Перегляд за Автор "Dydo, R."

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  • Ескіз
    Документ
    Analysis of Precision of Finding the Destroyed Remains Buildings on Photos using MobileNetV3 and ViT Neural Networks
    (2025) Dydo, R.; Sobko, O.; Molchanova, M.; Mazurets, O.; Мазурець, Олександр Вікторович
    This study presents a comparative analysis of the precision and recall of MobileNetV3 and Vision Transformer (ViT) neural networks in detecting destroyed building remains from photographic data. Using a curated dataset of disaster-zone images, both models were trained and evaluated on key performance metrics. Results show that while both architectures performed well, ViT consistently achieved higher accuracy and generalization, particularly in complex material classes. The findings support the use of ViT in high-precision post-disaster assessment systems and highlight its potential for integration into automated, real-time damage detection platforms.
  • Ескіз
    Документ
    Approach for Using Neural Network BERT-GPT2 Dual Transformer Architecture for Detecting Persons Depressive State
    (2024) Mazurets, O.; Tymofiiev, I.; Dydo, R.; Мазурець, Олександр Вікторович
    The paper proposed the method of using neural network BERT-GPT2 dual transformer architecture for detecting persons depressive state designed to transform input data in the form of text and trained neural network BERT-GPT2 dual transformer architecture model into output data in the form of the numerical assessment of the presence of persons depressive state. Experiments were conducted with the use of the given developed software complex for detecting persons depressive state, which testify to the correctness of the proposed approach. From the performed performance study, the dual architecture did not make a single error during classification
  • Ескіз
    Документ
    Datalogic structure for intelligent system for areas localization in photos with the remains of buildings using neural network
    (2025) Dydo, R.; Sobko, O.; Klimenko, V.; Mazurets, O.; Мазурець, Олександр Вікторович
    This paper presents the development of a datalogic structure for an intelligent system designed to detect and localize areas in photographic images that contain remains of destroyed buildings using neural networks. The system integrates pre-processing, object detection via the YOLO model, and multiclass classification of building materials. A relational database was designed to store and manage information about images, segments, detected materials, experiments, and classification metrics. This structured approach ensures efficient data handling, supports analytical reporting, and enables retraining of models based on historical data, contributing to more accurate and scalable damage assessment solutions in post-conflict or disaster-struck areas.