About the Book
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).
Table of Contents:
Part 1: INTRODUCTION
1. Introduction to digital twin computing
2. Introduction to AI/ML
3. Basics of Blockchain Technology
4. Convergence of Intelligence: Exploring the Integration of AI, ML and Blockchain
Part 2: INTEGRATION OF AI/ML AND BLOCKCHAIN IN DIGITAL TWIN
5. Synergizing AI/ML and Digital Twin Computing
6. Leveraging Blockchain in Digital Twin Systems
7. Blockchain for collaborative AI/ML in DT computing
8. Blockchain for decentralized and secure AI/ML in DT computing
9. Blockchain for IoT-enabled digital twin
10. Converging Technologies for Innovation of Digital Twin
Part 3: EMERGING APPLICATIONS
11. Production optimization/lifecycle management in smart manufacturing (Factory digital twin)
12. Damage Detection and Predictive Maintenance in Smart Infrastructures based on Digital Twining approach
13. Prediction and Remediation of Cancer Using Digital Twins: A Comprehensive Review
14. Selected Applications of AI-Based Digital Twins for Industry 4.0/5.0
Part 4: ADVANCED TOPICS AND FUTURE DIRECTIONS
15. Emerging Trends in Digital Twin Technologies
16. Advancing Real-Time Insights: Leveraging AI Digital Twins for Enhanced System and Optimization
17. Digital Twin Computing: Recent Evolution, Challenges, and Future Directions
18. Future Trends in AI-Enhanced Digital Twins: From Autonomous Systems to Quantum Integration
19. Ethical consideration and regulatory challenges
20. Future Perspectives on AI/ML and Blockchain
21. Security, Privacy, and Trust Frameworks for AI-Driven Digital Twin Ecosystems
About the Author :
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.
