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Morgan Kaufmann

    • Foundations of Cloud Computing

      • 1st Edition
      • December 1, 2025
      • Robert Shimonski
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 4 7 9 0
      • eBook
        9 7 8 0 4 4 3 2 1 4 8 1 3
      Foundations of Cloud Computing provides readers with a guidebook to navigating the field of Cloud Computing, including the guiding principles, key concepts, history, terminology, state-of-the-art in research, and a roadmap to where the field intersects and interacts with related fields of research and development. In this age of total connectivity, researchers need to be able to communicate and collaborate with a wide range of colleagues across multiple disciplines. This book helps researchers from all fields understand what Cloud Computing is, how it works, and how to speak the language of collaboration with developers and researchers who specialize in the field. With a complete and in-depth foundation in the key concepts of the field, and a roadmap to where and how Cloud Computing intersects across the domains of scientific research and application development, this book gives readers everything they need to navigate and apply this important, ubiquitous technology.

    • Consensus

      • 1st Edition
      • December 1, 2025
      • Ali Ahmadian + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 0 4 3 9 9
      • eBook
        9 7 8 0 4 4 3 4 0 4 4 0 5
      Consensus: Fueling Blockchain Innovation and DApp Expansion explores the complexities of consensus mechanisms in order to shed light on emerging trends, best practices, and real-world applications that can fuel blockchain innovation while encouraging the dissemination of DApps across various industries. Additionally, the book bridges a crucial gap in the literature by providing in-depth insights into the role of consensus mechanisms in shaping the future of blockchain technology and decentralized applications. This book delves into the fundamentals of blockchain technology along with the roles and significance of vital consensus mechanisms, their underlying principles, formal specifications, functional characteristics, architecture, frameworks, and potential across diverse blockchain applications. Moreover, the book meticulously explores classification, performance metrics, and design parameters. It offers a comprehensive comparative analysis of these mechanisms, shedding light on their computational and communication complexity, strengths, weaknesses, and suitability. Additionally, the book delves into future research directions, highlighting emerging trends and areas requiring further investigation. It also addresses the efforts underway to address existing challenges and open issues within the realm of consensus mechanisms, ensuring a comprehensive understanding of the state-of-the-art in this pivotal aspect of blockchain technology. Due to the wide range of availability and evolving new consensus mechanisms, selecting an optimal and suitable consensus for a specific blockchain application is one of the crucial challenges in the development and innovation of blockchain systems. This book has also a discussion on appropriate selection algorithms based on multi-attribute decision-making for specific blockchain systems and DApps development.

    • Mathematical Modeling for Big Data Analytics

      • 1st Edition
      • November 3, 2025
      • Passent El-Kafrawy + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 6 7 3 5 2
      • eBook
        9 7 8 0 4 4 3 2 6 7 3 6 9
      Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

    • Multimodal Learning Using Heterogeneous Data

      • 1st Edition
      • November 1, 2025
      • Saeid Eslamian + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 5 2 8 9
      • eBook
        9 7 8 0 4 4 3 2 7 5 2 9 6
      Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities.The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.

    • Successful User Experience

      • 2nd Edition
      • October 31, 2025
      • Elizabeth Rosenzweig
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 0 8 2 4
      • eBook
        9 7 8 0 4 4 3 2 9 0 8 3 1
      Successful User Experience: Strategy and Roadmaps, Second Edition provides a hands-on guide for pulling all of the User Experience (UX) pieces together to create a strategy that includes tactics, tools, and methodologies. Leveraging material honed in user experience courses and over 35 years in the field, the author explains the value of strategic models to refine goals against available data and resources. You will learn how to think about UX from a high level, design the UX while setting goals for a product or project, and how to turn that into concrete actionable steps.This book demonstrates how to translate high-level planning into concrete, actionable steps. It explains the connection between Design Thinking and creating a great user experience, and guides the reader in setting effective UX goals for their product or project You’ll learn how to choose the right tools and methodologies at each stage of the product lifecycle. Starting with UX acceptance as a foundation, the book builds on this with practical steps and real-world case studies, helping you develop a comprehensive strategy-from the big picture of product design, development, and commercialization, to leveraging UX for stronger, more successful products.

    • Foundations of Computer Engineering

      • 1st Edition
      • October 15, 2025
      • Marilyn Wolf
      • English
      • Paperback
        9 7 8 0 4 4 3 1 5 8 9 2 6
      • eBook
        9 7 8 0 4 4 3 1 5 8 9 1 9
      Foundations of Computer Engineering is a complete introductory textbook for freshman and sophomore students taking a first course in computer engineering. This new text covers everything today’s students will need to go from almost no computer-specific knowledge to understanding the design of computer systems, from their fundamental hardware components and mathematical abstractions to their use in solving real-world problems. Covering all the major themes of 21st century computer engineering, including logic and computers, software, and circuits, instructors will find that this book provides a single coherent reference to guide students through their course.

    • Quantum Computational AI

      • 1st Edition
      • September 11, 2025
      • Long Cheng + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 0 2 5 9 6
      • eBook
        9 7 8 0 4 4 3 3 0 2 6 0 2
      Quantum Computational AI: Algorithms, Systems, and Applications is an emerging field that bridges quantum computing and artificial intelligence. With rapid advancements in both areas, this book serves as a vital resource, capturing the latest theories, algorithms, and practical applications at their intersection. It aims to be both informative and accessible, making it perfect for academics, researchers, industry professionals, and students eager to lead in these technologies. The book explores quantum algorithms, system design, and demonstrates real-world applications across various sectors. It provides a comprehensive understanding of how quantum principles can advance AI, revealing unprecedented possibilities and benefits.

    • Recent Developments in Theory and Applications of Fractional Order Systems

      • 1st Edition
      • August 11, 2025
      • Mehmet Yavuz + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 3 9 5 2 6
      • eBook
        9 7 8 0 4 4 3 2 3 9 5 3 3
      Recent Developments in Theory and Applications of Fractional Order Systems presents a rigorous and thorough analysis of various aspects of Fractional Calculus. The book provides readers with a thorough understanding of fundamental concepts and methods of applied mathematics utilized in a variety of scientific and engineering disciplines. The authors present each computational modeling concept with a definition, methods, theorems, and observations followed by typical application problems and step-by-step solutions. Each topic is covered in detail, followed typically by several meticulously worked out examples and a problem set containing many additional related problems.In addition, the book discusses recent developments and the latest research on Fractional Calculus and its applications, demonstrating important applications in Engineering, Computer Science, Management, Social Science, and the Humanities.

    • Data Science for Teams

      • 1st Edition
      • July 30, 2025
      • Harris V. Georgiou
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 4 0 6 8
      • eBook
        9 7 8 0 4 4 3 3 6 4 0 7 5
      Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.

    • Theoretical Foundations of Quantum Computing

      • 1st Edition
      • July 25, 2025
      • Daowen Qiu
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 7 0 4 7
      • eBook
        9 7 8 0 4 4 3 2 7 7 0 5 4
      Theoretical Foundations of Quantum Computing is an essential textbook for introductory courses in the quantum computing discipline. Quantum computing represents a paradigm shift in understanding computation. This textbook delves into the principles of quantum mechanics that underpin this revolutionary technology, making it invaluable for undergraduate and graduate students in computer science and related fields. Structured into eight meticulously crafted chapters, it covers everything from the historical context of quantum computing to advanced theories and applications. The book includes core topics such as basic models, quantum algorithms, cryptography, communication protocols, complexity, and error correction codes.Each chapter builds upon the last, ensuring a robust understanding of foundational concepts and cutting-edge research. It serves as both a foundational resource for students and a comprehensive guide for researchers interested in quantum computing. Its clarity makes it an excellent reference for deepening understanding or engaging in advanced research.

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