Modern artificial intelligence (AI) has achieved success by learning statistical patterns from data and framing many problems as prediction tasks. However, prediction alone cannot answer questions such as why an outcome occurred or how an intervention would ch…
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 struc…
The study of nuclear materials is strongly complemented by computational materials science (CMS), which provides an alternative means of investigating radiation-induced phenomena, defect evolution, and thermophysical properties—all of which can be hazardous, t…
This thesis investigates how fairness is addressed within machine learning pipelines and proposes a framework for adversarial fairness testing through a human-in-the-loop approach. Although several fairness metrics and mitigation techniques have been proposed,…
s systèmes d’apprentissage automatique (AA), y compris les modèles classiques, les réseaux de neurones profonds (DNN) et les grands modèles de langage (LLMs), sont de plus en plus déployés dans des domaines à enjeux élevés tels que la santé, le recrutement, la…
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 scenari…
Modern machine learning (ML) systems are increasingly required to operate in dynamic environments, where models must adapt to new information, remove obsolete or sensitive knowledge, and satisfy fairness and safety requirements. This thesis addresses these cha…
This thesis explores the challenges presented by label noise in machine learning (ML) for health. Label noise is unavoidable in real-world data; it can arise from variability in measurement, human annotation errors, and aleatoric uncertainty in the ground trut…
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in natural language processing (NLP) and software engineering (SE). However, their increasing adoption has raised critical concerns about trustworthiness, inclu…
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 out…
In this information age, machine learning (ML) applications drive smart living through innovations such as personalized healthcare, intelligent transportation, and smart homes. However, despite these advancements, businesses and industries continue to face sig…
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 effectiv…
أنا مساعد المستودع. اسألني عن الرسائل والأطاريح حسب الموضوع أو الجامعة أو الدولة أو السنة أو الدرجة، أو اطلب أعداداً وإحصاءات.
يجيب من قاعدة بيانات المستودع فقط، ولا يخترع سجلات أو أرقاماً. لا يُحفظ ما تكتبه على الخادم.
قد يخطئ المساعد أحياناً، فراجع المصادر المرفقة.
