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Secure multi-party computation for privacy-preserving data mining – Complete Phd and Masters Thesis

[ad_1] Introduction 1.1 Introduction 1.2 Background of study 1.3 Problem Statement 1.4 Objective of study 1.5 Limitation of study 1.6 Scope of study 1.7 Significance of study 1.8 Structure of the Thesis 1.9 Definition of…

Explainable deep learning for autonomous driving – Complete Phd and Masters Thesis

[ad_1] Introduction Autonomous driving is an emerging technology that has the potential to revolutionize transportation by making vehicles safer, more efficient, and convenient. Deep learning, a subset of artificial intelligence, has shown great promise in…

Quantum-resistant key exchange protocols – Complete Phd and Masters Thesis

[ad_1] Introduction The rise of quantum computing technology poses a significant threat to the security of current key exchange protocols used in cryptographic systems. Quantum computers have the potential to break traditional encryption schemes, rendering…

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) for scalable blockchain – Complete Phd and Masters Thesis

[ad_1] Introduction: Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) have emerged as a powerful tool for enhancing the scalability and privacy of blockchain networks. These cryptographic protocols allow for the verification of computations without revealing…

Multi-modal learning for affective computing – Complete Phd and Masters Thesis

[ad_1] Introduction: Multi-modal learning for affective computing is a rapidly growing field that aims to enhance the capabilities of computer systems to recognize, interpret, and respond to human emotions through multiple modalities such as facial…

Interpretable machine learning for clinical decision support – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, machine learning algorithms have shown significant potential in providing decision support for various domains, including healthcare. However, the black-box nature of many machine learning models often poses challenges in their…

Differential privacy for federated analytics – Complete Phd and Masters Thesis

[ad_1] Introduction Differential privacy has emerged as a promising technique for protecting the privacy of individuals in datasets while allowing for meaningful analysis to be conducted. With the rise of federated analytics, which involves the…

Zero-shot learning for open-domain question answering – Complete Phd and Masters Thesis

[ad_1] Introduction Zero-shot learning (ZSL) is a challenging task in the field of artificial intelligence and machine learning, where the goal is to develop models that can answer questions on unseen topics without any prior…

Federated reinforcement learning for autonomous drone swarms – Complete Phd and Masters Thesis

[ad_1] Introduction Autonomous drone swarms have gained significant attention in various fields such as surveillance, search and rescue operations, and environmental monitoring. However, the effective coordination and collaboration among drones in a swarm pose a…

Transfer learning for few-shot image classification – Complete Phd and Masters Thesis

[ad_1] Introduction Transfer learning has gained significant attention in the field of deep learning due to its ability to leverage knowledge gained from one task to improve learning performance on another related task. This is…

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