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).
1. Part 1: Introduction - p. P1 2. Chapter 1 Introduction to digital twin computing - p. 3 3. Chapter 2 Introduction to artificial intelligence and machine learning - p. 33 4. Chapter 3 Basics of blockchain technology - p. 85 5. Chapter 4 Convergence of intelligence: Exploring the integration of artificial intelligence, machine learning and blockchain - p. 115 6. Part 2: Integration of artificial intelligence/machine learning and blockchain in digital - p. P2 7. Chapter 5 Synergizing artificial intelligence/machine learning and digital twin computing - p. 143 8. Chapter 6 Leveraging blockchain in digital twin systems - p. 163 9. Chapter 7 Blockchain for collaborative artificial intelligence/machine learning in digital twin computing - p. 189 10. Chapter 8 Blockchain for decentralized and secure artificial intelligence/machine learning in DT computing - p. 211 11. Chapter 9 Blockchain for IoT-enabled digital twin - p. 231 12. Chapter 10 Converging technologies for innovation in digital twinning - p. 249 13. Part 3: Emerging applications - p. P3 14. Chapter 11 Production optimization/lifecycle management in smart manufacturing (factory digital twin) - p. 301 15. Chapter 12 Damage detection and predictive maintenance in smart infrastructures based on digital twining approach - p. 319 16. Chapter 13 Prediction and remediation of cancer using digital twins: A comprehensive review - p. 351 17. Chapter 14 Selected applications of AI-based digital twins for industry 4.0/5.0 - p. 371 18. Part 4: Advanced topics and future directions - p. P4 19. Chapter 15 Emerging trends in digital twin technologies - p. 389 20. Chapter 16 Advancing real-time insights: Leveraging artificial intelligence digital twins for enhanced system and optimization - p. 425 21. Chapter 17 Digital twin computing: Recent evolution, challenges, and future directions - p. 445 22. Chapter 18 Future trends in AI-enhanced digital twins: From autonomous systems to quantum integration - p. 467 23. Chapter 19 Ethical consideration and regulatory challenges - p. 491 24. Chapter 20 Future perspectives on AI/ML and blockchain - p. 525 25. Chapter 21 Surity, privacy, and trust frameworks for artificial intelligence-driven digital twin ecosystems - p. 551 26. Index - p. 569
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).
1. Part 1: Introduction - p. P1 2. Chapter 1 Introduction to digital twin computing - p. 3 3. Chapter 2 Introduction to artificial intelligence and machine learning - p. 33 4. Chapter 3 Basics of blockchain technology - p. 85 5. Chapter 4 Convergence of intelligence: Exploring the integration of artificial intelligence, machine learning and blockchain - p. 115 6. Part 2: Integration of artificial intelligence/machine learning and blockchain in digital - p. P2 7. Chapter 5 Synergizing artificial intelligence/machine learning and digital twin computing - p. 143 8. Chapter 6 Leveraging blockchain in digital twin systems - p. 163 9. Chapter 7 Blockchain for collaborative artificial intelligence/machine learning in digital twin computing - p. 189 10. Chapter 8 Blockchain for decentralized and secure artificial intelligence/machine learning in DT computing - p. 211 11. Chapter 9 Blockchain for IoT-enabled digital twin - p. 231 12. Chapter 10 Converging technologies for innovation in digital twinning - p. 249 13. Part 3: Emerging applications - p. P3 14. Chapter 11 Production optimization/lifecycle management in smart manufacturing (factory digital twin) - p. 301 15. Chapter 12 Damage detection and predictive maintenance in smart infrastructures based on digital twining approach - p. 319 16. Chapter 13 Prediction and remediation of cancer using digital twins: A comprehensive review - p. 351 17. Chapter 14 Selected applications of AI-based digital twins for industry 4.0/5.0 - p. 371 18. Part 4: Advanced topics and future directions - p. P4 19. Chapter 15 Emerging trends in digital twin technologies - p. 389 20. Chapter 16 Advancing real-time insights: Leveraging artificial intelligence digital twins for enhanced system and optimization - p. 425 21. Chapter 17 Digital twin computing: Recent evolution, challenges, and future directions - p. 445 22. Chapter 18 Future trends in AI-enhanced digital twins: From autonomous systems to quantum integration - p. 467 23. Chapter 19 Ethical consideration and regulatory challenges - p. 491 24. Chapter 20 Future perspectives on AI/ML and blockchain - p. 525 25. Chapter 21 Surity, privacy, and trust frameworks for artificial intelligence-driven digital twin ecosystems - p. 551 26. Index - p. 569
