Artificial Intelligence, Machine Learning and Blockchain in Digital Twin Computing (Advances in Digital Twin Computing and Sensor Networks) - Softcover

9780443439148: Artificial Intelligence, Machine Learning and Blockchain in Digital Twin Computing (Advances in Digital Twin Computing and Sensor Networks)

Softcover

ISBN 10: 0443439141 ISBN 13: 9780443439148

Verlag: Elsevier, 2026

Artificial Intelligence, Machine Learning and Blockchain in Digital Twin Computing explores the synergy between artificial intelligence, machine learning, blockchain technology, and digital twin computing. The book overviews each technology, establishing a clear understanding of their individual roles and potential when combined. The second section delves into the integration of these technologies, focusing on key themes such as enhancing system simulations, ensuring data integrity, and enabling secure, real-time decision-making. Practical applications and case studies are used to illustrate how this convergence can drive innovation in industries like manufacturing, healthcare, and smart cities. Final sections look ahead, discussing emerging trends, challenges, and future opportunities.

Digital twin computing is the bridge between the real and virtual worlds. Digital twin computing also is the mirror that reflects the real world into the virtual world. Blockchain technology can refine the digital twins (DTs) by ensuring transparency, decentralized data storage, data immutability, and peer-to-peer communication in various applications. DT provides a powerful tool able to generate a huge amount of training data for machine learning algorithms (MLAs).

Explores the seamless integration of artificial intelligence, machine learning, blockchain, and digital twin computing for enhanced system performance
Features real-world examples from industries such as manufacturing, healthcare, and smart cities
Highlights the latest research trends and emerging opportunities in this interdisciplinary field
Offers solutions to challenges in implementing these technologies
Discusses future trends and potential advancements in the field

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�ber die Autorinnen und Autoren

Dr Parikshit is a senior member IEEE and is Professor, Dean Research and Development and Head - Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology, Pune, India. He completed his Ph. D from Aalborg University, Denmark and continued as Post Doc Researcher at CMI, Copenhagen, Denmark. He has 23 + years of teaching and research experience. He is an ex-member of the Board of Studies in Computer Engineering, Ex-Chairman Information Technology, Savitribai Phule Pune University and various Universities and autonomous colleges across India. He has 15 patents, 200+ research publications (Google Scholar citations-2950 plus, H index-25 and Scopus Citations are 1550 plus with H index -18, Web of Science citations are 438 with H index - 10) and authored/edited 56 books with Springer, CRC Press, Cambridge University Press, etc. He is editor in chief for IGI Global International Journal of Rough Sets and Data Analysis, Inter-science International Journal of Grid and Utility Computing, member-Editorial Review Board for IGI Global – International Journal of Ambient Computing and Intelligence and reviewer for various journals and conferences of the repute. His research interests are Machine Learning, Data Science, Algorithms, Internet of Things, Identity Management and Security. He is guiding 8 PhD students in the area of IoT and machine learning and SIX students have successfully defended their PhD under his supervision from SPPU. He is also the recipient of “Best Faculty Award� by Sinhgad Institutes and Cognizant Technologies Solutions. He has delivered 200 plus lectures at national and international level.

Dr. Vijay R. Sonawane holds a Ph.D. in Computer Engineering from K.L. University, Vijayawada, and has over 20 years of academic and research experience in computer science and information technology. His areas of expertise include Artificial Intelligence, Machine Learning, Blockchain Technology, Cloud Computing, Data Security, Explainable AI, and Digital Twins.

He has an extensive research record with 24+ journal publications indexed in SCI, Scopus, and Web of Science, along with several international conference papers published through Springer and IEEE. His contributions span advanced research topics such as blockchain-enabled data security, optimization techniques, greenhouse energy efficiency, cloud-based authentication, and AI-driven healthcare systems. He also serves as a reviewer for reputed journals such as Applied Artificial Intelligence (Taylor & Francis).

Dr. Vijay R. Sonawane has completed multiple funded research projects, and secured grants from AICTE, BCUD, MHRD, and MSFDA. His innovative work includes developing IoT systems, healthcare kiosks, and the Krushi Basket mobile application. He has also filed multiple Indian patents in emerging technologies.

An active academic leader, he has served as Head of Department, contributed to achieving NBA accreditation, established Centers of Excellence (DELL EMC, Red Hat & AWS Academy), and organized numerous national-level FDPs, conferences, and workshops. He has also contributed significantly to curriculum development with Pune University (SPPU) through syllabus design and faculty orientation programs.

Von der hinteren Coverseite

Digital twin computing is the bridge between the real and virtual worlds. Digital twin computing also is the mirror that reflects the real world into the virtual world. Blockchain technology can refine the digital twins (DTs) by ensuring transparency, decentralized data storage, data immutability, and peer-to-peer communication in various applications. DT provides a powerful tool able to generate a huge amount of training data for machine learning algorithms (MLAs). On the other hand, AI/ML based/driven DT offers many advantages for optimization, prediction, damage detection/predictive maintenance/predictive modeling/decision support, lifecycle management for the real physical assets. Artificial Intelligence, Machine Learning and Blockchain in Digital Twin Computing offers a comprehensive exploration of the synergy between artificial intelligence, machine learning, blockchain technology, and digital twin computing. Structured into three main sections, the book begins with a foundational overview of each technology, establishing a clear understanding of their individual roles and potential when combined. The second section delves into the integration of these technologies, focusing on key themes such as enhancing system simulations, ensuring data integrity, and enabling secure, real-time decision-making. Practical applications and case studies are used to illustrate how this convergence can drive innovation in industries like manufacturing, healthcare, and smart cities. The final section looks ahead, discussing emerging trends, challenges, and future opportunities in this evolving field. By blending theory with practical insights, this book serves as both an educational resource and a practical guide for professionals, researchers, and students seeking to harness the power of these advanced technologies in complex, real-world environments.

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