Healthcare data protection in our mutually connected era has emerged as an issue of serious concern with private patient information, which has been exposed more often due to data violations and cyber-attacks. Network structures CNN and LSTM as part ...
Distributed Collaborative Machine Learning (DCML) offers a promising alternative to address privacy concerns in centralized machine learning. Split learning (SL) and Federated Learning (FL) are two effective learning approaches within DCML. Recently,...
Cyber defense systems face increasingly sophisticated threats that rapidly evolve and exploit vulnerabilities in complex environments. Traditional approaches which often rely on centralized monitoring and static rule-based detection, struggle to adap...
Internal threats are becoming more common in today's cybersecurity landscape. This is mainly because internal personnel often have privileged access, which can be exploited for malicious purposes. Traditional detection methods frequently fail due to ...
Medical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring. However, sharing sensitive raw medical data with third parties for analysi...
The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain ...
The dynamical growth of cyber threats in IoT setting requires smart and scalable intrusion detection systems. In this paper, a Lean-based hybrid Intrusion Detection framework using Particle Swarm Optimization and Genetic Algorithm (PSO-GA) to select ...
Amid substantial capital influx and the rapid evolution of online user groups, the increasing complexity of user behavior poses significant challenges to cybersecurity, particularly in the domain of vulnerability prediction. This study aims to enhanc...
This article examines the importance of patient-centered research in radiology with an emphasis on incorporating the patient perspective to improve patient-reported outcomes (PROs) and research relevance. The methods for effective patient engagement ...
The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitiv...
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