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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.

BooksJournals

    • Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics

      • 1st Edition
      • February 1, 2026
      • Sayooj Aby Jose + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 2 3 4 0
      • eBook
        9 7 8 0 4 4 3 3 3 2 3 5 7
      Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics offers a comprehensive exploration of innovative methodologies at the intersection of mathematics, biology, and medicine. This book delves into advanced mathematical modeling, artificial intelligence, and computational intelligence, providing essential tools for understanding and managing complex disease dynamics. Covering a wide range of topics, including fractional-order modeling, optimal control strategies, and privacy-preserving technologies, it addresses critical challenges in public health and healthcare systems. With contributions from leading experts, this volume bridges theoretical advancements and practical applications, making it an invaluable resource for researchers, healthcare professionals, and academics seeking interdisciplinary solutions to global health issues.

    • Digital Supply Chain Transformation

      • 1st Edition
      • April 1, 2026
      • Vinaytosh Mishra
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 9 9 5 0
      • eBook
        9 7 8 0 4 4 3 3 3 9 9 6 7
      Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions delves into the intricate world of supply chain management, emphasizing the role of digital transformation in modern supply chains. Through a blend of theoretical learning and practical applications, readers will gain a deep understanding of foundational supply chain principles while exploring emerging trends and technologies reshaping the industry. Topics such as system dynamics modelling, machine learning, artificial intelligence, and end-to-end visibility will be explored in-depth, equipping readers with the tools and knowledge needed to excel in the rapidly evolving landscape of supply chain management. There is a growing recognition that supply chain management can be significantly improved by leveraging modern technologies, such as machine learning and generative AI, to enhance efficiency and accuracy. Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions helps readers:Comprehend core principles and elements of supply chain management and its pivotal role in businesses and industries.Recognize the significance of digital transformation in supply chains, understanding the tools, technologies, and strategies essential for a successful transformation.Evalu... the importance of end-to-end supply chain visibility and employ methods and technologies to enhance this visibility in practical scenarios.Apply system dynamics modelling techniques to address complex supply chain problems and optimize supply chain processes.Utilize supply chain analytics and tools to make data-driven decisions, enhancing efficiency and reducing operational costs.Understand the potential of machine learning and artificial intelligence in supply chains, applying these technologies to innovate and solve real-world supply chain challenges.Identify and analyze emerging trends in supply chain management, anticipating future challenges and opportunities in the field.Demonstrate the ability to apply theoretical knowledge to practical scenarios, devising solutions for real-world supply chain challenges.Criticall... evaluate supply chain strategies, technologies, and solutions, recommending improvements and innovations based on informed analysis.

    • Essentials of Big Data Analytics

      • 1st Edition
      • February 1, 2026
      • Pallavi Chavan + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 2 0 6 2
      • eBook
        9 7 8 0 4 4 3 4 5 2 0 7 9
      Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments.

    • Autonomous Vehicle Safety Solutions

      • 1st Edition
      • January 1, 2026
      • Aparna Kumari
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 5 2 8 0
      • eBook
        9 7 8 0 4 4 3 3 3 5 2 9 7
      Autonomous Vehicle Safety Solutions: Foundations, Technologies, and Prospects for the Future addresses the dynamically evolving state of the art that traverses and brings together automotive engineering, electronics engineering, and computer science. Its technical overview of smart vehicles' capabilities, propelled by headway in artificial intelligence and sensor technologies, enables in-depth understanding of the safety-assurance complexities that guide their design and operation.The comprehensive volume distinguishes itself for its treatment of several exciting, emerging opportunities and trends, including machine learning algorithms, V2X connectivity, and cybersecurity. Up-to-date applications are featured through practical examples and expert insights, bringing additional value to a discourse which underscores the critical need for ongoing safety advancements and adaptability in the fast-moving context of autonomous driving systems, with their related implications on sustainability and society as a whole.The outcome is a cornerstone resource for academia and industry alike that encourages further transformative, interdisciplinary investigations to bring safety solutions to maturity and subsequent rigorous testing for validation standards to be defined, thus realizing, in a future not distant from now, a scenario where self-driving vehicles can co-exist seamlessly with traditional modes of transportation and also integrate reliably, efficiently, and without risks within the larger infrastructure.

    • Learning-Driven Game Theory for AI

      • 1st Edition
      • February 1, 2026
      • Mehdi Salimi + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 3 8 5 2 3
      • eBook
        9 7 8 0 4 4 3 4 3 8 5 3 0
      Learning-Driven Game Theory for AI: Concepts, Models, and Applications offers in-depth coverage of recent methodological and conceptual advancements in various disciplines of Dynamic Games, namely differential and discrete-time dynamic games, evolutionary games, repeated and stochastic games, and their applications in a variety of fields, such as computer science, biology, economics, and management science. In this book, the authors bridge the gap between traditional game theory and its modern applications in artificial intelligence (AI) and related technological fields. The dynamic nature of contemporary problems in robotics, cybersecurity, machine learning, and multi-agent systems requires game-theoretic solutions that go beyond classical methods. The book delves into the rapidly growing intersection of pursuit differential games and AI, focusing on how these advanced game-theoretic models can be applied to modern AI systems, making it an indispensable resource for both academics and professionals. The book also provides a variety of applications demonstrating the practical integration of AI and game theory across various disciplines, such as autonomous systems, federated learning, and distributed decision-making frameworks. The book also explores the use of game theory in reinforcement learning, swarm intelligence, multi-agent coordination, and cybersecurity. These are critical areas where AI and dynamic games converge. Each chapter covers a different facet of dynamic games, offering readers a comprehensive yet focused exploration of topics such as differential and discrete-time games, evolutionary dynamics, and repeated and stochastic games. The absence of static games ensures a concentrated focus on the dynamic, evolving problems that are most relevant today.

    • Computational Intelligence in Surveillance Systems Using Image Processing

      • 1st Edition
      • March 1, 2026
      • Jay Kumar Pandey + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 4 0 8 2
      • eBook
        9 7 8 0 4 4 3 3 6 4 0 9 9
      Traditional surveillance systems struggle to process large volumes of visual data, identify specific objects or behaviors, and adapt to dynamic environments. Computational intelligence, which encompasses techniques like artificial intelligence (AI), machine learning (ML), and computer vision, offers powerful tools to address these challenges by enabling automated analysis, pattern recognition, and decision-making based on visual data. Computational Intelligence in Surveillance Systems Using Image Processing addresses the unique challenges and ethical considerations of applying AI and ML, offering a nuanced understanding of the regulatory landscape. It provides insights into the responsible development and deployment of technologies to unlock the transformative potential of computational intelligence to revolutionize surveillance systems and advance the capabilities of security and monitoring across various sectors.

    • Quantum Theory, Decision Making and Social Dynamics

      • 1st Edition
      • February 1, 2026
      • Tofigh Allahviranloo + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 4 9 0 7
      • eBook
        9 7 8 0 4 4 3 3 6 4 9 1 4
      Quantum Theory, Decision Making and Social Dynamics is an innovative exploration of the intersection of quantum theory, decision-making, and social networks. As quantum theory continues to permeate various fields, there is a growing need for accessible resources that elucidate its principles and applications. This book aims to fill that gap by illuminating the complex interplay between quantum theory and social dynamics, particularly in the context of decision-making. It addresses the challenges of understanding and applying quantum theory in social dynamics and equips readers with the knowledge to harness its potential in decision-making processes. The book is organized into nine chapters, each delving into a specific aspect of quantum theory and its applications. Chapter 1 provides an overview of quantum theory, tracing its historical development and key principles. Chapter 2 explores the integration of quantum theory with fuzzy systems, highlighting real-world applications and emerging trends. In Chapter 3, the focus shifts to the application of quantum theory in social networks, including quantum social network models and case studies. Chapter 4 examines decision-making processes within social networks, while Chapter 5 introduces and analyzes quantum decision-making models. Chapter 6 delves into quantum graph theory, discussing quantum graphs, walks, and algorithms. The subsequent chapters address the challenges and limitations of quantum decision-making (Chapter 7), future directions for the field (Chapter 8), and conclude with a summary of key points and final thoughts (Chapter 9). Quantum Theory, Decision Making and Social Dynamics is an invaluable resource for graduate students, researchers, and professionals in the fields of applied mathematics, computer science, physics, and social sciences interested in applying quantum approaches to the study of decision-making. These readers often face challenges in understanding complex quantum concepts, applying quantum theory to social networks, and utilizing quantum decision-making models. This book provides a solid foundation in quantum theory, introduces its applications in diverse areas, and prepares readers to navigate the quantum age with confidence. By offering a comprehensive overview and practical insights, this book empowers its audience to appreciate the profound impact of quantum theory on social network dynamics and decision-making.

    • Integrated Photonics for Sensing Applications

      • 1st Edition
      • April 1, 2026
      • Anu Agarwal + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 6 5 7 8 5
      • eBook
        9 7 8 0 4 4 3 2 6 5 7 9 2
      Photonic Integrated Circuits for Sensing Applications delves into the fascinating world of sensors within the realm of integrated photonics. The book begins with a historical overview, tracing the evolution of spectroscopic sensing techniques such as FTIR, Raman, SPR, and reflectometry, each contributing to the field’s growth. It emphasizes the transformative potential of photonic integrated circuit (PIC) sensor systems by showcasing their advantages in achieving low SWAP-C metrics (size, weight, power, and cost) while maintaining high performance. Complete with technical insights, the book sets the stage for understanding how PICs are revolutionizing sensing applications across diverse industries.Beyond the introductory scope, the book thoroughly examines the components that constitute PIC sensor systems, including waveguides (operating below and above 1550 nm), ring resonators, photonic crystals, and MZ interferometers. It also explores integrated systems designed for chem–bio sensing applications, leveraging biofunctionalization and sorbent technologies. With attention to manufacturing scalability, topics such as materials, PDK development, and sensor packaging are addressed, ensuring readers grasp the practical aspects of producing advanced sensor systems at scale.

    • High-Performance Computing

      • 1st Edition
      • April 1, 2026
      • Oscar Diez
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 1 3 4 8
      • eBook
        9 7 8 0 4 4 3 4 5 1 3 5 5
      The exponential growth in computational needs across industries has created a demand for a modern guide that consolidates both theoretical and hands-on knowledge of high performance computing (HPC). High-Performance Computing: Applications and Frontiers offers a comprehensive and up-to-date reference to HPC. Sitting alongside the companion text, Foundations of High Performance Computing, High-Performance Computing: Applications and Frontiers builds on the fundamental knowledge to examine specialized tools, emerging trends including parallel and distributed computing, GPU programming, cloud-based HPC, and quantum computing, that define HPC's role in cutting-edge research and industry applications. The volume is tailored for advanced students, researchers, and professionals ready to explore advanced HPC programming, optimization, and applications, and features topics such as domain-specific implementations, machine learning, and the future of HPC. High-Performance Computing: Applications and Frontiers covers essential modern topics such as containerization, AI-driven HPC, hybrid/multi-cloud HPC strategies, or advancements in GPU and quantum computing. Readers will find High-Performance Computing: Applications and Frontiers is a current, versatile reference that blends theoretical principles with practical examples, preparing them to navigate both traditional and emerging HPC challenges effectively.

    • Harnessing Artificial Intelligence to Ensure Diverse Global Teams

      • 1st Edition
      • March 1, 2026
      • Harish Garg + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 4 1 3 5 9
      • eBook
        9 7 8 0 4 4 3 3 4 1 3 6 6
      Harnessing Artificial Intelligence to Ensure Diverse Global Teams explores new research and applications of AI which can be used to address the distinct challenges of diverse, distributed teams. Incorporating compelling case studies and strategic guidance, the book demonstrates how AI can be developed and applied within systems and programs to promote inclusion, break down barriers, and enhance collaboration in cross-cultural organizational settings. Providing case studies and examples, this book equips computer scientists and engineers with actionable strategies for integrating AI seamlessly into programs and applications designed for diverse global teams. With contributions from experts in AI and team dynamics, this book will benefit leaders and team members seeking to leverage AI for improved teamwork across geographical and cultural boundaries, providing an up-to-date resource for uniting, uplifting, and optimizing global collaborations through inclusive artificial intelligence.

Related subjects

Applications

Artificial intelligence

Business intelligence

Computer systems organization

Computer vision and pattern recognition

Computing milieux

Data

Graphics cad hci

Hardware and architecture

Information systems

Internet and web technology

Software

Theory and mathematics