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


- Pp. i
Sangeeta Sonania
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- Pp. ii-iii (2)
Prasad Lokulwar, Basant Verma, N. Thillaiarasu, Kailash Kumar, Mahip Bartere, Dharam Singh
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List of Contributors

- Pp. iv-v (2)

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Cutting Edge Techniques of Adaptive Machine Learning for Image Processing and Computer Vision

- Pp. 1-18 (18)
P. Sasikumar*, T. Saravanan
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Algorithm For Intelligent Systems

- Pp. 19-30 (12)
Pratik Dhoke*, Pranay Saraf, Pawan Bhalandhare, Yogadhar Pandey, H.R. Deshmukh, Rahul Agrawal
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Clinical Decision Support System for Early Prediction of Congenital Heart Disease using Machine learning Techniques

- Pp. 31-41 (11)
Ritu Aggarwal*, Suneet Kumar
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A Review on Covid-19 Pandemic and Role of Multilingual Information Retrieval and Machine Translation for Managing its Effect

- Pp. 42-58 (17)
Mangala Madankar*, Manoj Chandak
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An Empirical View of Genetic Machine Learning based on Evolutionary Learning Computations

- Pp. 59-75 (17)
M. Chandraprabha, Rajesh Kumar Dhanaraj*
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High-Performance Computing for Satellite Image Processing Using Apache Spark

- Pp. 76-91 (16)
Pallavi Hiwarkar*, Mangala S. Madankar
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Artificial Intelligence and Covid-19: A Practical Approach

- Pp. 92-109 (18)
Md. Alimul Haque*, Shameemul Haque, Samah Alhazmi, D.N. Pandit
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Intelligent Personalized E-Learning Platform using Machine Learning Algorithms

- Pp. 110-126 (17)
Makram Soui*, Karthik Srinivasan*, Abdulaziz Albesher*
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Automated Systems using AI in the Internet of Robotic Things: A New Paradigm for Robotics

- Pp. 127-144 (18)
T. Saravanan*, P. Sasikumar
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Missing Value Imputation and Estimation Methods for Arrhythmia Feature Selection Classification Using Machine Learning Algorithms

- Pp. 145-163 (19)
Ritu Aggarwal*, Suneet Kumar
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Analysis of Abstractive Text Summarization with Deep Learning Technique

- Pp. 164-196 (33)
Shruti J. Sapra Thakur, Avinash S. Kapse*
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Advanced Topics in Machine Learning

- Pp. 197-212 (16)
Sana Zeba*, Md. Alimul Haque, Samah Alhazmi, Shameemul Haque
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Subject Index

- Pp. 213-221 (9)

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