For healthcare systems utilizing Wireless Body Area Networks (WBANs), maintaining the
network's diverse Quality of Service (QoS) metrics necessitates effective communication …

The precise classification of medical images is crucial in various healthcare applications,
especially in fields like disease diagnosis and treatment planning. In recent times, machine …

Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms
that can effectively work with decentralized data sources (eg, hospitals) without acquiring …

In healthcare, the primary objective is to improve the individuals' well-being by addressing a
range of health issues through accurate disease detection. Central to this process is …

With the arrival of Artificial Intelligence (AI), the Internet of Things (IoT), machine learning,
and deep learning methodologies, data-driven medical applications have appeared as a …

In recent years, machine learning has made significant progress. Federated learning is one
of the new techniques of machine learning, but the estimated costs of implementing it stand …

Fading channels are a common problem in real-world wireless communication
environments, causing signal deterioration over the propagation path. This study portrays …

In the domain of smart health devices, the accurate detection of physical indicators levels
plays a crucial role in enhancing safety and well-being. This paper introduces a cross device …

In the era of pervasive computing, fog computing has emerged as a paradigm that extends
cloud services to the edge of the network, enabling real-time data processing and reducing …

Анотація Набув подальшого розвитку метод оптимізації IoT інфраструктури із
застосуванням туманних обчислень, який на відміну від відомих методів здійснює …