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Elsevier

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.

  • Curcumin-Based Nanomedicines as Cancer Therapeutics

    • 1st Edition
    • Amirhossein Sahebkar + 1 more
    • English
    Curcumin-based Nanomedicines as Cancer Therapeutics presents a consistent and thorough overview of nanocurcumin applications in cancer treatments. It brings together the novel applications of nanocurcumin in biological milieu as well as helps readers to define the major gaps in knowledge that can lead to significant scientific discoveries.Nanocurc... has been widely explored for the treatment of various cancers; however, the scientific literature is inconsistent in style and structure and scattered across many sources. By providing an explicit account on vital aspects on nanocurcumin-based anticancer delivery approaches and discussing the perspectives of the technologies explored so far based upon the findings outlined, the book offers updated and in-depth knowledge on the topic in one single source written by global leading experts.In addition, the book aims to stimulate the interest of the academic researchers, industrial scientists, businessmen, and young scholars to address key multidisciplinary challenges faced by nanotechnologists to foster the desired collaboration among biologists, chemists, physicists, engineers, and clinicians to find proper and efficient new cancer treatments.

  • Putting AI in the Critical Loop

    Assured Trust and Autonomy in Human-Machine Teams
    • 1st Edition
    • Prithviraj Dasgupta + 6 more
    • English
    Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams takes on the primary challenges of bidirectional trust and performance of autonomous systems, providing readers with a review of the latest literature, the science of autonomy, and a clear path towards the autonomy of human-machine teams and systems. Throughout this book, the intersecting themes of collective intelligence, bidirectional trust, and continual assurance form the challenging and extraordinarily interesting themes which will help lay the groundwork for the audience to not only bridge knowledge gaps, but also to advance this science to develop better solutions. The distinctively different characteristics and features of humans and machines are likely why they have the potential to work well together, overcoming each other's weaknesses through cooperation, synergy, and interdependence which forms a “collective intelligence.” Trust is bidirectional and two-sided; humans need to trust AI technology, but future AI technology may also need to trust humans.

  • Artificial Intelligence and Machine Learning for Open-world Novelty

    • 1st Edition
    • Volume 134
    • English
    Artificial Intelligence and Machine Learning for Open-world Novelty, Volume 134 in the Advances in Computers series presents innovations in computer hardware, software, theory, design and applications, with this updated volume including new chapters on AI and Machine Learning for Real-world problems, Graph Neural Network for learning complex problems, Adaptive Software platform architecture for Aerial Vehicle Safety Levels in real-world applications, OODA Loop for Learning Open-world Novelty Problems, Privacy-Aware Crowd Counting Methods for Real-World Environment, AI and Machine Learning for 3D Computer Vision Applications in Open-world, and PIM Hardware accelerators for real-world problems.Other sections cover Irregular Situations in Real-World Intelligent Systems, Offline Reinforcement Learning Methods for Real-world Problems, Addressing Uncertainty Challenges for Autonomous Driving in Real-World Environments, and more.

  • Federated Learning

    Theory and Practice
    • 1st Edition
    • Lam M. Nguyen + 2 more
    • English
    Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors.

  • Internet of Things: Architectures for Enhanced Living Environments

    • 1st Edition
    • Volume 133
    • Goncalo Marques
    • English
    Internet of Things: Architectures for Enhanced Living Environments, Volume 133 presents interesting chapters on a variety of timely topics, including Explainable Artificial Intelligence for Enhanced Living Environments: A Study on User Perspective, Human behavioral anomaly pattern mining within an IoT environment: an exploratory study, Indoor Activity Localization Technologies for Assisted Living: Opportunities, Challenges, and Future Directions, Smart Indoor Air Quality Monitoring for Enhanced Living Environments and Ambient Assisted Living, Usability evaluation for the IoT use in Enhanced Living Environments, Roadmap to the elderly enhanced living and care environments: applications and challenges on the Internet of Things domain, and much more.

  • Engineering Simulation and its Applications

    Algorithms and Numerical Methods
    • 1st Edition
    • Xin-She Yang
    • English
    Engineering Simulation and its Applications: Algorithms and Numerical Methods covers the essential quantitative methods needed for engineering simulations, introducing optimization techniques that can be used in the design of systems to minimize cost and maximize efficiency. This book serves as a reference and textbook for courses such as engineering simulation, design optimization, mathematical modeling, numerical methods, data analysis, and engineering management. Diverse coverage of the various subject areas within the field puts the essential topics into a single book for easy access for graduates and senior undergraduates. It also serves as a reference book for lecturers and industrial practitioners.

  • Trolley Crash

    Approaching Key Metrics for Ethical AI Practitioners, Researchers, and Policy Makers
    • 1st Edition
    • Peggy Wu + 3 more
    • English
    The prolific deployment of Artificial Intelligence (AI) across different fields has introduced novel challenges for AI developers and researchers. AI is permeating decision making for the masses, and its applications range from self-driving automobiles to financial loan approvals. With AI making decisions that have ethical implications, responsibilities are now being pushed to AI designers who may be far-removed from how, where, and when these ethical decisions occur.Trolley Crash: Approaching Key Metrics for Ethical AI Practitioners, Researchers, and Policy Makers provides audiences with a catalogue of perspectives and methodologies from the latest research in ethical computing. This work integrates philosophical and computational approaches into a unified framework for ethical reasoning in the current AI landscape, specifically focusing on approaches for developing metrics. Written for AI researchers, ethicists, computer scientists, software engineers, operations researchers, and autonomous systems designers and developers, Trolley Crash will be a welcome reference for those who wish to better understand metrics for ethical reasoning in autonomous systems and related computational applications.

  • Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

    • 1st Edition
    • D. Jude Hemanth
    • English
    Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The book's authors provide readers with an in-depth look at the challenges and associated solutions, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered that will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas.

  • The Essential Criteria of Graph Databases

    • 1st Edition
    • Ricky Sun
    • English
    The Essential Criteria of Graph Databases collects several truly innovative graph applications in asset-liability and liquidity risk management to spark readers’ interest and further broaden the reach and applicable domains of graph systems. Although AI has incredible potential, it has three weak links: 1. Blackbox, lack of explainability, 2. Silos, slews of siloed systems across the AI ecosystem, 3. Low-performance, as most of ML/DL based AI systems are SLOW. Hence, fixing these problems paves the road to strong and effective AI.

  • Human-Computer Interaction

    An Empirical Research Perspective
    • 2nd Edition
    • I. Scott MacKenzie
    • English
    Human-Computer Interaction: An Empirical Research Perspective is the definitive guide to empirical research in HCI. The book begins with foundational topics, including historical context, the human factor, interaction elements, and the fundamentals of science and research. From there, the book progresses to the methods for conducting an experiment to evaluate a new computer interface or interaction technique. There are detailed discussions and how-to analyses on models of interaction, focusing on descriptive models and predictive models. Writing and publishing a research paper is explored with helpful tips for success.Throughout the book, readers will find hands-on exercises, checklists, and real-world examples. This is a must-have, comprehensive guide to empirical and experimental research in HCI – an essential addition to your HCI library.

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