The advent of Large Language Models (LLMs) has significantly influenced the field of artificial intelligence, offering remarkable text generation capabilities through their vast number of parameters.These advancements have established new benchmarks across var…
Autonomous vehicles are gaining popularity rapidly for transportation safety and efficiency. However, to achieve fully automated driving without human intervention, cars need to be aware of traffic and environmental dynamics. One key component of their awarene…
With federated learning, local learners train a shared global model using their own data, and report model updates to a server to aggregate and then update the global model. Such a learning paradigm may suffer from two attacks: privacy attacks by the untrusted…
Machine Learning (ML) has revolutionized various fields, from disease prediction to credit risk evaluation, by harnessing abundant data scattered across diverse sources. However, transporting data to a trusted server for centralized ML model training is not on…
Federated learning, while promising for its ability to train models across decentralized data sources, presents both security challenges and solutions.Its security depends on how well these challenges are addressed.On one hand, federated learning can be secure…
In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To p…
The growing population around the globe has a significant impact on various sectors including the labor force, healthcare, and the global economy. The healthcare sector is among the most affected sectors by the growing population due to the increasing demand f…
Quantifying the inherent uncertainty in an automated medical image analysis is crucial to guarantee the safe deployment of deep learning models. However, these models, often referred to as black boxes, are known to produce errors with high confidence, potentia…
With the increasing ubiquity of Artificial Intelligence (AI) in daily life and into more critical applications, explainability has been brought under the spotlight due to ethical, social, and legal concerns. Explainability is particularly important in applicat…
Mutual learning is an emerging field in intelligent systems which takes inspiration from naturally intelligent agents and attempts to explore how agents can communicate and cooperate to share information and learn more quickly. While agents in many biological…
Digital twinning has enabled the real-time usage of sensor readings from a variety of systems, such as industrial machines and medical patients.These time-series data offer opportunities to accurately predict the system's future behaviour, e.g. by predicting w…
Human action recognition from videos has a wide range of applications, including video surveillance and security, human-computer interaction, robotics, health care, etc. Nowadays, 3D skeleton-based action recognition has drawn increasing attention thanks to th…
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قد يخطئ المساعد أحياناً، فراجع المصادر المرفقة.
