Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …

The use of metaheuristics is widespread for optimization in both scientific and industrial
problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf …

The activity of walking through hilly country for pleasure. He is an avid athlete and loves
mountain walking. Mountaineering is a terrifying quest used for mathematical optimization …

Emotion recognition has shown many valuable roles in people's lives under the background
of artificial intelligence technology. However, most existing emotion recognition methods …

Feature selection (FS), an important pre-processing step in the fields of machine learning
and data mining, has immense impact on the outcome of the corresponding learning …

Abstract Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in …

Recognition of human actions from visual contents is a budding field of computer vision and
image understanding. The problem with such a recognition system is the huge dimensions …

Classification accuracy provided by a machine learning model depends a lot on the feature
set used in the learning process. Feature selection (FS) is an important and challenging …

Classification accuracy achieved by a machine learning technique depends on the feature
set used in the learning process. However, it is often found that all the features extracted by …

DNA microarray experiments generate thousands of gene expression values that provide
information about the state of cells and tissues. Though these expressive values are useful …