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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

  • Full-Stack Web Development from the Ground Up

    Principles, Practices, and Technologies
    • 1st Edition
    • Christopher D Hundhausen
    • English
    Full-Stack Development from the Ground Up: Principles, Practices, and Technologies addresses the growing need for a comprehensive upper-division computer science textbook that provides in-depth treatment of full-stack web development using the modern web development technologies that students are likely to encounter in industry. Professional full-stack web developers who are capable of developing both the front-end user interfaces and back-end databases and services for dynamic websites are in high demand. The book begins by laying a foundation in HTML, CSS and JavaScript—the building blocks of client-side web development.It then explores one particular web development stack in detail: MERN, which stands for MongoDB, Express.js, React.js and Node.js. Together, these four technologies provide powerful support for full-stack web development in a single programming language—JavaScript. The crucial final step in the web development process is deploying apps to a server, so users can interact with them. This book simplifies deployment by focusing on just one web deployment environment: Amazon Web Services (AWS), and only those AWS tools that are absolutely necessary to deploy MERN applications.

  • Tcl/Tk

    A Developer's Guide
    • 4th Edition
    • Clif Flynt
    • English
    Tcl/Tk: A Developer's Guide, Fourth Edition is an essential resource for computer professionals, from systems administrators to programmers. It covers new Tcl features, expanded Tcl-OO coverage, web technology using Rivet and SQLite, and AI integration with AWS. The book also delves into Tcl's standard tools, multi-faceted nature, and extensibility, making it ideal for developing GUIs, client/server middleware, and web applications. Readers will quickly learn to code in Tcl and extend its capabilities with the inclusion of numerous code examples and case studies.The updated edition includes over 150 pages on the latest Tcl extensions, proven techniques, and tools for effective programming. Extensive code snippets and online tutorials enhance understanding, while case studies provide practical insights. The book also discusses Tcl's role as the hidden "secret sauce" in commercial applications, highlighting its graphics and control infrastructure. With a vibrant user community and evolving API, Tcl/Tk remains a powerful and versatile programming platform for both beginners and experienced programmers.

  • Learning-Driven Game Theory for AI

    Concepts, Models, and Applications
    • 1st Edition
    • Mehdi Salimi + 1 more
    • English
    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.

  • Foundations of Cloud Computing

    • 1st Edition
    • Robert Shimonski
    • English
    Foundations of Cloud Computing provides readers with a guidebook to navigating the field of Cloud Computing, including the guiding principles, key concepts, history, terminology, state-of-the-art in research, and a roadmap to where the field intersects and interacts with related fields of research and development. In this age of total connectivity, researchers need to be able to communicate and collaborate with a wide range of colleagues across multiple disciplines. This book helps researchers from all fields understand what Cloud Computing is, how it works, and how to speak the language of collaboration with developers and researchers who specialize in the field. With a complete and in-depth foundation in the key concepts of the field, and a roadmap to where and how Cloud Computing intersects across the domains of scientific research and application development, this book gives readers everything they need to navigate and apply this important, ubiquitous technology.

  • Essentials of Big Data Analytics

    Applications in R and Python
    • 1st Edition
    • Pallavi Chavan + 2 more
    • English
    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.

  • Digital Transformation in Artificial Systems

    Engineering Requirements and Political, Economic, and Philosophical Challenges
    • 1st Edition
    • Mirko Farina + 3 more
    • English
    The last decade has seen exponential growth in the development of digital technologies. This has led to significant shifts in the political arena as well as in the economy, precipitating a series of revolutionary changes in the fabric of our societies, which have had far-reaching consequences and effects on the way we relate and connect to each other. Digital Transformation in Artificial Systems: Engineering Requirements and Political, Economic, and Philosophical Challenges focuses on analyzing the engineering requirements as well as the political consequences, overarching the philosophical and ethical implications of this transformation, especially in relation to its application in artificial systems. In this context, the concept of digital transformation (understood as the practice of redefining models, functions, operations, processes, and activities by leveraging technological advancements to build efficient digital environments) has become increasingly important. This book brings together key concepts, ideas, and frameworks related to this idea. It promotes an inclusive and responsible digital transformation capable of addressing the constraints on the global digital divide, deepening cooperation in digitization, industrialization, and innovation, while furthering our understanding of the ethical and moral challenges associated with such a development. The distinctive and most original element of the book is its interdisciplinarity. It will allow readers to gather crucial insights that will be instrumental to better understand the reach of the forthcoming AI revolution, its multidimensionality, and its potential impact on the people and society.

  • Home Digital Twins

    • 1st Edition
    • Tuan Anh Nguyen
    • English
    The introduction of sensor networks into the home environment allows automation into the home, utilising internet of things (IoT) devices to programme, for example, door opening, room luminosity, room temperature, and humidity. Digital Twin technology extends the capabilities of the smart home, collecting and analysing data from the Activities of Daily Living (ADLs) and applying Human Activity Recognition (HAR) algorithms to provide adaptive services, including minimizing power consumption, improving healthcare, and enhancing overall well-being. However, there are many challenges when applying these algorithms to the smart home due to the variability and sparsity of sensor data, which can lead to a mismatch between the real home and the digital home. Home Digital Twins explores how digital twin technologies can serve as a bridge between the real home and the virtual home. It explains how IoT devices, wearable sensors, artificial intelligence (AI), and machine learning (ML) can aid in the diagnosis, prediction and prevention of abnormal/critical/ex... problems in real home environments, and shows how digital twin computing can support the management of home energy consumption to optimise everyday living. The book also considers the security and ethical implications of introducing digital twin technology in the home to ensure the technology benefit the lives of the home's inhabitants.

  • Challenges and Applications of Generative Large Language Models

    • 1st Edition
    • Anitha S. Pillai + 2 more
    • English
    Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

  • Engineering Generative AI-Based Software

    • 1st Edition
    • Miroslaw StaroÅ„
    • English
    Engineering Generative-AI Based Software discusses both the process of developing this kind of AI-based software and its architectures, combining theory with practice. Sections review the most relevant models and technologies, detail software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, explore various architectural styles and tactics for such systems, including different programming platforms, and show how to create robust licensing models. Finally, readers learn how to manage data, both during training and when generating new data, and how to use generated data and user feedback to constantly evolve generative AI-based software.As generative AI software is gaining popularity thanks to such models as GPT-4 or Llama, this is a welcomed resource on the topics explored. With these systems becoming increasingly important, Software Engineering Professionals will need to know how to overcome challenges in incorporating GAI into the products and programs they develop.

  • Fundamentals of Statistics for Researchers and Data Analysts

    • 1st Edition
    • Shashi Chiplonkar + 1 more
    • English
    Fundamentals of Statistics for Researchers and Data Analysts explains statistical methods, and the assumptions and prerequisites for applying various analytical tools from an statistical point of view. Statistical analysis has become indispensable in almost all fields of science, business, industry and medicine, for evidence-based decision making and forecasting. However, due to lack of fundamental understanding of statistics, results of data analysis often remain inconclusive or erroneous. In addition, data analysts or even statistical advisers may not be familiar with the subject area of the data, leading to inaccurate application of statistical tools and interpretation of results. To address these issues, this book breaks down the concepts of statistics into accessible, practical explanations with real-world examples. The book is organized by first explaining what statistical thinking is and how one should proceed with formulating their question in terms of a statistical hypothesis. Then step by step, topics are explained in detail, including data generation by choice of proper study design, data collection methods, identifying outliers, methods of data analysis, and finally interpretation of results to help make the required decision. Essential statistical methods such as classification techniques, correlation analysis, regression models, probability distributions, model building and statistical tests of significance are explained with live datasets using Excel and SPSS. Fundamentals of Statistics for Researchers and Data Analysts instructs readers on the precise methodology of analyzing data and interpretation of statistical results to arrive at a valid conclusion. Readers can use the same methodology from the case studies given in the book for their own applications and research by replacing the variables in the examples with the variables from their own datasets. The book ensures that readers are well-prepared for data-driven roles in various sectors.

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