RSTA 2023 : 5th International Symposium on Rough Sets: Theory and Applications
Call For Papers
The RSTA symposium is devoted to the state-of-the-art and future perspectives of rough sets considered from both a theoretical standpoint and real-world applications. Rough set theory is a versatile mathematical framework that has proven successful in artificial intelligence, knowledge representation, approximate reasoning, data mining, machine learning, and pattern recognition, among other areas. The symposium is devoted to all the mentioned areas, with an additional emphasis on problems of modeling artificial intelligence processes using rough set-based techniques.
The aim is to showcase the latest research results, exchange new ideas, and facilitate collaborations among experts in the field. Participants are encouraged to submit papers that address fundamental and applied research problems in rough set theory, as well as their applications in diverse fields. We also encourage scientists from other research fields to participate to initiate discussions, and collaborations on other methods of approximate reasoning, data exploration, and computations.
The symposium will provide an opportunity for interdisciplinary exchange and collaboration among scientists from diverse backgrounds, including mathematics, computer science, statistics, physics, engineering, and social sciences. The RSTA symposium will allow staying up-to-date with the state-of-the-art in rough set theory and its applications, and to discuss future research directions and opportunities.
Relevant topics include but are not limited to:
Logics from rough sets
Clustering and rough sets
Dominance-based rough sets
Near sets and proximity
Fuzzy-rough hybrid methods
Game-theoretic rough sets
Scalability and rough sets
Rough neural computing
Evolutionary computation and rough set
Rough sets in education research
Three-way decision making
Analytic Hierarchy Process and decision making
Assistive technology and adaptive sensing systems
Artificial immune systems and rough sets
Machine learning and rough sets