Перегляд за Автор "Tyschenko, O.O."

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  • Ескіз
    Документ
    Approach for Comparative Analysis of Effectiveness of using MobileNetV3 and ViT Neural Network Models for Graphical Localization of Destroyed Buildings Remains Areas
    (2025) Didur, V.O.; Molchanova, M.O.; Tyschenko, O.O.; Mazurets, O.V.; Мазурець, Олександр Вікторович
    This study presents a comparative analysis of MobileNetV3 and Vision Transformer (ViT) neural networks for graphical localization of destroyed building remains. A custom software solution was developed to process images from robotic systems, train both models on a labeled dataset, and evaluate their performance in realistic conditions. Results showed that both architectures achieved high accuracy, with ViT offering strong classification precision and MobileNetV3 excelling in efficiency for edge deployment. The findings highlight each model's potential for disaster response applications involving automated debris analysis.
  • Ескіз
    Документ
    Effectiveness Research of Method for Values Forecasting of Epidemiological Danger Indicators by Means of Neural Network Modeling
    (2024) Mazurets, O.V.; Ovcharuk, O.M.; Tyschenko, O.O.; Zalutska, O.O.; Мазурець, Олександр Вікторович
    The aim of the study is effectiveness research of method for values forecasting of epidemiological danger indicators by means of neural network modeling. The method for values forecasting of epidemiological danger indicators using neural network modeling was studied, which allows, based on input data in the form of a sample of time-dependent values of a specified parameter during the studied period, to receive output data in the form of a sample with predicted values of the parameter for further forecasting of the level of epidemiological danger using neural network modelling, and uses a recurrent temporal neural network with one convolutional layer to predict parameter values from their time series.