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Morgan Kaufmann

    • Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

      • 1st Edition
      • Manuel González Canché
      • English
      Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. The book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools—just as people do not need to know C++ or TypeScript to benefit from Microsoft Word. The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies, along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements.

    • 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.

    • 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.

    • Data Compression for Data Mining Algorithms

      • 1st Edition
      • Xiaochun Wang
      • English
      Data Compression for Data Mining Algorithms tackles the important problems in the design of more efficient data mining algorithms by way of data compression techniques and provides the first systematic and comprehensive description of the relationships between data compression mechanisms and the computations involved in data mining algorithms. Data mining algorithms are powerful analytical techniques used across various disciplines, including business, engineering, and science. However, in the big data era, tasks such as association rule mining and classification often require multiple scans of databases, while clustering and outlier detection methods typically depend on Euclidean distance for similarity measures, leading to high computational costs.Data Compression for Data Mining Algorithms addresses these challenges by focusing on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations involved in tasks such as feature selection and similarity computation. The book features the latest developments in both lossless and lossy data compression methods and provides a comprehensive exposition of data compression methods for data mining algorithm design from multiple points of view.Key discussions include Huffman coding, scalar and vector quantization, transforms, subbands, wavelet-based compression for scalable algorithms, and the role of neural networks, particularly deep learning, in feature selection and dimensionality reduction. The book’s contents are well-balanced for both theoretical analysis and real-world applications, and the chapters are well organized to compose a solid overview of the data compression techniques for data mining. To provide the reader with a more complete understanding of the material, projects and problems solved with Python are interspersed throughout the text.

    • 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.

    • Understanding Models Developed with AI

      Including Applications with Python and MATLAB Code
      • 1st Edition
      • Ömer Faruk Ertuğrul + 2 more
      • English
      Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide on the intricacies of AI models and their real-world applications. The book demystifies complex AI methodologies by providing clear explanations and practical examples that are reinforced with Python and MATLAB program codes. Its content structure emphasizes a practical, applications-driven approach to understanding AI models, with hands-on coding examples throughout each chapter. Readers will find the tools they need 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.As the primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results, and bias (data and algorithm) management, this resource give researchers and developers what they need to be able to not only implement AI models, but also interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable.

    • 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.

    • Digital Design using VerilogHDL

      VLSI Modeling, Coding and Verification
      • 1st Edition
      • Shilpi Birla + 2 more
      • English
      Digital Design using VerilogHDL: VLSI Modeling, Coding and Verification covers the concepts of digital logic design, including, logic simplification and optimization for digital circuit synthesis and implementation, design and integration of logics (combinational and sequential) in the building of digital circuits and systems, the practical aspects of number systems, the use of VerilogHDL in the logic design, testbench verification, and the synthesis of digital circuits and systems with HDL code examples. Users will find an approach to the design, integration, verification, and synthesizing of a digital logic circuit, complete with coding examples.

    • The Governance of Artificial Intelligence

      • 1st Edition
      • Tshilidzi Marwala
      • English
      The Governance of Artificial Intelligence stands out as a comprehensive guide that unifies the essential dimensions of artificial intelligence—values, data, algorithms, computing, applications, and governance—within a single volume. The book offers expert guidance on each of these topics, blending engineering insight with governance strategies. It proposes a holistic approach to AI governance, emphasizing the importance of proactive and balanced policies that foster innovation while safeguarding ethical standards. Prioritizing social welfare and human rights, this work advocates for maximizing AI’s benefits and minimizing its risks through effective, integrative governance structures.Moreover, the book highlights the need for a versatile governance model that draws from various disciplines and champions diversity. It stresses the importance of leveraging existing regulatory frameworks, ethical guidelines, and industry standards, while encouraging active collaboration among governments, businesses, civil society, and academia. Structured into six sections and 33 chapters, the book systematically explores core principles, data concerns, algorithms, computing, practical applications, and governance challenges, making it a crucial resource for understanding the evolving landscape of AI oversight.

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