Keynote Speakers

Keynote Speaker I

Prof. Latif Ladid, University of Luxembourg, Luxembourg

Founder & President, IPv6 FORUM ( )
Member of 3GPP PCG (Board) (
Founding Chair, 5G World Alliance ( ) 
Chair, ETSI IPv6 Industry Specification Group : 
IEEE Steering Committee Member: 5G, IoT
Chair, IEEE ComSoC IoT subcommittee ( )
Chair, IEEE ComSoC 5G subcommittee ( 
Vice Chair, IEEE ComSoC SDN-NFV subcommittee: 
Emeritus Trustee, Internet Society - ISOC (
IPv6 Ready & Enabled Logos Program Board ( 
World summit Award Board Member ( )
Research Fellow @ University of Luxembourg on multiple European Commission Next Generation Technologies IST Projects
Member of 3GPP2 PCG ( 
Member of UN Strategy Council
Member of Future Internet Forum EU Member States (representing Luxembourg) Luxembourg, June 2017.

Keynote Speaker II

Prof. Dimitrios Georgakopoulos, Swinburne University of Technology, Australia

Prof. Georgakopoulos is the Director of the Key IoT Lab at the Digital Innovation Platform of Swinburne University of Technology. Before that was Research Director at CSIRO’s ICT Centre and Executive Director of the Information Engineering Laboratory, which was the largest Computer Science program in Australia.  Before CSIRO, he held research and management positions in several industrial laboratories in the US, including Telcordia Technologies (where he helped found two of Telcordia’s Research Centers in Austin, Texas, and Poznan, Poland); Microelectronics and Computer Corporation (MCC) in Austin, Texas; GTE (currently Verizon) Laboratories in Boston, Massachusetts; and Bell Communications Research in Piscataway, New Jersey. He was also a full Professor at RMIT University, and he is currently an Adjunct Prof. at the Australian National University and a CSIRO Adjunct Fellow. Prof. Georgakopoulos is an internationally known expert in IoT, process management, and data management. He has received 20+ industry and academic awards. His 170+ journal and conference publications, which include three seminal papers in the areas Service Computing, Workflow Management, Context Management for the Internet of Things (IoT), have received 12,400+ citations. Dimitrios’ research has attracted significant external research funding ($35M+) from various industry and government research funding agencies, ranging from DARPA and ARDA in the USA, to the Framework Program in the EU, to the Department of Human Services and 50+ industry partners in Australia.

Invited Speakers

Invited  Speaker I

Assoc. Prof. Danilo Pelusi, University of Teramo, Italy
Danilo Pelusi received the degree in Physics from the University of Bologna (Italy) and the Ph.D. degree in Computational Astrophysics from the University of Teramo (Italy). Currently, he is an Associate Professor of Computer Science at the Department of Communication Sciences, University of Teramo. Editor of Springer, Elsevier and CRS books, and Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence (2017-2020), IEEE Access (2018-present), IEEE Transactions on Neural Networks and Learning Systems (2022-present) and IEEE Transactions on Intelligent Transportation Systems (2022-present), he is Guest Editor for Elsevier, Springer, MDPI and Hindawi journals. Keynote speaker, Guest of Honor and Chair of IEEE conferences, he is inventor of international patents on Artificial Intelligence. World’s 2% Top Scientist 2021 and 2022, his research interests include Fuzzy Logic, Neural Networks, Information Theory, Machine Learning and Evolutionary Algorithms.

Invited  Speaker II

Dr. Jia Uddin, Woosong University, South Korea

Dr. Jia Uddin is an Assistant Professor in Artificial Intelligence and Big Data Department, at Endicott College, Woosong University, South Korea, and an Associate Professor (On Leave), Computer Science and Engineering Department at Brac University, Dhaka, Bangladesh. He received Ph.D. in Computer Engineering from the University of Ulsan, Korea, in January 2015 and M.Sc. in Telecommunications from Blekinge Institute of Technology, Sweden in June 2010. He was an Assistant Professor in the CSE department at BRAC University and the CCE department at International Islamic University Chittagong, Bangladesh. He was invited as a visiting faculty member at the School of Computing, Staffordshire University, Stoke-on-Trent, United Kingdom funded by a European Union Grant in April 2017, was invited as a Professor at Telkom University, Indonesia in Summer 2021, and University of Foggia, Italy in April 2023.
Dr. Jia received the Best Research Faculty award in the 2016 academic year at BRACU for his outstanding research contributions in the area of multimedia signal processing. He is supervising several undergraduate and graduate thesis students and his research students’ papers won Best paper awards in several international conferences: ICEEICT-2016, ICCIT-2016, IEEE ICAEE- 2017, ICERIE-2017, ICMIP2019, IHCI2020, and IVIC2021. Dr. Jia is the author of 3 books related to Data Science and Computer Vision published by Woosong publisher and has 50 SCI/Scopus indexed Journal publications. Dr. Jia is involved with different research communities at home and abroad by serving as a member of the organizing committee, technical committee, technical Session Chair, and reviewers in different peer-reviewed journals: IEEE Access, Multimedia Tools and Applications (Springer), Journal of Supercomputing (Springer), Wireless Personal Communication, SAI Journal, Neural Computing and Applications (Springer), Journal of Information Processing Systems, etc. His research interests include fault diagnosis, computer vision, and multimedia signal processing.

Speech Title: Industry 4.0 Smart Factory: Industrial Fault Diagnosis using Deep Learning Architectures
Abstract: In industry 4.0, artificial intelligence (AI) based smart devices are widely used in various applications such as Smart-Governance, Smart-healthcare, Smart-city, Smart-home, Smart-factory, etc. Different sensors are using to collect real-time data from the environment and then the processed data are used in the AI models. Earlier AI models are mostly machine learning-based models where feature engineering plays a vital role in the diagnosis (detection and prediction). However, the features are environment dependent and the optimal features change with the environments. With the advancement of AI, deep learning models are used nowadays, where deep features are automatically extracted for diagnosis. The major concerns of deep learning architectures are computational complexity and the models are data hungry. However, limited datasets are available in industrial environments. To address, the issues, nowadays, to deploy the deep learning-based diagnosis models in portable devices different key techniques like transfer learning (cross domain, domain specific), self-supervised learning, few shot-learning, etc. are playing a vital role in the smart diagnosis in industrial environments.