Metaheuristic algorithms are widely known as efficient solutions for solving problems of
optimization. These algorithms supply powerful instruments with significant engineering …

Fault detection, isolation and optimal control have long been applied to industry. These
techniques have proven various successful theoretical results and industrial applications …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

There are several types of neural networks (NNs) that are widely used for data classification
tasks. The supervised learning NN is an advanced network with a training algorithm for …

In this manuscript, a new quantum computing-based optimization algorithm is proposed to
solve multiple-objective mixed cost-effective emission dispatch (MEED) problem of electrical …

In recent years, Quantum Computing witnessed massive improvements in terms of available
resources and algorithms development. The ability to harness quantum phenomena to solve …

Power generation system largely depends on fossil fuels to generate electricity. Due to
various reasons, the reserves of fossil fuels are declining and will become too expensive in …

Pattern classification represents a challenging problem in machine learning and data
science research domains, especially when there is a limited availability of training samples …

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to
find a way to reduce the data dimensionality. Gene selection problem has a large number of …

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf
optimizer (GWO) has a wide range of applications in practical problems. As a kind of local …