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Scrivener Publishing100 Cummings Center, Suite 541J Beverly, MA 01915-6106

Artificial Intelligence and Soft Computing for Industrial Transformation

Series Editor: Dr S. Balamurugan (sbnbala@gmail.com)

Scope: Artificial Intelligence and Soft Computing Techniques play an impeccable role in industrial transformation. The topics to be covered in this book series include Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Fuzzy Logic, Genetic Algorithms, Particle Swarm Optimization, Evolutionary Algorithms, Nature Inspired Algorithms, Simulated Annealing, Metaheuristics, Cuckoo Search, Firefly Optimization, Bio-inspired Algorithms, Ant Colony Optimization, Heuristic Search Techniques, Reinforcement Learning, Inductive Learning, Statistical Learning, Supervised and Unsupervised Learning, Association Learning and Clustering, Reasoning, Support Vector Machine, Differential Evolution Algorithms, Expert Systems, Neuro Fuzzy Hybrid Systems, Genetic Neuro Hybrid Systems, Genetic Fuzzy Hybrid Systems and other Hybridized Soft Computing Techniques and their applications for Industrial Transformation. The book series is aimed to provide comprehensive handbooks and reference books for the benefit of scientists, research scholars, students and industry professional working towards next generation industrial transformation.

Publishers at Scrivener Martin Scrivener ( martin@scrivenerpublishing.com) Phillip Carmical ( pcarmical@scrivenerpublishing.com)

Edited by

S. Balamurugan,

Anupriya Jain,

Sachin Sharma,

Dinesh Goyal,

Sonia Duggal

and

Seema Sharma

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

© 2022 Scrivener Publishing LLC

For more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-68174-8

Cover image: Pixabay.ComCover design Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book was designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide. A brief description of the topics covered in each chapter is given below.

–In Chapter 1, “Introduction to Nature-Inspired Computing,” Dr. N. M. Saravana Kumar, K. Hariprasath, N. Kaviyavarshini and A. Kavinya introduce a new discipline that strives to develop new computing techniques through observing how naturally occurring phenomena behave to solve complex problems in environmental situations. Characterization of nature-inspired algorithms are also discussed.

–In Chapter 2, “Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning,” Dr. P. Mary Jeyanthi and Dr. A. Mansurali introduce various hybridized algorithms in the field of machine learning (ML) along with their applications. This chapter emphasizes the characteristics of a genetic algorithm (GA) which helps machine learning in GA’s consideration of genes (variables).