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Visar resultat 1 - 5 av 136 avhandlingar som matchar ovanstående sökkriterier.
1. Visual Analytics for Explainable and Trustworthy Machine Learning
Sammanfattning : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. LÄS MER
2. Machine Learning for Wireless Link Adaptation : Supervised and Reinforcement Learning Theory and Algorithms
Sammanfattning : Wireless data communication is a complex phenomenon. Wireless links encounter random, time-varying, channel effects that are challenging to predict and compensate. Hence, to optimally utilize the channel, wireless links adapt the data transmission parameters in real time. LÄS MER
3. Automated Malware Detection and Classification Using Supervised Learning
Sammanfattning : Malware has been one of the key concerns for Information Technology security researchers for decades. Every year, anti-malware companies release alarming statistics suggesting a continuous increase in the number and types of malware. LÄS MER
4. Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things : Enhancing COVID-19 & Early Sepsis Detection
Sammanfattning : 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. LÄS MER
5. Self-supervised Representation Learning for Visual Domains Beyond Natural Scenes
Sammanfattning : This thesis investigates the possibility of efficiently adapting self-supervised representation learning on visual domains beyond natural scenes, e.g., medical imagining and non-RGB sensory images. LÄS MER