Amrutanshu Panigrahi – författare

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.

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.