Background and objectives Artificial intelligence (AI) has branched out to various
applications in healthcare, such as health services management, predictive medicine …

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …

Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI systems are …

Simple Summary For automated cancer diagnosis on medical imaging, explainable artificial
intelligence technology uses advanced image analysis methods like deep learning to make …

Communicable diseases are transmitted through water, food, contaminated surfaces, bodily
fluids, air. In such a situation, staying in home isolation for fewer chronic health problems …

This article unravels the intricate connections between climate change, food systems, and
human health, offering a comprehensive exploration within a concise framework focused on …

Abstract Machine learning is fast becoming one of the central solutions to various real-world
problems. Thanks to powerful hardware and large datasets, training a machine learning …

Background/Objectives: Stroke stands as a prominent global health issue, causing con-
siderable mortality and debilitation. It arises when cerebral blood flow is compromised …

Background: Machine learning approaches, including deep learning, have demonstrated
remarkable effectiveness in the diagnosis and prediction of diabetes. However, these …

Machine Learning models have been frequently used in transcriptome analyses.
Particularly, Representation Learning (RL), eg, autoencoders, are effective in learning …