FYI
-----Original Message----- From: Van-Nam HUYNH Sent: Sunday, May 21, 2023 8:33 AM
[Apologies for cross-posting. Your help in circulating this call for papers to potentially interested parties is highly appreciated.] ———————————————————————————————————————————————————— CALL FOR PAPERS ———————————————————————————————————————————————————— IUKM 2023: The Tenth International Symposium on INTEGRATED UNCERTAINTY in KNOWLEDGE MODELLING and DECISION MAKING
Kanazawa, JAPAN, November 02-04, 2023
Conference website: https://www.jaist.ac.jp/IUKM/IUKM2023/
Submission link: https://cmt3.research.microsoft.com/IUKM2023
Submission deadline: May 31, 2023 [Extended deadline] ———————————————————————————————————————————————————— The International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM) aims to provide a forum for exchanges of research results, ideas for and experience of application among researchers and practitioners involved with all aspects of uncertainty management and application.
The Tenth International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2023) will be held in Kanazawa, Japan during 02-04 November, 2023, and be jointly organized by Japan Advanced Institute of Science and Technology (JAIST), University of Science (Vietnam National University Ho Chi Minh City), Vietnam, and Osaka University, Japan.
Submission Guidelines:
All papers must be original and not simultaneously submitted to another journal or conference. The authors are invited to submit their full papers by May 31, 2023. The submissions will be peer-reviewed for originality and scientific quality. Authors are requested to prepare their papers in the Springer format. Submissions should not exceed 12 pages and must be submitted as PDF electronically through the conference's CMT submission page.
List of Topics
Relevant topics for the IUKM 2023 Symposium include (but are not limited to) the following:
— Theory and Methodology
• Uncertainty formalisms: Bayesian probability, Dempster-Shafer theory, imprecise probability, random sets, rough sets, fuzzy sets and interval methods
• Modelling uncertainty and inconsistency in big data
• Learning and reasoning with uncertainty
• Logics for reasoning under uncertainty
• Uncertainty modelling in machine learning and deep learning
• Information fusion and knowledge integration in uncertain environments
• Decision making under various types of uncertainty
• Aggregation operators for decision making
• Copulas for dependence modelling
• Granular and soft computing
• Computational intelligence— Application
• Data mining and knowledge discovery
• Multi-agent reinforcement learning
• Ontology engineering and Semantic Web
• Privacy preserving data analysis
• Intelligent data analysis and modelling
• Agents and argumentation
• Natural language processing
• Medical informatics and bioinformatics
• Ranking and recommendation systems
• Big data and cloud computing
• Social network analysis and mining
• Sensor fusion
• System identification and modelling
• Diagnosis and reliability
• Decision support systems
• Kansei/affective engineering
• Service computing
• Engineering management
• Supply chain management
• Environmental management
• Economics and econometrics
• Statistics and Applications——————————————————————————————————————————— Publication
As done in previous editions, the proceedings of IUKM 2023 will be published by Springer-Verlag in the Lecture Notes in Artificial Intelligence series, and be available at the Conference.
Authors of selected accepted IUKM 2023 papers will be also invited to submit substantially extended and updated version of their work for possible inclusion in a special issue of Annals of Operations Research.
General Chairs: Masahiro Inuiguchi (Osaka University, Japan) Youji Kohda (Japan Advanced Institute of Science and Technology, Japan)
Program Chairs: Van-Nam Huynh (Japan Advanced Institute of Science and Technology, Japan) Bac H. Le (University of Science, Vietnam National University (VNU)-Ho Chi Minh City, Vietnam) Katsuhiro Honda (Osaka Prefecture University, Japan)