Radioelectronic and Computer Systems
type: channel
description: Founded in: 2003 Field of science and issues in focus: The journal reflects the scientific achievements of the scholars of higher education institutions, research institutes, industrial enterprises, potential applicants of academic degrees and titles in the field of electronic, computer and information systems in the following areas: design, technology and organization of production of radio engineering systems and complexes; development of computer systems; the use of information technology and information technology in the management of enterprises, programs and projects. The edition is included in the List of Scientific Professional Publications of Ukraine (Category "A") by the Ministry of Education and Science of Ukraine № 1017 of 27.09.2021. Specialties (ISCED-F 2013): 0533 (104) Physics 0541 (113) Mathematics 0588 (163) Inter-disciplinary programmes and qualifications involving natural sciences, mathematics and statistics 0612 (123, 125, 126) Database and network design and administration 0613 (121, 122) Software and applications development and analysis 0688 (124) Inter-disciplinary programmes and qualifications involving Information and Communication Technologies 0714 (171, 172, 173, 174, 175, 176) Electronics and automation 0716 (173) Motor vehicles, ships and aircraft 0788 (163) Inter-disciplinary programmes and qualifications involving engineering, manufacturing and construction INDEXING: Scopus (from 2019) ISSN: 1814-4225 (Print), 2663-2012 (Online) Media identifier No. R30-01718 (Decision dated 10/26/2023 No. 1146). Indexing: Vernadsky National Library of Ukraine; NRAT;«Ukrainika naukova»; «Dzerelo»; EBSCO; <a href="https://doaj.org/toc/2663-2012?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22terms%22%3A%7B%22index.issn.exact%22%3A%5B%221814-4225%22%2C%222663-2012%22%5D%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22match_all%22%3A%7B%7D%7D%7D%7D%2C%22size%22%3A100%2C%22sort%22%3A%5B%7B%22created_date%22%3A%…
publisher: Національний аерокосмічний університет "Харківський авіаційний інститут"
language: en-US
publication name: Radioelectronic and Computer Systems
issn: 1814-4225
copyright: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License that allows others to share (copy and redistribute the material in any medium or format), adapt, remix, transform, and build upon the material: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
items: Modeling of defensive actions for protecting military and civilian facilities from massive wave attacks by enemy strike drones
items: The impact of the number representation format in parameterized text queries on the accuracy of 3D model generation
items: Models for industry differentiation in decision-making systems with an application to the Ukrainian economy
items: Empirical evaluation of feature selection and machine learning techniques to recommend clones for software refactoring
items: A bayesian-driven feedforward neural network model for Kafka cluster latency forecasting
items: An integrated approach to forecasting software system quality using bayesian correction, multi-criteria optimization, and meta-learning
items: On Kamada-Kawai graph layout with graph neural networks
items: Intelligent system for real-time detection and classification of solar panel defects
items: Deep information-extreme machine learning for autonomous UAV based on decursive data structure for semantic segmentation of digital image of a region
items: Method for optimizing descriptors clustering in the feature database of a content-based image retrieval system
items: Method for matching satellite and UAV images for visual place recognition with cross-view color normalization
items: A method for improving the robustness of neural network model for aerial image matching
items: Using a hybrid attention mechanism as a method to improve the efficiency of network intrusion detection systems
items: GraphSAGE optimization for insurance risk assessment: balancing performance and efficiency
items: A general method for real-time detection of information threats with a Ukraine case study
items: Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
items: Information technology for automation of server infrastructure management using DevOps tools

Modeling of defensive actions for protecting military and civilian facilities from massive wave attacks by enemy strike drones
type: item
description: he conditions of modern hybrid warfare require the creation of the necessary defense potential to protect military facilities and critical infrastructure from massive attacks by enemy strike drones. A feature of repelling drone air attacks is the use of a diverse arsenal of defensive means (anti-drone means (ADM), electronic warfare (EW), anti-aircraft warfare (AAW), interceptor drones (ID), etc.). Therefore, it is relevant to conduct research on modeling defensive actions and planning the protection of military and civilian facilities from attacking missions of enemy strike drones. The subject of the study is a mathematical and simulation model used for planning the protection of military and civilian facilities against swarm attacks by enemy strike drones.The purpose of the study is to simulate the planning of defensive actions against enemy air attacks, under conditions of limited capabilities, which will ensure the rational use of military resources. Tasks to be solved: analyze the sequence of defensive actions; analyze the most threatening places for the formation and launch of enemy strike drones; justify the creation of the defense of critical military and civilian facilities; create the necessary defense potential to protect critical military and civilian facilities; model possible scenarios for the launch and movement of enemy strike drones; demonstrate the feasibility and effectiveness of the proposed approach using an illustrated example. Mathematical methods and models used: system analysis of defensive anti-drone actions; qualitative assessment of defensive action options using linguistic variables; lexicographic ordering of options to highlight relevant locations for the protection of military and civilian facilities; integer (boolean) programming method to substantiate the defensive potential of protection, in conditions of limited capabilities; multi-agent modeling to analyze and predict possible scenarios of an enemy attack mission using strike dron…
creator: Oleg Fedorovich
creator: Olga Malyeyeva
creator: Andrіi Humennyi
creator: Oleksandr Leshchenko
creator: Yuliia Leshchenko
creator: Ganna Pliekhova
rights: Copyright (c) 2025 Oleg Fedorovich, Olga Malyeyeva, Andrіi Humennyi, Oleksandr Leshchenko, Yuliia Leshchenko
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 3
ending page: 20
doi: 10.32620/reks.2025.3.01

The impact of the number representation format in parameterized text queries on the accuracy of 3D model generation
type: item
description: <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">The subject matter <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">of the article is the analysis of the artificial intelligence models for generating 3D objects based on a text query with different formats for representing numerical parameters. <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">The goal of the work<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; wo…
creator: Olesia Barkovska
creator: Liubov Bukharova
creator: Igor Ruban
creator: Oleksii Liashenko
creator: Andriy Kovalenko
creator: Vitalii Serdechnyi
rights: Copyright (c) 2025 Olesia Barkovska, Liubov Bukharova, Igor Ruban, Oleksii Liashenko, Andriy Kovalenko, Vitalii Serdechnyi
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 21
ending page: 36
doi: 10.32620/reks.2025.3.02

Models for industry differentiation in decision-making systems with an application to the Ukrainian economy
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">This article is devoted to the study of the problem of using adaptive models of differentiation of sectors of the real sector of the economy as a key component of modern decision support systems (DSS). The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">subject<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> of the study is models of differentiation of real sectors of the Ukrainian economy for integration into decision support systems to optimize public administration. This research <strong style="color: #000000; font-family: Verdana, Arial, Helvet…
creator: Alina Hlushko
creator: Oleksandr Laktionov
creator: Alina Yanko
creator: Oleksandr Isaiev
rights: Copyright (c) 2025 Alina Hlushko, Oleksandr Laktionov, Alina Yanko, Oleksandr Isaiev
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 37
ending page: 52
doi: 10.32620/reks.2025.3.03

Empirical evaluation of feature selection and machine learning techniques to recommend clones for software refactoring
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">article’s subject matter<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> deals with the management of software clones. Software clones are duplicate code fragments that can exist in the same or different software files. Software clone detection and management has become a well-established research area. Software clones should be managed to minimize their ill-effects, as the presence of clones can increase the software’s maintenance cost and resource requirements. Refactoring is a commonly used tec…
creator: Manpreet Kaur
creator: Dhavleesh Rattan
creator: Madan Lal
rights: Copyright (c) 2025 Manpreet Kaur, Dhavleesh Rattan, Madan Lal
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 53
ending page: 67
doi: 10.32620/reks.2025.3.04

A bayesian-driven feedforward neural network model for Kafka cluster latency forecasting
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">subject matter <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">of this article is the process of designing the architecture of a Feedforward neural network model based on the discrete Bayesian Network and a new method for setting the initial weights that connect neurons across layers. The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: …
creator: Olha Solovei
creator: Tetiana Honcharenko
rights: Copyright (c) 2025 Olha Solovei, Tetiana Honcharenko
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 68
ending page: 87
doi: 10.32620/reks.2025.3.05

An integrated approach to forecasting software system quality using bayesian correction, multi-criteria optimization, and meta-learning
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The aim of this study is to improve modern methods for forecasting the quantitative characteristics of software system quality to enhance reliability, efficiency, and adaptability in dynamic IT environments. To achieve this, an integrated forecasting approach was developed that combines adaptive Bayesian noise correction, probabilistic ensembles with weighted risk adjustment, hybrid multi-criteria optimization, graph models of metric interdependencies, and meta-learning for forecast adaptation. The scientific novelty lies in the proposed ensemble integration and adaptive mechanisms that increase forecasting robustness while accounting for uncertainty and metric dependencies. The methods were validated on the GitLab CE system. The experimental results confirmed measurable improvements: forecasting error was reduced from 18.7% to 4.2%, execution time decreased by 36.8%, CPU and memory consumption dropped by up to 20%, and system reliability indicators (downtime, fault tolerance) improved by more than 60%. These metrics confirm that the proposed approach strengthens reliability, efficiency, and adaptability of software quality forecasting compared to traditional methods.
creator: Anton Shantyr
creator: Olha Zinchenko
creator: Kamila Storchak
creator: Viktor Vyshnivskyi
creator: Olga Morozova
rights: Copyright (c) 2025 Anton Shantyr, Olha Zinchenko, Kamila Storchak, Viktor Vyshnivskyi, Olga Morozova
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 88
ending page: 100
doi: 10.32620/reks.2025.3.06

On Kamada-Kawai graph layout with graph neural networks
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">Traditional force-directed layout algorithms, such as the Kamada-Kawai (KK) method, are widely used for graph visualization due to their ability to produce aesthetically pleasing layouts. However, these algorithms can be computationally intensive for large graphs. <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">The subject<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; le…
creator: Olena Linnyk
creator: Lyudmyla Polyakova
creator: Iryna Zaretska
rights: Copyright (c) 2025 Olena Linnyk, Lyudmyla Polyakova, Iryna Zaretska
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 101
ending page: 110
doi: 10.32620/reks.2025.3.07

Intelligent system for real-time detection and classification of solar panel defects
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">subject matter <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">of the study in the article involves the process of detecting and classifying defects in solar panels in real-time using unmanned aerial vehicles (UAV) and artificial intelligence technologies. The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-in…
creator: Lesia Dubchak
creator: Yevgeniy Bodyanskiy
creator: Oleg Savenko
creator: Volodymyr Kochan
creator: Anatoliy Sachenko
rights: Copyright (c) 2025 Lesia Dubchak, Yevgeniy Bodyanskiy, Oleg Savenko, Volodymyr Kochan, Anatoliy Sachenko
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 111
ending page: 121
doi: 10.32620/reks.2025.3.08

Deep information-extreme machine learning for autonomous UAV based on decursive data structure for semantic segmentation of digital image of a region
type: item
description: <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">The subject of the research<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> is functional categorical models of deep information-extreme machine learning based on linear and hierarchical data structures, methods for optimizing machine learning parameters based on information criteria and constructing a decursive binary data tree for a given alphabet of recognition classes. <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">The aim of the research<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 4…
creator: Valerii Cheranovskyi
creator: Mykyta Myronenko
creator: Serhii Kovalevskyi
creator: Roman Kraskovskyi
creator: Mykhailo Otroshchenko
rights: Copyright (c) 2025 Valerii Cheranovskyi, Mykyta Myronenko, Serhii Kovalevskyi, Roman Kraskovskyi, Mykhailo Otroshchenko
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 122
ending page: 136
doi: 10.32620/reks.2025.3.09

Method for optimizing descriptors clustering in the feature database of a content-based image retrieval system
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">subject<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> of this study is the method of grouping image descriptors that are placed in the feature database of search systems. This study aims to develop a method for optimizing the clustering of descriptors in Big Data storage, represented by a Multidimensional Cube data model. Further use of the formed clusters for effective search in content-based image retrieval systems. The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, san…
creator: Stanislav Danylenko
creator: Kirill Smelyakov
rights: Copyright (c) 2025 Stanislav Danylenko, Kirill Smelyakov
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 137
ending page: 149
doi: 10.32620/reks.2025.3.10

Method for matching satellite and UAV images for visual place recognition with cross-view color normalization
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">subject of this<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> article is visual place recognition (VPR), specifically matching satellite images with images captured by unmanned aerial vehicles (UAVs). VPR is critical for autonomous UAV navigation, particularly in GPS-denied environments such as urban canyons or areas with significant infrastructure coverage where GNSS signals are unreliable. Despite its practical importance, accurately matching UAV images to satellite imagery remains challenging…
creator: Volodymyr Vozniak
creator: Oleksander Barmak
creator: Iurii Krak
rights: Copyright (c) 2025 Volodymyr Vozniak, Oleksander Barmak, Iurii Krak
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 150
ending page: 165
doi: 10.32620/reks.2025.3.11

A method for improving the robustness of neural network model for aerial image matching
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">Neural network–based image matching techniques are increasingly employed in aerial image analysis—particularly for UAV navigation, localization, and mapping. However, their sensitivity to structured visual distortions (e.g., shadows, illumination changes, and terrain variability) limits robustness under real-world conditions. Addressing this challenge, we propose a training methodology that enhances both robustness and cross-domain generalization of feature-matching models by integrating adversarial procedural noise with activation‐function modification. During training, structured noise patterns (Perlin, Gabor, and Worley) are synthesized and applied in an adversarial manner, while the standard ReLU activation is replaced by a hybrid LeakyReLU6 to mitigate sensitivity to local perturbations. We evaluate our approach on both detector-based (SuperPoint + SuperGlue) and detector-free (LoFTR) architectures using the Aerial Image Matching Benchmark Dataset and further assess cross-domain performance on the HPatches dataset. Experimental results show that our method yields over 4 % absolute improvements in matching precision and recall on noisy test data for both classes of models. Ablation studies confirm that these gains are attributable to the synergistic effect of procedural noise and LeakyReLU6. Moreover, models trained with our procedure exhibit significantly smaller performance drops when transferred to HPatches, demonstrating enhan…
creator: Viacheslav Moskalenko
creator: Alona Moskalenko
creator: Yuriy Moskalenko
rights: Copyright (c) 2025 Viacheslav Moskalenko, Alona Moskalenko, Yuriy Moskalenko
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 166
ending page: 176
doi: 10.32620/reks.2025.3.12

Using a hybrid attention mechanism as a method to improve the efficiency of network intrusion detection systems
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">subject matter<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> of this article is a HybridAttention mechanism integrated into a deep neural architecture for Network Intrusion Detection Systems (NIDS). This study <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: nor…
creator: Andrii Nikitenko
creator: Yevhen Bashkov
rights: Copyright (c) 2025 Andrii Nikitenko, Yevhen Bashkov
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 177
ending page: 188
doi: 10.32620/reks.2025.3.13

GraphSAGE optimization for insurance risk assessment: balancing performance and efficiency
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">subject of<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> this article is the application and optimization of Graph Neural Networks, specifically the GraphSAGE (Graph SAmple and aggreGatE) architecture, for insurance risk assessment in volatile environments. This study <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: star…
creator: Oleksandr Lutsenko
creator: Serhii Shcherbak
rights: Copyright (c) 2025 Oleksandr Lutsenko, Serhii Shcherbak
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 189
ending page: 201
doi: 10.32620/reks.2025.3.14

A general method for real-time detection of information threats with a Ukraine case study
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">subject <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">matter is a general set of methods and system architecture for text analytics, enabling real-time detection and monitoring of information threats, validated through a Ukrainian case study. It integrates sentiment analysis, polarity-inversion handling, and machine-learning–based thematic classification. The research is especially relevant in the context of hybrid warfare, where the information environment becomes a battlefield of disinformatio…
creator: Khrystyna Lipianina-Honcharenko
creator: Myroslav Komar
creator: Hennadii Bohuta
creator: Ihor Ihnatiev
creator: Khrystyna Yurkiv
creator: Oleg Illiashenko
creator: Lesia Bilovus
rights: Copyright (c) 2025 Khrystyna Lipianina-Honcharenko, Myroslav Komar, Hennadii Bohuta, Ihor Ihnatiev, Khrystyna Yurkiv, Oleg Illiashenko, Lesia Bilovus
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 202
ending page: 230
doi: 10.32620/reks.2025.3.15

Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">The subject of the article is the study and comparison of two approaches to modelling the battery discharge of a CubeSat satellite: analytical using equivalent circuit and machine learning. The article aims to make a reasoned choice of the approach to modelling the battery discharge of a CubeSat satellite. Modelling the battery discharge of a satellite will enable the prediction of the consequences of disconnecting the autonomous power system and ensure the fault tolerance of equipment in orbit. Therefore, the selected study is relevant and promising. This study focuses on the analysis of CubeSat satellite data, based explicitly on orbital data samples of the power system, which include data available at the time of the article’s publication. The dataset contains data on the voltage (mV), current (mA), and temperature (Celsius) of the battery and solar panels attached to the five sides of the satellite. In this context, two approaches are considered: analytical modelling based on physical laws and machine learning, which uses empirical data to create a predictive model. Results: A comparative analysis of the modeling results reveals that the equivalent circuit approach has the advantage of transparency, as it identifies possible parameters that facilitate understanding of the relationships. However, the model is less flexible to environmental changes or non-standard satellite behavior. The machine learning model demonstrated more accu…
creator: Ihor Turkin
creator: Lina Volobuieva
creator: Andriy Chukhray
creator: Oleksandr Liubimov
rights: Copyright (c) 2025 Ihor Turkin, Lina Volobuieva, Andriy Chukhray, Oleksandr Liubimov
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 231
ending page: 244
doi: 10.32620/reks.2025.3.16

Using a hybrid attention mechanism as a method to improve the efficiency of network intrusion detection systems
type: item
description: <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">The subject<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> of this study is various types of network traffic in modern computer networks with a complex structure and a certain degree of self-similarity. Efficient use of network resources and ensuring the quality of service to subscribers are important tasks of computer networks. The probability of losing a message due to buffer storage device overflow is an important parameter in determining the quality of service (QoS). The mathematical model should be used to estimate this parameter. Recent advancements have resulted in many different models of packet loss probability in a router buffer. However, many models do not consider the traffic characteristics of various modern applications and protocols. The traffic in modern computer networks has a complex structure and often has a certain degree of self-similarity. Currently, a large number of models are available for estimating the probability of packet loss due to buffer overflow. <strong style="color: #000000; font-family: Verdana, …
creator: Kyrylo Rukkas
creator: Anastasiia Morozova
creator: Ievgen Meniailov
creator: Myroslav Momot
rights: Copyright (c) 2025 Kyrylo Rukkas, Anastasiia Morozova, Ievgen Meniailov, Myroslav Momot
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 245
ending page: 256
doi: 10.32620/reks.2025.3.17

Information technology for automation of server infrastructure management using DevOps tools
type: item
description: <span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">This research presents an automated server infrastructure management system integrating Python, Terraform, Ansible, MySQL, and the DigitalOcean API for dynamic DNS management, tailored for educational environments requiring rapid provisioning of uniform server configurations. It automates server deployment on the Hetzner platform, configuration standardization, and horizontal and vertical scaling. <strong style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">Objective<span style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> to develop a scalable, automated infrastructure…
creator: Ivan Byzov
creator: Sergiy Yakovlev
rights: Copyright (c) 2025 Ivan Byzov, Sergiy Yakovlev
date: 2025-09-10
publication date: 2025-09-10
volume: 2025
number: 3
starting page: 257
ending page: 269
doi: 10.32620/reks.2025.3.18