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Congratulations!! ICACT2024 Outstanding Paper Award on Author Homepage now!!
 
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ICACT2024 Conference Program - Final



ICACT2024 Conference Proceedings (Click!)

Plenary Session - Keynote Speech
Title : Regional Innovation On Industrial Digital Transformation
Time : 17:00 Feb. 05 (Mon), 2024
Speaker : Prof. KWON YEONG-IL, PhD. Head of Graduate School of Management of Advanced Technology, Hoseo University, Korea.
He is currently a Professor at the Graduate School of Industrial Digital Transformation, Hoseo University, South Korea. He received the M.Sc. degree from the Department of Computer Science, KAIST (Korea Advanced Institute of Science and Technology) in 1998 and the Ph.D. degree in the Department of IT Service Management from Soongsil University in 2015. Dr. Kwon made a lot of contributions to the national ICT development while serving as a General Director at the National Information Society Agency, so called NIA for implementing e-Government, Big data and AI, Open Government Data for many years. He was also a Member of the Big data Policy Committee on National Tax Agency and National Statistics Agency. He has published many books and articles on Big data, Global management of technology, etc. His research interests include big data, platform business, mobile edge computing and smart factory

Abstract : Digital Transformation (DX) requires establishing a strategic direction to innovate both public and private sectors along with the development of artificial intelligence technology. Industrial digital transformation requires local innovation that applies intelligent technology, especially to the factories of semiconductor, automobile, and manufacturing companies. Hoseo University is located in Chungnam Province which has many industrial complexes for small and medium-sized businesses. As one of the cutting-edge technologies in the manufacturing field, cloud computing and edge computing must be implemented using 5G-based MEC (Mobile Edge Computing) technology. Hoseo University is applying the ... More

Title : Gradual Process Mining
Time : 17:00 Feb. 05 (Mon), 2024
Speaker : Prof. Kwanghoon Pio Kim, Ph.D.
He is a Full Professor in the Division of AI Computer Science and Engineering as well as the Founder and Supervisor of the Data and Process Engineering Research Laboratory, Kyonggi University, South Korea. At Kyonggi University, he had been the Dean of the Computerization and Informatics Institute from 2017 to 2021; Since 2007, he has been in charge of the Founder and the Director of the Contents Convergence Software Research Institute, where he has been leading and fulfilling a multi-million-dollar research project that will be continuously supported and funded by the National Research Foundation of Korea, from 2020 to 2029, ever since the institute was officially designated as the National Science and Engineering Research Institute by the Ministry of Education, South Korea, in 2020. He received B.S. degree in computer science from Kyonggi University in 1984. And he received M.S. degree in computer science from Chungang University in 1986. He also received his M.S. and Ph.D. degrees from the computer science department at the University of Colorado Boulder, in 1994 and 1998, respectively. He had worked as researcher and developer at Aztek Engineering, American Educational Products Inc., and IBM in USA, as well as at Electronics and Telecommunications Research Institute (ETRI) in South Korea. In present, he is fulfilling the presidency of the KSII (Korean Society for Internet and Information) from 2023 to 202. He has been in charge of a country-chair (Korea) and ERC vice-chair of the Workflow Management Coalition.

Abstract : The workflow (and business process) literature has been looking for efficient redesigning and reengineering approaches because of not only that workflow and business procedures are becoming massively large-scaled and complicated, but also, nevertheless, that their life-cycle and recycling (reengineering) periods are becoming swiftly shorter. As depicted in the figure, the business process life-cycle management with business process redesign, reengineering, and restructuring approaches is needed to be supported by a process mining system performing the process discovery and rediscovery functionality. The process mining system is based upon the workflow and business process enactment audit and ... More

Tutorial Session
Title : Decoding the Logistics Intelligence Spectrum: a tutorial on Artificial Intelligence in Smart Logistics
Time : 15:30 Feb. 04 (Sun) 2024
Speaker : Prof. HEE-CHEOL KIM, PhD. Head of College of AI Convergence, Inje University, Korea (south)
HEE-CHEOL KIM received the B.Sc. degree from the Department of Mathematics, the M.Sc. degree from the Department of Computer Science, Sogang University, South Korea, and the Ph.D. degree in the Department of Numerical Analysis and Computing Science from Stockholm University, Sweden, in 2001. He is currently a Professor with the Department of AI Software and the Head of College of AI Convergence, Inje University, South Korea. He is also the President of Korea Institute of Information and Communication Engineering. He has published more than 200 papers in journals and conference proceedings. His research interests include machine learning, computer vision, digital healthcare, and smart logistics.

Abstract : The tutorial delves into the foundational aspects of AI in logistics, encompassing machine learning, deep learning, and the crucial steps of data collection and preprocessing. The application of AI in basic logistics functions is elucidated, with a focus on route optimization and the role of AI in demand forecasting and inventory management. The tutorial then advances to discuss how AI enhances decision-making processes, providing insights into real-time analytics for decision support and intelligent risk management in logistics. Sensor integration and the Internet of Things (IoT) take center stage as the tutorial explores leveraging sensor data for heightened visibility and the impact of ... More

Title : Anomalies Detection in Industrial Time Series Data Using Machine Learning Algorithms
Time : 15:30 Feb. 04 (Sun) 2024
Speaker : Prof. Babu Kaji Baniya, PhD. Computer Science and Information Systems, Bradley University, Peoria Illinois.USA
Babu Kaji Baniya holds a B.E. degree in Computer Engineering from Pokhara University, Nepal, which he obtained in 2005. He further pursued his education and completed an M.E. and Ph.D. in Department of Computer Science and Engineering from Chonbuk National University, Republic of Korea in 2015. Following his doctoral studies, he gained valuable experience as a postdoctoral researcher in the Department of Computer Science and Biomedical Engineering at the University of South Dakota. He then served as an assistant professor in the Department of Computer Science and Digital Technologies at Grambling State University, Louisiana. Currently, he holds the position of assistant professor in the Department of Computer Science and Information Systems at Bradley University, located in Peoria, Illinois. Dr. Baniya was awarded with the National Science Foundation grant in 2021 for his research on “iMed-Sec: Exploring Hardware-Assisted Solutions for Energy-Efficient Low-Overhead Security and Privacy for the Internet-of-Medical-Things”. He also received the Caterpillar fellowship 2023 under the title “Deep based Skin Cancer Detection and Classification”. Throughout his career, he has taught a wide range of Computer Science courses at both the graduate and undergraduate levels. His research interests span several key areas, including audio signal processing, information retrieval, cybersecurity, bioinformatics, IoT, and machine learning His research interests encompass various areas, such as audio signal processing, information retrieval, cybersecurity, bioinformatics, IoT, and machine learning. His specific research focus involves applying machine learning and deep learning algorithms to secure the Internet of Medical Things (IoMT). Dr. Baniya is also a member of IEEE.

Abstract : Anomalies Detection in Industrial Time Series Data Using Machine Learning Algorithms
The consistent evolution of new technologies brings advancements over previous generations, providing increased maneuverability, enhanced automation through AI control, and improved communication capabilities, whether through wired or wireless means. The remote control of systems, facilitated by wired or wireless connectivity, necessitates the development of innovative concepts to address existing security issues and confront emerging challenges. These technologies utilize diverse scientific methods, processes, algorithms, and systems to extract crucial knowledge or features from data in various forms, ... More

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   ICACT2023 Conference & Journal Papers published via IEEE Xplore on Mar. 29, 2023  
All papers of ICACT2023 sent to IEEE Xplore DL on 03-20-2023.
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