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 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.

AIoT (Artificial Intelligence of Things) and Big Data Analytics are catalyzing a healthcare revolution. This book is an accessible volume that summarizes the information available. In this book, researchers explore how AIoT and Big Data can seamlessly integrate into healthcare, enhancing medical services and devices while adhering to established protocols. The book demonstrates the crucial role of these technologies during healthcare crises like the COVID-19 pandemic. It presents novel solutions and computational techniques powered by AIoT, Machine Learning, and Deep Learning, providing a new frontier in healthcare problem-solving. Key Features: Real-Life Illustrations: Real-world examples showcase AIoT and Big Data in action, highlighting their impact in healthcare. Comprehensive Exploration: The book offers a thorough examination of AIoT, Big Data, and their harmonious synergy within the healthcare landscape. Visual Aids: Complex concepts become approachable through diagrams, flowcharts, and infographics, making technical processes and system designs more digestible. Ethical Insights: Delving into the ethical dimensions of AIoT and Big Data, it addresses concerns like data bias, patient consent, and transparency in healthcare. Forward-Looking Discourse: The book engages with emerging trends, potential innovations, and the future direction of AIoT and Big Data, making it a compass for healthcare transformation. Researchers, whether from academia, industry, or research and development organizations, interested in AIoT, Big Data, artificial intelligence, and healthcare optimization, will find this book informative. It also serves as an update for tech enthusiasts who want to explore the future of healthcare powered by AI and data.

Stay informed about recent trends and groundbreaking research driving innovation in the AI-IoT landscape. AI, a simulated form of natural intelligence within machines, has revolutionized various industries, simplifying daily tasks for end-users. This book serves as a handy reference, offering insights into the latest research and applications where AI and IoT intersect. The book includes 12 edited chapters that provide a comprehensive exploration of the synergies between AI and IoT. The contributors attempt to address engineering opportunities and challenges in different fields. Key Topics: AI and IoT in Smart Farming: Explore how these technologies enhance crop yield and sustainability, revolutionizing agricultural practices. AIoT (Artificial Intelligence of Things): Understand the amalgamation of AI and IoT and its applications, particularly focusing on smart cities and agriculture. Smart Healthcare and Predictive Disease Analysis: Uncover the crucial role of AI and IoT in early disease prediction and improving healthcare outcomes. Applications of AI in Various Sectors: Explore how AI contributes to sustainable development, sentiment analysis, education, autonomous vehicles, fashion, virtual trial rooms, and more. Each chapter has structured sections with summaries and reference lists, making it an invaluable resource for researchers, professionals, and enthusiasts keen on understanding the potential and impact of these technologies in today's rapidly evolving world.

This book explains different applications of supervised and unsupervised data engineering for working with multimedia objects. Throughout this book, the contributors highlight the use of Artificial Intelligence-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, automation in vehicle manufacturing, data science and automation in electronics industries. The book presents seven chapters which present use-cases for AI engineering that can be applied in many fields. The book concludes with a final chapter that summarizes emerging AI trends in intelligent and interactive multimedia systems. Key features: - A concise yet diverse range of AI applications for multimedia data engineering - Covers both supervised and unsupervised machine learning techniques - Summarizes emerging AI trends in data engineering - Simple structured chapters for quick reference and easy understanding - References for advanced readers This book is a primary reference for data science and engineering students, researchers and academicians who need a quick and practical understanding of AI supplications in multimedia analysis for undertaking or designing courses. It also serves as a secondary reference for IT and AI engineers and enthusiasts who want to grasp advanced applications of the basic machine learning techniques in everyday applications.

Basics of Python Programming: A Quick Guide for Beginners is an essential companion to mastering the Python programming language. The book presents information about Python in 12 structured chapters with a strong emphasis on fundamentals and practical information. Starting with basic operators, functions and expressions, contents explain file handling, exception handling and modules. The book concludes with advanced topics such as object oriented programming and machine learning. Key Features: Fundamental Focus: Covers the core concepts of Python programming to build a strong foundation in python programming in an easy-to-understand format. Practical Demonstrations: Learn by doing. This textbook includes hands-on practical demonstrations that reinforce your understanding of Python concepts. IDE Guidance: Includes programming and installation guidance for Python-supporting Integrated Development Environments (IDEs). Explores Python Frameworks: Introduces Python frameworks such as Matplotlib, TensorFlow, PyTorch, Scikit-Learn, and NLTK for complex projects. Python for Machine Learning: Gives a preliminary understanding of Python for machine learning tasks for data science and AI applications. Basics of Python Programming: A Quick Guide for Beginners is the perfect starting point for aspiring students, programmers and tech enthusiasts. Whether you're a student looking to build a solid foundation in Python or an industry professional venturing into machine learning and artificial intelligence, this textbook has you covered.