Feature selection (FS) is a popular data pre-processing technique in machine learning to
extract the optimal features to maintain or increase the classification accuracy of the dataset …

This paper proposes a hybrid sine cosine butterfly optimization algorithm (m-SCBOA), in
which a modified butterfly optimization algorithm is combined with sine cosine algorithm to …

This study aims to use a machine learning (ML)-based enhanced diagnosis and survival
model to predict heart disease and survival in heart failure by combining the cuckoo search …

Feature selection (FS) methods play essential roles in different machine learning
applications. Several FS methods have been developed; however, those FS methods that …

Nowadays, because of the enormous increase in load demand, the electrical distribution
system faces problems like poor system efficiency due to high I 2 R losses and poor voltage …

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for
feature selection is proposed. DMO is a novel technique of the swarm intelligence …

Abstract According to the World Health Organization, millions of infections and a lot of
deaths have been recorded worldwide since the emergence of the coronavirus disease …

Feature selection is one of the main issues in machine learning algorithms. In this paper, a
new binary hyper-heuristics feature ranks algorithm is designed to solve the feature …

Aquila Optimization (AO) is a recently proposed meta-heuristic algorithm, which has been
proved to be more competitive than other meta-heuristic algorithms in function optimization …

In data mining, feature selection (FS) has become a significant data pre-processing tool that
maximises the model's generalisation and minimises the feature size. Due to the large …