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Elsevier

Morgan Kaufmann

  • Artificial Intelligence Modeling for Dynamical Problems

    • 1st Edition
    • Snehashish Chakraverty + 2 more
    • English
    Artificial Intelligence Modeling for Dynamical Problems provides a comprehensive exploration of AI-driven methodologies tailored to address the intricate challenges posed by dynamical systems. The chapters in this book delve into cutting-edge techniques, including scientific machine learning, operator learning, fuzzy logic, and optimization algorithms, highlighting their applications in structural dynamics, fluid dynamics, robotics, and wave dynamics. Readers will gain insights into innovative state-of-the-art approaches such as Physics-Informed Neural Networks (PINNs) for precise navigation and control in autonomous vehicles, convolutional neural networks (CNNs) for frequency dynamics recognition, and data-driven models for market sentiment analysis. Additionally, the book explores the role of uncertainty modeling, statistical inference, and hybrid AI techniques, such as Type-2 fuzzy fractional modeling, in advancing the field of dynamical system analysis. Each chapter combines foundational principles with practical applications, including recent investigations, making it a valuable resource for researchers, practitioners, students of STEM seeking to apply theoretical AI models to real-world dynamical problems.

  • A Comprehensive Guide to R Programming for Data Analytics

    • 1st Edition
    • Parul Acharya
    • English
    A Comprehensive Guide to R Programming for Data Analytics provides a comprehensive presentation of univariate and multivariate statistical models within the general linear model and generalized linear model framework to analyze simple and complex data using R software. This book presents popular R packages that are used in data mining (e.g., caret-classification and regression, lubridate-dates and times, string-R for string data) and visualization (e.g., ggplot, ggthemes, ggtext). The R packages used to analyze data using a particular statistical model are explained through real-world and publicly available datasets. R codes are presented in a manner that helps readers understand the program code syntax.Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn R programming to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues in R syntax, as well as definitions of key terms. Checkpoints are included to recap the concepts learned in each chapter. The book helps readers enhance their conceptual understanding and practical application of statistical models to real-world datasets, and enables readers to gain competency in R programming, which is an important skill in today’s data-driven market.

  • Metaheuristic Optimization for Social Good

    • 1st Edition
    • Thompson Stephan + 2 more
    • English
    Metaheuristic Optimization for Social Good presents a comprehensive guide that shows how powerful optimization algorithms—genetic algorithms, swarm intelligence, evolutionary strategies, and other heuristics—can be tailored to address critical societal problems. Whereas existing references tend to focus on either the technical underpinnings of metaheuristics or broad “AI for social impact” overviews, this book demonstrates how metaheuristic approaches can be systematically and ethically applied to domains such as healthcare, urban planning, environmental stewardship, and policy-making. This handbook directly addresses these challenges by providing a structured approach to metaheuristics, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques, and diverse real-world applications. The book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume. The book presents theoretical advances, hybrid model architectures, and practical applications, providing comprehensive coverage that combines metaheuristics with machine learning and data-driven simulation to enhance decision making for social good. The authors provide real-world case studies that cover the intersection of advanced optimization algorithms and multiple social domains in one volume, with technical depth in each topic. Readers learn how to ensure optimization results align with values such as fairness, equity, and sustainability, preparing readers to not only build effective solutions, but also ones that are socially acceptable and beneficial.

  • Analytical and Computational Modeling of Complex Systems

    A Systematic Approach to Understanding Real-World Complexity
    • 1st Edition
    • Florentin Paladi + 3 more
    • English
    Analytical and Computational Modeling of Complex Systems: A Systematic Approach to Understanding Real-World Complexity presents modern studies of complex systems by methods of statistical physics, numerical calculation, and computer modeling. Using general methods of investigating the branching (bifurcations) of solutions for nonlinear equations, the authors present an exhaustive analysis of the order parameter dependences on the control parameter in a small vicinity of the equilibrium values of parameters, including the stability analysis of the equilibrium states, and the asymptotic behavior of the order parameter dependences on the control parameter (bifurcation diagrams). The authors also present concepts and techniques used to model heterogeneous multi-agent interactions, complex neural networks, biochemical reaction networks in cells, and the dynamic behavior of economic systems over time. The book serves as both a learning resource and a practical reference for those engaged in the study of complex systems and their diverse real-world implementations. The book also offers a systematic learning path from theory to applications. It provides case studies that bridge the gap between mathematical formalism and complex systems modeling and simulation. Emphasizing hybrid and probabilistic modeling techniques, the book ensures greater flexibility for real-world applications. Covering a broad range of topics, it supports interdisciplinary use across departments such as physics, computer science, engineering, economics, and more.

  • 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.

  • 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.

  • 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.

  • 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 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.

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