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Books in Computer science

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Federated Learning for Digital Healthcare Systems

  • 1st Edition
  • Agbotiname Lucky Imoize, Fatos Xhafa + 2 more
  • Agbotiname Lucky Imoize, Fatos Xhafa, Mohammad S Obaidat + 1 more
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 8 9 7 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 8 9 6 - 6
Modern healthcare systems facilitate the collection of critical medical data for statistical evaluation and inference using machine learning, however, the application of ML in healthcare data analytics has not been fully exploited due to the proliferation of security and privacy concerns. The potential of machine learning is also limited by insufficient data, posing a significant impediment to the transition from research to clinical practice. Over the past five years, Federated Learning has been introduced to strengthen the performance of machine learning. In federated learning, artificial intelligence models are trained with data from multiple sources. In this case, data anonymity, security, privacy and integrity are maintained, thus removing potential barriers to data sharing. Additionally, models trained by federated learning have shown favorable progress in the agreement with models obtained from centrally hosted data sets. A successfully implemented federated learning model can produce unbiased decisions which facilitate better-informed decision making in precision medicine.Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance. In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems.
Federated Learning for Digital Healthcare Systems

Metaheuristic Optimization Algorithms

  • 1st Edition
  • Laith Abualigah
  • Laith Abualigah
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 9 2 5 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 9 2 6 - 0
Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. The book provides readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm that is followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies.
Metaheuristic Optimization Algorithms

Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing

  • 1st Edition
  • Shufei Li, Pai Zheng + 1 more
  • Shufei Li, Pai Zheng and Lihui Wang
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 9 4 3 - 7
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 9 4 4 - 4
Proactive Human–Robot Collaboration Toward Human-Centric Smart Manufacturing is driven by an appreciation of manufacturing scenarios where human and robotic agents can understand each other’s actions and conduct mutual-cognitive, predictable, and self-organizing teamwork. Modern factories’ smart manufacturing transformation and the evolution of relationships between humans and robots in manufacturing tasks set the scene for a discussion on the technical fundamentals of state-of-the-art proactive human–robot collaboration; these are further elaborated into the three main steps (i.e., mutual-cognitive and empathic coworking; predictable spatio-temporal collaboration; self-organizing multiagent teamwork) to achieve an advanced form of symbiotic HRC with high-level, dynamic-reasoning teamwork skills. The authors then present a deployment roadmap and several case studies, providing step-by-step guidance for real-world application of these ground-breaking methods which crucially contribute to the maturing of human-centric, sustainable, and resilient production systems. The volume proves to be an invaluable resource that supports understanding and learning for users ranging from upper undergraduate/graduate students and academic researchers to engineering professionals in a variety of industry contexts.
Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing

Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

  • 1st Edition
  • Rajesh Kumar Tripathy + 1 more
  • Rajesh Kumar Tripathy and Ram Bilas Pachori
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 4 1 4 1 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 4 1 4 0 - 9
Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine learning methods such as supervised learning, unsupervised learning, semi-supervised learning, and meta-learning combined with different signal processing techniques such as multivariate data analysis, time-frequency analysis, multiscale analysis, and feature extraction techniques for the detection of cardiovascular diseases, heart valve disorders, hypertension, and activity monitoring using ECG, PPG, and PCG signals.In addition, this book also includes the applications of digital signal processing (time-frequency analysis, multiscale decomposition, feature extraction, non-linear analysis, and transform domain methods), machine learning and deep learning (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine learning and deep learning models combined with signal processing for cardiovascular data analysis are also covered.
Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

Cognitive Science, Computational Intelligence, and Data Analytics

  • 1st Edition
  • Vikas Khare, Ankita Jain + 1 more
  • Vikas Khare, Ankita Jain and Sanjeet Kumar Dwivedi
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 6 0 7 8 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 6 0 7 9 - 0
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.
Cognitive Science, Computational Intelligence, and Data Analytics

Computational Intelligence Techniques for Sustainable Supply Chain Management

  • 1st Edition
  • Sanjoy Kumar Paul + 1 more
  • Sanjoy Kumar Paul and Sandeep Kautish
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 8 4 6 4 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 8 4 6 5 - 9
Computational Intelligence Techniques for Sustainable Supply Chain Management presents state-of-the-art computational intelligence techniques and applications for supply chain sustainability issues and logistic problems, filling the gap between general textbooks on sustainable supply chain management and more specialized literature dealing with methods for computational intelligence techniques. This book focuses on addressing problems in advanced topics in the sustainable supply chain and will appeal to practitioners, managers, researchers, students, and professionals interested in sustainable logistics, procurement, manufacturing, inventory and production management, scheduling, transportation, and supply chain network design.
Computational Intelligence Techniques for Sustainable Supply Chain Management

Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem

  • 1st Edition
  • Dominique J Monlezun
  • Dominique J Monlezun
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 5 9 7 - 1
  • eBook
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Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem: A Humanity Worth Saving is the first comprehensive book showing how trustworthy AI can revolutionize decolonized global public health. It explains how it works as an ecosystem and how it can be fixed to equitably empower us all to solve the defining crises of our era, from poverty to pandemics, climate to conflicts, debt to divisions. It is written from the first-hand perspective of the world’s first triple doctorate trained physician-data scientist and ethicist who has cared for more than 10,000 patients and authored 5 AI textbooks and more than 400 scientific and ethics papers. This essential resource integrates science, political economics, and ethics to unite our unique cultures, belief systems, institutions, and governments. In doing so, it is meant to give humanity a fighting chance against shared existential threats through cooperation and managed strategic competition for integral sustainable development.Taking seriously diverse voices, perspectives, and insights from the Global North and the Global South, this book uses concrete examples backed up by clear explanations to elucidate the current failures, emerging successes, and societal trends of global public health. It shows how a small number of powerful governments and corporations—amid digitalization, deglobalization, and demographic shifts—dominate global health, and how we can re-engineer a better future for it both societally and technologically. The book spans health breakthroughs in federated data architectures, machine learning, deep learning, swarm learning, quantum computing, blockchain, agile data governance and solidarity, value blocks (of democracies and autocracies), adaptive value supply chains, social networks, pandemics, health financing, universal health coverage, public–private partnerships, healthcare system design, precision agriculture, clean energy, human security, and multicultural global ethics. This book therefore is meant to provide a clear, coherent, and actionable guide equipping students, practitioners, researchers, policymakers, and leaders in digital technology, public health, healthcare, health policy, public policy, political economics, and ethics to generate the solutions that will define humanity’s next era—while recovering what that humanity means, and why it is worth saving.
Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem

The Theory and Practice of Intelligent Algorithms

  • 1st Edition
  • Han Huang
  • Han Huang
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 7 5 8 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 7 5 9 - 3
In this book, the latest achievements of the computation time analysis theory and practical applications of intelligent algorithms are set out. There are five chapters: (1) new method of intelligent algorithm computation time analysis; (2)Application of intelligent algorithms in computer vision; (3)Application of intelligent algorithms in logistics scheduling; (4)Application of intelligent algorithms in software testing; and (5) application of intelligent algorithm in multi-objective optimization. The content of each chapter is supported by papers published in top journals. The authors introduce the work of each part, which mainly includes a brief introduction (mainly for readers to understand) and academic discussion (rigorous theoretical and experimental support), in a vivid and interesting way through excellent pictures and literary compositions. To help readers learn and make progress together, each part of this book provides relevant literature, code, experimental data, and so on.
The Theory and Practice of Intelligent Algorithms

Cognitive Assistant Supported Human-Robot Collaboration

  • 1st Edition
  • Cecilio Angulo, Alejandro Chacón + 1 more
  • Cecilio Angulo, Alejandro Chacón and Pere Ponsa
  • June 1, 2024
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 2 1 3 5 - 4
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 2 1 3 6 - 1
Cognitive Assistant Supported Human-Robot Collaboration covers the design and development of cognitive assistants in the smart factory era, its application domains, challenges, and current state-of-the-art in assistance systems with collaborative robotics and IoT technologies, standards, platforms, and solutions. This book also provides a sociotechnical view of collaborative work in human-robot teams, investigating specific methods and techniques to analyze assistance systems. This provides readers with a comprehensive overview of how cognitive assistants function and work in human-robot teams.
Cognitive Assistant Supported Human-Robot Collaboration

TinyML for Edge Intelligence in IoT and LPWAN Networks

  • 1st Edition
  • Bharat S Chaudhari, Sheetal N Ghorpade + 2 more
  • Bharat S Chaudhari, Sheetal N Ghorpade, Marco Zennaro + 1 more
  • June 1, 2024
  • English
  • Paperback
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  • eBook
    9 7 8 - 0 - 4 4 3 - 2 2 2 0 3 - 0
TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to IoT and LPWANs. It starts by providing the foundations of IoT/LPWANs, low power embedded systems and hardware, the role of artificial intelligence and machine learning in communication networks in general and cloud/edge intelligence. It then presents the concepts, methods, algorithms and tools of TinyML. Practical applications of the use of TinyML are given from health and industrial fields which provide practical guidance on the design of applications and the selection of appropriate technologies.
TinyML for Edge Intelligence in IoT and LPWAN Networks