Feature Selection (FS) is a special preprocessing step in Machine Learning (ML) that
reduces the number of unwanted features in datasets to increase the accuracy of ML …

Feature selection plays a pivotal role in preprocessing data for machine learning (ML)
models. It entails choosing a subset of pertinent features to enhance the model's accuracy …

The exponential growth of digital text documents presents a significant challenge for text
classification algorithms, as the vast number of words in each document can hinder their …

The patient admission scheduling problem (PASP) has been studied for many years as one
of the most important scheduling problems in the health sector. The primary goal of PASP is …

Abstract Machine learning (ML) and deep learning (DL) techniques have demonstrated
significant potential in the development of effective intrusion detection systems. This study …

The development of new algorithms can aid researchers and professionals in resolving
problems that were once unsolvable or discovering superior solutions to problems that were …

A random initialization of the search particles is a strong argument in favor of the deployment
of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is …

Feature selection (FS) is a crucial data preprocessing technique that selects important
features to enhance learning efficiency, yet it encounters challenges due to the high …

Abstract Currently, the Internet of Things (IoT) generates a huge amount of traffic data in
communication and information technology. The diversification and integration of IoT …

Abstract Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition
marked by challenges in social communication, sensory processing, and behavioral …