Data Analytics for IoT Applications explains how advanced computational methods support real-world IoT solutions. The book offers a clear and practical overview of how AI, machine learning, and big data analytics strengthen IoT systems across healthcare, agriculture, security, smart environments, and industrial safety. Going from intrusion detection and neural network optimization to smart sensing, energy-efficient communication, and early warning systems. It also showcases applied use cases such as disease detection, facial recognition, livestock monitoring, robotics in forensics, fraud detection, and smart water management, blending theory with hands-on applications. Key Features: · Integrates AI, machine learning, and big data with IoT systems. · Demonstrates practical applications through real-world case studies. · Examines privacy, security, and ethical considerations in IoT networks. · Explores emerging trends including nature-inspired algorithms, edge computing, and robotics.

This book explores cutting-edge methodologies, integration strategies, and secure frameworks that combine AI capabilities with IoT systems to enhance performance, decision-making, and scalability. Covering a wide range of crucial topics, including AI-driven predictive maintenance, healthcare IoT, smart cities, industrial automation, edge computing, cybersecurity challenges, and real-time data analytics. The book brings forward domain-specific innovations and solutions, illustrating how AI techniques such as machine learning, deep learning, and natural language processing are advancing IoT applications. Highlighting technical challenges, solutions, and future directions, the book offers practical guidance for researchers and practitioners working on intelligent IoT frameworks. Key Features: Explore real-world AI-IoT integration strategies Analyze applications in healthcare, smart homes, and industrial IoT Address edge and fog computing frameworks Examine security models and threat mitigation approaches Highlight regulatory and ethical implications Present expert-contributed chapters with case studies

Reinforcement Learning: Foundations and Applications combines rigorous theory with real-world relevance to introduce readers to one of the most influential branches of modern Artificial Intelligence. Walking readers through the essential principles, algorithms, and techniques that define reinforcement learning (RL), the book highlights how RL enables intelligent systems to learn from interaction and optimize decision-making in domains such as robotics, autonomous control, game AI, finance, and healthcare. The book opens with foundational RL concepts, including Markov Decision Processes, dynamic programming, and the exploration–exploitation dilemma. It then progresses to advanced material covering policy gradient methods, actor–critic architectures, deep reinforcement learning models, and multi-agent systems. Dedicated application chapters demonstrate how RL drives adaptive control, sequential decision-making, and practical problem-solving—supported by case studies, diagrams, and algorithm pseudocode. Rich with examples, research insights, and implementation guidance, this book equips readers with both the conceptual understanding and applied perspective needed to master reinforcement learning. Key Features Blends foundational RL theory with practical, application-driven case studies. Explains both model-based and model-free reinforcement learning approaches. Covers cutting-edge methods including Deep Q-Networks, continuous control, and reward shaping. Presents clear diagrams, pseudocode, and implementation notes to support hands-on learning. Highlights current challenges, limitations, and emerging research directions in RL.

A comprehensive exploration of the intersection between Generative Artificial Intelligence (GAI) and Wireless Sensor Networks (WSNs) , two transformative technologies reshaping data-driven systems. This book examines how generative AI can enhance wireless networks through advanced data analysis, anomaly detection, predictive modeling, and optimization, while also addressing the security risks and ethical challenges of its deployment. Beginning with an overview of GAI and its evolution, the book guides readers through real-world examples, case studies, and frameworks that demonstrate how generative AI can unlock new levels of efficiency and performance in WSNs. Key Features - Examines how Generative AI enhances wireless networks through advanced data analysis, anomaly detection, predictive modeling, and optimization - Demonstrates methods to improve network efficiency, reliability, and adaptability using AI-driven approaches - Addresses critical security risks and ethical challenges linked to the deployment of Generative AI - Explores innovative applications that integrate Generative AI into next-generation wireless systems - Evaluates the impact of AI-driven decision-making on corporate governance and technological adoption - Provides practical insights and case studies illustrating real-world implementations

The Role of IoT and Big Data in the Fifth Industrial Revolution highlights the shift from automation-driven Industry 4.0 to the human-centered, intelligent ecosystem of Industry 5.0. The book integrates perspectives from IoT, Big Data analytics, Artificial Intelligence, Digital Twins, and 5G-enabled IIoT to demonstrate how these innovations are transforming manufacturing, energy systems, healthcare, urban planning, and human-machine interaction. The chapters explore IoT architectures, smart fault detection systems, big data driven optimization, and industrial cybersecurity, while dedicated sections delve into Digital Twins across multiple sectors, advanced facial expression recognition for human machine interfaces, and IoT-based energy management solutions. Real-world case studies and practical insights illustrate how emerging technologies are enabling sustainable, efficient, and resilient industrial environments. By bridging theoretical foundations with applied advancements, this book serves as a critical resource for understanding the technological pillars and practical implications of the Fifth Industrial Revolution. Key Features -Explores foundational and emerging concepts in IoT, AI, Big Data, and Digital Twins. -Demonstrates real-world industrial applications through practical case studies. -Addresses security, sustainability, and ethical challenges in smart technology deployment. -Integrates cross-disciplinary perspectives spanning manufacturing, healthcare, energy, and urban systems.

A comprehensive and practice-oriented exploration of blockchain technology, tracing its evolution from Bitcoin to Ethereum and beyond. This volume explains blockchain’s foundational principles, such as decentralization, immutability, and cryptographic security, before moving into real-world applications across finance, supply chain management, healthcare, governance, and emerging tech ecosystems. With contributions from leading researchers and practitioners, the book examines both opportunities and challenges ranging from scalability and energy consumption to regulatory clarity and security. Readers also gain insights into cutting-edge developments like smart contracts, decentralized finance (DeFi), identity systems, Layer 2 solutions, DAOs, and blockchain’s synergy with IoT and AI. Key Features Explains the core principles of blockchain, from decentralization to cryptographic security. Traces the evolution from Bitcoin and Ethereum to DeFi, smart contracts, and dApps. Demonstrates applications across finance, healthcare, supply chain, governance, and identity management. Addresses pressing challenges, including scalability, energy use, regulatory frameworks, and security threats. Explores future trends like interoperability, DAOs, Layer 2 solutions, and integration with IoT and AI.

Recent Advancements in Computational Intelligence: Concepts, Methodologies and Applications (Part 1) is an in-depth exploration of computational intelligence (CI), a rapidly evolving field blending artificial intelligence, machine learning, and data-driven problem-solving. The book focuses on biologically inspired methods such as neural networks, fuzzy logic, swarm intelligence, and evolutionary algorithms; it showcases how CI addresses complex real-world problems marked by uncertainty and incomplete data. Integrating theory with Industry 4.0 applications, the book spans diverse domains including healthcare, autonomous systems, cybersecurity, and smart computing. Structured across thematic sections like Social Computing, High-Performance Computing, Network Science, Smart Computing, and Intelligent Communications, it covers topics like deep learning for autonomous vehicles, AI-driven healthcare diagnostics, graph theory in cybersecurity, and reinforcement learning for generative AI. With contributions from international experts, it bridges foundational principles with applied research and case studies. Key Features: • Integrates emerging trends such as generative AI, edge computing, and reinforcement learning. • Demonstrates practical relevance through real-world case studies and industrial applications. • Promotes interdisciplinary understanding across computer science, engineering, and healthcare. • Provides actionable methodologies for researchers and practitioners advancing CI innovation. • Highlights the fusion of theory and practice in intelligent, adaptive systems.

Artificial Intelligence: Unleashing the Future builds a robust conceptual framework on artificial intelligence and machine learning before diving into practical applications. Beginning with a historical overview and foundational techniques in machine learning and deep learning the book’s chapters explore the deployment of AI in agriculture through smart irrigation, plant disease detection, and robotic farming; in cybersecurity via intelligent threat detection and defense tools; in healthcare through diagnostic AI, robotic surgery, and telemedicine; and in education by enabling personalized learning and adaptive technologies. Ethical, regulatory, and privacy concerns are also thoughtfully addressed, ensuring readers grasp both the opportunities and the complexities of AI integration. Key Features • Trace AI’s evolution and emerging architectures • Apply machine learning across real-world sectors • Explore sector-specific case studies and implementations • Understand ethical, legal, and data privacy issues • Gain insights into future trends in AI deployment.

A Primer on Wireless Technology and IoT Basics covers the journey of wireless networks from the early 1G systems to the anticipated capabilities of 6G, while also explaining essential wireless technologies such as GSM, WLAN, WPANs, MANETs, and Wireless Sensor Networks (WSNs). It further delves into how these technologies underpin IoT ecosystems, emphasizing their role in data collection, transmission, and smart system integration. Chapters explore key developments in wireless communication, types of networks, sensor architectures, IoT system layers, and real-world implementation challenges. Special attention is given to IoT applications across diverse sectors and how wireless protocols support their functionality. This book is particularly useful for readers seeking a strong foundational understanding of wireless systems and IoT, making it ideal for both technical and non-technical audiences. Key Features: Explain key generations of wireless technology from 1G to 6G. Clarify concepts related to GSM, WLAN, WPANs, MANETs, and WSNs Illustrate IoT architecture, protocols, and system layers Highlight real-world IoT use cases and applications Address implementation challenges and data handling in IoT systems

Digital Deception: Uncovering the Dark Side of AI in Social Networks is a critical investigation into how artificial intelligence silently influences, manipulates, and, at times, undermines digital interactions across social platforms. Bridging disciplines such as computer science, sociology, and ethics, the book exposes how AI technologies contribute to misinformation, surveillance, identity manipulation, and psychological exploitation in the digital sphere. With chapters on algorithmic bias, deep fakes, federated learning, and intrusion detection, the book reveals the hidden mechanisms that shape user behavior and societal discourse. It explores the ethical implications of AI-powered content curation, privacy violations, and the rise of automated cyberattacks-while proposing regulatory and technological countermeasures. Case studies and real-world examples illustrate the consequences of unchecked AI deployment and the erosion of trust in online spaces. Key features: Examines misinformation, digital surveillance, and algorithmic bias Presents real-world case studies and AI behavior models Highlights privacy concerns and ethical frameworks Proposes AI-driven defenses and user empowerment strategies