Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics,
cognitive psychology, decision theory, neuroscience, linguistics, cybernetics, and computer …

The aim of this paper is to present the use of an innovative approach based on MCDM
methods as the main component of a consumer Decision Support System (DSS) by …

The future of work embodies changes to the workplace, work, and workforce, which require
additional occupational safety and health (OSH) stakeholder attention. Examples include …

The increasing availability of large collections of electronic health record (EHR) data and
unprecedented technical advances in deep learning (DL) have sparked a surge of research …

Established approaches to assuring safety-critical systems and software are difficult to apply
to systems employing ML where there is no clear, pre-defined specification against which to …

The data-driven identification of disease states and treatment options is a crucial challenge
for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing …

Accurate sleep stage classification is significant for sleep health assessment. In recent
years, several machine-learning based sleep staging algorithms have been developed, and …

Counterfactual explanations (CEs) are a practical tool for demonstrating why machine
learning classifiers make particular decisions. For CEs to be useful, it is important that they …

Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation
and, specifically, artificial ambient intelligence with individualisation to support engagement …