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

個数：

予約

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

Mahalle, Parikshit Narendra (EDT)/ Sonawane, Vijay (EDT)

ウェブストア価格 ¥44,774（本体¥40,704）
Elsevier - Health Sciences Division（2026/08発売）
外貨定価 US$ 200.00
ポイント 407pt

現在予約受付中です。出版後の入荷・発送となります。
（重要：表示されている発売日は予定となり、発売が延期、中止、生産限定品で商品確保ができないなどの理由により、ご注文をお取消しさせていただく場合がございます。予めご了承ください。）

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版／ページ数 400 p.
言語 ENG
商品コード 9780443439148

Full 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).

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