Keynote Speakers

Gyu Myoung Lee joined the Liverpool John Moores University (LJMU), UK in 2014, as a Senior Lecture in the department of Computer Science and was promoted to a Reader in 2017 and a Professor in 2020. He is also with KAIST Institute for IT convergence, Daejeon, Rep. of Korea, as an Adjunct Professor from 2012. Before joining the LJMU, he worked with the Institut Mines-Telecom, Telecom SudParis from 2008. Until 2012, he was invited to work with the Electronics and Telecommunications Research Institute (ETRI), Rep. of Korea. He worked as a research professor in KAIST, Rep. of Korea and as a guest researcher in National Institute of Standards and Technology (NIST), USA, in 2007. He worked as a visiting researcher in the University of Melbourne, Australia, in 2002. Furthermore, he also has work experience in industries in Rep. of Korea. His research interests include Internet of Things, Web of Things, computational trust, knowledge centric networking and services considering all vertical services, Smart Grid, energy saving networks, cloud-based big data analytics platform and multimedia networking and services. Dr. Lee has been actively participating in standardization meetings including ITU-T SG 13 (Future Networks and cloud) and SG20 (IoT and smart cities and communities), IETF and oneM2M, etc., and currently serves as a Rapporteur of Q16/13 (Knowledge centric trustworthy networking and services) and Q4/20 (e/Smart services, applications and supporting platforms) in ITU-T. He is also the chair of ITU-T Focus Group on Data Processing and Management (FG-DPM) to support IoT and smart cities & communities. He has contributed more than 300 proposals for standards and published more than 100 papers in academic journals and conferences. He received several Best Paper Awards in international and domestic conferences and served as a reviewer of IEEE journals/conference papers and an organizer/member of committee of international conferences. He is a Senior Member of IEEE.

Speech Title: "AI powered Trustworthy Decentralized Internet"

Abstract: Artificial Intelligence (AI) and Internet of Things (IoT) are very important technologies for the future, and recently there has been a lot of research activity to combine AI and IoT, called AIoT (Artificial Intelligence powered Internet of Things). In addition, data is becoming essential to support AI-based solutions with human interactions. Blockchain is revolutionizing the way transactions are recorded as a machine to create trust. In this context, this talk will introduce key concepts, features and characteristics of the Decentralized Internet (i.e., Web 3.0 and its vision as the Internet of Value), taking into account emerging ICTs integrating AIoT and Blockchain, and related EU projects such as GAIA-X. From the research on decentralized Internet research for Web 3.0, many researchers have recognized that there are security, privacy and trust concerns to realize a user-centric approach for decentralization. To cope with the negative effects of the decentralized Internet, it's necessary to build a trustworthy infrastructure with AI for the future digital economy toward the Internet of Value. Therefore, starting from the new economic paradigm for cyberspace, data ecosystem and its characteristics, this talk will present future directions for realizing decentralized Internet with AI-powered trust technology.

Ayman Alzaatreh is a Professor in the Department of Mathematics and Statistics at the American University of Sharjah, UAE, which he joined in 2017. His research interests include generalizing statistical distributions arising from the hazard function, statistical inference of probability models, characterization of distributions, bivariate and multivariate weighted distributions, feature selection, Bayesian statistics, and data mining. He is the author or co-author of more than 100 refereed publications, in addition to several book chapters and numerous conference proceedings with top international publishers.

Speech Title: "Feature Selection Technique Based on Relative Belief Ratio for Multi-Class Classification Problems"

Abstract: In this talk, a Bayesian approach to feature selection, namely the Relative Belief Ratio (RBR) [1], is presented as a filter method for both binary and multi-class classification problems. Unlike wrapper or embedded methods, the filter approach evaluates the importance of features independently of any specific learning algorithm, making it computationally efficient, broadly applicable, and less prone to overfitting. The RBR quantifies the evidence in favor of a feature’s association with the target variable, providing a principled Bayesian framework for ranking and selecting features. The proposed method is demonstrated on several benchmark datasets, highlighting its effectiveness, scalability, and interpretability across diverse classification tasks.