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Elsevier

Morgan Kaufmann

  • Trustworthy LLMs

    Principles and Challenges
    • 1st Edition
    • Mohit Kumar
    • English
    Despite numerous advantages, LLMs have trust, transparency, accountability, and reliability issues due to development with "black-box" approaches, which make it difficult to understand how LLMs create specific outputs. Trustworthy LLMs: Principles and Challenges presents the fundamental concepts of trustworthy LLMs, then proceeds to address the foremost challenges researchers and developers face in developing reliable and trustworthy LLMs. The book begins by presenting the main research branches of artificial intelligence along with the principles of LLMs, from pre-training to fine tuning, and, ultimately, trustworthy LLMs. Readers will learn about the chief technical principles of LLMs, including attention mechanism, transformers, and transfer learning. The methodologies used for development of ChatGPT have been explained as a case study for comprehensive understanding of the concepts involved in LLMs. Readers will also learn about the integration of XAI with LLM, and other key frontiers in trustworthy LLM development, including the synergy between deep learning and LLMs, as well as case studies on GPT-4 and OPT-1.3B. The book concludes with chapters on key challenges and future research approaches for developing trustworthy LLMs.

  • Artificial Intelligence and Machine Learning for Safety-Critical Systems

    A Comprehensive Guide
    • 1st Edition
    • Rajiv Pandey + 3 more
    • English
    Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource on the challenges and mitigation strategies involved in their design. The book's authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems. In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models. In essence, the goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety.

  • Measuring the User Experience

    Collecting, Analyzing, and Presenting UX Metrics
    • 4th Edition
    • Bill Albert
    • English
    Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics provides the quantitative analysis training that students and professionals need. This book presents an update on the first resource that focused on how to quantify user experience. Now in its fourth edition, the authors have continued to expand on the area of behavioral and physiological metrics. The book also contains new research and updated examples, several new case studies, and new examples using the most recent version of Excel.

  • Modeling Systems with SysML v2

    The Systems Modeling Language
    • 1st Edition
    • Sanford Friedenthal + 2 more
    • English
    Whether you have previous experience modeling with SysML v1 or are new to modeling with SysML, Modeling Systems with SysML v2: The Systems Modeling Language provides the understanding and guidance to help you gain a basic level of competency in SysML v2, prepare for SysML v2 certification, and advance your modeling skills over time. SysML v2 is the next generation systems modeling language that can be used to model complex systems that span a wide range of application domains. It provides the ability to specify requirements, behavior, structure, analysis, and verification of interacting systems and their components while maintaining traceability across the system model. SysML v2 enhances the precision, expressiveness, consistency, usability, extensibility and interoperability of systems modeling. It provides both graphical and textual notations that facilitates stakeholder understanding and enables modern practices with agile workflows. SysML also includes an application programming interface (API) that facilitates interoperability across digital engineering tools and models. The formal semantic and syntactic foundation for SysML v2 enables technologies such as artificial intelligence (AI) with large language models (LLM) to interact with the SysML model. The combination of SysML v2's textual notation and standard API, along with a formal foundation and consistent usage patterns, facilitate automation to support model validation, workflow enhancements, and integration with other models and tools. This book provides a comprehensive and practical guide for modeling systems with SysML v2. With their unique perspective as leading contributors to the language, Friedenthal and Seidewitz provide a full description of the language along with a quick reference guide and practical examples to help you use SysML v2. The book begins with guidance on the most commonly used features to help you get started quickly, explaining the benefits of a model-based approach and providing an overview of the language and how to apply SysML v2 to model systems. The authors include a comprehensive description of SysML v2 that provides a detailed understanding that can serve as a foundation for modeling with SysML v2, and as a reference for practitioners. The book includes methods for applying model-based systems engineering using SysML v2 to specify and design systems, and how these methods can help manage complexity.

  • Advances in Multimodal Large Language Models for Healthcare

    Methods and Applications
    • 1st Edition
    • Hari Mohan Pandey + 4 more
    • English
    Advances in Multimodal Large Language Models for Healthcare: Methods and Applications provides valuable insights on Large Language Models in healthcare applications for researchers, academics, and practitioners. The book explains key concepts, including artificial intelligence, machine learning, deep learning, and the evolution of neural networks and transformer models. It then covers generative AI and LLMs for a wide spectrum of healthcare applications, including mental health, clinical decision support, interactive system design, and sensitive analysis. Readers will find this to be a valuable deep dive into the emergent intersection of LLMs and health care, with guidance into applications, technical and programming methods, and more.Although LLMs have shown some promising results in the healthcare sector, numerous challenges need to be addressed before they can be used in patient care. The two key issues with the adoption of LLMs regarding healthcare settings are reliability, transparency, interpretation of results and bias (data and algorithm) management. Unless properly and adequately validated, there may be incorrect medical information provided by the LLM-based systems, which can lead to misdiagnosis or hazardous treatment errors. At this point, LLMs have not only been used for decision making or documentation, they have also proven to be useful in patient engagement through QA systems, medical chatbots, and virtual healthcare.

  • Advanced Methods in Mathematics and Data Science

    Concepts and Applications
    • 1st Edition
    • Mehmet Yavuz + 2 more
    • English
    Advanced Methods in Mathematics and Data Science: Concepts and Applications provides a comprehensive guide to topics in which mathematical modelling and applications play a pivotal role, including computational models in physics and chemistry, statistical models in life science, data analysis in engineering, as well as finance and social science applications. The book provides a clear and step-by-step presentation of advanced mathematical methods at the intersection of machine learning, artificial intelligence, big data analytics, and statistics, including control theory, topology, and nonlinear dynamic systems. The book progresses from concepts and foundational methods in the early chapters, to inter-disciplinary applications in computational mathematics, mathematical biology, fractional calculus, nonlinear dynamical systems, and data-driven optimization. The methods used to analyze data using a particular computational model are thoroughly explained through real-world and publicly available data sets. Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues, as well as definitions of key terms. The book helps readers enhance their conceptual understanding and practical application of computational methods to real-world data sets, and enables readers to gain competency at the intersection of advanced mathematics and AI, which is an important skill in today’s data-driven market.

  • Advanced Methods for Optimizing and Accelerating Deep Learning Models

    • 1st Edition
    • Patrick Siarry + 3 more
    • English
    Advanced Optimization and Acceleration Techniques for Deep Learning Models provides a comprehensive guide to enhancing deep learning models' efficiency, scalability, and performance, including large language models (LLMs). As AI systems grow in complexity, optimizing their training and deployment has become critical for achieving higher accuracy, faster inference, and reduced computational costs. This book explores cutting-edge optimization strategies, from gradient descent refinements and hyperparameter tuning to model compression, pruning, and hardware acceleration. AI is evolving rapidly, but existing deep learning resources often focus on building models rather than optimizing them for efficiency and scalability. As deep learning applications expand into cloud computing, edge AI, and real-time decision-making, a dedicated resource on optimization is essential. This book addresses this gap by providing a structured approach to making deep learning networks faster, more cost-effective, and more sustainable.

  • Functional Equations and Stability Theory

    Methods and Applications in Science and Engineering
    • 1st Edition
    • Ali Turab + 2 more
    • English
    Functional Equations and Stability Theory: Methods and Applications in Science and Engineering presents current advancements in the theory of functional equations and their stability, with a particular emphasis on their applications across science, engineering, and computational domains. Functional equations—long-stand... pillars of mathematical analysis—have experienced a transformative evolution through the lens of Hyers–Ulam stability theory and its generalizations, which now intersect with key disciplines including artificial intelligence, cognitive science, and cybersecurity. This book provides readers with both principles of classical theory along with contemporary applications, thereby advancing the discourse on functional equations beyond its traditional confines. The book provides readers with both foundational studies and emerging methodologies, exploring how stability properties—such as Ulam–Hyers and generalized stability—are not only central to pure mathematics but also critically relevant to the modeling of real-world systems. By presenting methods that span theoretical results, fixed point methods, approximation techniques, and interdisciplinary case studies, this book offers a comprehensive perspective for readers interested in both mathematical rigor and real-world impact.The book features chapters on advanced topics including classical and generalized stability of Cauchy, Jensen, Drygas, and Pexider-type equations; functional equations in restricted domains and abstract metric spaces; fixed point techniques for analyzing nonlinear and operatorial equations; stability in functional inequalities and mixed-type systems; applications to cognitive modeling, behavioral dynamics, and learning systems; functional frameworks in information theory, entropy, and communication systems; operator-valued and matrix functional equations; stability-informed modeling in cybersecurity, robotics, and intelligent environments; and, approximation and numerical approaches to stability problems. As such, the authors address both computational and mathematical dimensions, empowering readers to apply functional equations and stability theory to contemporary scientific problems.

  • Full-Stack Web Development from the Ground Up

    Principles, Practices, and Technologies
    • 1st Edition
    • Christopher D. Hundhausen
    • English
    Web development is one of the most highly sought after skills in the software industrytoday. Full-Stack Web Development From the Ground Up: Principles, Practices, andTechnologies presents a systematic and modern treatment of this core area of softwaredevelopment, starting with the fundamentals of front-end development using HTML, CSS,and JavaScript; moving to front-end development with React; and culminating in server-sidedevelopme... using Express.js, Node.js, and MongoDB.Using the widely adopted MERN stack (MongoDB, Express.js, React.js, and Node.js), thisundergraduate textbook explores full-stack web development through the step-bystepconstruct... of a pedagogical version of SpeedScore (see https://speedscore.o... asophisticated full-stack web application that supports the international speedgolf community.The book’s emphasis is not on specific web development technologies, but rather on theunderlying concepts, principles, and best practices of web development. This approach laysa solid foundation for students to become proficient web developers who can adapt to therapidly changing landscape of web development technologies.

  • Observing the User Experience

    A Practitioner's Guide to User Research
    • 3rd Edition
    • Elizabeth Goodman + 1 more
    • English
    Observing the User Experience: A Practitioner's Guide to User Research, Third Edition helps readers bridge the gap to understand what users want and need from their product. Filled with real-world experience and a wealth of practical information, the book presents a complete toolbox of techniques to help designers, developers, and other stakeholders see through the eyes of their users. Sections discuss the benefits of end-user research and the ways it fits into the development of useful, desirable, and successful products and present techniques for understanding people’s needs, desires, and abilities, providing a basis for developing better products, whether Web, software, or mobile-based.Final chapters explain the communication and application of research results.

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