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Morgan Kaufmann

    • Foundations of Quantum Programming

      • 2nd Edition
      • April 29, 2024
      • Mingsheng Ying
      • English
      • Paperback
        9 7 8 0 4 4 3 1 5 9 4 2 8
      • eBook
        9 7 8 0 4 4 3 1 5 9 4 3 5
      Foundations of Quantum Programming, Second Edition provides a systematic exposition of the subject of quantum programming. Emphasis is placed on foundational concepts, methods, and techniques that can be widely used for various quantum programming models and languages. The book describes how programming methodologies developed for current computers can be extended for quantum computers, along with new programming methodologies that can effectively exploit the unique power of quantum computing. In addition, this resource introduces a chain of quantum programming models from sequential to parallel and distributed programming in the paradigm of superposition-of-dat... to the paradigm of superposition-of-pro... content presents a series of logical and mathematical tools for verification and analysis of quantum programs, including invariant generation, termination analysis, and abstract interpretation.

    • Theory of Structured Parallel Programming

      • 1st Edition
      • April 26, 2024
      • Yong Wang
      • English
      • Paperback
        9 7 8 0 4 4 3 2 4 8 1 4 6
      • eBook
        9 7 8 0 4 4 3 2 4 8 1 5 3
      Theory of Structured Parallel Programming is a comprehensive guide to structured parallel programming corresponding to traditional structured sequential programming. The book provides readers with comprehensive coverage of theoretical foundations of structured parallel programming, including analyses of parallelism and concurrency, truly concurrent process algebras, building block-based structured parallel programming, modelling and verification of parallel programming language, modelling and verification of parallel programming patterns, as well as modeling and verification of distributed systems.There have been always two ways to approach parallel computing: one is the structured way, and the other is the graph-based (true concurrent) way. The structured way is often based on the interleaving semantics, such as process algebra CCS. Since the parallelism in interleaving semantics is not a fundamental computational pattern (the parallel operator can be replaced by alternative composition and sequential composition), the parallel operator often does not occur as an explicit operator, such as in the mainstream programming languages C, C++, Java, et al.

    • Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment

      • 1st Edition
      • April 18, 2024
      • Alma Y Alanis + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 2 3 4 1 9
      • eBook
        9 7 8 0 4 4 3 2 2 3 4 0 2
      Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bioinspired techniques such as modeling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by an extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modeling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in the early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks present in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with an extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose-tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modeling, prediction, and classification.

    • Computational Intelligence and Blockchain in Complex Systems

      • 1st Edition
      • March 26, 2024
      • Fadi Al-Turjman
      • English
      • Paperback
        9 7 8 0 4 4 3 1 3 2 6 8 1
      • eBook
        9 7 8 0 4 4 3 1 3 2 7 4 2
      Computational Intelligence and Blockchain in Complex Systems: System Security and Interdisciplinary Applications provides readers with a guide to understanding the dynamics of AI, machine learning, and computational intelligence in blockchain, and how these rapidly developing technologies are revolutionizing a variety of interdisciplinary research fields and applications. This book examines the role of computational intelligence and machine learning in the development of algorithms to deploy blockchain technology across a number of applications, including healthcare, insurance, smart grid, smart contracts, digital currency, precision agriculture, and supply chain. The authors cover the unique and developing intersection between cyber security and blockchain in modern networks, as well as in-depth studies on cybersecurity challenges and multidisciplinary methods in modern blockchain networks. Readers will find mathematical equations throughout the book as part of the underlying concepts and foundational methods, especially the complex algorithms involved in blockchain security aspects for hashing, coding, and decoding. This book also provides readers with the most in-depth technical guide to the intersection of computational intelligence and blockchain, two of the most important technologies for the development of next-generation complex systems

    • Object-Oriented Analysis and Design for Information Systems

      • 2nd Edition
      • March 16, 2024
      • Raul Sidnei Wazlawick
      • English
      • Paperback
        9 7 8 0 4 4 3 1 3 7 3 9 6
      • eBook
        9 7 8 0 4 4 3 1 3 7 4 0 2
      Object-Oriented Analysis and Design for Information Systems, Second Edition clearly explains real object-oriented programming in practice. Expert author Raul Sidnei Wazlawick explains concepts such as object responsibility, visibility, and the real need for delegation in detail. The object-oriented code generated by using these concepts in a systematic way is concise, organized and reusable.The patterns and solutions presented in this book are based in research and industrial applications. You will come away with clarity regarding processes and use cases and a clear understanding of how to expand a use case. Wazlawick clearly explains how to build meaningful sequence diagrams. Object-Oriented Analysis and Design for Information Systems illustrates how and why building a class model is not just placing classes into a diagram. You will learn the necessary organizational patterns so that your software architecture will be maintainable. The Second Edition includes all new content shifting the focus of the book to agile software development, including Scrum software project management, BPMN diagrams, user stories, and Python code examples.

    • Many-Sorted Algebras for Deep Learning and Quantum Technology

      • 1st Edition
      • February 3, 2024
      • Charles R. Giardina
      • English
      • Paperback
        9 7 8 0 4 4 3 1 3 6 9 7 9
      • eBook
        9 7 8 0 4 4 3 1 3 6 9 8 6
      Many-Sorted Algebras for Deep Learning and Quantum Technology presents a precise and rigorousdescription of basic concepts in quantum technologies and how they relate to deep learning and quantum theory. Current merging of quantum theory and deep learning techniques provides the need for a source that gives readers insights into the algebraic underpinnings of these disciplines. Although analytical, topological, probabilistic, as well as geometrical concepts are employed in many of these areas, algebra exhibits the principal thread; hence, this thread is exposed using many-sorted algebras. This book includes hundreds of well-designed examples that illustrate the intriguing concepts in quantum systems. Along with these examples are numerous visual displays. In particular, the polyadic graph shows the types or sorts of objects used in quantum or deep learning. It also illustrates all the inter and intra-sort operations needed in describing algebras. In brief, it provides the closure conditions. Throughout the book, all laws or equational identities needed in specifying an algebraic structure are precisely described.

    • Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

      • 1st Edition
      • January 19, 2024
      • D. Jude Hemanth
      • English
      • Paperback
        9 7 8 0 4 4 3 2 2 0 0 9 8
      • eBook
        9 7 8 0 4 4 3 2 2 0 1 0 4
      Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The book's authors provide readers with an in-depth look at the challenges and associated solutions, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered that will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas.

    • Human-Computer Interaction

      • 2nd Edition
      • January 12, 2024
      • I. Scott MacKenzie
      • English
      • Paperback
        9 7 8 0 4 4 3 1 4 0 9 6 9
      • eBook
        9 7 8 0 4 4 3 1 4 0 9 7 6
      Human-Computer Interaction: An Empirical Research Perspective is the definitive guide to empirical research in HCI. The book begins with foundational topics, including historical context, the human factor, interaction elements, and the fundamentals of science and research. From there, the book progresses to the methods for conducting an experiment to evaluate a new computer interface or interaction technique. There are detailed discussions and how-to analyses on models of interaction, focusing on descriptive models and predictive models. Writing and publishing a research paper is explored with helpful tips for success.Throughout the book, readers will find hands-on exercises, checklists, and real-world examples. This is a must-have, comprehensive guide to empirical and experimental research in HCI – an essential addition to your HCI library.

    • Fractional Difference, Differential Equations, and Inclusions

      • 1st Edition
      • January 11, 2024
      • Saïd Abbas + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 3 6 0 1 3
      • eBook
        9 7 8 0 4 4 3 2 3 6 0 2 0
      Fractional Difference, Differential Equations, and Inclusions: Analysis and Stability is devoted to the existence and stability (Ulam-Hyers-Rassias stability and asymptotic stability) of solutions for several classes of functional fractional difference equations and inclusions. Covered equations include delay effects of finite, infinite, or state-dependent nature, and tools used to establish the existence results for the proposed problems include fixed point theorems, densifiability techniques, monotone iterative technique, notions of Ulam stability, attractivity and the measure of non-compactness, as well as the measure of weak noncompactness. The tools of fractional calculus are found to be of great utility in improving the mathematical modeling of many natural phenomena and processes occurring in the areas of engineering, social, natural, and biomedical sciences. All abstract results in the book are illustrated by examples in applied mathematics, engineering, biomedical, and other applied sciences.

    • Synthetic Data and Generative AI

      • 1st Edition
      • January 9, 2024
      • Vincent Granville
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 8 5 7 6
      • eBook
        9 7 8 0 4 4 3 2 1 8 5 6 9
      Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.

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