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.

Advancing Intelligence: Exploring Smart Systems and Future Innovations with Machine Learning is a comprehensive volume collection examining how Artificial Intelligence (AI) and Machine Learning (ML) are reshaping industries, societies, and technologies worldwide. Written by global experts, the series combines theoretical foundations with practical applications, showcasing how intelligent systems address pressing challenges across healthcare, agriculture, urban development, business intelligence, and cybersecurity. Volume 2: Transformative Healthcare Solutions Powered by AI & ML investigates intelligent tools for precision medicine, medical informatics, wearable devices, and patient-centric care, demonstrating how AI enhances diagnosis and treatment outcomes. Volume 3: AI and ML Solutions Driving Modern Farming and Urban Innovation explores sustainable farming, smart agriculture, environmental monitoring, and intelligent city infrastructures for improved quality of life. Volume 4: AI-Driven Competitive Intelligence and Next-Generation Security focuses on financial analytics, business intelligence, cybersecurity, and digital forensics, emphasizing strategies for resilience in a data-driven era. Key Features Integrates AI and ML into healthcare, agriculture, urban, and business systems. Applies intelligent tools to improve diagnosis, sustainability, efficiency, and security. Analyzes case studies and global perspectives for real-world relevance. Explores future-ready innovations shaping intelligent, resilient ecosystems. Addresses both societal opportunities and technical challenges of AI adoption.

Introduction: Advancing Intelligence: Exploring Smart Systems and Future Innovations with Machine Learning is a comprehensive volume collection examining how Artificial Intelligence (AI) and Machine Learning (ML) are reshaping industries, societies, and technologies worldwide. Written by global experts, the series combines theoretical foundations with practical applications, showcasing how intelligent systems address pressing challenges across healthcare, agriculture, urban development, business intelligence, and cybersecurity. Volume 2: Transformative Healthcare Solutions Powered by AI & ML investigates intelligent tools for precision medicine, medical informatics, wearable devices, and patient-centric care, demonstrating how AI enhances diagnosis and treatment outcomes. Volume 3: AI and ML Solutions Driving Modern Farming and Urban Innovation explores sustainable farming, smart agriculture, environmental monitoring, and intelligent city infrastructures for improved quality of life. Volume 4: AI-Driven Competitive Intelligence and Next-Generation Security focuses on financial analytics, business intelligence, cybersecurity, and digital forensics, emphasizing strategies for resilience in a data-driven era. Key Features Integrates AI and ML into healthcare, agriculture, urban, and business systems. Applies intelligent tools to improve diagnosis, sustainability, efficiency, and security. Analyzes case studies and global perspectives for real-world relevance. Explores future-ready innovations shaping intelligent, resilient ecosystems. Addresses both societal opportunities and technical challenges of AI adoption.

Advancing Intelligence: Exploring Smart Systems and Future Innovations with Machine Learning is a comprehensive volume collection examining how Artificial Intelligence (AI) and Machine Learning (ML) are reshaping industries, societies, and technologies worldwide. Written by global experts, the series combines theoretical foundations with practical applications, showcasing how intelligent systems address pressing challenges across healthcare, agriculture, urban development, business intelligence, and cybersecurity. Volume 2: Transformative Healthcare Solutions Powered by AI & ML investigates intelligent tools for precision medicine, medical informatics, wearable devices, and patient-centric care, demonstrating how AI enhances diagnosis and treatment outcomes. Volume 3: AI and ML Solutions Driving Modern Farming and Urban Innovation explores sustainable farming, smart agriculture, environmental monitoring, and intelligent city infrastructures for improved quality of life. Volume 4: AI-Driven Competitive Intelligence and Next-Generation Security focuses on financial analytics, business intelligence, cybersecurity, and digital forensics, emphasizing strategies for resilience in a data-driven era. Key Features Integrates AI and ML into healthcare, agriculture, urban, and business systems. Applies intelligent tools to improve diagnosis, sustainability, efficiency, and security. Analyzes case studies and global perspectives for real-world relevance. Explores future-ready innovations shaping intelligent, resilient ecosystems. Addresses both societal opportunities and technical challenges of AI adoption.

With contributions from leading researchers and industry experts, this book examines cutting-edge applications, integration models, and sustainable solutions across sectors including finance, agriculture, healthcare, IoT, and smart cities. Chapters cover blockchain-enabled fintech operations, fraud detection, deep learning–driven intrusion detection, AI-enhanced smart contracts, and data-driven healthcare innovations. Case studies, methodologies, and future-oriented insights demonstrate how these technologies can foster secure, efficient, and sustainable ecosystems. By bridging theoretical foundations with practical implementations, this book offers readers a roadmap to navigate the opportunities and challenges shaping the next generation of intelligent, blockchain-powered systems. Key Features Integrates blockchain with machine learning for real-world applications. Applies advanced analytics, automation, and AI models to enhance blockchain ecosystems. Develops secure solutions in fintech, agriculture, healthcare, IoT, and smart cities. Evaluates case studies and frameworks addressing challenges and vulnerabilities. Explores sustainable, future-ready trends shaping intelligent systems.

AI for Our Planet: How Artificial Intelligence Can Solve Global Challenges brings together insights from leading scholars and industry experts to examine the responsible and strategic use of AI in addressing pressing global challenges, including climate change, resource scarcity, environmental degradation, ethical governance, and socio-economic inequality. Across sixteen multidisciplinary chapters, the book positions AI not only as a transformative technological tool but also as a framework for building a sustainable and equitable future. Featuring real-world case studies on AI-driven governance, ethical and regulatory frameworks, climate diplomacy, circular economies, renewable energy, and sustainable resource management, the book offers forward-looking insights into AI’s expanding global impact. Key Features Multidisciplinary perspectives integrating AI, sustainability, governance, and public policy Evidence-based discussions supported by real-world case studies Coverage of ethical, regulatory, and responsible AI deployment framework Practical insights into AI applications for climate action, resource management, and circular economy models Forward-looking analysis of AI’s role in global development and environmental stewardship

This book deals with the methods of theoretical and experimental research of antennas, which are based on the electromagnetic (EM) theory. The theory of thin antennas explains antenna analysis, as thin antennas represent one of the main types of radiators and are extensively applied as both independent antennas and as elements of more complicated antennas.

The contents of the book cover new methods of antenna analysis and design, including the theory of electrically coupled lines, the methods of complex potential, compensation, impedance line, and mathematical programming among others. New results from these experiments are also detailed. The complex potential method is generalized to inhomogeneous media and is employed in conical and parabolic problems. The theory of electrically coupled lines allows considering theoretical problems of multi-conductor cables and multi-radiator antennas. The compensation method permits creating a weak field area in the near region of a transmitting antenna. The method of impedance line is applied to antennas with loads, and the method of mathematical programming deals with selecting loads to develop antennas with the required characteristics.

The methods described within this text enable engineers to calculate parameters for new types of antennas, particularly, the self-complementary antennas. In the general case, similar properties pertain to the three-dimensional self-complementary antennas, their variants are also considered in the book. Close attention is also paid to the field compensation in the near region of a transmitting antenna and to the creation of the ‘dark spot’.

The book is an essential reference for graduates and researchers requiring knowledge on the design and engineering of new antennas as well as the underlying electromagnetic and mathematical principles.