Перегляд за Автор "Hladun, O."

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  • Ескіз
    Документ
    Method for Determining the Person Emotional State in Real Time by Neural Networks Tools
    (2024) Hladun, O.; Molchanova, M.; Zalutska, O.
    The paper proposes method for determining the person emotional state in real time by means of neural networks, which allows for transformation of input data in the form of trained neural network model of convolutional architecture and video stream into output data that includes information about person's emotional state, presented in the form of emotional tags that correspond to emotions: joy, sadness, anger, disgust, fear, surprise and neutral.
  • Ескіз
    Документ
    Real Time Detection the Person Emotion State Using Neural Network
    (2024) Hladun, O.; Mazurets, O.; Molchanova, M.; Sobko, O.; Мазурець, Олександр Вікторович
    The method of determining the emotional state of a person in real time by neural networks tools is proposed, which uses a convolutional neural network and allows detecting 7 basic emotional states of a person with an accuracy of more than 80% for each of the emotions.
  • Ескіз
    Документ
    Research on the effectiveness of classifying the remains of destroyed buildings using MobileNetV3 neural network architecture
    (2025) Hladun, O.; Zalutska, O.; Klimenko, V.; Mazurets, O.; Мазурець, Олександр Вікторович
    The study investigates the effectiveness of the MobileNetV3 neural network architecture in classifying the remains of destroyed buildings, a task of increasing relevance due to the widespread destruction of infrastructure caused by military actions, natural disasters, and industrial accidents. A software application with a graphical interface was developed to enable interactive analysis of photo data using pre-trained models. The system allows users to classify construction debris into multiple categories with high accuracy and reliability. Experimental results demonstrated strong performance metrics, including an overall accuracy of 95% and high values for precision, recall, and F1-score across ten classes of construction materials. The model showed excellent discriminative capability, as evidenced by ROC curves with AUC values close to 1.00. The solution holds promise for practical applications such as automated sorting of construction waste and monitoring of damage zones, contributing to more efficient disaster response and recovery operations.