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>