Перегляд за Автор "Sobko, O.V."

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  • Ескіз
    Документ
    Practical Implementation of Neural Network Method for Stress Features Detection by Social Internet Networks Posts
    (2024) Mazurets, O.V.; Sobko, O.V.; Molchanova, M.O.; Zalutska, O.O.; Yurchak, A.V.; Мазурець, Олександр Вікторович
    The article considers a neural network method for stress features detection by social internet network posts, designed for automated analysis of text messages posted on social networks in order to identify signs of stress in posts. Based on the designed functional and design architectures of the information system for detecting stress in posts, the software implementation was carried out to study the effectiveness of the developed neural network method for stress features detection by social internet network posts. The practical implementation of the neural network method has determined that the developed method allows detecting stress features in social Internet network posts with an accuracy of 90%.
  • Ескіз
    Документ
    Segmentation of textile prints with contour-stable color masks for industrial stencil printing using artificial intelligence
    (2025) Lianskorunskyi, K.O.; Klimenko, V.I.; Sobko, O.V.
    This paper presents an AI-driven approach for segmenting textile prints to obtain contour-stable binary or multi-class masks suitable for industrial stencil printing and color separation. The method combines neural network segmentation with minimalistic, topologically motivated post-processing to ensure accurate, reproducible boundaries under moderate variations in lighting, shooting conditions, and prepress transformations. Unlike traditional planar metrics, the proposed evaluation prioritizes boundary stability, contour integrity, and technological suitability for cliché preparation. Experimental results on complex textile textures demonstrate consistent mask quality, reduced manual refinement, lower ink consumption, and fewer defects in serial printing. The solution supports sustainable textile production by decreasing prepress energy and material waste while increasing process repeatability and printing accuracy. Further work will expand the dataset, refine boundary corrections for specific fabrics, and formalize end-to-end evaluation protocols from digital segmentation to control print.