Herald of Advanced Information Technology
Game-theoretic method of decentralized load balancing in microservice architectures
The paper presents a game-theoretic method of decentralized load balancing in microservice architectures, aimed at improving the efficiency of request distribution among service instances without using centralized controllers. The main idea is to represent the balancing process as a non-cooperative potential game in which each microservice is considered an autonomous agent seeking to minimize its own cost function. Unlike traditional algorithms such as Round Robin and Least Connections, the proposed approach is based on adaptive adjustment of agent strategies depending on the current state of the system, which ensures the achievement of Nash equilibrium and a stable load distribution. The mathematical model of game-theoretic method of decentralized load balancing takes into account the intensity of the request flow, the throughput of each node, and the quadratic component of the cost associated with resource overload. To optimize the decision-making process, a stochastic Softmax-update dynamics are used, which approximate the gradient descent of the potential function. This allows the system to gradually balance the load even in the presence of asynchronous updates and communication delays between nodes. It has been proven that the process converges to a stationary state in polynomial time, ensuring scalability and predictable behavior in large distributed environments. An experimental study conducted in the SimPy simulation environment demonstrated that the proposed method significantly outperforms classical algorithms in key metrics. Under peak load, the Game-Theoretic Load Balancer algorithm reduced the average system response time compared to the Round Robin and Least Connections algorithms. The standard deviation of processor utilization decreased, indicating a more balanced workload distribution and the absence of overloaded computing nodes. The obtained results confirm the analytical stability, convergence, and practical effectiveness of the game-theoretic approach. The developed method ensures adaptive self-regulation of the system, minimizes the risk of overload, and increases the reliability of microservice architectures. Future research should focus on integrating game-theoretic method of decentralized load balancing with cloud orchestrators and extending the model to multi-level games in hybrid computing environments.
Information technologies and computer systems
Information technologyand computer systems
Copyright (c) 2025 Herald of Advanced Information Technology
Unsupervised Re-identification architecture based on segmented tracklets for animal behavior analysis
In this paper, we present Mask-TAUDL, an advanced unsupervised re-identification architecture that combines instance segmentation, unsupervised deep learning, and tracklet association for detailed analysis of object behavior in long-term recordings. It combines the Mask R-CNN dual-stream detector/segmenter with dual ResNet-18 backbones and the unsupervised deep learning module based on tracklet association (TAUDL). Mask R-CNN provides accurate object localization and binary masks from which we construct tracklets with improved segmentation. The two ResNet-18 streams use these masks to extract appearance and motion-sensitive features at the tracklet level, which are combined into a common feature descriptor. The TAUDL module operates directly on the masked tracklet features and co-trains discriminative embeddings and cross-session associations without manual labeling. The proposed Mask-TAUDL architecture trains a model so that features of a single individual remain close in embedding space over time, while providing a clear separation of features between different individuals. Integrating pure masked regions with temporally aggregated features helps suppress spurious variations caused by shadows, reflections, or overlapping objects. Long-term animal re-identification is challenging due to frequent overlaps, appearance drift, and subtle visual differences between individuals, and most existing solutions rely on large annotated datasets, which limits their applicability in real-world laboratory settings. The Mask-TAUDL architecture overcomes these limitations by explicitly modeling temporally consistent, mask-refined tracks and training embeddings that preserve identity in a fully unsupervised manner. Mask-TAUDL is designed for animal behavior studies, namely small laboratory species such as mice and fish observed in closed or semi-structured arenas, where reliable long-term identity tracking is essential for quantitative behavioral analysis, longitudinal experiments, and high-throughput screening.
Theoretical aspects of computer science, programming and data analysis
Theoretical aspects of computer science, programming anddata analysis
Comparative analysis of signal processing techniques for multimode polymer fiber interferometers
This study explores speckle pattern matching methods for deformation sensing using multimode polymer fiber interferometers. The research highlights the benefits of multimode polymer optical fibers, including their flexibility, cost-efficiency, and compatibility with complex structures, making them ideal for strain sensing applications. The primary focus is to evaluate and compare advanced image matching techniques, specifically the sum of squared differences, zero mean normalized cross-correlation, and phase-only correlation. These methods are assessed based on essential metrics such as linearity, precision, and signal-to-noise ratio. Experimental results indicate that phase-only correlation outperforms the other techniques in overall performance, offering combination of high linearity, moderate precision, and strong noise rejection. The normalized cross-correlation also demonstrates competitive results, while the sum of squared differences, despite its strengths in linearity, shows lower precision and weaker resistance to noise. The findings provide a practical framework for selecting suitable signal processing techniques to optimize multimode polymer fiber interferometers for various sensing demands. This work offers novel contributions to the field, highlighting the unique strengths and limitations of each method. These insights are expected to guide the future design and development of advanced fiber-based sensing systems.
Estimation of task-specific manipulability scores for a robotic manipulator in vacuuming scenarios
Reliable vacuum-cleaning trajectories require selecting joint configurations from which a robotic arm can continue motion in task-relevant directions without encountering self-collision, joint-limit violations, or singularities. Classical manipulability measures describe dexterity using the Jacobian but do not account for the geometric constraints and directional motion patterns typical in floor-cleaning tasks. This work presents a data-driven method for estimating a task-specific manipulability score that reflects how easily a seven degrees-of-freedom robotic arm equipped with a floor-contact vacuum tool can continue motion in six primitive directions (forward, backward, left, right, clockwise rotation, counterclockwise rotation). A dataset of one hundred and seventy-six thousand and twenty valid joint configurations was generated by sweeping a four-dimensional grid of feasible end-effector poses, computing up to three inverse-kinematics solutions per pose, and simulating incremental movements with collision checking. Each configuration was assigned a directional score based on inverse-kinematics reachability, collision outcomes, and distance-dependent penalties. A fully connected neural network was trained to regress six scores from the seven joint angles. The model achieved a denormalized Mean Absolute Error of approximately two point zero for translational directions and one point fifty-five to one point sixty for rotational directions (approximately eight percent of the full score range), while enabling extremely fast inference–around ninety-five thousand eight hundred and eighty evaluations per second on a consumer GPU. By shifting the computationally expensive stage of collision checking and inverse-kinematics sampling to offline preprocessing, the method provides a lightweight surrogate for online motion planning. The learned score can help planners avoid unfavorable configurations and maintain consistent vacuuming trajectories. Limitations include the absence of environment-aware terms, stochastic inverse-kinematics sampling, and hand-tuned scoring parameters. Future work will focus on integrating obstacle information, improving label generation, and embedding the score into trajectory optimization frameworks for more robust real-world operation.
Development of a product pricing algorithm using RFM strategy for user cohorts using machine learning methods
This paper presents a comprehensive approach to developing a dynamic product pricing algorithm that integrates RFM (Recency, Frequency, Monetary) customer segmentation strategy with machine learning methods. The research addresses the critical challenge of personalizing pricing strategies for different user cohorts in competitive retail environments, particularly for supermarkets and online marketplaces. The study extends the classical RFM model by incorporating price elasticity of demand coefficients to create a cohort-based pricing framework. Using the K-Nearest Neighbors (KNN) algorithm, the authors developed an automated classification system that accurately segments customers based on their behavioral characteristics, achieving 100% classification accuracy for repeat purchasers. The methodology comprises five key stages: data collection and preprocessing, RFM score calculation using a quartile-based ranking, customer segmentation into five distinct groups (Champions, Loyal Users, Potential Loyalists, At-Risk, and Lost), training a machine learning model for segment prediction, and cohort formation based on the timing of the first purchase. The research implements a pricing algorithm that selectively applies discounts to target segments (“Potential Loyalists” and “At Risk” customers) who demonstrate inelastic demand and have maintained activity for at least six months. Experimental results demonstrate that strategic discount application not only reduces customer churn but also increases overall revenue through enhanced purchase volume. The proposed framework, implemented in the R programming language within the RStudio environment, provides businesses with a data-driven decision support tool for optimizing personalized pricing strategies while maintaining profitability. This approach enables companies to balance customer retention efforts with revenue maximization by utilizing sophisticated behavioral analytics and predictive modeling.
Information technologies in socio-economic, organisational and technical systems
Information technology in socio-economic,organisational and technical systems
Comparative analysis of chaotic and deterministic methods to territory coverage by drone swarms
This study proposes a hybrid chaotic-deterministic method for controlling a swarm of unmanned aerial vehicles, integrating social interactions such as separation, cohesion, alignment, and binding with an attraction-driven search strategy. The two-level anti-stagnation system operates at the level of individual agents (‘micro’) and the collective swarm (‘macro’) to prevent local minima and maintain controlled trajectory smoothing. The method uses double course conditioning to balance smooth trajectories with stochastic exploration. Meanwhile, an attractiveness function evaluates potential movement directions quantitatively based on territory novelty, distance factors and course stability. Social interaction forces – repulsion, cohesion, alignment and centroid binding – ensure swarm stability and collision avoidance throughout mission execution. Comparative experimental validation was conducted through multiple simulation launches for each method in irregular polygonal territories. Both the chaotic and deterministic waypoint-based approaches demonstrated exceptional mission reliability, achieving a target coverage threshold in all trials, thereby confirming complete success rates. In terms of coverage efficiency, the chaotic method achieved superior average territory completeness compared to the deterministic approach, representing a measurable improvement. However, this enhanced coverage precision comes at a significant computational time cost: the chaotic method required substantially longer average mission duration compared to the optimized waypoint method. The chaotic approach also exhibited notably higher variability in results, reflecting the inherently stochastic nature of exploration-based methods. Thus, while the chaotic method demonstrates superior coverage efficiency, it exhibits inferior time efficiency compared to the deterministic baseline. These findings quantify the fundamental trade-off between thorough exploration and time efficiency in unmanned aerial vehicle swarm operations, providing empirical evidence to inform mission-critical deployment decisions. The results suggest that chaotic methods are best suited to scenarios that prioritize comprehensive coverage and adaptability, such as search and rescue operations where undetected casualties would be a critical failure, while deterministic approaches are more effective in time-sensitive missions with predictable environments. The reliability of both methodologies, combined with the quantification of performance differences, enables the selection of methods based on evidence, aligned with specific operational requirements, mission constraints and acceptable risk-time-accuracy trade-off parameters.
Road traffic accident classification using a sparse videotransformer and adaptive fragmentation
In this work, we propose a simple yet effective approach for classifying short road traffic video clips into car accident and normal scenes. From each clip 8 frames are uniformly sampled across the sequence to ensure that key events are preserved even in longer videos. Based on the Farnebäck optical flow map, an adaptive fragment selection is performed, where the patch size (eight/sixteen/thirty-two pixels) is determined for each region of the base grid. Smaller patches are used in areas with intensive motion (to capture finer details), while larger patches are used in static regions (to reduce computations). The selected fragments are non-overlapping, resized to a uniform scale, and converted into feature vectors. The architecture operates in two stages. First, a spatial transformer processes each frame independently; attending only to the selected fragments this drastically reduces the number of feature tokens. Second, a temporal transformer processes the sequence of classify tokens (compact per-frame representations), aggregating temporal dynamics across frames. This space-to-time factorization significantly lowers computational cost and memory consumption while maintaining high informativeness in motion-intensive regions. To address class imbalance, we employ a weighted cross-entropy loss (or focal loss emphasizing hard examples) and weighted random sampling during training. Optical flow maps and fragment lists are precomputed and cached on disk, which accelerates training epochs even on CPUs without specialized hardware. Evaluation was conducted on the Car Crash Dataset (one thousand and five hundred accident and three thousand normal videos) using an eighty to twenty percent train-test split with preserved class proportions. The proposed method achieved Accuracy = 0.864 and Macro-F1 = 0.851. Preliminary comparisons show that our approach outperforms both the baseline uniform-patch Vision Transformers and traditional temporal aggregation schemes. The key advantage of the method lies in combining motion-guided feature reduction with a two-stage spatial-temporal processing pipeline, making the model suitable for realistic computational constraints (CPU-level inference) while maintaining high sensitivity to short and localized accident events. The approach is easily scalable and can be integrated with self-supervised pertaining techniques (e.g., masked video reconstruction). All experimental conditions, hyperparameters, and configurations are documented to ensure full reproducibility.
Development of a geoinformation method for assessing the accessibility of medical services in community infrastructure development projects
The article develops a geoinformation method that provides a solution to the scientific and applied problem of assessing the accessibility of medical services in community infrastructure development projects. Solving this problem is the basis for planning community infrastructure development during their post-war reconstruction. The relevance of the study is determined by the need to improve the effectiveness of management decisions regarding the assessment of medical service accessibility to community populations in conditions of spatial unevenness in the distribution of medical infrastructure facilities and limited resources. The work aims to develop a geoinformation method for assessing the accessibility of medical services for the population of communities, based on the use of open geospatial data from the OpenStreetMap framework, which takes into account the topology of the road network, the spatial distribution of medical facilities, the density and distribution of the population in the communities, and the time parameters of access to medical services. The proposed method is implemented on the basis of open data using OpenStreetMap tools, OSMnx and NetworkX libraries, which ensure flexibility, scalability and transparency of calculations. A distinctive feature of the method is the automated determination of areas of community access to medical services based on the construction of isochrones around medical infrastructure facilities, taking into account the type of facility and the acceptable time of arrival of community residents to them. The developed geoinformation method was tested for adequacy. It was found that deviations are within 1 minute or 0.3 km. The results of the experimental application of the method on the example of a typical territorial community showed its ability to identify ‘grey areas’, i.e. settlements that are not sufficiently covered by the network of medical institutions, and to assess the impact of changes in the structure of the medical network on the overall accessibility of medical services to the population. The practical value of the study lies in the creation of an algorithm that forms the basis for the development of a system to support management decisions on the strategic development of communities. In particular, in terms of assessing the population's access to medical services for planning community infrastructure development projects. The proposed method can be further expanded by integrating demographic forecasts, population mobility scenarios, and data on the state of road infrastructure, which opens up prospects for its use in a wider range of community infrastructure development project planning tasks.
Information technologies in energy systems engineering and manufacturing
Information technology in energy engineering and manufacturing
Analysis of methods for supporting personalization in IT education
The increasing complexity of digital learning environments and the growing demand for individualized educational experiences underscore the importance of developing effective methods for personalizing practical tasks. The purpose of this study is to analyze existing approaches to the personalization of practical tasks in IT education and to identify their advantages, limitations, and potential for integration into digital learning ecosystems. The main objectives include the identification of technological solutions that enable automated generation and assessment of personalized practical tasks, and their effectiveness for supporting adaptive and scalable learning processes. The research methodology is based on a comparative analysis of scholarly sources, existing educational platforms, and practical implementations of personalized tasks. Special attention is given to the development of conceptual frameworks that enable personalization not only at the level of task complexity but also in terms of content variability, learning pathways, and assessment mechanisms. The review identifies the most effective strategies for achieving scalable personalization in digital education. The analysis also reveals the limitations of current solutions. The critical limitations are: insufficient support for comprehensive practical task parametrization, high technical barriers for instructors, incomplete learning management system integration, security vulnerabilities in local deployment scenarios, and limited automation in assessment processes. These constraints highlight the need for more sophisticated, user-friendly solutions. The findings provide a foundation for developing integrated information technologies that combine personalization methods with adaptive learning and artificial intelligence tools, supporting the evolution of digital learning ecosystems toward more effective, scalable, and integrity-focused educational approaches. The scientific novelty of the research lies in the systematic comparison of personalization methods and the identification of criteria for their effectiveness in IT education. The practical significance is determined by the possibility of integrating the proposed approaches into digital platforms, thereby improving the quality of training, optimizing the learning process, and ensuring the flexibility required by modern educational ecosystems.
Information model of remote sensing of soil
This paper presents a comprehensive information model and open architecture for unmanned aerial vehicle (UAV)-based remote sensing of soil. The development of remote sensing technologies using unmanned aerial vehicles (UAVs) and sensors of various measurement methods makes it possible to contactless detect various anomalies. The reliability of information after its processing is achieved by a combination of measurement methods, the correct choice of contactless measurement tools, and the use of machine learning methods for data processing and training on known anomalies. The modular UAV platform supports adaptive sensor integration and real-time data fusion for precision agriculture, environmental monitoring, and post-conflict applications. The information model defines the structure and flow of data between UAV sensors, onboard processors, and the ground control station, incorporating a digital twin for dynamic environmental modeling. A minimum set of necessary tools has been selected, based on the measurements and processing of which there will be enough information to identify anomalies in the soil with a high degree of reliability. The scope of application of the obtained results is quite extensive: from precision farming, including monitoring crop diseases and chemical non-contact soil analysis, to identifying foreign inclusions and anomalies in areas where military actions took place.
A model for keyword spotting in voice signal for specialized computer systems
Keyword spotting in voice signal is a crucial task for specialized, low-resource computer systems, such as ground drones, particularly when operating under challenging conditions with limited computational power and without reliable cloud access. This paper presents a novel, modular model for efficient keyword spotting that does not rely on deep neural networks. The model's core principle is the differential weighting of Mel-Frequency Cepstral Coefficients , prioritizing those coefficients most discriminative for phonetic content. The architecture incorporates robust signal conditioning, dynamic feature extraction (including delta and delta-delta derivatives), the transformation of acoustic features into compact string-based "fingerprints", and final classification using the Levenshtein distance. Experimental validation, conducted on a Ukrainian-language corpus of drone commands with lexicons of up to 200 words, demonstrated the model's high performance and scalability. The system achieved an F1-score of 0.92 under ideal conditions and showed significant resilience in noisy environments, maintaining an F1-score of 0.78 at a 5dB signal-to-noise ratio. Furthermore, the proposed system significantly outperformed a baseline version (using only basic Mel-Frequency Cepstral Coefficients without derivatives or normalization) by up to 33 percentage points in F1-score under challenging conditions. The study validates that this optimized classical Keyword Spotting approach provides an effective and fully autonomous solution for edge computing applications where resource efficiency and independence from cloud infrastructure are paramount, especially in critical scenarios like military operations.
Development and optimization of distributed high-performance systems with real-time data consistency
This article examines the theoretical and applied aspects of building distributed high-performance systems capable of processing large volumes of data with minimal latency while maintaining real-time consistency guarantees. The relevance of the research is determined by the rapid growth of data-intensive domains such as high-frequency trading, telemedicine, smart cities, the Internet of Things, and autonomous transport, where even millisecond delays can have critical consequences. The study focuses on the analysis of modern architectural models, including microservices, CQRS, Lambda and Kappa architectures, and event-driven systems with Apache Kafka. Methods of performance optimization are explored in detail, covering asynchronous request handling, caching, replication, and load balancing, with special attention to edge computing and 5G-based infrastructures. Particular emphasis is placed on theoretical and practical aspects of data consistency, including CAP-theorem (Consistency Availability Partition tolerance) trade-offs, consensus algorithms (Paxos, Raft), and CRDT (Conflict-free Replicated Data Type) structures. The results demonstrate that no universal architectural solution exists, but hybrid approaches combining strong and eventual consistency models can ensure reliability in mission-critical domains. The proposed analytical model for evaluating performance under different workload profiles enables the selection of optimal system configurations, balancing throughput, latency, and consistency requirements. The scientific novelty of the research lies in the integrated framework that unites architectural patterns, optimization techniques, and consensus mechanisms, as well as in the development of an analytical evaluation model that had not been sufficiently presented in previous studies. The practical significance is manifested in the formulation of recommendations for the design of fault-tolerant, scalable infrastructures for finance, telecommunications, healthcare, IoT, and smart cities.
AI-Generated video evaluation by fragment processing
Recent advances in generative AI have led to the development of techniques to generate visually realistic synthetic video. As a result, there is a growing demand for detectors capable of distinguishing between AI-generated videos. In this paper we propose a compact, fragment-based representation of video frames that enables robust spatial-temporal analysis and new approach to discrimination between real and synthetically generated footage. To achieve this goal, each frame is divided into fragments and a square matrix of size BxB is formed. Next, we compute the dominant singular value for every fragment, yielding a square S-map. This construction preserves local structure while normalizing geometry, so standard 2-D operators can be applied uniformly. We analyze spatial organization via 2-D Discrete Cosine Transform (DCT) energies and temporal change with robust thresholding to form a binary change mask. Then we apply Connected Component Labeling (CCL) on the binary change mask (4- or 8-connectivity) and compute the area of the Largest Connected Component (LCC). We derive an LCC time series that measures the spatial concentration of change. Empirically, synthetic videos exhibit higher rates of near-binary LCC toggling, longer plateaus, increased mass at rational steps, and fewer unique levels than real videos - signatures consistent with temporal quantization and procedural dynamics. The pipeline is lightweight (fragment-wise rank-1 SVD + CCL on a small grid), auditably interpretable, and suitable for batch screening and edge devices. It attains ROC-AUC ≈ 0.86 and TNR ≈ 0.94 on mixed-resolution datasets with further gains from per-granularity calibration.
Uncertainty-aware multi-objective refactoring for code duplication
Code clones are recurring code fragments that may hinder software maintainability if not properly managed. While many clone detection tools exist, they often stop at identification and provide no clear guidance on whether a detected clone group should be refactored, how to do so, or in what order. This paper presents a machine learning–based method for recommending clone refactorings with prioritization and confidence estimation. The proposed approach represents code fragments using abstract syntax trees, program dependency graphs, and semantic embeddings from a pre-trained CodeBERT model. In addition, version control data is used to extract evolutionary features such as churn, age, and co-change patterns. A multi-class classifier predicts refactoring types, while open-set recognition techniques identify uncertain cases and flag them as unknown. Effort and benefit estimation models help prioritize suggestions based on a cost-effectiveness ratio. We evaluated the method on four open-source Java projects using a manually labeled dataset of 600 clone groups. The system achieves a macro-F1 score of zero point seven six on known refactoring types and an AUROC of zero point nine one for unknown detection. Prioritized recommendation quality reaches NDCG@3 of zero point eight nine, showing strong alignment with expert assessments. The results indicate that clone refactoring can be effectively supported through integrated code representation, uncertainty modeling, and prioritization. The approach transforms clone analysis from a passive task into an actionable process.
artificial intelligence in software engineering
Method for building ensemble classifiers of structured and unstructured data based on a unified approach
Effectively classifying heterogeneous data, including structured and unstructured data, is essential in diverse fields such as healthcare, finance, information security, and audio content analysis. This study aims to develop a unified approach for constructing ensemble classifiers capable of handling diverse data formats within a single framework, enhancing classification accuracy and robustness. The methodology integrates feature extraction and data preprocessing techniques, transforming heterogeneous datasets to a standardized numerical format suitable for ensemble learning. Eight base classifiers including K-nearest neighbors, support vector machines, random forest, extreme gradient boosting, logistic regression, multilayer perception, convolution neural networks and long short-term memory networks–were trained with optimized hyperparameters. The ensemble classification uses stacking with various aggregation types such as hard voting, soft voting, and soft voting using Gompertz fuzzy ranking to effectively combine model predictions while accounting for uncertainty and noise. Experimental evaluation across five datasets, covering medical diagnosis, credit risk, emotion recognition, music genres and deepfake detection–demonstrates consistent improvement in accuracy and F1-score metrics, with gains up to 8 percent compared to the best individual classifiers. The approach proves particularly effective for unstructured audio data, where temporal and spectral dependencies pose significant challenges. The results underscore the versatility the proposed unified ensemble methodology in addressing class imbalance and noise offering a scalable solution adaptable to various domains. This work contributes a comprehensive framework facilitating the development of robust classifiers for complex real-world data and paves the way for future research integrating heterogeneous data sources within cohesive predictive models.
Improving the performance of сlustering with wavelet function by using inequality constraints
Clustering methods based on gradient estimation are common in automated control and diagnostic systems, where reliable data processing is needed under noise and multimodality. Their use, however, is constrained by low robustness and high computational costs. Wavelet-based approaches are relevant because they enhance noise immunity and improve efficiency. The purpose of this work is to develop and study a clustering method that employs wavelet functions to introduce constraints and ensure stable performance under noisy conditions. The research included analysis of existing approaches, development of a wavelet-based method with inequality-type second-order constraints, creation of an algorithm for its implementation, and experimental evaluation. The proposed method relies on wavelet transforms with hyperbolic functions, which reduce the number of oracle calls, decrease computational stages, and accelerate convergence in classification and clustering problems. Experiments show that the method shortens the search time for the optimum by about one and a half to seven times at different signal-to-noise ratios, with a moderate increase in error of roughly five to fifteen percent for the De Jong test function. On synthetic datasets, the gain in computation time exceeded one point one compared with the baseline method. In a practical case of reliability assessment for resistors used in critical equipment, efficiency improved by nearly eight percent. Finally, the novelty lies in the clustering method with constraints-inequalities defined by wavelet processing. This can increase the computational speed in conditions of high noise levels, asymmetric objective functions, and small data samples.
On the computational complexity of cascade GL-models for fault-tolerant multiprocessor systems
The article addresses the problem of evaluating the computational complexity of basic cascade GL-models used for modeling the behavior of fault-tolerant multiprocessor systems in the flow of failures. The purpose of this work is to reduce the complexity of such models by optimal selection of their parameters. It has been demonstrated that a single system usually corresponds to an entire family of cascade GL-models, differing in cascade depth and parameters, with each having its own computational complexity. To simplify the process of modeling the system behavior under a flow of failures, it is advisable to choose the cascade GL-model configuration with the lowest complexity. However, additional constraints on the model, such as cascade depth limitations, must also be considered. This work applies an empirical-analytical research method. An analysis of computational complexity for cascade GL-models was conducted using specially developed software, which automated model construction for various combinations of parameters. Subsequently, a comparative analysis of the complexity of their edge function expressions was performed to identify dependencies on parameter values. Experimental studies were carried out for fault-tolerant multiprocessor systems with varying numbers of processors and different maximum allowable failure multiplicities (but not exceeding half of the total number of processors in the system). It was shown that cascade GL-models typically have significantly lower computational complexity compared to standard basic GL-models, especially for systems with a small maximum number of allowed failures. However, in cases where the allowed number of failures equals or exceeds half of the processor count, standard models may become less complex. Based on the conducted analysis, practical recommendations for selecting the parameters of cascade GL-models were formulated for the first time. In particular, the lowest complexity is achieved when the fault tolerance coefficient of the auxiliary model is minimized at each cascade level; however, this leads to a maximal cascade depth. If cascade depth is limited, the lowest complexity is achieved by evenly or nearly evenly distributing the fault-tolerance coefficients among auxiliary models. If an even distribution is impossible, it is advisable to place higher-value coefficients at deeper cascade levels. Experimental results demonstrate that the application of the proposed recommendations can significantly reduce the overall complexity of edge function expressions in the cascade GL-model compared to the basic GL-model, with the effectiveness of the approach increasing as the system size grows.
Internet of Energy based cellular structures for distributed Microgrids
In the face of evolving global energy challenges and the urgent need for resilient, decentralized, and digitized power systems, the concept of the Internet of Energy (IoE) emerges as a transformative framework for modern energy infrastructure. This paper explores the development and implementation of IoE-based cellular structures within distributed Microgrids, with a focus on Ukraine’s critical need for energy security and system restoration. Embracing the “3D” paradigm – Decarbonization, Decentralization, and Digitalization –the proposed architecture organizes energy systems into interconnected energy cells. These cells leverage distributed energy resources, demand-side management, and Energy Cloud 4.0 technologies to enable real-time energy balancing, market participation, and transactive energy operations. The paper presents a comprehensive structure for Microgrids based on IoE principles, detailing energy cell typologies, control algorithms for cost optimization, and strategies for grid flexibility and reliability. It highlights how cellular architectures can ensure autonomous operation, enhance resilience to outages, and foster integration with the European energy market. Through modeling and simulation, the paper validates the feasibility of this approach, demonstrating significant improvements in energy cost efficiency, stability, and sustainability. To organize the work and interaction of cells, 5 types of energy cells (generators, prosumers, passive consumers, electric vehicles, utility) have been identified that interact and have impacts on all participants of the Microgrid: economic impacts (changes in consumer electricity bills and revenues from energy sales), technical impacts (changes in the network scheme, approaches to network planning and operation) and social impacts (support, reliability of consumption, reduction of CO2 emissions). The technical, economic, and social benefits of transitioning to IoE-based cellular Microgrids are a foundational element in rebuilding and future-proofing Ukraine’s energy sector.
A multi-criteria scoring metric for evaluating deep learning models in bitcoin price forecasting
The increasing computational demands of deep learning have raised concerns about the environmental sustainability of artificial intelligence applications, particularly in high-frequency domains such as financial forecasting. This paper addresses the need for more holistic evaluation criteria by proposing a multi-criteria scoring metric for deep learning models used in Bitcoin price forecasting. The purpose of the study is to develop a performance metric that balances predictive accuracy with computational efficiency and environmental impact. The method involves combining traditional accuracy measures with training time, energy consumption, and carbon emissions into a unified performance score, calculated using a logistic scoring function. The metric was validated by applying it to forty-two configurations of Long Short-Term Memory models trained on historical Bitcoin price data. Each configuration was assessed for its forecasting accuracy, energy use and emissions (measured using a carbon-tracking tool). The results show that simpler Long Short-Term Memory models can offer competitive accuracy while significantly reducing training time and emissions. The highest-performing model achieved a balance of all criteria, while deeper architectures with marginal accuracy gains incurred disproportionate environmental costs. The study concludes that the proposed scoring metric offers a practical and scalable solution for selecting deep learning models under sustainability constraints, supporting more responsible Artificial Intelligence deployment in real-world settings.
Fuzzy models of wireless components sensor networks
The article addresses the problem of modeling wireless sensor network components operating under uncertainty and the influence of external factors. The relevance of the research stems from the growing need for reliable and autonomous monitoring and control systems in industrial environments, especially under conditions where wired communication is not feasible or consistent. Classical models with strictly defined input parameters fail to adequately reflect the dynamics and unpredictability of sensor node behavior and communication links, which limits the accuracy of performance assessment and complicates the design of robust systems. The aim of this study is to develop a formalized approach for describing wireless sensor network behavior under uncertainty using fuzzy logic and semantic representation. The proposed solution is based on a generalized modeling framework that utilizes the theory of fuzzy sets and supports both crisp and fuzzy input/output variables. Two universal formulations are introduced: one that models relationships solely between fuzzy variables, and another that links crisp parameters with fuzzy outputs. These models serve as a flexible basis for representing typical processes in wireless sensor networks, where parameters are often only partially known, qualitatively estimated, or described in linguistic terms. Variables are mapped into an orthogonal semantic space, allowing the use of membership functions for further reasoning and integration into ontological structures. Although detailed structural models of sensor nodes and communication channels are not included in this version and will be presented in a separate follow-up study, the current work lays the methodological groundwork for their development. The scientific novelty lies in the creation of a unified fuzzy modeling framework that supports both abstract and data-driven representations of uncertainty in wireless sensor networks. The practical significance is seen in the applicability of the proposed approach to designing adaptive, context-aware sensor networks capable of operating reliably in challenging and resource-constrained environments. The introduced models provide enhanced flexibility, semantic interpretability, and accuracy in representing real-world operational conditions.
A model for constructing neural network systems forrecognizing emotions of text fragments
Emotion Recognition in text is a crucial task in Natural Language Processing, particularly relevant given the exponential growth of textual data from social media and voice interfaces. However, developing effective emotion recognition systems for low-resource languages, such as Ukrainian, faces significant challenges, including linguistic informality, dialectal variations, and cultural specificities. This paper introduces a modular model (framework) for developing neural network-based tools for recognizing emotions in Ukrainian text fragments. The model encompasses a comprehensive data preprocessing pipeline, flexible architectural choices (including approaches based on Word to Vector, Long Short-Term Memory, and Transformers), and rigorous validation using standard metrics and interpretability methods. As part of an experimental study, two prototypes were implemented and compared: a lightweight classifier based on FastText and a more powerful classifier based on pretrained RoBERTa-base, both trained to recognize seven basic emotions. The results demonstrate that RoBERTa-base achieves high accuracy, significantly outperforming FastText and a baseline translation-based approach, yet it demands substantially more computational resources for inference. The study underscores the importance of creating Ukrainian-language corpora to enhance recognition capabilities and highlights the critical trade-off between accuracy and efficiency. It provides practical recommendations for model selection based on resource constraints and performance requirements for emotion analysis tasks in the Ukrainian language.
Analysis of existing approaches to automated refactoring of object-oriented software systems
Automated refactoring plays a crucial role in the maintenance and evolution of object-oriented software systems, where improving internal code structure directly impacts maintainability, scalability, and technical debt reduction. This paper presents an extended review of current approaches to automated refactoring, emphasizing methodological foundations, automation levels, the application of artificial intelligence, and practical integration into CI/CD workflows. We examine rule-based, graph-based, machine learning–based (CNNs, GNNs, LLMs), and history-aware (MSR) techniques, along with hybrid systems incorporating human-in-the-loop feedback. The taxonomy of refactoring types is aligned with established terminology – particularly Fowler’s classification – distinguishing structural, semantic (architectural), and behavioral transformations, all grounded in the principle of behavior preservation. Formal models are introduced to describe refactorings as graph transformations governed by preconditions and postconditions that ensure semantic equivalence between program versions. The paper provides a concrete example of a transformation generated by the DeepSmells tool, demonstrating the «before/after» change and explaining the rationale behind the AI-driven recommendation. The study also addresses the challenges of explainability and semantic drift, proposing mitigation strategies such as SHAP-based analysis, attention visualization in transformer architectures, integration with formal verification tools (e.g., SMT solvers, symbolic execution), and explainable AI recommendations. Special attention is given to the limitations of automated refactoring in dynamically typed languages (e.g., Python, JavaScript), where the lack of static type information reduces the effectiveness of traditional techniques. Generalization to multilingual systems is supported through models like CodeBERT, CodeT5, and PLBART, which operate over token-level, syntactic, and graph-based representations to enable language-agnostic refactoring. The paper also discusses real-world integration of automated refactoring into CI/CD environments, including the use of bots, refactoring-aware quality gates, and scheduled transformations applied at commit or merge time. Practical examples illustrate the verification of behavior preservation through regression testing or formal methods. This work targets software engineers, researchers, and tool developers engaged in intelligent software maintenance and automated quality assurance. By offering a consolidated classification, tool selection criteria, and practical scenarios, the paper delivers applied value for designing custom refactoring solutions or adopting existing technologies across diverse project constraints–ranging from safety-critical systems to large-scale continuous delivery pipelines.
Mykola A. Hodovychenko Mykola A. Hodovychenko
Adaptive super-resolution integration to enhance object detection on low-quality unmanned aerial vehicle imagery
The article addresses the problem of improving the accuracy of object detection in images captured by unmanned aerial vehicles under conditions of reduced spatial resolution and the presence of noise artifacts. The relevance of this research is driven by the practical need to maintain the reliability of computer vision systems in challenging field environments, where conventional detection algorithms tend to lose effectiveness. The aim of the study is to enhance the robustness of object detection in low-quality unmanned aerial vehicles imagery through the development of an adaptive preprocessing mechanism based on deep neural network–driven image super-resolution. The proposed approach involves the dynamic activation of the super-resolution module only in cases where image quality or detector confidence is insufficient. Within the framework of the study, a combination of the high-accuracy two-stage model Faster R-CNN and prior image upscaling using Real-ESRGAN is employed. An adaptive logic for triggering the image enhancement module is introduced, which is activated solely when the detector's confidence level falls below a defined threshold, thereby reducing computational overhead without compromising recognition performance. An experimental evaluation of the proposed method was conducted using unmanned aerial vehicles imagery degraded by various distortions, including blur, noise, and compression artifacts. The results demonstrate consistent improvements in detection accuracy across all tested image degradation types while maintaining acceptable processing time. The practical value of this research lies in its applicability to autonomous monitoring systems, search-and-rescue missions, and situational analysis tasks based on unmanned aerial vehicles video streams. The proposed approach opens up opportunities for further optimization by incorporating additional components, such as lightweight preliminary object filtering modules.
From classification to taxonomy: Automated structuring of vehicle repair names in multilingual corpora
This study introduces and rigorously validates a hybrid, five-stage Natural Language Processing pipeline that transforms unstructured, bilingual repair-order text into fully navigable, hierarchical action taxonomy – bridging the gap between flat keyword classification and business-grade knowledge organization. Addressing the limitations of both traditional and modern Natural Language Processing methods in technical, noisy, and domain-specific datasets, the proposed methodology integrates advanced lemmatization, manual core dictionary creation, semantic filtering, transformer-based classification, and embedding-driven clustering. Building on advanced Ukrainian lemmatization, dynamic semantic filtering, multilingual sentence embeddings, and density clustering, the pipeline systematically overcomes the noise, code-switching, and “long-tail” rarity that typify real-world automotive datasets. Tested on a corpus of over 4.3 million service records, the approach achieves over 92 % cluster coherence with minimal manual annotation. The resulting taxonomy unlocks four immediate industrial benefits: enterprise-wide repair analytics and benchmarking across branches and brands; intent-aware chatbots capable of precise service triage and automated quotation; inventory and workforce optimization through fine-grained job statistics; and a practical blueprint for industry-level standardization of repair nomenclature and data exchange. In sum, the work demonstrates that combining minimal expert input with modern embedding techniques and density clustering can automate taxonomy induction at industrial scale, setting a new benchmark for digital transformation initiatives that depend on accurate structuring of noisy technical language.
Organization of computations in clusters using transparent parallelizing principles
Methods of consructing of the systems identification and recognition requirements significant computational resources and therefore require usage of parallel systems, such as clusters or computers with multiple processors or processors with multiple cores. In this paper cluster computing organization principles based on transparent parallelizing are considered. Questions that arise while implementing this technology as a parallel calculations framework are described. Described technology has been implemented as a framework on Java programming language. Architecture of such framework is shown and functionality of its parts is described. The concept of a value ID and the concept of an unready value ID have been proposed to implement the proposed principles. The ID of a value is an ID that should be assigned to each value that is used as input or output parameter of procedure. These assignments are cluster-wide and are used to replace sending parameter value with sending its ID. The same values are often used in different calls in parallel programs, so using IDs allows the framework to save traffic. IDs of unready values are created each time during a procedure call and are assigned to the output parameters of the procedure. They are used to get the value of parameter in the moment of the first access. Also they are passed to the server as a part of information about an order. When the execution of an order is finished, value IDs are obtained for values of output parameters of the order and these IDs are assigned to the corresponding IDs of unready values. RMI technology has been used to implement communication between server and clients. Also JDBC has been used to implement storing of final and intermediate computations results to external database. In this paper is to propose method of execution time characteristics analysis for parallel applications that have been created using the technology of orders based transparent parallelizing. Its efficiency has been proven by solving the problem of determination of diagnostic value of formed features diagnostics on a cluster of 2, 3, 5 and 10 computers. The result of multiplication of execution time by number of processors has grown by not more than 1.13% when using 2, 3 or 5 computers instead of one, and by not more than 3.25% when using 10 computers instead of one during this experiment. The closest analogue of offered approach is T-system.
Moment of light pressure in the medium with resistance
The paper describes the simulation of fast rotational motion of a dynamically asymmetric satellite relative to the centre of mass under the influence of the joint effect of the moment of forces of light pressure and resistance. It is assumed that the surface of the spacecraft is a surface of revolution. The medium creates a weak resistance proportional to the angular velocity of the rigid body's own rotation relative to the centre of mass. Orbital motions with an arbitrary eccentricity are considered given. The mathematical model of satellite motion in this formulation is described by a rigid system of differential equations: the fast variables are Euler angles, and the slow variables are the modulus of the angular momentum vector, the kinetic energy, and the angles of orientation of the angular momentum vector in space. Averaging is performed over the Euler-Poinsot motion. The averaged system of equations of body motion allows numerical simulation of the satellite's motion relative to the centre of mass. The study is carried out in a dimensionless form for a multiparameter system of equations. For numerical calculation, an implicit third-order Adams method is used to integrate systems of differential equations. A personal computational package was developed for the constructed mathematical model of the satellite, as well as a library for calculating the complete elliptic integrals of the first and second kinds. Numerical calculation allows one to obtain the functions of modulating the modulus of the satellite kinetic moment vector, its orientation angles to the orbit, as well as the satellite kinetic energy values. The analysis of the influence of the parameters of the problem on the nature of the motion of the satellite relative to the centre of mass is carried out. A qualitative picture was obtained of the influence of the initial values ​​of the angles of orientation of the kinetic moment vector, the geometry of the masses, the eccentricity of the orbit, the characteristic numbers of disturbing moments on the hodograph character of the kinetic moment vector. The hodograph of the kinetic moment vector in three-dimensional space is simulated for various values of the system parameters. To construct three-dimensional objects on the scene, according to the carried out numerical calculations, we developed our own software using DirectX technology in C# language, simulating a virtual laboratory of a numerical experiment.
Algorithmization of the failed subschemes localization process
Approaches to the algorithmization of the process of localizing faulty subschemes of a wide class of electrical devices (in particular, electrical and electronic) are considered, appropriate algorithms are proposed, and the possibility of their application in practical applications is shown. In particular, it was noted that in modern conditions, with increasing requirements for the reliability of electrical devices and expanding the set of functions performed by them, not only the full feasibility of monitoring the operability of these devices at the production and operation stages should be considered, but also ensuring the potential possibility of diagnosing the developed devices (systems ) - at the design stage. At the same time, the observed complication of the methodological support of individual procedures and the whole diagnostic process as a whole is indicated, which, on the one hand, is connected with the need to carry out the most complete and comprehensive diagnosis, and on the other hand, determines a sharp increase in computing resources and labor costs for the implementation of a diagnostic experiment. One of the possible solutions to this contradiction is the formalization and subsequent algorithmization of diagnostic procedures, which ensures the automation of the latter and, as a result, reduces the time of diagnosis and improves its quality. The proposed algorithmic tools implement model-oriented methods for the localization of faulty subschemes of electrical devices (systems), in particular, the method of training and testing characteristics. A distinctive feature of these methods and the algorithmic support considered in the work is the use of models of the devices under study during a diagnostic experiment, which makes it possible to form and test functionally necessary (ideally, any) health hypotheses of the latter.
algorithmization of diagnostic procedures
Classifying mixed patterns of proteins in microscopic images with deep neural networks
Nowadays, accurate diagnosis of diseases, their treatment and prognosis is a very acute problem of modern medicine. By studying information about human proteins, you can identify differentially expressed proteins. These proteins are potentially interesting biomarkers that can be used for an accurate diagnosis, prognosis, or selection of individual treatments, especially for cancer. A surprising finding from this research is that we have relatively few proteins that are tissue specific. Almost half of all proteins are categorized as housekeeping proteins, expressed in all cells. Only 2,300 proteins in the human body have been identified as tissue enriched, meaning they have elevated expression levels in certain tissues. Thanks to advances in high-throughput microscopy, images are generated too quickly for manual evaluation. Consequently, the need for automating the analysis of biomedical images is as great as ever to speed up the understanding of human cells and diseases. Historically, the classification of proteins was limited to individual patterns in one or more cell types, but in order to fully understand the complexity of a human cell, models must classify mixed patterns according to a number of different human cells. The article formulates the problem of image classification in medical research. In this area, classification methods using deep convolutional neural networks are actively used. Presented article gives a brief overview of the various approaches and methods of similar research. As a dataset was taken “The Human Protein Atlas”, that presents a tissue-based map of the human proteome, completed in 2014 after 11 years of research. All protein expression profiling data is publicly accessible in an interactive database, enabling tissue-based exploration of the human proteome. It was done an analysis of the work and the methods that were used during the research. To solve this problem, the deep neural network model is proposed taking into account the characteristics of the domain and the sample under study. The neural network model is based on Inception-v3 architecture. Optimization procedure contains combination of several tweaks for fast convergence: stochastic gradient descent with warm restarts (learning rate schedule for exploring different local minima), progressive image resizing (training starts from small resolution and sequentially increases each cycle of SGDR). We propose new method for threshold selection for F1 measure. Developed model can be used to create an instrument integrated into the medical system of intellectual microscopy to determine the location of the protein from a high-performance image.
Volterra neural network construction in the nonlinear dynamic systems modeling problem
The features of using the theory of Volterra series and neural networks in problems of nonlinear dynamic systems modeling are considered. A comparative analysis of methods for constructing models of nonlinear dynamic systems based on the theory of Volterra series and neural networks is carried out; areas of effective application of each method are indicated. The problem statement is formulated, consisting in the creation of a mathematical apparatus for transforming models of nonlinear dynamic systems derived from the Volterra series apparatus into an artificial neural network of a certain structure. The three-layer structure of a direct signal propagation neural network has been substantiated and investigated for represent nonlinear dynamic systems. It is outlined a class of systems that can be efficiently approximated by this network. The dependence of the Volterra kernels coefficients and the weighting coefficients of the hidden layer of the three-layer forward-propagation neural network is established. An algorithm for constructing an artificial neural network based on the Volterra series is given. The results of computer simulation of nonlinear dynamic systems using the Volterra neural network and direct signal propagation neural network are presented. The analysis of experimental data confirms the effectiveness of using Volterra neural networks in problems of modeling nonlinear dynamic systems. Conclusions and recommendations on the effective use of Volterra neural networks for modeling nonlinear dynamic systems are made.
On the construction of a software architecture for nuclear systems on a crystal
The article discusses how to build software architecture for multi-core systems on a chip (SoC), based on asymmetric and symmetric multiprocessing, the hypervisor. Asymmetric multiprocessing is a port for several operating systems on physically separate processor cores. In symmetric multiprocessing in systems with core isolation, one OS is launched on several cores. OS SMP- system is ported without user intervention with a growing number of cores. Since all cores are managed by a single OS, message transfer between cores can occur at the L1 data cache level, providing faster communication with less jitter. Kernel isolation allows you to reserve a kernel for a hard real-time application, protecting it from the influence of other high-performance kernels, which for the software architecture allows you to select your operating system without creating low-level software when managing multiple operating systems. The hypervisor refers to a low-level software system. It manages several independent operating systems that are at a higher level. Developing multi-core systems-on-chip offerings focused on the embedded market are well suited for asymmetric multiprocessing configurations. This architecture is useful for developers who use the performance of a real-time operating system in combination with a diverse set of Linux kernel functions. The article discusses the software and hardware solutions contained in the XAPP1079 environment, which are required to run Linux on a single Zynq-7000 All Programmable system on a chip, and open source applications on the second core. Designing systems based on systems on a chip for high-performance and а real-time applications requires an optimal solution taking into account the factors: data transfer time; separation of the operating system. A system solution for high-performance and real-time applications using a symmetric multiprocessor processing architecture with kernel isolation provides low latency, jitter and real-time system operation, while maintaining software SoC scalability. Programmable logic integrated circuits containing multi-core subsystems have an efficient architecture with symmetric multiprocessing of data to ensure a compromise between the actual data transfer time and the low latency of their processing. The advantages of using symmetric multiprocessing manifest themselves if the load is distributed among several resources. In this case, the time required to complete the task is reduced. However, the performance gain brought about by a simple multiplication of the number of performers will not necessarily be linear. Some tasks should be performed only sequentially. Multi-core systems are able to process packages much more efficiently than single-core ones - but only if they are managed by optimized software. It is expedient to develop multi-core computing software, including an OS with support for symmetric and asymmetric multiprocessor data processing architectures, an embedded hypervisor, high-speed packet processing modules, and an exhaustive set of tools for the entire cycle of multi-core computing systems. The results of such development will find application in multiprocessor supercomputers and server applications, in terminal devices, access aggregators and basic devices - where the highest throughput is required.