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%7B%22order%22%3A%22desc%22%7D%7D%5D%2C%22_source%22%3A%7B%7D%7D" target="_blank">DOAJ; Ulrichsweb Global Serials Directory; OCLC WorldCat; Index Copernicus; CiteFactor; AcademicKeys; Infobase Index; WorldCat; Google Scholar; Scillit; OUCI Issues and articles from 2016 have a DOI from CrossRef Founder: National Aerospace University «Kharkiv Aviation Institute» Periodicity: 4 issues per year. Editor-in-Chief: Kharchenko Vyacheslav Executive secretary: Leshchenko Oleksandr Editorial office address: 61070, Kharkiv, 17 V. Manka Str., chair 302. Tel.: +38(057)7884381, +38(067)1205222 E-mail: ntrio@khai.edu Archives of issues are HERE & HERE This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Національний аерокосмічний університет "Харківський авіаційний інститут"
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Modeling of defensive actions for protecting military and civilian facilities from massive wave attacks by enemy strike drones
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 drones. The following results were obtained: a systematic presentation of planning defensive actions against massive attacks by enemy strike drones was proposed; the most threatening places for the formation and launch of swarms of enemy strike drones were substantiated; the relevant locations of anti-drone means for protecting military facilities and dual infrastructure facilities were identified; the necessary defense potential was created to protect against air attacks in conditions of limited capabilities; a multi-agent simulation model was developed to analyze and predict possible scenarios for the launch and flight of enemy strike drones to the locations of critical military and civilian facilities. Conclusions. The results of the study allow us to substantiate and plan defensive anti-drone actions to protect military facilities and dual-purpose infrastructure facilities. The scientific novelty of the proposed approach lies in the scientific substantiation of defensive actions to protect military and civilian facilities from massive attacks by enemy strike drones based on the use of the developed complex of original and new mathematical and simulation models.
Copyright (c) 2025 Oleg Fedorovich, Olga Malyeyeva, Andrіi Humennyi, Oleksandr Leshchenko, Yuliia Leshchenko
The impact of the number representation format in parameterized text queries on the accuracy of 3D model generation
<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; 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 to evaluate and compare the effectiveness of the Hunyuan3D 2.0, Michelangelo and SDFusion models depending on the format of the text input queries using the following metrics: generation time, CLIP-Similarity and Chamfer Distance. 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;">tasks<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 be solved are: 1) to conduct a systematic review of modern generative 3D models regarding their ability to process queries with numerical parameters presented in different formats (decimals, verbal representations, fractions); 2) to conduct a quantitative and qualitative evaluation of generative 3D models using the metrics of generation time, CLIP-Similarity and Chamfer Distance, taking into account the visual similarity to text descriptions and geometric similarity to Ground Truth objects; 3) to analyse the obtained results of generative text-to-3D models evaluation for parameterized 3D objects generation tasks for further implementation in user interface applications. <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<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; 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;">methods<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; 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;">used<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;"> in this work are machine learning methods, methods of vector representation of text and images, statistical methods of evaluating results, and a heuristic method of forming text queries. <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<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; 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;">results<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;"> show that there is no universal generative 3D model that is capable of creating objects that fully correspond to a parameterised text query. The proposed methodology enables the formation of input text sequences containing such numerical representations, including decimals, verbal representations or fractions, for further analysis of the accuracy of object generation. <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;">Conclusions<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;">. Similar efficiency of training 3D generative models in the joint latent space of text features containing numerical parameters and using datasets with precisely defined geometric characteristics of objects was confirmed. The results show that the Hunuyan3D-2.0 model is suitable for further research and modification to adapt the used methods to create personalised 3D objects with given numerical parameters, such as a prosthetic cover.
Copyright (c) 2025 Olesia Barkovska, Liubov Bukharova, Igor Ruban, Oleksii Liashenko, Andriy Kovalenko, Vitalii Serdechnyi
Models for industry differentiation in decision-making systems with an application to the Ukrainian economy
<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, 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;">aims<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 and validate adaptive models of industry differentiation into clusters (groups) to improve the effectiveness of decision-making systems applied to the real sector of Ukraine’s economy. 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;">research object<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 the process of sectoral differentiation, which allows determining the structural features and patterns of economic sector functioning. DSS architecture is proposed that integrates multifactor analysis and machine learning algorithms for automated selection of strategic scenarios. For clustering, we used production volume indicators and the number of strategically important enterprises in Ukraine for the pre-war period (2015–2021), which serve as a benchmark model for comparative analysis. A comparative assessment of the effectiveness of the classical K-means, DBSCAN, and Ensemble model algorithms was conducted with quantitative verification of the results using the Silhouette Score and Davies-Bouldin Score metrics. Empirical analysis showed that the DBSCAN and Ensemble models provide the highest quality of clustering (Silhouette Score 0.8387; Davies-Bouldin Score 0.0777), forming a reliable grouping of economic sectors. DSS module was developed based on the results obtained to form indicative tactical support measures, in particular, infrastructure strengthening of high-potential clusters and structural reorganization of vulnerable ones. <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;">Conclusions.<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 developed models form a universal methodological framework that is suitable for use in different countries, particularly in countries with a “peaceful” economy. DSS specialists can use the research results to identify key sectors of the economy, develop adaptive policies, and increase the stability and competitiveness of economic systems in a dynamic environment.
Copyright (c) 2025 Alina Hlushko, Oleksandr Laktionov, Alina Yanko, Oleksandr Isaiev
Empirical evaluation of feature selection and machine learning techniques to recommend clones for software refactoring
<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 technique for managing clones. A software clone detection tool can detect many clones from the software, but not all detected clones are suitable for refactoring. A developer needs a subset of detected clones that can be easily refactored. This study aims to suggest software clones for refactoring using machine learning techniques. This study evaluates the performance of fourteen machine-learning algorithms and investigates the influence of three feature selection methods on clone recommendation accuracy. 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;">tasks<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 be solved are as follows: selecting appropriate features from datasets, developing machine learning-based models that can suggest suitable clones for refactoring, and selecting an effective machine learning and feature selection algorithm for recommending clones for refactoring. 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;">methods<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;"> used for feature selection are correlation, InfoGain, and ReliefF. The study is conducted on datasets from six open-source software written in Java. The experimental <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;">results<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;"> show that the Decision Tree and LogitBoost classifiers achieve the highest accuracy of 94.44 % on the Lucene dataset. ReliefF yields the best performance among the feature selection methods, particularly when used with the Decision Tree algorithm. 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: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">concludes<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;"> that Random Committee, Random Forest, and Decision Tree perform best when paired with correlation, InfoGain, and ReliefF, respectively. Overall, the Decision Tree classifier, combined with the ReliefF feature selection method, delivers the highest average precision, recall, and F-score across datasets.
Copyright (c) 2025 Manpreet Kaur, Dhavleesh Rattan, Madan Lal
A bayesian-driven feedforward neural network model for Kafka cluster latency forecasting
<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: 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;">goal<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 to develop a Neural Network model designed to forecast end-to-end latency in a Kafka cluster. The proposed model can be used as a tool to predict the end-to-end latency of Kafka clusters based on the given configuration parameters and performance metrics. This study resolved the following tasks: developed and validated a discrete Bayesian network to understand the factors influencing end-to-end latency in Kafka clusters; conducted a sensitivity analysis on the discrete Bayesian network; created a matrix with initial weights derived from the sensitivity analysis in the Bayesian network to initialize weights in FFNN model; designed FFNN architecture for predicting the Kafka cluster end-to-end latency and configured its parameters; trained and evaluated the designed FFNN model. Methods from theories were used to conduct the research: big data processing, probabilistic graphical models and Bayesian inference theory, artificial neural networks and deep learning theories, graph theory, and machine learning optimization. The following <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;">results<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;"> were obtained: a trained FFNN model Mean Square Error showed consistent decrease across epochs, so we concluded that the model can be deployed and used as a tool to forecast Apach Kafka latency for given configuration parameters and performance metrics. The comparison of the Mean Square Error values when FFNN model is initialized with weights derived from the strength of influence in the Bayes Network and FFNN model which is set the same initial weights but scaled by Kaiming He factor demonstrated that Kaiming He scaling factor primarily improves the initial phase of training by stabilizing weight initialization. Therefore, we recommend scaling the initial weights as specified in our method to optimize FFNN training process. <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;">Conclusions<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 scientific novelty of the results obtained is as follows: 1) a new methodology for defining the architecture of a Feedforward Neural Network (FFNN) based on the discrete Bayesian network structure is introduced; 2) the initial weights that connect neurons across layers are set.
Copyright (c) 2025 Olha Solovei, Tetiana Honcharenko
An integrated approach to forecasting software system quality using bayesian correction, multi-criteria optimization, and meta-learning
<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.
Copyright (c) 2025 Anton Shantyr, Olha Zinchenko, Kamila Storchak, Viktor Vyshnivskyi, Olga Morozova
On Kamada-Kawai graph layout with graph neural networks
<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; 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;">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 is graph layout. 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;">aim<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 research is to explore the application of graph neural networks (GNNs) to improve the KK algorithm for graph layouts, resulting in a novel hybrid approach named KKNN. The key <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;">tasks <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;">addressed in this study include: 1. Development of the KKNN layout algorithm by integrating GNN-based reparameterization from the NeuLay-2 approach with the KK algorithm. 2. Evaluation of computational efficiency by comparing the computational performance of KKNN with both the original KK algorithm and the NeuLay-2. 3. Assessment of layout quality, particularly by examining the symmetry preservation, energy minimization, and aesthetic criteria such as minimal edge crossings and balanced node placement. 4. Testing on various graph types, including both random and highly structured (symmetric) graphs. 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;">methods <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;">used are: GNN-based layout reparameterization, inspired by NeuLay-2; Kamada-Kawai graph layout algorithm; performance metrics, including time-to-convergence, energy minimization, and symmetry preservation. Our experiments demonstrate the following <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;">results:<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;"> 1. KKNN converges to the energy minimum faster and achieves a lower energy state compared to the original KK. 2. KKNN not only reduces computational time but also better preserves graph symmetries compared to NeuLay-2. <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;">Conclusions.<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 study underscores the potential of integrating neural networks with traditional graph layout algorithms, presenting a promising approach for efficient and high-quality graph visualization. KKNN not only enhances computational performance but also ensures visually interpretable layouts. This hybrid approach offers a pathway for future research in graph visualization, where combining deep learning techniques with classical algorithms may open new possibilities for handling complex, large-scale graphs in a visually coherent and computationally efficient manner.
Copyright (c) 2025 Olena Linnyk, Lyudmyla Polyakova, Iryna Zaretska
Intelligent system for real-time detection and classification of solar panel defects
<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-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;">goal<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 to develop an intelligent system that combines an active monitoring methodology with Fuzzy BSB-based model for real-time detection and classification of solar panel defects. This will allow for the timely detection of defects and reduce the costs of repairing or replacing solar panels. 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;"> tasks<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 be solved are: to develop a method for active monitoring of the condition of panels based on laser scanning; to integrate algorithms for data processing and classification of defects in real time; to investigate the application of the Fuzzy BSB (Braine-State-in-the-Box) model to increase the stability of classification under conditions of noise and incomplete data. The methods used are: active laser scanning from UAVs, fuzzy neural network algorithms, the Fuzzy BSB associative memory model, as well as methods for analyzing images and feature vectors. The following <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;">results<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;"> were obtained. A methodology for detecting defects at the transportation stage and during the operation of solar panels is proposed. A Fuzzy BSB model is proposed for classifying detected defects, which is capable of providing an accuracy of about 80% even under conditions of significant noise and class overlap. It is found that the system effectively distinguishes the main types of defects, in particular cracks, contamination, shading, and mechanical damage, demonstrating competitive advantages compared to traditional passive methods. <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;">Conclusions.<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 scientific novelty of the results obtained is as follows: 1) adapting the combination of associative memory and fuzzy logic in the Fuzzy BSB model to the classification of solar panel defects, which allows to increase the reliability of this classification in conditions of incomplete or noisy data; 2) the concept of integrating active laser scanning with intelligent analysis algorithms is proposed, which opens up prospects for creating flexible and adaptive systems for monitoring the condition of solar power plants.
Copyright (c) 2025 Lesia Dubchak, Yevgeniy Bodyanskiy, Oleg Savenko, Volodymyr Kochan, Anatoliy Sachenko
Deep information-extreme machine learning for autonomous UAV based on decursive data structure for semantic segmentation of digital image of a region
<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: 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 to improve the accuracy of machine learning for an autonomous UAV for semantic segmentation of a digital image of a region obtained via an optoelectronic observation channel. This goal is achieved by developing a method of deep information-extreme machine learning for an on-board recognition system of an autonomous UAV using a decursive binary data structure. <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;">A new method<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 deep information-extreme machine learning for autonomous UAVs has been developed, based on a hierarchical data structure in the form of a decursive binary tree. The novelty of the method lies in the maximization of the average interclass code distance within a given dimensionality of the Hamming feature space by optimizing the selection level of coordinates of statistically averaged binary realizations of the recognition classes. At the same time, the level of depth of information-extreme machine learning according to the principle of deferred decisions is determined by the number of parameters of the system's functioning that are optimized according to the information criterion. This approach, unlike neural-like structures, provides flexibility for the onboard recognition system during retraining in the event of an expansion of the recognition class alphabet. The Kullback-Leibler information measure modified by the authors serves as a criterion for optimizing machine learning parameters. In addition, the proposed method involves the transformation of the input training matrix into a working binary matrix specified in the Hamming space, which in the process of machine learning adapts to its maximum accuracy. <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;">Results:<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;"> Based on the results of deep information-extreme machine learning, error-free decision rules based on the training matrix were constructed within the framework of a geometric approach. It is shown that the accuracy of the deep information-extreme machine learning is affected by the sequence of optimization of the parameters of the recognition system. The results of functional testing and cross-validation have confirmed the high accuracy of information-extreme machine learning for an autonomous UAV, as demonstrated by semantic segmentation of a digital image of a region. <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;">Conclusions:<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;"> For the first time, a method of deep information-extreme machine learning based on a hierarchical data structure in the form of a decursive binary tree has been developed, which, unlike the known ones, additionally optimizes the level of selection for coordinates of binary averaged vectors of recognition features.
Copyright (c) 2025 Valerii Cheranovskyi, Mykyta Myronenko, Serhii Kovalevskyi, Roman Kraskovskyi, Mykhailo Otroshchenko
Method for optimizing descriptors clustering in the feature database of a content-based image retrieval system
<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, 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;">task<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 to: analyze modern approaches and solutions for forming groups of image descriptors in the feature database; formulate the problem of the clustering method in the Multidimensional Cube and the requirements for its optimization; develop an abstract optimization method; develop specific optimization algorithms for different types of model placement in memory; develop metrics; perform experiments and compare the results with analogs. 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;">methodology<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;"> includes analyzing the process of forming groups using different methods, determining their advantages and disadvantages, applying numerical methods to optimize the clustering process, conducting experiments with data sets available on the Internet, evaluating the effectiveness of the optimization method, and generating result tables for comparison with analogs. The following <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;">results<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;"> were obtained: a method for optimizing the clustering of descriptors was developed for use in the Multidimensional Cube data model, and optimization algorithms were developed for model placement in random-access memory and databases. The results of clustering, determined by the metrics of time spent and cluster filling, were compared with those of descriptor clustering performed using the k-means algorithm and the Product Quantization approach with the implementation of Inverted Multi-Index. The results showed that the use of the model with the developed optimizations demonstrates that the quality of descriptor clustering is not worse than when using Inverted Multi-Index and better in terms of time spent than when using k-means or Inverted Multi-Index. <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;">Conclusions<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 developed method for optimizing descriptor clustering significantly improves the distribution of descriptors within the Multidimensional Cube model and makes it a good alternative for use in content-based image retrieval systems.
Copyright (c) 2025 Stanislav Danylenko, Kirill Smelyakov
Method for matching satellite and UAV images for visual place recognition with cross-view color normalization
<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 due to significant viewpoint, scale, illumination, and texture discrepancies. Traditional approaches that rely on handcrafted descriptors or classical local features often fail under such cross-view conditions. 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: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">aims<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 design a robust visual place recognition method for matching UAV and satellite imagery, employing deep learning-based embeddings and advanced color normalization to improve reliability across cross-view scenarios. 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;">tasks<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;"> addressed in this article are: firstly, designing a YOLO-based method is designed for extracting global image embeddings, which utilizes YOLO’s multi-scale feature extraction capabilities to encode semantically significant landmarks in the scene. Second, a novel preprocessing technique based on aligning statistical color distributions between UAV and satellite images was developed and implemented to enhance their visual congruence. Finally, these components are integrated into a complete VPR system and evaluated for effectiveness using the challenging VPAIR dataset, emphasizing urban settings. 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;">methods<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;"> employed include deep learning techniques, particularly fine-tuning a YOLO11 neural network on a dataset specifically annotated for building segmentation. Statistical alignment techniques based on cumulative distribution functions (CDF) were used to standardize image appearances between the two distinct image domains. <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;">Conclusions.<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 experiments demonstrate significant improvements in UAV-to-satellite image matching performance using the proposed method. Fine-tuning YOLO11 specifically for building segmentation resulted in a robust embedding generation method that achieved high segmentation accuracy (F1-score of 0.722). The color preprocessing technique further improved the recognition performance, with Recall@1 reaching 19.5% for urban terrain within a localization radius of 3, substantially outperforming the traditional methods. This study provides an effective solution for UAV localization tasks, particularly in complex urban environments, highlighting the importance of integrated embedding extraction and domain-specific image preprocessing in cross-view visual place recognition.
Copyright (c) 2025 Volodymyr Vozniak, Oleksander Barmak, Iurii Krak
A method for improving the robustness of neural network model for aerial image matching
<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 enhanced generalization relative to conventionally trained counterparts. To our knowledge, this is the first work to combine adversarial procedural‐noise training with activation‐function constraints for aerial image matching. Beyond improved noise resistance, our method advances cross-domain applicability and is readily extendable to diverse neural‐network architectures.
Copyright (c) 2025 Viacheslav Moskalenko, Alona Moskalenko, Yuriy Moskalenko
Using a hybrid attention mechanism as a method to improve the efficiency of network intrusion detection systems
<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: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">aims<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 and study a HybridAttention mechanism based on a combination of global (self-attention) and local (dynamic local attention) models to improve the quality of traffic classification in real-time NIDS. 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;">tasks<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 be solved are as follows: analyzing the applicability of existing attention mechanisms in network intrusion detection; integrating various attention types into a CNN-BiGRU architecture; developing a HybridAttention mechanism based on dynamic window alignment; optimizing the model using Optuna; and experimentally evaluating its performance on benchmark datasets using standard classification metrics. 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;">methods<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;"> used are: deep learning modeling with CNN-BiGRU architecture, integration of various attention mechanisms, including a novel HybridAttention, hyperparameter optimization using Optuna, and performance evaluation based on standard classification metrics. 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;">results<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 show that the proposed HybridAttention mechanism demonstrates superiority over individual types of attention in all key metrics. The model achieved up to 99.85% accuracy on the NSL-KDD dataset training data and demonstrated strong generalization on the UNSW-NB15 dataset, achieving up to 98.06% accuracy in multi-class classification and up to 99.20% in binary classification. The proposed model also outperformed state-of-the-art approaches for processing unbalanced data and detecting various types of attacks. <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;">Conclusions<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 scientific novelty of the results obtained is as follows: a HybridAttention mechanism combining self-attention and dynamic local attention was developed to enhance sequential pattern recognition in network traffic; the CNN-BiGRU architecture was improved by integrating multiple attention modules; systematic hyperparameter optimization using Optuna improved generalization on imbalanced data; and the proposed model outperformed existing approaches on benchmark datasets in detecting both known and novel cyberattacks.
Copyright (c) 2025 Andrii Nikitenko, Yevhen Bashkov
GraphSAGE optimization for insurance risk assessment: balancing performance and efficiency
<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: 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;">aims<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 robust and efficient GraphSAGE-based framework for insurance risk assessment that balances predictive performance with computational efficiency. This is achieved by systematically exploring various GraphSAGE architectures, optimizing hyperparameters, and implementing regularization techniques to prevent overfitting. The effectiveness of different configurations is evaluated through empirical analysis to find the optimal balance between model performance (accuracy) and efficiency (computational speed and memory usage). 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;">tasks<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 be accomplished in this study include: designing and implementing a synthetic graph generation process that accurately represents the complexities of insurance risk data; conducting a systematic exploration of GraphSAGE architectures, varying the number of layers (2, 3, 4) and hidden channels (64, 128, 256); investigating the impact of different learning rates (0.1, 0.01, 0.001) on model convergence and stability; analyzing the effectiveness of various regularization techniques, including dropout (0.1 to 0.5) and weight decay (1e-05 to 0.0001); evaluating different training strategies, including the optimal number of epochs (100 to 300) and the implementation of early stopping; assessing the performance of different loss functions in handling outliers common in insurance data; and developing a comparison framework to facilitate informed decision-making in model selection for insurance risk assessment tasks. 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;">methods<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;"> used in this study are: employing an experimental approach, utilizing the PyTorch Geometric library for implementing GraphSAGE models, deploying the models and testing them on the cloud infrastructure, developing a custom graph generation algorithm to create realistic insurance risk scenarios, incorporating factors such as health scores, smoking status, and regular check-ups, and a grid search strategy for hyperparameter optimization, combined with cross-validation, regularization techniques to prevent overfitting, and employment of early stopping mechanisms. 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;">quantitative results<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;"> were confirmed by generating synthetic graphs that simulate realistic insurance risk scenarios and by conducting experiments to test different model configurations. One key finding is that a 2-layer GraphSAGE model with 128 hidden channels achieved performance comparable to more complex architectures, demonstrating that simpler models can be effective for insurance risk assessment tasks. <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;">Conclusions<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 novelty of the results is as follows: 1) the relatively simple GraphSAGE architectures, such as 2-layer models with 128 hidden channels, can achieve performance comparable to more complex models in insurance risk assessment tasks. This suggests that the inherent structure of insurance risk data may not always require deep, elaborate neural networks to capture essential patterns. 2) the research underscores the importance of tailored regularization strategies, with deeper models generally requiring stronger regularization to combat overfitting. The investigation into training dynamics reveals the role of learning rate selection and early stopping strategies, with shallower models benefiting from higher learning rates, whereas deeper architectures require more conservative learning rates for stable convergence. The consistent performance of the Smooth L1 loss function across various model architectures demonstrates its suitability for insurance risk assessment tasks. 3) a foundation for the effective application of GraphSAGE models in insurance risk assessment is established, emphasizing the importance of a balanced approach to model design that considers not only predictive performance but also computational efficiency and practical deployment considerations.
Copyright (c) 2025 Oleksandr Lutsenko, Serhii Shcherbak
A general method for real-time detection of information threats with a Ukraine case study
<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 disinformation, manipulative campaigns and cognitive influence. 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;">goal<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 to develop and experimentally validate a comprehensive information technology system for automated threat detection in the Ukrainian information space, built on the principles of Responsible Artificial Intelligence (Responsible AI) and modern natural language processing techniques. 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;">objectives<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 formation of a multilingual corpus of news and social media texts; implementation of a sentiment analysis module that incorporates polarity inversion; development of a hybrid thematic classification method that combines keyword dictionaries with machine learning model ensembles; and the construction of a Responsible AI Evaluation (RAIE) framework with indicators for fairness, transparency and user satisfaction. The obtained <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;">results<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;"> confirm all five proposed hypotheses: the developed sentime оьnt analysis module achieves macro-F1 = 0.85 and reduces MAE by 18.2% compared to the baseline model; the polarity inversion detection algorithm allows automatic reversal of sentiment score in manipulative texts, improving the detection of hostile narratives; the hybrid thematic classification achieves macro-F1=0.83, with latency of 55 ms/document and throughput of 18 documents/second; integration of all modules into a unified pipeline improves recall by 10.4% without significant increase in latency; the RAIE conceptual model ensures ΔF1 ≤ 5%, an expert user satisfaction score of 4.14/5 and less than 10% latency overhead. The conclusions demonstrate that the proposed system effectively combines high accuracy in identifying information threats with the principles of ethical AI, transparency and user trust, making it practically valuable for national cybersecurity centres, CERTs and OSINT platforms. <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;">Conclusions.<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 scientific novelty<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;"> <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;">lies in the development of novel methods: a context-sensitive sentiment analysis approach tailored to military-related vocabulary; a polarity inversion algorithm for detecting covert hostility; a hybrid thematic classification model combining machine learning with expert dictionaries; an integrated information processing architecture with >17 documents/second throughput; a Responsible AI evaluation model incorporating Fairness Gap, Model Cards and User Satisfaction Score.
Copyright (c) 2025 Khrystyna Lipianina-Honcharenko, Myroslav Komar, Hennadii Bohuta, Ihor Ihnatiev, Khrystyna Yurkiv, Oleg Illiashenko, Lesia Bilovus
Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
<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 accurate results, as it can account for complex dependencies and adapt to actual conditions, even when they deviate from theoretical assumptions. However, the model requires prior training on a large amount of data and is less well understood in terms of physical laws. General conclusions. The equivalent circuit approach provides high accuracy and reliability under known conditions, but it is limited when external parameters change. The machine learning approach demonstrates better overall accuracy and stability, especially under variable or unpredictable conditions, but requires a large amount of high-quality data and more complex interpretation. Thus, the most effective approach may be a hybrid one, where the analytical model serves as the basis and machine learning is used as a tool for refining or compensating for inaccuracies.
Copyright (c) 2025 Ihor Turkin, Lina Volobuieva, Andriy Chukhray, Oleksandr Liubimov
<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, 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<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 work is to perform a comparative analysis of such models and provide recommendations for their use and to estimate the influence of network traffic fractality on the probability of packet loss in a router due to buffer overflow. <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 tasks to be solved<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; 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;">are<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;"> as follows: 1) to conduct an analysis of analytical models that describe the packet loss probability in a router considering the influence of fractality and without it; 2) to construct the dependencies of the packet loss probability in the router on the data transmission channel load for different buffer capacity values, the Hurst exponent, and traffic deviation; and 3) to describe the dependences of the packet loss probability on the buffer capacity for different channel load values. <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;">Comparative<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;"> analysis of various methods of fractal traffic modeling and simulation with different storage capacity, Hurst exponent, deviation coefficients, and channel load factor values is used. <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 following results were obtained:<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;"> 1) The M/M/1 queuing system model gives the most optimistic estimate. This estimate can be used as a lower bound for the message loss probability for a given buffer capacity and a channel load factor; 2) the highest message loss probability was observed when using queuing systems with a Hurst exponent of 0.95; 3) the packet loss probability also increased with an increasing traffic fractality and deviation coefficient; 4) the influence of fractality decreased with an increase in the buffer capacity was found; 5) an objective estimation of the message loss probability due to a router buffer overflow can only be made by considering the nature of the traffic. <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;">Conclusions<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 main contribution of this research is that various types of network traffic have a fractal nature, and the traditional methods of route service specification, such as traffic using the M/M/1 queuing model, give more errors. Because of the research conducted to reduce the impact of traffic fractality, increasing the capacity of buffer storage devices is necessary.
Copyright (c) 2025 Kyrylo Rukkas, Anastasiia Morozova, Ievgen Meniailov, Myroslav Momot
Information technology for automation of server infrastructure management using DevOps tools
<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 management system that can adapt to dynamic educational and operational requirements. <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;">Methodology<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;">: Python scripts have been utilized to generate Terraform configurations, thereby facilitating the creation of servers within the Hetzner cloud provider. The script employs the DigitalOcean API to automate Domain Name System (DNS) records, while Ansible is employed to ensure consistent server configurations. MySQL plays a pivotal role in providing real-time infrastructure monitoring and 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;"> Scientific Novelty<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 proposed system represents a significant advance in the field of scientific innovation by addressing the critical issue of infrastructure as code (IaC) optimization. It achieves this advancement by employing a formal M/G/с queue model, a methodical approach that has been empirically validated through analytical and experimental analyses. The efficacy of this model is evident in its ability to reduce deployment time by 50% compared to conventional IaC tools such as Puppet, Chef, and Ansible. Furthermore, its superior performance is pronounced, with a 90% reduction in deployment time when compared to manual methods. <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;">Results<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 results of the experiment show that when using the Terraform infrastructure management tool, the deployment time of computing nodes remains unchanged regardless of their number. Specifically, deploying both two and five servers on the Hetzner platform takes an average of 270 seconds. This indicates a high degree of process parallelism and the scalability of the solution at this stage of infrastructure initialization. The configuration process is completed in 30-40 seconds. These results indicate a 90% reduction in configuration errors and an 80% reduction in costs for deploying 100 servers per month for laboratory or test tasks. The script allows for the execution of server templates only when necessary, for example, during laboratory sessions. The startup time is 4 minutes and 30 seconds, which enables the rapid provision of a working number of servers, sites, or applications for training. <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;">Conclusions<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 system has been shown to enhance deployment efficiency, reduce operating costs, and broaden the range of possible applications in education, scientific research, and business. <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;">Future 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;">: Planned enhancements include multi-cloud integration (AWS, Google Cloud) for improved resilience, Kubernetes orchestration for containerized workloads, a web-based management interface to enhance usability, and machine learning–based predictive analytics for optimized resource scaling. These upgrades will expand the system’s flexibility and applicability.
Copyright (c) 2025 Ivan Byzov, Sergiy Yakovlev