Prof. Chiharu Ishii
Hosei University, Japan
Biography: Chiharu Ishii received his PhD in Mechanical Engineering from Sophia University, Japan in 1997. He worked at Ashikaga Institute of Technology between 1997 and 2002, at Kogakuin University between 2002 and 2009, and at Shibaura Institute of Technology between 2009 and 2010. He has been working at Hosei University since 2010, and currently working as a Professor with the Department of Mechanical Engineering, Faculty of Science and Engineering at Hosei University. Dr. Chiharu Ishii has received several awards such as The Best Paper Award in the area of Tactile and Haptic Interfaces at the 4th International Conference on Human System Interaction (IEEE HSI 2011); Best Paper Award at the 1st International Conference on Computer Science, Electronics and Instrumentation (ICCSE 2012); Best Presentation Award at the International Conference on Intelligent Mechatronics and Automation (ICIMA 2013); Excellent Oral Presentation Award at the 4th International Conference on Soft Computing & Machine Intelligence (ISCMI 2017); 3rd Prize, Excellent Paper Award at the 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021). He is currently a member of IEEE, SICE, JSME, RSJ, IEEJ and JSCAS. His research interests are in medical robotics and assistive technology.
Prof. Ahmed CHEMORI
LIRMM, University of Montpellier, CNRS, Montpellier, France
Biography: Ahmed Chemori received his M.Sc. and Ph.D. degrees, both in automatic control from Polytechnic Institute of Grenoble, France, in 2001 and 2005 respectively. During the year 2004/2005 he has been a Research and Teaching assistant at Laboratoire de Signaux et Systèmes (LSS - Centrale Supelec) and University Paris 11. Then he joined Gipsa-Lab (Former LAG) as a CNRS postdoctoral researcher.
He is currently a senior CNRS researcher in Automatic control and Robotics for the French National Center for Scientific Research (CNRS), at the Montpellier Laboratory of Computer Science, Robotics and Microelectronics (LIRMM).
His research interests include nonlinear (adaptive and predictive) control and their real-time applications in different fields of robotics (underactuated robotics, parallel robotics, underwater robotics, humanoid robotics and wearable robotics). He is the author of more than 160 scientific publications, including international journals, patents, books, book chapters and international conferences. He co-supervised 19 PhD theses (including 17 defended) and more than 40 MSc theses. He served as a TPC/IPC member or associate editor for different international conferences and he organized different scientific events (e.g. PKM 2016 and PKM 2018 Summer Schools, WIR 2017 and Robo-Rehab 2019 workshops). He has been a visiting researcher/professor at different institutions (NTNU - Norway, Tohoku University - Japan, EPFL - Switzerland, TUT - Estonia, HUST - China, UPC - China, CINVESTAV - Mexico, UPT - Mexico, Chiang Mai University - Thailand, KAUST - Saudi Arabia, ENIT - Tunisia, ENSIT - Tunisia, UMC - Algeria, etc). He has also delivered various plenary/keynote lectures at different international conferences.
Assoc. Prof. Sansanee Auephanwiriyakul
Chiang Mai University, Thailand
Biography: Sansanee Auephanwiriyakul received the B.Eng. (Hons.) degree in electrical engineering from the Chiang Mai University, Thailand (1993), the M.S. degree in electrical and computer engineering and Ph.D. degree in computer engineering and computer science, both from the University of Missouri, Columbia, in 1996, and 2000, respectively. After receiving her Ph.D. degree, she worked as a post-doctoral fellow at the Computational Intelligence Laboratory, University of Missouri-Columbia. She is currently an Associate Professor in the Department of Computer Engineering and a deputy director of the Biomedical Engineering Institute, Chiang Mai University, Thailand. Dr. Auephanwiriyakul is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). She is an Associate Editor of the IEEE Transactions on Fuzzy System, the IEEE Transactions on Neural Networks and Learning Systems, IEEE Computational Intelligence Magazine, IEEE Transactions on Artificial Intelligence, Engineering Applications of Artificial Intelligence, and ECTI Transactions on Computer and Information Technology. She was also an Editorial Board of several prominent journals. She was a general chair of the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2016). She will be a general chair of the IEEE World Congress on Computational Intelligence (WCCI) 2024 (IEEE International Conference on Fuzzy Systems 2024). She was a Technical Program Chair, Organizing Committee in several major conferences including the IEEE International, Conference Fuzzy Systems. She is also a member of several important IEEE CIS technical committees.
Speech Title: Fuzzy Pattern Recognition in Data Analysis
Abstract: Data Analysis is a process to analyze data in terms of representing, describing, evaluating, interpreting the data using statistical methods. Data can come in the form of statistical representation or a vector of numbers in which numeric pattern recognition algorithms can deal with this type of data set. Another type of data can be in the form of syntactic data. For this type of data set, there is another research branch in pattern recognition called syntactic pattern recognition that is able to analyze it. Each sample in syntactic data set is normally represented as a string. The strings in the same data set can have different lengths. Also, the string does not have any mathematical meaning that we can calculated as if they are vectors of numbers.
One of the popular theories used in data analysis is Fuzzy set theory, an extension of the classical set introduced by Lotfi Zadeh in 1965. Since then, there are many theories and applications developed based on Fuzzy set theory. In this talk, there are three parts on the utilization of the Fuzzy pattern recognition in data analysis. First, we will show how to develop a fuzzy algorithm in a decision making when the data are a collection of fuzzy vectors (a vector of fuzzy numbers). Another is how to incorporate the Fuzzy set theory into a set of feature generation in the classification problem. The last part of the talk is how to incorporate the Fuzzy set theory into string grammar pattern recognition. All algorithms in this talk are developed at Computational Intelligence Research Laboratory, Chiang Mai University. In each part of the talk, we will show applications of these algorithms in several real-world problems, e.g., sign language translation system, face recognition, health applications and person identification.