Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …

Machine learning is an expanding field with an ever-increasing role in everyday life, with its
utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this …

In this study, a weighted federated learning approach is proposed for electrocardiogram
(ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

Federated learning methods offer secured monitor services and privacy-preserving
paradigms to end-users and organisations in the Internet of Things networks such as smart …

In the modern interconnected world, intelligent networks and computing technologies are
increasingly being incorporated in industrial systems. However, this adoption of advanced …

Artificial intelligence and machine learning have recently attracted considerable attention in
the healthcare domain. The data used by machine learning algorithms in healthcare …

Existing domain adaptation (DA) methods typically require access to source domain data,
which raises privacy concerns due to the sensitive information contained in …

Technological advances in smart devices and applications targeting the Internet of
Healthcare Things provide a perfect environment for using Machine Learning-based …

To identify epilepsy, Electroencephalography (EEG) is an important and common tool used
to study the electrical activity of the human brain. The machine learning-based classifier is …