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Explainable deep learning for time series forecasting – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, deep learning has become a popular and powerful approach for solving complex machine learning problems, including time series forecasting. However, one of the main challenges with deep learning models is…

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) for privacy-preserving auctions – Complete Phd and Masters Thesis

[ad_1] Introduction Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are powerful cryptographic tools that allow one party to prove to another party the knowledge of certain information without revealing the information itself. This technology has…

Federated transfer learning for cross-device federated optimization – Complete Phd and Masters Thesis

[ad_1] Introduction Federated transfer learning is a cutting-edge technique that allows for the sharing of knowledge across multiple devices while preserving data privacy and security. This emerging field has gained significant attention in recent years…

Quantum machine learning for quantum state preparation – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum machine learning (QML) has emerged as an interdisciplinary field that combines quantum physics and machine learning techniques to solve complex computational problems. One of the key areas of research in QML is…

Quantum-inspired optimization for network resource allocation – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum-inspired optimization has emerged as a promising approach for solving complex optimization problems in various domains. In the context of network resource allocation, the efficient utilization of resources is crucial for ensuring high…

Interpretable machine learning for loan default prediction – Complete Phd and Masters Thesis

[ad_1] Introduction Machine learning algorithms are increasingly being used in a variety of applications, including loan default prediction. However, the black-box nature of many machine learning models makes it challenging to understand the reasons behind…

Differential privacy for privacy-preserving social network analysis – Complete Phd and Masters Thesis

[ad_1] Introduction In today’s digital age, the amount of data generated through social networks is enormous, making it an invaluable resource for researchers in various fields such as sociology, computer science, and economics. However, this…

Zero-shot learning for semantic segmentation – Complete Phd and Masters Thesis

[ad_1] Introduction: Zero-shot learning for semantic segmentation is a cutting-edge research topic in the field of computer vision, with the potential to revolutionize the way we approach image segmentation tasks. Traditional semantic segmentation methods require…

Federated reinforcement learning for multi-robot systems – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, there has been a growing interest in the development of multi-robot systems for various applications such as search and rescue, surveillance, and environmental monitoring. However, coordinating multiple robots to work…

Homomorphic encryption for secure natural language processing – Complete Phd and Masters Thesis

[ad_1] Introduction With the increasing use of cloud computing and outsourced data processing, the need for secure methods of handling sensitive data has become more crucial. Homomorphic encryption is a promising solution that allows for…

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