Machine Learning and Blockchain: Challenges, Future Trends and Sustainable Technologies

With contributions from leading researchers and industry experts, this book examines cutting-edge applications, integration models, and sustainable solutions across sectors including finance, agriculture, healthcare, IoT, and smart cities. Chapters cover blockchain-enabled fintech operations, fraud detection, deep learning–driven intrusion detection, AI-enhanced smart contracts, and data-driven healthcare innovations. Case studies, methodologies, and future-oriented insights demonstrate how these technologies can foster secure, efficient, and sustainable ecosystems. By bridging theoretical foundations with practical implementations, this book offers readers a roadmap to navigate the opportunities and challenges shaping the next generation of intelligent, blockchain-powered systems.

Key Features Integrates blockchain with machine learning for real-world applications.

Applies advanced analytics, automation, and AI models to enhance blockchain ecosystems.

Develops secure solutions in fintech, agriculture, healthcare, IoT, and smart cities.

Evaluates case studies and frameworks addressing challenges and vulnerabilities.

Explores sustainable, future-ready trends shaping intelligent systems.

Readership

For researchers, graduate students, and academicians in computer science, IT, and data science, as well as industry practitioners, fintech innovators, and blockchain developers.

© 2026 Bentham Science Publishers (E-bog): 9789815324211

Udgivelsesdato

E-bog: 13. januar 2026