Перегляд за автором "Pomazan, V."

Зараз показано 1 - 3 з 3
  • Зображення мініатюри
    Публікація
    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.
  • Зображення мініатюри
    Публікація
    Development of an application for recognizing emotions using convolutional neural networks
    (International Journal of Academic Information Systems Research, 2023) Pomazan, V.; Tvoroshenko, I.; Gorokhovatskyi, V.
    This paper explores the potential of using convolutional neural networks (CNN) for emotion recognition in marketing research of advertising. Discusses how CNN can be trained to accurately recognize emotions conveyed in advertisements and how this information can be used to gain insights into consumer behavior. By analyzing consumer emotions in response to different types of advertising, marketers can better tailor their advertising campaigns to elicit positive emotional responses and improve overall effectiveness. Overall, the paper demonstrates the potential of CNN-based emotion recognition as a valuable tool to optimize advertising strategies and improve consumer engagement.
  • Зображення мініатюри
    Публікація
    Handwritten character recognition models based on convolutional neural networks
    (International Journal of Academic Engineering Research, 2023) Pomazan, V.; Tvoroshenko, I.; Gorokhovatskyi, V.
    This paper explores the possibility of using various convolutional neural network models to recognize handwritten Arabic characters. It discusses which of the existing convolutional neural network models best suits this task. The results are analyzed based on which conclusions are drawn regarding the effectiveness or ineffectiveness of specific convolutional neural network models. The paper provides an analysis of the efficiency of convolutional neural network models for the task of recognizing handwritten Arabic characters.