Book chapter contributions are invited for submission in the book "Machine Learning and Blockchain – Challenges, Future Trends and Sustainable Technologies". Bentham Science publisher will publish the book. Only confirmed authors will be allowed to contribute their full chapters.  

Submission Guidelines

All chapters must be original and not simultaneously submitted to another journal or conference, or book. Initially, authors are supposed to submit the abstract only. The full chapter needs to be submitted after acceptance. But if you have the full chapter ready, then you can submit the full chapter directly.  Submission will be done through easychair only.

Abstract Submission Deadline: 31st October 2022

List of Topics (but are not limited to)

The main focus of this book is to bring all the related managerial applications of blockchain and machine learning into a single platform so that Undergraduate and Postgraduate students, Researchers, Academicians, and Industry people can easily understand. This edited book aims to provide the concepts of related technologies and novel findings of the researchers through its Chapter Organization. The primary audience for the book incorporates specialists, researchers, graduate understudies, designers, experts, and managers doing research in this domain. The edited book will be organized into independent chapters to give readers great readability, adaptability, and flexibility.

The chapters are invited on the topics mentioned below (*tentative).

• Blockchain foundations, new design, and privacy
• Cyber-physical systems and Blockchain
• Security and data integrity with blockchain
• Cyberattacks on blockchains
• IoT platform based on Blockchain or/and deep learning
• P2P communication protocol
• Blockchain-based social media
• Distributed Database Technologies for Blockchain
• Permissioned vs. permission-less paradigms
• Reinforcement learning
• Deep learning models for achieving safety
• Blockchain in connected and autonomous vehicles
• Blockchain and Machine Learning/Artificial Intelligence
• Learning at the edge of the networks
• IoT-driven intelligence and incorporate deep learning models
• Vision, Image Processing, and Environment Perception
• Intelligent Automation
• Operational and Policy issues in Automation
• Big Data and Deep Learning

Contact

• Keshav Kaushik, University of Petroleum and Energy Studies, Dehradun, INDIA. Email: officialkeshavkaushik@gmail.com
• Dr. Rewa Sharma, Department of Computer Engineering, J.C Bose University of Science and Technology, YMCA, Faridabad, Haryana, India. Email: rewa10sh@gmail.com
• Dr. Ayodeji Olalekan Salau, Department of Electrical/Electronics and Computer Engineering, Afe Babalola University Ado-Ekiti, Nigeria. Email: ayodejisalau98@gmail.com