Data Management and Security in Blockchain Systems offers a comprehensive exploration of how blockchain technology is reshaping the landscape of data management and security. This book addresses key aspects of blockchain-based systems, including data integrity, transparency, and tamper resistance, making it an essential resource for students, researchers, and professionals. Covering topics from blockchain-enabled IoT traffic management to the integration of AI for enhanced security, this book presents solutions to current challenges such as cyberattacks, smart grid security, and scalable network designs. Each chapter is thoughtfully structured to provide readers with a solid understanding of blockchain applications in diverse domains. Perfect for those seeking to understand blockchain’s potential to secure and manage data in an increasingly interconnected world. Key Features: - Comprehensive overview of data management and security in blockchain networks. - Practical insights into IoT, smart grids, and AI integration. - In-depth analysis of cybersecurity challenges and solutions.

Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions examines cutting-edge applications, from blockchain-powered IoT innovations in healthcare systems to intelligent health profile management, remote patient monitoring, and healthcare credential verification. Additionally, the book extends its insights into blockchain-enabled IoT applications in smart agriculture, highlighting AI-driven technologies for health management and sustainable practices. With expert analyses, case studies, and practical guidance, this book offers readers a roadmap for implementing these technologies to improve efficiency, security, and data management in healthcare. It is an invaluable resource for industry professionals, researchers, and students interested in the future of healthcare technology. Key Features: - Exploration of blockchain and IoT applications in healthcare and agriculture - In-depth case studies and expert analyses - Practical insights into technology challenges and benefits

Demystifying Emerging Trends in Machine Learning (Volume 2) offers a deep dive into emerging and trending topics in the field of machine learning (ML). This edited volume showcases several machine learning methods for a variety of tasks. A key focus of this volume is the application of text classification for cybersecurity, E-commerce, sentiment analysis, public health and web content analysis. The 49 chapters highlight a wide variety of machine learning methods including SVNs, K-Means Clustering, CNNs, DCNNs, among others. Each chapter includes accessible information through summaries, discussions and reference lists. This comprehensive volume is essential for students, researchers, and professionals eager to understand the emerging trends reshaping machine learning today.

Computational Intelligence and its Applications explores cutting-edge advancements in machine learning, data science, and their practical applications. This comprehensive guide caters to college students, researchers, and professionals equipping readers with the knowledge to navigate and leverage the latest trends in computational intelligence effectively. Key Features: - Emerging Technologies: Covers post-quantum cryptography, secure communication, and advancements in chatbot technology. - Practical Applications: Includes stock price prediction, cloud computing, and data-driven DNA sequencing. - Security and Detection: Enhances intrusion detection and analyzes crop disease using machine learning. - Natural Language Processing: Explores sentiment analysis and the impact of big data on natural language processing. - Advanced Techniques: Discusses blockchain technology, remote sensing, and depression detection on social media.

Data Recovery Techniques for Computer Forensics is a practical and comprehensive reference designed for professionals, students, and researchers in digital forensics, data recovery, and information security. This handbook provides clear, structured guidance on essential principles and practical techniques for recovering lost or compromised digital data in forensic investigations. The book begins with the fundamentals of data recovery and examines the major causes of data loss, including software errors and hardware failures. It then explores contemporary data protection technologies and delves into the structure and organization of hard disks, laying a solid foundation for understanding data storage systems. Specialized chapters cover the recovery and management of various file systems, including FAT16, FAT32, and NTFS, along with methods for partition recovery and an introduction to dynamic disk management. The final section introduces essential data security software used to protect and recover digital information. Key Features Covers basic and applied data recovery concepts for forensic applications Explains causes of data loss and modern data protection technologies Detailed chapters on hard disk structure, data organization, and partition recovery Practical guidance on managing and recovering FAT16, FAT32, and NTFS file systems Introduces dynamic disk configurations and essential data security tools

Advanced Machine Learning for Complex Medical Data Analysis is a definitive guide to leveraging machine learning to solve critical challenges in medical data analysis. This book discusses cutting-edge methodologies, from predictive modeling to neural networks, tailored to address the unique complexities of medical and healthcare data. It combines theoretical frameworks with practical applications, ensuring readers gain a comprehensive understanding of both concepts and real-world implementations. The book covers diverse topics, including medical image denoising, the transformative role of GANs, IoT applications in healthcare, early disease detection using speech data, and COVID detection using autoencoders. It also explores the impact of big data, statistical approaches to medical analytics, and public health improvements through technology. Key Features: - Practical insights into deploying advanced machine learning models for healthcare. - Real-world case studies on diverse diseases and datasets. - Cutting-edge topics like explainable AI, federated learning, and ethical considerations. - Methods for improving data accuracy, efficiency, and privacy.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries, reshaping the way we interact with technology, and driving innovation across multiple disciplines. Advancements in Artificial Intelligence and Machine Learning is a comprehensive exploration of the latest developments, applications, and challenges in AI and ML, offering insights into cutting-edge research and real-world implementations. This book is a collection of twelve chapters, each exploring a distinct application of Artificial Intelligence (AI) and Machine Learning (ML). It begins with an overview of AI’s transformative role in Next-Gen Mechatronics, followed by a comprehensive review of key advancements and trends in the field. The book then examines AI’s impact across diverse sectors, including energy, digital communication, and security, with topics such as AI-based aging analysis of power transformer oil, AI in social media management, and AI-driven human detection systems. Further chapters address sentiment analysis, visual analysis for image processing, and the integration of AI in smart grid networks. The volume also covers AI applications in hardware security for wireless sensor networks, drone robotics, and crime prevention systems. The final set of chapters highlight AI’s role in healthcare and automation, including an AI-assisted system for women’s safety in India and the use of EfficientNet B0 CNN architecture for brain tumor detection and classification. Together, these chapters showcase the versatility and growing influence of AI and ML across critical modern industries. Key features A multidisciplinary approach covering AI applications in robotics, cybersecurity, healthcare, and digital transformation in 12 organized chapters. A focus on contemporary challenges and solutions in AI and ML across industries. Research-driven insights from experts and practitioners in the field. Practical discussions on AI-driven automation, security, and intelligent decision-making systems.

Beyond Blockchain: Reviewing the Impact and Evolution of Decentralized Networks (Part 1) explores how blockchain and decentralized systems are transforming key industries and building a smarter, more connected world. The book covers practical applications such as Ethereum smart contracts in supply chains, blockchain for sustainable agriculture, climate monitoring, education, and revolutionizing healthcare, especially when combined with AI. Written in clear, simple language, it helps readers understand complex technologies without a technical background. Key Features: - Beginner-friendly guide to blockchain, smart contracts, and decentralized systems - Real-world use cases in supply chains, agriculture, education, and healthcare - Insights into integrating AI, IoT, and quantum computing with blockchain - Focus on sustainability, data security, and ethical tech use - Easy-to-read chapters with clear themes and practical insights.

Analysis of Boundedness and Safeness in a Petri Net-Based Specification of Concurrent Control Systems provides a comprehensive study of concurrent control systems using Petri net models, with a focus on boundedness and safeness analysis. Designed for both academic and industry professionals, it bridges the gap between theoretical rigor and practical applications, offering essential insights for advancing knowledge in the field. The content covers foundational principles of control systems and Petri nets before exploring advanced analysis techniques. Topics include algorithms, computational complexity, and different Petri net classes. The book also addresses boundedness and safeness properties, presenting verification methods, case studies, and experimental results. Trends, challenges, and future directions are also discussed in the book equipping readers with practical tools to innovate in concurrent system design. Key Features: - In-depth coverage of Petri net-based concurrent system modeling and analysis. - Clear explanations supported by real-world examples and case studies. - A balanced approach combining theory and application.

Advances in AI for Financial, Cyber, and Healthcare Analytics: A Multidisciplinary Approach comprehensively explores how artificial intelligence and machine learning are reshaping decision-making, predictive modelling, and operational strategies across three critical sectors—finance, cybersecurity, and healthcare. Across nine chapters, the book delves into the foundations of financial analytics and explores AI’s role in market prediction, fraud detection, and risk analysis. It progresses into healthcare applications such as disease classification using ResNet, ethical implications of AI decisions, and the evolution of human-centred, edge-driven healthcare systems. In the cybersecurity domain, it addresses predictive threat modelling, smart home authentication, and biometric identification through advanced AI techniques. Key features: Unifies financial, healthcare, and cyber analytics through AI-driven solutions Demonstrates practical implementations with code examples and case studies Covers cutting-edge technologies like CNN-LSTM, attention models, and edge computing Addresses ethical, technical, and human-centred dimensions of AI