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Elsevier

Books in Artificial intelligence expert systems and knowledge based systems

  • Smart and Sustainable Healthcare Systems

    From Algorithms to Patient Outcomes
    • 1st Edition
    • Shivi Sharma + 3 more
    • English
    Smart and Sustainable Healthcare Systems: From Algorithms to Patient Outcomes addresses the transformative evolution in healthcare driven by artificial intelligence, machine learning, telemedicine, and digital health technologies. This reference responds to the growing need for sustainable, personalized, and efficient healthcare delivery by bridging technical innovation with clinical application across diverse medical and engineering disciplines. The content spans intelligent decision support systems, IoT and wearable technologies, secure electronic medical records, telemedicine solutions, and automation in diagnostics and treatment. The book includes detailed chapters on AI in clinical pathways, predictive healthcare analytics, brain tumor classification, and sustainable telehealth innovations, complemented by real-world case studies and future outlooks. This reference benefits researchers, healthcare engineers, clinicians, and policymakers by providing in-depth exploration of emerging technologies, challenges, and practical solutions in Healthcare 4.0. It promotes sustainable healthcare practices, ethical AI use, and advances in telemedicine, equipping professionals with knowledge to design and implement intelligent healthcare systems that enhance patient care and resource efficiency.

  • Lightweight Secure and Intelligent Approaches for Medical Devices

    Trends and Future Frontiers
    • 1st Edition
    • R. Praveen + 1 more
    • English
    Lightweight Secure and Intelligent Approaches for Medical Devices: Trends and Future Frontiers addresses the rapidly evolving intersection of artificial intelligence, Internet of Medical Things (IoMT), and edge computing within healthcare. This reference meets the critical demand for resource-efficient, secure, and trustworthy AI models tailored specifically for embedded medical devices operating under stringent constraints. By integrating advances in lightweight AI, medical device security, and intelligent data processing, the book fills a vital gap in current literature, offering researchers, engineers, and healthcare professionals a comprehensive resource to develop next-generation medical technologies. The book is organized into four parts, beginning with foundational concepts including the evolution of AI in healthcare devices, lightweight model design, and medical device security standards. It progresses to predictive intelligence and reliability, covering AI techniques for disease monitoring, device calibration, and personalized medicine. The security and privacy section explores network architectures, privacy-preserving AI, lightweight cryptography, and defenses against adversarial attacks. The final part presents emerging trends such as edge AI, digital twins, explainable AI, augmented and virtual reality applications, and quantum-safe security protocols. Each chapter combines theoretical insights with practical case studies and research perspectives to guide real-world deployment. Lightweight Secure and Intelligent Approaches for Medical Devices: Trends and Future Frontiers serves as an essential reference for researchers, medical device developers, cybersecurity professionals, and graduate students aiming to innovate secure and intelligent healthcare solutions in resource-constrained environments.

  • System of Systems Engineering

    Innovations, Challenges, and Future Directions
    • 1st Edition
    • Bedir Tekinerdogan + 1 more
    • English
    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.

  • 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.

  • Digital Twins and Predictive Modeling in Robotic Surgery

    • 1st Edition
    • Mudassir Khan + 3 more
    • English
    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.

  • Healthcare 5.0

    Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data
    • 1st Edition
    • Yugal Kumar + 2 more
    • English
    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.

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

    • 1st Edition
    • Manoj Diwakar + 3 more
    • English
    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.

  • Green Intrusion Detection Systems for IoT

    • 1st Edition
    • Saeid Jamshidi + 3 more
    • English
    Green Intrusion Detection Systems for IoT tackles the pressing security challenges posed by the rapid expansion of the Internet of Things (IoT). The book delves into innovative, lightweight security models and energy-aware IDS mechanisms that strike a balance between security efficacy, computational efficiency, and environmental sustainability. Sections discuss the transformative role of IoT and the need for sustainable security solutions, highlight the distinctions between traditional and Green IDS, focus on lightweight security models essential for resource-constrained IoT devices, and delve into energy-efficient network designs.Additional sections explore green IDS mechanisms, including machine learning and distributed approaches, IoT vulnerabilities and mitigation strategies, practical examples of sustainable IDS in various smart environments, real-world case studies, and future directions in sustainable IoT security. The book concludes with actionable recommendations that align technological advancements with global sustainability goals.

  • Digital Twins

    Core Principles and AI Integration
    • 1st Edition
    • Bedir Tekinerdogan + 1 more
    • English
    Digital Twins: Core Principles and AI Integration offers a structured and up-to-date overview of digital twin technology, combining foundational principles with the rapidly growing role of artificial intelligence (AI). This book introduces the core concepts, modeling approaches, and software and systems engineering foundations needed to design and implement digital twins effectively. It then explores architectural methods, lifecycle management, interoperability, and the alignment between physical systems and their digital representations. A central part of this book focuses on data science and AI-enabled digital twins, demonstrating how machine learning, deep learning, generative AI, and autonomous agents enhance predictive analytics, optimization, anomaly detection, and automated decision-making. Integration with Internet of Things (IoT), cloud–edge infrastructures, big data analytics, and XR technologies further shows how intelligent digital twins evolve into adaptive and interactive systems. Real-world applications from manufacturing, agriculture, food systems, energy, mobility, healthcare, and urban environments illustrate the practical value of AI-driven digital twins. This book concludes with key challenges and future directions, including trustworthy AI, security, data governance, and the scaling of digital twin ecosystems.

  • Digital Transformation in Artificial Systems

    Engineering Requirements and Political, Economic, and Philosophical Challenges
    • 1st Edition
    • Mirko Farina + 3 more
    • English
    The last decade has seen exponential growth in the development of digital technologies. This has led to significant shifts in the political arena as well as in the economy, precipitating a series of revolutionary changes in the fabric of our societies, which have had far-reaching consequences and effects on the way we relate and connect to each other. Digital Transformation in Artificial Systems: Engineering Requirements and Political, Economic, and Philosophical Challenges focuses on analyzing the engineering requirements as well as the political consequences, overarching the philosophical and ethical implications of this transformation, especially in relation to its application in artificial systems. In this context, the concept of digital transformation (understood as the practice of redefining models, functions, operations, processes, and activities by leveraging technological advancements to build efficient digital environments) has become increasingly important. This book brings together key concepts, ideas, and frameworks related to this idea. It promotes an inclusive and responsible digital transformation capable of addressing the constraints on the global digital divide, deepening cooperation in digitization, industrialization, and innovation, while furthering our understanding of the ethical and moral challenges associated with such a development. The distinctive and most original element of the book is its interdisciplinarity. It will allow readers to gather crucial insights that will be instrumental to better understand the reach of the forthcoming AI revolution, its multidimensionality, and its potential impact on people and society.

  • Edge Intelligence

    Advanced Deep Transfer Learning for IoT Security
    • 1st Edition
    • Jawad Ahmad + 5 more
    • English
    Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.

  • Multilevel Quantum Metaheuristics

    Applications in Data Exploration
    • 1st Edition
    • Siddhartha Bhattacharyya + 4 more
    • English
    Multilevel Quantum Metaheuristics: Applications in Data Exploration explores the most recent advances in hybrid quantum-inspired algorithms. Combining principles of quantum mechanics with metaheuristic techniques for efficient data optimization, this book examines multilevel quantum systems characterized by qudits and higher-level quantum states as more robust alternatives to conventional bilevel quantum approaches. It introduces novel multilevel applications of quantum metaheuristics for addressing optimization problems in areas including function optimization, data analysis, scheduling, and signal processing. The book also showcases real-world examples, case studies, and contributions that emphasize the effectiveness of proposed multilevel techniques over existing bilevel methods. Researchers, professionals, and engineers working on intelligent computing, quantum computing, data processing, clustering, and analysis, and those interested in the synergies between quantum computing, metaheuristics, and multilevel quantum systems for enhanced data exploration and analysis will find this book to be of great value.

  • Challenges and Applications of Generative Large Language Models

    • 1st Edition
    • Anitha S. Pillai + 2 more
    • English
    Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

  • Edge Artificial Intelligence

    Algorithms, Applications, Challenges and Ethical Issues
    • 1st Edition
    • Parikshit Narendra Mahalle + 3 more
    • English
    Edge Artificial Intelligence: Algorithms, Applications, Challenges and Ethical Issues introduces the essentials of Edge AI and machine learning. It delves into the architecture, algorithms, and applications of Edge AI, offering insights into regulation and governance. Real-world case studies and practical examples are included, providing readers with the knowledge and tools to harness the transformative power of Edge AI. This book also addresses the ethical considerations and regulatory aspects of deploying AI at the edge.In addition to offering a clear understanding of real-time decision-making, enhanced privacy, and efficient applications, this book empowers both technical and nontechnical readers by providing practical insights, case studies, and ethical considerations. It helps users implement and govern Edge AI in a responsible and effective manner.

  • Edge Intelligence in Cyber-Physical Systems

    Foundations and Applications
    • 1st Edition
    • Wei Yu
    • English
    Edge Intelligence in Cyber-Physical Systems: Foundations and Applications provides a comprehensive overview of best practices for building edge intelligence into cyber-physical systems. This book covers the foundations and applications of synergizing machine learning at the edge of CPS, leveraging an edge computing infrastructure. Divided into four parts, the first section of the book reviews the foundations, principles, and representative application domains of CPS. The second part covers machine learning, edge computing, and their needs in CPS, defining edge intelligence and its principles, challenges, and research directions. The third part presents tutorials and foundational research works on realizing edge intelligence in representative CPS. The fourth part explores the problem space of threats and countermeasures in building edge intelligence into CPS. Researchers, graduate students and professionals in computer science, data science, and electrical engineering will find this to be a valuable resource on the principles and applications of edge intelligence in cyber-physical systems as well as the development of interdisciplinary techniques to advance the field.

  • Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT)

    • 1st Edition
    • Yacine Atif + 1 more
    • English
    Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT) is a reference for IT engineers and decision-makers who may engage in IoT platform pilot projects. The resources covered in this book help establish plans for sustainable operations and management and assist with the long-term procurement of relevant IoT technologies. The aim of the book is to be exhaustive and holistic by pointing out numerous issues and related solution options that guide with daily challenges when deploying and running IoT platforms.The book is divided into three parts where each part includes relevant theoretical chapters and applied case studies. Part One focuses on architectural and federation options for the design and implementation of IoT platforms that foster strategic collaboration opportunities. Part Two addresses vertical security challenges across IoT platform layers. Finally, Part Three shows how IoT is driving the digital transformation wheel through existing and forthcoming case studies.

  • Interdependent Human-Machine Teams

    The Path to Autonomy
    • 1st Edition
    • William Lawless + 3 more
    • English
    Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy, trust by the public, and trust by the users and AI systems of their users, integrating concepts from various disciplines such as AI, machine learning, social sciences, quantum mechanics, and systems engineering. In this book, world-class researchers, engineers, ethicists, and social scientists discuss what machines, humans, and systems should discuss with each other, to policymakers, and to the public.It establishes the meaning and operation of “shared contexts” between humans and machines, policy makers, and the public and explores how human-machine systems affect targeted audiences (researchers, machines, robots, users, regulators, etc.) and society, as well as future ecosystems composed of humans, machines, and systems.

  • Empowering IoT with Big Data Analytics

    • 1st Edition
    • Mohamed Adel Serhani + 2 more
    • English
    Empowering IoT with Big Data Analytics provides comprehensive coverage of major topics, tools, and techniques related to empowering IoT with big data technologies and big data analytics solutions, thus allowing for better processing, analysis, protection, distribution, and visualization of data for the benefit of IoT applications and second, a better deployment of IoT applications on the ground. This book covers big data in the IoT era, its application domains, current state-of-the-art in big data and IoT technologies, standards, platforms, and solutions. This book provides a holistic view of the big data value-chain for IoT, including storage, processing, protection, distribution, analytics, and visualization.Big data is a multi-disciplinary topic involving handling intensive, continuous, and heterogeneous data retrieved from different sources including sensors, social media, and embedded systems. The emergence of Internet of Things (IoT) and its application to many domains has led to the generation of huge amounts of both structured and unstructured data often referred to as big data.

  • Data Analytics for Intelligent Transportation Systems

    • 2nd Edition
    • Mashrur Chowdhury + 2 more
    • English
    Data Analytics for Intelligent Transportation Systems, Second Edition provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Other sections provide extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies.All fundamentals/concept... presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. In addition, they will discover how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning.

  • Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem

    A Humanity Worth Saving
    • 1st Edition
    • Dominique J. Monlezun
    • English
    Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem: A Humanity Worth Saving is the first comprehensive book showing how trustworthy AI can revolutionize decolonized global public health. It explains how it works as an ecosystem and how it can be fixed to equitably empower us all to solve the defining crises of our era, from poverty to pandemics, climate to conflicts, debt to divisions. It is written from the first-hand perspective of the world’s first triple doctorate trained physician-data scientist and ethicist who has cared for more than 10,000 patients and authored 5 AI textbooks and more than 400 scientific and ethics papers. This essential resource integrates science, political economics, and ethics to unite our unique cultures, belief systems, institutions, and governments. In doing so, it is meant to give humanity a fighting chance against shared existential threats through cooperation and managed strategic competition for integral sustainable development.Taking seriously diverse voices, perspectives, and insights from the Global North and the Global South, this book uses concrete examples backed up by clear explanations to elucidate the current failures, emerging successes, and societal trends of global public health. It shows how a small number of powerful governments and corporations—amid digitalization, deglobalization, and demographic shifts—dominate global health, and how we can re-engineer a better future for it both societally and technologically. The book spans health breakthroughs in federated data architectures, machine learning, deep learning, swarm learning, quantum computing, blockchain, agile data governance and solidarity, value blocks (of democracies and autocracies), adaptive value supply chains, social networks, pandemics, health financing, universal health coverage, public–private partnerships, healthcare system design, precision agriculture, clean energy, human security, and multicultural global ethics. This book therefore is meant to provide a clear, coherent, and actionable guide equipping students, practitioners, researchers, policymakers, and leaders in digital technology, public health, healthcare, health policy, public policy, political economics, and ethics to generate the solutions that will define humanity’s next era—while recovering what that humanity means, and why it is worth saving.

  • Securing Next-Generation Connected Healthcare Systems

    Artificial Intelligence Technologies
    • 1st Edition
    • Deepak Gupta + 1 more
    • English
    Securing Next-Generation Connected Healthcare Systems: Artificial Intelligence Technologies focuses on the crucial aspects of IoT security in a connected environment, which will not only benefit from cutting-edge methodological approaches but also assist in the rapid scalability and improvement of these systems. This book shows how to utilize technologies like blockchain and its integration with IoT for communication, data security, and trust management. It introduces the security aspect of next generation technologies for healthcare, covering a wide range of security and computing methodologies.Resear... data scientists, students, and professionals interested in the application of artificial intelligence in healthcare management, data security of connected healthcare systems and related fields, specifically on data intensive secured systems and computing environments, will finds this to be a welcomed resource.

  • Machine Learning

    A Constraint-Based Approach
    • 2nd Edition
    • Marco Gori + 2 more
    • English
    Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.The book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, including many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.

  • Intelligent Environments

    Advanced Systems for a Healthy Planet
    • 2nd Edition
    • P. Droege
    • English
    The promises and realities of digital innovation have come to suffuse everything from city regions to astronomy, government to finance, art to medicine, politics to warfare, and from genetics to reality itself. Digital systems augmenting physical space, buildings, and communities occupy a special place in the evolutionary discourse about advanced technology. The two Intelligent Environments books edited by Peter Droege span a quarter of a century across this genre. The second volume, Intelligent Environments: Advanced Systems for a Healthy Planet, asks: how does civilization approach thinking systems, intelligent spatial models, design methods, and support structures designed for sustainability, in ways that could counteract challenges to terrestrial habitability? This book examines a range of baseline and benchmark practices but also unusual and even sublime endeavors across regions, currencies, infrastructure, architecture, transactive electricity, geodesign, net-positive planning, remote work, integrated transport, and artificial intelligence in understanding the most immediate spatial setting: the human body. The result of this quest is both highly informative and useful, but also critical. It opens windows on what must fast become a central and overarching existential focus in the face of anthropogenic planetary heating and other threats—and raises concomitant questions about direction, scope, and speed of that change.

  • AI Computing Systems

    An Application Driven Perspective
    • 1st Edition
    • Yunji Chen + 5 more
    • English
    AI Computing Systems: An Application Driven Perspective adopts the principle of "application-driven, full-stack penetration" and uses the specific intelligent application of "image style migration" to provide students with a sound starting place to learn. This approach enables readers to obtain a full view of the AI computing system. A complete intelligent computing system involves many aspects such as processing chip, system structure, programming environment, software, etc., making it a difficult topic to master in a short time.

  • Data Mining

    Concepts and Techniques
    • 4th Edition
    • Jiawei Han + 2 more
    • English
    Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.

  • Recent Trends in Computational Intelligence Enabled Research

    Theoretical Foundations and Applications
    • 1st Edition
    • Siddhartha Bhattacharyya + 4 more
    • English
    The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few. Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.

  • Machine Learning, Big Data, and IoT for Medical Informatics

    • 1st Edition
    • Pardeep Kumar + 2 more
    • English
    Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.

  • Automated Theorem Proving: A Logical Basis

    • 1st Edition
    • D.W. Loveland
    • English
    Fundamental Studies in Computer Science, Volume 6: Automated Theorem Proving: A Logical Basis aims to organize, augment, and record the major conceptual advances in automated theorem proving. The publication first examines the role of logical systems and basic resolution. Discussions focus on the Davis-Putnam procedure, ground resolution, semantic trees, general resolution procedure, basic concepts of first-order logic, refutation procedures, and preparation of formulas. The text then takes a look at the refinements of resolution, including unit preference and set-of-support, ordered clause deductions, and setting and linear refinements. The monograph tackles subsumption, resolution with equality, and resolution and problem reduction format. Topics include problem reduction format, paramodulation and linear refinements, paramodulation, and subsumption for linear and nonlinear procedures. The publication is a dependable reference for students and researchers interested in automated theorem proving.

  • Artificial Intelligence IV

    Methodology, Systems, Applications
    • 1st Edition
    • P. Jorrand + 1 more
    • English
    Presenting recent results and ongoing research in Artificial Intelligence, this book has a strong emphasis on fundamental questions in several key areas: programming languages, automated reasoning, natural language processing and computer vision.AI is at the source of major programming language design efforts. Different approaches are described, with some of their most significant results: languages combining logic and functional styles, logic and parallel, functional and parallel, logic with constraints.A central problem in AI is automated reasoning, and formal logic is, historically, at the root of research in this domain. This book presents results in automatic deduction, non-monotonic reasoning, non-standard logic, machine learning, and common-sense reasoning. Proposals for knowledge representation and knowledge engineering are described and the neural net challenger to classical symbolic AI is also defended.Finally, AI systems must be able to interact with their environment in a natural and autonomous way. Natural language processing is an important part of this. Various results are presented in discourse planning, natural language parsing, understanding and generation. The autonomy of a machine for perception of its physical environment is also an AI problem and some research in image processing and computer vision is described.

  • Automating Open Source Intelligence

    Algorithms for OSINT
    • 1st Edition
    • Robert Layton + 1 more
    • English
    Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data.

  • Hidden Semi-Markov Models

    Theory, Algorithms and Applications
    • 1st Edition
    • Shun-Zheng Yu
    • English
    Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.

  • Knowledge-Based Systems and Legal Applications

    • 1st Edition
    • Volume 36
    • T.J.M. Bench-Capon
    • English
    This book compiles the experience of the largest project in knowledge-based systems and the law yet undertaken. It provides an in-depth introduction to representation of law in computer programs, as well as more advanced discussion and description of large knowledge-based systems building, legal representation, cooperative work, and interface design in the context of the project.

  • Abstract Domains in Constraint Programming

    • 1st Edition
    • Marie Pelleau
    • English
    Constraint Programming aims at solving hard combinatorial problems, with a computation time increasing in practice exponentially. The methods are today efficient enough to solve large industrial problems, in a generic framework. However, solvers are dedicated to a single variable type: integer or real. Solving mixed problems relies on ad hoc transformations. In another field, Abstract Interpretation offers tools to prove program properties, by studying an abstraction of their concrete semantics, that is, the set of possible values of the variables during an execution. Various representations for these abstractions have been proposed. They are called abstract domains. Abstract domains can mix any type of variables, and even represent relations between the variables. In this work, we define abstract domains for Constraint Programming, so as to build a generic solving method, dealing with both integer and real variables. We also study the octagons abstract domain, already defined in Abstract Interpretation. Guiding the search by the octagonal relations, we obtain good results on a continuous benchmark. We also define our solving method using Abstract Interpretation techniques, in order to include existing abstract domains. Our solver, AbSolute, is able to solve mixed problems and use relational domains.

  • Social Sensing

    Building Reliable Systems on Unreliable Data
    • 1st Edition
    • Dong Wang + 2 more
    • English
    Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.

  • Foundations of Genetic Algorithms 1995 (FOGA 3)

    • 1st Edition
    • Volume 3
    • FOGA
    • English
    Foundations of Genetic Algorithms, 3 focuses on the principles, methodologies, and approaches involved in the integration of genetic algorithm into mainstream mathematics, as well as genetic operators, genetic programming, and evolutionary algorithms. The selection first offers information on an experimental design perspective on genetic algorithms; schema theorem and price's theorem; and fitness variance of formae and performance prediction. Discussions focus on representation-indep... recombination, representation-indep... mutation and hill-climbing, recombination and the re-emergence of schemata, and Walsh transforms and deception. The publication then examines the troubling aspects of a building block hypothesis for genetic programming and order statistics for convergence velocity analysis of simplified evolutionary algorithms. The manuscript ponders on stability of vertex fixed points and applications; predictive models using fitness distributions of genetic operators; and modeling simple genetic algorithms for permutation problems. Topics include exact models for permutations, fitness distributions of genetic operators, predictive model based on linear fitness distributions, and stability in the simplex. The book also takes a look at the role of development in genetic algorithms and productive recombination and propagating and preserving schemata. The selection is a dependable source of data for mathematicians and researchers interested in genetic algorithms.

  • Face Detection and Recognition on Mobile Devices

    • 1st Edition
    • Haowei Liu
    • English
    This hands-on guide gives an overview of computer vision and enables engineers to understand the implications and challenges behind mobile platform design choices. Using face-related algorithms as examples, the author surveys and illustrates how design choices and algorithms can be geared towards developing power-saving and efficient applications on resource constrained mobile platforms.

  • Artificial Intelligence in Real-Time Control 1989

    • 1st Edition
    • Hua-Tian Li + 2 more
    • English
    Papers presented at the workshop are representative of the state-of-the art of artificial intelligence in real-time control. The issues covered included the use of AI methods in the design, implementation, testing, maintenance and operation of real-time control systems. While the focus was on the fundamental aspects of the methodologies and technologies, there were some applications papers which helped to put emerging theories into perspective. The four main subjects were architectural issues; knowledge - acquisition and learning; techniques; and scheduling, monitoring and management.

  • Artificial Intelligence Planning Systems

    Proceedings of the First Conference (AIPS 92)
    • 1st Edition
    • James Hendler
    • English
    Artificial Intelligence Planning Systems documents the proceedings of the First International Conference on AI Planning Systems held in College Park, Maryland on June 15-17, 1992. This book discusses the abstract probabilistic modeling of action; building symbolic primitives with continuous control routines; and systematic adaptation for case-based planning. The analysis of ABSTRIPS; conditional nonlinear planning; and building plans to monitor and exploit open-loop and closed-loop dynamics are also elaborated. This text likewise covers the modular utility representation for decision-theoretic planning; reaction and reflection in tetris; and planning in intelligent sensor fusion. Other topics include the resource-bounded adaptive agent, critical look at Knoblock's hierarchy mechanism, and traffic laws for mobile robots. This publication is beneficial to students and researchers conducting work on AI planning systems.

  • Introduction to Machine Learning

    • 1st Edition
    • Yves Kodratoff
    • English
    A textbook suitable for undergraduate courses in machine learningand related topics, this book provides a broad survey of the field.Generous exercises and examples give students a firm grasp of theconcepts and techniques of this rapidly developing, challenging subject.Introduction to Machine Learning synthesizes and clarifiesthe work of leading researchers, much of which is otherwise availableonly in undigested technical reports, journals, and conference proceedings.Beginnin... with an overview suitable for undergraduate readers, Kodratoffestablishes a theoretical basis for machine learning and describesits technical concepts and major application areas. Relevant logicprogramming examples are given in Prolog.Introduction to Machine Learning is an accessible and originalintroduction to a significant research area.

  • Machine Learning Proceedings 1995

    Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12 1995
    • 1st Edition
    • Armand Prieditis + 1 more
    • English
    Machine Learning: Proceedings of the Twelfth International Conference on Machine Learning covers the papers presented at the Twelfth International Conference on Machine Learning (ML95), held at the Granlibakken Resort in Tahoe City, California on July 9-12, 1995. The book focuses on the processes, methodologies, principles, and approaches involved in machine learning, including inductive logic programming algorithms, neural networks, and decision trees. The selection first offers information on the theory and applications of agnostic PAC-learning with small decision trees; reinforcement learning with function approximation; and inductive learning of reactive action models. Discussions focus on inductive logic programming algorithm, collecting instances for learning, residual gradient algorithms, direct algorithms, and learning curves for decision trees of small depth. The text then elaborates on visualizing high-dimensional structure with the incremental grid growing neural network; empirical support for winnow and weighted-majority based algorithms; and automatic selection of split criterion during tree growing based on node location. The manuscript takes a look at learning hierarchies from ambiguous natural language data, learning with rare cases and small disjuncts, learning by observation and practice, and learning collection fusion strategies for information retrieval. The selection is a valuable source of data for mathematicians and researchers interested in machine learning.

  • Machine Learning

    An Artificial Intelligence Approach, Volume III
    • 1st Edition
    • Yves Kodratoff + 1 more
    • English
    Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.

  • COLT '89

    Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989
    • 1st Edition
    • COLT
    • English
    Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition. Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes. This book is a valuable resource for students and teachers.

  • Uncertainty in Artificial Intelligence

    Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, University of Washington, Seattle, July 29-31, 1994
    • 1st Edition
    • MKP
    • English
    Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994) covers the papers accepted for presentation at the Tenth Annual Conference on Uncertainty in Artificial Intelligence, held in Seattle, Washington on July 29-31, 1994. The book focuses on the processes, methodologies, and approaches involved in artificial intelligence, including approximations, computational methods, Bayesian networks, and probabilistic inference. The selection first offers information on ending-based strategies for part-of-speech tagging; an evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets; and probabilistic constraint satisfaction with non-Gaussian noise. The text then examines Laplace's method approximations for probabilistic inference in belief networks with continuous variables; computational methods, bounds, and applications of counterfactual probabilities; and approximation algorithms for the loop cutset problem. The book takes a look at learning in multi-level stochastic games with delayed information; properties of Bayesian belief network learning algorithms; and the relation between kappa calculus and probabilistic reasoning. The manuscript also elaborates on intercausal independence and heterogeneous factorization; evidential reasoning with conditional belief functions; and state-space abstraction for anytime evaluation of probabilistic networks. The selection is a valuable reference for researches interested in artificial intelligence.

  • Readings in Computer Vision

    Issues, Problem, Principles, and Paradigms
    • 1st Edition
    • Martin A. Fischler + 1 more
    • English
    The field of computer vision combines techniques from physics, mathematics, psychology, artificial intelligence, and computer science to examine how machines might construct meaningful descriptions of their surrounding environment. The editors of this volume, prominent researchers and leaders of the SRI International AI Center Perception Group, have selected sixty papers, most published since 1980, with the viewpoint that computer vision is concerned with solving seven basic problems:Reconstruct... 3D scenes from 2D imagesDecomposing images into their component partsRecognizing and assigning labels to scene objectsDeducing and describing relations among scene objectsDetermining the nature of computer architectures that can support the visual functionRepresenting abstractions in the world of computer memoryMatching stored descriptions to image representationEach chapter of this volume addresses one of these problems through an introductory discussion, which identifies major ideas and summarizes approaches, and through reprints of key research papers. Two appendices on crucial assumptions in image interpretation and on parallel architectures for vision applications, a glossary of technical terms, and a comprehensive bibliography and index complete the volume.

  • Practical Knowledge Engineering

    • 1st Edition
    • Richard Kelly
    • English
    This book provides knowledge engineers with practical methods for initiating, designing, building, managing, and demonstrating successful commercial expert systems. It is a record of what actually works (and does not work) in the construction of expert systems, drawn from the author's decade of experience in building expert systems in all major areas of application for American, European, and Japanese organizations.The book features:* knowledge engineering programming techniques* useful skills for demonstrating expert systems * practical costing and metrics* guidelines for using knowledge representation techniques* solutions to common difficulties in design and implementation

  • Case-Based Reasoning

    • 1st Edition
    • Janet Kolodner
    • English
    Case-based reasoning is one of the fastest growing areas in the field of knowledge-based systems and this book, authored by a leader in the field, is the first comprehensive text on the subject. Case-based reasoning systems are systems that store information about situations in their memory. As new problems arise, similar situations are searched out to help solve these problems. Problems are understood and inferences are made by finding the closest cases in memory, comparing and contrasting the problem with those cases, making inferences based on those comparisons, and asking questions when inferences can't be made.This book presents the state of the art in case-based reasoning. The author synthesizes and analyzes a broad range of approaches, with special emphasis on applying case-based reasoning to complex real-world problem-solving tasks such as medical diagnosis, design, conflict resolution, and planning. The author's approach combines cognitive science and engineering, and is based on analysis of both expert and common-sense tasks. Guidelines for building case-based expert systems are provided, such as how to represent knowledge in cases, how to index cases for accessibility, how to implement retrieval processes for efficiency, and how to adapt old solutions to fit new situations. This book is an excellent text for courses and tutorials on case-based reasoning. It is also a useful resource for computer professionals and cognitive scientists interested in learning more about this fast-growing field.

  • Introduction to Knowledge Systems

    • 1st Edition
    • Mark Stefik
    • English
    Focusing on fundamental scientific and engineering issues, this book communicates the principles of building and using knowledge systems from the conceptual standpoint as well as the practical. Previous treatments of knowledge systems have focused on applications within a particular field, or on symbol-level representations, such as the use of frame and rule representations. Introduction to Knowledge Systems presents fundamentals of symbol-level representations including representations for time, space, uncertainty, and vagueness. It also compares the knowledge-level organizations for three common knowledge-intensive tasks: classification, configuration, and diagnosis. The art of building knowledge systems incorporates computer science theory, programming practice, and psychology. The scope of this book is appropriately broad, ranging from the design of hierarchical search algorithms to techniques for acquiring the task-specific knowledge needed for successful applications. Each chapter proceeds from concepts to applications, and closes with a brief tour of current research topics and open issues. Readers will come away with a solid foundation that will enable them to create real-world knowledge systems using whatever tools and programming languages are most current and appropriate.

  • Reasoning About Plans

    • 1st Edition
    • James Allen + 3 more
    • English
    This book presents four contributions to planning research within an integrated framework. James Allen offers a survey of his research in the field of temporal reasoning, and then describes a planning system formalized and implemented directly as an inference process in the temporal logic. Starting from the same logic, Henry Kautz develops the first formal specification of the plan recognition process and develops a powerful family of algorithms for plan recognition in complex situations. Richard Pelavin then extends the temporal logic with model operators that allow the representation to support reasoning about complex planning situations involving simultaneous interacting actions, and interaction with external events. Finally, Josh Tenenberg introduces two different formalisms of abstraction in planning systems and explores the properties of these abstraction techniques in depth.

  • Machine Learning Proceedings 1989

    • 1st Edition
    • Alberto Maria Segre
    • English
    Proceedings of the Sixth International Workshop on Machine Learning covers the papers presented at the Sixth International Workshop of Machine Learning, held at Cornell University, Ithaca, New York (USA) on June 26-27, 1989. The book focuses on the processes, methodologies, techniques, and approaches involved in machine learning. The selection first offers information on unifying themes in empirical and explanation-based learning; integrated learning of concepts with both explainable and conventional aspects; conceptual clustering of explanations; and tight integration of deductive and inductive learning. The text then examines multi-strategy learning in nonhomogeneous domain theories; description of preference criterion in constructive learning; and combining case-based reasoning, explanation-based learning, and learning from instruction. Discussions focus on causal explanation of actions, constructive learning, learning in a weak theory domain, learning problem, and individual criteria and their relationships. The book elaborates on learning from plausible explanations, augmenting domain theory for explanation-based generalization, reducing search and learning goal preferences, and using domain knowledge to improve inductive learning algorithms for diagnosis. The selection is a dependable reference for researchers interested in the dynamics of machine learning.

  • Readings in Artificial Intelligence and Databases

    • 1st Edition
    • John Mylopoulos + 1 more
    • English
    The interaction of database and AI technologies is crucial to such applications as data mining, active databases, and knowledge-based expert systems. This volume collects the primary readings on the interactions, actual and potential, between these two fields. The editors have chosen articles to balance significant early research and the best and most comprehensive articles from the 1980s. An in-depth introduction discusses basic research motivations, giving a survey of the history, concepts, and terminology of the interaction. Major themes, approaches and results, open issues and future directions are all discussed, including the results of a major survey conducted by the editors of current work in industry and research labs. Thirteen sections follow, each with a short introduction.Topics examined include semantic data models with emphasis on conceptual modeling techniques for databases and information systems and the integration of data model concepts in high-level data languages, definition and maintenance of integrity constraints in databases and knowledge bases, natural language front ends, object-oriented database management systems, implementation issues such as concurrency control and error recovery, and representation of time and knowledge incompleteness from the viewpoints of databases, logic programming, and AI.