Search for dissertations about: "semi-supervised learning"
Showing result 1 - 5 of 22 swedish dissertations containing the words semi-supervised learning.
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1. Semi-supervised learning with self-supervision for closed and open sets
Abstract : Semi-supervised learning (SSL) is a learning framework that enables the use of unlabeled data with labeled data. These methods play a crucial role in reducing the burden of human labeling in training deep learning models. Many methods for SSL learn from unlabeled data through confidence-based pseudo-labeling. READ MORE
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2. Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things : Enhancing COVID-19 & Early Sepsis Detection
Abstract : This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection.It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. READ MORE
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3. Deep learning for news topic identification in limited supervision and unsupervised settings
Abstract : In today's world, following news is crucial for decision-making and staying informed. With the growing volume of daily news, automated processing is essential for timely insights and in aiding individuals and corporations in navigating the complexities of the information society. READ MORE
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4. Machine Learning Methods for Image Analysis in Medical Applications, from Alzheimer's Disease, Brain Tumors, to Assisted Living
Abstract : Healthcare has progressed greatly nowadays owing to technological advances, where machine learning plays an important role in processing and analyzing a large amount of medical data. This thesis investigates four healthcare-related issues (Alzheimer's disease detection, glioma classification, human fall detection, and obstacle avoidance in prosthetic vision), where the underlying methodologies are associated with machine learning and computer vision. READ MORE
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5. Adapting Deep Learning for Microscopy: Interaction, Application, and Validation
Abstract : Microscopy is an integral technique in biology to study the fundamental components of life visually. Digital microscopy and automation have enabled biologists to conduct faster and larger-scale experiments with a sharp increase in the data generated. READ MORE