Artificial Intelligence, Machine Learning and Blockchain in Digital Twin Computing
Series: Advances in Digital Twin Computing and Sensor Networks;
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72 647 Ft (69 188 Ft + 5% VAT)
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72 647 Ft
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Product details:
- Publisher Elsevier Science
- Date of Publication 1 August 2026
- ISBN 9780443439148
- Binding Paperback
- No. of pages400 pages
- Size 235x191 mm
- Weight 450 g
- Language English 700
Categories
Long description:
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
