The privacy-focused concept of Federated Learning (FL) allows local data processing
without disclosing patients' health details to a central server. However, its vulnerability to …

The next-generation wireless network is expected to be highly dense with a large number of
heterogeneous Internet of Things (IoT) devices, working in co-operation with various Small …

In modern healthcare monitoring, wearable sensors play a crucial role in collecting patient
data, especially for individuals with disabilities. However, analyzing this data presents …

In an emergency healthcare situation, delay between injury and treatment is one of the most
critical parameters with regard to survivability. Reduction in diagnosis/pre-treatment time by …

This work explores the coupling of Machine Learning (ML) and Wireless Body Area Network
(WBAN) data to develop highly effective models. To support resource-constrained WBANs …

The integration of cloud computing (CC) and Internet of Things (IoT) technologies in the
healthcare industry has significantly boosted the importance of real-time remote patient …

Contact tracing has become a widely adopted strategy to curb the spread of COVID-19,
enabling identification, evaluation, and management of individuals exposed to the virus …

Federated Learning (FL) enables local model training on devices while collaboratively
updating a global model on a server, ensuring user data privacy by keeping it on the device …

The rapid development of Wireless Body Area Network (WBAN) based remote healthcare
system allows real-time patient health monitoring. However, during emergency situations …

In an emergency healthcare situation, the time delay between injury and treatment is crucial
for patient survival. Real-time processing of ambulance data can significantly reduce …