This book explores the intersection of the Internet of Things (IoT) and Artificial Intelligence (AI) in sustaining a green environment, sustainable societies, and thriving industries. It offers a comprehensive exploration of how these technologies intersect and transform various sectors to enhance environmental conservation, societal well-being, and industrial progress. The book features a diverse array of case studies, methodologies, and notes on technological advancements. Readers will gain valuable insights into the impact of AI and IoT on sustainable initiatives through real-world examples, research findings, and discussions on future directions. Key themes AI in complex and versatile scenarios: Chapters 1 and 4 explore AI applications in combatant identification and COVID-19 monitoring IoT for efficiency and data-driven decision-making: Chapters 2, 3, and 7 focus on IoT implementations in battery monitoring for electric vehicles, healthcare systems, and precision farming AI for diagnostics and computer vision: Chapters 5, 9, and 13 highlight AI-driven solutions for plant disease detection, fetal spine disorder detection, and defect detection Industry applications: Chapters 6, 8, 10, 11, 12, 14, 15, 16, and 17 cover AI and IoT in healthcare, transportation, supply chain management, endangered species protection, crop management, and pollution detection, showcasing their transformative potential across various domains. This book is ideal for readers with multidisciplinary backgrounds, including researchers, academics, professionals, and students interested in IoT, AI, environmental sustainability, healthcare, agriculture, smart technologies, and industrial innovation.

This volume explores diverse applications for automated machine learning and predictive analytics. The content provides use cases for machine learning in different industries such as healthcare, agriculture, cybersecurity, computing and transportation. Key highlights of this volume include topics on engineering for underwater navigation, and computer vision for healthcare and biometric applications. Chapters 1-4 delve into innovative signal detection, biometric authentication, underwater AUV localization, and COVID-19 face mask detection. Chapters 5-9 focus on wireless pH sensing, differential pattern identification, economic considerations in off-grid hybrid power, high optimization of image transmission, and ANN-based IoT-bot traffic detection. Chapters 10-12 cover mixed-signal VLSI design, pre-placement 3D floor planning, and bio-mimic robotic fish. Finally, Chapters 13 and 14 explore underwater robotic fish and IoT-based automatic irrigation systems, providing a comprehensive overview of cutting-edge technological advancements. The book is a resource for academics, researchers, educators and professionals in the technology sector who want to learn about current trends in intelligent technologies.

This volume covers a wide array of topics related to research, technology and sustainability for technology researchers and educators. Chapter 1 explores the detection of fake news in a distributed environment. Material science is covered in Chapter 2, which explains the influence of MOS2, B4C, and graphite on the mechanical and dry sliding wear behavior of aluminum 7075 hybrid matrix composites. Chapter 3 focuses on sensors and antennas for smart sensor networks. Chapters 4 to 8 delve into various aspects of electrical and computer engineering, including induction motor condition monitoring, automatic conversion of building plans to graphs for robot navigation, and analysis of defects in microscopic and electroluminescent images using AI and image processing algorithms. Chapters 9 to 16 cover topics such as missing data prediction techniques, breast cancer diagnosis on mammography, groundwater contamination, biofertilizers, organic farming, and remediation using organisms. Sustainable development is a key theme in Chapters 17 to 26, addressing issues like sensor-based vehicle fuel theft detection, waste management techniques, bioremediation of soil contaminated with heavy metals, sustainable agriculture practices, and novel approaches in bioplastics and nanoremediation. Finally, Chapters 27 to 29 touch upon renewable energy and Industrial IoT, discussing research challenges in renewable energy sources, recent trends, and the transformation brought about by the Industrial Internet of Things.

This book offers comprehensive insights into digital forensics, guiding readers through analysis methods and security assessments. Expert contributors cover a range of forensic investigations on computer devices, making it an essential resource for professionals, scholars, and students alike. Chapter 1 explores smart home forensics, detailing IoT forensic analysis and examination of different smart home devices. Chapter 2 provides an extensive guide to digital forensics, covering its origin, objectives, tools, challenges, and legal considerations. Chapter 3 focuses on cyber forensics, including secure chat application values and experimentation. Chapter 4 delves into browser analysis and exploitation techniques, while Chapter 5 discusses data recovery from water-damaged Android phones with methods and case studies. Finally, Chapter 6 presents a machine learning approach for detecting ransomware threats in healthcare systems. With a reader-friendly format and practical case studies, this book equips readers with essential knowledge for cybersecurity services and operations. Key Features: 1.Integrates research from various fields (IoT, Big Data, AI, and Blockchain) to explain smart device security. 2.Uncovers innovative features of cyber forensics and smart devices. 3.Harmonizes theoretical and practical aspects of cybersecurity. 4.Includes chapter summaries and key concepts for easy revision. 5.Offers references for further study.

This reference reviews the architectural requirements of IT systems that are designed to digitally transform business operations. It is a compilation of 7 timely reviews that demonstrate how adopting emerging technologies and examining the security-based concerns can lead to innovation in the business sector. The aim of the book is to guide scholars and business consultants on IT and business frameworks that can help new and existing organizations navigate the challenges posed by disruptive technologies to create a competitive advantage. The reviews are contributed by experts in business and information technology. The chapters cover diverse topics related to technological advancements and digital security measures. Chapter 1 offers insights into accessing and securing patient medical records through a blockchain-based framework, detailing research methodology, scalability, and standards. Chapter 2 discusses cyber threats in IoT-connected cars, addressing vulnerabilities, attack methods, and defense strategies. Chapter 3 focuses on malware analysis and detection using machine learning techniques. Chapter 4 emphasizes on securing IoT-based home automation. Chapter 5 presents an IoT policy and governance reference architecture to ensure integrity and security across devices. Chapter 6 explores organizational security improvements to prevent deepfake ransomware. Finally, Chapter 7 examines the use of machine learning in credit card fraud detection, discussing challenges and control layers.

Optimization Techniques for Decision-making and Information Security is a scholarly compilation that has been edited by experts with specialized knowledge in the fields of decision theory and cybersecurity. Through the synthesis of an extensive array of information, this edited volume presents novel methodologies and approaches that forge a link between the critical domain of information security and the realm of decision-making processes. The publication commences with a fundamental investigation that establishes the theoretical foundations of information security-relevant decision-making models. The subsequent chapters present comprehensive evaluations of real-world applications, showcasing an assortment of optimization techniques. The book offers a wide range of perspectives on the practical implementation of data analysis in various domains, including but not limited to power generation and optimization, solid transportation problems, soft computing techniques, wireless sensor networks, parametric set-valued optimization problems, data aggregation optimization techniques, fuzzy linear programming problems, and nonlinear chaotic systems. The anthology concludes with a comprehensive summary of the most noteworthy observations and ramifications extracted from the projects of all contributors. Key features - Presents a wide variety of sophisticated optimization methodologies - Explores the intricate intersection of decision theory and the safeguarding of confidential information. - Emphasizes effectiveness in improving decision-making processes designed to strengthen information security measures. - Showcases practical examples in different industrial domains through case studies and real-world problems. - Provides guidance and contemplations on strengthening information security environments. - Includes scientific references for advanced reading This book serves as an essential reference for policymakers, researchers, and professionals who are learning about or working in information security roles.

Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies. The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains. Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management. Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.

The book gives a comprehensive overview of the evolving landscape of cyber-physical systems (CPS) with a primary focus on security challenges and innovative solutions. CPS, encompassing a wide array of applications from e-Health to smart grid and industry automation, is explored in depth through eight edited reviews. The book starts with an exploration of various threat detection and prevention techniques in IoT environments, followed by discussions on security in smart grid cyber-physical systems, and the integration of cyber-physical systems with game theory. It also covers important topics such as cyber-physical systems in healthcare, augmented reality challenges, network and computer forensic frameworks, and a review of industrial critical infrastructure perspectives. The journey from traditional data warehouses to data lakes is thoroughly examined, shedding light on the evolution of data storage methods. The final chapter explains intrusion detection in industrial critical infrastructure, reviewing feature selection and classification models. By navigating through these topics, the book equips readers with a comprehensive understanding of cybersecurity challenges and solutions in an era of automation and IoT technologies. This book is intended for a diverse readership, including professionals, researchers, and technology enthusiasts keen on exploring the intricacies of CPS, IoT security, data storage evolution, and industrial infrastructure protection. Key Features: -Analytical insights into cyber-physical systems security. -Thorough exploration of threat detection and prevention techniques. -Application-focused chapters covering smart grid, healthcare, and more. -Integration of game theory and augmented reality in cyber-physical systems. -Comprehensive overview on network and computer forensic frameworks.

This volume explores a diverse range of applications for automated machine learning and predictive analytics. The content provides use cases for machine learning in different industries such as healthcare, agriculture, cybersecurity, computing and transportation. Chapter 1 introduces an innovative device for automatically notifying and analyzing the impact of automobile accidents. Chapter 2 focuses on the detection of malaria using systematized image processing techniques. In Chapter 3, an intelligent technique based on LMEPOP and fuzzy logic for the segmentation of defocus blur is discussed. Predictive analytics is introduced in Chapter 4, providing an overview of this emerging field. Chapter 5 delves into discrete event system simulation, offering insights into its applications. The performance analysis of different hypervisors in OS virtualization is explored in Chapter 6. Load balancing in cloud computing is the subject of investigation in Chapter 7. Chapter 8 presents a survey on a facial and fingerprint-based voting system utilizing deep learning techniques. Chapter 9 explores IoT-based automated decision-making with data analytics in agriculture. Biometric recognition through modality fusion is investigated in Chapter 10. Chapter 11 offers a new perspective on evaluating machine learning algorithms for predicting employee performance. Pre-process methods for cardiovascular diseases diagnosis using CT angiography images are discussed in Chapter 12. Chapter 13 presents the implementation of a smart wheelchair using ultrasonic sensors and LabVIEW. Cryptography using the Internet of Things is the focus of Chapter 14. Chapter 15 explores machine learning applications for traffic sign recognition, and the book concludes with Chapter 16, which analyzes machine learning algorithms in healthcare. The book is a resource for academics, researchers, educators and professionals in the technology sector who want to learn about current trends in intelligent technologies.

This volume explores IoT architectures, their configuration, and operability in wireless sensor networks. The topics are spread across nine structured chapters covering fundamental and applied knowledge about wireless sensor networks using IoT devices. The book starts with an introduction to the subject, giving readers a quick overview of IoT enabled networks and bio-inspired approaches towards network design. This is followed by chapters explaining optimized routing protocols for accident detection, efficiency and performance analysis. The book concludes with four chapters dedicated to security applications of wireless networks, for homes, urban areas and businesses. Overall, the volume gives a balance of theoretical and practical information for readers. The book is intended as a resource for graduate and postgraduate students for understanding network design for home and embedded applications, specifically using single board computing devices. It also serves as a guide for networking courses and assessments.