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Elsevier

Libros en Ciencias de la Computación

  • AI Challenges for a Sustainable Future

    • 1 Edición
    • Kuldeep Kaswan + 2 más
    • Inglés
    Artificial Intelligence (AI) has emerged as a fundamental transformative force which can create revolutionary solutions to protect the environment while encouraging economic development and improving social progress. AI also brings potential threats for the planet and for future generations. AI Challenges for a Sustainable Future analyzes the role of AI in promoting sustainability while also addressing the significant challenges it introduces which threaten long-term ecological and social equilibrium. It examines the impact of AI biases, along with energy use optimization challenges and social economic problems that result from AI applications. The book presents analysis of natural resource governance and sustainable computing development, and offers solutions to aid in the achievement of worldwide Sustainable Development Goals (SDGs), and questions how we can control AI technology before it undermines future developments. Each chapter explores a key issue, highlights obstacles, and suggests technological, ethical, and policy-oriented strategies to lessen AI's adverse effects while enhancing its advantages for a sustainable future. The book advocates for energy-efficient AI algorithms, green computing, and the development of sustainable hardware. It also recommends decentralized AI models to decrease dependence on energy-intensive cloud computing. Additionally, it discusses policy frameworks aimed at fostering responsible AI development and achieving carbon-neutral AI systems.

  • Photogrammetry | Videogrammetry and Forensic Analysis of Digital Media

    • 1 Edición
    • William T.C. Neale + 1 más
    • Inglés
    Photogrammetry: Videogrammetry and Forensic Analysis of Digital Media is a deep dive into the mechanics of digital media, its structure, its embedded data, and the science of verifying, evaluating, and quantifying photographic and video evidence. In the world of digital media, a single photograph or video is rarely what it appears to be. Beneath the surface lies a complex puzzle of hidden layers, where diverse formats and invisible processing techniques can transform even the simplest clip into a labyrinth of technical challenges. This book serves as a rigorous roadmap for analysts and experts who operate in the realm of measurable facts and verifiable data, even in the face of increasingly sophisticated AI-generated material. The book is organized into two technical pillars: Photo Analysis & Photogrammetry: Spanning both visual interpretation and spatial quantification, this section establishes robust protocols for image authentication and validation before advancing into high-level photogrammetric methodologies. Readers will learn how to extract precise three-dimensional measurements from two-dimensional imagery with defensible, mathematically grounded accuracy. Video Analysis & Videogrammetry: This section traces the evolution of video analysis, from frame-by-frame examination to a comprehensive understanding of compression systems and the artifacts they introduce. It then progresses into advanced videogrammetry, including dynamic reconstruction and object tracking in three-dimensional space. Readers will explore the sophisticated world of videogrammetry: tracking objects through time and space to provide a definitive account of an object’s size and shape, orientation, and detailed dynamics.Written for cybersecurity and infosec professionals, forensic analysts, photogrammetrists, accident reconstructionists, and technical investigators, this authoritative manual provides the foundational knowledge and advanced methodologies required for high-level expert work. It equips readers to confidently address common challenges, correct distortions, and integrate complex data into defensible conclusions. This is an essential resource for professionals tasked with transforming photographic and video evidence into accurate, reliable, and quantifiable representations of reality.

  • Principles of Medical Biohybrid Microrobots

    • 1 Edición
    • Veronika Magdanz + 1 más
    • Inglés
    Principles in Medical Biohybrid Microrobots explores the field of biohybrid magnetic microrobots, explaining the evolution of locomotion at the microscale and provides the general theoretical framework to describe the hydrodynamics of swimming microorganisms. This book helps readers take the relevant environmental conditions of biohybrid microrobots into consideration, such as viscosity, fluid flow, and interactions with other objects, as well as mapping the potential environment during applications of biohybrid microrobots inside the human body.This book envisions the potential impact of microrobots in biomedical applications and will be a practical and useful guide to graduate students, senior undergraduate students, researchers, and professionals in biomedical engineering, as well as medical researchers, not only for the fundamental knowledge, but also the technical and experimental know-how for designing, fabricating, and operating biohybrid microrobots.

  • System of Systems Engineering

    Innovations, Challenges, and Future Directions
    • 1 Edición
    • Bedir Tekinerdogan + 1 más
    • Inglés
    System of Systems Engineering: Innovations, Challenges, and Future Directions focuses on the many aspects of System of Systems Engineering. Part I, Foundations of System of Systems Engineering, introduces the field, characterizes and classifies SoS, and discusses key concepts. Part II, Governance and Management of SoSE, covers strategic governance, policy and regulatory frameworks, and leadership and decision-making in SoSE projects. Part III, Methodologies and Tools, explores systems thinking and modeling approaches, lifecycle management, and interoperability and integration strategies. Part IV, AI and System of Systems Engineering, delves into leveraging AI for enhanced decision-making, machine learning applications, AI-driven automation and control, and ethical considerations.Final... Part V, Case Studies and Emerging Challenges, presents real-world applications in defense and aerospace, smart cities, healthcare, environmental and energy systems, and discusses future directions and research opportunities. This book offers significant benefits to graduate students, researchers, and professionals in software engineering, systems engineering, aerospace engineering, defense, telecommunications, and other fields where SoSE is relevant.

  • Federated Learning for the Metaverse

    Applications in Virtual Environments
    • 1 Edición
    • Noor Zaman Jhanjhi + 3 más
    • Inglés
    Federated Learning for the Metaverse: Applications in Virtual Environments provides readers with insights into how federated learning, a decentralized machine learning paradigm, can be strategically applied to address critical aspects of the metaverse. The book covers a wide range of topics, including privacy-preserving personalization, security, collaboration, adaptive learning environments, real-time communication, decentralized governance, language understanding, immersive learning experiences, avatar customization, and dynamic scene rendering.

  • Trustworthy LLMs

    Principles and Challenges
    • 1 Edición
    • Mohit Kumar
    • Inglés
    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.

  • Computer Vision

    Principles, Algorithms, Applications, Learning
    • 6 Edición
    • E. R. Davies + 1 más
    • Inglés
    Computer Vision: Principles, Algorithms, Applications, Learning, Sixth Edition clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This new sixth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject.

  • Measuring the User Experience

    Collecting, Analyzing, and Presenting UX Metrics
    • 4 Edición
    • Bill Albert
    • Inglés
    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
    • 1 Edición
    • Sanford Friedenthal + 2 más
    • Inglés
    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.

  • UX of AI

    Understanding and Designing Intelligent User Experiences
    • 1 Edición
    • Rahul Divekar
    • Inglés
    Artificial Intelligence (AI) has become so ubiquitous within technology that all technological design involves some aspect of AI behind the scenes. Knowing how AI technologies relate to the user experience (UX)/user interface (UI)/product design field and how they can be applied will be a key skill for both designers and technologists. The UX of AI: Understanding and Designing Intelligent User Experiencesoffers a critically understanding of the benefits and limitations of the integration of AI and UX. Bridging the gap between artificial intelligence technologies and experience design, and written for UX/UI/product/servic... designers, researchers, and students, it demystifies complex AI concepts and demonstrates their practical applications in creating intuitive, responsive, and personalized user experiences. The book begins by establishing fundamental AI concepts in accessible language, avoiding unnecessary technical jargon while providing readers with a solid understanding of Large Language Models (LLMs), machine learning, natural language processing, computer vision, and complex architectures that combine various AI technologies to form innovative interactions and products. It then explores the unique design challenges and opportunities presented by AI-powered interfaces. The book also offers exercises that instructors can use as interactive classroom activities or homework assignments.

  • Formal Verification Testbenches

    Using Patterns for Reusable and Repeatable VLSI Design Quality
    • 1 Edición
    • Erik Seligman + 2 más
    • Inglés
    Formal Verification (FV) has become an essential technology in the verification of IP, core, or SOC design. The authors' previous book, “Formal Verification: An Essential Toolkit for Modern VLSI Design”, offered the definitive guide to design and validation, with advice to help working engineers integrate these techniques into their work. However, understanding the technology is only the beginning: to really use FV effectively, there are many practical considerations in creating effective testbenches. It’s important to use the right formal tools depending on the preferred design style, project phase, and verification goals. Formal Verification Testbenches: Using Patterns for Reusable and Repeatable VLSI Design Quality is designed to provide that guidance, to assist the transition from initial FV usage to FV being the main workhorse of the validation flow. In addition to describing general principles of FV testbench development that apply to any design style, the book takes a deep dive into real testbenches for specific examples: arbiters, sequence controllers, memory controllers, fsm-heavy control blocks, clock gating designs, and dot-product accumulate blocks. It also highlights new opportunities within the field, for example using AI to plan and execute FV. Formal Verification Testbenches: Using Patterns for Reusable and Repeatable VLSI Design Quality enables a design team to confidently plan and execute a project whose primary validation method will be formal verification.

  • Digital Twins and Predictive Modeling in Robotic Surgery

    • 1 Edición
    • Mudassir Khan + 3 más
    • Inglés
    Digital Twins and Predictive Modeling in Robotic Surgery addresses the transformative convergence of digital twin technology, predictive analytics, and robotic surgery. This reference responds to the critical need for a comprehensive resource that bridges simulation science, artificial intelligence, and real-time robotic execution, facilitating enhanced surgical precision, patient-specific planning, and intraoperative decision-making. The content is organized into four parts covering foundational intelligent surgical systems, predictive modeling and simulation, clinical applications with case studies, and challenges alongside future directions. Topics include AI integration, IoT-enabled sensor networks, digital twin architectures encompassing the entire surgical ecosystem, surgical workflow modeling, risk assessment, human-robot interaction, technology acceptance, ethical and regulatory considerations. Real-world examples from smart hospitals worldwide illustrate practical implementation. This books benefits biomedical engineers, researchers, and healthcare system architects by providing interdisciplinary insights, practical implementation frameworks, and cutting-edge case studies. It supports improved surgical planning, precision, and decision-making, empowering end users to advance the adoption of intelligent, AI-powered robotic surgery systems in clinical practice and research environments.

  • Federated Intelligence in Medicine

    AI-Driven Robotics for Secure and Intelligent Healthcare Systems
    • 1 Edición
    • Manisha Guduri + 3 más
    • Inglés
    Federated Intelligence in Medicine: AI-Driven Robotics for Secure and Intelligent Healthcare Systems explores the rapidly evolving field where medical robotics intersects with cutting-edge artificial intelligence and secure data-sharing technologies. Covering a comprehensive range of topics, the book begins with foundational knowledge in medical robotics and Generative AI, progressing to advanced concepts such as federated learning and architectural modeling tailored for AI-driven robotic systems. Readers will explore algorithms enhancing surgical robotics, remote robotic surgeries, and human-robot interaction. The text also tackles AI-driven diagnostic robots, privacy preservation through homomorphic encryption, and differential privacy and emerging medical imaging techniques integrated with robotics.Later chapters examine the synergy of large language models, agent AI, and edge AI with robotic technologies, providing both theoretical frameworks and practical case studies that illuminate real-world applications and challenges. This book is an invaluable resource for researchers and academicians engaged in artificial intelligence, robotics, and medical technology. It also serves undergraduate and graduate students in Biomedical Engineering, Electronics and Electrical Engineering, and Computer Science and Engineering.

  • Digital Signal Processing with MATLAB®

    From Fundamentals to AI Applications
    • 1 Edición
    • Samir I. Abood + 1 más
    • Inglés
    Digital Signal Processing with MATLAB®: From Fundamentals to AI Applications provides coverage of discrete-time signals and systems, and lays out the mathematical fundamentals of digital signal processing (DSP) systems. The book contains a solution to large-scale signal processing problems, namely the development of methods and algorithms that increase (a) operational efficiency under different uncertainty conditions and (b) system stability rate. It offers an algebraic approach to digital signal processing, including algorithms related to digital systems, the setting of parameters, plus operational estimation, detection, classification, resolution, and reliability. Digital Signal Processing with MATLAB®: From Fundamentals to AI Applications begins with a thorough explanation of MATLAB® in recognition of the Accreditation Board for Engineering and Technology (ABET) requirements for integrating computer tools into DSP. The principles of DSP are presented using a step-by-step approach; each chapter starts with an introduction that states the chapter objectives, and ends with a discussion of practical applications of DSP, including providing problems that help the reader understand how the concepts are used in real-life situations. Sometimes, problems are solved in two or three ways to facilitate a deeper understanding and comparison of different approaches. Digital Signal Processing with MATLAB®: From Fundamentals to AI Applications teaches the fundamental principles of digital signal processing, and explains how MATLAB® can be used in the development of methods and algorithms for DSP.

  • Advanced Methods in Mathematics and Data Science

    Concepts and Applications
    • 1 Edición
    • Mehmet Yavuz + 2 más
    • Inglés
    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

    • 1 Edición
    • Patrick Siarry + 3 más
    • Inglés
    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.

  • Advances in Machine Learning for Engineering Applications and Sustainable Development

    • 1 Edición
    • Volumen 145
    • Inglés
    Advances in Machine Learning for Engineering Applications and Sustainable Development, published as Volume 145 of the serial Advances in Computers, provides a comprehensive and timely exploration of how artificial intelligence (AI) and machine learning (ML) are transforming engineering practice and advancing sustainability goals. The volume brings together contributions from leading researchers to examine the integration of data-driven methods in concrete technology, digital fabrication, structural health monitoring, materials engineering, and industrial process optimisation. It highlights emerging paradigms such as AI-driven 3D printing, predictive analytics for failure detection, lifecycle and environmental impact assessment, and circular economy strategies.Spanning foundational methods and real-world implementations, the volume emphasises sustainable materials, eco-efficient construction processes, and intelligent system design. It pays particular attention to the use of ML in improving resource efficiency, enabling recycling of industrial by-products, and enhancing the performance of green materials such as biochar-based composites. By combining theoretical insights, mathematical modelling, and application-oriented case studies, this volume serves as a critical reference for advancing resilient, efficient, and environmentally responsible engineering systems.

  • Functional Equations and Stability Theory

    Methods and Applications in Science and Engineering
    • 1 Edición
    • Ali Turab + 2 más
    • Inglés
    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
    • 1 Edición
    • Christopher D. Hundhausen
    • Inglés
    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.

  • Neural Networks

    A Concise Theoretical Foundation
    • 1 Edición
    • Aydin Sarraf + 1 más
    • Inglés
    Neural Networks: A Concise Theoretical Foundation provides a comprehensive and structured overview of Neural Networks, covering basics as well as advanced topics. The book begins with basic concepts such as perceptrons, gradient-based learning, and optimization, then progresses through convolutional and recurrent networks, attention mechanisms, graph neural networks, and transformer-based models. Further sections explore generative models, reinforcement learning, biological perspectives, and the development of large-scale intelligent systems. The material is intended for students, researchers, and professionals who aim for both theoretical depth and practical understanding of modern Neural Network methods.This book was written to bring clarity and structure to the subject by presenting a unified framework that connects mathematical foundations with recent innovations. It aims to support learners and practitioners who want a single reference that explains both the fundamental ideas and the developments that have shaped current research and technology in artificial intelligence.

  • Knowledge Graphs and Large Language Models

    Current Approaches, Challenges, and Future Directions
    • 1 Edición
    • Sanju Tiwari + 3 más
    • Inglés
    Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions explores the cutting-edge fusion of two powerful artificial intelligence technologies: Large Language Models (LLMs) and Knowledge Graphs (KGs). The book is structured to provide a comprehensive understanding of this emerging field. Chapters introduce the synergy between LLMs and KGs, delve into the capabilities and challenges of LLMs, focus on the structure, function, and significance of KGs, present a conceptual framework for bridging LLMs and KGs, discuss techniques for their integration, explore how LLMs can enhance KGs and vice versa, and showcase applications of LLM-KG synergy across various domains.Final sections addresses ethical, social, and technical challenges and future innovations. The book concludes by summarizing key insights and advancements in intelligent systems. This is an essential resource for graduate students, researchers, and professionals in computer science. It offers valuable insights for adopting LLMs, KGs, and their advanced applications in research and product development. By bridging the gap between these technologies, this book equips readers with the knowledge to drive innovation and enhance the capabilities of intelligent systems.

  • Healthcare 5.0

    Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data
    • 1 Edición
    • Yugal Kumar + 2 más
    • Inglés
    Healthcare 5.0: Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data addresses the urgent need for innovation in today’s complex healthcare data landscape that is characterized by pandemics, aging populations, and chronic conditions. The book introduces the concept of ‘Healthcare 5.0’ as an interconnected, data-driven, and patient-centric framework where advanced technologies such as AI, ML, IoMT, Big Data, and Large Language Models (LLMs) converge to optimize care, streamline operations, and deliver personalized, predictive solutions. The book moves from core AI applications in electronic health records, drug discovery, data management, and privacy, through cutting-edge big data analytics for disease forecasting and diagnosis.It explores new research advances in the Internet of Medical Things, including connected device architectures and their fusion with AI for dynamic decision-making. The third section focuses on data analytics in telemedicine, remote care, system usability, and integration in Healthcare 5.0. The personalized healthcare section details analysis and applications in AI- and IoT-powered assistance, and real-time monitoring. The last section explores the development of LLMs and their applications in medical imaging, clinical decision support, predictive analytics, system architectures, as well as the ethical challenges of their deployment in healthcare.

  • Next-Generation VAPT

    Combining OSINT and AI-ML for Enhanced Vulnerability Assessment and Penetration Testing
    • 1 Edición
    • Ravi Sheth + 4 más
    • Inglés
    Next-Generation VAPT: Combining OSINT and AI/ML for Enhanced Vulnerability Assessment and Penetration Testing empowers information security threat intelligence with the fusion of Open-Source Intelligence Tools (OSINT) and Artificial Intelligence and Machine Learning (AI/ML) to identify and detect vulnerabilities more efficiently and effectively. The book highlights the concept of OSINT for gathering actionable threat intelligence and AI/ML for automating threat detection, which ultimately enhances VAPT processes. The authors explain concepts for assessing the security of networks, wireless networks, performing VAPT on mobile devices, and conducting red teaming, which helps end users deal with upcoming threats at an early stage.It focuses on using machine intelligence for developing and validating exploits and AI-driven exploitation testing, which helps field experts discover and exploit vulnerabilities more quickly and accurately. Readers will find essential ethical and legal considerations in AI/ML and OSINT-based VAPT, ensuring responsible and compliant testing practices, accountability, and transparency. Industry Case Studies with solutions and safeguards are an integral part of the books applied approach.

  • Observing the User Experience

    A Practitioner's Guide to User Research
    • 3 Edición
    • Elizabeth Goodman + 1 más
    • Inglés
    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.

  • Deep Learning Models for Continuous Authentication on Mobile Devices

    • 1 Edición
    • Yantao Li + 3 más
    • Inglés
    Sensor-based continuous authentication has emerged as a critical approach for strengthening mobile security, enabling persistent user verification without disrupting device usage. However, the field faces significant hurdles, including limited training data, complex feature representation, environmental noise, and the strict resource constraints of mobile hardware.Deep Learning Models for Continuous Authentication on Mobile Devices provides a unified and structured treatment of data-driven continuous authentication, presenting a systematic study of sensor-based continuous authentication on mobile devices, focusing on modern machine learning and deep learning techniques. It guides readers in designing, analyzing, and deploying reliable systems that effectively balance security, robustness, and computational efficiency. Featuring data augmentation strategies for data scarcity, multi-sensor feature fusion, discriminative feature learning via two-stream CNNs, data synthesis using conditional Wasserstein GANs, lightweight networks for efficient deployment, neural architecture search for automated optimization, and neuromorphic computing with spiking neural networks,Deep Learning Models for Continuous Authentication on Mobile Devices balances methodological rigor with practical system design, offering robust solutions for real-world mobile security.

  • Autonomous Intelligence

    Designing the Future of Agentic Systems
    • 1 Edición
    • Shubham Mahajan + 1 más
    • Inglés
    Autonomous Intelligence: Designing the Future of Agentic Systems provides insights on how autonomous technologies—from robots to AI-driven systems— are transforming industries and daily life. The book explores how these intelligent agents operate independently, making decisions and taking actions without constant human intervention. Through clear explanations and real-world examples, the book serves as a gateway for both novices and experts as it simplifies the complexities of autonomous systems. Sections cover Foundations of AI and Autonomy; Understanding Agentic Intelligence; Technology Enablers and Architectures; Design, Safety, and Deployment; Sectoral Applications of Agentic AI; Societal, Ethical, and Future Considerations.The book also considers how these technologies function, while also addressing concerns about their integration, limitations, potential benefits, and possible risks. In a rapidly evolving field, the book looks at latest trends, applications, and research, and at the ethical and legal considerations, especially in areas like employment, privacy, and accountability.

  • Probabilistic Modelling for Advanced Data Analysis

    • 1 Edición
    • Amit Kumar Tyagi + 1 más
    • Inglés
    Probabilistic Modelling for Advanced Data Analysis provides a practical and rigorous guide for data practitioners to effectively implement probabilistic models in real-world scenarios. The book strikes a balance between high-level intuition and technical derivations, offering step-by-step explanations, real-world case studies, and Python implementation examples. The authors offer specific solutions that include modeling and quantifying uncertainty in data-driven decision-making, applying Bayesian inference to real-world problems and implementing scalable probabilistic models for large-scale datasets, all of which contribute to explainable and trustworthy AI.This book presents readers with theoretical foundations and practical applications of probabilistic modeling, providing a structured approach for researchers, data scientists, and industry professionals. It meets the increasing demand for uncertainty-aware AI models, Bayesian inference, and probabilistic graphical models across various fields of research.

  • Ethics of Blockchain by Design

    • 1 Edición
    • Muthu Ramachandran
    • Inglés
    Ethics of Blockchain by Design offers a comprehensive, design-centered framework that integrates ethical principles into every stage of blockchain system development. Rooted in systems engineering, the book systematically explores how ethical requirements can be specified, implemented, and validated within decentralized technologies. It goes beyond theoretical discussions by providing actionable methodologies, case studies, governance models, and compliance tools, making it essential reading for blockchain architects, developers, students, and policymakers. This book positions itself at the intersection of engineering, ethics, and decentralized innovation, and builds on the author’s extensive contributions to AI ethics, secure software systems, and blockchain in healthcare.The increasing integration of blockchain into critical sectors such as finance, healthcare, and governance has prompted a pressing need to address ethical considerations from the ground up. While blockchain offers revolutionary potential for transparency, decentralization, and trust, it also introduces profound challenges around accountability, privacy, inclusion, and governance. Current literature often focuses on the technological and economic dimensions of blockchain but overlooks its ethical implications, leaving a critical gap in both research and practice.

  • The Deterministic Universe

    Exploring Chaos, Free Will, Prediction, and Modeling
    • 1 Edición
    • Paul A. Gagniuc
    • Inglés
    The Deterministic Universe: Exploring Chaos, Free Will, Prediction, and Modeling equips readers with the tools to learn the foundational concepts of chaos, randomness, and determinism through examples and applied case studies. The book helps readers gain insights into how deterministic algorithms handle complex, chaotic data, providing an interdisciplinary exploration of chaos theory, determinism, and free will grounded in scientific principles, computational models, and philosophical insights. The content builds on established theories in physics, bioinformatics, and systems biology, weaving them into broader existential questions. The material emphasizes the interplay between randomness, noise, and order, providing a fresh lens to view the universe.The book connects these ideas to practical tools like random number generators and nonlinear equations, machine learning algorithms, computational and predictive models, extending their implications to biological systems, human thought, and decision-making. By addressing both scientific fundamentals and philosophical debates, it bridges abstract ideas with real-world phenomena and demonstrates the role of randomness and noise in predictive models and simulations, thus helping readers understand the limits of computational systems in mimicking real-world processes.

  • Generative AI Risks and Benefits within Human-Machine Teams

    • 1 Edición
    • William Lawless + 1 más
    • Inglés
    Generative AI Risks and Benefits within Human-Machine Teams delves into the foundational principles, metrics, and applications of human-machine systems, addressing the legal ramifications of autonomy, public trust, and bidirectional AI systems. This book brings together world-class researchers, engineers, philosophers, social scientists, and other experts to discuss the critical aspects of generative AI, its risks, and benefits. The authors aim to establish a shared context between humans and machines, regulators, the public, and other stakeholders, exploring how these systems impact targeted audiences and society at large. The book combines human-centered computing and autonomous human-machine teams to provide a comprehensive understanding of generative AI's potential and challenges. The book is structured to guide readers through a detailed exploration of these topics. It begins with an introduction to the core concepts of human-machine collaboration and the next generation of large language models. The discussion then moves to practical applications, such as Human-AI collaboration for energy communities, autonomous human-machine team advances, and human-AI collaboration in the design process. The book also delves into adaptive collaboration patterns for logic modeling, assessing multimodal large language models in resolving visual ambiguities, and developing team context-aware collaborative AI assistants. Additionally, it explores the taxonomy of teamwork support for collaborative AI efforts, trust management in human-AI collaboration, and provides a distributed teaming testbed for human-machine collaboration in space missions. Finally, it addresses the limits of classical team science in interdependent human-machine teams.Generative AI Risks and Benefits within Human-Machine Teams is an essential resource for computer scientists and systems engineers focused on designing and theorizing about the development of autonomous systems. By providing in-depth insights into the integration of generative AI within human-machine teams, this book equips professionals with the knowledge to navigate the complexities of AI autonomy, enhance collaboration, and address the ethical and technical challenges associated with these advanced technologies.

  • Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support

    • 1 Edición
    • Manoj Diwakar + 3 más
    • Inglés
    Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support examines how to fuse imaging, genomics, electronic health records, and wearable sensor data into clinically actionable insights. As healthcare data becomes increasingly diverse and voluminous, there is a pressing need for integrative methodologies that preserve information across modalities while maintaining interpretability and safety. Current resources either focus on single-modality AI or domain-specific applications, leaving practitioners with fragmented guidance. This volume defines a cohesive, data-centric framework for multimodal predictive diagnostics and clinical decision support, addressing methodological foundations, reproducible pipelines, and real-world translation challenges.

  • Digital Transformation and Equitable Global Health

    A Future-Ready Perspective
    • 1 Edición
    • Arletty Pinel + 2 más
    • Inglés
    Digital Transformation and Equitable Global Health: A Future-Ready Perspective presents a collective body of knowledge and global experiences that demonstrate current status and future trends in the use of exponential technologies and their potential for poverty reduction, improving health outcomes, strengthening health systems, and transforming traditional development aid structures. The book uses a translational innovation perspective to guide the reader—regardless of their area of expertise—on the rationale behind the co-creation of human-centered, affordable, and sustainable digital solutions.It addresses the interest of professionals from multiple areas (e.g., technology, health, social development, global financing), and it is a valuable resource for professionals, social scientists, practitioners, researchers, instructors, and undergraduate and graduate students interested in understanding the challenges and complexities of global public health and the applied uses of health technologies for equitable access to primary health care and universal health coverage.

  • ARM Processors for Power Electronic Applications

    Fundamentals to Programming
    • 1 Edición
    • P. Ponnambalam + 2 más
    • Inglés
    ARM Processors for Power Electronic Applications: Fundamentals to Programming teaches the concept of controlling power converters using the ARM processor, from its fundamentals to implementation procedure, including using advanced controllers for energy management. The book discusses coding the ARM processor for different power converters and a variety of applications. It covers the step-by-step procedure in the design of advanced controllers, the ARM processor and parallel processing, creating driver circuits to interface the ARM processor with power circuits, the peripherals to ARM processors so it can used to control power converters and coding for power circuits.

  • Lightweight, Real-time Deep Learning Models for Healthcare Applications

    • 1 Edición
    • Tina Babu + 2 más
    • Inglés
    Lightweight, Real-time Deep Learning Models for Healthcare Applications addresses the pressing need for deploying efficient artificial intelligence models in clinical environments constrained by limited computational resources. As AI adoption accelerates in healthcare, this reference targets the gap between advanced deep learning research and practical real-time applications, emphasizing model optimization, privacy, and regulatory compliance. The content comprehensively covers model compression techniques such as pruning, quantization, knowledge distillation, and neural architecture search, alongside federated learning frameworks and real-time inference optimization. Detailed chapters present hardware-aware strategies, security considerations, continuous learning, and regulatory validation. The book also provides extensive case studies and implementation blueprints demonstrating practical deployment on devices like Raspberry Pi, NVIDIA Jetson, and mobile health platforms. This book benefits researchers, clinicians, and healthcare technology professionals by offering practical solutions to develop, validate, and implement AI models that maintain diagnostic accuracy while meeting clinical constraints. It equips students and educators with theoretical foundations and hands-on examples to foster innovation in biomedical AI, ultimately enhancing diagnostic capabilities and patient care in diverse healthcare settings.

  • Company Digital Twins

    Emerging Trends, Technologies, and Applications
    • 1 Edición
    • Joseph Bamidele Awotunde
    • Inglés
    Company Digital Twins, a new volume in the Advances in Digital Twin Computing and Sensor Networks series, explores virtual companies based on digital twin technologies. The book covers foundational concepts, emerging trends, underlying technologies, practical applications, future directions, data security, privacy concerns, integration issues, and the scalability of digital twins across large companies. It is essential reading for anyone who needs to benefit from the capabilities of digital twins within the organization. The timeliness of this book lies in the rapid, current digital transformation and the increasing complexity of systems requiring real-time, intelligent management to achieve competitive advantages and sustainability goals.The concept of the digital twin, a virtual representation of a physical system, has gained significant attention due to advancements in Artificial Intelligence, Internet of Things, and data analytics. It plays a crucial role in enhancing efficiency, sustainability, and decision-making across industries, such as manufacturing, healthcare, and urban planning.

  • Advanced Concepts in Grey Wolf Optimizer

    Leading the Pack in Advanced Optimization
    • 1 Edición
    • Seyedali Mirjalili
    • Inglés
    Advanced Concepts in Grey Wolf Optimizer: Leading the Pack in Advanced Optimization provides in-depth coverage of recent theoretical advancements in GWO, as well as advanced methods to handle issues such as multiple objectives, constraints, binary variables, large search spaces, dynamic goals, and uncertain data. This book assumes familiarity with optimization fundamentals and therefore dives directly into multi-objective, constrained, binary, and dynamic-environment variants, as well as GWO-ML/LLM hybrids. Extensive real-world case studies in areas such as energy systems, supply-chain design, LLM fine-tuning, robotics, and finance ensure that both scholars and engineers can translate the material into deployable solutions. The authors present important new theories, hybrids with Machine Learning/Deep Learning, and hybrid methods that increase GWO’s performance. The use of generative AI to improve this algorithm and make it more generic is also explored, along with diverse applications across multiple fields to illustrate the practical utility and versatility of the methods presented. Written by some of the world’s most highly cited researchers in the field of artificial intelligence, algorithms, and machine learning, the book serves as an advanced resource for researchers and practitioners interested in applying and developing the Grey Wolf Optimizer.

  • High-Performance Computing

    Applications and Frontiers
    • 1 Edición
    • Oscar Diez
    • Inglés
    High-Performance Computing: Applications and Frontiers serves as an essential, up-to-date guide for those seeking to master the rapidly evolving field of HPC. Building on foundational concepts, this volume delves into specialized tools and emerging technologies shaping modern research and industry, such as parallel and distributed systems, GPU programming, quantum computing, and cloud-based solutions. Designed for advanced students, researchers, and professionals, it prepares readers to tackle real-world challenges by combining theoretical insights with practical expertise, offering a clear roadmap for navigating both established and cutting-edge HPC landscapes.The book further distinguishes itself by exploring domain-specific applications, machine learning, and the future trajectory of high-performance computing. Key topics include containerization, AI-powered HPC, hybrid and multi-cloud strategies, as well as recent advancements in GPU and quantum technologies. With its blend of theory and hands-on examples, this book is a versatile reference that meets the growing demand for a comprehensive resource in today's data-driven world.

  • Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure

    • 1 Edición
    • V. Subramaniyaswamy + 3 más
    • Inglés
    Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure offers a comprehensive exploration of how AI and data science are revolutionizing the electric vehicle (EV) industry. It guides readers through the basic concepts of EV technology and explains how machine learning and blockchain optimize battery management, predictive maintenance, and secure fault detection. The book highlights cutting-edge techniques like sensor fusion and computer vision for autonomous driving, alongside real-time analytics and edge computing for low-latency AI applications. It also covers intelligent charging infrastructure, route optimization, and renewable energy integration and shares insights into cybersecurity, business models, and demand forecasting, complemented by practical case studies.This book is a useful resource for researchers, scientists, advanced students, software engineers, data scientists, R&D professionals, and other industrial personnel working at the intersection of computer science, electrical engineering, artificial intelligence, data science, and machine learning with an interest in advancing AI and ML applications in electric vehicle technologies.

  • AI-Driven Diagnostics for 6G-Enabled Smart Healthcare

    • 1 Edición
    • Sangeeta Kumari + 3 más
    • Inglés
    AI-Driven Diagnostics for 6G-Enabled Smart Healthcare explores the transformative integration of artificial intelligence (AI) and next-generation 6G networks in the healthcare sector. The book begins by highlighting the evolution of healthcare technology and the critical role of AI-driven diagnostics, emphasizing how 6G facilitates real-time, ultra-reliable communication. Key features of 6G, such as ultra-low latency and massive connectivity are discussed, showcasing their impact on advanced healthcare applications like remote diagnostics and patient monitoring. In addition, the integration of AI in medical diagnostics is examined, focusing on machine learning and deep learning techniques that enhance disease detection through medical imaging and clinical data analysis.Users will also find content that explores the benefits of remote patient monitoring, particularly for underserved populations, and delves into edge AI for localized, low-latency diagnostic processing. Other sections cover real-time imaging diagnostics are highlighted and address predictive analytics, detailing AI models that forecast diseases and the role of IoT devices and wearables in healthcare diagnostics. Final sections cover ethical considerations and regulatory challenges.

  • Robotics for Intervention in Healthcare

    From Technology to Clinical Practice
    • 1 Edición
    • Françoise J Siepel
    • Inglés
    Robotics for Intervention in Healthcare: From Technology to Clinical Practice bridges the gap between deep-core robotic intervention technology and clinical aspects, including content that is appropriate for physicians and clinicians. The book gives insights on the importance of connectivity in early stages, thoroughly addressing which aspects are important to improve the innovation chain.

  • Blockchain-Enabled Innovations in Location-Based Services for Healthcare

    • 1 Edición
    • Ajay Kumar Gupta + 3 más
    • Inglés
    Blockchain-Enabled Innovations in Location-Based Services for Healthcare addresses the growing need for secure, intelligent healthcare solutions integrating location-based services (LBS), Internet of Things (IoT), and blockchain technologies. This reference explores how these technologies transform healthcare delivery improving autonomy and real-time care while safeguarding sensitive data. It responds to the challenges posed by the proliferation of wearable sensors and smart medical devices by proposing decentralized, privacy-preserving frameworks.The book’s comprehensive chapters cover blockchain trust models, adaptive privacy for patient trajectories, wearable computing, deep learning for medical imaging, secure communication in healthcare IoT, and AI-powered diagnostics. Contributions from global experts provide case-driven analyses and technical depth, addressing current challenges in healthcare data security, interoperability, and predictive modeling. The interdisciplinary approach integrates computer science, data privacy, and healthcare informatics, making it a valuable resource for researchers and professionals.Blockc... Innovations in Location-Based Services for Healthcare benefits academic researchers, industry practitioners, graduate students, and policymakers by offering cutting-edge knowledge in secure, decentralized healthcare systems. It equips readers with advanced methodologies and practical solutions to design inclusive healthcare technologies, fostering innovation in smart health monitoring, medical image analysis, and data-driven diagnostics within privacy-compliant frameworks.

  • Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders

    • 1 Edición
    • Hanif Heidari + 1 más
    • Inglés
    Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders discusses the latest developed methods in IoT and its applications in neurological disorders that emphasize end-user requirements. Intelligent IoT is used to explore the intersection between medicine, data science, biomedical engineering, and healthcare systems. A comprehensive overview of modelling and analyzing the requirements of people with neurological disorders is presented in this book. Signals and images of biological activity are collected and analyzed based on patient specifications to facilitate more accurate diagnosis and treatment. The book also discusses cutting-edge AI methods for IoT devices designed to treat neurological conditions.

  • Modern Bioinformatics

    From Molecular Insights to Biomedical Innovation
    • 1 Edición
    • Kolla Bhanu Prakash + 2 más
    • Inglés
    Modern Bioinformatics: From Molecular Insights to Biomedical Innovation explores the rapidly advancing field of bioinformatics, addressing a critical gap in current literature by integrating quantum biology and quantum biosensors with computational bioinformatics. The book offers a thorough exploration of bioinformatics from foundational concepts to translational applications. It begins with molecular structures of proteins, DNA, and RNA, advancing through nano-molecular visualization and structural domain analysis. Readers gain insights into deep learning techniques like AlphaFold for protein structure prediction, AI applications in genomics, and molecular interactions critical to function. The text further covers computational methods in drug discovery, forensic bioinformatics, and molecular evolution.A unique emphasis is placed on the convergence of nanotechnology and quantum innovations in molecular diagnostics and biosensing, including quantum biosensors and quantum computing applications. The final sections focus on computational strategies for drug target identification, biomarker discovery, and clinical trial analysis, providing a holistic view of modern bioinformatics workflows. This book is an indispensable reference for advanced researchers, graduate students, and professionals in bioinformatics, molecular biology, and biomedical engineering seeking to integrate quantum computing and nanotechnology into their work.

  • Usability Testing Essentials

    Ready, Set ...Test!
    • 3 Edición
    • Carol M. Barnum
    • Inglés
    Usability Testing Essentials: Ready, Set ...Test!, Third Edition presents a practical, step-by-step approach to learning the entire process of planning and conducting a usability test. The book explains how to analyze and apply results and what to do when confronted with budgetary and time restrictions. This is the ideal book for anyone involved in usability or user-centered design—from students to seasoned professionals. Updated throughout, this book reflects the latest approaches, tools, and techniques needed to begin usability testing or to advance in this area.

  • Advances in Medical Imaging

    From Behavioral Analysis to Disease Prevention and Rehabilitation
    • 1 Edición
    • Dilber Uzun Ozsahin + 4 más
    • Inglés
    Advances in Medical Imaging: From Behavioral Analysis to Disease Prevention and Rehabilitation presents a comprehensive exploration of the rapidly evolving field of medical imaging at the crossroads of clinical science, behavioral research, and public health. This book addresses the growing need to understand how advanced imaging technologies—such as MRI, PET, and ultrasound—are transforming healthcare beyond traditional diagnosis. By capturing structural, functional, metabolic, and molecular processes, these innovations provide new insights into brain function, lifestyle impacts, chronic disease, and rehabilitation, supporting the shift toward personalized and preventive medicine.The book is organized into five thematic sections covering a wide range of topics. Early chapters focus on the integration of neuroimaging with behavioral science, highlighting brain–behavior relationships through cutting-edge imaging modalities and cognitive software. Subsequent sections examine the effects of sedentary lifestyles on brain health, vascular function assessed by ultrasound, and musculoskeletal aging through imaging biomarkers. Additional chapters explore chronic disease management, yoga and cognitive well-being, stroke recovery, traumatic brain injury rehabilitation, and the expanding role of imaging in oncology and systemic diseases. Throughout, the book emphasizes the use of artificial intelligence, radiomics, and multimodal imaging to enhance precision medicine and population health research.Advances in Medical Imaging: From Behavioral Analysis to Disease Prevention and Rehabilitation offers valuable insights for researchers, clinicians, and healthcare professionals interested in the applications of medical imaging across multiple disciplines. It highlights imaging’s critical role in advancing personalized care, supporting preventive strategies, and fostering innovation in modern healthcare.

  • Artificial Intelligence in Brain Disorders

    Innovations in Diagnosis and Treatment
    • 1 Edición
    • Pranav Kumar Prabhakar + 3 más
    • Inglés
    Artificial Intelligence in Brain Disorders: Innovations in Diagnosis and Treatment focuses on the utilization of AI and machine learning to enhance current practices in the diagnosis and treatment of neurological disorders. Each chapter provides in-depth exploration of specific areas where AI can improve existing methodologies, offering practical guidance, case studies, and research findings that can be directly applied in the field. It explains the application of AI in diagnosing and treating major neurological illnesses and showcases the potential of AI in predicting diseases such as epilepsy and neurodegenerative disorders.As such, this book offers a detailed overview of AI and machine learning techniques relevant to neurological research.

  • Symbiotic Planning for Urban Futures

    A Paradigm for Human-AI Co-Creation
    • 1 Edición
    • Zhong-Ren Peng
    • Inglés
    Symbiotic Planning for Urban Futures: A Paradigm for Human-AI Co-Creation presents a framework for harnessing artificial intelligence in urban planning while preserving democratic control over urban futures. The book establishes symbiotic planning as a falsifiable paradigm grounded in five technology-neutral axioms and operationalized through governed friction, in which AI acts as a governed co-creator across the CORE framework: Collaboration, Options, Refinement, and Execution. It shows how planners can guide AI systems to generate options, support evidence-based refinement, translate values into constraints, and communicate context and equity considerations to decision-makers. By treating equity as a primary design constraint, the book offers a practical foundation for more transparent, accountable, and democratic urban governance in an algorithmic age.This book will be of value to urban planners, civic technologists, policymakers, researchers, and students seeking to understand how AI can support public-sector decision-making without displacing human judgment or public accountability.

  • Agile Systems Engineering with SysML v2 and AI

    • 2 Edición
    • Bruce Powel Douglass
    • Inglés
    Agile Systems Engineering with SysML v2 and AI, Second Edition presents a practical vision of systems engineering in which requirements, structure, behavior, and analysis are captured as precise engineering data—while still addressing the “big system” concerns of safety, security, reliability, privacy, and performance in an agile context. World-renowned author and speaker Dr. Bruce Powel Douglass shows how agile methods, model-based systems engineering (MBSE), and artificial intelligence (AI), work together to reduce ambiguity, expose defects earlier, and sustain end-to-end traceability from stakeholder intent to verification evidence.This edition goes beyond concepts by providing usable, repeatable workflows for modern programs—covering incremental, agile, and DevSecOps-oriented lifecycles and the concrete process steps and gates that make them executable in practice. Rather than treating modeling as documentation, the book treats SysML v2 as a semantic backbone for capturing requirements, architecture, interfaces, behaviors, constraints, and verification intent in one coherent source of truth.New to this edition is an introduction to SysML v2 and an entire chapter on AI and modern MBSE, showing where AI assistants provide leverage, how to apply quality-control gates to keep outputs trustworthy, and how to integrate AI into real engineering workflows without surrendering correctness. Each chapter includes AI prompt patterns for MBSE—ready-to-use prompt structures for generating SysML v2 model elements, extracting and normalizing requirements from external sources, reconciling terminology, and reviewing models against project rules and acceptance criteria. Throughout, Douglass equips systems engineers with concrete methods to prevent specification defects, improve system quality, and reduce rework—so teams can move faster and build with greater confidence

  • Neuro-Symbolic AI

    Integrating Neural Networks and Symbolic Reasoning
    • 1 Edición
    • Sarika Jain + 3 más
    • Inglés
    Neuro-Symbolic AI: Integrating Neural Networks and Symbolic Reasoning explores the convergence of two historically distinct paradigms in artificial intelligence—data-dr... neural networks and logic-based symbolic reasoning. This book presents a comprehensive roadmap of this emerging hybrid discipline, offering deep theoretical insights, practical methodologies, and transformative applications across diverse research sectors, including healthcare, finance, engineering, and autonomous systems. It is structured into four parts—Foundational Principles, Hybrid Models and Techniques, Real-World Applications, and Emerging Challenges, bringing together cutting-edge research and expert perspectives to highlight how Neuro-Symbolic AI enhances interpretability, reasoning capabilities, and trust in intelligent systems.While neural networks have achieved remarkable success in perception and pattern recognition tasks, they often lack the reasoning, transparency, and generalizability that symbolic systems excel at. Conversely, symbolic AI lacks the flexibility and scalability of deep learning. This handbook directly addresses these challenges by providing a structured approach to Neuro-symbolic AI, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques (e.g., knowledge graphs, compositionality, category theory), and diverse real-world applications. This book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume.

  • Generative Artificial Intelligence for Neuroimaging

    Methods and Applications
    • 1 Edición
    • Deepika Koundal + 1 más
    • Inglés
    Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. The book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare.Finally, the book addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.

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