Digital Twin for Smart Manufacturing: Emerging Approaches and Applications provides detailed descriptions on how to integrate and optimize novel digital technologies for smart manufacturing. The book discusses digital twins, which combine the industrial internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. In addition, they provide an effective way to integrate technologies like cyber-physical systems into a smart manufacturing system, potentially optimizing the entire business process and operating procedure of the manufacturing firm. Drawing on the latest research, the book addresses the topics and technologies key to successful implementation of a smart manufacturing system, including augmented and virtual reality, big data and energy management. Broader subjects such as additive manufacturing and robotics are also covered in this context, covering every aspect of production.

In healthcare, a digital twin is a digital representation of a patient or healthcare system using integrated simulations and service data. The digital twin tracks a patient's records, crosschecks them against registered patterns and analyses any diseases or contra indications. The digital twin uses adaptive analytics and algorithms to produce accurate prognoses and suggest appropriate interventions. A digital twin can run various medical scenarios before treatment is initiated on the patient, thus increasing patient safety as well as providing the most appropriate treatments to meet the patient's requirements. Digital Twin Technologies for Healthcare 4.0 discusses how the concept of the digital twin can be merged with other technologies, such as artificial intelligence (AI), machine learning (ML), big data analytics, IoT and cloud data management, for the improvement of healthcare systems and processes. The book also focuses on the various research perspectives and challenges in implementation of digital twin technology in terms of data analysis, cloud management and data privacy issues. With chapters on visualisation techniques, prognostics and health management, this book is a must-have for researchers, engineers and IT professionals in healthcare as well as those involved in using digital twin technology, AI, IoT & big data analytics for novel applications.

Metaverse Technologies in Healthcare includes different areas of Metaverse healthcare and provides comprehensive knowledge and methodological information. With a strong focus on data analysis, the book utilizes the possibilities of data to increase the effectiveness of healthcare organizations. Healthcare services aim to maintain the physical, mental, social and emotional well-being of human lives while the Metaverse is a blend of technological trends- Artificial Intelligence (AI), Augmented Reality (AR) and Virtual Reality (VR). Collectively, they can deliver treatments and medicines; lowering costs, and substantially enhancing patient outcomes. In 20 chapters Metaverse Technologies in Healthcare redefines the digital health experience. It describes the advantages of the Metaverse-based NFT technology in healthcare sector facilitating NFT avatars to interact with targeted audiences, discusses the aspects of their projects with like-minded people resulting in interdisciplinary solutions . Healthcare entities ranging from hospitals chains to fitness companies, will benefit from this title.

Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing. Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector.

Blockchain-Based Systems for a Paradigm Shift in the Energy Grid explores the technologies and tools to utilize blockchain for energy grids and assists professionals and researchers to find alternative solutions for the future of the energy sector. The focus of this globally edited book is on the application of blockchain technology and the balance between supply and demand for energy and where it is achievable. Looking at the integration of blockchain and how it will make the network resistant to any failure in sub-components, this book has very clearly explores the areas of energy sector that need in-depth study of Blockchain for expanding energy markets. Meeting the demands of energy by local trading, verifying use of green energy certificates and providing a greater understanding of smart energy grids and Blockchain use cases. Exhaustively exploring the use of Blockchain for energy, this reference useful for all those in the energy industry looking to avoid disruption in the grid and sustain and control successful flow of electricity.

Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with the focus on the technologies, enabling the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery and consumption. The impact for food security may be significant and this book explores ways to implement CPS effectively and appropriately. Technology, especially computing technology, can play a significant in the field of agriculture by processing digitized data to solve the complex agronomic, agricultural demand and supply issues that impact the food supply chain, and ultimately food security. In Agri 4.0, the cyber physical system synchronously interacts with agricultural systems to control and execute the operation autonomously. Digitalization of agriculture integrates digital computers to assist the processes of agriculture with its digitized data and its allied technology including AI, Computer Vision, Big data, Block chain and IoT. Agri 4.0 digitalizes, estimate, plan, predict, and produce the optimum agricultural inputs and outputs for the required for commercial purposes. It can be used to get a fair, transparent and accountable process to serve the stakeholders. The convergence of IoT, ML, Big data and 5G networks have opened new possibilities to explore and exploit the cyber physical agricultural systems. The management and practices of smart multi-layer architecture and smart supply chain are one of the key application areas in Agri 4.0. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices and government policies in Agri 4.0.

Cognitive Computing for Internet of Medical Things (IoMT) offers a complete assessment of the present scenario, role, challenges, technologies, and impact of IoMT-enabled smart healthcare systems. It contains chapters discussing various biomedical applications under the umbrella of the IoMT. Key Features Exploits the different prospects of cognitive computing techniques for the IoMT and smart healthcare applications Addresses the significance of IoMT and cognitive computing in the evolution of intelligent medical systems for biomedical applications Describes the different computing techniques of cognitive intelligent systems from a practical point of view: solving common life problems Explores the technologies and tools to utilize IoMT for the transformation and growth of healthcare systems Focuses on the economic, social, and environmental impact of IoMT-enabled smart healthcare systems This book is primarily aimed at graduates, researchers and academicians working in the area of development of the application of the of the application of the IoT in smart healthcare. Industry professionals will also find this book helpful.

AI-Powered IoT in the Energy Industry: Digital Technology and Sustainable Energy Systems looks at opportunities to employ cutting-edge applications of artificial intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) in designing and modeling energy and renewable energy systems. The book's main objectives are to demonstrate how big data can help with energy efficiency and demand reduction, increase the usage of renewable energy sources, and assist in transitioning from a centralized system to a distributed, efficient, and embedded energy system. Contributions cover the fundamentals of the renewable energy sector, including solar, wind, biomass, and hydrogen, as well as building services and power generation systems. Chapters also examine renewable energy property prediction methods and discuss AI and IoT prediction models for biomass thermal properties. Covers renewable energy sector fundamentals; Explains the application of big data in distributed energy domains; Discusses AI and IoT prediction methods and models.

The book IoT and Big Data Analytics (IoT-BDA) for Smart Cities - A Global Perspective, emphasizes the challenges, architectural models, and intelligent frameworks with smart decisionmaking systems using Big Data and IoT with case studies. The book illustrates the benefits of Big Data and IoT methods in framing smart systems for smart applications. The text is a coordinated amalgamation of research contributions and industrial applications in the field of smart cities. Features: Provides the necessity of convergence of Big Data Analytics and IoT techniques in smart city application Challenges and Roles of IoT and Big Data in Smart City applications Provides Big Data-IoT intelligent smart systems in a global perspective Provides a predictive framework that can handle the traffic on abnormal days, such as weekends and festival holidays Gives various solutions and ideas for smart traffic development in smart cities Gives a brief idea of the available algorithms/techniques of Big Data and IoT and guides in developing a solution for smart city applications This book is primarily aimed at IT professionals. Undergraduates, graduates, and researchers in the area of computer science and information technology will also find this book useful.

Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks. Features: Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care. Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery. Summarizes several strategies, analyses, and optimization methods for patient healthcare. Focuses on drug discovery and development by cloud computing and data-driven research The targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals.

The main aim of Healthcare 4.0: Health Informatics and Precision Data Management is to improve the services given by the healthcare industry and to bring meaningful patient outcomes by applying the data, information and knowledge in the healthcare domain. Features: * Improves the quality of health data of a patient * Presents a wide range of opportunities and renewed possibilities for healthcare systems * Gives a way for carefully and meticulously tracking the provenance of medical records * Accelerates the process of disease-oriented data and medical data arbitration * Brings meaningful patient health outcomes * Eradicates delayed clinical communications * Helps the research intellectuals to step down further toward the disease and clinical data storage * Creates more patient-centered services The precise focus of this handbook is on the potential applications and use of data informatics in healthcare, including clinical trials, tailored ailment data, patient and ailment record characterization and health records management.

Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.