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Books in Computer science

The Computing collection presents a range of foundational and applied content across computer and data science, including fields such as Artificial Intelligence; Computational Modelling; Computer Networks, Computer Organization & Architecture, Computer Vision & Pattern Recognition, Data Management; Embedded Systems & Computer Engineering; HCI/User Interface Design; Information Security; Machine Learning; Network Security; Software Engineering.

  • Internet of Things Security

    Attacks, Tools, Techniques and Challenges
    • 1st book:metaData.edition
    • Preeti Mishra contributors.plusContributors
    • publicationLanguages:en
    Internet of Things Security: Attacks, Tools, Techniques and Challenges delves into the essential aspects of safeguarding devices within the IoT ecosystem. This book provides a comprehensive overview of the IoT architecture, its vast applications, and the difficulties in securing such an expansive infrastructure. Readers gain insight into various security vulnerabilities present at each layer of IoT architecture, alongside viable security solutions. The book thoroughly examines distinct access control and authentication strategies, including intrusion detection systems (IDS), misuse detection, anomaly detection, DNS-based security solutions, and the implementation of artificial intelligence (AI) in cognitive IoT security.Additionall... the book explores microservice architecture for distributed IoT security and blockchain-based IoT security solutions.

  • Feature Extraction and Image Processing for Computer Vision

    • 5th book:metaData.edition
    • Mark Nixon contributors.plusContributors
    • publicationLanguages:en
    Feature Extraction and Image Processing for Computer Vision, Fifth Edition is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated, providing a link between theory and implementation. Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation.

  • Cybersecurity Defensive Walls in Edge Computing

    • 1st book:metaData.edition
    • Agbotiname Lucky Imoize contributors.plusContributors
    • publicationLanguages:en
    Cybersecurity Defensive Walls in Edge Computing dives into the creation of robust cybersecurity defenses for increasingly vulnerable edge devices. This book examines the unique security challenges of edge environments, including limited resources and potentially untrusted networks, providing fundamental concepts for real-time vulnerability detection and mitigation through novel system architectures, experimental frameworks, and AI/ML techniques. Researchers and industry professionals working in cybersecurity, edge computing, cloud computing, defensive technologies, and threat intelligence will find this to be a valuable resource that illuminates critical aspects of edge-based security to advance theoretical analysis, system design, and practical implementation of defensive walls. With a focus on fast-growing edge application scenarios, this book offers valuable insights into strengthening real-time security for the proliferation of interconnected edge devices.

  • Federated Learning in Metaverse Healthcare

    Personalized Medicine and Wellness
    • 1st book:metaData.edition
    • Shubham Mahajan contributors.plusContributors
    • publicationLanguages:en
    Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and personalized care. The book introduces federated learning, emphasizing its advantages over traditional centralized machine learning in healthcare. It provides a historical context and discusses the technological advancements that led to the emergence of metaverse healthcare. Privacy-preserving methods crucial for protecting sensitive healthcare data within federated learning environments are also examined, underscoring the importance of secure communication protocols. Other important points include the transformation of healthcare delivery through virtual environments, remote consultations, and immersive experiences.The role of telemedicine in facilitating remote diagnostics and consultations via virtual platforms, and the applications of augmented reality wearables for real-time health monitoring and wellness tracking are detailed. Additionally, the book discusses federated learning's ability to deliver personalized treatment plans tailored to individual patient needs, its role in predictive modeling for disease risks and prevention, as well as virtual health coaches offering personalized guidance for wellness management. The challenges and ethical dilemmas of metaverse healthcare and federated learning, along with potential solutions, are also considered.

  • Quantum Computational AI

    Algorithms, Systems, and Applications
    • 1st book:metaData.edition
    • Long Cheng contributors.plusContributors
    • publicationLanguages:en
    Quantum Computational AI: Algorithms, Systems, and Applications is an emerging field that bridges quantum computing and artificial intelligence. With rapid advancements in both areas, this book serves as a vital resource, capturing the latest theories, algorithms, and practical applications at their intersection. It aims to be both informative and accessible, making it perfect for academics, researchers, industry professionals, and students eager to lead in these technologies. The book explores quantum algorithms, system design, and demonstrates real-world applications across various sectors. It provides a comprehensive understanding of how quantum principles can advance AI, revealing unprecedented possibilities and benefits.

  • Metaverse in the Healthcare Industry

    Potential Applications, Tools, and Techniques
    • 1st book:metaData.edition
    • Hemachandran Kannan contributors.plusContributors
    • publicationLanguages:en
    Metaverse in the Healthcare Industry: Potential Applications, Tools, and Techniques explores the intersection of rapidly evolving metaverse technology and its implications for the healthcare sector. The book provides a comprehensive overview of virtual and augmented reality, artificial intelligence, and other immersive technologies that have gained significant attention within the context of the metaverse in healthcare. It emphasizes how these technologies can revolutionize healthcare by enabling virtual clinics, patient education, remote medical training, therapeutic interventions, and much more. By bringing together diverse perspectives, it contributes to the ongoing discourse on the future of healthcare delivery and innovation.This book also delves into technical aspects of metaverse development and its integration with existing healthcare systems. It discusses ethical considerations and challenges associated with implementing metaverse technologies in healthcare settings. Additionally, it highlights the potential to reshape the healthcare landscape by fostering innovation, improving patient care, and revolutionizing medical training and research. Readers will gain insights into applications, tools, and techniques for leveraging the metaverse to create more effective and accessible healthcare solutions.

  • Applied Mathematical Modeling for Biomedical Robotics and Wearable Devices

    • 1st book:metaData.edition
    • S. Sountharrajan contributors.plusContributors
    • publicationLanguages:en
    Applied Mathematical Modelling for Biomedical Robotics and Wearable Devices delves into the innovative convergence of mathematical frameworks and biomedical engineering. The book begins by exploring how advanced mathematical modelling underpins the development and optimization of robotic systems and wearable technologies tailored for medical applications. With a strong emphasis on practical implementation, it serves as a bridge between theoretical concepts and real-world engineering challenges in the healthcare sector. Readers will gain insights into the transformative role of mathematical techniques that drive precision, functionality, and human-centric design in cutting-edge medical technologies.The book also covers interdisciplinary applications, integrating domains like biomechanics, sensor technology, and data analytics. By highlighting case studies and real-world scenarios, it showcases practical advancements in wearable devices that monitor health metrics and robotic systems that assist in surgical procedures.

  • Healthcare Applications of Neuro-Symbolic Artificial Intelligence

    • 1st book:metaData.edition
    • Boris Galitsky
    • publicationLanguages:en
    Healthcare Applications of Neuro-Symbolic Artificial Intelligence provides a comprehensive introduction to the field of neuro-symbolic (NS) artificial intelligence (AI), presenting the most recent advances in deep learning and integration of NS systems and large language models (LLMs). This book evaluates traditional approaches, current approaches, as well as the author’s own approach to NS, to create hybrid architectures and reasoning techniques to overcome the limitations of most existing AI systems such as deep learning, neural networks, and symbolic AI.This book will be a welcome resource for researchers and graduate students in AI, natural language processing, and biomedical informatics, as well as professionals in software development looking to redesign current systems to leverage LLMs through the health application of NS architecture.

  • Minds, Machines, and Misinformation

    Decoding Bias, Algorithms, and Trust
    • 1st book:metaData.edition
    • Don Donghee Shin
    • publicationLanguages:en
    Algorithms have become the key organizer through which power is enacted in our society. A huge amount of data regarding our daily routines are monitored and analyzed to make recommendations that manage, control, and lead our behaviors in everyday life. AI, Humans, and Misinformation: How Does AI Alter Human Behavior and How Do Humans Influence Algorithmic Misinformation? is a guide to understanding the dynamics of AI and misinformation in human contexts by addressing meaningful questions—How does AI alter human behavior and how do humans influence algorithmic decision-making? In answering these questions, this book examines the role of misinformation, disinformation, and fake news, and shows readers how to develop AI methods and algorithms that combat misinformation by using AI design choices that provide users and developers alike with meaningful control over AI. This book brings together various perspectives on algorithms into an integrated conceptual framework, and provides a broad socio-technical analysis, addressing critical and ethical issues of misinformation and fake news. The book offers a compelling insight into the misinformation phenomenon and the future of AI-based society. Readers will find an integrated technical analysis of the logic and social implications of algorithmic processes. Reporting from the cutting edge of critical technical methods and research, the result is useful and constructive for developing the relations between algorithms and humans. This is an imperative methodology for understanding what is at stake as industry and government use AI to reshape the world.

  • Data Science for Teams

    20 Lessons from the Fieldwork
    • 1st book:metaData.edition
    • Harris V. Georgiou
    • publicationLanguages:en
    Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.

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