Перегляд за Автор "Iurii, Krak"

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  • Ескіз
    Документ
    Explainable deep learning: A visual analytics approach with transition matrices
    (Multidisciplinary Digital Publishing Institute, 2024-03-29) Radiuk, Pavlo; Olexander, Barmak; Manziuk, Eduard; Iurii, Krak
    The non-transparency of artificial intelligence (AI) systems, particularly in deep learning (DL), poses significant challenges to their comprehensibility and trustworthiness. This study aims to enhance the explainability of DL models through visual analytics (VA) and human-in-the-loop (HITL) principles, making these systems more transparent and understandable to end users. In this work, we propose a novel approach that utilizes a transition matrix to interpret results from DL models through more comprehensible machine learning (ML) models. The methodology involves constructing a transition matrix between the feature spaces of DL and ML models as formal and mental models, respectively, improving the explainability for classification tasks. We validated our approach with computational experiments on the MNIST, FNC-1, and Iris datasets using a qualitative and quantitative comparison criterion, that is, how different the results obtained by our approach are from the ground truth of the training and testing samples. The proposed approach significantly enhanced model clarity and understanding in the MNIST dataset, with SSIM and PSNR values of 0.697 and 17.94, respectively, showcasing high-fidelity reconstructions. Moreover, achieving an F1m score of 77.76% and a weighted accuracy of 89.38%, our approach proved its effectiveness in stance detection with the FNC-1 dataset, complemented by its ability to explain key textual nuances. For the Iris dataset, the separating hyperplane constructed based on the proposed approach allowed for enhancing classification accuracy. Overall, using VA, HITL principles, and a transition matrix, our approach significantly improves the explainability of DL models without compromising their performance, marking a step forward in developing more transparent and trustworthy AI systems.
  • Ескіз
    Документ
    Method of facial geometric feature representation for information security systems
    (CEUR-WS, 2022-06-17) Kalyta, Oleg; Iurii, Krak; Barmak, Olexander; Wojcik, Waldemar; Radiuk, Pavlo
    Throughout human history, emotional manifestations have played a major role in interpersonal interaction among humans in all areas of society. In particular, information security systems for visual surveillance, based on recognizing emotional states by facial expressions, have recently become highly relevant. In this paper, we propose a method of representing geometric facial features, which aims to enhance the functioning of visual surveillance for information security systems. The method is designed to automatically reflect the facial expressions of human emotions in the form of quantitative characteristics of geometric shapes. It uses software-generated landmarks for constructing specific geometric characteristics of the face, which serve as input data for the method. Our method consists in forming seven geometric shapes based on predefined landmarks, with the subsequent quantitative expression of these shapes. The method derives quantitative features of seven forms, which are further used to identify emotional facial states. We validated the proposed method using hyperplane classification and compared its performance with analogs. As such, the classification model, which was constructed based on the proposed method, achieved a classification accuracy of 92.73% and slightly surpassed the analogs in other statistical indicators. Overall, the results of computational experiments confirmed the effectiveness of the proposed method for identifying changes in a person’s emotional state by facial expressions. In addition, the use of simple mathematical calculations in our method has significantly reduced the computational complexity against analogs.