The 8th International Conference on System Reliability and Safety
Sicily, Italy - November 20-22, 2024

Session Keynote Lectures

Elena Zaitseva, University of Zilina, Slovakia

Elena Zaitseva received the M.S. and Ph.D. degrees in computer science from the Belarus State University of Informatics and Radioelectronics, Minsk, Belarus, in 1989 and 1994, respectively. From 1996 to 2004, she was with Belarus State Economic University. Since 2004, she has been with the Department of Informatics, University of Žilina, Slovakia. Since 2016, she has been working as a Professor with the Department of Informatics.  Her research interests include mathematical methods in reliability and safety analysis, classification problems, and algebra logic-based methods application in reliability evaluation of complex systems. She is the author of more than 100 articles. She has led international and national projects thematically related to reliability analysis and its use in applications such as information technology, healthcare and ecology. She is a member of the Technical Committee of the European Safety and Reliability Association and the Chair of the Reliability Association Chapter of Czechoslovakia Section of IEEE.

Speech Title: Machine Learning based Reliability Analysis of Multi-State System
The development of the system's model is an important step in reliability analysis. The system's model in reliability analysis similar to other knowledge areas is used as an explanatory and research tool. In reliability analysis, this model is mathematical and allows us to determine quantitative characteristics to evaluate the behavior of the original system. Typically such a mathematical model approximates the behavior of the initial system and has some uncertainty. However, this uncertainty can increase significantly if the initial data for building the model is uncertain and incompletely specified. The uncertainty of the mathematical model also causes incorrect estimates of the system's behavior and its reliability. Therefore, it is important to develop methods that allow us to take into account the uncertain nature of the initial data and, first of all, take into account epistemic uncertainty in initial data. The methods of Machine learning, in particular classification, can be effective for the development of a mathematical model of a Multi-State System (MSS) based on incompletely specified and uncertain data. 

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