Machine Learning and Artificial Intelligence in Today's Perspective

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Authors

Rajeev Kumar Dang (ed)
University Institute of Engineering and Technology Panjab University SSG Regional Centre, Hoshiarpur, Punjab-146021
https://orcid.org/0000-0001-8190-5781
Sukhvinder Singh Bamber (ed)
University Institute of Engineering and Technology Panjab University SSG Regional Centre, Hoshiarpur, Punjab-146021
https://orcid.org/0000-0002-1749-2940

Keywords:

Machine Learning, Artificial Intelligence, Combinational Circuits, Cloud Computing, Data Mining, Blockchain, Agriculture

Synopsis

This book is about the Machine Learning (ML) and the applications of Artificial Intelligence (AI) in the various fields of ML. The authors have explained the use of AI in some of the eminent fields of ML to expand its scope of applicability like: Computing, Agriculture, Healthcare.

This book describes the basic building blocks of AI that can be implemented in ML to improve its effectiveness. Also, describes the integration of AI with Cloud to expand its impact / Computing area. As a part of computing process in ML, this book also describes the Data Discretization in Data Mining using ML. This book further evaluates the stack organization explaining the online / remote computing using ML and AI. Additionally, the Blockchain based Voting System using Smart Contract highlights and explains the use of ML and AI. 

This book in the modern perspective also elaborates the use of ML in resilient agriculture. Further, the use of ML & AI in healthcare applications like: Breast Cancer Detection & Classification and Brain Cancer treatments has been explained.

Finally, the dark side of AI mainly the generated threats and attacks have been explained in this book.

To conclude, the growing impact of Machine Learning in combination with Artificial Intelligence has been described in this book. We sincerely hope that our work will contribute in providing further research directions in the area of Machine Learning and Artificial Intelligence.

Chapters

References

Feuerriegel, Stefan, Jochen Hartmann, Christian Janiesch, and Patrick Zschech. “Generative AI.” Business & Information Systems Engineering 66, no. 1 (2024): 111-126.

Huang, Ken, Yang Wang, and Xiaochen Zhang. “Foundations of generative AI.” In Generative AI security: theories and practices, pp. 3-30. Cham: Springer Nature Switzerland, 2024.

Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. “Generative Adversarial Nets.” Advances in neural information processing systems 27 (2014).

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. “Attention is all you need.” Advances in neural information processing systems 30 (2017).

Kingma, Diederik P., and Max Welling. Auto-Encoding Variational Bayes. arXiv:1312.6114v10, pp. 1-14, 2013.

Downloads

Front Pages

Published

29 December 2025
Copyright (c) 2025 Rajeev Kumar Dang; Surinder Singh Khurana, Parvinder Singh, Kounser Ali Mir, Mina Bakhtiyar Ahmad Khan, Umar Farooq, Neeru Mago, Sukhvinder Singh Bamber, Rajeev Kumar Dang, Naveen Dogra, Balwinder Kaur, Rajinder Singh, Gurpreet Singh; Sukhvinder Singh Bamber; Anam Bansal

Details about the available publication format: E-Book

E-Book

ISBN-13 (15)

978-93-7185-555-6

Details about the available publication format: Book (Paperback)

Book (Paperback)

ISBN-13 (15)

978-93-7185-321-7

How to Cite

Dang, R. K., & Bamber, S. S. . (Eds.). (2025). Machine Learning and Artificial Intelligence in Today’s Perspective. Deep Science Publishing. https://doi.org/10.70593/978-93-7185-555-6
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