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                <text>Tyagi, Amit Kumar; Tiwari, Shrikant; Arumugam, Senthil Kumar; Sharma, Avinash Kumar</text>
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                <text>Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing</text>
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                <text>Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing;pp.1-585</text>
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                <text>Tyagi A.K., Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India; Tiwari S., School of Computing Science and Engineering (SCSE), Galgotias University, Uttar Pradesh, Greater Noida, India; Arumugam S.K., Department of Professional Studies, Christ University, Bengaluru, India; Sharma A.K., Dept. of Computer Science and Engineering, Sharda University, Greater Noida, India</text>
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                <text>An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin-an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical systems behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AIs capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience: This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.  2024 Scrivener Publishing LLC.</text>
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                <text>Prasanth A., Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&amp;amp;D Institute of Science and Technology, Chennai, India; Dhanaraj R.K., Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India; Sabharwal M.; Sharma V., Computer Science Department, Christ University, Bengaluru, India; Kadry S.</text>
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                <text>Explore the future of logistics and disaster management with this essential guide to the design, applications, and challenges of integrating advanced drone technology into intelligent transportation systems. Drones are quickly becoming an essential technology for navigating inaccessible areas, especially during emergency situations. However, the implementation of these drones requires strict standards, policies, and procedures. Currently, drones are being used in several industrial and service sectors, extending the possibilities of handling transportation and logistics. The future of transportation is based on unmanned vehicles, and it is important to identify their challenges and futuristic applications. Drones for Transportation Logistics and Disaster Management introduces the essential aspects of the technological advancement of drones, the challenges faced in current practices, and their advanced applications. The book describes future intelligent and resilient transportation systems backed by the Internet of Vehicle Things, the problems of big data analytics, and optimization techniques for in-house supply-chain management. Using a global multi-sector perspective, this volume will comprehensively cover essential components of drone systems, including their modeling, design, and maintenance, making it an essential guide for anyone looking to the future of disaster management.  2026 Scrivener Publishing LLC.</text>
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                <text>Manoj R.K., Department of Electrical and Electronics Engineering, Academy of Maritime Education and Training, Deemed to be University, Tamil Nadu, India; Senthilnathan S., Department of Electronics and Communication Engineering, School of Engineering and Technology, Christ University, Bangalore, India; Selvi S.A., Computer Science and Engineering, Holy Cross Engineering College, Tamil Nadu, India; Kumar T.A., Computer Science and Engineering, IFET College of Engineering (Autonomous), Tamil Nadu, India; Balamurugan S., iRCS, an Indian Technological Research &amp;amp; Consulting Firm, Coimbatore, Tamil Nadu, Coimbatorex, India</text>
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                <text>For industry practitioners, academic researchers, and governance professionals alike, this book offers both clarity and depth in one of the most important domains of modern technology. As AI matures, trust and risk management will define its success-and this book lays the groundwork for achieving that vision. As AI continues to permeate sectors ranging from healthcare to finance, ensuring that these systems are not only powerful but also accountable, transparent, and secure, is more critical than ever. This book offers a vital exploration into the intersection of trustworthiness, risk mitigation, and security governance in artificial intelligence systems, serving as a definitive guide for professionals, researchers, and policymakers striving to build, deploy, and manage AI responsibly in high-stakes environments. Using a comprehensive approach, it explores how to integrate technical safeguards, organizational practices, and regulatory alignment to manage the unique risks posed by AI, including algorithmic bias, data misuse, adversarial attacks, and opaque decision-making. The result is a strategic approach that not only identifies vulnerabilities, but also promotes resilient, auditable, and trustworthy AI ecosystems. At its core, AI TRiSM is a forward-looking concept that embraces the realities of AI in production environments. The framework moves beyond traditional static models of governance to propose dynamic, adaptive controls that evolve alongside AI systems. Through real-world case studies, the book outlines how tools like model cards, bias audits, and zero-trust architectures can be embedded into the AI development lifecycle. Readers will find the volume:  Introduces concepts to stay ahead of regulations and build trustworthy AI systems that customers and stakeholders can rely on;  Addresses security threats, bias, and compliance gaps to avoid costly AI failures;  Explores proven frameworks and best practices to deploy AI responsibly and strategies to outperform;  Provides comprehensive guidance through real-world case studies and contributions from industry and academia. Audience AI and machine learning engineers, data scientists, cybersecurity and risk management specialists, academics, researchers, and policymakers specializing in AI ethics, security, and risk management.  2026 Scrivener Publishing LLC.</text>
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                <text>Sharma A., School of Sciences, Christ University, Pune, India; George M., Faculty of Economics, University of Economics and Finance (UEF), Ho Chi Minh City, Viet Nam; Pandey P., School of Commerce, Finance and Accountancy, Christ University, Pune, India; Sharma M., School of Commerce, Finance and Accountancy, Christ University, Pune, India; Joshi V., 3E- Excess Edge Experts, Jaipur, India</text>
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                <text>Thomas S., Department of Physics and Electronics, CHRIST (Deemed to Be University), Karnataka, Bengaluru, India; Royaud I., CNRS, IJL, Universitde Lorraine, Nancy, France; Chapron D., LMOPS, CentraleSupec, Universitde Lorraine, Metz, France; Pont M., SI2M 304, Jean Lamour Institute, Universitde Lorraine, Nancy, France</text>
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                <text>Jose S., Textile Manufacture and Textile Chemistry Division, ICAR- Central Sheep and Wool Research Institute, Rajasthan, Avikanagar, India; Thomas S., Mahatma Gandhi University, Kerala, Kottayam, India, Department of Physics and Electronics, CHRIST (Deemed to Be University), Karnataka, Bengaluru, India, Department of Chemical Sciences, University of Johannesburg, Johannesburg, South Africa, TrEST Research Park, College of Engineering, GEM Building, Kerala, Trivandrum, India, Center of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand; Kadumudi F.B., Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark</text>
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                <text>&lt;a href="https://doi.org/10.1201/9781003476344" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003476344&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105034147991?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105034147991?origin=resultslist&lt;/a&gt;</text>
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                <text>Balakrishnan C., CHRIST University Deemed to be University, Bengaluru, India; Jayapriya J., Department of Computer Science, CHRIST University Deemed to be University, Karnataka, Bengaluru, India; Vinay M., Head of the Department, CHRIST University Deemed to be University, India; Singh S.K., Department of Bioinformatics, Alagappa University, Tamil Nadu, India; Manivannan N., Brunel School of Design, United Kingdom, Brunel University, London, United Kingdom</text>
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                <text>The book presents a comprehensive overview of emerging IoT technologies, IoT devices and sensors and their role in the healthcare industry. It offers recent developments in the creation, application, and impact of new IoMT data security, IoMT devices used in a variety of applications, including telemedicine, monitoring patients, and intelligence-based medical data analysis. It includes discussions on the use of medical data analysis to make various predictions and diagnoses that lead to the right treatment. The security of medical data from Internet of Things (IoT) devices, where there is a risk of data leakage during transmission from the device to the server, is also discussed.  Offers Software Application Framework for Sustainable Healthcare  Includes feature case studies and real-world examples of IoT devices and technologies in use in healthcare settings  Provide insight into the security risks associated with connected medical devices and the measures that can be taken to mitigate these risks.  Discusses various integration strategies and technologies, including data interoperability standards, application programming interfaces (APIs), and other tools  Contains discussions on emerging IoT technologies, such as blockchain and edge computing, as well as their potential applications in healthcare This book is for researchers, professionals and students interested in the latest advancements on Internal of Medical Things.  2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors.</text>
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                <text>ISBN: 978-104050436-9; 978-103274893-1;</text>
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