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  • Документ
    Statistical data analysis models for determining the relevance of structural image descriptions
    (IEEE Access, 2023) Daradkeh Yousef Ibrahim; Gorokhovatskyi, V.; Tvoroshenko, I.; Gadetska, S.; Al-Dhaifallah Mujahed
    The aim of the research is to improve the effectiveness of image recognition methods according to the description in the form of a set of keypoint descriptors. The focus is on increasing the speed of analysis and processing of description data while maintaining the required level of classification efficiency. The class of the image is provided as a description of the etalon. It is proposed to transform the description by implementing a statistical system of features for non-intersecting data fragments. The developed method is based on the aggregation of data distribution values within the description, the basis of which is the bit representation of the descriptors. Statistical features are calculated as the frequency of occurrence of the fixed value of a fragment on a set of description data and thus reflect the individual properties of images. Three main classifier models are analyzed: calculating the measure of data relevance in the form of distributions; assigning each of the descriptors to defined classes (voting); using the apparatus of statistical data analysis to decide on the significance of the difference between the distributions of the object and etalons. The results of software modeling of methods and calculations of statistical significance of differences based on distributions for training sets of images are represented. Using distributions instead of a set of descriptors increases the processing speed by hundreds of times, while the classification accuracy is maintained at a sufficient level and does not deteriorate compared to traditional voting.
  • Документ
    Application of video data classification models using convolutional neural networks
    (International Journal of Academic and Applied Research, 2023) Tvoroshenko, I.; Pomazan, V.; Gorokhovatskyi, V.; Kobylin, O.
    This paper explores the classification of video data with convolutional neural networks. It discusses how convolutional neural network architecture can do this task with fastness and maximum efficiency. By analyzing the different convolutional neural network models model was proposed that showed great results in this video classification task. The strengths and weaknesses of the neural network model for video classification have been identified, and the prospects for further work have also been outlined.