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.

<p>AI for Our Planet: How Artificial Intelligence&nbsp;Can Solve Global Challenges brings together insights from leading scholars and industry experts to examine the responsible and strategic use of&nbsp;AI&nbsp;in addressing pressing global challenges, including climate change, resource scarcity, environmental degradation, ethical governance, and socio-economic inequality. &nbsp;</p><p>Across sixteen multidisciplinary chapters, the book positions&nbsp;AI&nbsp;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&nbsp;AI-driven governance, ethical and regulatory frameworks, climate diplomacy, circular economies, renewable energy, and sustainable resource management, the book offers forward-looking insights into&nbsp;AI’s expanding global impact.</p><p><br></p><p>Key Features</p><ul><li><p>Multidisciplinary perspectives integrating&nbsp;AI, sustainability, governance, and public policy</p></li><li><p>Evidence-based discussions supported by real-world case studies<br></p></li><li><p>Coverage of ethical, regulatory, and responsible&nbsp;AI&nbsp;deployment framework<br></p></li><li><p>Practical insights into&nbsp;AI&nbsp;applications for climate action, resource management, and circular economy models</p></li><li><p>Forward-looking analysis of&nbsp;AI’s role in global development and environmental stewardship</p></li></ul>

Recent Advancements in Computational Intelligence: Concepts, Methodologies and Applications (Part 2) explores cutting-edge developments in computational intelligence (CI), a dynamic field at the intersection of artificial intelligence, machine learning, and data science. Emphasising biologically inspired techniques such as neural networks, fuzzy logic, swarm intelligence, and evolutionary algorithms, the book demonstrates how CI solves real-world challenges involving uncertainty, ambiguity, and incomplete information. Aligned with Industry 4.0, it bridges theoretical foundations with practical applications across diverse domains, including healthcare, autonomous systems, cybersecurity, and smart computing.Structured into thematic sections, the book covers Social Computing, High-Performance Computing, Network Science, Smart Computing, and Intelligent Communications. Key chapters explore deep learning for autonomous vehicles, AI-driven healthcare diagnostics, graph theory for cybersecurity, edge computing in IoT, and reinforcement learning for generative AI. Case studies highlight innovations such as myelin segmentation in pediatric MRI, personalised marketing AI, and secure password generation with multi-factor authentication. Contributions from global experts offer a balanced blend of theoretical rigour and applied research.-Comprehensive coverage integrating CI concepts with emerging trends like generative AI and edge computing-Real-world case studies demonstrating CI’s transformative potential in industry-An interdisciplinary approach connecting computer science, engineering, and healthcare-Practical methodologies for researchers and implementable strategies for practitioners.

<p>Artificial Intelligence,&nbsp;Big Data, and&nbsp;Internet&nbsp;of&nbsp;Things&nbsp;for&nbsp;Sustainable Industry&nbsp;and&nbsp;Infrastructure Development&nbsp;explains how&nbsp;AI,&nbsp;IoT,&nbsp;Big Data, and Blockchain&nbsp;reshape&nbsp;industries&nbsp;by enabling real-time monitoring, improving resource efficiency, predicting climate risks, and strengthening ethical decision-making in&nbsp;sustainable&nbsp;infrastructure and smart&nbsp;industrial&nbsp;systems.</p><p>The book connects digital innovation with responsible business and healthcare practices, showing practical uses in waste reduction, supply chain&nbsp;sustainability, disaster forecasting, traffic automation, online safety, and trust-building systems for digital markets.</p><p>Key Features</p><ul><li><p>Reduce production waste using big data analytics<br><br></p></li><li><p>Boost operational efficiency and lower industrial costs<br><br></p></li><li><p>Predict climate risks using AI and historical datasets<br><br></p></li><li><p>Authenticates e-commerce reviews using ML and NLP<br><br></p></li><li><p>Decentralised digital industries using blockchain systems<br><br></p></li><li><p>Optimises sustainable supply chain logistics with data science<br><br></p></li><li><p>Forecasts natural disasters for smart cities using AI models<br><br></p></li><li><p>Improves disaster resilience using fractal and fractal-based analysis<br><br></p></li><li><p>Automates adaptive traffic signalling using Arduino and IR sensors<br><br></p></li><li><p>Detects cyberbullying using AI for faster moderation</p></li></ul>

<div><div>Smart Healthcare: Leveraging AI and Cloud Technologies for Enhanced Medical Solutions&nbsp;explores the rapidly evolving convergence of artificial intelligence and cloud computing in modern healthcare systems. This volume brings together cutting-edge research and practical insights that demonstrate how AI and cloud-enabled solutions are improving diagnostics, treatment planning, patient monitoring, data management, and operational efficiency, providing a balanced perspective on both technological innovation and real-world healthcare needs.</div><div><p>The chapters present an integrated view of AI techniques such as machine learning and computer vision alongside cloud-based infrastructures that enable scalable, secure, and efficient healthcare delivery. Through real-world case studies and applied examples, the book illustrates measurable improvements in clinical decision-making and healthcare workflows. It also critically examines key challenges, including data privacy, security, interoperability, and regulatory compliance, while proposing practical strategies to address them. Looking ahead, the book discusses emerging trends such as edge computing, blockchain, and intelligent robotics and their potential to further advance smart healthcare ecosystems.</p><p>Key Features:</p><ul><li><p>Examines AI- and cloud-driven innovations in healthcare delivery<br><br></p></li><li><p>Demonstrates real-world applications through case studies and examples<br><br></p></li><li><p>Addresses data security, privacy, and regulatory challenges<br><br></p></li><li><p>Explores emerging technologies shaping the future of smart healthcare</p></li></ul></div></div>

<p>Advanced Explorations in Machine Learning,&nbsp;Computer Vision, and IoT focuses on the convergence of&nbsp;machine learning&nbsp;algorithms,&nbsp;computer vision&nbsp;techniques, and Internet of Things (IoT) infrastructures to enable scalable, adaptive, and real-time intelligent applications. </p><p>Balancing strong theoretical foundations with system-level design considerations, the book serves as a structured guide for readers interested in how&nbsp;advanced&nbsp;mathematical models and&nbsp;learning&nbsp;paradigms drive modern AI-enabled IoT ecosystems.</p><p>The book begins with the mathematical, probabilistic, and computational principles underlying&nbsp;machine learning&nbsp;and visual&nbsp;intelligence, with subsequent chapters exploring linear and nonlinear models, kernel methods, neural networks, deep&nbsp;learning&nbsp;architectures, and optimisation techniques. </p><p>The integration of&nbsp;computer vision&nbsp;with IoT data pipelines, edge and cloud computing, wireless communication, and multi-agent systems is examined in detail.&nbsp;Advanced&nbsp;topics such as generative models, reinforcement&nbsp;learning, fuzzy&nbsp;intelligence, explainable AI, and real-world case studies demonstrate practical deployments in healthcare, smart environments, and autonomous systems.</p><h3>Key Features</h3><ul><li><p>Balanced coverage of theoretical foundations and practical system design<br><br></p></li><li><p>Clear mathematical intuition supporting&nbsp;advanced&nbsp;learning&nbsp;and&nbsp;vision&nbsp;models<br><br></p></li><li><p>Covers emerging topics including explainable AI, generative models, and reinforcement&nbsp;learning<br><br></p></li><li><p>Application-driven case studies with integrated perspectives on&nbsp;machine learning,&nbsp;computer vision, and IoT technologies</p></li></ul>