Editors: Prasad Lokulwar, Basant Verma, N. Thillaiarasu, Kailash Kumar, Mahip Bartere, Dharam Singh

​Machine Learning Methods for Engineering Application Development

eBook: US $69 Special Offer (PDF + Printed Copy): US $110
Printed Copy: US $76
Library License: US $276
ISBN: 978-981-5079-19-7 (Print)
ISBN: 978-981-5079-18-0 (Online)
Year of Publication: 2022
DOI: 10.2174/97898150791801220101


This book is a quick review of machine learning methods for engineering applications. It provides an introduction to the principles of machine learning and common algorithms in the first section. Proceeding chapters summarize and analyze the existing scholarly work and discuss some general issues in this field. Next, it offers some guidelines on applying machine learning methods to software engineering tasks. Finally, it gives an outlook into some of the future developments and possibly new research areas of machine learning and artificial intelligence in general.

Techniques highlighted in the book include: Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural network. Finally, it also intends to be a reference book.

Key Features:

  • - Describes real-world problems that can be solved using machine learning
  • - Explains methods for directly applying machine learning techniques to concrete real-world problems
  • - Explains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT).
  • - It does not require prior knowledge of the machine learning

This book is meant to be an introduction to artificial intelligence (AI), machine earning, and its applications in Industry 4.0. It explains the basic mathematical principles but is intended to be understandable for readers who do not have a background in advanced mathematics.


Students, general readers and industry professionals


As I reviewed the manuscript prior to writing this Foreword, I was fascinated by many unique features that I would like to share with you. The book can best be described as concise yet detailed. There is more useful information packed into its 12 chapters than seen in most books twice its size. Therefore, it gives me great pleasure to contribute to this foreword.

This book is an enthusiastic celebration of many Machine Learning Techniques for Engineering Applications. It is also a unique tribute to many academicians and researchers who were involved in their study and contributed to society. Still another element is provided by many interesting technical details and an abundance of illustrations in the form of figures and tables. On top of that, there are innumerable machine learning algorithms for Intelligent Systems, Computational Linguistics, Natural Language Processing, Information Retrieval, Neural Networks, Social Networks, Recommender Systems, etc., indeed, to anyone with a fascination with the world of machine learning. This book can be read on two different levels. First, it may be read by ordinary people with a limited, if any, scientific background. Throughout, the book has been written with this audience in mind. The second group of readers will be represented by professionals from academia, government agencies and researchers. I do feel that everybody in the scientific community agrees with the content and ideas put forth in this book, and I hope that the information and knowledge presented will become a useful guideline for the research community and scholars.

This book contains so much useful information, and the chapters contain many pearls. I hope that this book will become a primer for teachers, teacher educators, and professional developers, helping teachers across the world to learn, teach, and practice machine learning techniques for various applications.

Sangeeta Sonania
Software Developer