des systèmes assistés par ordinateur basés sur des modèles basés sur la physique ont également été développés pour fournir des informations objectives et quantitatives. Cependant, l'utilisation de ces systèmes dans la pratique clinique de routine reste encore difficile en raison de plusieurs limitations : 1) le manque de d'informations 3D à partir d'images ou la dépendance des caméras sélectionnés ; 2) le manque d'analyse du visage en termes de reconnaissance d'expression et d'analyse de symétrie et 3) la limitation de la capacité prédictive des modèles de mouvement du visage avec des propriétés biomécaniques émergentes. Les objectifs de la thèse ont été de développer des solutions d'ingénierie innovantes pour un système d'aide à la décision assistée par ordinateur de nouvelle génération pour l'analyse faciale et la rééducation fonctionnelle. La thèse a fourni quatre contributions principales : 1) la reconstruction rapide de la forme du visage en 3D à partir d'une seule image 2D en utilisant des approches d'apprentissage en profondeur ; 2) l'amélioration de la reconnaissance des expressions faciales à l'aide d'un ensemble de points 3D et d'un apprentissage profond géométrique ; 3) l'analyse de la symétrie du visage basée sur de nouveaux descripteurs et la différence entre deux populations différentes (caucasienne et asiatique) et 4) la proposition d'un nouveau cadre de modélisation pour l'apprentissage du mouvement facial en couplant l'apprentissage par renforcement et la modélisation par éléments finis pour l'apprentissage et la prédiction du mouvement facial. En conclusion, la thèse a proposé un cadre pour le développement et le déploiement un système d'aide à la décision assistée par ordinateur de bout en bout pour guider et optimiser le programme de rééducation fonctionnelle.",
            "source_url": "https:\/\/openalex.org\/W7151240088",
            "pdf_url": "https:\/\/theses.hal.science\/tel-03813649v1\/document",
            "doi": "10.70675\/716db43fzfaf8z4480za272z71992a6e873c",
            "handle": "",
            "repository_name": "theses.fr (ABES)",
            "source_record_id": "W7151240088",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1041,
            "title": "Deep learning with synthetic, temporal, and adversarial supervision",
            "title_ar": "",
            "title_en": "Deep learning with synthetic, temporal, and adversarial supervision",
            "author": "Ankush Gupta",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Oxford",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; cnn; rnn; object-detection; video-analysis",
            "keywords": "",
            "abstract": "In this thesis we explore alternatives to manually annotated training examples for supervising the training of deep learning models. Specifically, we develop methods for learning under three different supervision paradigms, namely — (1) synthetic data, (2) temporal data, and (3) adversarial supervision for learning from unaligned examples. The dominant application domain of our work is text spotting, i.e. detection and recognition of text instances in images. We learn text localisation networks on synthetic data, and harness an adversarial discriminator for training text recognition networks using no paired training examples. Further, we exploit the changing pose of objects in temporal sequences (videos) to learn object landmark detectors. The unifying objective is to scale deep learning methods beyond manually annotated training data. We develop a large-scale, realistic synthetic scene text dataset. Armed with this large annotated dataset of scene images, we train a novel, fast fully-convolutional text detection network, and show excellent performance on real images. This generalisation from synthetic to real images, confirms the verisimilitude of our rendering process. The dataset, SynthText in the Wild, has been widely adapted by the research community, and has enabled the development of end-to-end text spotting models. While synthetic text can be readily generated, it needs to be adapted for the specific application domain. However, unaligned examples of text-images, and valid language sentences are abundant. With this in mind, we develop a method for text recognition which learns from such unaligned data. We cast the text recognition problem as one of aligning the conditional distribution of strings predicted from given text images, with lexically valid strings. This alignment is induced through an adversarial discriminator which tries to distinguish the predicted and real text strings apart. Our method achieves excellent text recognition accuracy, using no labelled training examples. Temporal sequences (videos) of objects encode changes in their pose. We develop a method to harness this, and learn object landmark detectors, which consistently track object parts across different poses and instances. We achieve this by conditionally generating a future frame given a past frame, and a sparse keypoint like (learnt) representation extracted from the future frame. We demonstrate generality of our method by learning landmarks for human faces (where we outperform existing landmark detectors), articulated human body, and rigid 3D objects, with no modification to the method. Finally, we propose one-step inductive training for improving generalisation in recurrent neural networks to longer sequences. We restrict the recurrent state to a spatial memory map which tracks the regions of the image which have been accounted for, and train the network for valid evolution of this map. We show excellent generalisation to much longer sequences on two sequential visual recognition tasks — joint localisation and recognition of multiple lines of text, and counting objects in aerial images.",
            "source_url": "https:\/\/openalex.org\/W2964327310",
            "pdf_url": "https:\/\/ora.ox.ac.uk\/objects\/uuid:0a9d2c4a-0e8d-4722-9e0b-3f247efa89e8\/files\/m9652124287993d30be22dafbd8178674",
            "doi": "10.5287\/ora-2nqvgy7gz",
            "handle": "",
            "repository_name": "Oxford University Research Archive (ORA) (University of Oxford)",
            "source_record_id": "W2964327310",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1042,
            "title": "Visual Aided Deep Learning Applications for Underwater Operations",
            "title_ar": "",
            "title_en": "",
            "author": "Martin Skaldebø",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2019,
            "language": "sv",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; generative-ai; robotics; ai-applications; cnn; object-detection; gan; ai-industry",
            "keywords": "",
            "abstract": "Den 4. industrielle revolusjonen har pågått de siste fire årene og resultater har begynt å vise seg i industrimarkedet. Google slipper stadig ny teknologi som kan slå mennesker i et mangfold av strategispill og de har utviklet kunstig inteligens som kan lære å gå på egenhånd. Statlige instanser og klassifikasjonsselskaper sammarbeider mot regler og retningslinjer for autonome skip, Yara Birkeland er et skip som er planlagt ferdig i 2020 som vil gå mot full autonom operasjon innen 2022. Autonomi i undervannsoperasjoner erfarer også vekst som vises igjen i økning i markedet. Maskinlæring er forventet å være en bidragsyter i skiftet mot en mer autonom fremtid. Metoder for innhenting av relevant informasjon i undervannsapplikasjoner som eksisterer idag bruker hovedsakelig akkustiske sensorer. Metoder for informasjonsinhenting som bruker kamerabilder er sjeldent brukt, men forbedringer innen nevrale nettverk, spesielt Convolutional Neural Networks (CNN), har vist lovende resultater. Denne rapporten vil undersøke bruken av slike metoder for undervannsoperasjoner. Rapporten trekker spesielt frem to problemstillinger, som er fordelt på to vedlagte artikler. Den første problemstillingen anngår realitetsgapet, som beskriver fenomenet som oppstår når kunnskap blir overført mellom domener, mer presist mellom det kunstige simuleringsdomenet og den virkelige verden. Eksisterende metoder angående dette har blitt undersøkt og sammenlignet for å danne et utsnitt av eksisterende metoder. Grunnet det tøffe miljøet under vann har bruken av kamerabilder til informasjonsinnhenting vært liten. Dårlig belysning, maritim snø, et miljø i kontinuerlig endring etc. gjør det vanskelig å modellere et undervannsmiljø. Det vil derfor nesten garantert eksistere et realitetsgap mellom det simulerte domenet og den virkelige verden. I løpet av de siste årene har flere rammeverk som angriper realitetsgapet oppstått og blitt publisert. Generative Adversarial Networks (GAN) har muligens stått frem som en av de mest lovende metodene for domenetransformasjon de siste årene. Denne rapporten undersøker en metode av dette rammeverket kalt CycleGAN, som introduserer en sykelkonsistens egenskap i det orginale GAN rammeverket. CycleGAN har vist gode resultater inne flere disipliner og har i denne rapporten for første gang blitt anvendt på undervannsapplikasjoner. Metoden består av å ta et bilde som input og reprodusere det samme bildet i et annet domene som output, e.g. ta et generert bilde fra simuleringsdata av et undervannspanel som input og produsere et virkelighetsnært bilde av det samme undervannspanelet. I denne rapporten er rammeverket anvendt på to forskjellige dataset med bilder fra undervannsmiljøer og en god kartlegging mellom simuleringsbaserte domener og den virkelige verden er oppnådd. Den andre problemstillingen omhandler optisk basert objekt detektering ved bruk av monokularkamera. Deep learning metoder for objekt detektering er undersøkt i unison med metoder for å ekstrakte relevante egenskaper fra de respektive detektorene for å bidra i et dynamsik posisjonerings (DP) system. En detekteringsalgoritme basert på YOLOv3 er anvendt på et dataset sammensatt av bilder fra marin kybernetisk laboratoriet (MC-lab) ved NTNU. Datasettet inkluderer bilder av et kjent objekt i bassenget i laboratoriet. Når detekteringsalgoritmen er ferdig trent og kan vellykket detektere det relevante objectet, er en skaleringsfunksjon samt et DP-system for undervannsdronen BlueROV utviklet. BlueROV2 utfører DP relativ til det kjente objektet ved å innhente relevant informasjon angående posisjon og avstander ved hjelp av detekteringsalgoritmen. Systemet fungerer tilfredstillende og klarer å utføre DP med korrekt informasjonsinnhenting av både posisjon og avstander til objektet fra kamerabilder.",
            "source_url": "https:\/\/openalex.org\/W2999495304",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "NORA - Norwegian Open Research Archives",
            "source_record_id": "W2999495304",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1043,
            "title": "Classification in Bone Scintigraphy Images Using Convolutional Neural Networks",
            "title_ar": "",
            "title_en": "Classification in Bone Scintigraphy Images Using Convolutional Neural Networks",
            "author": "Johnny Dang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Lund University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2016,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; cnn",
            "keywords": "",
            "abstract": "The main goal of this thesis is to design and implement a convolutional neural network (CNN) to classify whether hotspots in bone scintigraphy images represent cancer metastases, caused by prostate cancer, or some other physiological process. A side task was included, where another CNN was designed and trained to classify whether a bone scintigraphy image image is acquired from the front of the patient (anterior) or from the back (posterior). The CNNs were implemented using the deep learning frameworks Deeplearning4J and Keras, and were trained on labelled data sets provided by Exini Diagnostics AB, Lund, Sweden. The performance of the CNNs were evaluated using various metrics commonly used in machine learning contexts, for instance accuracy and Receiver Operating Characteristics. The final trained CNN used for the classification of hotspots reached an accuracy of 89% on a test set consisting of images that had not been used for training. The corresponding CNN for the side task reached an accuracy of 99%. These results indicate that CNNs can work well for both classification problems.",
            "source_url": "https:\/\/openalex.org\/W2507947736",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Lund University Publications Student Papers (Lund University)",
            "source_record_id": "W2507947736",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1045,
            "title": "Classification of ulcer images using convolutional neural networks",
            "title_ar": "",
            "title_en": "Classification of ulcer images using convolutional neural networks",
            "author": "Charlotta Aguirre Nilsson ; Medina Velic",
            "supervisor": "",
            "degree_type": "master",
            "degree_evidence": "نص السجل: «master»",
            "university": "Chalmers University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; transfer-learning; cnn; image-classification; medical-imaging",
            "keywords": "",
            "abstract": "The use of artificial intelligence has increased within a variety of different fields the last decade, including the area of health care.Machine learning algorithms have already been successfully used in e.g.skin cancer detection in images, indicating its potential for being applied to wound treatment by automatically classifying wound images.In addition to reducing patient discomfort, a machine learning based solution for wound image classification could lower the costs for treatment of wounds.Today, this cost corresponds to the third biggest health care cost for Västra Götalandsregionen, the health care sector in Västra Götaland.This master thesis is a collaboration between the electronics and software development company QRTECH and Västra Götalandsregionen with the aim of creating an image-based aid to be used in the diagnosis of venous leg ulcers.A total of 300 manually annotated images depicting various ulcers for training a network have been provided by Alexandra Forssgren, specialist in dermatology and head of the dermatology department at Skaraborg Hospital.A deep learning based algorithm, using a pre-trained version of the VGG19 convolutional neural network, has been implemented to perform automated binary classification in order to separate venous leg ulcers from other ulcers.Since the annotated ulcer data set was limited, transfer learning was used by pre-training on ImageNet as well as on a set of dermoscopic skin images.The network was implemented in Python using the deep learning library Keras with TensorFlow as backend.To improve performance and reduce overfitting, methods such as dropout, L1\/L2 regularisation and data augmentation were applied.Augmentation included rotation, flipping, contrast enhancement as well as added noise and modified brightness.The best set-up was selected based on the performance metrics accuracy, precision and recall.For the test set, the final network reached an accuracy, precision and recall of approximately 85 %, 82 % and 75 %, respectively.Most certainly, the limited size of the data set affected the results negatively and a larger data set would increase the variety of the training examples, possibly leading to better performance.However, the performance metrics are still promising considering the small data set which proves the potential of a machine learning based solution for ulcer image classification.",
            "source_url": "https:\/\/openalex.org\/W2889535768",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Chalmers Publication Library (Chalmers University of Technology)",
            "source_record_id": "W2889535768",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1046,
            "title": "Image Classification with Convolutional Neural Networks in the Presence of Optical Aberrations and Stray Light",
            "title_ar": "",
            "title_en": "Image Classification with Convolutional Neural Networks in the Presence of Optical Aberrations and Stray Light",
            "author": "Page King",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "The University of Arizona",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-classification",
            "categories": "machine-learning; deep-learning; computer-vision; responsible-ai; cnn; image-classification; fairness",
            "keywords": "",
            "abstract": "As optical systems advance in capability, they present new engineering challenges. This work focuses on two such challenges: controlling stray light in imaging systems with increasingly demanding out-of-field stray light requirements and understanding the relationship between imaging hardware requirements and image classification performance with Convolutional Neural Networks (CNNs). These topics are unified by investigating CNN classification performance in the presence of stray light from optical scatter.Chapter 1 introduces the field of stray light. Stray light is light that takes an unintended path relative to the intended design path within an optical system. This section introduces mechanisms yielding stray light in an optical system, controls to manage their impact, and the engineering of stray light analysis. The purpose of stray light analysis is to discern stray light mechanisms driving performance and efficiently manage their effects to satisfy stray light performance requirements. Chapter 2 explores the out-of-field stray light performance metric, Point Source Transmittance (PST) and delves into how various stray light controls impact this metric for an optical system. Emphasis is placed on sunshades, which are designed to suppress stray light from sources outside the optical system field of view. While PST is a fundamental metric quantifying an optical system’s out-of-field stray light performance, imaging systems often have the concern of stray light from extended sources like the Sun, Moon, and Earth in their scene. Chapter 3 presents a method of using a PST to calculate an extended source transmittance for an arbitrary source. This approach enables flexibility in how stray light requirements are defined and managed on an effort. Transitioning to the second focus of this work, Chapter 4 introduces CNNs. In the field of machine learning, a CNN is a deep learning algorithm that can be applied to image classification. CNNs perform convolution and pooling operations to learn and extract features of targets in a training set of imagery. When presented with new imagery, CNNs extract learned features to classify targets in a scene. Chapter 5 delves into how CNN architectures are impacted by imaging hardware requirements. CNNs pretrained on pristine imagery are given imagery degraded by progressive amounts of optical aberration and correlated to the consequent degradation in classification performance. The Modulation Transfer Function (MTF) proves a critical image quality metric, as CNNs learn and recognize spatial frequency content of their targets. The layered structure of CNNs, in which successive operations reduce the spatial resolution of the input image, introduces a bias toward learning lower spatial frequency content. This is supported by classification performance with various optical aberrations; CNNs can be particularly robust against degradation with optical aberrations (that did not exist in training imagery), so long as they do not introduce a zero crossing or significant degradation in contrast for the spatial frequency content of features that were learned for a given target. This discussion is extended to the stray light mechanism of optical scatter. CNNs are generally robust against optical scatter, but an important exception is when bright sources are present in a scene. Optical scatter (and other stray light mechanisms) in the presence of bright sources can raise the noise background or saturate imagery and degrade the contrast and apparent color of targets for classification.",
            "source_url": "https:\/\/openalex.org\/W7213385102",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UA Campus Repository (The University of Arizona)",
            "source_record_id": "W7213385102",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1049,
            "title": "Convolutional neural network for hyperspectral image classification",
            "title_ar": "",
            "title_en": "Convolutional neural network for hyperspectral image classification",
            "author": "Nanqi Yuan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2016,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-classification",
            "categories": "deep-learning; computer-vision; cnn; image-classification",
            "keywords": "",
            "abstract": "Hyperspectral Image Classification is an important research problem in remote sensing.Classification is one of the most popular topic in hyperspectral remote sensing. In the last twenty years, a huge quantity of methods were proposed to deal with the hyperspectral data classification problem. Deep learning has been shown to be very promissing for this problem. However, existing deep learning methods only try to learn features from a pixel\/region independently without considering the dependency between different pixels\/regions.This project will employ Convolutional Neural Networks for learning features based on the spatial-spectral information of hyperspectral images. Experiments are conducted on benchmark datasets.",
            "source_url": "https:\/\/openalex.org\/W2528086488",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W2528086488",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1053,
            "title": "A deep convolutional neural network architecture for image classification",
            "title_ar": "",
            "title_en": "A deep convolutional neural network architecture for image classification",
            "author": "Willem Lodewikus Pretorius",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Stellenbosch University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "South Africa",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "deep-learning; computer-vision; nlp; cnn; image-classification; object-detection; video-analysis; speech-recognition",
            "keywords": "",
            "abstract": "ENGLISH SUMMARY : Convolutional Neural Networks (CNNs), a specialised form of Neural Networks (NNs), are wellknown for their state-of-the-art results obtained in Computer Vision (CV) and Deep Learning (DL) tasks throughout the past few years. Some of the exciting application areas of CNNs include image classi cation, object detection, video processing, natural language processing, and speech recognition. The powerful learning ability of deep CNNs is primarily owed to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in hardware technology has accelerated the research done in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of di erent activation and loss functions, parameter optimisation, regularisation, and architectural innovations by using di erent layer structures. Therefore, the objective of this study is based on image classi cation and object detection tasks, that is, creating custom-designed CNN architectures for deployment on real-world datasets while comparing these custom-designed architectures to those state-of-the-art architectures found in literature while comparing di erent optimisation procedures and activations functions. All major developments of CNNs are discussed and critically considered, with a view to improve CNNs in the context of the number of parameters used to obtain satisfactory results and additionally to obtain a better understanding of the term known as a `black box' which is usually associated with CNNs such that they are complex models with little understanding in the way how their classi cations are done. The most promising modern CNN architectures with associated hyperparameters are further explored by means of empirical work. Evaluation is done on the validity of ndings reported in the literature and comments are made on the e ectiveness of recent proposals through the use of ve di erent real-world datasets. The empirical work done will be complemented by additional coded notebooks that could be used to implement state-of-the-art techniques, as well as for comparative and model assessment experiments.",
            "source_url": "https:\/\/openalex.org\/W3135789880",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "SUNScholar (Stellenbosch University)",
            "source_record_id": "W3135789880",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1055,
            "title": "Evolving Convolutional Neural Networks for Image and Text Classification Tasks",
            "title_ar": "",
            "title_en": "Evolving Convolutional Neural Networks for Image and Text Classification Tasks",
            "author": "Trevor Londt",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; generative-ai; predictive-analytics; optimization; cnn",
            "keywords": "",
            "abstract": "In the Machine Learning field, Convolutional Neural Networks (CNNs) have successfully been applied to image and text classification tasks. Innovative research in CNN design, such as the usage of deeper models and data augmentation, has resulted in significantly increased performance. However, the resultant growth in CNN complexity from these innovations has required a corresponding increase in expert skills to design them. Furthermore, CNN architectures and data augmentation techniques designed for a classification task in one domain may not generalise to other classification tasks in a different domain, meaning the difficult and costly design process must be repeated. Evolutionary Deep Learning Neural Architecture Search (EDLNAS) has been used to automatically evolve performant CNNs for various classification tasks, including image and text classification. However, the approach suffers from high computation costs, often resulting in algorithms needing to focus on a single task for a single domain, thereby missing the opportunity to achieve algorithm generalisability. This thesis aims to investigate and improve upon the efficiency, effectiveness, and generalisability of EDLNAS of CNNs for image and text classification tasks without using data augmentation. Achieving this goal can mitigate three significant problems: the high-computational cost of EDLNAS algorithms, the generalisability problem, and the problem of collecting additional training data or implementing bespoke data augmentation techniques that require costly expert domain knowledge. This thesis proposes a new, configurable, and extensible version of Cellular Encoding (CE), including supporting network structures designed to represent and construct novel CNN architectures. The encoding successfully represents various novel CNN architectures of varying width and depth, suitable for image or text classification tasks. This thesis proposes a data augmentation-free EDLNAS method to automatically evolve novel CNN architectures for image and text classification tasks, using the proposed CE representation and predesigned state-of-the-art micro-architectures. The results show that peer-competitive CNNs can be evolved in less than one GPU day across image or text domains. Next, this thesis proposes to evolve micro-architectures to boost the performance in evolving CNN architectures for image and text classification tasks without data augmentation. The results show that this is achieved without increasing the number of GPU days required. This thesis proposes the first data augmentation-free cooperative coevolution algorithm to decompose a CNN architecture into four constituent sub-architectures, each evolved separately but in cooperation, to synthesise performant CNN architectures. A new population structure and novelty injection mechanism are proposed to create a better balance between exploration and exploitation. Furthermore, a persistent offline database is created, storing tens of thousands of evolved CNN architectures and associated performance metrics. The results show that highly competitive CNN architectures can be evolved in less than one GPU day, with the algorithm achieving a good balance between exploration and exploitation during the evolution process. Finally, this thesis proposes the first EDLNAS method that models a database of stored CNN architectures and performance metrics to create a deep neural network prediction model to predict the potential classification accuracy of a candidate CNN architecture, thereby removing the need to conduct back-propagation training during the evolutionary process. The method is able to evolve a large population of CNN architectures for both image and text classification tasks in seconds, making the evolutionary process near real-time while still achieving state-of-the-art peer-competitive results.",
            "source_url": "https:\/\/openalex.org\/W4404658997",
            "pdf_url": "",
            "doi": "10.26686\/wgtn.27897288",
            "handle": "",
            "repository_name": "Open Access Victoria University of Wellington | Te Herenga Waka (Figshare)",
            "source_record_id": "W4404658997",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1056,
            "title": "Implementation of Convolutional Neural Networks in FPGA for Image Classification",
            "title_ar": "",
            "title_en": "Implementation of Convolutional Neural Networks in FPGA for Image Classification",
            "author": "Mark Espinosa",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; cnn; image-classification; speech-recognition",
            "keywords": "",
            "abstract": "Machine Learning and Deep Learning its sub discipline are gaining popularity quickly. Machine Learning algorithms have been successfully deployed in a variety of applications such as Natural Language Processing, Optical Character Recognition, and Speech Recognition. Deep Learning particularly is suited to Computer Vision and Image Recognition tasks. The Convolutional Neural Networks employed in Deep Learning Neural Networks train a set of weights and biases which with each layer of the network learn to recognize key features in an image. This work set out to develop a scalable and modular FPGA implementation for Convolutional Neural Networks. It was the objective of this work to attempt to develop a system which could be configured to run as many layers as desired and test it using a currently defined CNN configuration, AlexNet. This type of system would allow a developer to scale a design to fit any size of FPGA from the most inexpensive to the costliest cutting-edge chip on the market. The objective of this work was achieved, and all layers were accelerated including Convolution, Affine, ReLu, Max Pool, and Softmax layers. The performance of the design was assessed, and it was determined its maximum achievable performance was approximately 3 GFLOPS.",
            "source_url": "https:\/\/openalex.org\/W3217604117",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "California State University ScholarWorks (system-wide DSpace)",
            "source_record_id": "W3217604117",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:57",
            "updated_at": "2026-11-15 16:52:57"
        },
        {
            "id": 1060,
            "title": "Design Space Exploration of Convolutional Neural Networks for Image Classification",
            "title_ar": "",
            "title_en": "Design Space Exploration of Convolutional Neural Networks for Image Classification",
            "author": "Prasham Shah",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Purdue University System",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; cnn; image-classification; object-detection; image-segmentation; video-analysis",
            "keywords": "",
            "abstract": "Computer vision is a domain which deals with the goal of making technology as efficient as human vision. To achieve that goal, after decades of research, researchers have developed algorithms that are able to work efficiently on resource constrained hardware like mobile or embedded devices for computer vision applications. Due to their constant efforts, such devices have become capable for tasks like Image Classification, Object Detection, Object Recognition, Semantic Segmentation, and many other applications. Autonomous systems like self-driving cars, Drones and UAVs, are being successfully developed because of these advances in AI. Deep Learning, a part of AI, is a specific domain of Machine Learning which focuses on developing algorithms for such applications. Deep Learning deals with tasks like extracting features from raw image data, replacing pipelines of specialized models with single end-to-end models, making models usable for multiple tasks with superior performance. A major focus is on techniques to detect and extract features which provide better context for inference about an image or video stream. A deep hierarchy of rich features can be learned and automatically extracted from images, provided by the multiple deep layers of CNN models. CNNs are the backbone of Computer Vision. The reason that CNNs are the focus of attention for deep learning models is that they were specifically designed for image data. They are complicated but very effective in extracting features from an image or a video stream. After AlexNet won the ILSVRC in 2012, there was a drastic increase in research related with CNNs. Many state-of-the-art architectures like VGG Net, GoogleNet, ResNet, Inception-v4, Inception-Resnet-v2, ShuffleNet, Xception, MobileNet, MobileNetV2, SqueezeNet, SqueezeNext and many more were introduced. The trend behind the research depicts an increase in the number of layers of CNN to make them more efficient but with that, the size of the model increased as well. This problem was fixed with the advent of new algorithms which resulted in a decrease in model size. As a result, today we have CNN models, which are implemented on mobile devices. These mobile models are compact and have low latency, which in turn reduces the computational cost of the embedded system. This thesis resembles similar idea, it proposes two new CNN architectures, A-MnasNet and R-MnasNet, which have been derived from MnasNet by Design Space Exploration. These architectures outperform MnasNet in terms of model size and accuracy. They have been trained and tested on CIFAR-10 dataset. Furthermore, they were implemented on NXP Bluebox 2.0, an autonomous driving platform, for Image Classification.",
            "source_url": "https:\/\/openalex.org\/W3150947729",
            "pdf_url": "",
            "doi": "10.25394\/pgs.13350125",
            "handle": "",
            "repository_name": "Purdue e-Pubs (Purdue University System)",
            "source_record_id": "W3150947729",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 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": 1066,
            "title": "Structured Deep Neural Networks for Speech Recognition",
            "title_ar": "",
            "title_en": "Structured Deep Neural Networks for Speech Recognition",
            "author": "Chunyang Wu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Cambridge",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; speech-recognition",
            "keywords": "",
            "abstract": "Deep neural networks (DNNs) and deep learning approaches yield state-of-the-art performance in a range of machine learning tasks, including automatic speech recognition. The multi-layer transformations and activation functions in DNNs, or related network variations, allow complex and difficult data to be well modelled. However, the highly distributed representations associated with these models make it hard to interpret the parameters. The whole neural network is commonly treated a ``black box''. The behaviours of activation functions and the meanings of network parameters are rarely controlled in the standard DNN training. Though a sensible performance can be achieved, the lack of interpretations to network structures and parameters causes better regularisation and adaptation on DNN models challenging. In regularisation, parameters have to be regularised universally and indiscriminately. For instance, the widely used L2 regularisation encourages all parameters to be zeros. In adaptation, it requires to re-estimate a large number of independent parameters. Adaptation schemes in this framework cannot be effectively performed when there are limited adaptation data. This thesis investigates structured deep neural networks. Special structures are explicitly designed, and they are imposed with desired interpretation to improve DNN regularisation and adaptation. For regularisation, parameters can be separately regularised based on their functions. For adaptation, parameters can be adapted in groups or partially adapted according to their roles in the network topology. Three forms of structured DNNs are proposed in this thesis. The contributions of these models are presented as follows. The first contribution of this thesis is the multi-basis adaptive neural network. This form of structured DNN introduces a set of parallel sub-networks with restricted connections. The design of restricted connectivity allows different aspects of data to be explicitly learned. Sub-network outputs are then combined, and this combination module is used as the speaker-dependent structure that can be robustly estimated for adaptation. The second contribution of this thesis is the stimulated deep neural network. This form of structured DNN relates and smooths activation functions in regions of the network. It aids the visualisation and interpretation of DNN models but also has the potential to reduce over-fitting. Novel adaptation schemes can be performed on it, taking advantages of the smooth property that the stimulated DNN offer. The third contribution of this thesis is the deep activation mixture model. Also, this form of structured DNN encourages the outputs of activation functions to achieve a smooth surface. The output of one hidden layer is explicitly modelled as the sum of a mixture model and a residual model. The mixture model forms an activation contour, and the residual model depicts fluctuations around this contour. The smoothness yielded by a mixture model helps to regularise the overall model and allows novel adaptation schemes.",
            "source_url": "https:\/\/openalex.org\/W2804696297",
            "pdf_url": "",
            "doi": "10.17863\/cam.23363",
            "handle": "",
            "repository_name": "Apollo (University of Cambridge)",
            "source_record_id": "W2804696297",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1071,
            "title": "Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks",
            "title_ar": "",
            "title_en": "Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks",
            "author": "Suhas Pillai",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Rochester Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "deep-learning; computer-vision; nlp; medical-ai; cnn; rnn; image-classification; speech-recognition",
            "keywords": "",
            "abstract": "Millions of people around the world are diagnosed with neurological disorders like Parkinson’s, Cerebral Palsy or Amyotrophic Lateral Sclerosis. Due to the neurological damage as the disease progresses, the person suffering from the disease loses control of muscles, along with speech deterioration. Speech deterioration is due to neuro motor condition that limits manipulation of the articulators of the vocal tract, the condition collectively called as dysarthria. Even though dysarthric speech is grammatically and syntactically correct, it is difficult for humans to understand and for Automatic Speech Recognition (ASR) systems to decipher. With the emergence of deep learning, speech recognition systems have improved a lot compared to traditional speech recognition systems, which use sophisticated preprocessing techniques to extract speech features. In this digital era there are still many documents that are handwritten many of which need to be digitized. Offline handwriting recognition involves recognizing handwritten characters from images of handwritten text (i.e. scanned documents). This is an interesting task as it involves sequence learning with computer vision. The task is more difficult than Optical Character Recognition (OCR), because handwritten letters can be written in virtually infinite different styles. This thesis proposes exploiting deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for offline handwriting recognition. For speech recognition, we compare traditional methods for speech recognition with recent deep learning methods. Also, we apply speaker adaptation methods both at feature level and at parameter level to improve recognition of dysarthric speech.",
            "source_url": "https:\/\/openalex.org\/W2612106303",
            "pdf_url": "https:\/\/scholarworks.rit.edu\/cgi\/viewcontent.cgi?article=10561&context=theses",
            "doi": "",
            "handle": "",
            "repository_name": "RIT Scholar Works (Rochester Institute of Technology)",
            "source_record_id": "W2612106303",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1072,
            "title": "Speech Recognition in noisy environment using Deep Learning Neural Network",
            "title_ar": "",
            "title_en": "Speech Recognition in noisy environment using Deep Learning Neural Network",
            "author": "A. A. Nasef",
            "supervisor": "",
            "degree_type": "phd",
            "degree_evidence": "اسم المستودع يخص أطاريح الدكتوراه: National Repository of Dissertations in Serbia",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Serbia",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; nlp; speech-recognition",
            "keywords": "",
            "abstract": "Recent researches in the field of automatic speaker recognition have shown that methods based on deep learning neural networks provide better performance than other statistical classifiers. On the other hand, these methods usually require adjustment of a significant number of parameters. The goal of this thesis is to show that selecting appropriate value of parameters can significantly improve speaker recognition performance of methods based on deep learning neural networks. The reported study introduces an approach to automatic speaker recognition based on deep neural networks and the stochastic gradient descent algorithm. It particularly focuses on three parameters of the stochastic gradient descent algorithm: the learning rate, and the hidden and input layer dropout rates. Additional attention was devoted to the research question of speaker recognition under noisy conditions. Thus, two experiments were conducted in the scope of this thesis. The first experiment was intended to demonstrate that the optimization of the observed parameters of the stochastic gradient descent algorithm can improve speaker recognition performance under no presence of noise. This experiment was conducted in two phases. In the first phase, the recognition rate is observed when the hidden layer dropout rate and the learning rate are varied, while the input layer dropout rate was constant. In the second phase of this experiment, the recognition rate is observed when the input layers dropout rate and learning rate are varied, while the hidden layer dropout rate was constant. The second experiment was intended to show that the optimization of the observed parameters of the stochastic gradient descent algorithm can improve speaker recognition performance even under noisy conditions. Thus, different noise levels were artificially applied on the original speech signal.",
            "source_url": "https:\/\/openalex.org\/W3083659827",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "National Repository of Dissertations in Serbia",
            "source_record_id": "W3083659827",
            "other_sources": "",
            "verification_status": "verified",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1074,
            "title": "SPEECH EMOTION RECOGNITION USING DEEP NEURAL NETWORKS",
            "title_ar": "",
            "title_en": "SPEECH EMOTION RECOGNITION USING DEEP NEURAL NETWORKS",
            "author": "Raiymbek Mustazhapov",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nazarbayev University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Kazakhstan",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "deep-learning; nlp; cnn; sentiment-analysis; speech-recognition",
            "keywords": "",
            "abstract": "There is an apparent evolving interest in speech emotion recognition (SER), one of the particular cases of a broader problem of multimedia pattern recognition. SER is considered to possess the capability to enhance the communication efficiency between human and artificial intelligence providing an emotional context to the machine. The field has been developing fast with the emergence and increase in accessibility of deep learning techniques recently. This potential critical benefit and novel techniques have drawn the attention of many specialists in the field and generated a great number of research papers that furnish diverse intricate methods. One of such methods involving various data augmentation techniques has demonstrated high performance in this field. This paper performs an analysis of various simple augmentation methods to attempt to improve existing models. Particularly, this research focuses on state of the art CNN models for RAVDESS, EMO-DB, and IEMOCAP datasets, and exploits temporal, spatial, and spectral transformations of sound as an underlying method for augmentation. As a result of exploiting simple augmentations, we achieved an increase in performance for IEMOCAP model and positive effects comparable to their complex counterparts for other datasets.",
            "source_url": "https:\/\/openalex.org\/W7197763834",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Nazarbayev University Repository (Nazarbayev University)",
            "source_record_id": "W7197763834",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1079,
            "title": "Joint Training Methods for Tandem and Hybrid Speech Recognition Systems using Deep Neural Networks",
            "title_ar": "",
            "title_en": "Joint Training Methods for Tandem and Hybrid Speech Recognition Systems using Deep Neural Networks",
            "author": "Zhang, Chao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Cambridge",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "deep-learning; nlp; speech-recognition",
            "keywords": "",
            "abstract": "Hidden Markov models (HMMs) have been the mainstream acoustic modelling approach for state-of-the-art automatic speech recognition (ASR) systems over the past few decades. Recently, due to the rapid development of deep learning technologies, deep neural networks (DNNs) have become an essential part of nearly all kinds of ASR approaches. Among HMM-based ASR approaches, DNNs are most commonly used to extract features (tandem system configuration) or to directly produce HMM output probabilities (hybrid system configuration). Although DNN tandem and hybrid systems have been shown to have superior performance to traditional ASR systems without any DNN models, there are still issues with such systems. First, some of the DNN settings, such as the choice of the context-dependent (CD) output targets set and hidden activation functions, are usually determined independently from the DNN training process. Second, different ASR modules are separately optimised based on different criteria following a greedy build strategy. For instance, for tandem systems, the features are often extracted by a DNN trained to classify individual speech frames while acoustic models are built upon such features according to a sequence level criterion. These issues mean that the best performance is not theoretically guaranteed. This thesis focuses on alleviating both issues using joint training methods. In DNN acoustic model joint training, the decision tree HMM state tying approach is extended to cluster DNN-HMM states. Based on this method, an alternative CD-DNN training procedure without relying on any additional system is proposed, which can produce DNN acoustic models comparable in word error rate (WER) with those trained by the conventional procedure. Meanwhile, the most common hidden activation functions, the sigmoid and rectified linear unit (ReLU), are parameterised to enable automatic learning of function forms. Experiments using conversational telephone speech (CTS) Mandarin data result in an average of 3.4% and 2.2% relative character error rate (CER) reduction with sigmoid and ReLU parameterisations. Such parameterised functions can also be applied to speaker adaptation tasks. At the ASR system level, DNN acoustic model and corresponding speaker dependent (SD) input feature transforms are jointly learned through minimum phone error (MPE) training as an example of hybrid system joint training, which outperforms the conventional hybrid system speaker adaptive training (SAT) method. MPE based speaker independent (SI) tandem system joint training is also studied. Experiments on multi-genre broadcast (MGB) English data show that this method gives a reduction in tandem system WER of 11.8% (relative), and the resulting tandem systems are comparable to MPE hybrid systems in both WER and the number of parameters. In addition, all approaches in this thesis have been implemented using the hidden Markov model toolkit (HTK) and the related source code has been or will be made publicly available with either recent or future HTK releases, to increase the reproducibility of the work presented in this thesis.",
            "source_url": "https:\/\/openalex.org\/W2935927048",
            "pdf_url": "",
            "doi": "10.17863\/cam.38848",
            "handle": "",
            "repository_name": "Apollo (University of Cambridge)",
            "source_record_id": "W2935927048",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1087,
            "title": "Single-Channel Speech Enhancement Based on Deep Neural Networks",
            "title_ar": "",
            "title_en": "Single-Channel Speech Enhancement Based on Deep Neural Networks",
            "author": "Zhiheng Ouyang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Concordia University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "deep-learning; nlp; generative-ai; cnn; speech-recognition; gan",
            "keywords": "",
            "abstract": "Speech enhancement (SE) aims to improve the speech quality of the degraded speech. Recently, researchers have resorted to deep-learning as a primary tool for speech enhancement, which often features deterministic models adopting supervised training. Typically, a neural network is trained as a mapping function to convert some features of noisy speech to certain targets that can be used to reconstruct clean speech. These methods of speech enhancement using neural networks have been focused on the estimation of spectral magnitude of clean speech considering that estimating spectral phase with neural networks is difficult due to the wrapping effect. As an alternative, complex spectrum estimation implicitly resolves the phase estimation problem and has been proven to outperform spectral magnitude estimation. In the first contribution of this thesis, a fully convolutional neural network (FCN) is proposed for complex spectrogram estimation. Stacked frequency-dilated convolution is employed to obtain an exponential growth of the receptive field in frequency domain. The proposed network also features an efficient implementation that requires much fewer parameters as compared with conventional deep neural network (DNN) and convolutional neural network (CNN) while still yielding a comparable performance. Consider that speech enhancement is only useful in noisy conditions, yet conventional SE methods often do not adapt to different noisy conditions. In the second contribution, we proposed a model that provides an automatic \"on\/off\" switch for speech enhancement. It is capable of scaling its computational complexity under different signal-to-noise ratio (SNR) levels by detecting clean or near-clean speech which requires no processing. By adopting information maximizing generative adversarial network (InfoGAN) in a deterministic, supervised manner, we incorporate the functionality of SNR-indicator into the model that adds little additional cost to the system. We evaluate the proposed SE methods with two objectives: speech intelligibility and application to automatic speech recognition (ASR). Experimental results have shown that the CNN-based model is applicable for both objectives while the InfoGAN-based model is more useful in terms of speech intelligibility. The experiments also show that SE for ASR may be more challenging than improving the speech intelligibility, where a series of factors, including training dataset and neural network models, would impact the ASR performance.",
            "source_url": "https:\/\/openalex.org\/W3199713199",
            "pdf_url": "https:\/\/spectrum.library.concordia.ca\/986846\/1\/Ouyang_MASc_F2020.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Spectrum Research Repository (Concordia University)",
            "source_record_id": "W3199713199",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1097,
            "title": "Object detection and distance measurement with computer vision",
            "title_ar": "",
            "title_en": "Object detection and distance measurement with computer vision",
            "author": "Zhu, Jun",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; robotics; ai-applications; object-detection; ai-industry",
            "keywords": "",
            "abstract": "With the development of computer and robotic technology, it becomes more and more common for robots to do hard, regular work for human. The appearance of robots has already changed humanity’s daily life and improved the efficiency of industry production. Among different kinds of robot applications, object detection and distance measurement are especially important for industry production. Like quality examination, product sorting and dimension measurement. These robots in these applications are programmable, high speed and high efficiency. To do object detection and distance measurement, deep learning and time of flight camera are widely used in industry. Engineers have already spent a lot of time doing related research. However, it is not easy and cheap to implement these methods. Deep learning needs so much computing power and industrial cameras are very expensive. Hence, in this report, the aim is to explore some easy ways to do object detection and distance measurement for some easy application in single background. To achieve this goal, some external sensors are necessary. Later the report will explain the theoretical basis and the experimental results of these two different ways. Then there will be a short discussion about the future development, which can improve the efficiency of these two methods covered in the report.",
            "source_url": "https:\/\/openalex.org\/W2904725650",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W2904725650",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1098,
            "title": "Object detection, recognition and classification using computer vision and artificial intelligence approaches",
            "title_ar": "",
            "title_en": "Object detection, recognition and classification using computer vision and artificial intelligence approaches",
            "author": "Gharib Ismail Al Matroushi",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Loughborough University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; quantum-ml; data-mining; object-detection; image-segmentation; ai-industry",
            "keywords": "",
            "abstract": "Object detection and recognition has been used extensively in recent years to solve numerus challenges in different fields.Due to the vital roles they play, object detection and recognition has enabled quantum leaps in many industry fields by helping to overcome some serious challenges and obstacles.For example, worldwide security concerns have drawn the attention and stimulated the use of highly intelligent computer vision technology to provide security in different environments and in diverse terrains.In addition, some wildlife is at present exposed to danger and extinction worldwide.Therefore, early detection and recognition of potential threats to wildlife have become essential and timely.The extent of using computer vision and artificial intelligence to convert the seemingly insecure world to a more secure one has been widely accepted.Such technologies are used in monitoring, tracking, organising, analysing objects in a scene and for a number of other countless purposes.With the goal of further expanding the application domains of computer vision and artificial intelligence, a novel algorithm to classify mixed industrial airborne waste particles is developed under the research context of this thesis.Initially, a dataset of industrial airborne particles was collected and imaged for this purpose.All images were captured using an advanced microscopic camera.The particles are segmented from the background image using K-means clustering and Hue, Saturation and Value (HSV) colour space features.Subsequently, the Hue, Saturation and Value (HSV) colour histogram data and Gray Level Co-occurrence Matrix (GLCM) texture features are combined into one feature vector of size 278 features, for use in dust sample classification.A feature selection algorithm is employed to minimise the number of features to ten, without compromising the accuracy that can be obtained when using all features.This significant minimisation of features used in classification would improve the processing time.A further computer vision system proposed in this thesis is for automated road surface type recognition aimed at use within autonomous vehicles.Traditional feature driven machine learning based algorithm and a deep learning based approach are proposed to recognise 5 different road surfaces namely: asphalt, concreate, rough, blocks and sand.Firstly, a novel algorithm is developed to exclude irrelevant objects and to create the region of interest within a snapshot image of the road surface.This segmentation algorithm eliminates unnecessary objects being considered for processing as the road in which the vehicle is being driven, by dividing the input image into top and bottom parts, to attempt a fast, region-dependent segmentation.Subsequently, the algorithm uses regiongrowing to create region of interests.For the recognition task, a feature based algorithm that uses a combination of colour histogram, Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern Feature Selection ...........",
            "source_url": "https:\/\/openalex.org\/W2892376930",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Loughborough University Institutional Repository (Loughborough University)",
            "source_record_id": "W2892376930",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1100,
            "title": "COMPUTER VISION AND DEEP LEARNING WITH APPLICATIONS TO OBJECT DETECTION, SEGMENTATION, AND DOCUMENT ANALYSIS",
            "title_ar": "",
            "title_en": "COMPUTER VISION AND DEEP LEARNING WITH APPLICATIONS TO OBJECT DETECTION, SEGMENTATION, AND DOCUMENT ANALYSIS",
            "author": "Xianzhi Du",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Maryland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; cnn; object-detection; image-segmentation",
            "keywords": "",
            "abstract": "There are three work on signature matching for document analysis. In the first work, we propose a large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbors for query signatures. We show that our algorithm can achieve a high accuracy even when few signatures are collected from one same person and perform fast matching when dealing with a large dataset. In the second work, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. We demonstrate considerable improvement over state of the art methods. In the third work, we present a partial signature matching method using graphical models. In additional to the second work, modified shape context features are extracted from the contour of signatures to describe both full and partial signatures. Hierarchical Dirichlet processes are implemented to infer the number of salient regions needed. The results show the effectiveness of the approach for both the partial and full signature matching. There are three work on deep learning for object detection and segmentation. In the first work, we propose a deep neural network fusion architecture for fast and robust pedestrian detection. The proposed network fusion architecture allows for parallel processing of multiple networks for speed. A single shot deep convolutional network is trained as an object detector to generate all possible pedestrian candidates of different sizes and occlusions. Next, multiple deep neural networks are used in parallel for further refinement of these pedestrian candidates. We introduce a soft-rejection based network fusion method to fuse the soft metrics from all networks together to generate the final confidence scores. Our method performs better than existing state-of-the-arts, especially when detecting small-size and occluded pedestrians. Furthermore, we propose a method for integrating pixel-wise semantic segmentation network into the network fusion architecture as a reinforcement to the pedestrian detector. In the second work, in addition to the first work, a fusion network is trained to fuse the multiple classification networks. Furthermore, a novel soft-label method is devised to assign floating point labels to the pedestrian candidates. This metric for each candidate detection is derived from the percentage of overlap of its bounding box with those of other ground truth classes. In the third work, we propose a boundary-sensitive deep neural network architecture for portrait segmentation. A residual network and atrous convolution based framework is trained as the base portrait segmentation network. To better solve boundary segmentation, three techniques are introduced. First, an individual boundary-sensitive kernel is introduced by labeling the boundary pixels as a separate class and using the soft-label strategy to assign floating-point label vectors to pixels in the boundary class. Each pixel contributes to multiple classes when updating loss based on its relative position to the contour. Second, a global boundary-sensitive kernel is used when updating loss function to assign different weights to pixel locations on one image to constrain the global shape of the resulted segmentation map. Third, we add multiple binary classifiers to classify boundary-sensitive portrait attributes, so as to refine the learning process of our model.",
            "source_url": "https:\/\/openalex.org\/W2891789699",
            "pdf_url": "",
            "doi": "10.13016\/m2vt1gs9h",
            "handle": "",
            "repository_name": "University Libraries (University of Maryland)",
            "source_record_id": "W2891789699",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1101,
            "title": "Automated Activity Discovery and Object Detection With Computer Vision: Towards Unsupervised Learning for Breakfast to Surgery",
            "title_ar": "",
            "title_en": "Automated Activity Discovery and Object Detection With Computer Vision: Towards Unsupervised Learning for Breakfast to Surgery",
            "author": "Michael Zhang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Harvard University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; data-mining; predictive-analytics; cnn; object-detection; image-segmentation; video-analysis",
            "keywords": "",
            "abstract": "While deep learning in computer vision has enjoyed tremendous success in recent years, much progress remains to be made in important tasks. Many state-of-the-art vision models follow a supervised-learning paradigm, making them dependent on a large amount of labeled training data. This limits both their potential learning capabilities, as well as their application domains. In this work, we present contributions to expand the state-of-the-art in computer vision along both these dimensions, focusing on how to both perceive activities and objects in the video domain. We first tackle the problem of unsupervised activity discovery, where given a collection of untrimmed and unlabeled videos, we wish to learn a semantically meaningful embedding of the data which can be used to segment the data into \"discovered\" activities. To expand the learning capabilities of computer vision models, we avoid the intensive bottleneck of amassing a large annotated dataset, and seek to do so without requiring explicit training labels. We consider unsupervised activity discovery from the perspective of the inherently hierarchical nature of activities, e.g. a single complex activity may be modeled as a sequence of smaller sub-activities. Recognizing dependencies across this spectrum of complexities may therefore lead to greater video understanding. Accordingly, we introduce a hyperbolic embedding representation for video data to simultaneously capture hierarchical and semantic relationships in video data. While motivated by their prior success in modeling explicitly hierarchical data found in language, here we show how to leverage hyperbolic representations for the implicitly hierarchical nature of video data. We demonstrate that our hyperbolic video embeddings approach learn representations that significantly outperform the previous state-of-the-art for unsupervised activity segmentation on the Breakfast and 50Salads datasets, and that our hierarchical embeddings naturally allow discovery of activities at multiple levels of complexity. Following this, we next consider the second challenge of expanding the application domains of state-of-the-art computer vision models. We focus on open, or non-laparoscopic surgery, which represents the vast majority of all operating room procedures. Despite this prominence, few tools exist to objectively evaluate these techniques at scale, and current efforts involve human expert-based visual assessment. We therefore leverage a state-of-the-art convolutional neural network architecture for object detection to detect operating hands in open surgery videos. Automated assessment was expanded by combining model predictions with a fast object tracker to enable surgeon-specific hand tracking. To train our model, we used publicly available videos of open surgery from YouTube and annotated these with spatial bounding boxes of operating hands. Our model's spatial detections of operating hands significantly outperforms the detections achieved using pre-existing hand-detection datasets, and allow for insights into intra-operative movement patterns and economy of motion. Finally, we consider how to combine both advances in capability and application domain to focus on automatic activity discovery in surgery videos. Using our developed unsupervised learning algorithm, we demonstrate that we can discover multiple surgical activities in various operating procedures from just a collection of untrimmed and unlabeled YouTube surgery videos. We leverage a state-of-the-art deep learning architecture to extract base features, which we then encode into temporal and hierarchically-aware hyperbolic embeddings. We can then discover an arbitrary number of activities based on our clustering and decoding procedure. Through qualitative evaluation we show how our segmentations align with changes in video activity over time. Based on the alignment of our video segmentations, we present further insights and evidence into the hierarchical nature of activities. In all, our contributions present a step towards fully automated video activity understanding and discovery in real world domains.",
            "source_url": "https:\/\/openalex.org\/W3081564847",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)",
            "source_record_id": "W3081564847",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1104,
            "title": "Computer Vision: Object recognition with deep learning applied to fashion items detection in images",
            "title_ar": "",
            "title_en": "Computer Vision: Object recognition with deep learning applied to fashion items detection in images",
            "author": "Hélder Filipe de Sousa Russa",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Portugal",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "",
            "categories": "deep-learning; computer-vision",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W2771513231",
            "pdf_url": "https:\/\/repositorio-aberto.up.pt\/bitstream\/10216\/107862\/2\/220803.pdf",
            "doi": "10.34626\/wt5z-5c59",
            "handle": "",
            "repository_name": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)",
            "source_record_id": "W2771513231",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1108,
            "title": "Object Localization In Computer Vision",
            "title_ar": "",
            "title_en": "Object Localization In Computer Vision",
            "author": "Qwider. Mesmoudii ; Mohamed. Zigh",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Algeria",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "machine-learning; deep-learning; computer-vision; predictive-analytics; image-segmentation",
            "keywords": "",
            "abstract": "In this work, we have introduced the computer vision discipline that includes several tasks, such as object recognition, segmentation and detection. solving the object localization task usually needs a set of features such as HAAR cascade or HOG these features must change according to the type of the localized object and this create an additional overhead. With the arrival of deep learning era it will be possible to both learn the representation subproblem (feature extraction) and the regression\/classification subproblem. We leverage the concepts offered by deep learning and use them to tackle our localization problem which consists of recognizing the category of the main object (in our case cat or dog), in addition to the drawing of a bounding box around the detected object. our model mainly consists of a feature extractor termed Xception and a set of dense layers that capture the categories and the bounding box. We also notice that the proposed model has achieved a classification rate of 100% and an IoU rate of 78% on the validation dataset. As a perspective of this work, we can compare our results with more elaborated feature extractors such as SeNet,ResNet; Additionally, we can extend the presented model to tackle the detection task which assume that the image contains several objects of different classes",
            "source_url": "https:\/\/openalex.org\/W7135311009",
            "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/43ed13c9-c488-4f1d-a43f-6b3867546a29\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen",
            "source_record_id": "W7135311009",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1109,
            "title": "Computer vision for driver assistance systems",
            "title_ar": "",
            "title_en": "Computer vision for driver assistance systems",
            "author": "Sandhya Sridhar",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Georgia Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; object-detection; video-analysis",
            "keywords": "",
            "abstract": "The objective of the proposed thesis is to illustrate the training, validation and evaluation of vehicle detection algorithms using computer vision and deep learning methods, and vehicle tracking in video sequences. Object detection poses a major challenge in localizing the object and finding its exact coordinates, in addition to determining its presence in the image. Objects in varying scales must also be detected in the image, and the algorithm chosen for object detection must be feasible for real-time detection. The research work focuses on a traditional machine learning approach and a deep learning approach for overcoming the object detection challenges, and object tracking algorithms, to improve the accuracy of object detection.",
            "source_url": "https:\/\/openalex.org\/W2807511107",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "SMARTech Repository (Georgia Institute of Technology)",
            "source_record_id": "W2807511107",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1111,
            "title": "Development of a robot vision system for object detection and localization",
            "title_ar": "",
            "title_en": "Development of a robot vision system for object detection and localization",
            "author": "Shuqi Ning",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "deep-learning; computer-vision; robotics; ai-applications; object-detection; ai-industry",
            "keywords": "",
            "abstract": "With the increasing speed of computer operation, machine vision has a wide prospect in robot positioning and navigation, human-computer interaction, industrial measurement and other fields. This project aims to develop a robot vision system which involves object detections and object localization. The object detection using a fixed camera is often limited by the angle and filed in the environment and therefore it is difficult to detect a wide range of objects. The project used a Jidetech Outdoor 2MP Waterproof 20X Zoom POE PTZ camera, which can rotate 360 degrees to detect the whole environment. Due to its inability to detect distance, a vision algorithm combining depth camera and PTZ camera is proposed, and a robot vision system is developed to estimate the 3D depth information of the whole environment. The main work includes the following aspects. Firstly, the composition and principle of object detection algorithm under deep learning are studied, and YOLOv2 algorithm is selected as the object detection algorithm. Then, based on the darkflow framework, the collected data sets are trained, and the accuracy is ensured to meet the test requirements. Secondly, the working principle of D45i RGB-D camera is studied and it is used to detect and measure the object. Then, by controlling of the rotation angle of PTZ camera, the detection of object is realized under PTZ camera. According to the position of the object, two detection algorithms are developed to obtain the relative position form the camera with the object. Therefore, the RGB-D camera can be calibrated with respect to the PTZ camera to estimate the depth information so as to determine the location of the object. Finally, through the comparison of the experimental results, the error data is obtained and analyzed so as to improve the algorithms for localization of the object with better accuracy.",
            "source_url": "https:\/\/openalex.org\/W3036670756",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W3036670756",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1144,
            "title": "Deep Learning for text data mining: Solving spreadsheet data classification.",
            "title_ar": "",
            "title_en": "Deep Learning for text data mining: Solving spreadsheet data classification.",
            "author": "Aleksandr Kimashev",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; nlp; data-mining; cnn; rnn",
            "keywords": "",
            "abstract": "This project developed for the Avito LOOPS company. Research goals was to investigate existing algorithms and implementations of Deep Learning, to understand their applicability to text mining, to design a solution that incorporates theoretical and practical aspects, to run classification experiments on different data sets so that the pros and cons of different techniques can be understood. Classification of the text was necessary for the spreadsheet columns classification. The work used convolutional and recurrent neural networks, trained on samples from five classes. Also, was made an attempt to classify unknowns for a neural network of classes, with an ensemble of four networks.",
            "source_url": "https:\/\/openalex.org\/W2766751493",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "BIBSYS Brage (BIBSYS (Norway))",
            "source_record_id": "W2766751493",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:58",
            "updated_at": "2026-11-15 16:52:58"
        },
        {
            "id": 1155,
            "title": "Mining heterogeneous enterprise data",
            "title_ar": "",
            "title_en": "Mining heterogeneous enterprise data",
            "author": "Xinxin Jiang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Technology Sydney",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "data-mining",
            "categories": "machine-learning; deep-learning; ai-applications; data-mining; ai-industry",
            "keywords": "",
            "abstract": "Heterogeneity is becoming one of the key characteristics inside enterprise data, because the current nature of globalization and competition stress the importance of leveraging huge amounts of enterprise accumulated data, according to various organizational processes, resources and standards. Effectively deriving meaningful insights from complex large-scaled heterogeneous enterprise data poses an interesting, but critical challenge. The aim of this thesis is to investigate the theoretical foundations of mining heterogeneous enterprise data in light of the above challenges and to develop new algorithms and frameworks that are able to effectively and efficiently consider heterogeneity in four elements of the data: objects, events, context, and domains. Objects describe a variety of business roles and instruments involved in business systems. Object heterogeneity means that object information at both the data and structural level is heterogeneous. The cost-sensitive hybrid neural network (Cs-HNN) proposed leverages parallel network architectures and an algorithm specifically designed for minority classification to generate a robust model for learning heterogeneous objects. Events trace an object’s behaviours or activities. Event heterogeneity reflects the level of variety in business events and is normally expressed in the type and format of features. The approach proposed in this thesis focuses on fleet tracking as a practical example of an application with a high degree of event heterogeneity. Context describes the environment and circumstances surrounding objects and events. Context heterogeneity reflects the degree of diversity in contextual features. The coupled collaborative filtering (CCF) approach proposed in this thesis is able to provide context-aware recommendations by measuring the non-independent and identically distributed (non-IID) relationships across diverse contexts. Domains are the sources of information and reflect the nature of the business or function that has generated the data. The cross-domain deep learning (Cd-DLA) proposed in this thesis provides a potential avenue to overcome the complexity and nonlinearity of heterogeneous domains. Each of the approaches, algorithms, and frameworks for heterogeneous enterprise data mining presented in this thesis outperform the state-of-the-art methods in a range of backgrounds and scenarios, as evidenced by a theoretical analysis, an empirical study, or both. All outcomes derived from this research have been published or accepted for publication, and the follow-up work has also been recognised, which demonstrates scholarly interest in mining heterogeneous enterprise data as a research topic. However, despite this interest, heterogeneous data mining still holds increasing attractive opportunities for further exploration and development in both academia and industry.",
            "source_url": "https:\/\/openalex.org\/W2910858813",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "OPUS - Open Publications of UTS Scholars (University of Technology Sydney)",
            "source_record_id": "W2910858813",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1162,
            "title": "Interpretable time-series forecasting with multi-model deep learning and natural language processing (NLP) driven explainable artificial intelligence (XAI)",
            "title_ar": "",
            "title_en": "Interpretable time-series forecasting with multi-model deep learning and natural language processing (NLP) driven explainable artificial intelligence (XAI)",
            "author": "Jiztom Kavalakkatt Francis",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "xai",
            "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; xai",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7131292683",
            "pdf_url": "",
            "doi": "10.31274\/td-20260223-66",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W7131292683",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1167,
            "title": "Explainable Artificial Intelligence Methods for Spiking Neural Networks",
            "title_ar": "",
            "title_en": "Explainable Artificial Intelligence Methods for Spiking Neural Networks",
            "author": "Jane Jung",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Auckland University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "New Zealand",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "xai",
            "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; ai-applications; predictive-analytics; multimodal; xai; ai-cybersecurity; ai-smart-cities",
            "keywords": "",
            "abstract": "As the demand for energy-efficient Artificial Intelligence (AI) grows, Spiking Neural Networks (SNNs) have emerged as a leading neuromorphic alternative to traditional deep learning. However, the complex, non-linear temporal dynamics of SNNs often result in a black box nature, hindering their adoption in high-stakes domains such as cybersecurity and clinical healthcare. This thesis proposes a novel framework, the SNN-based MLP (SNN-MLP), which utilises a non-spiking surrogate model to interpret the decision-making logic of a trained SNN. By mapping the high-dimensional activity of spiking ensembles to a differentiable architecture, the framework enables the application of post-hoc Explainable Artificial Intelligence (XAI) techniques, such as Shapley Additive ExPlanation (SHAP), to provide transparent feature-level explanations. The framework is validated through two diverse case studies. The first case study evaluates network traffic for detection, and demonstrates a successful translation of single-dimensional SNNs into Multi-Layer Perceptrons (MLPs) and the resultant SNN-MLP successfully identifies malicious features with up to 87% accuracy, and also aligns in terms of interpretability with a baseline MLP. The second case study applies the framework to personalised depression modelling using more complex SNN architectures along with multimodal datasets. The results demonstrate that the SNN-MLP functions as a high-fidelity surrogate for multi-dimensional SNNs as well, identifying clinically relevant triggers-primarily anxiety and dietary factors-that align with established benchmarks. While the study notes challenges regarding data scarcity and class imbalance in clinical settings, the consistent overlap between the SNN and its surrogate proves that spiking architectures can achieve competitive predictive performance without sacrificing interpretability. Thus, this thesis provides a foundation for mathematically grounded, energy-efficient, and explainable AI, offering a pathway towards the deployment of explainable SNN systems in safety-critical and security infrastructures.",
            "source_url": "https:\/\/openalex.org\/W7155577599",
            "pdf_url": "https:\/\/openrepository.aut.ac.nz\/bitstreams\/da44f9c0-126b-4a59-9ac6-39d35ec9f7cd\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Tuwhera (Auckland University of Technology)",
            "source_record_id": "W7155577599",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1169,
            "title": "Intuitive Explainable Artificial Intelligence for Molecular Design",
            "title_ar": "",
            "title_en": "Intuitive Explainable Artificial Intelligence for Molecular Design",
            "author": "Alec Lamens",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Bonn",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Germany",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "xai",
            "categories": "machine-learning; deep-learning; medical-ai; responsible-ai; expert-systems; self-semi-supervised; predictive-analytics; xai",
            "keywords": "",
            "abstract": "With the onset of artificial intelligence (AI), machine learning (ML) approaches have become increasingly embedded into the drug discovery pipeline with the aim of accelerating research and reducing cost. Exemplary applications include quantitative structure-property relationship modeling and de novo design of candidate molecules. However, the practical impact of these ML models has been limited by the opaque nature of their predictions, termed the black-box problem. The proliferation of large-scale public chemical datasets combined with advances in high-performance computing has driven a shift from traditional ML approaches toward more complex deep learning (DL) methods. While powerful, these models further exacerbate the inability to rationalize model outcomes. Therefore, the field of explainable AI (XAI) has attracted substantial interest to improve the transparency of model decisions. Given the wide range of available XAI approaches and lack of established practices, these methods are often applied without careful consideration for their suitability to the task at hand. This represents a problem in drug discovery where interpretable explanations are critical to guide experimental follow-up. To address this issue, this dissertation proposes the adaptation of established XAI approaches to operate on the level of chemical structure and facilitate interpretable ML for molecular design. The first study demonstrates the use of Shapley additive explanations (SHAP) to analyze feature sets driving the correct prediction of compound promiscuity. This is followed by a benchmarking study comparing several methods for the calculation of feature importance values, revealing inconsistencies across approaches that complicate the interpretation of feature-importance explanations. To support chemically meaningful interpretability, SHAP values are then combined with counterfactual reasoning, yielding the SHAP-CF methodology. Thereafter, a novel counterfactual method is introduced that generates candidate counterfactuals through the iterative recombination of molecular cores and substituents. This method is evaluated on a challenging multi-class kinase inhibitor prediction task, demonstrating its capacity to generate large numbers of counterfactuals, enabling in-depth analysis. Next, a closely related XAI concept, contrastive explanations, is adapted for molecular design by introducing rational chemical modifications to test compounds through the exchange of scaffolds or substituents with structural analogues, termed MolCE. This approach is shown to provide chemically relevant contrastive explanations that enable direct causal insights into the structural factors that drive model predictions. Finally, MolAnchor is introduced, a domain-adapted variant of the rule-based Anchors method. By conforming the principles underlying the Anchors framework to operate on retrosynthetically meaningful molecular fragments, MolAnchor produces interpretable fragment-level decision rules. A follow-up study showcases how the if-then formulation of the fragment rules is particularly complementary to causal reasoning. Taken together, the studies presented hereindemonstrate the utility of XAI methods that have been augmented with domain-specific information to provide chemically intuitive explanations for predictive models applied to molecular design.",
            "source_url": "https:\/\/openalex.org\/W7164705685",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "bonndoc (University of Bonn)",
            "source_record_id": "W7164705685",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1171,
            "title": "eXAI: an innovative ensemble technique with explainable artificial intelligence applied to computer vision in critical contexts",
            "title_ar": "",
            "title_en": "eXAI: an innovative ensemble technique with explainable artificial intelligence applied to computer vision in critical contexts",
            "author": "Miguel Jose das [UNESP] Neves",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; responsible-ai; predictive-analytics; cnn; multimodal; xai; fairness",
            "keywords": "",
            "abstract": "The growing complexity of Deep Learning models, particularly Convolutional Neural Networks (CNNs), presents a fundamental challenge in critical applications: the opacity of their decisionmaking processes. This thesis addresses this interpretability gap by proposing and validating a methodological framework termed eXAI (Ensemble Explainable Artificial Intelligence), designed to create image analysis systems that merge high predictive performance with transparency. Conceptually inspired by Ensemble Learning, the framework’s novelty lies not in combining predictive models, but in synergistically integrating an ensemble of explainability methods specifically combining SHAP for pixel-level attribution with Grad-CAM for region-level localization to provide a multi-faceted interpretation of a single model’s decisions. The framework’s flexibility and efficacy are demonstrated through two case studies in distinct domains. The first, focused on Diabetic Retinopathy diagnosis, implements a stacking architecture that integrates deep visual features from a CNN with structured clinical data, classified by a LightGBM (LGBM) meta-model. This multimodal approach achieved outstanding accuracy, while the XAI analysis validated the clinical relevance of its decision factors. The second case study, in digital forensics, applies a direct classification CNN to detect Seam Carving image manipulations. Here, XAI was crucial in revealing that the model learned to identify subtle structural distortion artifacts rather than relying on semantic content. The collective results demonstrate that the eXAI framework is a robust and adaptable approach for building trustworthy AI models for critical contexts. By delivering comprehensive and understandable explanations, this methodology enhances the confidence in, and practical utility of, artificial intelligence in both healthcare and information security.",
            "source_url": "https:\/\/openalex.org\/W7120300663",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W7120300663",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1172,
            "title": "An Explainable Hybrid Artificial Intelligence Framework for Stock Market Prediction Using Financial Time-Series and Market Sentiment",
            "title_ar": "",
            "title_en": "An Explainable Hybrid Artificial Intelligence Framework for Stock Market Prediction Using Financial Time-Series and Market Sentiment",
            "author": "Anshul Manhas",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "machine-learning; deep-learning; nlp; responsible-ai; predictive-analytics; multimodal; sentiment-analysis; xai",
            "keywords": "",
            "abstract": "This dissertation presents an explainable hybrid artificial intelligence framework for stock market prediction using financial time-series information and market sentiment. The study investigates the integration of historical market observations, technical indicators, sequential deep-learning models, financial sentiment analysis and explainable artificial intelligence within a unified forecasting framework. The methodology addresses data preprocessing, temporal alignment, feature engineering, model development, multimodal feature integration and evaluation. Particular emphasis is placed on preventing information leakage through chronological data handling and on improving the interpretability of model predictions. The dissertation provides a structured research framework for investigating whether financial sentiment provides complementary information to numerical market features in stock-market forecasting.",
            "source_url": "https:\/\/openalex.org\/W7204049726",
            "pdf_url": "",
            "doi": "10.5281\/zenodo.22065590",
            "handle": "",
            "repository_name": "Zenodo (CERN European Organization for Nuclear Research)",
            "source_record_id": "W7204049726",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1192,
            "title": "Machine Learning with Conformal Prediction for Predictive Maintenance tasks in Industry 4.0 : Data-driven Approach",
            "title_ar": "",
            "title_en": "Machine Learning with Conformal Prediction for Predictive Maintenance tasks in Industry 4.0 : Data-driven Approach",
            "author": "Liu, Shuzhou ; Mulahuko, Mpova",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Jönköping University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-industry",
            "keywords": "",
            "abstract": "This thesis is a cooperation with Knowit, Östrand \\& Hansen, and Orkla. It aimed to explore the application of Machine Learning and Deep Learning models with Conformal Prediction for a predictive maintenance situation at Orkla. Predictive maintenance is essential in numerous industrial manufacturing scenarios. It can help to reduce machine downtime, improve equipment reliability, and save unnecessary costs. In this thesis, various Machine Learning and Deep Learning models, including Decision Tree, Random Forest, Support Vector Regression, Gradient Boosting, and Long short-term memory, are applied to a real-world predictive maintenance dataset. The Orkla dataset was originally planned to use in this thesis project. However, due to some challenges met and time limitations, one NASA C-MAPSS dataset with a similar data structure was chosen to study how Machine Learning models could be applied to predict the remaining useful lifetime (RUL) in manufacturing. Besides, conformal prediction, a recently developed framework to measure the prediction uncertainty of Machine Learning models, is also integrated into the models for more reliable RUL prediction. The thesis project results show that both the Machine Learning and Deep Learning models with conformal prediction could predict RUL closer to the true RUL while LSTM outperforms the Machine Learning models. Also, the conformal prediction intervals provide informative and reliable information about the uncertainty of the predictions, which can help inform personnel at factories in advance to take necessary maintenance actions. Overall, this thesis demonstrates the effectiveness of utilizing machine learning and Deep Learning models with Conformal Prediction for predictive maintenance situations. Moreover, based on the modeling results of the NASA dataset, some insights are discussed on how to transfer these experiences into Orkla data for RUL prediction in the future.",
            "source_url": "https:\/\/openalex.org\/W7113257904",
            "pdf_url": "https:\/\/hj.diva-portal.org\/smash\/get\/diva2:1765779\/FULLTEXT01",
            "doi": "",
            "handle": "",
            "repository_name": "Jonkoping University Library (Jönköping University)",
            "source_record_id": "W7113257904",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1196,
            "title": "Deep learning for automobile predictive maintenance under Industry 4.0",
            "title_ar": "",
            "title_en": "Deep learning for automobile predictive maintenance under Industry 4.0",
            "author": "Chong Chen",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Cardiff University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; ai-applications; self-semi-supervised; predictive-analytics; rnn; ai-cybersecurity; ai-industry; ai-smart-cities",
            "keywords": "",
            "abstract": "Industry 4.0 refers to the fourth industrial revolution, which has boosted the development of the world. An important target of Industry 4.0 is to maximize the asset uptime so to improve productivity and reduce the production and maintenance cost. The emerging techniques such as artificial intelligence (AI), industrial Internet of things (IIoT) and cyber-physical system (CPS) have accelerated the development of data-orientated application such as predictive maintenance (PdM). Maintenance is a big concern for an automobile fleet management company. An accurate maintenance prediction can be helpful to avoid critical failure and avoid further loss. Deep learning is a type of prevailing machine learning algorithm which has been widely used in big data analytics. However, how to establish a maintenance prediction model based on historical maintenance data using deep learning has not been investigated. Moreover, it is worthwhile to study how to build a prediction model when the labelled data is insufficient. Furthermore, surrounding factors which may impact automobile lifecycle have not been concerned in the state-of-the-art. Hence, this thesis will focus on how to pave the way for automobile PdM under Industry 4.0. This research is structured according to four themes. Firstly, different from the conventional PdM research that only focuses on modelling based on sensor data or historical maintenance data, a framework for automobile PdM based on multi-source data is proposed. The proposed framework aims at automobile TBF modelling, prediction, and decision support based on the multi-source data. There are five layers designed in this framework, which are data collection, cloud data transmission and storage, data mapping, pre-processing and integration, deep learning for automobile TBF modelling, and decision support for PdM. This framework covers the entire knowledge discovery process from data collection to decision support. Secondly, one of the purposes of this thesis is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. An accurate automobile TBF iv Abstract prediction can bring tangible benefits to a fleet management company. Different from the existing studies that adopted sensor data for failure time prediction, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed based on the historical maintenance data for TBF modelling and prediction. CoxPHDL is able to tackle the data sparsity and data censoring issues that are common in the analysis of historical maintenance data. Firstly, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox PHM is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental results have demonstrated the merits of the proposed approach. Thirdly, a large amount of labelled data is one of the critical factors to the satisfactory algorithm performance of deep learning. However, labelled data is expensive to collect in the real world. In order to build a TBF prediction model using deep learning when the labelled data is limited, a new semi-supervised learning algorithm called deep learning embedded semi-supervised learning (DLeSSL) is proposed. Based on DLeSSL, unlabelled data can be estimated using a semi-supervised learning approach that has a deep learning technique embedded so to expand the labelled dataset. Results derived using the proposed method reveal that deep learning (DLeSSL based) outperforms the benchmarking algorithms when the labelled data is limited. In addition, different from existing studies, the effect on algorithm performance due to the size of labelled data and unlabelled data is reported to offer insights for the deployment of DLeSSL. Finally, automobile lifecycle can be impacted by surrounding factors such as weather, traffic, and terrain. The data contains these factors can be collected and processed via geographical information system (GIS). To introduce these GIS data into automobile TBF modelling, an integrated approach is proposed. This is the first time that the surrounding factors are considered in the study of automobile TBF modelling. Meanwhile, in order to build a TBF prediction model based on multi-source data, a new deep learning architecture called merged-LSTM (M-LSTM) network is designed. Abstract v Experimental results derived using the proposed approach and M-LSTM network reveal the impacts of the GIS factors. This thesis aims to research automobile PdM using deep learning, which provides a feasibility study for achieving Industry 4.0. As such, it offers great potential as a route to achieving a more profitable, efficient, and sustainable fleet management.",
            "source_url": "https:\/\/openalex.org\/W3168907897",
            "pdf_url": "https:\/\/orca.cardiff.ac.uk\/137968\/1\/2021ChenCPhD.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "ORCA Online Research @Cardiff (Cardiff University)",
            "source_record_id": "W3168907897",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1198,
            "title": "Predictive Maintenance Using Machine Learning",
            "title_ar": "",
            "title_en": "",
            "author": "Mateyaunga Innocent",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Algeria",
            "governorate": "",
            "publication_year": 2020,
            "language": "fr",
            "research_domain": "deep-learning",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; ai-applications; predictive-analytics; rnn; ai-industry",
            "keywords": "",
            "abstract": "Ces dernières années, le Deep Learning a attiré une attention croissante, d’où son application dans la maintenance prédictive. Il s’agit d’un domaine de recherche en évolution qui a le potentiel d’influencer divers domaines d’application et, par conséquent, son utilisation pour les applications de gestion de la santé des systèmes et équipements complexes et couteux doit être mis à profit. Cette thèse présente une revue systématique de la gestion de la santé du système basée sur l’intelligence artificielle (IA) en mettant l’accent sur les tendances récentes de l’apprentissage profond (DL) dans le domaine: différents types de modèles d’apprentissage automatique (ML) qui peuvent être utilisés pour prédire la défaillance d’une machine .Au début, la revue des publications précédentes est effectué, puis différents modèles (LSTM, GRU et SRN) sont utilisés sur l’ensemble de données NASA Turbofan afin de déterminer le meilleur modèle, les versions CuDNN (CuDNNLSTM, CuDNNGRU) sont également appliquées et le temps d’exécution est comparé aux temps de base (LSTM, GRU). Le CuDNNGRU s’avère être la meilleure méthode en raison de son temps d’exécution (30,6 s) et de sa exactitude compétitive (0,9827) et de sa précision (0,9452).",
            "source_url": "https:\/\/openalex.org\/W7135298816",
            "pdf_url": "https:\/\/dspace.univ-tlemcen.dz\/bitstreams\/3dec6713-7835-4c54-92a5-a7922e1ca84a\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Dépôt Institutionnel de lUniversité de Tlemcen",
            "source_record_id": "W7135298816",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1200,
            "title": "Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms",
            "title_ar": "",
            "title_en": "Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms",
            "author": "Kayalvizhi Lakshmanan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Swansea University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; ai-industry; ai-smart-cities",
            "keywords": "",
            "abstract": "The importance of Predictive Maintenance is critical for engineering industries, such as manufacturing, aerospace and energy. Unexpected failures cause unpredictable downtime, which can be disruptive and high costs due to reduced productivity. This forces industries to ensure the reliability of their equip-ment. In order to increase the reliability of equipment, maintenance actions, such as repairs, replacements, equipment updates, and corrective actions are employed. These actions affect the flexibility, quality of operation and manu-facturing time. It is therefore essential to plan maintenance before failure occurs.Traditional maintenance techniques rely on checks conducted routinely based on running hours of the machine. The drawback of this approach is that maintenance is sometimes performed before it is required. Therefore, conducting maintenance based on the actual condition of the equipment is the optimal solu-tion. This requires collecting real-time data on the condition of the equipment, using sensors (to detect events and send information to computer processor).Predictive Maintenance uses these types of techniques or analytics to inform about the current, and future state of the equipment. In the last decade, with the introduction of the Internet of Things (IoT), Machine Learning (ML), cloud computing and Big Data Analytics, manufacturing industry has moved forward towards implementing Predictive Maintenance, resulting in increased uptime and quality control, optimisation of maintenance routes, improved worker safety and greater productivity.The present thesis describes a novel computational strategy of Predictive Maintenance (fault diagnosis and fault prognosis) with ML and Deep Learning applications for an FG304 series external gear pump, also known as a domino pump. In the absence of a comprehensive set of experimental data, synthetic data generation techniques are implemented for Predictive Maintenance by perturbing the frequency content of time series generated using High-Fidelity computational techniques. In addition, various types of feature extraction methods considered to extract most discriminatory informations from the data. For fault diagnosis, three types of ML classification algorithms are employed, namely Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB) algorithms. For prognosis, ML regression algorithms, such as MLP and SVM, are utilised. Although significant work has been reported by previous authors, it remains difficult to optimise the choice of hyper-parameters (important parameters whose value is used to control the learning process) for each specific ML algorithm. For instance, the type of SVM kernel function or the selection of the MLP activation function and the optimum number of hidden layers (and neurons).It is widely understood that the reliability of ML algorithms is strongly depen-dent upon the existence of a sufficiently large quantity of high-quality training data. In the present thesis, due to the unavailability of experimental data, a novel high-fidelity in-silico dataset is generated via a Computational Fluid Dynamic (CFD) model, which has been used for the training of the underlying ML metamodel. In addition, a large number of scenarios are recreated, ranging from healthy to faulty ones (e.g. clogging, radial gap variations, axial gap variations, viscosity variations, speed variations). Furthermore, the high-fidelity dataset is re-enacted by using degradation functions to predict the remaining useful life (fault prognosis) of an external gear pump.The thesis explores and compares the performance of MLP, SVM and NB algo-rithms for fault diagnosis and MLP and SVM for fault prognosis. In order to enable fast training and reliable testing of the MLP algorithm, some predefined network architectures, like 2n neurons per hidden layer, are used to speed up the identification of the precise number of neurons (shown to be useful when the sample data set is sufficiently large). Finally, a series of benchmark tests are presented, enabling to conclude that for fault diagnosis, the use of wavelet features and a MLP algorithm can provide the best accuracy, and the MLP al-gorithm provides the best prediction results for fault prognosis. In addition, benchmark examples are simulated to demonstrate the mesh convergence for the CFD model whereas, quantification analysis and noise influence on training data are performed for ML algorithms.",
            "source_url": "https:\/\/openalex.org\/W3211660692",
            "pdf_url": "",
            "doi": "10.23889\/suthesis.58703",
            "handle": "",
            "repository_name": "Cronfa (Swansea University)",
            "source_record_id": "W3211660692",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1202,
            "title": "Machine Learning for Predictive Maintenance: A Boeing 747 Bleed Air Valves case study",
            "title_ar": "",
            "title_en": "Machine Learning for Predictive Maintenance: A Boeing 747 Bleed Air Valves case study",
            "author": "Erik IJzermans",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Delft University of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Netherlands",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; medical-ai; ai-applications; predictive-analytics; ai-industry",
            "keywords": "",
            "abstract": "The newest generation of aircraft has seen a strong increase in sensor data generated on-board. The available data has the potential to indicate the health state of individual components based on which their maintenance requirements can be determined, a maintenance strategy called Condition Based Maintenance. Predictive Maintenance is a specific condition based maintenance strategy that aims to determine these requirements in advance by predicting failures from the sensor data. It has the potential to reduce costly unanticipated maintenance or unnecessarily conservative maintenance. In the short term it could add significant value for components that are currently subject to a reactive maintenance policy. In the long term it could potentially disrupt the traditional maintenance practice of periodic inspection. One of the main challenges in applying Predictive Maintenance in the aviation industry is translating the large amounts of sensor data into a reliable failure prediction, a process called prognostics. In this study, state-of-the-art machine learning, and specifically deep learning models, have been investigated for their potential for prognostics. A case study has been performed at KLM Royal Dutch Airlines on the Boeing 747 Bleed Air Valves, traditionally some of the most challenging components from a maintenance perspective. It has been shown that fully self-learning algorithms can be used for prognostics, enabling the implementation of one of the first real-life predictive maintenance implementations.",
            "source_url": "https:\/\/openalex.org\/W2898020487",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Research Repository (Delft University of Technology)",
            "source_record_id": "W2898020487",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1208,
            "title": "أثرمنهج التعلم العميق القائم على الذكاء الاصطناعي على مهارة كتابة اللغة العربية لدى طلاب الصف الحادي عشر في المدرسة الثانوية الحكومية الثانية بيوجياكرتا ٢٠٢٦ = ATHR MANHAJ AL-TA'ALLUM AL-'AMIQ AL-QA'IM 'ALA AL-DHAKA' AL-ISTINA'I 'ALA MAHARAT KITABAT AL-LUGHAH AL-'ARABIYYAH LADA TULLAB AL-SAFF AL-HADI 'ASHAR FI AL-MADRASAH AL-THANAWIYYAH AL-HUKUMIYYAH AL-THANIYAH YOGYAKARTA 2026",
            "title_ar": "أثرمنهج التعلم العميق القائم على الذكاء الاصطناعي على مهارة كتابة اللغة العربية لدى طلاب الصف الحادي عشر في المدرسة الثانوية الحكومية الثانية بيوجياكرتا ٢٠٢٦ = ATHR MANHAJ AL-TA'ALLUM AL-'AMIQ AL-QA'IM 'ALA AL-DHAKA' AL-ISTINA'I 'ALA MAHARAT KITABAT AL-LUGHAH AL-'ARABIYYAH LADA TULLAB AL-SAFF AL-HADI 'ASHAR FI AL-MADRASAH AL-THANAWIYYAH AL-HUKUMIYYAH AL-THANIYAH YOGYAKARTA 2026",
            "title_en": "",
            "author": "NIM.: 22104020014 Sri Fatimah",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Sunan Kalijaga State Islamic University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Indonesia",
            "governorate": "",
            "publication_year": 2026,
            "language": "ar",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; nlp; transformers; arabic-nlp",
            "keywords": "",
            "abstract": "Penelitian ini dilatarbelakangi oleh masih rendahnya keterampilan menulis Bahasa Arab siswa kelas XI MAN 2 Yogyakarta, khususnya dalam menulis kalimat dan paragraf sederhana. Penelitian ini bertujuan untuk mengetahui pengaruh pendekatan deep learning berbasis Artificial Intelligence (AI) terhadap keterampilan menulis Bahasa Arab siswa kelas XI MAN 2 Yogyakarta. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis kuasi-eksperimen dan desain non-equivalent control group design. Populasi penelitian adalah seluruh siswa kelas XI MAN 2 Yogyakarta tahun ajaran 2025\/2026 dengan teknik pengambilan cluster random sampling. Pengumpulan data dilakukan melalui pretest, posttest, dan observasi partisipan. Analisis data menggunakan uji Wilcoxon, uji Mann-Whitney U, dan uji N-Gain. Hasil penelitian menunjukkan bahwa pendekatan deep learning berbasis AI tidak berpengaruh secara signifikan terhadap keterampilan menulis Bahasa Arab siswa kelas XI MAN 2 Yogyakarta. Hasil uji Mann-Whitney U menunjukkan nilai signifikansi sebesar 0,221 pada analisis pertama dan 0,133 pada analisis kedua, yang berarti lebih besar dari 0,05. Selain itu, hasil uji N-Gain memperoleh nilai sebesar 0,041 yang termasuk kategori rendah. Dengan demikian, pendekatan deep learning berbasis AI hanya menunjukkan peningkatan yang rendah dan tidak signifikan secara statistik terhadap keterampilan menulis Bahasa Arab siswa.",
            "source_url": "https:\/\/openalex.org\/W7164774459",
            "pdf_url": "https:\/\/digilib.uin-suka.ac.id\/id\/eprint\/76688\/1\/22104020014_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Digital Library UIN Sunan Kalijaga (Sunan Kalijaga State Islamic University)",
            "source_record_id": "W7164774459",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:52:59",
            "updated_at": "2026-11-15 16:52:59"
        },
        {
            "id": 1248,
            "title": "Reinforcement Learning on Resource Bounded Systems",
            "title_ar": "",
            "title_en": "Reinforcement Learning on Resource Bounded Systems",
            "author": "Jaden B. Travnik",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Alberta",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Canada",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; reinforcement-learning; ai-applications; predictive-analytics; ai-industry",
            "keywords": "",
            "abstract": "Recent advancements in reinforcement learning have made the field interesting to academia and industry alike. Many of these advancements depend on deep learning as a means to approximate a value function or a policy. This dependency usually relies on high performance hardware (e.g., a graphics processing unit, GPU) and applications of deep learning are often limited to domains where a substantial amount of time is allowed for a prediction to be made or an action to be chosen. Although these criteria cover many important use cases, the growing popularity of the ``Internet of Things,'' wearable electronics, and the advancement of myoelectric prosthetic limbs presents a rapidly growing real-time domain of resource bounded systems that are not properly suited for deep learning yet could still benefit from the application of reinforcement learning. Furthermore, many of these systems are limited by the physical space they must occupy. This restricts the size of the hardware and thereby the computational and memory resources that can be used. Despite these restrictions, demand for prompt actions from receptive systems continues to grow. To address these problems, I first highlight the difficulties one is faced with when implementing reinforcement learning on a system which is deployed in an asynchronous environment and introduce a new metric of performance by measuring the time it takes for a system to react to an observed state of an asynchronous environment. Secondly, I develop a class of algorithms that addresses these issues by reordering the algorithmic components to minimize reaction time. Thirdly, to minimize both the time and memory necessary to compute function approximation, I introduce a novel linear function approximation method, selective Kanerva coding (SKC), that allows a reinforcement learning agent to perform behaviors reactively in real-time while using less memory and computation time than the standard linear approach of tile coding. I also show that SKC is less sensitive to the curse of dimensionality than tile coding making SKC a significant step towards accurately representing high dimensional data on resource bounded systems. Moreover, I show that SKC can make the inclusion of more sensory modalities more feasible, which can increase prediction accuracy when those modes of sensation are relevant to the prediction. Finally, I present an exploration of the meta-parameters of SKC and evaluate the performance of two different variations of SKC against the original formulation. These findings are imperative to the current state of the field of reinforcement learning as they form a challenging perspective that is contrary to the current direction of the field's focus on deep learning. I form this argument by emphasizing the impracticality of deep learning in domains of resource bounded systems deployed in real-time environments, establishing the limitations on available computation and memory of these systems, and address these issues by proposing new insights, algorithms, and representations.",
            "source_url": "https:\/\/openalex.org\/W2809564569",
            "pdf_url": "",
            "doi": "10.7939\/r39g5gv5s",
            "handle": "",
            "repository_name": "ERA: Education and Research Archive (University of Alberta)",
            "source_record_id": "W2809564569",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1271,
            "title": "Transformer-based kernels for Natural Language Processing",
            "title_ar": "",
            "title_en": "Transformer-based kernels for Natural Language Processing",
            "author": "David Morán Pomés",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; nlp; transfer-learning; rnn; transformers",
            "keywords": "",
            "abstract": "Kernel methods (KM) and Artificial Neural Networks (ANN) are two of the main families of methods that are more widely spread nowadays in the world of Machine Learning, being the Support Vector Machine (SVM) and the Deep Learning Neural Network (DLNN) its most known members. One of the main benefits of both families is how they can be adapted to work with different kinds of input data, even though they do it in a different way: KM rely on different kernel functions and ANN rely on specific network architectures. In the specific world of Natural Language Processing, both families have specific resources to deal with texts as inputs. While KM rely on specific kernels like the string kernel, ANN rely on Recurrent Neural Network (RNN) architectures and, more recently, on Transformer architectures. Even though KM were the first ones to dive into the NLP world, ANN are nowadays the state-of-the-art in all kinds of NLP tasks. The main reason of this success is the fact that ANN perform as a nonlinear parametric feature extractor, which processes the text in a complex way that lets the model learn an adequate representation of the input. On the other side, kernel functions do only rely on fixed similarity measures to contain that information, which is less flexible and powerful. This is the reason why we developed a new kernel function, called Transformer kernel. This kernel function is a parametric model like an ANN, but instead of computing a nonlinear representation of the input, it directly computes a similarity measure that corresponds to an inner product in some implicit feature space. The main idea of this model is that it can be pre-trained in a generic task using the whole Wikipedia corpus (like the different Transformer models for NLP tasks), but the resulting model can be directly used as a kernel function for any kind of kernel method (like the SVM). In order to do so, we build this model by stacking two different components: A Transformer Encoder block (based on the BERT architecture) and a newly designed kernel function called Attention Kernel. By using this kernel function with real text datasets, we have shown that it provides results that are much better than the ones obtained by the use of the string kernel. We have also seen that, due to the fact that our model is built as a Neural Network and can be executed on a GPU, the Transformer kernel can compute the kernel matrix of a dataset much faster than the classical string kernel. Results are very promising, and suggest that further study of the behaviour of this kernel function with different datasets for pre-training and different final tasks will be very interesting. Moreover, it has to be considered if it is possible to adapt this kernel in order to combine the text input with other kinds of data (like numerical or categorical variables), or if the kernel can be fine-tuned into a specific task before it is used as a kernel function.",
            "source_url": "https:\/\/openalex.org\/W3138190555",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W3138190555",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1272,
            "title": "Cyberbullying identification and classification using transformer models",
            "title_ar": "",
            "title_en": "Cyberbullying identification and classification using transformer models",
            "author": "Wachiraporn Tapaopong",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; nlp; ai-applications; transfer-learning; predictive-analytics; transformers; ai-cybersecurity",
            "keywords": "",
            "abstract": "The widespread use of social media platforms has made cyberbullying an increasingly prevalent issue, causing physical, emotional, and mental harm to victims. This study focuses on utilizing Natural Language Processing techniques to enhance the identification and analysis of cyberbullying in hazardous social media messages. To achieve this goal, we utilized the transfer learning and fine-tuning techniques with the Transformer models, specifically BERT, RoBERTa, ALBERT, DistilBERT, and ConvBERT, to compare the effectiveness of category prediction. The study tested two methods to evaluate the effectiveness of automated cyberbullying detection. The first involves six categories: religion, age, ethnicity, gender, not cyberbullying, and other types of cyberbullying. The second method did not include \"other types of cyberbullying.\" The findings revealed that DistilBERT was the model with the best performance for multi-class text classification with the least amount of training time. This research emphasized the value of automated cyberbullying detection by using the potential of Natural Language Processing techniques to reduce the negative consequences of cyberbullying. Overall, this study contributed to the development of effective and efficient approaches for identifying and classifying cyberbullying in order to prevent and intervene in cyberbullying. Implication of thematic paper: The implications of this research are significant in terms of addressing the issue of cyberbullying, which is becoming increasingly prevalent due to the widespread use of social media platforms. The use of Transformer models for cyberbullying identification and classification has the potential to significantly improve the accuracy and efficiency of automated detection systems. These models can learn to recognize and categorize cyberbullying behaviors in a way that is more nuanced and accurate than traditional machine learning approaches by utilizing deep learning. This could help social media platforms more effectively identify and address instances of cyberbullying, ultimately leading to a more secure and encouraging online environment for all users. Furthermore, the use of Transformer models for cyberbullying detection could also have broader implications for the fields of natural language processing and machine learning, as it demonstrates the potential for these models to effectively analyze and understand complex, human-generated text data.",
            "source_url": "https:\/\/openalex.org\/W7220849862",
            "pdf_url": "",
            "doi": "10.67546\/mu.th.6437949",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W7220849862",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1275,
            "title": "Exploring deep learning for earth system forecasting",
            "title_ar": "",
            "title_en": "Exploring deep learning for earth system forecasting",
            "author": "Zhihan Gao",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; predictive-analytics",
            "keywords": "",
            "abstract": "HKUST Electronic Theses Exploring deep learning for earth system forecasting by Zhihan Gao thesis 2024 1 online resource (xx, 132 pages) : illustrations (some color) Conventionally, Earth system (e.g., weather and climate) forecasting relies on numerical simulations using complex physical",
            "source_url": "https:\/\/openalex.org\/W4405916824",
            "pdf_url": "",
            "doi": "10.14711\/thesis-991013340437803412",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4405916824",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1276,
            "title": "Deep Learning for Chatbots",
            "title_ar": "",
            "title_en": "Deep Learning for Chatbots",
            "author": "Vyas Ajay Bhagwat",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "San Jose State University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "",
            "categories": "machine-learning; deep-learning; nlp",
            "keywords": "",
            "abstract": "Natural Language Processing (NLP) requires modelling complex relationships between the semantics of the language. While traditional machine learning techniques are used for NLP, the models built for conversations, called chatbots, are unable to be truly generic. While chatbots have been made with traditional machine learning techniques, deep learning has allowed the complexities within NLP to be easier to model and can be leveraged to build a chatbot which has a real conversation with a human. In this project, we explore the problems and techniques used to build chatbots and where improvements can be made. We analyze different architectures to build chatbots and propose a hybrid model, partly retrieval-based and partly generation-based which gives the best results.",
            "source_url": "https:\/\/openalex.org\/W4231594475",
            "pdf_url": "https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=1645&context=etd_projects",
            "doi": "10.31979\/etd.9hrt-u93z",
            "handle": "",
            "repository_name": "San José State University ScholarWorks (San Jose State University)",
            "source_record_id": "W4231594475",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1277,
            "title": "Is deep learning the same as deep learning? : perspectives on the construct of deep learning",
            "title_ar": "",
            "title_en": "Is deep learning the same as deep learning? : perspectives on the construct of deep learning",
            "author": "Karianne Strauman",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W2884223569",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "BIBSYS Brage (BIBSYS (Norway))",
            "source_record_id": "W2884223569",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1278,
            "title": "Deep Learning Macroeconomics",
            "title_ar": "",
            "title_en": "Deep Learning Macroeconomics",
            "author": "Rafael Rockenbach da Silva Guimarães",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transfer-learning",
            "categories": "machine-learning; deep-learning; transfer-learning; predictive-analytics",
            "keywords": "",
            "abstract": "Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Although macroeconomists already apply transfer learning when assuming a given a priori distribution in a Bayesian context, estimating a structural VAR with signal restriction and calibrating parameters based on results observed in other models, to name a few examples, advance in a more systematic transfer learning strategy in applied macroeconomics is the innovation we are introducing. We explore the proposed strategy empirically, showing that data from different but related domains, a type of transfer learning, helps identify the business cycle phases when there is no business cycle dating committee and to quick estimate a economic-based output gap. Next, since deep learning methods are a way of learning representations, those that are formed by the composition of multiple non-linear transformations, to yield more abstract representations, we apply deep learning for mapping low-frequency from high-frequency variables. The results obtained show the suitability of deep learning models applied to macroeconomic problems. First, models learned to classify United States business cycles correctly. Then, applying transfer learning, they were able to identify the business cycles of out-of-sample Brazilian and European data. Along the same lines, the models learned to estimate the output gap based on the U.S. data and obtained good performance when faced with Brazilian data. Additionally, deep learning proved adequate for mapping low-frequency variables from high-frequency data to interpolate, distribute, and extrapolate time series by related series.",
            "source_url": "https:\/\/openalex.org\/W4226128270",
            "pdf_url": "",
            "doi": "10.48550\/arxiv.2201.13380",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W4226128270",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1279,
            "title": "ENHANCED DEEP LEARNING NEURAL NETWORKS FOR CLASSIFICATION AND FORECASTING PROBLEMS IN EMBEDDED SYSTEMS",
            "title_ar": "",
            "title_en": "ENHANCED DEEP LEARNING NEURAL NETWORKS FOR CLASSIFICATION AND FORECASTING PROBLEMS IN EMBEDDED SYSTEMS",
            "author": "Pentsos, Vasileios",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Southern Illinois University Carbondale",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2025,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; predictive-analytics; cnn; rnn; transformers; image-classification; ai-smart-cities",
            "keywords": "",
            "abstract": "The widespread adoption of deep learning has led to a surge in demand for the efficient deployment of Deep Neural Networks (DNNs) on embedded and resource-constrained systems. These platforms are increasingly tasked with performing complex inference workloads in real time, yet they must do so under strict limitations on energy consumption, computation, and memory resources. As DNN models grow in complexity to support advanced applications such as time-series forecasting and image classification, ensuring their efficient execution without compromising performancebecomes a key design challenge. This calls for tailored algorithmic and architectural solutions that can meet application-specific constraints while maximizing the benefits of modern hardware accelerators.This dissertation proposes a set of algorithmic-based approaches aimed at enhancing the efficiency and adaptability of deep learning models for embedded systems. The research centers on three primary contributions: (i) a hybrid Long Short-Term Memory (LSTM)-Transformer architecture optimized for multi-step residential power load forecasting, integrating sequence modeling and attention mechanisms for improved accuracy under training time constraints, (ii) a lightweight hierarchical deep neural network enhancement that augments baseline classifiers through cascading binaryConvolutional Neural Networks (CNNs) and Vision Transformers, achieving higher classification accuracy and reduced inference time, and (iii) an energy-aware scheduling framework for DNN inference, featuring dynamic batch size selection, GPU frequency adjustment, and concurrent task mapping across multi-GPU systems to minimize energy use without violating real-time deadlines.In summary, this dissertation presents a comprehensive approach to designing and managing deep learning workflows on embedded platforms, emphasizing performance-efficiency trade-offs. The proposed methods demonstrated significantimprovements in both predictive accuracy and resource utilization, compared to existing solutions, offering practical pathways for deploying DNNs in sustainable, computing environments.",
            "source_url": "https:\/\/openalex.org\/W7111621194",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "OpenSIUC (Southern Illinois University Carbondale)",
            "source_record_id": "W7111621194",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1280,
            "title": "Deep learning neural network for image processing",
            "title_ar": "",
            "title_en": "Deep learning neural network for image processing",
            "author": "Xue-Qing Ma",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; cnn; image-classification; speech-recognition",
            "keywords": "",
            "abstract": "Deep learning is a new research direction in the field of machine learning. It is a subclass of machine learning. Inspired by the way the human brain works, deep learning is a learning process that uses deep neural networks to address feature expression. In many neural network models, convolutional neural network (CNN) can be considered the most successful neural network model in recent years. It is widely used in computer vision, natural language processing, speech and image recognition because of its satisfying performance. However, CNN is a deep neural network, which contains many layers and parameters. Thus, it may take a long time and be very difficult to train the network. What is more, the accuracy of the neural network should also be guaranteed. In this dissertation, I tried to maximize the accuracy of CNN with the least amount of training time. I improved the accuracy of the CNN model by adding two more convolutional and pooling layers based on MATLAB, and used different optimization methods including Adam optimizer, dropout layers, batch normalization layers and adaptive learning rate to improve the network performance based on PyTorch. The experimental results showed that the test accuracy of the CNN in MATLAB improved by 3％ after adding layers and the final accuracy of the test dataset has reached 99.218％ by applying the optimization methods in PyTorch. In conclusion, the accuracy of convolution neural network can be significantly improved by adding more convolution layers with less time and the above optimization methods can also effectively improve the network performance.",
            "source_url": "https:\/\/openalex.org\/W3081926688",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W3081926688",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1281,
            "title": "Deep Learning, Neural Networks and Aerospace Applications",
            "title_ar": "",
            "title_en": "Deep Learning, Neural Networks and Aerospace Applications",
            "author": "Liting Zhou",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W2901693003",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W2901693003",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1282,
            "title": "Deep learning : neural networks for object detection",
            "title_ar": "",
            "title_en": "",
            "author": "Alejandro Garcia Miñano",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Universitat Autònoma de Barcelona",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Spain",
            "governorate": "",
            "publication_year": 2018,
            "language": "es",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "deep-learning; computer-vision; medical-ai; object-detection; medical-imaging",
            "keywords": "",
            "abstract": "En la actualidad se está llevando a cabo unan gran mejoría en los sistemas basados en detección de objetos, con el incremento de potencia de las GPUS, cada vez es más viable realizar entrenamientos en conjuntos de datos lo suficientemente grandes como para lograr una buena precisión, los detectores de una fase se están imponiendo ante los detectores de dos fases por su principal ventaja de obtener tiempos de inferencia mucho menores, esto abre una gran cantidad de usos para este tipo de detectores como pueden ser sistemas de vigilancia en tiempo real, control del tráfico. En este articulo se analiza el detector de una fase conocido como RetinaNet, que utiliza las técnicas ya conocidas por los detectores de una fase pero que introduce como novedad una función de error capaz de incrementar la precisión obtenida hasta este momento con los detectores de una fase, solventando el mayor problema que comparten todos ellos, que es el desbalanceo en la detección de objetos.",
            "source_url": "https:\/\/openalex.org\/W2912333791",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona)",
            "source_record_id": "W2912333791",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1283,
            "title": "DeepSR a Deep Learning Neural Network for Speech Reading",
            "title_ar": "",
            "title_en": "DeepSR a Deep Learning Neural Network for Speech Reading",
            "author": "Basuki Winoto",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Harvard University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; predictive-analytics; cnn; rnn; video-analysis; speech-recognition",
            "keywords": "",
            "abstract": "Speech reading or lip reading is the understanding of spoken language while watching the speaker. It is a natural extension to increase comprehension when hearing becomes challenging. One attentively observes the speaker’s mouth movement as it forms the word. Speech reading is a skill, in addition to hand sign language, that helps with comprehension. It is an interesting skill yet difficult to learn. This thesis describes the development of a deep learning model that translates mouth movement into words. The model stacks a Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) for learning the sequence of mouth images. The model is trained with a dataset from the GRID corpus. This corpus contains video recordings of 34 speakers with each speaks 1000 sentences in English. The trained model is capable of predicting words from the mouth video frames with a word accuracy of 52.25%, far exceeding human accuracy of 14.47%.",
            "source_url": "https:\/\/openalex.org\/W3081906677",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Digital Access to Scholarship at Harvard (DASH) (Harvard University)",
            "source_record_id": "W3081906677",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1284,
            "title": "Deep Learning Neural Network Based on Image Filters",
            "title_ar": "",
            "title_en": "Deep Learning Neural Network Based on Image Filters",
            "author": "MD Sajib",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2022,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "",
            "categories": "deep-learning; computer-vision",
            "keywords": "",
            "abstract": "The picture is worth thousands of words, so it is necessary to enhance images.Removing noise from color images is a very active research scope in image processing.This paper presents the implementation and comparison of mean, median, gaussian, conservative, and Crimmins speckle removal.The effectiveness of the restoration filters is measured using performance metrics like the Peak signal to noise ratio (PSNR), Mean square error (MSE), and Structure Similarity Index Method (SSIM).Results and analysis show that mean, median, and gaussian are comparatively better than conservative and Crimmins in restoring the degraded image.The study also clarifies that filters SRCNN performs better all the time.",
            "source_url": "https:\/\/openalex.org\/W7215174292",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/5e4e8b25-374f-45e5-94fb-5e44dc39e5b4\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215174292",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1285,
            "title": "Image Denoising via Deep Learning Neural Networks and Transformers",
            "title_ar": "",
            "title_en": "Image Denoising via Deep Learning Neural Networks and Transformers",
            "author": "Jundong Wang",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Tampere University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "transformers",
            "categories": "deep-learning; computer-vision; transformers",
            "keywords": "image denoising, deep learning, residual learning, Transformers.",
            "abstract": "This thesis investigates the implementation of image denoising using Neural Networks (NN) in conjunction with wavelet transform techniques. For the purpose of data simulation, images are intentionally corrupted with additive white Gaussian noise (AWGN) at various noise levels. Additionally, the study includes the examination of both random noise and real-world noise scenarios. This research has an emphasis on implementing several novel neural network architectures and strategies. By integrating the wavelet transform, the research aims to leverage its multi-resolution analysis capability to further enhance denoising performance. The proposed methods underscore the ability of residual learning and Transformers to integrate both local and global features, which efficiently capture fine-grained local details and long-range global dependencies. Keywords: image denoising, deep learning, residual learning, Transformers.",
            "source_url": "https:\/\/openalex.org\/W7133094216",
            "pdf_url": "https:\/\/trepo.tuni.fi\/bitstream\/10024\/160429\/2\/WangJundong.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Tampere University Institutional Repository (Tampere University)",
            "source_record_id": "W7133094216",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1286,
            "title": "Data Augmentation Methods for Deep Learning Neural Networks",
            "title_ar": "",
            "title_en": "Data Augmentation Methods for Deep Learning Neural Networks",
            "author": "Md. Sharif",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7215584607",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215584607",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1287,
            "title": "Image Data Augmentation Methods with Deep Learning Neural Networks",
            "title_ar": "",
            "title_en": "Image Data Augmentation Methods with Deep Learning Neural Networks",
            "author": "Mohammad Hossain",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2024,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "cnn",
            "categories": "deep-learning; computer-vision; ai-applications; cnn; ai-agriculture",
            "keywords": "",
            "abstract": "Image data enhancement is a vital method in the field of deep learning for enhancing model performance and generalization capabilities.This thesis explores various image data augmentation methods and their effectiveness in improving the performance of deep learning neural networks.The study begins with an overview of image data augmentation techniques, including rotation, flipping, scaling, cropping, translation, brightness adjustment, contrast adjustment, and Gaussian noise addition.Each augmentation method is examined in detail, discussing its impact on the variety and richness of the training collection.Following the theoretical overview, a series of experiments are conducted using widely used deep learning models such as Convolutional Neural Networks (CNNs), VGG, and MobileNet.These experiments aim to evaluate the performance of different augmentation methods in terms of model accuracy, robustness, and generalization on benchmark datasets.The results of the experiments demonstrate the effectiveness of certain augmentation techniques in improving model performance, while also highlighting potential trade-offs and considerations for real-world applications.Additionally, the study investigates the impact of augmentation parameters such as magnitude, probability, and combination strategies on model training dynamics and convergence.This thesis provides valuable insights into the role of image data augmentation in deep learning and offers practical guidance for selecting and implementing augmentation methods to enhance the effectiveness of neural networks across various computer vision assignments.",
            "source_url": "https:\/\/openalex.org\/W7215158281",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/13972c2f-f64d-4f28-8b7c-b18f3a88a358\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W7215158281",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1288,
            "title": "Rhythm interpretation using deep learning neural networks",
            "title_ar": "",
            "title_en": "Rhythm interpretation using deep learning neural networks",
            "author": "Daniel Eidsvåg",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Norway",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "ai-industry",
            "categories": "machine-learning; deep-learning; medical-ai; ai-applications; ai-industry",
            "keywords": "",
            "abstract": "Out-of-hospital cardiac arrest (OHCA) is a leading cause of death in the industrialized world, with an estimated annual incidence that varies by 52.5 (in Asia), 86.4 (in Europe), 98.1 (in North America), and 111.9 (in Australia) per 100,000 person-years. Lethal ventricular arrhythmias are the most frequent causes of OHCA. A defibrillation shock is an effective treatment, and early defibrillation is one of the key factors in survival from OHCA. However, chest compressions, ventilations, and drug play a key role in the treatment of cardiac arrest. Under resuscitation, automated external defibrillator (AED) require peri-shock pauses to analyze for a shockable rhythm. This peri-shock are associated with a decrease in survival to hospital discharge. The objective of this thesis is to determine if different artificial neural network (ANN) structures can be used as a classifier, to determine the underlying heart rhythm under chest compressions to remove peri-shock pauses during cardiac arrest. The analysis is conducted from data obtained from 394 OHCA patients, where two datasets were used. Both containing 3-second segments with clinical rhythm annotations resulting in 2446 and 422415 segments. Results in this thesis suggest that there is no clear best method in the different neural network methods for ECG data. However, FNN demonstrates the most promising results with an accuracy of 52.30%. This result emphasizes the problem regarding classification of ECG data with compression artifacts.",
            "source_url": "https:\/\/openalex.org\/W2756701130",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "BIBSYS Brage (BIBSYS (Norway))",
            "source_record_id": "W2756701130",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1289,
            "title": "Hand gesture recognition using deep learning neural networks",
            "title_ar": "",
            "title_en": "Hand gesture recognition using deep learning neural networks",
            "author": "Norah M. Alnaim",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Brunel University London",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "speech-recognition",
            "categories": "machine-learning; deep-learning; computer-vision; nlp; medical-ai; predictive-analytics; cnn; video-analysis; speech-recognition",
            "keywords": "",
            "abstract": "Human Computer Interaction (HCI) is a broad field involving different types of interactions including gestures.Gesture recognition concerns non-verbal motions used as a means of communication in HCI.A system may be utilised to identify human gestures to convey information for device control.This represents a significant field within HCI involving device interfaces and users.The aim of gesture recognition is to record gestures that are formed in a certain way and then detected by a device such as a camera.Hand gestures can be used as a form of communication for many different applications.It may be used by people who possess different disabilities, including those with hearing-impairments, speech impairments and stroke patients, to communicate and fulfil their basic needs.Various studies have previously been conducted relating to hand gestures.Some studies proposed different techniques to implement the hand gesture experiments.For image processing there are multiple tools to extract features of images, as well as Artificial Intelligence which has varied classifiers to classify different types of data.2D and 3D hand gestures request an effective algorithm to extract images and classify various mini gestures and movements.This research discusses this issue using different algorithms.To detect 2D or 3D hand gestures, this research proposed image processing tools such as Wavelet Transforms and Empirical Mode Decomposition to extract image features.The Artificial Neural Network (ANN) classifier which used to train and classify data besides Convolutional Neural Networks (CNN).These methods were examined in terms of multiple parameters such as execution time, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood, negative likelihood, receiver operating characteristic, area under ROC curve and root mean square.This research discusses four original contributions in the field of hand gestures.The first contribution is an implementation of two experiments using 2D hand gesture video where ten different gestures are detected in short and long distances using an iPhone 6 Plus with 4K resolution.The experiments are performed using WT and EMD for feature extraction while ANN and CNN for classification.The second contribution comprises 3D hand gesture video experiments where twelve gestures are recorded using holoscopic imaging system camera.The third contribution pertains experimental work carried out to detect seven common hand gestures.Finally, disparity experiments were performed using the left and the right 3D hand gesture videos to discover disparities.The results of comparison show the accuracy results of CNN being 100% compared to other techniques.CNN is clearly the most appropriate method to be used in a hand gesture system.",
            "source_url": "https:\/\/openalex.org\/W3085860926",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Brunel University Research Archive (BURA) (Brunel University London)",
            "source_record_id": "W3085860926",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1290,
            "title": "REFINING DEEP LEARNING NEURAL NETWORKS FOR AUTONOMOUS VEHICLE NAVIGATION",
            "title_ar": "",
            "title_en": "REFINING DEEP LEARNING NEURAL NETWORKS FOR AUTONOMOUS VEHICLE NAVIGATION",
            "author": "Marcea Ascencio",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Naval Postgraduate School",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-classification",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; ai-applications; cnn; image-classification; ai-agriculture",
            "keywords": "",
            "abstract": "Machine learning methods have recently increased in appearance in navigation and guidance applications by means of image classification. This thesis sought to advance the ongoing Electrical and Computer Engineering (ECE) Control Systems and Robotics Laboratory project in developing a system that will autonomously navigate across the Naval Postgraduate School (NPS) campus. In pursuit of providing a robust navigation and guidance solution to an autonomous robotic vehicle, a convolutional neural network (CNN) was trained to classify significant objects around NPS. In addition to increasing the number of objects that the neural network could classify, the network was also trained with varying image augmentation techniques applied to the training and validation images. A variety of tests were performed to evaluate the accuracy of the model when exposed to different objects and regions throughout the campus. The tests also included running the image classification model against images that were altered with crop, blur, rotation, and noise. The results demonstrated high classification accuracy and asserted that the output was robust when faced with poor image quality. This work established a strong baseline for more CNN output integration into the navigation and guidance solution of the robotic vehicle.",
            "source_url": "https:\/\/openalex.org\/W3197256413",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)",
            "source_record_id": "W3197256413",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1291,
            "title": "On the Optimal Deployment of Deep Learning Neural Networks on Field Programmable Gate Arrays",
            "title_ar": "",
            "title_en": "On the Optimal Deployment of Deep Learning Neural Networks on Field Programmable Gate Arrays",
            "author": "Ahmad Shawahna",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "King Fahd University of Petroleum and Minerals",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Saudi Arabia",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W7167161047",
            "pdf_url": "https:\/\/eprints.kfupm.edu.sa\/id\/eprint\/142340\/1\/Dissertation_Shawahna.pdf",
            "doi": "",
            "handle": "",
            "repository_name": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)",
            "source_record_id": "W7167161047",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1292,
            "title": "Deep Learning Neural Networks-Based Edge Detection in Mechanical Components",
            "title_ar": "",
            "title_en": "Deep Learning Neural Networks-Based Edge Detection in Mechanical Components",
            "author": "Rahman",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "object-detection",
            "categories": "machine-learning; deep-learning; computer-vision; ai-applications; cnn; object-detection; ai-industry",
            "keywords": "",
            "abstract": "For the past few decades, the traditional deep neural network was one of the vital parts of machine learning methods, but in recent years the trend has been changed with the development of machine vision technology.Nowadays convolutional neural network is used in most image detection-related sectors.Detecting the special features from the images such as hole detection, edge detection, or detection of any corners can be easily and effectively done using the convolutional neural network.The system can estimate and identify the location of possible objects for object detection.This research aims to detect the special features from the images and determine the coordinates of the edges then convey those data to the industrial computer, which is used for the accurate assembling of the mechanical parts in the industry.The research is started with capturing the images of the mechanical disc.Next, the targeted parts of the images subjected to be detected are labeled using image labeling tools.In the next step, all the images are used in training.After the training, the weight value obtained from the training is used in the python code for detecting the features.A graphical user interface is made for operating with the data and result demonstration.In the result section, all the required edges are identified correctly in the first step.In the second step, the mean distances between the two edges are determined using the Euclidean method.Lastly, the holes are detected and the center and the coordinates of the center of the holes are also identified.",
            "source_url": "https:\/\/openalex.org\/W3184637092",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W3184637092",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1293,
            "title": "Deep Learning Neural Network based Survival Analysis: Application in Brain Stroke",
            "title_ar": "",
            "title_en": "Deep Learning Neural Network based Survival Analysis: Application in Brain Stroke",
            "author": "Nasrin Someeh",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W3213784345",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3213784345",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1294,
            "title": "Customer Move Prediction In Electricity Sales Using Deep Learning Neural Networks",
            "title_ar": "",
            "title_en": "Customer Move Prediction In Electricity Sales Using Deep Learning Neural Networks",
            "author": "J. Laakso",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Tampere University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; predictive-analytics",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W3035970992",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Tampere University Institutional Repository (Tampere University)",
            "source_record_id": "W3035970992",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1295,
            "title": "Analyzes of Braİn Blood Vessels Using Deep Learning Neural Networks",
            "title_ar": "",
            "title_en": "",
            "author": "Tuğçe Göksu",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "FSM Vakif University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Turkey",
            "governorate": "",
            "publication_year": 2022,
            "language": "tr",
            "research_domain": "computer-vision",
            "research_subdomain": "image-segmentation",
            "categories": "deep-learning; computer-vision; cnn; image-segmentation",
            "keywords": "",
            "abstract": "Çok katmanlı ileri beslemeli bir yapay sinir ağı modeli olan evrişimsel sinir ağı (ing: Convolutional Neural Network - CNN) özellikle görüntüler üzerinden özellik çıkarılması ve analizlerinin yapılması aşamasında sıklıkla kullanılmaktadır. Yapılan bu tez çalışmasında, iki boyutlu olarak elde edilen gerçek beyin damar ağı görüntüleri kullanılarak, derin öğrenme sinir ağı tabanlı otomatik damar ağı bölütlemesi gerçekleştirilen bir sistem ortaya konulmuştur. Görüntülerdeki damar ağı bölütlemesi için üç farklı derin öğrenme sinir ağı mimarisi kullanılmıştır. Kullanılan derin öğrenme sinir ağı mimarileri sırası ile Otomatik kodlayıcı (AutoEncoder), U-Net ve ResNet+U-Net ağlarıdır. Otomatik bölütleme adımında kullanılan derin öğrenme sinir ağı mimarilerinin eğitimi için oluşturulan veri setinde, eğitim için 1806 adet test için ise 34 adet görüntü kullanılarak eğitim ve test işlemi tamamlanmıştır. Üç ağ mimarisinden elde edilen eğitilmiş ağ modeli ile yapılan test işleminde en yüksek doğruluk başarım değerini ,31 ile ResNet+U-Net ağı vermiştir. Bölütleme işleminden sonra, vasküler analiz aşamasından önce, bölütlenmiş görüntüler üzerinde ikilileştirme (ing: binarization) işlemi uygulanarak, bölütlenmiş görüntülerdeki sadece beyin damar ağının belirginleştirilmesi sağlanmıştır. Tez çalışmasının bir sonraki aşamasında ise bölütlenmiş beyin damar ağı görüntüleri kullanılarak vasküler analiz işlemi gerçekleştirilmiştir. Yapılan vasküler analiz ile beyin damar ağının, vasküler alan fraksiyonu, dallanma noktası sayısı (ing: branching point), dal sayısı (ing: branch number) ve vasküler uzunluk (damar uzunluğu) metrikleri hesaplanmıştır. Elde edilen sonuçlar, medikal alanda kullanılan angiotool ve fiji uygulamaları ile karşılaştırılmıştır. Doğruluk, birleşimlerin kesişimi (ing: Intersection Over Union-IoU), zar katsayısı (ing: dice coefficient), kesinlik (ing: precision) ve duyarlılık (ing: recall) değerlendirme kriterleri ile değerlendirmeler yapılmıştır. Tez çalışması kapsamında yazılan uygulama, var olan analiz uygulamaları (Angiotool ve Fiji makrosu) ile aynı veri seti kullanılarak test edilerek sonuçları karşılaştırılmıştır. Analiz sonuçları, tez çalışmasında yapılan uygulamanın medikal alanda kullanılan diğer uygulamalara kıyasla değerlendirme metriklerine göre daha iyi sonuçlar verdiğini göstermektedir.",
            "source_url": "https:\/\/openalex.org\/W7140350108",
            "pdf_url": "https:\/\/acikerisim.fsm.edu.tr\/bitstreams\/adbd5ff2-2ec2-4696-a4f1-5e9c2718c519\/download",
            "doi": "",
            "handle": "",
            "repository_name": "DSpace@FSM (FSM Vakif University)",
            "source_record_id": "W7140350108",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1296,
            "title": "Adaptive Second-order Derivative Approximate Greatest Descent Optimization for Deep Learning Neural Networks",
            "title_ar": "",
            "title_en": "Adaptive Second-order Derivative Approximate Greatest Descent Optimization for Deep Learning Neural Networks",
            "author": "Hong Hui Tan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Curtin University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Australia",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "",
            "categories": "deep-learning",
            "keywords": "",
            "abstract": "Backpropagation using Stochastic Diagonal Approximate Greatest Descent (SDAGD) is a novel adaptive second-order derivative optimization method in updating weights of deep learning neural networks. SDAGD applies two-phase switching strategy to seek for solution at far using long-term optimal trajectory and automatically switch to Newton method when nearer to optimal solution. SDAGD has the advantages of steepest training roll-off rate, adaptive adjustment of step-length and the ability to deal with vanishing gradient issues in deep architecture.",
            "source_url": "https:\/\/openalex.org\/W3008992957",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "eSpace (Curtin University)",
            "source_record_id": "W3008992957",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1297,
            "title": "Medical image analysis with machine learning techniques and deep learning neural networks",
            "title_ar": "",
            "title_en": "",
            "author": "Γεώργιος Γιαννουλόπουλος ; Giannoulopoulos, George",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Piraeus",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Greece",
            "governorate": "",
            "publication_year": 2020,
            "language": "el",
            "research_domain": "medical-ai",
            "research_subdomain": "medical-imaging",
            "categories": "machine-learning; deep-learning; computer-vision; medical-ai; medical-imaging",
            "keywords": "",
            "abstract": "Οι πρόσφατες εξελίξεις στον τομέα της μηχανικής μάθησης στη μηχανική όραση θα εξεταστούν ,ενώ ένα υποσύνολο αυτών των αλγορίθμων θα δοκιμαστεί στο πρόβλημα της ταξινόμησης δερματικών παθήσεων. Πιο συγκεκριμένα, η απόδοση των βαθιών νευρωνικών δικτύων στη ταξινόμηση εικόνων θα παρουσιαστεί και δοκιμαστεί. Τέλος, μια συγκεκριμένη οικογένεια βαθιών νευρωνικών δικτύων που λέγονται EfficientNets θα εκπαιδευτεί στο σύνολο δεδομένων του ISIC challenge του 2019.",
            "source_url": "https:\/\/openalex.org\/W3081787540",
            "pdf_url": "",
            "doi": "10.26267\/unipi_dione\/239",
            "handle": "",
            "repository_name": "Dione (University of Piraeus)",
            "source_record_id": "W3081787540",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1298,
            "title": "PLACE-BASED NAVIGATION FOR AUTONOMOUS VEHICLES WITH DEEP LEARNING NEURAL NETWORKS",
            "title_ar": "",
            "title_en": "PLACE-BASED NAVIGATION FOR AUTONOMOUS VEHICLES WITH DEEP LEARNING NEURAL NETWORKS",
            "author": "Magee, Ashleigh",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Naval Postgraduate School",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United States",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-classification",
            "categories": "machine-learning; deep-learning; computer-vision; robotics; transfer-learning; cnn; image-classification",
            "keywords": "",
            "abstract": "Accurate navigation is crucial to successfully deploying autonomous vehicles but is often limited by GPS reliance. The purpose of this thesis was to develop an image-based navigation solution that does not require GPS. This work builds on an ongoing research project, which is to develop a mobile robot that can navigate anywhere on the Naval Postgraduate School campus, inside and outside of buildings. This work focused on indoor navigation using image classification, and images were classified using a convolutional neural network (CNN). Transfer learning was used to reduce CNN training time and increase learning efficiency. The trained CNN was integrated into a waypoint loop algorithm that ran autonomously on the robot, stopping when it had correctly identified its desired location. Tests of the CNN classification showed a high success rate, but that it was also susceptible to variations in light and similar types of images. Increasing the data set will improve the classification results and allow for use in a variety of applications. This system will also benefit from improved indoor localization techniques.",
            "source_url": "https:\/\/openalex.org\/W7112451897",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)",
            "source_record_id": "W7112451897",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1299,
            "title": "Autonomous control of a mobile robot with incremental deep learning neural networks",
            "title_ar": "",
            "title_en": "Autonomous control of a mobile robot with incremental deep learning neural networks",
            "author": "Isabella Glöde",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2021,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "ai-industry",
            "categories": "deep-learning; robotics; ai-applications; ai-industry",
            "keywords": "",
            "abstract": "Over the last few years autonomous driving had an increasingly strong impact on the automotive industry. This created an increased need for artificial intelligence algo- rithms which allow for computers to make human-like decisions. However, a compro- mise between the computational power drawn by these algorithms and their subsequent performance must be found to fulfil production requirements. In this thesis incremental deep learning strategies are used for the control of a mobile robot such as a four wheel steering vehicle. This strategy is similar to the human approach of learning. In many small steps the vehicle learns to achieve a specific goal. The usage of incremental training leads to growing knowledge-base within the system. It also provides the opportunity to use older training achievements to improve the system, when more training data is available. To demonstrate the capabilities of such an algorithm, two different models have been formulated. First, a more simple model with counter wheel steering, and second, a more complex, nonlinear model with independent steering. These two models are trained incrementally to follow different types of trajectories. Therefore an algorithm was established to generate useful initial points. The incremental steps allow the robot to be positioned further and further away from the desired trajectory in the environ- ment. Afterwards, the effects of different trajectory types on model behaviour are investigated by over one thousand simulation runs. To do this, path planning for straight lines and circles are introduced. This work demonstrates that even simulations with simple network structures can have high performance.",
            "source_url": "https:\/\/openalex.org\/W3160493819",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)",
            "source_record_id": "W3160493819",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1300,
            "title": "A Deep Learning (Neural Network) Approach for ECG Heartbeat Classification: Arrhythmia Detection",
            "title_ar": "",
            "title_en": "A Deep Learning (Neural Network) Approach for ECG Heartbeat Classification: Arrhythmia Detection",
            "author": "B.E. Salami",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Eastern Finland",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2020,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "cnn",
            "categories": "deep-learning; medical-ai; cnn",
            "keywords": "",
            "abstract": "Nearly 17 million people die every year due to cardiovascular disease according to World Health Organization and one of the diseases associated with the human heart is arrhythmia.Arrhythmia is the irregularity and abnormality of heartbeat rhythm.To detect arrhythmia, ECG (Electrocardiogram) is used for diagnosing it, but in order to interpret well ECG signals, many automated analyses method have been proposed over the years but not all gave a satisfactory result.However, Deep learning especially Convolutional Neural Network has been successfully used for the detection and classification of ECG signal because it is an end to end model, which means it does not require pre-processing before network training except normalizing the dataset.In this thesis, a 1D-Convolutional Neural Network model (1D-CNN) was proposed, which is mainly used for signal and time-series data.A total of 7636 consisting of 5 different classes of arrhythmias were extracted which includes Normal sinus rhythm n, Atrial Premature beat, Premature ventricular contraction, Atrial fibrillation, and Atrial flutter from the MIT -BIH Arrhythmia database.By experimenting with 5 different 1D-CNN layered model, different results were achieved.The results obtained all showed that increasing or decreasing the number of layers with respect to changing the values of hyperparameters can affect performance results of a network. .",
            "source_url": "https:\/\/openalex.org\/W3112906421",
            "pdf_url": "https:\/\/erepo.uef.fi\/bitstreams\/c66b8559-4efd-4f89-8416-963a6dc5b6c6\/download",
            "doi": "",
            "handle": "",
            "repository_name": "UEF eRepo (University of Eastern Finland)",
            "source_record_id": "W3112906421",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1301,
            "title": "Deep Learning: Neural Network Applications and Architectures in Sequence and Image Classification",
            "title_ar": "",
            "title_en": "Deep Learning: Neural Network Applications and Architectures in Sequence and Image Classification",
            "author": "Alexander Stec",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "computer-vision",
            "research_subdomain": "image-classification",
            "categories": "deep-learning; computer-vision; image-classification",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W3005728568",
            "pdf_url": "",
            "doi": "10.21985\/n2-0bg0-v403",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W3005728568",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1309,
            "title": "Sentiment Analysis of Twitter Data Using Machine Learning and Deep Learning Methods",
            "title_ar": "",
            "title_en": "Sentiment Analysis of Twitter Data Using Machine Learning and Deep Learning Methods",
            "author": "Kundan Reddy Manda",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "machine-learning; deep-learning; nlp; generative-ai; cnn; rnn; sentiment-analysis",
            "keywords": "",
            "abstract": "Background: Twitter, Facebook, WordPress, etc. act as the major sources of information exchange in today's world. The tweets on Twitter are mainly based on the public opinion on a product, event or topic and thus contains large volumes of unprocessed data. Synthesis and Analysis of this data is very important and difficult due to the size of the dataset. Sentiment analysis is chosen as the apt method to analyse this data as this method does not go through all the tweets but rather relates to the sentiments of these tweets in terms of positive, negative and neutral opinions. Sentiment Analysis is normally performed in 3 ways namely Machine learning-based approach, Sentiment lexicon-based approach, and Hybrid approach. The Machine learning based approach uses machine learning algorithms and deep learning algorithms for analysing the data, whereas the sentiment lexicon-based approach uses lexicons in analysing the data and they contain vocabulary of positive and negative words. The Hybrid approach uses a combination of both Machine learning and sentiment lexicon approach for classification. Objectives: The primary objectives of this research are: To identify the algorithms and metrics for evaluating the performance of Machine Learning Classifiers. To compare the metrics from the identified algorithms depending on the size of the dataset that affects the performance of the best-suited algorithm for sentiment analysis. Method: The method chosen to address the research questions is Experiment. Through which the identified algorithms are evaluated with the selected metrics. Results: The identified machine learning algorithms are Naïve Bayes, Random Forest, XGBoost and the deep learning algorithm is CNN-LSTM. The algorithms are evaluated with respect to the metrics namely precision, accuracy, F1 score, recall and compared. CNN-LSTM model is best suited for sentiment analysis on twitter data with respect to the selected size of the dataset. Conclusion: Through the analysis of results, the aim of this research is achieved in identifying the best-suited algorithm for sentiment analysis on twitter data with respect to the selected dataset. CNN-LSTM model results in having the highest accuracy of 88% among the selected algorithms for the sentiment analysis of Twitter data with respect to the selected dataset.",
            "source_url": "https:\/\/openalex.org\/W2974440996",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W2974440996",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1314,
            "title": "Machine learning techniques for sentiment analysis and emotion recognition in natural language",
            "title_ar": "",
            "title_en": "Machine learning techniques for sentiment analysis and emotion recognition in natural language",
            "author": "Spyridon Kardakis",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "Greece",
            "governorate": "",
            "publication_year": 2019,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "machine-learning; deep-learning; nlp; data-mining; sentiment-analysis",
            "keywords": "",
            "abstract": "The field of Textual Sentiment Analysis has been met with increased interest by the research community in recent years due to the rise of social media and the Internet. The vast amount of opinion-heavy user-generated content that is available to us, whether that is a product\/service review or an opinion on an event, shows that effective Sentiment Analysis is needed. However, automatic knowledge extraction about the opinion and emotional state of people can be a very challenging task. This thesis studies the fields of Machine Learning and Deep Learning in-depth, in order to perform Sentiment Analysis and by extension Emotion Recognition classification tasks. A novel Hidden Markov Model-based approach is proposed where a single model is trained for each class label with the help of clustering and a lexicon. Overall, the main goal is to present a variety of Machine Learning models, ranging from basic all the way to state-of-the-art approaches, and implement them in real-world datasets. Initially, the theory behind the aforementioned fields and the related literature is introduced. Then, we present the mathematical background of the proposed approaches as well as expand on their usage for text classification and its challenges. The task at hand is supervised text classification. Additionally, a survey of the available datasets in the Sentiment Analysis domain is performed and the field of Ensemble Learning is explored. Finally, we implement and evaluate the proposed models on benchmark datasets using k-fold cross-validation and come to conclusions regarding each algorithm’s ability to recognize peoples' opinions and emotions. From the experimental results it is observed that the proposed Hidden Markov Models and Deep Neural Networks with word embeddings achieve very high performance, proving that they are potent and suitable tools for Sentiment Analysis and classification tasks in general.",
            "source_url": "https:\/\/openalex.org\/W2985845076",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Νημερτής",
            "source_record_id": "W2985845076",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1317,
            "title": "Machine learning technicques for aspect based sentiment analysis",
            "title_ar": "",
            "title_en": "Machine learning technicques for aspect based sentiment analysis",
            "author": "Chen, Haonan",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "Nanyang Technological University",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Singapore",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "machine-learning; deep-learning; nlp; rnn; sentiment-analysis",
            "keywords": "",
            "abstract": "Aspect Based Sentiment Analysis (ABSA) is a field of study where sentiments on certain aspects or characteristics of entities are obtained, analyzed, and aggregated from text. Since ABSA facilitates analyzing sentiments at a fine-grained level, it has gained significant attention over the past few years. In particular, ABSA caters real-world applications such analyzing sentiments from product reviews, tweets and emails focusing solely on user intended aspects. In recent research, ABSA has predominantly leveraged on Machine Learning (ML) techniques such as Representation Learning, Kernel Methods and Deep Learning. This dissertation focuses on empirically evaluating two recently proposed ML techniques for ABSA – Support Vector Machine (SVM)-based and Recurrent Neural Networks (RNNs)-based approaches on several datasets. To this end, we re-implemented two state-of-the-art ABSA frameworks which use these classifiers and compared them on the aforementioned ABSA datasets in terms of both accuracy and efficiency. Through these large-scale evaluations, we infer the following: (i) SVMs produce accuracies which are comparable to that of RNNs but they are much computationally lighter, (ii) When there is a significant imbalance among the classes in a multi-class ABSA setting, RNNs perform much better than SVMs. Furthermore, we observe that RNN, as it leverages on word embeddings, are particularly more suited for semantics based ABSA. However, due to its huge computational demands (e.g., large number of GPU cores and high GPU memory), we could not explore the full realm of its performance on our experimental setup which had limited computing resources.",
            "source_url": "https:\/\/openalex.org\/W2750897301",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "DR-NTU (Nanyang Technological University)",
            "source_record_id": "W2750897301",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:00",
            "updated_at": "2026-11-15 16:53:00"
        },
        {
            "id": 1324,
            "title": "Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis",
            "title_ar": "",
            "title_en": "Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis",
            "author": "Soujanya Poria",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Stirling",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "United Kingdom",
            "governorate": "",
            "publication_year": 2017,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "deep-learning; computer-vision; nlp; ai-applications; multimodal; sentiment-analysis; ai-education",
            "keywords": "",
            "abstract": "Emotions and sentiments play a crucial role in our everyday lives. They aid decision-making, learning, communication, and situation awareness in human-centric environments. Over the past two decades, researchers in artificial intelligence have been attempting to endow machines with cognitive capabilities to recognize, infer, interpret and express emotions and sentiments. All such efforts can be attributed to affective computing, an interdisciplinary field spanning computer science, psychology, social sciences and cognitive science. Sentiment analysis and emotion recognition has also become a new trend in social media, avidly helping users understand opinions being expressed on different platforms in the web. In this thesis, we focus on developing novel methods for text-based sentiment analysis. As an application of the developed methods, we employ them to improve multimodal polarity detection and emotion recognition. Specifically, we develop innovative text and visual-based sentiment-analysis engines and use them to improve the performance of multimodal sentiment analysis. We begin by discussing challenges involved in both text-based and multimodal sentiment analysis. Next, we present a number of novel techniques to address these challenges. In particular, in the context of concept-based sentiment analysis, a paradigm gaining increasing interest recently, it is important to identify concepts in text; accordingly, we design a syntaxbased concept-extraction engine. We then exploit the extracted concepts to develop conceptbased affective vector space which we term, EmoSenticSpace. We then use this for deep learning-based sentiment analysis, in combination with our novel linguistic pattern-based affective reasoning method termed sentiment flow. Finally, we integrate all our text-based techniques and combine them with a novel deep learning-based visual feature extractor for multimodal sentiment analysis and emotion recognition. Comparative experimental results using a range of benchmark datasets have demonstrated the effectiveness of the proposed approach.",
            "source_url": "https:\/\/openalex.org\/W2620364470",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "Stirling Online Research Repository (University of Stirling)",
            "source_record_id": "W2620364470",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:01",
            "updated_at": "2026-11-15 16:53:01"
        },
        {
            "id": 1329,
            "title": "Hybrid Machine Learning and Time-Series Modelling to Forecast Product Demand: A CRISP-DM Approach",
            "title_ar": "",
            "title_en": "Hybrid Machine Learning and Time-Series Modelling to Forecast Product Demand: A CRISP-DM Approach",
            "author": "Niklas Virtanen",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "University of Vaasa",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Finland",
            "governorate": "",
            "publication_year": 2026,
            "language": "en",
            "research_domain": "nlp",
            "research_subdomain": "sentiment-analysis",
            "categories": "machine-learning; deep-learning; nlp; responsible-ai; ai-applications; predictive-analytics; sentiment-analysis; xai; ai-industry",
            "keywords": "",
            "abstract": "Efficient demand forecasting can yield significant competitive advantage for companies in today’s market, making it a crucial process in supply chain management. By exploring and implementing modern demand forecasting models, encompassing time-series, machine learning, deep learning and hybrid approaches, companies can gain benefits in important business areas such as strategic planning, operational efficiency and customer service. This research is done in collaboration with a case company, and it aims to identify and develop a product demand forecasting model that improves forecasting accuracy compared against a baseline approach and to identify and analyze key demand drivers that influence demand. To meet these goals, an industry standard CRISP-DM framework is utilized to develop and compare multiple time-series, machine learning and their hybrid combination models to find the best performing forecasting solution. Key demand drivers are identified and analyzed by combining SHAP values with statistical analysis. The results of this study indicate that a hybrid demand forecasting model combining Holt-Winters Exponential Smoothing and Elastic Net Regression outperformed the other models in the comparison and improved the forecasting accuracy significantly compared to the baseline approach. The results also suggest that product demand is driven by trend and seasonal factors, short-term sales pricing and momentum as well as macroeconomic leading indicators such as industry sentiment, interest rates and euro strength. Despite the significant improvement in forecasting accuracy and practical insights through the identified demand drivers, research limitations such as limited amount of training data, small test set and lack of true causal analysis of the relationships between features and demand are acknowledged to emphasize the need for future research. The development and evaluation of the HW-Elastic Net model contributes to current research by highlighting the importance of exploring hybrid models in demand forecasting, especially on small and noisy real-world datasets.",
            "source_url": "https:\/\/openalex.org\/W7198431739",
            "pdf_url": "https:\/\/osuva.uwasa.fi\/bitstreams\/6769593d-1bc1-41d8-9aac-273cac1b970a\/download",
            "doi": "",
            "handle": "",
            "repository_name": "Osuva (University of Vaasa)",
            "source_record_id": "W7198431739",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:01",
            "updated_at": "2026-11-15 16:53:01"
        },
        {
            "id": 1338,
            "title": "Financial time series forecasting : from machine learning to deep learning",
            "title_ar": "",
            "title_en": "Financial time series forecasting : from machine learning to deep learning",
            "author": "Karl Sant Fournier",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "predictive-analytics",
            "categories": "machine-learning; deep-learning; predictive-analytics",
            "keywords": "",
            "abstract": "",
            "source_url": "https:\/\/openalex.org\/W2965789559",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W2965789559",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:01",
            "updated_at": "2026-11-15 16:53:01"
        },
        {
            "id": 1341,
            "title": "MACHINE LEARNING STRATEGIES TO PREDICT OIL FIELD PERFORMANCE AS TIME-SERIES FORECASTING",
            "title_ar": "",
            "title_en": "MACHINE LEARNING STRATEGIES TO PREDICT OIL FIELD PERFORMANCE AS TIME-SERIES FORECASTING",
            "author": "ISABEL FIGUEIRA DE ABREU GONCALVES",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2023,
            "language": "en",
            "research_domain": "deep-learning",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; predictive-analytics; rnn",
            "keywords": "",
            "abstract": "Precisely forecasting oil field performance is essential in oil reservoir planning and management.Nevertheless, forecasting oil production is a complex nonlinear problem due to all geophysical and petrophysical properties that may result in different effects with a bit of change.Thus, all decisions to be made during an exploitation project must consider different efficient algorithms to simulate data, providing robust scenarios to lead to the best deductions.To reduce the uncertainty in the simulation process, recent studies have efficiently introduced machine learning algorithms for solving reservoir engineering problems since they can extract the maximum information from the dataset.This thesis proposes using two machine learning techniques to predict the daily oil production of an offshore reservoir.Initially, the oil rate production is considered a time series and is pre-processed and restructured to fit a supervised learning problem.The Random Forest model is used to forecast a one-time step, which is an extension of decision tree learning, widely used in regression and classification problems for supervised machine learning.Regardless, the restrictions of this approach lead us to a more robust model, the LSTM RNN's, which are proposed by several studies as a suitable deep learning technique for time series modeling.Various configurations of LSTM RNN's were constructed to implement single-step and multi-step oil rate forecasting and down-hole pressure was incorporated to the inputs.For testing the robustness of the proposed models, we use four different datasets, three of them synthetically generated and one from a public real dataset, the Volve oil field, as a case study to conduct the experiments.The results indicate that the Random Forest model could sufficiently estimate the one-time step of the oil field production, and LSTM could handle more inputs and adequately estimate multiple-time steps of oil production.",
            "source_url": "https:\/\/openalex.org\/W4381617672",
            "pdf_url": "https:\/\/www.maxwell.vrac.puc-rio.br\/62916\/62916.PDF",
            "doi": "10.17771\/pucrio.acad.62916",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W4381617672",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:01",
            "updated_at": "2026-11-15 16:53:01"
        },
        {
            "id": 1344,
            "title": "High-variance multivariate time series forecasting using machine learning",
            "title_ar": "",
            "title_en": "High-variance multivariate time series forecasting using machine learning",
            "author": "N. Katardjiev",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "KTH Royal Institute of Technology",
            "university_source": "repository_host",
            "college": "",
            "department": "",
            "country": "Sweden",
            "governorate": "",
            "publication_year": 2018,
            "language": "en",
            "research_domain": "medical-ai",
            "research_subdomain": "rnn",
            "categories": "machine-learning; deep-learning; medical-ai; predictive-analytics; rnn",
            "keywords": "",
            "abstract": "There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-specific transformations and considerations are usually required. This research aims to examine the issue by modeling four types of machine- and deep learning algorithms - support vector machine, random forest, feed-forward neural network, and a LSTM neural network - on a high-variance, multivariate time series to forecast trend changes one time step in the future, accounting for lag.The models were trained on clinical trial data of patients in an alcohol addiction treatment plan provided by a Uppsala-based company. The results showed moderate performance differences, with a concern that the models were performing a random walk or naive forecast. Further analysis was able to prove that at least one model, the feed-forward neural network, was not undergoing this and was able to make meaningful forecasts one time step into the future. In addition, the research also examined the effec tof optimization processes by comparing a grid search, a random search, and a Bayesian optimization process. In all cases, the grid search found the lowest minima, though its slow runtimes were consistently beaten by Bayesian optimization, which contained only slightly lower performances than the grid search.",
            "source_url": "https:\/\/openalex.org\/W2995738585",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "KTH Publication Database DiVA (KTH Royal Institute of Technology)",
            "source_record_id": "W2995738585",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:01",
            "updated_at": "2026-11-15 16:53:01"
        },
        {
            "id": 1374,
            "title": "Ad Campaigns Generation Using Deep Learning Approach",
            "title_ar": "Ad Campaigns Generation Using Deep Learning Approach",
            "title_en": "",
            "author": "Atef Ahmed",
            "supervisor": "",
            "degree_type": "unspecified",
            "degree_evidence": "",
            "university": "",
            "university_source": "",
            "college": "",
            "department": "",
            "country": "",
            "governorate": "",
            "publication_year": 2019,
            "language": "ar",
            "research_domain": "deep-learning",
            "research_subdomain": "rnn",
            "categories": "deep-learning; rnn",
            "keywords": "",
            "abstract": "الحملة الإعلانية عبارة عن مجموعة من الرسائل الإعلانية التي تشارك نفس الفكرة والتي تحدد الرسالة الترويجية للعلامات التجارية والمنتجات والخدمات. الحملات عادة تستخدم عدة وسائط لترويج المنتجات والخدمات مثل النصوص أو الصور والفيديو. الحملات النصية تستخدم في محركات البحث مثل جوجل وبينج (Bing) , بينما حملات الصور والفيديو تستخدم في منصات الشبكات الاجتماعية واليوتيوب. يعد إنشاء إعلان نصي يدويا تحديا كبيرا ويحتاج إلى خبير لإنشاء هذا الإعلان الذي يجب أن يستهدف العملاء. وهذا يتطلب الكثير من الوقت والمال والجهد لأنه يتعين على الخبير إنشاء واختبار حملات نصية عديدة تتطابق مع اهتمام واحتياجات العميل. بالإضافة إلى ذلك، فإن بعض الشركات لديها فهم محدود لعملائها بسبب أن بيانات العملاء مبعثرة في أماكن متعددة مثل رسائل البريد الإلكتروني وأوامر الشراء، لذلك، تحتاج الشركات إلى فهم شامل لاتخاذ الإجراءات والقرارات في الوقت المناسب. في هذا البحث، قمنا بتطوير نظام قادر على توليد حملات إعلانية نصية لمحركات البحث مثل Google وBing استنادا إلى الكلمات الرئيسية التي يبحث عنها العميل. التقنية الخاصة بنا تتكون من أربع مراحل رئيسية. المرحلة الأولى هي جمع بيانات عالية الجودة، أما المرحلة الثانية تتضمن معالجة البيانات، والمرحلة الثالثة هي تطوير نظام إنشاء حملات الإعلانات باستخدام الشبكة العصبية المتكررة (RNN) والتعلم العميق. هذا العمل يختبر معماريات مختلفة مثل LSTM وGRU. المرحلة الأخيرة في هذا البحث هي التقييم. قمنا بإجراء العديد من التجارب لتحديد أفضل نموذج وذلك بالتحقق لأقل قيمة للخطأ أثناء عميلة التعلم والـ Perplexity (PPL) للنص الذي تم إنشاؤه. يتم تقييم النتائج باستخدام مقياسين وهما قابلية القراءة للنص المولد مدى علاقته بالكلمات المفتاحية. تم تقييم مقياس قابلية القراءة بواسطة مختصين بشريين ومكتبة برمجية، في حين تم تقييم علاقة النص المولد بالكلمات المفتاحية فقط بواسطة تقييم بشري. يظهر نتائج التقييم أن النصوص التي تم إنشاؤها بواسطة الشبكة العصبية العميقة (DNN) على الأغلب سهلة القراءة ولها علاقة بالكلمات المفتاحية.",
            "source_url": "https:\/\/openalex.org\/W2980533915",
            "pdf_url": "",
            "doi": "",
            "handle": "",
            "repository_name": "",
            "source_record_id": "W2980533915",
            "other_sources": "",
            "verification_status": "needs_review",
            "created_at": "2026-11-15 16:53:01",
            "updated_at": "2026-11-15 16:53:01"
        }
    ]
}