Over the past few years, neural networks have exhibited remarkable results for various
applications in machine learning and computer vision. Weight initialization is a significant …

Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation (BP) based …

The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …

Abstract Underwater Image Enhancement (UIE) is a crucial preprocessing step for
underwater vision tasks. Addressing the challenge of training supervised deep learning …

Prediction of multi-dimensional time-series data, which may represent such diverse
phenomena as climate changes or financial markets, remains a challenging task in view of …

Wind power can be considered one of the most important green sources of electric power.
The prediction of wind power is necessary to boost the power grid operations' efficiency and …

Solving computational fluid dynamics problems requires using large computational
resources. The computational time and memory requirements to solve realistic problems …

Crowd stampedes and incidents are critical threats to public security that have caused
countless deaths during the past few decades. To avoid crowd stampedes, real-time crowd …

Neural Style Transfer (NST) is a class of software algorithms that allows us to transform
scenes, change/edit the environment of a media with the help of a Neural Network. NST …

Most popular recommender systems learn the embedding of users and items through
capturing valuable information from user–item interactions or item knowledge graph (KG) …