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Elsevier

Libros en Ciencias de la Computación

  • Foundations of Human-Computer Interaction

    Designing for Cognitive Alignment
    • 1 Edición
    • Robert Atkinson
    • Inglés
    Foundations of Human-Computer Interaction: Designing for Cognitive Alignment reframes human-computer interaction (HCI), usability, and user-centered design by focusing on the conditions under which cognition stabilizes over time. It provides an integrated account of HCI by bringing together cognitive science, neuroscience, and design principles to explain how systems shape perception, regulate attention, and support stable reasoning across repeated encounters. This approach ensures that graduate and undergraduate students not only understand core theoretical frameworks but also recognize how design decisions influence reasoning, decision-making, and cognitive effort in real-world contexts. The book emphasizes structured learning and iterative design processes, making the material accessible to both novices and advanced learners. It also addresses contemporary challenges such as AI-driven systems, adaptive interfaces, and large-scale personalization, offering a framework for understanding how misalignment emerges as instability—seen in repetition, delayed decisions, fragmented attention, and unresolved effort—and how design can support clarity, recovery, and trust in responsible ways

  • Smart Wearable IoT

    Principles and Implementation of Development Modules with Wireless Biomedical SOC
    • 1 Edición
    • Shuenn-Yuh Lee + 1 más
    • Inglés
    Smart Wearable IoT: Principles and Implementation of Development Modules with Wireless Biomedical SoC focuses on the development of intelligent wearable technology integrated with the Internet and various platforms. The book provides detailed guidance on building a user-friendly development platform that features intelligent wearable systems, including bio-signal SoCs/modules, user-friendly websites/apps, and artificial intelligence (AI) systems on edge/cloud. By exploring specific case studies, such as the ECG-based fatigue analysis system, readers will gain fundamental knowledge in biosignal acquisition and processing.This hands-on approach enables users to understand the integration of digital signal processing and artificial intelligence in analyzing physiological data, ultimately enhancing their skills in developing innovative wearable solutions.

  • Pioneering Autonomous Technology: A Deep Dive into Hyper Automation

    • 1 Edición
    • Volumen 143
    • Inglés
    Pioneering Autonomous Technology: A Deep Dive into Hyper Automation, Volume 143 in the Advances in Computers series, showcases the transformative role of artificial intelligence, deep learning, and machine learning in creating safer, more efficient, and socially inclusive environments. This volume explores how autonomous technologies are reshaping healthcare, transportation, agriculture, and urban living. Chapters cover important topics such as a Survey on Deep Learning Based Autonomous Medical Diagnosis Models for Chronic Disease Identification, Human-Robot Interaction: Ensuring Safe and Effective Coordination, Blazing Trails: Cutting-Edge Technologies Revolutionizing Forest Fire Screening, Connected Communities: Fostering Social Inclusion and Equity in Smart Cities through IoT Integration, and much more.Additional chapters cover a Preliminary Study of Retinal Biomarkers Detection on Fundus Images for the Diagnosis of Alzheimer’s and Parkinson’s Diseases, Deep Learning Driven Classification of Sweet Lime Leaves: A Superior CNN Approach for Agricultural Automation, Machine Learning in Healthcare: Advancements, Applications, and Challenges, Feature Extraction and Object Recognition in Autonomous Systems, The Future of Autonomous Vehicles: Industry Trends, Technologies, and Challenges, Autonomous Systems: Shaping the Future of Industries and Transportation, and many other topics that will be of interest to readers.

  • Data Compression for Data Mining Algorithms

    • 1 Edición
    • Xiaochun Wang
    • Inglés
    Data Compression for Data Mining Algorithms tackles the important problems in the design of more efficient data mining algorithms by way of data compression techniques and provides the first systematic and comprehensive description of the relationships between data compression mechanisms and the computations involved in data mining algorithms. Data mining algorithms are powerful analytical techniques used across various disciplines, including business, engineering, and science. However, in the big data era, tasks such as association rule mining and classification often require multiple scans of databases, while clustering and outlier detection methods typically depend on Euclidean distance for similarity measures, leading to high computational costs.Data Compression for Data Mining Algorithms addresses these challenges by focusing on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations involved in tasks such as feature selection and similarity computation. The book features the latest developments in both lossless and lossy data compression methods and provides a comprehensive exposition of data compression methods for data mining algorithm design from multiple points of view.Key discussions include Huffman coding, scalar and vector quantization, transforms, subbands, wavelet-based compression for scalable algorithms, and the role of neural networks, particularly deep learning, in feature selection and dimensionality reduction. The book’s contents are well-balanced for both theoretical analysis and real-world applications, and the chapters are well organized to compose a solid overview of the data compression techniques for data mining. To provide the reader with a more complete understanding of the material, projects and problems solved with Python are interspersed throughout the text.

  • Distributed AI in the Modern World

    Technical and Social Aspects of Interacting Intelligent Agents
    • 1 Edición
    • Andrei Olaru + 3 más
    • Inglés
    Distributed AI in the Modern World: Technical and Social Aspects of Interacting Intelligent Agents presents state-of-the-art insights into the various forms of distribution of artificial intelligence, with practical application instances. Sections provide readers with practical solutions at an architectural level, with solutions presented on the distribution of the learning process and the utilization of machine learning models in a distributed system, tools that enable the distribution and interaction of artificial learning entities, how multi-agent systems and machine learning can be combined, the physical embodiment of intelligent agents, and the interaction of intelligent computing units bound to physical space.Following sections emphasize the challenges that are common to all scenarios and solutions that apply in a wider range of cases. This book does not analyze the internal workings of machine learning models (for instance, in the case of multi-agent reinforcement learning), but instead provides readers with an overview of the challenges brought by the need of artificially intelligent entities to interact with other entities and with their environments, along with practical solutions at an architectural level.

  • AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice

    • 1 Edición
    • Olfa Boubaker + 1 más
    • Inglés
    AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice examines the transformative role of AI and data science in improving diagnosis, treatment, and healthcare delivery. It shows how machine learning, deep learning, and advanced signal and image analysis enable breakthroughs in genomics, multi-omics integration, biomedical imaging, EEG-based seizure prediction, and real-time physiological monitoring. The book highlights AI-driven stratification of complex syndromes such as sepsis, stroke, and acute respiratory distress syndrome, demonstrating how data-driven models support early detection, personalized interventions, and actionable clinical decisions.The volume also presents system-level innovations, including AI-based forecasting for dialysis, blood supply management, and telemedicine optimization. It addresses ethical and regulatory challenges, fairness, transparency, data governance, and clinical validation, providing a practical roadmap for healthcare professionals, engineers, researchers, and policymakers. By integrating responsible, human-centered AI into precision medicine, the book illustrates clear pathways to enhance patient care, improve outcomes, and promote equitable healthcare.

  • AI and Data Science in Medical Research

    • 1 Edición
    • Olfa Boubaker
    • Inglés
    AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies.The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.

  • Artificial Intelligence Applications in Emerging Healthcare Technologies

    • 1 Edición
    • Miguel Antonio Wister Ovando + 2 más
    • Inglés
    Artificial Intelligence Applications in Emerging Healthcare Technologies presents the latest advances and state-of-the-art methods and applications of computer science and emerging AI technologies in health and medicine. The book explores the impact of artificial intelligence (AI) in healthcare for medical decision-making and data analysis, tackling topics such as cloud computing, cybersecurity, the internet of things, natural language processing, virtual health, data science applied to healthcare, personalized medicine, imaging, diagnosis, drug discovery, and diseases, among others.Chapters present adaptations or improvements on previous models and algorithms to process data from different sources. Other chapters investigate new formulations for the optimization of known procedures and algorithms. Finally, all chapters use experimental methods to study problems of interest in healthcare. This is a great resource for researchers and students who want to learn how machine learning algorithms and other data science techniques have been implemented to solve healthcare-related problems.

  • Digital Supply Chain Transformation

    Implementing Technology, Analytics, and Data-Driven Solutions
    • 1 Edición
    • Vinaytosh Mishra
    • Inglés
    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 are explored in-depth, equipping readers with the tools and knowledge needed to excel in the rapidly evolving landscape of supply chain management. Readers will learn how 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, understand the tools, technologies, and strategies essential for a successful transformation, evaluate the importance of end-to-end supply chain visibility, employ methods and technologies to enhance this visibility in practical scenarios, and apply system dynamics modeling techniques to address complex supply chain problems to optimize supply chain processes, and much more.

  • Foundations of High-Performance Computing

    A Comprehensive Guide to Systems, Concepts, and Programming
    • 1 Edición
    • Oscar Diez
    • Inglés
    Foundations of High Performance Computing: A Comprehensive Guide to Systems, Concepts, and Programming offers a comprehensive reference on high-performance computing (HPC). Laying the groundwork for the companion text, High-Performance Computing: Applications and Frontiers, this book serves as a comprehensive introduction to the fundamental concepts, systems, and introductory programming techniques. It focuses on infrastructure, programming basics, and practical access to HPC systems, covering the evolution of HPC and offering an architectural overview of HPC systems. In addition, it explores resource management and performance metrics in parallel systems, discussing practical access to HPC and considering the concepts behind distributed and shared memory. Other sections focuses on OpenMP for multithreading, MPI programming, GPU computing, OpenACC, and CUDA, and parallel I/O, data management, filesystems, cloud based HPC, and virtualization. The book concludes with a reference and glossary, equipping the reader with a thorough grounding in the basics of HPC and providing a comprehensive, multidisciplinary guide on the principles and concepts that underpin high performance computing. It is designed for graduate students, early-career professionals, and researchers seeking to build a solid foundation in HPC.

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