Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …

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 …

We are living in an era of big data, where the process of generating data is continuously
been taking place with each coming second. Data that is more varied and extremely …

BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …

Background An appropriate sample size is essential for obtaining a precise and reliable
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …

Abstract The emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-
2) in China at December 2019 had led to a global outbreak of coronavirus disease 2019 …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

Abstract Developing Big Data applications has become increasingly important in the last few
years. In fact, several organizations from different sectors depend increasingly on …

Electroencephalogram (EEG) signals are progressively growing data widely known as
biomedical big data, which is applied in biomedical and healthcare research. The …

In the past, data in which science and engineering is based, was scarce and frequently
obtained by experiments proposed to verify a given hypothesis. Each experiment was able …