Feature selection is becoming a relevant problem within the field of machine learning. The
feature selection problem focuses on the selection of the small, necessary, and sufficient …

Artificial Intelligence techniques, such as optimization algorithms, have become essential for
success in many fields. Therefore, most researchers, especially in computer and …

Datasets comprise a collection of features; however, not all of these features may be
necessary. Feature selection is the process of identifying the most relevant features while …

This study presents a comparative analysis of four Machine Learning (ML) models used to
map wildfire susceptibility on Hawaiʻi Island, Hawaiʻi. Extreme Gradient Boosting …

People around the globe are suffering from different types of brain tumors. So, early
prediction of brain tumors can save human lives. This work focused on implementing a …

To address the challenge of distinguishing the health status of bearings, in this paper, a
health index (HI) is developed through utilization of the multiple target time-varying black …

The study explores the application of convolutional neural networks (CNN) trained with
black widow optimization (BWO) algorithms to enhance eco-friendly wastewater treatment …

Feature selection is the process of extracting an optimal subset feature from a primary
feature set to minimize data dimensionality. The hybrid metaheuristic is the most common …

Seeing something as reality (VR) is the term used to describe the visual perception of virtual
reality, general assembly designs, patterns, and their conversion into part entities. In this …

Recent technological advances in medical diagnosis have led to the generation of high-
dimensional datasets. The presence of redundant and irrelevant features in these datasets …