In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world
has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data …

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

Concise and unambiguous assessment of a machine learning algorithm is key to classifier
design and performance improvement. In the multi-class classification task, where each …

Diabetes is a dreadful disease identified by escalated levels of glucose in the blood.
Machine learning algorithms help in identification and prediction of diabetes at an early …

Heart disease, alternatively known as cardiovascular disease, encases various conditions
that impact the heart and is the primary basis of death worldwide over the span of the past …

Nowadays, machine learning algorithms have become very important in the medical sector,
especially for diagnosing disease from the medical database. Many companies using these …

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 …

Being a second most cause of mortality worldwide, cancer has been identified as a perilous
disease for human beings, where advance stage diagnosis may not help much in …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …

Data science is likely to lead to major changes in cardiovascular imaging. Problems with
timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The …