Decentralized Futures

Navigating the Integration of Blockchain Technology with Artificial Intelligence
1. Auflage 2026
ISBN: 978-1-041-11166-5
Verlag: Taylor & Francis Ltd

Buch, Englisch, 322 Seiten, Format (B × H): 156 mm x 234 mm

This book provides readers with a comprehensive grasp of blockchain technology by exploring its applications, security measures, and protocols along with the need for safe blockchain networks, and the incorporation of artificial intelligence (AI) improving blockchain performance.

Decentralized Futures: Navigating the Integration of Blockchain Technology with Artificial Intelligence seeks to provide readers with a thorough understanding of blockchain technology, delving into use cases, security, and the integration of AI. The book initially focuses on the characteristics of blockchain, along with different frameworks. The authors delve into machine learning techniques to identify suspicious and fraudulent transaction patterns, such as in behavior analysis, and examine AI-based clustering and classification techniques to break apart blockchain data for easier processing and more efficient analysis. In addition, this book also has a deep dive into the consensus mechanisms that makes blockchain more effective. The book contains case studies from real-world applications, and readers will learn by applying the take-home messages and guidance in these case studies to their works. The book explores the integration of AI with blockchain to make decentralized futures in an effective way. It will also give an explorative idea of opportunities and challenges of this integration.

This book targets a mixed audience of data scientists, engineers, researchers, academics, and students on different levels to share and contribute cutting-edge technologies, methodologies, frameworks, and practices in blockchain integrated with AI.

Zielgruppe

Postgraduate and Professional Reference

Autoren/Hrsg.

Panigrahi, Amrutanshu

Pati, Abhilash

Jain, Prince

Liu, Haipeng

Inhaltsverzeichnis

Chapter 1 Introduction to Blockchain Technology: A Revolutionary Paradigm Shift Chapter 2 Integrating Internet of Things (IoT) and Blockchain: Current Applications, Challenges, and Future Perspectives Chapter 3 Blockchain Technology: Foundations, Challenges, and Future Prospects Chapter 4 Biologically Plausible Spiking Neural Networks for Blockchain Technology: A Review of Synergies and Challenges Chapter 5 Enhancing Security and Transparency in AI with Blockchain Chapter 6 Enhancing UPI security with AI: An Ensemble-Based Fraud Detection Model Chapter 7 Decentralized Machine Learning: Opportunities and Challenges Chapter 8 Enhancing Supply Chain Sustainability Through Blockchain Technology: A Scholarly Analysis Chapter 9 BASIS-IoT: Blockchain and AI-integrated SDN-based Framework for Smart Irrigation Systems in IoT Environments Chapter 10 Decentralized Identity and AI-Powered Identity Management Chapter 11 IMBM: Integrating Metaverse, Blockchain, and Machine Learning models for effective Disease diagnosis Chapter 12 Modelling Herding and Contagion Effects in Digital Currencies: A Comparative Study of Holt-Winters and Deep Learning Approaches Chapter 13 Privacy-Preserving AI Through Blockchain and Federated Learning Chapter 14 Blockchain-Based Intelligent Transportation Management System

Über Autor(innen)

Dr. Amrutanshu Panigrahi is an accomplished academic and researcher, currently serving as Assistant Professor in the Department of Computer Science and Engineering at Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India. Demonstrating a strong commitment to international research collaboration, Dr. Panigrahi is currently undertaking postdoctoral research at Lincoln University College, Malaysia. His core research interests lie at the intersection of blockchain technology, ML, DL, and cloud computing. Dr. Panigrahi has published more than 100 research articles in reputable SCIE, ESCI, Scopus-indexed journals, and international conferences. He is an editorial board member of BMC Artificial Intelligence, Edelweiss Applied Science and Technology journal, and Discover Artificial Intelligence. He also serves as a reviewer for several SCIE, ESCI, and Scopus-indexed journals.

Dr. Abhilash Pati is currently working as Assistant Professor in the Department of CSE, FET-ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India, and also a postdoctoral fellow at Lincoln University College in Malaysia. He qualified UGC-NET (Assistant Professor Only) in December 2019. His research interests include IoT, fog computing, ML, and DL, and he has more than 100 publications in SCIE, ESCI, and Scopus-indexed journals and/or conferences. He is an editorial board member of various journals, including Discover Artificial Intelligence, Big Data and Computing Visions, BMC Artificial Intelligence, and Edelweiss Journal of Engineering and Technology. He is also an active reviewer for various SCIE, ESCI, and Scopus-indexed international journals. He has authored three textbooks.

Dr. Prince Jain is an accomplished academic, researcher, and innovator in machine learning, artificial intelligence, and advanced engineering. He currently serves as Assistant Professor (Research Cadre) at Parul Institute of Technology, Parul University, Vadodara. He earned his Ph.D. in Electronics Engineering from Punjab Engineering College, Chandigarh, where he developed a strong foundation in interdisciplinary research. Recognized among the world’s top 2% scientists in 2025 by Elsevier–Stanford, Dr. Jain’s work spans cutting-edge areas such as metamaterials, IoT-based healthcare systems, and biomedical signal processing. With prior academic experience at reputable institutions such as Chandigarh University, he is dedicated to advancing knowledge through research, teaching, and innovation. His contributions reflect a commitment to solving real-world problems using technology, while mentoring students and collaborating on impactful scientific developments across multiple domains.

Dr. Haipeng Liu is a researcher and academic professional specializing in wearable healthcare technologies, biomedical sensing, and intelligent monitoring systems. He is affiliated with Coventry University, where he actively contributes to research and postgraduate supervision, particularly on projects focused on cost-effective healthcare solutions using wearable sensors. His work centers on post-stroke rehabilitation monitoring, leveraging emerging technologies such as artificial intelligence, the Internet of Medical Things (IoMT), and cloud-based diagnostics to enable continuous, remote patient care. Dr. Liu has also contributed to scholarly publications, including reviews on advanced wearable sensor systems for healthcare applications. Beyond research, he mentors students and promotes diversity initiatives, including supporting female participation in STEM through international scholarship programs. His contributions reflect a commitment to advancing accessible, technology-driven healthcare and improving patient outcomes through innovative engineering solutions.