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 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, security frameworks, and the integration of artificial intelligence. The book initially focuses on the fundamental characteristics of blockchain, alongside various implementation frameworks. The authors explore 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. Additionally, this book provides an in-depth examination of consensus mechanisms that enhance blockchain effectiveness. Through real-world applications, readers will gain practical insights by applying the lessons and guidance from these examples to their own work. The book investigates how AI integration with blockchain creates effective decentralized futures, while also providing an exploratory perspective on the opportunities and challenges inherent in this convergence.
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 the Blockchain integrated with AI.