The 9th International Conference on Fuzzy Systems and Data Mining (FSDM 2023)

Abstract: To be updated

Biography: Prof.Witold Pedrycz (Life Fellow, IEEE) received the M.Sc., Ph.D., and D.Sci. degrees from the Silesian University of Technology, Gliwice, Poland. He is currently a Professor and a Canada Research Chair (CRC) in computational intelligence with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. He is also with the Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland. His research interests include computational intelligence, fuzzy modeling, knowledge discovery, data mining, and fuzzy control, including fuzzy controllers, pattern recognition, knowledge-based neural networks, granular and relational computing, and software engineering. He has published numerous articles in these areas. He is also the author of 15 research monographs. He has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. He is also the Editor-in-Chief of Information Sciences (Elsevier), WIREs Data Mining and Knowledge Discovery (Wiley), and the International Journal of Granular Computing (Springer). He served as the Editor-in-Chief for the IEEE Transactions on Systems Man and Cybernetics—Part A and the President of IFSA.

Abstract: To be updated

Biography: Prof. Guoyin Wang is a Vice-President of Chongqing University of Posts and Telecommunications, China. His research interests include rough set, granular computing, data mining, cognitive computing, big data and artificial intelligence. He is also a fellow of IRSS/CAAI/CCF. He is the editor-in-chief of more than 10 periodicals such as Int. J. of Approximate Reasoning, Trans. on Rough Sets, Chinese journal of computers and Journal of Intelligent Systems. He has published 23 academic monographs (including editors), published more than 300 academic papers in international journals and conferences and they have been cited more than 10,000 times. He was selected a Chinese highly cited scholar from 2014 to 2019. He was a Chair of more than 20 international conferences. His more information can be obtained at https://faculty.cqupt.edu.cn/wanggy/zh_CN/index.htm.

Abstract: The keynote speech will provide a round overview picture of the developments in the field of evolving fuzzy systems (EFS) achieved during the last two decades since their first time appearance at the beginning of this century.
Opposed to conventional fuzzy systems, EFS can be learnt from data (streams) on the fly during (fast) on-line processes (typically in form of data streams) in an incremental and mostly single-pass manner. They enjoy a flexible model structure that is able to automatically self-evolve and self-adapt to changes in the process, as e.g. caused by system drifts, new operation modes or dynamic environmental conditions. As being equipped with specific structural components in form of linguistically readable rules, they are able to offer some sort of interpretability and thus to gain insights for operators and experts into system behaviors and dependencies.
They stand for a very important topic in the field of Soft Computing to address modelling problems in nowadays real-world applications with quickly increasing complexity, more and more implying a shift from batch off-line model design phases (as conducted since the 80ties) to permanent on-line (active) model teaching and adaptation cycles toward enriched human-machine interactions. Furthermore, they are widely used in the context of on-line data stream mining and incremental extraction of models and knowledge from huge data bases and Big Data and therefore a fruitful contribution to the research fields of Evolving Adaptive Intelligent Systems and incremental, on-line Machine Learning.
A particular emphasis in this speech will be placed on advanced aspects in terms of improved robustness, interpretability and explainability as well as increasing the useability of EFS (Part 2 of the tutorial), comprising issues in the direction of drift handling, incremental curse of dimensionality reduction, dealing with prediction uncertainty, on-line active learning as well as on-line EFS ensemble methods.
Another emphasis will be placed on the application of EFS in the field of quality control and predictive maintenance (Part 3 of the tutorial), which are key applications within the context of “Industry 4.0” and “Factories of the Future (FoF)” Objectives within the current EU framework programmes, to discuss recent advances and to point out still open problems. Thereby, the tutorial speaker will address three variants for on-line monitoring and supervision of industrial (production) systems:
• Retrospective quality control based on user-friendly on-line, adaptive visual inspection systems
• Immediate quality control based on on-line plausibility analysis and condition monitoring of process data
• Predictive quality control (maintenance) based on forecasting models with ideally large prediction horizons (addressing multi-stage processes)

Biography: Edwin Lughofer received his PhD-degree from the Johannes Kepler University Linz (JKU) in 2005. He is currently Key Researcher with the Fuzzy Logic Laboratorium Linz / Institute for Mathematical Methods in Medicine and Data Based Modeling (JKU) in the Softwarepark Hagenberg.
He has participated in several basic and applied research projects on European and national level, with a specific focus on topics of Industry 4.0 and FoF (Factories of the Future). He has published more than 250 publications in the fields of evolving fuzzy systems and fuzzy modeling from data, data stream mining and modeling, on-line learning and modeling with human interaction, active learning, machine learning methods in classification and clustering, explainability and interpretability aspects, transfer learning, anomaly and fault detection and diagnosis, quality control and predictive maintenance, including more than 100 journals papers in SCI-expanded impact journals, a monograph on “Evolving Fuzzy Systems” (Springer, Heidelberg Berlin), an edited book on “Learning in Non-stationary Environments” (Springer, New York) and an edited book on “Predictive Maintenance in Dynamic Systems” (Springer, New York). In sum, his publications received 8800 references achieving an h-index of 50. He is member of the editorial boards and/or associate editor of the international journals Information Sciences, IEEE Transactions on Fuzzy Systems, Evolving Systems, Information Fusion, Soft Computing, Complex and Intelligent Systems, International Journal of Big Data and Analytics in Healthcare and Innovations, the general chair of the IEEE Conference on EAIS 2014 in Linz, the publication chair of IEEE EAIS 2015, 2016, 2017, 2018, 2020 and 2022, the program co-chair of the International Conference on Machine Learning and Applications (ICMLA) 2018, the tutorial chair of IEEE SSCI Conference 2018, the publication chair of the 3rd INNS Conference on Big Data and Deep Learning 2018, and the Area chair of the FUZZ-IEEE 2015 conference in Istanbul. He co-organized 18 special issues and around 20 special sessions in international journals and conferences. In 2006 he received the best paper award at the International Symposium on Evolving Fuzzy Systems, in 2013 the best paper award at the IFAC conference in Manufacturing Modeling, Management and Control (800 participants) and in 2016 the best paper award at the IEEE Intelligent Systems Conference.
More details about Edwin, please refer to his personal homepages via
https://www.jku.at/en/institute-for-mathematical-methods-in-medicine-and-data-based-modeling/about-us/team/edwin-lughofer/
https://www.flll.jku.at/staff/edwin/