Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …

Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …

We have recently seen many successful applications of recurrent neural networks (RNNs)
on electronic medical records (EMRs), which contain histories of patients' diagnoses …

The use and creation of machine‐learning‐based solutions to solve problems or reduce
their computational costs are becoming increasingly widespread in many domains. Deep …

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Explainability and interpretability are two critical aspects of decision support systems.
Despite their importance, it is only recently that researchers are starting to explore these …

In recent years, with the rapid development of deep learning technology, great progress has
been made in computer vision, image recognition, pattern recognition, and speech signal …

Neurons are the fundamental units of neural networks. In this paper, we propose a method
for explaining neural networks by visualizing the learning process of neurons. For a trained …