Digital Twin Technology

Dr. Gopal Chaudhary Bharati Vidyapeeth's college of engineering Paschim Vihar, Delhi gopal.chaudhary88@gmail.com, gopal.bvcoe@bharatividyapeeth.edu Dr. Gopal Chaudhary is currently working as an assistant professor in Bharati Vidyapeeth's College of Engineering, Guru Gobind Singh Indraprastha University, Delhi, India. He holds a Ph.D. in Biometrics at the division of Instrumentation and Control engineering, Netaji Subhas Institute of Technology, University of Delhi, India. He received the B.E. degree in electronics and communication engineering in 2009 and the M.Tech. degree in Microwave and optical communication from Delhi Technological University (formerly known as Delhi College of Engineering), New Delhi, India, in 2012. He has 30 publications in refereed National/International Journals & Conferences (Elsevier, Springer, Inderscience) in the area of Biometrics and its applications. His current research interests include soft computing, intelligent systems, information fusion and pattern recognition. He has organized many conferences and special issues. Dr. Manju Khari Netaji Subhas University of Technology, East Campus ( Formerly Ambedkar Institute of Advanced Communication Technologies And Research) Delhi manjukhari@yahoo.co.in, manjukhari@aiactr.ac.in Dr. Manju Khari an Assistant Professor in Netaji Subhas University of Technology, East Campus, Delhi, India. She is also the Professor- In-charge of the IT Services of the Institute and has experience of more than twelve years in Network Planning & Management. She holds a Ph.D. in Computer Science & Engineering from National Institute Of Technology Patna and She received her master's degree in Information Security from Ambedkar Institute of Advanced Communication Technology and Research, formally this institute is known as Ambedkar Institute Of Technology affiliated with Guru Gobind Singh Indraprastha University, Delhi, India. Her research interests are software testing, information security, optimization, Image processing and machine learning. She has 100+ published papers in refereed National/International Journals & Conferences (viz. IEEE, ACM, Springer, Inderscience, and Elsevier), 06 book chapters in a springer. She is also co-author of two books published by NCERT of Secondary and senior Secondary School. Dr. Mohamed Elhoseny Faculty of Computers and Information, Mansoura University, Egypt Mohamed_elhoseny@mans.edu.eg Dr. Mohamed Elhoseny is currently an Assistant Professor at the Faculty of Computers and Information, Mansoura University. Dr. Elhoseny has been appointed as an ACM Distinguished Speaker from 2019 to 2022. Collectively, Dr. Elhoseny authored/co-authored over 85 ISI Journal articles in high-ranked and prestigious journals such as IEEE Transactions on Industrial Informatics (IEEE), IEEE Transactions on Reliability (IEEE), Future Generation Computer Systems (Elsevier), and Neural Computing and Applications (Springer). Besides, Dr. Elhoseny authored/edited 15 international books (eleven published by Springer, two published by Taylor& Francis, one published by Elsevier, and one published by IGI-Global). His research interests include Smart Cities, Network Security, Artificial Intelligence, Internet of Things, and Intelligent Systems. Dr. Elhoseny serves as the Editor-in-Chief of International Journal of Smart Sensor Technologies and Applications (IGI Global). Moreover, he is an Associate Editor of many journals such as IEEE Journal of Biomedical and Health Informatics (IEEE), IEEE Access (IEEE), Scientific Reports (Nature), IEEE Future Directions (IEEE), Remote Sensing (MDPI), and International Journal of E-services and Mobile Applications(IGI Global), Applied Intelligence (Springer). Moreover, he served as the co-chair, the publication chair, the program chair, and a track chair for several international conferences published by IEEE and Springer. Dr. Elhoseny is the Editor-in-Chief of the Studies in Distributed Intelligence Springer Book Series, the Editor-in-Chief of The Sensors Communication for Urban Intelligence CRC Press-Taylor& Francis Book Series, and the Editor-in-Chief of The Distributed Sensing and Intelligent Systems CRC Press-Taylor& Francis Book Series. He was granted several awards by diverse funding bodies such as the Young Researcher Award in Artificial Intelligence from the Federation of Arab Scientific Research Councils in 2019, Obada International Prize for young distinguished scientists 2020, the Egypt State Encouragement Award in 2018, the best Ph.D. thesis in Mansoura University in 2015, the SRGE best young researcher award in 2017, and the membership of The Egyptian Young Academy of Science (EYAS) in 2019. Besides, he is a TPC Member or Reviewer in 50+ International Conferences and Workshops. Furthermore, he has been reviewing papers for 80+ International Journals including IEEE Communications Magazine, IEEE Transactions on Intelligent Transportation Systems, IEEE Sensors Letters, IEEE Communication Letters, Elsevier Computer Communications, Computer Networks, Sustainable Cities and Society, Wireless Personal Communications, and Expert Systems with Applications. Dr. Elhoseny has been invited as a guest in many media programs to comment on technologies and related issues.

Chapter 1: Digital Twin Technology: An Evaluation

Chapter 2: Digital Twin: Towards Internet of Drones

Chapter 3: Digital Twin in Agriculture Sector: Detection of Disease using Deep Learning

Chapter 4: Crop Diseases Detection and Prevention using AI and Machine Learning Techniques

Chapter 5: Architecture of Digital Twin for Network Forensic Analysis Using NMAP and WireShark

Chapter 6: Wind catchers as earth building: Digital Twins vs green sustainable architecture

Chapter 7: Digital Twin and the Detection and Location of DoS attacks to Secure Cyber-Physical UAS

Chapter 8: Digital twin techniques in Recognition of Human Action using the fusion of Convolutional Neural Network

Chapter 9: eVote - A Decentralised Voting Platform

Chapter 10: Nessus: A vulnerability scanner tool in network forensic

Chapter 11: Case Studies Related to Depression Detection Using Deep Learning Techniques