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Morgan Kaufmann

Designing Technology for an Aging Population
Towards Universal Design
2nd Edition
Jeff Johnson + 1 more
English
Designing User Interfaces for an Aging Population: Towards Universal Design, Second Edition explores the unique needs of older adults in today’s digital landscape. The authors examine this demographic’s wide-ranging sensory, cognitive, physical, and emotional characteristics, connecting each to the challenges and opportunities older users face with technology. Backed by hundreds of global research studies, the book provides actionable design guidelines to enhance satisfaction and usability for seniors. Updated to reflect the latest advances in AI, robotics, and speech recognition, it offers fresh examples and case studies to keep designers informed about emerging trends.Beyond demographics and design principles, the book highlights common pitfalls in technology that can reduce accessibility for older adults. It discusses strategies for involving seniors directly in research and design, ensuring their voices shape digital innovation. The authors emphasize that older users remain underserved and often overlooked in technology studies, urging designers to broaden their approach. By addressing these gaps, the book helps professionals create more inclusive interfaces that better serve a rapidly growing segment of the technology-using population.
Encyclopedia of Multi-Attribute Decision Making (MADM)
1st Edition
Gholamreza Haseli + 2 more
English
Encyclopedia of Multi-Attribute Decision Making (MADM) is a comprehensive guide that presents all of the current methods in MADM in one volume. In recent years, numerous MADM methods have been introduced, each of them having been rapidly developed by numerous researchers using fuzzy sets and fuzzy numbers. This book presents all the existing methods in a simple way, including Sections on Weighting Methods, Extensions for the MADM Methods, Ranking Methods, and Outranking Methods. All methods chapters have a consistent structure, enabling easier learning of the methods. Each of the methods chapters presents two numerical examples for each method, one simple example with less than six criteria and six alternatives, and one complex example with six or more criteria and six or more alternatives. In addition, most of the methods chapters are written by the original developers of the method, ensuring insight into and practical application of MADM. The book is also filled with over 200 full-color figures illustrating the methods and their applications. Encyclopedia of Multi-Attribute Decision Making (MADM) demystifies the complex world of MADM, blending theoretical concepts with hands-on practices and case studies. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of MADM.
Complexity in Mathematical Biology for Sustainable Development
Modeling Climate, Disease, and Ecosystems through Difference, Differential, and Fractional Theory
1st Edition
Fatma Bozkurt
English
Complexity in Mathematical Biology for Sustainable Development: Modeling Climate, Disease, and Ecosystems through Difference, Differential, and Fractional Theory introduces new mathematical methods to derive complex modeling solutions for a wide range of engineering and scientific research applications, providing a practical and rigorous guide for data practitioners to effectively implement complex models in real-world scenarios. The book strikes a balance between high-level mathematical theory and technical derivations, offering step-by-step explanations, real-world case studies, and clear introductions to advanced mathematical models. The author offers specific solutions that include modeling and quantifying complexity with emphasis placed on the growing need for interdisciplinary collaboration, the integration of real-time data into models, and the development of adaptive frameworks to address emerging global challenges such as pandemics, biodiversity loss, and climate uncertainty. The book is designed to meet the needs of a diverse primary audience, from graduate students to professionals in fields such as computer science, public health, environmental policy, applied mathematics, and biotechnology. By providing both theoretical foundations and practical applications, the book equips readers with the skills and knowledge to tackle pressing global challenges through mathematical models, making it a valuable resource for both academic and professional development.
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.
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.
Digital Twins
Core Principles and AI Integration
1st Edition
Bedir Tekinerdogan + 1 more
English
Digital Twins: Core Principles, System Engineering, and AI Integration provides a comprehensive overview of digital twin technology, a cutting-edge innovation that bridges the physical and digital worlds. 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 discusses 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. 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.
Hardware Security
A Hands-on Learning Approach
2nd Edition
Swarup Bhunia + 1 more
English
Hardware Security: A Hands On Learning Approach, Second Edition provides a broad, comprehensive, and practical overview of hardware security that encompasses all levels of the electronic hardware infrastructure. The book covers basic concepts like advanced attack techniques and countermeasures that are illustrated through theory, case studies, and well designed, hands on laboratory exercises for each key concept. The book is ideal as a textbook for upper level undergraduate students studying computer engineering, computer science, electrical engineering, and biomedical engineering, but is also a handy reference for graduate students, researchers and industry professionals.For academic courses, the book contains a robust suite of teaching ancillaries. Users of the book can access schematic, layout and design files for a printed circuit board for hardware hacking (i.e., the HaHa board), a suite of videos that demonstrate different hardware vulnerabilities, hardware attacks and countermeasures, and a detailed description and user manual for companion materials.
Essential Kubeflow
Engineering ML Workflows on Kubernetes
1st Edition
Prashanth Josyula + 2 more
English
Essential Kubeflow: Engineering ML Workflows on Kubernetes provides the tools needed to transform ML workflows from experimental notebooks to production-ready platforms. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms, including architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, efficiently scaling workloads, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. 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 solutions for real-world challenges.With this comprehensive guide to Kubeflow, a widely adopted open source MLOps platforms for automating ML workloads, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.
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.
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.

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