Feature selection has gained much consideration from scholars working in the domain of
machine learning and data mining in recent years. Feature selection is a popular problem in …

Feature selection is imperative in machine learning and data mining when we have high-
dimensional datasets with redundant, nosy and irrelevant features. The area of feature …

A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization
(PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO …

In data mining and machine learning, feature selection is a critical part of the process of
selecting the optimal subset of features based on the target data. There are 2n potential …

The research presented in this manuscript proposes a novel Harris Hawks optimization
algorithm with practical application for evolving convolutional neural network architecture to …

Feature selection or dimensionality reduction can be considered as a multi-objective
minimization problem with two objectives: minimizing the number of features and minimizing …

This study presents an extensive analysis of ten emerging swarm intelligence metaheuristic
techniques, namely Emperor Penguins Colony (EPC), Harris Hawks Optimizer (HHO) …

The abundance of artificial intelligence AI algorithms and growing computing power has
brought a disruptive revolution to the smart medical industry. Its powerful data abstraction …

Prediction and early detection of diseases have been an important field of research for a
long time to diagnose any disease. Machine‐learning (ML) algorithms have proved quite …

In voice pathology detection system, machine learning algorithms play an important role in
the classification process. Most of the developed systems in voice pathology detection used …