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Books in Artificial intelligence general

  • AI-Driven Urban Planning

    Shaping the Future of Cities
    • 1st Edition
    • Zhong-Ren Peng
    • English
    AI-Driven Urban Planning: Shaping the Future of Cities presents a comprehensive guide to the transformative potential of artificial intelligence in urban planning. This book equips readers with the knowledge to harness data, analytics, and AI for creating sustainable, equitable, and livable urban environments. Exploring diverse applications—from understanding human mobility patterns to enhancing disaster response strategies and optimizing design processes—the book offers practical projects and illustrates how AI is shaping contemporary urban landscapes. By addressing both theoretical and practical dimensions, this resource aims to empower students, professionals, and policymakers with a holistic understanding of Urban Planning AI.It is organized into five parts, each tackling crucial aspects of Urban Planning AI. It first introduces core concepts, types, mechanisms, and ethical considerations surrounding AI. Part II then discusses the history of computer applications in urban and regional planning. Part III focuses on AI Applications in Urban Planning, addressing critical domains such as transportation, environmental, housing, economic, participatory, and health and safety planning. Part IV tackles challenges and ethical considerations, emphasizing equity, transparency, and data-related issues. Lastly, Part V explores future pathways of urban planning AI, discussing current trends, future visions, and interdisciplinary approaches essential for effective governance and policymaking.

  • Digital Supply Chain Transformation

    Implementing Technology, Analytics, and Data-Driven Solutions
    • 1st Edition
    • Vinaytosh Mishra
    • English
    Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions delves into the intricate world of supply chain management, emphasizing the role of digital transformation in modern supply chains. Through a blend of theoretical learning and practical applications, readers will gain a deep understanding of foundational supply chain principles while exploring emerging trends and technologies reshaping the industry. Topics such as system dynamics modelling, machine learning, artificial intelligence, and end-to-end visibility are explored in-depth, equipping readers with the tools and knowledge needed to excel in the rapidly evolving landscape of supply chain management. Readers will learn how comprehend core principles and elements of supply chain management and its pivotal role in businesses and industries, recognize the significance of digital transformation in supply chains, understand the tools, technologies, and strategies essential for a successful transformation, evaluate the importance of end-to-end supply chain visibility, employ methods and technologies to enhance this visibility in practical scenarios, and apply system dynamics modeling techniques to address complex supply chain problems to optimize supply chain processes, and much more.

  • Advances in Multimodal Large Language Models for Healthcare

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

  • Artificial Intelligence in Brain Disorders

    Innovations in Diagnosis and Treatment
    • 1st Edition
    • Pranav Kumar Prabhakar + 3 more
    • English
    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.

  • Artificial Intelligence Applications in Emerging Healthcare Technologies

    • 1st Edition
    • Miguel Antonio Wister Ovando + 2 more
    • English
    Artificial Intelligence Applications in Emerging Healthcare Technologies presents the latest advances and state-of-the-art methods and applications of computer science and emerging AI technologies in health and medicine. It explores the impact of artificial intelligence (AI) in healthcare for medical decision-making and data analysis, tackling topics such as cloud computing, cybersecurity, the internet of things, natural language processing, virtual health, data science applied to healthcare, personalized medicine, imaging, diagnosis, drug discovery, and diseases, among others. It is a great resource for researchers and students to learn how machine learning algorithms and other data science techniques have been implemented to solve healthcare-related problems. Chapters present adaptations or improvements on previous models and algorithms to process data from different sources. Other chapters investigate new formulations for the optimization of known procedures and algorithms. Finally, all chapters use experimental methods to study problems of interest in healthcare.

  • Federated Learning

    Foundations and Applications
    • 1st Edition
    • Rajkumar Buyya + 2 more
    • English
    Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Sections cover fundamental concepts, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to coverage of the architectures, algorithms, and system models of Federated Learning, as well as security, privacy, and energy-efficiency techniques. Finally, the book presents various applications of Federated Learning through real-world case studies, illustrating both centralized and decentralized Federated Learning.Federated Learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchange of only model parameters between clients and servers, hence the addition of this new release is ideal for those interested in the topics presented.

  • Federated Learning for the Metaverse

    Applications in Virtual Environments
    • 1st Edition
    • Noor Zaman Jhanjhi + 3 more
    • English
    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.

  • Deep Learning Applications in Neuroinformatics

    Advances, Methods, and Perspectives
    • 1st Edition
    • Karthik Ramamurthy
    • English
    Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics, covering the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. The book addresses critical neurological disorders like Alzheimer’s disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder, bridging the gap between neuroscience and artificial intelligence. It is an ideal resource for researchers, practitioners, and students with insights from leading experts.

  • Distributed AI in the Modern World

    Technical and Social Aspects of Interacting Intelligent Agents
    • 1st Edition
    • Andrei Olaru + 3 more
    • English
    Distributed AI in the Modern World: Technical and Social Aspects of Interacting Intelligent Agents presents state-of-the-art insights into the various forms of distribution of artificial intelligence, with practical application instances. Sections provide readers with practical solutions at an architectural level, with solutions presented on the distribution of the learning process and the utilization of machine learning models in a distributed system, tools that enable the distribution and interaction of artificial learning entities, how multi-agent systems and machine learning can be combined, the physical embodiment of intelligent agents, and the interaction of intelligent computing units bound to physical space.Following sections emphasize the challenges that are common to all scenarios and solutions that apply in a wider range of cases. This book does not analyze the internal workings of machine learning models (for instance, in the case of multi-agent reinforcement learning), but instead provides readers with an overview of the challenges brought by the need of artificially intelligent entities to interact with other entities and with their environments, along with practical solutions at an architectural level.

  • AI Platforms as Global Governance for the Health Ecosystem

    The Future's Global Hospital
    • 1st Edition
    • Dominique J. Monlezun
    • English
    AI Platforms as Global Governance for the Health Ecosystem: The Future’s Global Hospital provides comprehensive and actionable approaches for readers to understand and optimize responsible AI to create global governance for the healthcare ecosystem. The book explores how AI platforms can transform hospitals and clinical practice by digitally unifying patients, providers, and payors, advancing healthcare for all. Users will find content that defines and explains the main hurdles and technical innovations in responsibly governing AI platforms for efficient, equitable, and sustainable global healthcare.Additiona... sections delve into the history, science, politics, economics, ethics, policy, and future of these AI platforms, and how governance efforts can work toward the common good. Written from the first-hand perspective of a practicing physician-data scientist and AI ethicist, the book maps out how to develop successful governance for AI platforms.