Federated learning is the process of developing machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …

Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …

Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the
field of ocular imaging over the last few years. Specifically, DL has been utilised to detect …

Purpose This study aims to summarize the critical issues in medical federated learning and
applicable solutions. Also, detailed explanations of how federated learning techniques can …

Public health emergencies have brought great challenges to the stability of the e-commerce
supply chain. Demand forecasting is a key driver for the sound development of e-commerce …

Objective. To review the progress of research on photoplethysmography‐(PPG‐) based
cuffless continuous blood pressure monitoring technologies and prospect the challenges …

Blood pressure (BP) determines whether a person has hypertension and offers implications
as to whether he or she could be affected by cardiovascular disease. Cuff-based …

Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data
privacy, existing passive mental health monitoring systems use alternative features such as …

Cardiovascular diseases are the leading causes of death, and blood pressure (BP)
monitoring is essential for prevention, diagnosis, assessment, and treatment …