Digital Twins for Sustainable Development

Erscheinungsjahr 2026
ISBN: 978-0-443-27388-9
Verlag: Elsevier Science

Buch, Englisch, 270 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

Digital Twins for Sustainable Development covers digital twins for sustainability as a virtual representation of a physical system or environment, such as a building, city, or natural ecosystem and how they are used to support sustainable development and management practices. The book demonstrates how data from a variety of sources, such as sensors, satellite imagery, and other monitoring tools can be used for advanced analytics and modeling techniques to simulate the system's behavior over time. This allows researchers and professionals in computer science to manage complex systems and promote sustainable development and resource management practices.

Autoren/Hrsg.

Emilia Balas, Valentina

Tiwari, Shamik

Garg, Lalit

Sohel, Ferdous

Jain, Anurag

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Technische Informatik Externe Speicher & Peripheriegeräte
• Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion User Interface Design & Benutzerfreundlichkeit

Inhaltsverzeichnis

1. Introduction of Digital Twins
2. Role of AI, ML and IoT in designing Digital Twins
3. Digital Twins in Precision Agriculture
4. Machine-Learning enabled Digital Twin in Manufacturing
5. Application of Digital Twins in Supply chain/Logistics Industry
6. Improving operational efficiency of healthcare operations using Digital Twins
7. Health Digital Twins as instruments for Precision Medicine
8. IoT and Digital Twins enabled Smart Learning in Education
9. Digital Twins for Managing Natural Resources
10. Urban Planning supported by Digital Twins
11. Simulate and prepare for natural disasters using Digital Twins
12. Overcoming sustainability challenges with Digital Twins

Über Autor(innen)

Tiwari, Shamik
Dr. Shamik Tiwari, currently working as Professor in School of Computer Science, University of Petroleum and Energy Studies, Dehradun. He has rich experience of around twenty years as an academician and researcher. His area of interest is digital image processing, computer vision, bio-metrics, predictive modelling, statistical modelling, machine learning, deep learning and health informatics. He has written many national and international publications including books in these fields. He has published around 60 research papers in reputed journals and conferences, 4 patents and 5 books. He associated with many professional bodies and organizations as consultant, mentor, guide, and associate member. He is also serving as 'Adjunct Faculty' for Data Science and 'Thesis Supervisor' of upGrad master's Program in collaboration with Liverpool John Moores University, UK.

Sohel, Ferdous
Ferdous Sohel received PhD degree from Monash University, Australia. He is currently a Professor in Information Technology at Murdoch University, Australia. Prior to joining Murdoch University in 2015, he was a Research Assistant Professor/Research Fellow at the School of Computer Science and Software Engineering, The University of Western Australia. His research interests include artificial intelligence and machine learning, computer vision, digital agriculture, image processing, pattern recognition, scene understanding, and video coding. Prof Sohel is a recipient of several research grants worth more than $4.5million. Notably, he is a recipient of the prestigious Discovery Project and Discovery Early Career Research Award (DECRA) funded by the Australian Research Council. He is a recipient of the Learning and Teaching grant on Work Integrated Learning and a Community grant for Invasive Ants detection and tracking from Australia's national competitive pools. He has also won two WA State Govt. funded competitive grants on shark hazard mitigation and digital pathology to improve cancer diagnosis. He is a recipient of the VC's Early Career Investigators award (UWA) and the best PhD thesis medal from Monash University. He has won several best paper awards. He is serving IEEE Transactions on Multimedia and IEEE Signal Processing Letters as an Associate Editor. He is a Fellow of the Higher Education Academy, a member of the Australian Computer Society and a senior member of the IEEE.

Garg, Lalit
Lalit Garg is a Senior Lecturer in Computer Information Systems at the University of Malta, Malta, and an honorary lecturer at the University of Liverpool, UK. He has been a researcher at the Nanyang Technological University, Singapore, and Ulster University, UK. He has supervised 200+ Master's dissertations, 2 DBA and 4 PhD theses and published 125+ high-impact publications in refereed journals/conferences/books, eight edited books and 22 patents. He has delivered several keynote speeches, organized/chaired international conferences, and consulted numerous public and private organizations for information systems implementation and management. His research interests are business intelligence, machine learning, data science, deep learning, cloud computing, mobile computing, the Internet of Things (IoT), information systems, management science and their applications, mainly in healthcare and medical domains. He participates in many EU and locally funded projects, including a one million euro Erasmus+ Capacity-Building project in Higher Education (CBHE), titled Training for Medical education via innovative eTechnology (MediTec), and Malta Council of science and technology's Space Research Funds. The University of Malta has awarded him the 2021-22 Research Excellence Award for exploring Novel Intelligent Computing Methods for healthcare requirements forecasting, allocation and management (NICE-Healthcare).

Jain, Anurag
Dr. Anurag Jain is an Associate Professor at the University of Petroleum and Energy Studies, Dehradun. He has been in the field of academia for the last two decades. He has published around 60 research papers in renowned journals and conferences. 14 students have successfully defended their M. Tech. dissertations under his guidance. Three students have completed their Ph.D. under his guidance. Currently, he is guiding three Ph.D. students in their research work. His research area is in the domain of scheduling and load balancing in cloud computing, machine learning, deep learning, and data science.