Keynote Speakers 主旨报告人
Prof. Gwanggil Jeon, IEEE Senior Member , Incheon National University, Korea
Gwanggil Jeon received the B.S., M.S., and Ph.D. (summa cum laude) degrees from the Department of Electronics and Computer Engineering, Hanyang University, Seoul, Korea, in 2003, 2005, and 2008, respectively. From 2009.09 to 2011.08, he was with the School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada, as a Post-Doctoral Fellow. From 2011.09 to 2012.02, he was with the Graduate School of Science and Technology, Niigata University, Niigata, Japan, as an Assistant Professor. From 2014.12 to 2015.02 and 2015.06 to 2015.07, he was a Visiting Scholar at Centre de Mathématiques et Leurs Applications (CMLA), École Normale Supérieure Paris-Saclay (ENS-Cachan), France. From 2016 to 2021, he was a Full Professor at Xidian University. From 2019 to 2020, he was a Prestigious Visiting Professor at Dipartimento di Informatica, Università degli Studi di Milano Statale, Italy. From 2019 to 2020 and 2023 to 2024, he was a Visiting Professor at Faculdade de Ciência da Computação, Universidade Federal de Uberlândia, Brasil. He is currently a professor at Incheon National University, Incheon. He was a general chair of IEEE SITIS 2023, and served as a workshop chairs in numerous conferences. Dr. Jeon is an Associate Editor of IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Elsevier Sustainable Cities and Society, IEEE Access, Springer Real-Time Image Processing, Journal of System Architecture, and Wiley Expert Systems. Dr. Jeon was a recipient of the IEEE Chester Sall Award in 2007, ACM’s Distinguished Speaker in 2022, the ETRI Journal Paper Award in 2008, and Industry-Academic Merit Award by Ministry of SMEs and Startups of Korea Minister in 2020. Dr. Jeon has published over 700 SCIE papers, among them 101 papers were published in IEEE. He also has 163 registered patents (in Korea, USA). His team was awarded 20 funded projects with 2 million USD.
Title: The Artificial Intelligence of Things framework improves the accuracy of human activity recognition
Abstract: As industries worldwide evolve with the rapid advancement of technology, traditional manufacturing sectors must also embrace innovation to remain competitive. In this keynote, we explore how Artificial Intelligence (AI) and Computer Vision technologies can transform the landscape of conventional manufacturing, with a special focus on the 3,000 traditional industrial companies located in Incheon’s Namdong Industrial Complex.We will introduce real-world applications where AI and Computer Vision have improved production efficiency, enhanced quality control, and optimized operational processes. Additionally, we will share success stories from university-industry collaborations that demonstrate how these technologies can be practically deployed, even in small and medium-sized enterprises (SMEs).This session aims to inspire and equip business leaders, engineers, and policymakers to envision a new era of innovation, where AI drives the sustainable growth and global competitiveness of traditional industries.
Prof. David Camacho, Universidad Politécnica de Madrid (UPM), Spain
David Camacho is full professor at Computer Systems Engineering Department of Universidad Politécnica de Madrid (UPM), and the head of the Applied Intelligence and Data Analysis research group (AIDA: https://aida.etsisi.uam.es) at UPM. He holds a Ph.D. in Computer Science from Universidad Carlos III de Madrid in 2001 with honors (best thesis award in Computer Science). He has published more than 300 journals, books, and conference papers (https://scholar.google.es/citations?hl=en&user=fpf6EDAAAAA). His research interests include Machine Learning (Clustering/Deep Learning), Computational Intelligence (Evolutionary Computation, Swarm Intelligence), Social Network Analysis, Fake News and Disinformation Analysis. He has participated/led more than 50 research projects (Spanish and European: H2020, DG Justice, ISFP, and Erasmus+), related to the design and application of artificial intelligence methods for data mining and optimization for problems emerging in industrial scenarios, aeronautics, aerospace engineering, cybercrime/cyber intelligence, social networks applications, or video games among others. He serves as Editor in Chief of Wiley's Expert Systems from 2023, and sits on the Editorial Board of several journals including Information Fusion, IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), Human-centric Computing and Information Sciences (HCIS), and Cognitive Computation among others.
Title: Unmasking the Shadows: A Dive into Cutting-Edge Strategies against Disinformation in Online Social Networks
Prof. Leixiao Li, Inner Mongolia University of Technology, China
Leixiao Li (Member, IEEE) was born in Shandong, China, in 1978. He received the M.A. degree in engineering from the Inner Mongolia University of Technology, Hohhot, China, in 2007 and the Ph.D. degree in engineering from Inner Mongolia Agricultural University, Hohhot, in 2019.,He is with the Inner Mongolia University of Technology, where he is currently a Professor with the School of Data Science and Application and also with the Research Center of Large-Scale Energy Storage Technologies, Ministry of Education of the People's Republic of China, Beijing, China. His research interests include cloud computing, data mining, and big data processing.
Title: Conceptualizations and Operationalizations of Speech Fluency: Implications for Second Language Teaching, Learning, and Assessment
Prof.Lisu Yu , Inner Mongolia University of Technology, China
Lisu Yu received the Ph.D. degree from the Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China, in 2019.,He is currently with the School of Information Engineering, Nanchang University, Nanchang, China. He was a Visiting Scholar with the University of Arkansas, Fayetteville, AR, USA, and the University of Houston, Houston, TX, USA, from 2017 to 2019. His main research interests include advanced wireless communications, coded modulation, nonorthogonal multiple access, machine learning, ultradense network, unmanned aerial vehicle, and visible light communication.,Dr. Yu has served as the Student Activities Chair for the IEEE Communication Society Chengdu Chapter and several International Conferences Technical Program Committee Chair/Member, the Section Chair, and the Special Track Chair, including IEEE BigData, IEEE ICMLA, and IEEE WCSP. He is currently serving as an Area Editor for the Physical Communication (Elsevier), an Editor for the Computer Communications (Elsevier), PeerJ Computer Science, Microelectronics and Computer, Journal of Wuhan University (Natural Science Edition), and Computer Technology and Development, and an Editorial Board Member for Journal of Electronics and Information Technology and IEEE Communications Society Technical Committee on Green Communications and Computing and Signal Processing and Computing for Communications Member.
Title: Efficient and Emission-Reducing Blockchain-Enabled IoT Networks
Abstract: In highly interconnected large-scale event and other Internet of Things (IoT) device-intensive scenarios, traditional terrestrial base stations have difficulty meeting the requirements of IoT devices for network speed and security, and have exacerbated carbon pollution. To this end, a blockchain-enabled unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) system is introduced to enhance communication efficiency and ensure the privacy of IoT devices. In this system, the Byzantine consensus algorithm is applied in the blockchain. Considering the pollution of reducing carbon dioxide emissions, a strategy for jointly optimizing the flight trajectories of UAVs, task offloading scheduling, and MEC computing resource allocation is formulated to minimize the system's carbon emissions and time delay while meeting MEC and blockchain computing tasks. However, due to the coupling of variables, this problem is very complex. Therefore, the original problem is decoupled into multiple subproblems, and the block coordinate descent method (BCD) and successive convex approximation method (SCA) are used for solving. Specifically, the UAV flight trajectories, task offloading scheduling, and MEC computing resource allocation are alternately optimized until convergence. Simulation results verify the effectiveness and good performance of the proposed algorithm in this study.