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Modern Programming Technologies in the Tasks of Identification and Classification of Military Aircraft Using Machine Learning Algorithms
(Lublin: Lublin University of Technology, 2024-12-16) Terletskyi, Taras; Kaidyk, Oleh
This article addresses the development of an intelligent military aircraft identification system using artificial intelligence, machine learning, and deep self-learning technologies to enhance national security and military efficiency. The system aims to automatically and accurately recognize and classify aircraft in images, offering advantages over traditional methods such as higher productivity, speed, accuracy, and the elimination of human error. The importance of deep learning solutions for threat detection and operational efficiency is emphasized. Modern visual data-based object recognition methods and tools are analysed. The methodology includes collecting and preprocessing data, developing a high-precision recognition system based on Yolov8, annotating objects with Roboflow, and creating training, validation, and testing subsets in the yolo format. The paper details the dataset formation process and presents satisfactory results in fast recognition of military aircraft with high classification accuracy. A comparative analysis of Yolov8, R-CNN, and GPT-4 models shows Yolov8's superiority in prediction accuracy and performance. The article describes the model management system for adjusting hyperparameters, selecting object categories, and initiating the training and forecasting process. Testing results demonstrate Yolov8's optimality for military aircraft identification, achieving accurate target identification in complex situations using advanced deep learning algorithms.
Analysis of Wildfires Using GIS Technologies (Case Study of Volyn Region, Ukraine)
(Aachen: CEUR Workshop Proceedings, 2026-02-07) Kaidyk, Oleh; Terletskyi, Taras; Zdolbitska, Nina; Zdolbitska, Nina
The paper proposed a GIS-based study, which allow analysis of the impact of wildfires using multispectral satellite images. A wildfire that occurred between 30 April and 3 May 2025 north of the village Birky, Kamin-Kashirsky district Volyn region (northwestern Ukraine), formed the basis of the study. Using channels in the near-infrared (NIR) and short-wave infrared (SWIR) ranges was found to be the most promising approach for promptly detecting wildfires and determining their long-term consequences. To interpret the study results, consequences and intensity of the wildfire were assessed using the US Geological Survey scale and a normalized burning coefficient. Creating forest fire intensity maps is key todeveloping vegetation restoration plans after fires and assessing the potential future impact on burnt areas.
The Probabilistic Approach to Automated Visual Navigation in Closed Spaces
(2026-02-07) Artemenko, Olga; Dorenskyi, Oleksandr; Terletskyi, Taras; Kaidyk, Oleh; Kramar, Oleh
The paper reviews current approaches to automated navigation tasks, such as localization and mapping, and proposes a different approach based on probability density approximation. The proposed system uses feature extraction to reduce computation complexity. Descriptors, are then used to match mapped features to a current view, and localization uses iterative pose estimation based on gradient descent. Current system limitations and weaknesses are also shown

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