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The goal of this book is to examine problems, concerns, and solutions for supporting digital twins with Big Data and Artificial Intelligence (AI). The book also explains how to stream real-time big data on an IoT platform using various ways and strategies.
A. Daniel is an Associate Professor in the School of Computing Science and Engineering, Galgotias University, India He has 12 years of experience in academics. Srinivasan Sriramulu is a professor in the School of Computing Science and Engineering, Galgotias University, India. He has more than 22 years of experience of teaching. He is expertise in image processing, Big Data, cloud, IoT and Artificial Intelligence. N Partheeban is a professor at the School of Computing Science and Engineering, Galgotias University. Santhosh Jayagopalan is with the Faculty in Computer Science, British Applied College, Umm Al Qwain, United Arab Emirates.
1. Digital Twin Past, Present and Future 2. Digital Twin Types and Design 3. Real Issues, Opportunities, and Open Investigations in Digital Twins 4. Twin Technology: Exploring Types and Applications of Digital Twins 5. The Convergence of Data Analytics, Digital Twins, and the IoT/IIoT: A New Era of Data-Driven Decision Making 6. Simulation Strategies for Analyzing of Data 7. Navigating the Complexities of Digital Twin Implementation: Challenges and Strategies for Success 8. Using Improved Finite Element Modelling to Combat Cardiovascular Disease: A Review of a Developing Area at the Intersection of Several Disciplines 9. Comprehensive Study of Digital Twin in Smart and Customized Healthcare 10. Sustainable Organic Farming in Indian Rural Areas with Aid of Internet of Things (IoT) 11. Predictive Analysis of Toxic Ions and Water Quality Based on Sensor Data Using LSTM and ARIMA Models 12. Digital Representation of Agriculture Forms 13. Accelerators for Clustering Applications in Machine Learning 14. Design of a Smart Healthcare Environment with Digital Twinning and Machine Learning 15. FloodWatch: Suggesting an IoT-Driven Flood Monitoring and Early Warning System for the Flood-Prone Cuddalore District in the Indian State of Tamilnadu
