Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …

Collective behavior provides a framework for understanding how the actions and properties
of groups emerge from the way individuals generate and share information. In humans …

High-throughput next-generation sequencing now makes it possible to generate a vast
amount of multi-omics data for various applications. These data have revolutionized …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

It is important to detect breast cancer as early as possible. In this manuscript, a new
methodology for classifying breast cancer using deep learning and some segmentation …

Abstract Human Action Recognition (HAR) is a challenging task used in sports such as
volleyball, basketball, soccer, and tennis to detect players and recognize their actions and …

The objective measurement of fatigue is of critical relevance in areas such as occupational
health and safety as fatigue impairs cognitive and motor performance, thus reducing …

Background: Technology has multiple potential applications to dementia from diagnosis and
assessment to care delivery and supporting ageing in place. Objectives: To summarise key …

In general, most of the existing convolutional neural network (CNN)-based deep-learning
models suffer from spatial-information loss and inadequate feature-representation issues …

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of
milliseconds, suggesting it may provide a new avenue for studying subsecond neural …