Srinivas Mettu - Machine Learning Algorithms and Applications

Scrivener Publishing
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Sustainable Computing and Optimization

The objective of the series is to bring together the global research scholars, experts, and scientists in the research areas of sustainable computing and optimization from all over the world to share their knowledge and experiences on current research achievements in these fields. The series aims to provide a golden opportunity for the global research community to share their novel research results, findings, and innovations to a wide range of readers. Data is everywhere and continuing to grow massively, which has created a huge demand for qualified experts who can uncover valuable insights from the data. The series will promote sustainable computing and optimization methodologies in order to solve real life problems mainly from engineering and management systems domains. The series will mainly focus on the real-life problems, which can suitably be handled through these paradigms.

Submission to the series:

Dr. Prasenjit Chatterjee
Department of Mechanical Engineering,
MCKV Institute of Engineering, Howrah - 711204, West Bengal, India
E-mail:

Dr. Morteza Yazdani
Department of Management, Universidad Loyola Andalucia, Seville, Spain
E-mail:

Dr. Dilbagh Panchal
Department of Industrial and Production Engineering,
Dr. B. R. Ambedkar National Institute of Technology (NIT) Jalandhar, Punjab, India
E-mail:

Publishers at Scrivener
Martin Scrivener ()
Phillip Carmical ()

Edited by

Mettu Srinivas,

National Institute of Technology, Warangal, India

G. Sucharitha,

Institute of Aeronautical Engineering, Hyderabad, India

Anjanna Matta

Faculty of Science and Technology, IFHE, Hyderabad, India

and

Prasenjit Chatterjee

MCKV Institute of Engineering, Howrah, India

This edition first published 2021 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

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-76885-2

Cover image: Pixabay.Com

Cover design by Russell Richardson

This book is based on research conducted at various leading technical institutions on different topics regarding the machine and deep learning platforms used throughout India. We are grateful to the many authors and co-authors who contributed their research work for use in the successful completion of this book. We are also grateful to our colleagues for their encouragement to start the process of working on the book, persevere throughout it, and finally publish it.

We gratefully acknowledge NIT Warangal and the Faculty of Science & Technology, ICFAI Foundation for Higher Education, Hyderabad, for providing the use of their facilities to carry out the work on this book. Finally, we would like to acknowledge with gratitude, the support and love from our parents, life partners and children.

Nowadays, machine learning has become an essential part of many commercial and industrial applications and research developments. It has expanded its roots in areas ranging from automatic medical diagnosis in healthcare to product recommendations in social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we try to show you how you yourself can easily adopt machine learning to build solutions for small applications and the best way to go about it. With the knowledge presented herein, you can build your own system to find the faults in a companys manufactured products and the fake profiles in social networks. This book clearly explains the various applications of machine and deep learning for use in the medical field, animal classification, gene selection from microarray gene expression data, sentiment analysis, fake profile detection in social media, farming sectors, etc.

For the ambitious machine learning specialists of today who are looking to implement solutions to real-world machine learning problems, this book thoroughly discusses the various applications of machine and deep learning techniques. Each chapter deals with the novel approach of machine learning architecture for a specific application and its results, including comparisons with previous algorithms. In order to present a unified treatment of machine learning problems and solutions, many methods based in different fields are discussed, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining. Furthermore, all learning algorithms are explained in a way that makes it easy for students to move from the equations in the book to a computer program.

The Editors

June 2020