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Books in Artificial intelligence general

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    • 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.

    • 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.

    • Engineering Generative AI-Based Software

      • 1st Edition
      • May 1, 2026
      • Miroslaw Staroń
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 6 0 6 4
      • eBook
        9 7 8 0 4 4 3 2 7 6 0 7 1
      Software Engineering professionals now face challenges in incorporating GAI into the products and programs they are developing. At this point, the knowledge about developing AI-based software is mostly based on classical AI, i.e., non-generative ML systems. Developers know how to use machine learning and, to some extent, how to include it in production systems. Engineering Generative-AI Based Software takes software development to the next level by using generative AI instead. Readers learn how to use text, image and audio models as part of larger software systems. The book discusses both the process of developing such software and the architectures for this kind of software, combining theory with practice. Generative AI software is gaining popularity thanks to such models as GPT-4 or Llama. More and more products use them as part of their feature portfolio, but this software is often limited to web applications or recommendation systems. Author Miroslav Staron shows readers how to tackle the challenges of professionally engineering generative AI-based systems. The book starts by reviewing the most relevant models and technologies in this area, both theoretically and practically. Once readers know the technologies, the book goes into details of software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, various architectural styles and tactics for such systems, and different programming platforms. The book also shows how to create robust licensing models and the technology to support them. Finally, readers learn how to manage data, both during the training and also when generating new data, as well as how to use the generated data and user feedback to constantly evolve generative AI-based software.

    • Distributed AI in the Modern World

      • 1st Edition
      • May 1, 2026
      • Andrei Olaru + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 6 7 9 5
      • eBook
        9 7 8 0 4 4 3 4 4 6 8 0 1
      Distributed AI in the Modern World: Technical and Social Aspects of Interacting Intelligent Agents presents several state-of-the-art insights into the various forms of distribution of artificial intelligence, with practical application instances. 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 environment along with practical solutions at an architectural level. Deployment, maintenance and monitoring of distributed machine learning systems brings about many practical challenges, dealing with the intelligent agents distributed across a network of heterogenous devices, or interacting with robots and humans alike. While these scenarios are very different, some challenges remain the same when interaction exists: discoverability, availability, communication language and formats, and efficiency in transferring significant amounts of information. The book provides readers with practical solutions at an architectural level, with solutions presented in three parts. Part 1 deals with the distribution of the learning process and the utilization of machine learning models in a distributed system. Part 2 deals with tools that enable the distribution and interaction of artificial learning entities and how multi-agent systems and machine learning can be combined. Part 3 deals with the physical embodiment of intelligent agents and the interaction of intelligent computing units bound to physical space. The three parts are followed by a conclusion, emphasizing the challenges that are common to all scenarios and solutions which apply in a wider range of cases.

    • Advanced Intelligence Methods for Data Science and Optimization

      • 1st Edition
      • April 1, 2026
      • Amir Hossein Gandomi + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 8 9 4 0 8
      • eBook
        9 7 8 0 4 4 3 2 8 9 4 1 5
      Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains.It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.

    • Digital Business Transformation in Healthcare

      • 1st Edition
      • April 1, 2026
      • Michael Mutingi
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 4 0 8 5
      • eBook
        9 7 8 0 4 4 3 3 3 4 0 9 2
      Digital Business Transformation in Healthcare: Advances, Models, Strategies and Frameworks provides a comprehensive view of digital transformation, outlining challenges and barriers while emphasizing critical success factors for healthcare digital transformation. The book introduces healthcare readiness and maturity models, enabling healthcare systems to choose appropriate roadmaps for digital transformation. By utilizing these models, healthcare providers can select effective and sustainable strategies for their digital transformation journey, avoiding the negative impacts of fragmented technology adoption. It is designed for both professionals already immersed in the healthcare industry and those seeking advanced knowledge in digital business transformation within healthcare.

    • Artificial Intelligence in Brain Disorders

      • 1st Edition
      • April 1, 2026
      • Pranav Kumar Prabhakar + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 7 2 2 1
      • eBook
        9 7 8 0 4 4 3 2 7 7 2 3 8
      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. This book offers a detailed overview of AI and machine learning techniques relevant to neurological research. The book 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.