Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …

Image processing technology has always been a hot and difficult topic in the field of artificial
intelligence. With the rise and development of machine learning and deep learning …

Feature selection aims at finding the minimum number of features that result in high
classification accuracy. Accordingly, the feature selection is considered as a multi-objective …

Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms
that has proven to be work more effectively in several challenging optimization tasks …

The feature selection problem is one of the pre-processing mechanisms to find the optimal
subset of features from a dataset. The search space of the problem will exponentially grow …

Feature selection is a vital pre-processing phase for most machine learning and data mining
courses. This article proposes new variants of the Harris hawk optimization called memory …

The rapid growth in biomedical datasets has generated high dimensionality features that
negatively impact machine learning classifiers. In machine learning, feature selection (FS) is …

Electromyography (EMG) processing is a fundamental part of medical research. It offers the
possibility of develo** new devices and techniques for the diagnosis, treatment, care, and …

Computations that mimic nature are known as nature-inspired computing. Nature presents a
wealthy source of thoughts and ideas for computing. The use of natural galvanized …

Due to the lack of experience and prior knowledge, the selection of the most informative
features has become one of the challenging problems in many applications. Recently, many …