Rough Sets: Theory and Applications

(RSTA)

This Thematic Track 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 Thematic Track 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 Thematic Track 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 Thematic Track 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.