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Books in Artificial intelligence

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    • Medical Imaging Application in Health Assessment and Disease Management

      • 1st Edition
      • May 1, 2026
      • Dilber Uzun Ozsahin + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 8 9 6 7 5
      • eBook
        9 7 8 0 4 4 3 2 8 9 6 8 2
      Medical Imaging Application in Health Assessment and Disease Management is an all-encompassing book that explores the transformative power of medical imaging in various fields of medicine. It showcases the latest advancements and applications of medical imaging modalities, ranging from neurology and oncology to audiology and osteoporosis. The book highlights the role of medical imaging in understanding and treating neurological conditions, assessing bone health, unraveling hearing disorders, and diagnosing and treating oncological conditions. It also delves into the potential of artificial intelligence and machine learning in improving cancer diagnosis and treatment. The book explores the use of medical imaging in observing mental health conditions such as autism spectrum disorder and stress-related behavioral changes. This comprehensive resource is essential for researchers and professional engineers in the fields of medical image computing/processing... computer science, artificial intelligence, radiology, neuroscience, and biomedical research.

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

      • 1st Edition
      • May 1, 2026
      • Hanif Heidari + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 4 1 3 3 5
      • eBook
        9 7 8 0 4 4 3 3 4 1 3 4 2
      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. The book explores Intelligent IoT and its use in exploring the intersection between medicine, data science, biomedical engineering, and healthcare systems. In addition, this release includes a comprehensive overview of modeling and analyzing the requirements of people with neurological disorders. Signals and images of biological activity are collected and analyzed based on patient specifications to facilitate more accurate diagnosis and treatment.Finally, the book also discusses cutting-edge AI methods for IoT devices designed to treat neurological conditions.

    • Deep Learning Applications in Neuroinformatics

      • 1st Edition
      • May 1, 2026
      • Karthik Ramamurthy
      • English
      • Paperback
        9 7 8 0 4 4 3 4 1 4 5 9 6
      • eBook
        9 7 8 0 4 4 3 4 1 4 6 0 2
      Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics. This book covers the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. It addresses critical neurological disorders like Alzheimer’s disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder. The book bridges the gap between neuroscience and artificial intelligence, making it ideal for researchers, practitioners, and students. It offers insights from leading experts and provides a clear pathway from fundamental concepts to advanced research and future trends in the field.

    • AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice

      • 1st Edition
      • May 1, 2026
      • Olfa Boubaker + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 5 5 4 6
      • eBook
        9 7 8 0 4 4 3 3 6 5 5 5 3
      AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice explores the transformative role of AI and data science in enhancing precision medicine, predictive analytics, and medical practice. The book covers diverse topics such as AI-driven personalized medicine, seizure prediction through EEG analysis, and the application of chaos theory in AI-driven healthcare. The volume also delves into medical practice and education, including ethical considerations, AI-driven supply chain management, and clinical documentation using natural language processing.Furthermo... it examines AI's role in telemedicine, patient engagement, and adherence, offering innovative solutions to improve healthcare delivery and outcomes.

    • Digital Twins

      • 1st Edition
      • May 1, 2026
      • Bedir Tekinerdogan + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 5 7 3 5
      • eBook
        9 7 8 0 4 4 3 4 5 5 7 2 8
      Digital Twins: Core Principles and AI Integration provides a comprehensive overview of digital twin technology, a cutting-edge innovation that bridges the physical and digital worlds. As digital twin technology evolves, its integration with various advanced digital solutions is becoming essential for achieving real-time insights and autonomous decision-making. Challenges include understanding the interoperability of these technologies, managing data complexity, ensuring security, and optimizing for low-latency environments. The authors demystify digital twin technology, providing a clear framework for understanding how to effectively implement and utilize digital twins. The book addresses common challenges such as data integration, security, scalability, and the alignment of digital twin models with actual physical processes. After presenting core concepts of digital twins for software engineering, the book progresses to a section on integration with advanced digital solutions such as AI, IoT, Cloud computing, Big Data Analytics, and Extended Reality (XR). Next, the authors provide readers with a thorough presentation of digital twins applications in a variety of settings and industry/research topics. Finally, the book concludes with a discussion of challenges and solutions, along with future trends in digital twins research and development.

    • Essential Kubeflow

      • 1st Edition
      • May 1, 2026
      • Prashanth Josyula + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 2 5 4 3
      • eBook
        9 7 8 0 4 4 3 4 5 2 5 5 0
      Essential Kubeflow: Engineering ML Workflows on Kubernetes equips readers with the tools to transform ML workflows from experimental notebooks to production-ready platforms with this comprehensive guide to Kubeflow, one of the most widely adopted open source MLOps platforms used to automate ML workloads. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides practical solutions for real-world challenges. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms: architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, scaling workloads efficiently, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. By the end of this book, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.

    • Artificial Intelligence and Machine Learning for Safety-Critical Systems

      • 1st Edition
      • May 1, 2026
      • Rajiv Pandey + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 5 9 7 3
      • eBook
        9 7 8 0 4 4 3 3 6 5 9 8 0
      Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide serves as a vital reference for engineers and system designers seeking to integrate AI and ML techniques into safety-critical environments. The book is meticulously structured into nine sections, each focusing on core applications and challenges unique to these high-stakes systems. Readers are guided through strategies that optimize resources, minimize failures, and bolster both system and public safety. With its practical approach, the guide aims to bridge the gap between advanced AI solutions and the rigorous demands of safety-critical industries.The book also delves into diverse domains such as pattern recognition, image processing, edge computing, IoT, encryption, and hardware accelerators. Each application area is explored to reveal the unique hurdles and solutions in deploying ML models in safety-sensitive contexts. Finally, the authors also emphasize the importance of explainable AI, ensuring model outputs are transparent and trustworthy rather than opaque. To further strengthen confidence in these systems, the text discusses legal, certification, and regulatory aspects, equipping readers with the tools necessary to achieve compliance and public trust.

    • Grey Wolf Optimizer

      • 1st Edition
      • May 1, 2026
      • Seyedali Mirjalili
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 6 2 4 6
      • eBook
        9 7 8 0 4 4 3 3 6 6 2 5 3
      Grey Wolf Optimizer: A Pack of Solutions for Your Optimization Problems offers in-depth coverage of recent theoretical advancements in GWO, as well as several variants, improvements, and hybrid approaches developed to enhance the GWO's performance and adaptability. 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. The GWO algorithm is an influential and rapidly advancing metaheuristic algorithm that has gained substantial attention across scientific and industrial domains. However, solving optimization problems using the GWO involves addressing various challenges, including but not limited to: handling multiple objectives, managing constraints, working with binary decision variables, navigating large-scale search spaces, adapting to dynamic objective functions, and dealing with noisy or uncertain parameters. This book directly addresses these needs by providing a thorough exploration of the GWO, offering a deep dive into the algorithm's foundations and presenting new developments to help researchers overcome common challenges. The book features numerous case studies and real-world examples across various fields, such as engineering, healthcare, finance, and environmental management. These applications demonstrate the versatility and effectiveness of the GWO in addressing complex, interdisciplinary challenges, making the content highly relevant and practical for readers. 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 essential resource for researchers and practitioners interested in applying and developing the Grey Wolf Optimizer.

    • AI Platforms as Global Governance for the Health Ecosystem

      • 1st Edition
      • May 1, 2026
      • Dominique J. Monlezun
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 5 0 5 6
      • eBook
        9 7 8 0 4 4 3 4 5 5 0 6 3
      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 as global governance for the healthcare ecosystem. 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. The book explores how AI platforms can transform hospitals and clinical practice by digitally unifying patients, providers, and payors, advancing healthcare for all. This book defines and explains the main hurdles and technical innovations in responsibly governing AI platforms for efficient, equitable, and sustainable global healthcare. It explores the history, science, politics, economics, ethics, policy, as well as the future of these AI platforms, and how governance efforts can work toward the common good.

    • Understanding Models Developed with AI

      • 1st Edition
      • May 1, 2026
      • Ömer Faruk Ertuğrul + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 1 6 3 9
      • eBook
        9 7 8 0 4 4 3 4 4 1 6 4 6
      Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide for readers looking to understand the intricacies of AI models and their real-world applications. This book demystifies complex AI methodologies by providing clear explanations and practical examples, reinforced with Python and MATLAB program code. It is an essential resource for readers who aim to develop and interpret AI models effectively. The primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results and bias (data and algorithm) management. Researchers and developers need to be able to not only implement AI models, but also to interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable. This book is a valuable reference that equips readers with the tools to build AI models along with the knowledge to make these models accessible and interpretable to stakeholders, thus fostering trust and reliability in AI systems. The book’s content structure emphasizes a practical, application-driven approach to understanding AI models, with hands-on coding examples throughout each chapter.

Related subjects

Artificial intelligence expert systems and knowledge based systems

Artificial intelligence general

Cognitive science

Computer human interaction

Evolutionary computation genetic algorithms programming

Fuzzy logic

Machine learning

Natural language and computational linguistics

Neural networks

Planning and scheduling

Reasoning

Robotics and autonomous systems