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» ERDPOU Code: 02066769 ROR of the founder: https://ror.org/048j5n646 DOI Prefix: 10.32620/recs 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 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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To 70 anniversary selebration of Professor Heorgii Kuchuk
Copyright (c) 2026 Vyacheslav Kharchenko
Information technology support for management decisions on the portfolio of dual education development projects
The article's subject is information technology for decision-making support in the processes of sustainable development of educational institutions, as well as a model for forming a rational portfolio of dual education development projects. The work aims to improve the efficiency and quality of management decisions when planning dual education learning processes in conditions of uncertainty by creating information technology for decision support using a portfolio model of action areas. This study has several objectives: 1) To develop a model for forming a portfolio, taking into account the hierarchical structure of the projects value criterion and the positions of stakeholders; 2) To formalize the division of project options into groups to which different development strategies should be applied; 3) Create an interactive visualization and dashboard for advanced analysis of candidate projects for the portfolio. Results. Technology is proposed to classify and divide project options into groups to which different strategies should be applied, and a combined portfolio of different directions (vectors) of action should be managed. A heuristic search optimisation methodology synthesizes a new algorithm for identifying and comparing situation characteristics for selecting non-dominated portfolios. Combining multi-criteria decision analysis (MCDA) and visual methodology simplifies evaluating and comparing candidate projects for the strategic portfolio. This makes this approach valuable for a group of developers in practical scenarios. Customised cross-filtering of visual report data using Power BI made it possible to compare projects by stakeholder groups, candidate projects, and those being successfully implemented. This ensured the management of a combined portfolio of different areas (vectors) of action. The proposed technology is demonstrated by selecting strategies for developing dual education projects by employer partners. Data from previous partnerships with regional companies helps the decision-making group adjust project strategies. Recommendations for action can be created even without information about the probability of a scenario. The conclusions of this study can be used in practice in complex decision-making scenarios that consider the interests of various stakeholders in the dual education system, the uncertainty of situations, and the subjectivity of decision-makers.
Copyright (c) 2026 Svitlana Kozyr, Valentyna Molokanova
An efficient threshold method for detecting R-peaks in electrocardiogram
Automated R-peak detection in electrocardiogram (ECG) signals is essential to heart rhythm analysis, with applications in heart rate monitoring, heart rate variability (HRV) assessment, arrhythmia diagnosis ets. Its accuracy, however, is highly sensitive to noise and artifacts present in the ECG recording. The study proposes a method and its software implementation for R-peak detection, based on signal smoothing followed by differentiation and the application of a thresholding approach. The method is designed for use in resource-constrained environments, such as portable and embedded monitoring systems. The objective of the study is to develop a computationally efficient and accurate method for the automatic detection of R-peaks in ECG signals, tailored to a personalized patient approach. The primary focus is on robustness to noise and QRS complex morphology, as well as the algorithm’s ability to operate under limited computational resources and in real-time conditions. The proposed method relies on staged ECG signal processing. To validate the approach and compare its effectiveness, existing studies in the field were analysed. The subject of the study involves processing ECG signals and addressing challenges in R-peak detection in ECGs recorded via Holter monitors by constructing a smoothed continuous signal based on discrete data obtained during digitization. The discrete nature of the initial signal complicates differentiation and the precise identification of characteristic points, specifically R-peaks, which play a crucial role in diagnosing cardiac conditions. The scientific novelty of this investigation lies in the use of a second-order piecewise polynomial approximation to represent the ECG signal. This approach enables noise reduction in the signal and represents the discrete signal as a continuous function together with its first derivative, thereby permitting the analytical computation of its derivative. Results involve the detection of R-peaks by analysing the derivative of the smoothed signal: regions with sharp changes characteristic of the QRS complex were identified, and an iterative smoothing scheme was developed, with the number of iterations determined by a proposed stopping criterion. The proposed method was implemented in software and tested on data from the open-access MIT-BIH Arrhythmia Database, which includes over 60 recordings and more than 100,000 annotated R-peaks. The results were compared with studies by other authors using the metric, based on standard precision and sensitivity metrics. Conclusions: The study proposes an effective and adaptive approach to ECG signal processing, ensuring reliable R-peak detection under conditions of significant noise, baseline drift, and physiological variability across patients. The obtained results demonstrated high performance metrics: up to 99.5% (average above 99.1%), consistently exceeding 99%, and in some recordings reaching 100%. Thus, the proposed approach is competitive, demonstrating high accuracy in detecting R-peaks in cases of arrhythmias, peak inversions, non-standard QRS complex morphology, and other challenging signal conditions. The study’s results can be applied in ECG analysis practice, particularly in the development of automated diagnostic systems or signal preprocessing before the application of classification methods.
Copyright (c) 2026 Mykola Yefremov, Andrey Liashko, Iurii Krak, Oleg Stelia
Modeling of an attack mission by strike drones under active enemy counteraction
The rapid growth of innovation in modern warfare has led to the need to consider the enemy's ability to build defenses using modern anti-drone measures when planning offensive missions with strike drones. Therefore, researching preventive actions is relevant when planning a massive attack aimed at overcoming obstacles to the movement of strike drones to enemy targets. The publication focuses on the development of analytical and simulation models to plan an attack while minimizing the risks posed by enemy anti-drone actions. The goal of this research is to develop a set of mathematical and simulation models for planning the movement of a swarm of attack drones under active enemy countermeasures. The tasks that need to be solved are as follows: systematically analyse actions for planning an attack mission in conditions of dynamic changes in the battlefield; select a subset from a set of relevant enemy targets for attack; create the required number of strike drones for attack in conditions of limited capabilities; create confusing trajectories for strike drones to successfully hit enemy targets; develop an algorithm for forming a flight route with minimal risk of drones being shot down (lost); and create a simulation model to simulate the movement of a swarm of strike drones towards targets under active enemy countermeasures. Mathematical methods and models used: system analysis to create a logical sequence of actions for conducting an attack with strike drones; qualitative and quantitative assessment of the characteristics of possible enemy targets; lexicographic ordering of options for selecting a subset of relevant enemy targets; integer (Boolean) to calculate the number of strike drones that will be involved in the attack; agent-based simulation modeling to form the movement of strike drones in enemy countermeasure conditions. The following results were obtained: a proposed systematic representation of the planning of an attack mission with strike drones under conditions of dynamic changes in circumstances on the battlefield; a justified selection of a subset of relevant targets for a massive attack; the required number of attacking drones was calculated under conditions of limited capabilities; an algorithm was developed to create a route for strike drones with minimal flight risks; and an agent-based model was created to simulate the flight of a swarm of strike drones for various scenarios of active countermeasures by the breacher. Conclusions: The results of the study provide a scientific basis for developing a plan for an attack mission by a swarm of strike drones under active enemy countermeasures, thereby facilitating the integration of air and ground combat operations and enabling the successful execution of the planned military operation. The novelty of the proposed approach lies in the scientific justification of the plan for an attack mission using strike drones in the face of active enemy countermeasures and dynamic battlefield conditions, supported by a developed set of original analytical and simulation models.
Copyright (c) 2026 Oleg Fedorovich, Oleksandr Prokhorov, Dmytro Krytskyi, Olga Malyeyeva, Oleksandr Leshchenko, Roman Trishch
Third-party library selection in IT projects under imperfect data using Dempster–Shafer theory
This study focuses on a method for selecting third-party libraries for IT projects, which involves systematizing evaluation criteria and applying Dempster–Shafer theory of evidence to model imperfect data. Imperfect data refer to incomplete, contradictory, unreliable, uncertain, imprecise, or ambiguous information. The goal is to enhance the validity and reliability of library selection and replacement decisions in the ever-changing landscape of modern projects while also minimizing associated risks. The tasks to be solved include: providing a systematic classification of evaluation criteria that comprehensively characterize third-party libraries; developing a selection method of third-party libraries that incorporates evaluations as imperfect data; integrating and combining evaluations from multiple heterogeneous sources; and demonstrating the application of the proposed method through an illustrative example that establishes confidence intervals for alternative libraries. The methods used are based on the Dempster–Shafer theory of evidence for modeling imperfect data and the Dubois–Prade disjunctive consensus rule for combining evaluations from independent sources. The results show that the proposed approach transforms subjective and imperfect evaluations into evidence, combines them according to the selected rule of evidence theory, and derives confidence intervals that express both guaranteed and possible degrees of support for each library alternative. This study confirms the effectiveness of applying Dempster–Shafer theory of evidence in multi-criteria decision-making contexts that resemble real-world project environments. Conclusions. The scientific novelty of this study lies in proposing, for the first time, a method for selecting third-party libraries based on the Dempster–Shafer theory of evidence, distinguished by a systematic taxonomy of evaluation criteria, including risk factors, and by combining evidence in support of candidate tools using the Dubois–Prade disjunctive consensus rule. The developed method extends the analytical capabilities of project decision-support systems by enabling comprehensive evaluation and risk-informed selection of third-party libraries in complex, dynamically evolving technological environments.
Copyright (c) 2026 Alexander Lysenko, Igor Kononenko
Method of ranking athletes in a decision support system in the process of preliminary selection of players
The topic of the article is the method of ranking athletes in a decision support system in the process of preliminary selection of players. The purpose of the is to develop an athlete ranking method that enables the integration of all available information types, facilitating informed decision-making, as selection accuracy directly impacts the quality of the resulting team. Research objectives: investigate the information types utilized in athlete ranking and identify appropriate methods for data representation and processing; to develop a method based on specific metrics, production rules, weighting coefficients, and an artificial neural network (ANN) model to generate player ratings within a DSS; to conduct an experiment to validate the effectiveness of the proposed method. Research results: The article proposes a hybrid ranking method that integrates a deterministic component (IF-THEN production rules) with an adaptive ANN model. This architecture combines expert heuristic knowledge with automated data-driven learning, ensuring both decision explainability and the model's robustness in complex conditions. At the first level, the system ranks candidates using predefined logical rules derived from expert assessments by coaches and analysts. These rules process formalized metrics such as physical development, fitness, and competition history, enabling initial screening based on basic functional suitability criteria for a specific position. At the second level, an ANN module is integrated to perform a multidimensional analysis of the players' numerical characteristics. By identifying latent non-linear dependencies between tactical and technical action indicators and anthropometric variables, the model provides a predictive assessment of overall potential. This approach is particularly effective in scenarios where manually formulated rules cannot capture the complex interdependencies between variables. The final output integrates a binary classification (fit/unfit) with a quantitative rating generated by the neural network. These results can be mapped to a formalized scale for further ranking or utilized as primary input for final coaching decisions. This structure ensures an optimal balance between interpretability, accuracy, and adaptability within the volleyball player selection process. Conclusions: The scientific novelty lies in combining a structured system of TTA indicators with role-specific weighting obtained through expert consensus, allowing for contextual adaptation. Additionally, the integration of a neural network component to identify latent dependencies between performance indicators dynamically represents a novel approach for short-term selection in volleyball. Further research will focus on developing adaptive player models and optimizing selection processes to enhance the effectiveness of decision-making within the DSS.
Copyright (c) 2026 Yelyzaveta Hnatchuk, Tetiana Hovorushchenko, Alina Hnatchuk
Intelligent technologies for target detection and engagement by UAVs based on artificial intelligence and simulation data
The subject matter of the article is the methodology for developing integrated intelligent systems that enable automated detection, classification, tracking, and engagement of targets by unmanned aerial vehicles (UAVs) using artificial intelligence, computer vision, and simulation modeling technologies. The goal is to design an architecture and experimental environment for evaluating the efficiency of deep learning models, particularly the YOLO family, in real-time operation across different hardware platforms, considering resource and power constraints, as well as to quantitatively assess improvements in key system characteristics. The tasks to be solved are: developing a microservice-based system architecture using Kubernetes and ROS2; creating specialized datasets for model training; integrating the AirSim, ArduPilot SITL, and Mission Planner simulators into a unified testing environment; and conducting comparative performance analysis of various YOLO versions on platforms ranging from low-power ARM/NPU to high-end GPU. The applied methods are simulation modeling, automated neural network training, model quantization, inference optimization using TensorRT, and statistical analysis of the obtained metrics. The following results were obtained. A unified experimental methodology and a hardware–software platform were developed, providing a complete research cycle—from data generation and annotation to model performance evaluation. Experimental tests demonstrated that YOLOv8n and YOLOv11n offer the best balance of accuracy and speed on low-resource platforms. The most effective onboard solutions were identified as the Google Coral Dev Board Micro and NVIDIA Jetson Orin Nano. A simulation testbed was deployed that successfully models UAV flight, target acquisition, and engagement processes. Conclusions. The results confirmed the feasibility of using simulation data and a microservice-based approach for developing autonomous intelligent UAV control systems, demonstrated measurable improvements in performance, computational efficiency, and energy consumption, as well as provided practical guidelines for selecting the optimal “model–platform” configuration. The scientific novelty of the obtained results lies in the creation of a comprehensive research environment that integrates modern artificial intelligence tools, simulation modeling, and hardware optimization into a unified reproducible structure, enabling objective evaluation of target detection and engagement algorithms in real time.
Copyright (c) 2026 Igor Sinitsyn, Anatoliy Doroshenko, Ivan Kyrylov, Iaroslav Omelianenko, Valentyn Smirnov, Olena Yatsenko
Simplified convolutional model for detecting vibration anomalies in helicopters operation
The challenge in vibration analysis for helicopters is to automate anomaly detection to indicate possible equipment failure, especially in high-stakes scenarios where labelled failure data is rare and highly desirable for training modern models such as convolutional neural networks or autoencoders which require a lot of such data and are often used for this kind of analysis. The study focuses on vibration data generated by helicopters, focusing on anomalies in these vibrations that may indicate imminent equipment failure. These vibrations are often complex and change under different operating conditions, making them a valuable source of diagnostic information. Objective and Approach. This study presents a novel model for detecting abnormal vibrations in helicopter operations, minimising dependency on failure data. The model uses Fast Fourier Transform (FFT) and convolutional techniques to reduce high-dimensional vibration data into a three-dimensional vector, enabling effective anomaly detection through distribution metrics and Z-score-based thresholding. Case Study. The model was evaluated using a real-world dataset of helicopter vibration signals. The study successfully differentiated between normal and abnormal conditions without relying on explicit failure labels, validating its applicability in maintenance scenarios where early fault detection is critical. Method. Model was developed using Fast Fourier Transform (FFT) and convolutions to reduce vibration data to a low-dimensional vector representation that allows anomaly detection using distribution metrics. This method increases adaptability and reduces data requirements, making it suitable for expensive equipment with limited or no fault data. In addition, this model can be very flexible and extended to a model with additional training. It uses the Fast Fourier Transform as the first step to get the frequency components of a signal buried in vibrations. After FFT, we can represent the vibration data as a multidimensional vector. An example can consist of around 45000 dimensions after that vector simplifies to reduce the dimensions. The model uses convolutions to get only a 3-dimensional vector from the 45,000-dimensional vector. After convolutions, the data saves a clear correlation, and it is still possible to represent the vibration sample as a vector in 3-dimensional space and save its properties. Three dimensions were chosen for visualisation comfort, and it still works correctly. These vectors are located in space in two separate clusters for 0 and 1 corresponding labels, and they can be used for the next iteration of analysis. On the next iteration, the model gets a mean from x,y, and z and checks the distributions for class 0 and class 1. This step is possible because values after all convolutions for x,y, and z are close to each other. As distributions of these vectors are close to a bell shape, the model uses distribution metrics to predict fault by using a Z-score of the distribution of normal vectors. In this case, the model is close to the zero-shot learning concept because the model requires only data about expected behaviour. Results. The developed model showed high accuracy at a particular configuration when using different matrix convolutions and a different approach to setting the limit of anomalous values. At the final stage of the testing, the model reached an accuracy of 99.568%, which, under specific conditions, is not considered an overfitted model since the initial input parameters were clearly defined and limited by specific data. The model also demonstrated that when changing the input conditions, the model can be easily configured to new parameters. Discussion. The proposed model proved highly adaptable and accurate in detecting anomalies with minimal historical data. While it showed robustness across different configurations, future improvements could focus on refining sensitivity thresholds and integrating additional predictive techniques for enhanced performance. Conclusions. Tests validate the model's adaptability and scalability, recommending its use for fault detection in inexpensive, data-limited environments like helicopters.
Copyright (c) 2026 Yurii Hodlevskyi, Tetiana Vakaliuk
Integrated hardware–software complex for wind turbine blade defect classification
The subject matter of this article is an integrated hardware–software complex for real-time detection and classification of wind turbine blade defects using an unmanned aerial vehicle (UAV) equipped with onboard edge-AI modules. The goal of the study is to develop and experimentally validate an autonomous embedded framework capable of collecting, processing, and interpreting blade-surface image data under field conditions while maintaining sufficient diagnostic accuracy and real-time performance on resource-constrained hardware. The tasks of the study are to design the architecture of the UAV-based inspection system; to implement an onboard processing pipeline including image acquisition, preprocessing, defect detection, classification, and georeferenced data association; to develop a lightweight YOLO-based visual detection stage combined with a neuro-fuzzy decision module; and to evaluate the diagnostic and computational performance of the proposed system on Raspberry Pi 5. The methods used include lightweight deep-learning-based visual analysis, neuro-fuzzy inference based on Gaussian membership functions and Wang–Mendel-type adaptation, telemetry-assisted defect georeferencing, and experimental evaluation under embedded deployment constraints. The results obtained show that the developed prototype provides stable onboard operation at 5–6 FPS for 640×480 video, supports local processing without cloud infrastructure, and ensures reliable defect-state recognition for four predefined blade-surface classes: Erosion, Corrosion, Crack, and Pristine. In the integrated validation workflow, the system achieved 88.1% classification accuracy, while the neuro-fuzzy decision module evaluated separately on YOLO-derived class-coefficient vectors achieved 94.0% accuracy with an MSE of 0.0686. Conclusions. The scientific novelty lies in the development of a fully autonomous UAV-based diagnostic architecture that combines onboard visual detection, telemetry fusion, and neuro-fuzzy decision support on limited embedded hardware, as well as in the adaptation of the neuro-fuzzy classifier for real-time multiclass blade-condition assessment
Copyright (c) 2026 Oleg Zastavnyy, Lesia Dubchak, Volodymyr Kochan, Oleg Savenko, Zenovii Bernas, Nadiia Vasylkiv
Multimodal detection of urban improvement indicators in public visual-text content using deep learning
The subject matter of the article is the automatic identification of user generated content about urban infrastructure improvement and construction within very large streams of social media and group messages, with a focus on filtering actionable posts from overwhelming background noise so that experts can see what needs restoration or investment first. The goal is to design, implement, and validate a multimodal deep learning system that receives any image and its accompanying text and decides whether the pair is relevant to city improvement topics such as roads, parks, buildings, lighting, cleanliness, accessibility, or playgrounds, and to provide a practical content filter that reduces manual triage for municipal teams. The tasks to be addressed are to base training on a single large scale public corpus rather than collecting new data, to define a clear relevant versus irrelevant decision target, to construct a multimodal neural architecture that learns from both visual content and written description, and to evaluate the approach against single modality baselines under the same conditions. The methods used rely on the Wikipedia based Image Text dataset known as WIT, which contains more than thirty-seven million image text examples with entity rich captions from many languages and is available for download on the Hugging Face platform, and on a fusion of a convolutional or vision transformer backbone for images with a transformer-based encoder for text that together form a unified classifier trained with standard supervised learning. Conclusions. Experiments show that the proposed approach accurately pinpoints posts related to urban improvement and that the multimodal design clearly surpasses the single modality baselines in the same conditions, which confirms that combining image evidence with textual context is advantageous for this filtering task and supports practical deployment as an automated screening stage. Scientific novelty lies in applying multimodal deep learning to the specific problem of real time content filtering in the urban planning domain, in framing the target as detection of relevant civic information within public image text communications using a single widely available corpus for training, and in demonstrating that a simple but principled fusion of computer vision and natural language processing can distill actionable information from very large volumes of online messages without manual collection of new training data.
Copyright (c) 2026 Oleksandr Poplavskyi, Yuliia Riabchun
Sentiment analysis of trending tweets using Spark NLP and deep learning: a benchmark study of CNN vs transformer models
This study investigates the implementation of different sentiment analysis models, exploring their theoretical foundations, robust evaluation criteria, and significant findings, and integrating natural language processing methods to preprocess data collected from social media, the main source of information. The goal of this study is to present notable advancements in sentiment classification by implementing innovative models, including Convolutional Neural Networks and Transformer-based architectures such as BERT, DistilBERT, and RoBERTa, with Spark NLP for preprocessing. The tasks to be performed include: collecting the dataset from Twitter/X focusing particularly on trending topics that appear daily; performing preprocessing steps using Spark NLP, a prominent and scalable NLP library built on Apache Spark to handle scalable and distributed processing of textual data; assigning the polarity for each tweet by applying VADER lexicon-based tool; pretraining both TextCNN and Transformer-based models on lexicon-based VADER labels under identical parameters; fine-tuning the models on manually annotated tweets; comparing their effectiveness, evaluating their strengths, weaknesses, and overall performance in sentiment classification that can guide model selection in resource-constrained settings. The methods used include Spark NLP pipelines, lexicon-based weak labeling, two-phase supervised learning on Convolutional Neural Networks (TextCNN), and Transformer-based models (BERT, DistilBERT, RoBERTa) with early stopping and learning-rate warm-up strategies, and finally the comparative evaluation metrics. The dataset used in this work (12,422 tweets, including 3,475 manually labeled tweets) is not large. The use of Apache Spark enables distributed data processing and supports scalability for larger datasets. The results show that the Transformer-based models outperformed TextCNN model in terms of classification accuracy and robustness. RoBERTa achieved the highest accuracy (85%), followed by BERT and DistilBERT (84%) and TextCNN (72%). DistilBERT balances the predictive performance and computational efficiency well. Conclusions. The scientific novelty of this study focuses particularly on the integration of Apache Spark NLP preprocessing, followed by a benchmarking of CNN and Transformer-based models trained on trending tweets in a hybrid two-phase learning strategy that combined lexicon-based weak supervision with human annotation under the same experimental conditions to provide methodological insights and select appropriate model in our sentiment classification experiments.
Copyright (c) 2026 Chaimaa Benyamani, Hassan Badi, Imad Badi, Aziz Khamjane
Toward self evolving quality assurance frameworks for AI driven intelligent energy management software
The subject matter of the article is the processes of designing and validating a self-evolving quality assurance (SEQA) framework for artificial intelligence (AI)-driven intelligent energy management software (IEMS). The goal is to develop a scalable and adaptive SEQA framework that enables continuous optimization and reliability- and trustworthiness-oriented quality assurance in dynamic, heterogeneous operational environments. The tasks to be solved are: to formalize a unified QA architecture integrating reinforcement learning for adaptive control and federated learning for distributed calibration; to develop a robust cross-domain adaptation mechanism ensuring energy-aware trust calibration; and to empirically validate the framework's performance against baseline models across multiple real-world energy datasets. The methods used are: reinforcement learning for policy-driven optimization, federated learning for privacy-preserving model aggregation, trust calibration techniques for reliability assessment, and experimental benchmarking on NASA, UCI, and OPSD datasets. The following results were obtained: the proposed SEQA framework successfully integrates reinforcement learning-based local adaptation with federated policy aggregation, achieving continuous self-evolution of QA performance across heterogeneous energy management scenarios; the cross-domain adaptation mechanism ensures robust generalization capability, with F1-scores exceeding 0.86 and reliability remaining above 0.91 under diverse operational conditions; experimental validation demonstrates consistent improvements in reliability (F1-score increases by 6–8%), calibration accuracy (Expected Calibration Error reduced to 0.024), and energy efficiency (up to 13%) compared to baseline QA models; the framework maintains stable performance under dynamic data distributions, with ablation studies confirming that each component—reinforcement learning, federated evolution, and continual replay—plays a critical role in enabling robust self-evolving quality assurance. Conclusions. The scientific novelty of the results obtained is as follows: 1) the proposed SEQA framework introduces a unified adaptive paradigm that synergistically combines reinforcement learning, federated calibration, and cross-domain adaptation, enabling autonomous, continuous quality evolution in IEMS; 2) the developed cross-domain mechanism achieves robust generalization and energy-aware performance balancing, addressing key limitations of static and single-domain QA approaches; 3) the extensive experimental validation demonstrates consistent improvements in reliability, calibration accuracy, and energy efficiency, confirming the framework's practical applicability for long-lifecycle industrial deployments; 4) the integration of adaptive aggregation intervals and policy pruning mechanisms minimizes redundancy during synchronization while maintaining near-linear scalability on distributed federated nodes, validating the framework's feasibility for deployment in real-world smart grid and industrial IoT environments
Copyright (c) 2026 Andriy Verlan, Wang Zhihai
Accelerating A* algorithm based search in time-dependent graphs with learned heuristics
Finding the shortest path in busy urban transport networks represented as time-dependent graphs is a complex task that demands algorithms with low computational complexity. Static heuristics based on geometric distance do not account for traffic dynamics, which limits the effectiveness of the A* algorithm. The subject of the study is methods for increasing the efficiency of the A* search algorithm by creating heuristic functions based on machine learning. The goal of the work is to reduce the number of node explorations of the A* algorithm in a time-dependent graph by training the heuristic function. The research tasks are to conduct a comparative analysis of machine learning models that can be used as a heuristic function (LightGBM, GNN, TGNN); develop a hybrid method where TGNN predicts the future physical state of the system; and experimentally evaluate the efficiency gain. The main contribution is a hybrid approach where a Temporal Graph Neural Network (TGNN) predicts the future speed at neighboring nodes, which is then integrated into a classical geometric formula to compute an admissible and consistent heuristic. This resolves the contradiction between the high average accuracy of ML forecasts and the strict requirements for A* consistency. The models were evaluated based on three key criteria: admissibility, consistency, and search space reduction. The best model was selected as the one satisfying all criteria while achieving the greatest reduction in search space. The model was trained on real GPS data and tested on the road graph of the city of Irpin (2,400 nodes, 5,600 edges). The TGNN-based heuristic reduced the search space by 41.8% compared to Dijkstra and by 27.9% compared to the classical heuristic, with an average execution time of 0.44–0.65 ms per query and a 99.92–100% optimality rate across multiple routing scenarios. Conclusions. The scientific novelty lies in proving that reformulating the ML model’s task from predicting total travel time to predicting a physical parameter (speed) effectively resolves the contradiction between ML forecast accuracy and A* consistency requirements, enabling the creation of adaptive heuristic functions for time-dependent pathfinding.
Copyright (c) 2026 Andrii Liashenko, Larysa Globa
Enhancing terahertz images corrupted by compact impulse noise: feasibility and practical recommendations
The subject matter of this article is the process of enhancing low-quality terahertz images through digital image processing. This study aims to investigate whether the visual quality of terahertz images corrupted by compact impulse noise can be improved while preserving their information value. The tasks to be solved are as follows: 1) to analyze impulse noise in real-life terahertz images obtained by raster scanning systems and to evaluate the typical range of its probabilities and localization features; 2) to create an adequate model for compact impulse noise generation; 3) to establish a relation between the probability of impulse noise, image spatial resolution, filtering settings, and the quality of the output images, and give the corresponding recommendations; and 4) to verify the obtained results on real-life terahertz data. The methods used are: mathematical modelling, numerical simulation, and statistical analysis. The following results were obtained. 1) The shape and localization characteristics of compact impulse noise in terahertz images acquired using a single-detector direct imaging setup were studied, and an appropriate generative statistical model was implemented. 2) The possibility of suppressing stripe-shaped compact impulse noise using a classical median filter and its modification with spatial adaptation was investigated through numerical simulations on a set of test images. 3) It was shown that for images no less than 100×100 pixels large and impulse noise probability up to 0.5, filtering with vertically oriented rectangular windows (9×1 to 15×1 pixels) allows quality improvement up to 45 dB according to PSNR and PSNRHVSM metrics, providing effective noise suppression while preserving object details. 4) The formulated recommendations’ adequacy was verified on real-life terahertz data, and the proposed approach’s workability in the presence of other distortions was confirmed. Conclusions. The scientific novelty of the obtained results is in the confirmation of the possibility of reliable restoration of terahertz images severely distorted by compact impulse noise without their re-acquisition along with providing practical recommendations for filtering settings and the requirements to the input terahertz data.
Copyright (c) 2026 Viktoriia Abramova, Sergiy Abramov, Linas Minkevičius, Ignas Grigelionis
Extending spectral indices from multispectral satellite data using U-Net segmentation
This study focuses on developing a unified, reproducible cloud-to-model pipeline for parcel-scale cropland delineation from multispectral Sentinel-2 and Landsat imagery in the Kur–Araz region. The goal of this study is to produce accurate, boundary-faithful, and computationally efficient farmland maps that remain transparent, scalable, and deployable on commodity hardware for use by government agencies, water authorities, and agricultural producers. The tasks to be addressed include the following: specification of the study scope, data sources, and evaluation protocol that integrates pixel-wise and boundary-sensitive accuracy metrics; construction of a multi-index feature stack in addition to surface reflectance bands, followed by screening of features for cross-seasonal stability; design and training of a memory-efficient U-Net architecture with a hybrid loss that simultaneously balances calibration and overlap; and validation of model generalization across different cropping seasons and neighboring subregions of Kur–Araz, with complete provenance tracked from preprocessing through evaluation. The methods used in the study include cloud-native preprocessing in Google Earth Engine, consisting of cloud masking, seasonal compositing, medoid and percentile mosaicking, and stratified patch sampling with spatial blocking. The datasets were exported to TFRecords and used to train a compact U-Net encoder–decoder network with skip connections and a hybrid objective function. Training involved strong normalization, data augmentation to account for agricultural scene variability, overlap-tiling for inference, and three ablation studies to isolate the contribution of individual vegetation indices. Complete logging of assets, random seeds, and parameters supported exact reruns of the pipeline, replication, and auditing. Conclusions. The scientific novelty of the results obtained is as follows: (1) a unified, provenance-complete pipeline was developed that directly integrates Google Earth Engine preprocessing with deep learning model training and boundary-aware evaluation, ensuring full reproducibility and auditability; (2) a resource-efficient U-Net architecture with skip connections and a hybrid overlap-weighted loss was designed and validated, which preserves parcel borders while maintaining calibration, outperforming heavier transformer-based backbones under limited compute budgets; (3) the complementarity of multi-index feature stacks was systematically quantified through ablation studies, demonstrating their role in mitigating spectral saturation and soil brightness effects and improving overlap fidelity. Finally, a standardized evaluation protocol was proposed that combines pixel-wise and boundary-sensitive metrics, allowing robust assessment of farmland delineation performance across seasons and spatial subregions.
Copyright (c) 2026 Artughrul Gayibov, Vagif Gasimov
Mathematical modeling of the thermal stress state of a ball with an eccentric heat-active spherical inclusion
Today, technical systems with internal heat generation are developed and used in various areas of industry, energy, construction, as well as in microelectronics, nanotechnology, biomedicine, technical chemistry, ecology, etc. The creation of such systems is usually preceded by their mathematical and computer modeling, in which issues of strength often play a primary role. The use of purely numerical methods for this purpose does not always give results of the required accuracy, and the impossibility of parametric description of the model does not allow solving optimization problems with them. Therefore, the creation of new methods for modeling such systems and their models is an actual scientific and practical task. The subject of the research of the present article is mathematical models of the thermoelastic state of a ball with a spherical inclusion having a heat release region, as well as methods for obtaining them. The goal of the study is to create a number of parametric models for studying temperature fields and stress fields in a piecewise homogeneous sphere with an internal heat-active region under different thermomechanical conditions on its boundary. To achieve this goal, it is necessary to solve a number of problems: to perform further development of the generalized Fourier method for the class of axisymmetric stationary thermoelastic problems for a ball with an eccentric heat-active spherical inclusion, using the developed method, obtain a number of models of the thermoelastic state of a ball in cases of eccentric and concentric inclusions with a certain area of heat generation in them, perform a rigorous justification of the proposed approach, conduct a wide computer experiment with the constructed models and an analysis of the thermomechanical characteristics obtained with its help, and draw conclusions based on the results of the study. The modeling methods used in this work are the generalized and ordinary Fourier methods. The following results were obtained in the work. A number of mathematical models of the axisymmetric stationary thermoelastic state of a ball with a spherical inclusion were constructed under the condition that the entire inclusion or part of it releases heat according to a harmonic law. The cases of a stationary surface of a ball and a constant temperature on its boundary, as well as a ball whose surface is loaded with normal pressure and is in conditions of heat exchange with the environment are considered. Modeling is carried out using the generalized Fourier method, which was further developed in the work. Determination of the parameters of the models is reduced to solving infinite systems of linear algebraic equations. For the two specified types of models, the case of concentric inclusion is considered separately. Linear algebraic systems of the fourth order are obtained to determine the parameters of the models. Using a new classical inequality, lower bounds for the moduli of the determinants of systems and the existence classes of the resulting models were obtained for the first time. Separately, a thermoelastic model of a ball with a spherical thermally active layer in the inclusion was obtained in a closed form. Different types of ball and inclusion materials were used in the numerical simulation. Calculations were performed for temperature fields and stress fields with changes in geometric parameters, values and density functions of heat sources. Rich graphic material was obtained and analyzed in the work. Scientific novelty: all the above results are new. Conclusions were made based on the results of the studies.
Copyright (c) 2026 Oleksii Nikolaev, Alina Krainychenko, Mariia Skitska
Mathematical modeling of combat operations with planning of target distribution coefficients for one of the opposing sides
The subject of the study is models of combat operations. The purpose of this study is to build mathematical and computer models to identify the optimal target distribution coefficients of the active side in order to inflict the greatest losses on the enemy at a given time for the case when the sides have several types of weapons and direct fire at each type of enemy combat units with some target distribution coefficients. Objectives: 1) to consider the case when the active side has one type of weapon, and the other side has two dissimilar types of weapons; 2) to generalize the problem to the case when the active side has n dissimilar types of weapons, and the other side has m dissimilar types of weapons; 3) to conduct a detailed study of the solution of the problem in the case when the sides have two dissimilar types of weapons. The following results were obtained: 1) a mathematical and computer models for the first case of the problem were built, the admissibility of the problem parameters was analyzed, and the corresponding numerical calculations were presented; 2) a mathematical model for the second case of the problem was created, the admissibility of the problem parameters was analyzed; 3) a mathematical and computer model for the third case was built and studied, the corresponding numerical calculations were presented, and the analysis was performed. Conclusions. The examples considered in this paper illustrate that, using a computer model, it is possible to predict how to allocate combat resources to fight the enemy to achieve success in combat at a given time if the enemy has two types of combat units. The problem has a solution in the general case, which requires maximizing a function of many variables at admissible lattice nodes, whose coordinates are the first side's objective coefficients. The paper shows how to find these admissible nodes. Numerical calculations before generalizing the problem demonstrate that it is possible by changing the time to predict the battle even in cases where the parties have several types of weapons.
Copyright (c) 2026 Oleksandr Fursenko, Nataliia Chernovol, Olha Udodova, Yaroslav Savchuk
Hybrid model for assessing competencies of cybersecurity service staff of civil aviation enterprises using gamification and fuzzy logic elements
The article focuses on improving cybersecurity in civil aviation by introducing a hybrid model that assesses cybersecurity service employees' competencies through gamification and fuzzy logic. The goal of this study is to develop a hybrid model for assessing personnel competencies in the cyber and information security services of civil aviation enterprises. The tasks to be solved are: to analyze the problem of aging knowledge and competencies of specialists; to develop a hybrid model for assessing competencies; to justify the model's feasibility through experimental testing at an airline; and to propose a taxonomy for assessing and certifying personnel. The following methods were used: fuzzy logic with linguistic variables and membership functions; gamification with interactive scenarios simulating cyber incidents; modeling using defuzzification rules and procedures; and experimental testing at an airline. The following results were obtained: the experiment conducted at airlines in the Republic of Kazakhstan proved the feasibility of this approach. Gamified scenarios simulating real cyber incidents allow you to interactively assess the current level of personnel competency without risk to real systems. Artificial intelligence provides deep data analysis, identifies individual and systemic knowledge gaps, and offers personalized learning paths. This testing technology not only allows accurate measurement of personnel's skills and competencies but also enables timely measures to improve their qualifications. The study substantiated the need to develop a taxonomy for building a portfolio of personnel skills and competencies in aviation enterprises’ cybersecurity services. Such a taxonomy will serve as the basis for establishing standardized norms and criteria for assessing personnel’s skills and competencies, enabling objective assessment and certification of cybersecurity specialists. In addition, its use will allow for the timely identification of the need for advanced personnel training, which is critically important in the context of an intensively changing cyber landscape. Conclusions. The scientific novelty of the results obtained is as follows: 1) a hybrid model for assessing the competencies of cybersecurity specialists in aviation enterprises has been developed, integrated with fuzzy logic and gamification elements, which ensures a realistic assessment of skills in a dynamic cyberspace; 2) a methodology for forming a taxonomy of skills and competencies has been substantiated, which will become the basis for forming standardized norms and criteria for assessment (certification); 3) the model’s effectiveness in identifying knowledge gaps and making it possible to form high-quality training trajectories for specialists has been confirmed by experimental testing of the model at an aviation enterprise.
Copyright (c) 2026 Kayrat Koshekov, Almat Sakhov, Nataliia Levchenko, Abay Koshekov
Proactive unmanned aerial systems cybersecurity analysis: combining a priori – a posteriori IMECA and penetration testing methods
The subject of this study is the proactive cybersecurity analysis process of Unmanned Aerial Systems (UAS). The goal of this study is to reduce cybersecurity risks in UAS operations by developing a proactive analysis method that combines systematic risk identification with empirical validation in a controlled environment. The objectives of the study are: (a) to justify the usefulness of proactive cybersecurity analysis for UAS, (b) to develop a corresponding method, (c) to experimentally demonstrate the usefulness and applicability of the proposed method experimentally, and (d) to derive and assess a list of recommended countermeasures. The methods used in the study include Intrusion Modes and Effects Criticality Analysis (IMECA) and penetration testing. The results of the study are as follows: (a) a staged method for proactive UAS cybersecurity analysis was developed, combining a priori assessment, controlled reproduction of selected intrusion scenarios, and a posteriori refinement of risk estimates; (b) a block diagram of proactive UAS cybersecurity analysis was developed to formalize the vulnerability identification and assessment process; (c) a SITL platform deployed on a workstation running Kali Linux was used for reconnaissance, vulnerability and misconfiguration scanning, and intrusion mode simulation; (d) preliminary identified intrusion modes were experimentally confirmed, which led to the discovery of 35 additional intrusion modes linked to a common initial access vulnerability in the Wi-Fi protocol, four of which fell into the unacceptable risk zone; (e) a priori and a posteriori IMECA tables and criticality matrices were constructed; and (f) recommended countermeasures were derived and assessed. The conclusions of the study are as follows: (a) the proposed method enables systematic identification of intrusion modes and empirical refinement of probability, severity, and risk estimates; (b) the scientific novelty lies in integrating the IMECA method with a priori prediction and a posteriori refinement based on observations from UAS penetration testing procedures; (c) the developed block diagram proved useful for formalizing vulnerability detection and minimizing uncertainty in risk assessment; (d) the proposed method’s usefulness and applicability were demonstrated on the SITL platform; and (e) no intrusion mode remains in the unacceptable risk zone following the potential implementation of the recommended countermeasures; three Wi-Fi-dependent modes retain a residual risk whose complete elimination requires an architectural rather than a configuration-level decision.
Copyright (c) 2026 Artem Abakumov, Vyacheslav Kharchenko, Peter Popov