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Elsevier

Libros en Ciencias de la Computación

  • Industry 4.0 and Sustainability

    Integrating Digital Technologies and Circular Models for a Sustainable Future
    • 1 Edición
    • M. Imran Khan + 2 más
    • Inglés
    The Fourth Industrial Revolution, commonly termed Industry 4.0, is transforming production and manufacturing through integration of advanced digital technologies like artificial intelligence (AI), industrial internet of things (IIoT), big data analytics, additive manufacturing, and advanced robotics. While much discussion has centered on potential economic impacts, Industry 4.0 also holds significant implications for sustainability across environmental, social, and economic dimensions. However, simply adopting technologies does not guarantee sustainability benefits, and improperly managed, they pose risks related to job losses, inequality, and rebound effects that must be addressed through integrative frameworks. Industry 4.0 and Sustainability will provide the first comprehensive, evidence-based perspective unifying technological, organizational, policy, and social considerations to strategically direct Industry 4.0 innovations towards improved sustainability across economic, environmental, and social dimensions. With applied case studies and integrated roadmaps spanning engineering, management, and policy, it fills a crucial gap in guiding Industry 4.0 deployments to accelerate progress on sustainable development goals.

  • Fundamentals of Statistics for Researchers and Data Analysts

    • 1 Edición
    • Shashi A. Chiplonkar + 1 más
    • Inglés
    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.

  • Smart City Computational Paradigms

    A Sustainable Approach
    • 1 Edición
    • Mohit Kumar + 3 más
    • Inglés
    Smart City Computational Paradigms: A Sustainable Approach describes the connections between state-of-the-art technologies while also providing a comprehensive overview for readers interested in advanced technologies. The smart city paradigm combines smart homes, smart healthcare, smart transportation, smart industry, smart environment, and smart energy to ensure sustainability, well-being, and comfortable living within the urban environment for the city's citizens. The book identifies challenges and proposed solutions and covers underlying theory, design techniques, classification, taxonomy, and analytical tools. Content primarily focuses on real-time applications, uncertainty solutions, and approaches with hands-on demonstrations for decision-making outcomes.This convergence of computational intelligence and IoT not only transforms data into actionable knowledge but also fosters the development of autonomous, efficient, and adaptive systems across diverse domains, ranging from smart cities to healthcare and industrial applications. The integration of computational intelligence with IoT enhances the capabilities of connected systems, making them smarter, more efficient, and better equipped to handle the complexities of the modern world.

  • AI and Data Science in Healthcare 5.0

    • 1 Edición
    • Olfa Boubaker
    • Inglés
    AI and Data Science in Healthcare 5.0 delves into the innovative developments in Healthcare 5.0, focusing on smart medical robots, devices, and connected hospitals. It explores the role of robotics in modern healthcare, including advanced wearables enhanced by deep learning. The volume also addresses the integration of blockchain technology, IoT, and cloud computing in healthcare, emphasizing real-time applications in precision healthcare. The discussion extends to energy optimization in smart hospitals, federated learning for IoMT networks, and advanced patient monitoring systems.The volume concludes with an overview of the future opportunities and challenges in Healthcare 5.0.

  • Edge Intelligence

    Advanced Deep Transfer Learning for IoT Security
    • 1 Edición
    • Jawad Ahmad + 5 más
    • Inglés
    Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.

  • Tcl/Tk

    A Developer's Guide
    • 4 Edición
    • Clif Flynt
    • Inglés
    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.

  • Adaptive AI in Sensor Informatics

    Methods, Applications, and Implications
    • 1 Edición
    • Karthik Ramamurthy + 4 más
    • Inglés
    Adaptive AI in Sensor Informatics: Methods, Applications, and Implications explores the growing need for efficient, interpretable, and reliable adaptive AI systems tailored to wireless sensor networks. The book highlights how adaptive AI strengthens collaboration between humans and artificial intelligence by enabling transparent decision-making processes. Aimed at academics, professionals, and students, it provides an accessible yet thorough guide to understanding the intersection of adaptive AI and sensor informatics, focusing on practical implementation and the development of models that are both trustworthy and user-friendly. Readers will gain insight into the essential role adaptive AI plays in advancing wireless sensor networks across various sectors.The book also examines the unique challenges and opportunities that arise when deploying adaptive AI in real-world sensor environments. It offers actionable advice for designing AI models that comply with regulations and support user confidence, especially in areas such as healthcare, environmental monitoring, smart cities, and industrial automation.

  • Multilevel Quantum Metaheuristics

    Applications in Data Exploration
    • 1 Edición
    • Siddhartha Bhattacharyya + 4 más
    • Inglés
    Multilevel Quantum Metaheuristics: Applications in Data Exploration explores the most recent advances in hybrid quantum-inspired algorithms. Combining principles of quantum mechanics with metaheuristic techniques for efficient data optimization, this book examines multilevel quantum systems characterized by qudits and higher-level quantum states as more robust alternatives to conventional bilevel quantum approaches. It introduces novel multilevel applications of quantum metaheuristics for addressing optimization problems in areas including function optimization, data analysis, scheduling, and signal processing. The book also showcases real-world examples, case studies, and contributions that emphasize the effectiveness of proposed multilevel techniques over existing bilevel methods. Researchers, professionals, and engineers working on intelligent computing, quantum computing, data processing, clustering, and analysis, and those interested in the synergies between quantum computing, metaheuristics, and multilevel quantum systems for enhanced data exploration and analysis will find this book to be of great value.

  • Challenges and Applications of Generative Large Language Models

    • 1 Edición
    • Anitha S. Pillai + 2 más
    • Inglés
    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.

  • Quantum Theory, Decision Making and Social Dynamics

    • 1 Edición
    • Tofigh Allahviranloo + 3 más
    • Inglés
    Quantum Theory, Decision Making, and Social Dynamics is a detailed exploration of the connection between quantum theory, decision-making, and social networks. As quantum theory expands into various fields, there is an increasing demand for accessible resources that clarify its principles and uses. This book aims to address that need by explaining the complex relationship between quantum theory and social dynamics, especially in decision-making contexts. It discusses the challenges of understanding and applying quantum theory in social settings and provides readers with the knowledge to leverage its potential in decision-making processes. The book is divided into eleven chapters, each focusing on a specific aspect of quantum theory and its applications. Chapter 1 introduces quantum theory, fuzzy logic, and social network analysis, highlighting key concepts like superposition, entanglement, and fuzzy influence within networks. Chapter 2 examines fuzzy sets, membership functions, and inference systems, with applications in devices, traffic management, and healthcare. Chapter 3 covers the mathematical framework of quantum mechanics and its philosophical paradoxes, connecting them to fuzzy logic models of uncertainty. Chapter 4 links social networks to quantum graphs, defining their topology, centrality, and entangled edges. Chapter 5 models social identity as a fuzzy quantum superposition, exploring identity collapse and coherence within networks. Chapter 6 relates quantum entanglement to social ties, proposing fuzzy–quantum graph models for interconnected systems. Chapter 7 analyses measures of irregularity in quantum graphs and applies these to financial networks. Chapter 8 integrates quantum cognition with fuzzy MCDM, employing various probability evaluation methods. Chapter 9 features case studies of fuzzy systems and their integration with quantum fuzzy graphs. Chapter 10 develops a quantum graph-based link prediction model for dynamic social networks. Chapter 11 concludes with a summary of the quantum–fuzzy framework, discussing its contributions, limitations, and future directions.

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