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Information × Registration Number 0224U000456, 0121U114705 , R & D reports Title Fulfillment of tasks of the Perspective plan of development of the scientific direction "Technical Sciences" of the West Ukrainian National University popup.stage_title Head Yatskiv Vasyl V., Доктор технічних наук Registration Date 08-01-2024 Organization Western Ukrainian National University popup.description2 Research object – the processes of analyzing data on disasters, their classification, and assessing the level of danger. The subject of the study is intelligent methods for determining the level of technological disasters in conditions of data absence, incompleteness, fuzziness, and uncertainty. The task aims at analyzing and processing data in cyber-physical systems; developing artificial intelligence methods (advancing artificial intelligence methods for assessing the level of technological disasters, including the development of an intelligent method that utilizes information collected from witnesses to assess the level of technological disasters). Research methods include machine learning, computer modeling, a systemic approach, and object-oriented analysis, design, and programming, functional and comprehensive system testing. Results and their novelty: The concept of assessing the consequences of technological disasters has been improved based on the use of an integrated approach to data collection and analysis, including the use of machine learning methods and statistical analysis. It distinguishes itself from existing approaches by its ability to deeply analyze complex interconnections and factors influencing disaster consequences. The information is used to develop more accurate forecasts and effective recommendations. An improved intelligent method for classifying the level (consequences) of technological disasters has been developed based on information collected from witnesses. Unlike existing methods that mainly rely on technical data and instrumental measurements, this method integrates eyewitness testimony and uses analytical machine learning methods for a more precise and timely assessment of danger levels, significantly reducing the time required to determine potential hazards in the region. Product Description popup.authors Vasylkiv Nadiya M. Dorosh Vitalii I. Zahorodnia Diana I. Kit Ivan R. Koval Vasyl S. Komar Myroslav P. Lipianina-Honcharenko Hrystyna V popup.nrat_date 2024-01-08 Close