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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology

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metadata of articles for the last 2 years

Spatial-temporal fusion of depth data for estimating three-dimensional distances

2026. T.14. № 8. id 2414

Yu X.

DOI: 10.26102/2310-6018/2026.59.8.013

Depth enhancement is commonly evaluated with pixel-wise errors, although robotic vision and three-dimensional metrology ultimately use geometric distances. This study develops and tests a protocol that evaluates enhancement methods by the error of Euclidean distances between back-projected point pairs. The primary experiments use the TUM Freiburg1 desk and xyz sequences. Additional validation includes the dynamic Freiburg3 walking_static sequence, controlled stress tests with moving depth boundaries, long range and elevated noise, three pseudo-reference variants, and a computational-cost benchmark. With temporal half-window N = 10, temporal mean fusion reduces distance RMSE by 24.07 % on desk and 23.66 % on xyz relative to raw depth. Direct metric comparison shows that temporal median has the lowest pixel MAE, whereas temporal mean has the lowest distance RMSE; Spearman rank correlations are 0.70 and 0.90. Temporal mean remains ranked first under all three pseudo-reference constructions. On the dynamic sequence, a temporal-stability gate retains 18.92 % of pixels and changes the method ranking, demonstrating that moving regions must be excluded or motion-compensated. For 640 × 480 frames at N = 10, the research CPU implementation requires 56.2 ms for temporal mean, 217.5 ms for temporal median, and 3155.6 ms for spatial-temporal median. The results delimit the practical use of the protocol and the systematic limitations of pseudo-reference evaluation.

Keywords: depth data, three-dimensional measurement, depth map, pseudo-reference, spatial-temporal fusion, geometric accuracy, depth enhancement

Learning analytics: predicting student performance based on student activity and performance data in e-learning course management systems

2026. T.14. № 9. id 2412

Danichev A.A. Liksonova D.I. Shestakov V.N.

DOI: 10.26102/2310-6018/2026.60.9.018

This study examines the problem of increasing the accuracy of predicting the learning outcomes of university students based on data on current academic performance in disciplines from electronic educational courses. The source data are numerical values – grades and activity indicators based on the results of each week of training – which may relate to different scale ranges and be highly sparse, making their subsequent use in forecasting algorithms difficult. Therefore, it is necessary to conduct high-quality preprocessing of the source information to construct a predictive mathematical model of learning outcomes. One proposed method for data processing is the use of percentiles, which effectively describe the distribution of features. The percentile method solves the issue of fairly comparing students enrolled in courses with differing difficulty levels by turning raw scores into relative rankings within a learner's peer group. This study presents a side-by-side comparison of two data processing techniques: the conventional centering-and-norming method versus the percentile-based strategy. A nonparametric estimator designed for the Nadaraya-Watson regression function is then employed to build the predictive model. Excerpts from computational experiments with varying numbers of groups are presented, yielding acceptable results from a practical perspective.

Keywords: educational data, learning analytics, student performance prediction, educational data mining, machine learning methods, data preprocessing, normalization, percentiles, electronic educational environment

Comparative modeling of computational load distribution heuristics in a hybrid cloud-fog architecture for IoT systems

2026. T.14. № 9. id 2405

Proshechkina N.V. Gasanov R.R. Zhityaikina V.D.

DOI: 10.26102/2310-6018/2026.60.9.007

This paper presents a comparative analysis of heuristics for workload distribution based on a hybrid cloud-fog architecture used for processing Internet of Things traffic. Such an architecture is optimal for applications requiring high performance and operating in real time, taking into account constraints on latency, power consumption and the load on computing nodes. Comparative modelling is carried out using a stochastic multi-criteria mathematical model of computational load distribution between fog and cloud resources. Constraints relating to capacity, latency, reliability and load balancing are applied to the model. Using iFogSim, a typical IoT scenario was modelled with variations in task flow intensity and a comparison of two distribution policies – a baseline and an optimised one. A dynamic analysis was carried out with the load varying according to the intensity of request arrivals. The results show that the optimised strategy reduces average latency, energy consumption, the proportion of SLA violations and the degree of fog node overload, ensuring more stable system operation as the load increases. With this approach, fog and cloud resources are utilised more efficiently, thereby improving the efficiency of IoT data processing. The practical significance of this work lies in the potential to apply the simulation results when developing intelligent load management algorithms for industrial IoT systems, smart cities, telemedicine and video surveillance systems.

Keywords: hybrid cloud-fog architecture, internet of Things, computational load distribution, multi criteria optimization, latency and energy consumption, iFogSim, SLA quality of service

An incremental method for updating a multidimensional cube based on a disordered stream of information system log events

2026. T.14. № 8. id 2404

Ufimtsev G.A. Zykov S.V.

DOI: 10.26102/2310-6018/2026.59.8.003

Information systems generate large volumes of event logs, which are used to analyze the operation of applications and services. In this case, events may arrive in the analytical circuit later than the moment of their actual occurrence and not in the original order. Such time inconsistency leads to errors in the construction of aggregated time-based indicators, while regular full recalculation of a multidimensional analytical cube requires significant computational costs. The purpose of the work is to develop an approach to updating a multidimensional cube based on the flow of log events, taking into account delays and violations of the order in which they arrive. The leading approach is to divide the final cube representation into a base cube and a compensation layer. Timely events update the base cube, while delayed events within a given compensation horizon add corrections to the compensation layer. Unique event identifiers are checked to exclude repeated event accounting. The paper presents a formal event model, describes rules for assigning events to cube cells by the occurrence time, proposes an incremental update algorithm, and provides a computational experiment on a software prototype. The experimental results show that the proposed method reduces aggregate errors compared with the window-based method and requires a smaller amount of repeated data processing compared with batch recalculation. The materials of the paper are of practical value for developing analytical pipelines for monitoring, audit, security event analysis, and user activity analysis.

Keywords: incremental updating, OLAP cube, event log, event flow, disordered data arrival, delayed events, compensation layer, stream data processing

Application of hybrid recommendation system model based on graph neural networks and feature description of users and communities

2026. T.14. № 7. id 2402

Азарнова Т.В. Mazur I.Y.

DOI: 10.26102/2310-6018/2026.58.7.008

In the conditions of growing competition in the social media market, there is an increasing need for personalized content recommendation tools that can increase user engagement. Traditional approaches to building recommendation systems based on collaborative filtering or content filtering have limitations in community recommendation tasks, where both the structure of social connections and the semantic description of groups are important. This article is aimed at studying a hybrid model of a recommendation system combining graph neural networks and feature description of users and communities. The model integrates information about user interactions with groups in the form of a graph, text embeddings of community descriptions obtained using transformer models, as well as numerical and categorical features of groups and users. The results of the study demonstrate a significant advantage of the proposed approach compared to baseline methods. A comparative analysis with popularity baseline and random baseline shows an improvement in the MAP@3 metric by 3.89 times relative to recommending popular groups and by 4045.9 times relative to random recommendation. The study is based on data from the OK social network, including information about 159,685 users and 54,425 communities.

Keywords: recommendation systems, graph neural networks, machine learning, personalization, social networks, collaborative filtering, embeddings

Geolocation determination from dashcam footage using ResNet

2026. T.14. № 7. id 2399

Baskhanov A.R.

DOI: 10.26102/2310-6018/2026.58.7.016

The relevance of this study is driven by the growing volume of street‑level imagery and the need for automatic georeferencing without relying on GPS metadata. Accordingly, this paper aims to assess the feasibility of determining photograph coordinates within a single city using deep neural networks. The leading research method is the application of convolutional neural networks ResNet18 and ResNet50 for latitude and longitude regression from 512×512-pixel frames. Data were collected using the open Mapillary platform, and a custom program with a 10,000‑cell grid was developed to ensure uniform coverage of Ufa. A total of 88,798 geotagged images were collected. The paper presents quantitative results of model training. The ResNet50‑based model achieves a median localization error of approximately 3.1 km after 13 epochs, with 92.6% of predictions falling within a 10 km radius of the ground truth. Smooth Grad‑CAM visualizations reveal that the model focuses on building facades, intersections, road markings, and other infrastructure elements – semantically meaningful landmarks for urban geolocation. The materials of the article are of practical value for automatic georeferencing systems for large collections of street‑level imagery and for the development of visual localization methods for autonomous vehicles. The work also provides a reproducible pipeline for city‑level image geolocation and analyses the behavioral attention of convolutional neural networks in this task.

Keywords: image geolocation, convolutional neural networks, resNet, urban scene analysis, deep regression, attention visualization, smooth Grad‑CAM, mapillary

Development of machine learning models for predicting gastrointestinal diseases

2026. T.14. № 9. id 2398

Almusawi M. Lyapuntsova E.V.

DOI: 10.26102/2310-6018/2026.60.9.014

The article addresses the problem of predicting pathological conditions of the gastrointestinal tract from endoscopic images using machine learning models. The relevance of the study is determined by the need to improve the reproducibility of preliminary assessment of endoscopic data and to reduce the risk of missing clinically significant images when processing large volumes of visual information. The aim of the study is to develop and experimentally evaluate a resource-efficient classification scheme for endoscopic images, taking into account not only the average recognition quality, but also the reliability of automatic decisions for pathological classes. The open Kvasir-v2 dataset containing 8000 images from eight classes was used as the experimental basis. A simple convolutional neural network, MobileNetV2, and EfficientNetB0 with transfer learning were compared. In addition, a selective scheme was proposed, combining the predictions of the two strongest models and sending uncertain images for manual review. The experiment showed that EfficientNetB0 achieved a macro-F-score of 0.8497 on the test set, while the combined prediction increased this value to 0.8681. The proposed selective scheme provided a macro-F-score of 0.9581 among automatically accepted decisions and reduced the number of dangerous automatic errors from 64 to 10. The obtained results demonstrate the potential of a clinically oriented approach in which automatic classification is supplemented by confidence control and assessment of the risk of missing a pathological condition.

Keywords: machine learning, endoscopic images, gastrointestinal tract, disease prediction, convolutional neural networks, transfer learning, selective classification, pathological conditions, medical decision support

Integrating information security and systems engineering into a multilingual sign language learning game

2026. T.14. № 8. id 2396

Ashrafi A. Philippovich Y.S. Mokhnachev V.S. Makhmud A.A.

DOI: 10.26102/2310-6018/2026.59.8.014

The development of accessible and secure digital tools is critical for inclusive education. While our prior work demonstrated the pedagogical efficacy of a multilingual sign language learning game-evidenced by a 35 % improvement in sign recognition and a System Usability Scale (SUS) score of 90/100-the transition to a production-ready, internet-connected platform introduces significant security and operational risks. To address this, this paper presents a unified framework that integrates four core disciplines-web application security analysis, software lifecycle automation (CI/CD), secure digital document workflows, and security information and event management (SIEM)-to systematically harden the platform and ensure its long-term resilience. We implement an integrated approach where continuous security testing, protected data workflows, and threat-informed development work in concert to ensure platform integrity. Results demonstrate the elimination of critical vulnerabilities, an 87.5 % reduction in high-severity issues within three months, and a 65.4 % improvement in mean time to remediate (MTTR). The secure certificate system achieved a 99.6 % issuance success rate and 100 % forgery detection, with public verification in under one second. This lab-validated methodology provides a practical blueprint for transforming innovative educational tools into resilient, production-ready systems.

Keywords: sign language, educational game, web application security, CI/CD automation, secure document flow, SIEM, data privacy, inclusive educational technologies

Methodology of system analysis of remote and hard-to-reach territories as complex systems

2026. T.14. № 8. id 2395

Serdechnyy D.V.

DOI: 10.26102/2310-6018/2026.59.8.005

This article examines the development management of remote and hard-to-reach territories of the Russian Federation, characterized by low population density, harsh climatic conditions, and high levels of infrastructure vulnerability. It substantiates the need to shift from sectoral and reactive management to proactive management based on systems analysis and predictive modeling. A formal model of a remote territory and its representation as a weighted directed graph have been developed, enabling a quantitative description of material, energy, and information flows. Integral indices of vulnerability and adaptive potential are proposed, as well as a matrix for classifying territories into nine resilience zones. A six-stage systems analysis algorithm has been developed that integrates remote sensing data collection, graph modeling, index calculation, scenario modeling, and the generation of management recommendations with feedback. The proposed methodology provides a foundation for building intelligent decision support systems for managing remote and hard-to-reach territories. Future research prospects lie in the development of hybrid modeling methods for creating full-scale digital twins of Arctic municipalities. Testing the methodology on pilot projects will refine the index weightings and create industry-specific databases for scaling up the proposed approach.

Keywords: systems analysis, predictive modeling, remote areas, infrastructure vulnerability, adaptive capacity, decision support system

Numerical modeling of one-dimensional wave processes in a bimodular medium: software implementation and convergence analysis

2026. T.14. № 7. id 2394

Polesia V.A. Dudko O.V. Lapteva A.A.

DOI: 10.26102/2310-6018/2026.58.7.015

Modern mechanics of deformable solids is actively advancing towards modeling bimodular materials – media with different resistance to tension and compression. This class includes both natural and artificial materials widely used in engineering practice. The difference in elastic moduli under tension and compression leads to a strong nonlinearity of the mechanical response, which under dynamic loading generates qualitatively complex wave patterns with the formation and interaction of strain discontinuities. As a result, the analytical solution of the corresponding boundary value problems becomes quite challenging due to numerous wavefront interactions. This paper presents the development, software implementation, and verification of a specialized system for the automated solution of boundary value problems of one-dimensional dynamics of bimodular elastic media under arbitrary boundary loading regimes. The system is based on a formalized iterative method for constructing the wave pattern, which uses piecewise-linear approximation of both the boundary conditions and the constitutive relations of the Myasnikov-Oleynikov bimodular medium. The software fully automates the construction of the wave pattern: it tracks the evolution of the wave system, classifies events, automatically generates and solves systems of nonlinear equations to determine the parameters of new local domains between wavefronts, and controls the evolutionarity of strain discontinuities and introduction of rigid layers. Verification was performed on test problems involving the formation of shock waves and rigid layers. A convergence analysis of the results against known analytical solutions confirms the high accuracy of the implemented method with an increasing number of boundary condition approximation nodes. Thus, the developed software tool is an effective instrument for automating the study of wave processes, providing physically correct modeling of the one-dimensional dynamic deformation of bimodular materials. The results obtained are of practical value to researchers in solid mechanics, specialists in numerical modeling, and engineers involved in the design of structures made of bimodular materials under dynamic loads.

Keywords: bimodular elastic medium, one-dimensional strain waves, boundary value problem, piecewise-linear approximation, evolutionarity of discontinuities, shock waves, rigid layers, numerical modeling, automation of calculations, convergence analysis

Mivar expert system for an access control system based on palm vein biometric identification

2026. T.14. № 6. id 2393

Grigorenko K.D. Gorenkov A.A. Belyaev I.A. Varlamov O.O.

DOI: 10.26102/2310-6018/2026.57.6.014

The paper addresses the task of designing an access control system (ACS) capable of making informed decisions not only on the basis of a biometric template but also taking into account the context: employee access level, zone of access, time-of-day policy and authentication history. An architecture of a complex intelligent system combining neural-network and logical levels of artificial intelligence is proposed. Biometric identification is implemented using a ResNet18 convolutional neural network adapted for grayscale palm-vein images and trained on a dataset of 834 subjects (8,340 images) using triplet metric learning with a classification head; Top-1 accuracy of 87.47 %, Top-5 of 96.58 %, Top-10 of 98.14 %, ROC-AUC of 0.9985 and EER of 1.64 % are achieved with an average confidence of correct matches equal to 0.908. The neural-network confidence together with five contextual parameters is passed to a mivar expert system (MES) implemented in the KESMI Wi!Mi Razumator environment. The MES contains three independent relations with shared inputs that produce an access decision, an alert level and a biometric reliability estimate. Shared inputs induce cross-edges in the bipartite solution graph, reflecting the multi-aspect nature of decision making in complex AI. A decision algorithm of 32 rules grouped into five priority tiers is developed. Testing on three representative scenarios demonstrates three distinct topologies of the solution graph – from a degenerate case to a full bipartite one. The results confirm the correctness of mivar logical inference and the scalability of the knowledge base without any change to the neural-network module.

Keywords: mivar expert system, ACS, biometric identification, palm veins, resNet18, triplet learning, complex artificial intelligence, KESMI, wi!Mi, neurosymbolic artificial intelligence

Method of blockchain-consensus distributed image classification

2026. T.14. № 7. id 2392

Konnikov E.A. Sandu N.R. Faizullin R.V.

DOI: 10.26102/2310-6018/2026.58.7.003

The study focuses on inter-node aggregation methods for model updates and local visual representations in distributed image classification systems. The paper aims to develop and experimentally validate a blockchain-consensus distributed image classification method that improves the robustness of the global model under heterogeneous, noisy, and partially malicious data sources. The proposed approach combines local class atlases, audit-based utility estimation of client updates, robust anomaly scoring of local trajectories, and blockchain-backed consensus weighting of node contributions. The blockchain layer is implemented as a reproducible software ledger for update provenance and audit logging. The experimental evaluation relies on the open CIFAR-10 and Olivetti Faces datasets in four scenarios: iid_clean, noniid_clean, noniid_noisy, and noniid_adversarial. The method is compared with Distributed Mean, Distributed Proximal, Distributed Trimmed Mean, and Atlas Consensus using Accuracy, Macro-F1, Balanced Accuracy, convergence dynamics, consensus weights, and statistical testing. The results show that the proposed method is not universally superior on clean distributions; however, in the target adversarial scenario it achieves the best performance among distributed schemes. The findings confirm the practical value of blockchain-consensus filtering of node updates for distributed visual data processing.

Keywords: blockchain, distributed image classification, inter-node aggregation, visual data, heterogeneous distributions, malicious nodes, robust aggregation, consensus, class atlas, information processes

A mathematical model for optimizing security measures for facilities based on matrix games

2026. T.14. № 9. id 2391

Sergeev N.P. Stepanov L.V. Zarubin V.S.

DOI: 10.26102/2310-6018/2026.60.9.013

The relevance of this study stems from the need to formalize the antagonistic confrontation between facility security measures and the operational vulnerabilities of systems while simultaneously accounting for uncertainty and resource limitations. Therefore, this article aims to develop a mathematical model that enables the rational selection of the optimal composition of protective measures in conflict situations. The leading approach to studying this problem is the framework of matrix games, which allows for a comprehensive consideration of the interaction of protective strategies and destructive factors based on uniform expert criteria. The article presents three options for forming a payoff matrix (homogeneous and heterogeneous strategies, direct expert assessment of mutual influence), reveals mechanisms for finding a solution in pure strategies through a saddle point, identifies conditions under which a solution exists only in mixed strategies, and substantiates a method for calculating the proportions of combining heterogeneous protective measures to achieve a guaranteed result. The article's materials are of practical value to specialists designing physical and information security systems, as well as to decision makers who allocate limited resources to protected facilities under deliberate countermeasures.

Keywords: mathematical modeling, game theory, matrix game, zero-sum game, facility security, security measures, vulnerability, expert assessment, saddle point, mixed strategies

Mivar expert system for identifying challenging observation conditions and selecting image correction methods for a single-camera vision system of an autonomous delivery robot

2026. T.14. № 7. id 2390

Milevich A.A. Ovchinnikov D.A. Varlamov O.O.

DOI: 10.26102/2310-6018/2026.58.7.002

The paper addresses the problem of intelligent decision support for the vision subsystem of a mobile autonomous delivery robot equipped with a single camera and operating under limited onboard computational resources without relying on cloud computing. The relevance of the study is determined by the degradation of image perception under insufficient illumination, overexposure, precipitation, haze, glare, digital noise, blur, partial occlusion, and lens contamination. A mivar expert system is proposed to formalize the domain knowledge in terms of parameters, relations, rules, and constraints. The user specifies 13 input parameters: ten normalized image features and three contextual attributes – location, time of day, and season. The system produces two semantically interpretable outputs: the current challenging observation condition and the recommended image enhancement action. A key feature of the model is its atomic relation structure: each relation contains no more than one conditional operator, while complex logic is represented as a chain of simple rules. Additional contextual service features are introduced to account for dense urban environment, nighttime city illumination, and seasonal effects when selecting the inference branch. A pretrained YOLO detector is used as the object recognition module, while the MES serves as an explainable and computationally efficient layer for interpreting challenging observation conditions. This combination makes the proposed approach suitable for local onboard deployment in an autonomous delivery robot. The obtained results confirm the feasibility of an explainable, modular, and extensible expert system for supporting a single-camera autonomous delivery robot under adverse observation conditions.

Keywords: mivar expert system, autonomous delivery robot, vision system, single-camera observation, challenging observation conditions, limited computational resources, explainable artificial intelligence, production rules, KESMI

Using systems analysis in designing intelligent protection systems against malicious software

2026. T.14. № 8. id 2389

Abedlhussain A.A. Lyapuntsova E.V.

DOI: 10.26102/2310-6018/2026.59.8.011

The paper addresses the problem of designing an intelligent protection system against malicious software under changing input flows, limited computational resources, and the need for stable operation on new data. The aim of the study was to show how systems analysis methods make it possible to move from a simple comparison of classification models to a justified choice of architecture, evaluation strategy, and update scenario. The experimental basis included 134435 objects, of which 57293 were malicious. After removing records without a recognized date and excluding the early archival fragment, an experimental subset of 123704 objects covering the period from August 2019 to September 2020 was formed. The study compares randomized and chronological evaluation schemes, baseline classification models, several feature reduction variants, and both single-stage and two-stage protection architectures. The results show that using 256 features in the first stage provides almost the same recognition quality as the full 2381-feature representation while requiring substantially lower computational costs. It is also shown that the two-stage architecture preserves the quality of the full model while sending only 0.25 percent of objects to the second stage and almost not increasing the average processing time per object. In addition, first-stage updating improves temporal robustness and reduces the risk of quality degradation on later monthly data segments.

Keywords: systems analysis, malicious software, intelligent protection system, chronological evaluation, two-stage architecture, feature reduction, adaptive updating, machine learning

Analysis of modern approaches and formalization of parameters for the management of mechanical ventilation modes

2026. T.14. № 5. id 2388

Frolov S.V. Sudakov D.E. Dolgov E.P.

DOI: 10.26102/2310-6018/2026.56.5.015

The relevance of this study is determined by the high need for respiratory support in intensive care unit patients (up to 50 % of patients) and the significant risk of ventilator-associated lung injury (VALI) due to suboptimal ventilator settings. Modern mechanical ventilators offer dozens of modes and over 50 adjustable parameters, creating a high cognitive load on the physician and increasing the likelihood of errors. The aim of this work is to systematize knowledge about modern mechanical ventilation modes and formalize key parameters of respiratory support for the subsequent development of intelligent clinical decision support systems (CDSS). The study employs methods of analytical review, classification, mathematical modeling of respiratory mechanics, and formalization of clinical criteria. An analysis of factors justifying the need for CDSS was performed: the complexity of interpreting respiratory mechanics (compliance, resistance, driving pressure), the high incidence of complications due to incorrect settings (barotrauma in 10–15 % of patients with plateau pressure >30 cm H₂O), time constraints for ICU physicians, and non-standardized nomenclature of modes across different manufacturers. A classification of ventilation modes by level of intelligence (from mandatory to fully automated) is provided, and key ventilation parameters (tidal volume, rate, pressures, flow, PEEP) are described in detail. Four groups of parameters for mode selection are formalized: lung mechanics (static compliance, resistance, plateau pressure, P0.1, driving pressure), gas exchange (PaO₂/FiO₂, PaCO₂, SpO₂), patient activity (respiratory rate, asynchrony signs), and hemodynamics (blood pressure, central venous pressure). Specific criteria for each parameter are proposed. A logical algorithm for mode selection based on formalized parameters is developed. The obtained results provide a foundation for building a production rule base for CDSS, enabling physicians in time-critical situations to receive justified recommendations. Further research should focus on clinical validation of the proposed criteria and the development of explainable artificial intelligence algorithms for personalizing respiratory support.

Keywords: mechanical ventilation, respiratory support, ventilator-associated lung injury, ventilation modes, clinical decision support, parameter formalization, respiratory mechanics, intelligent algorithms

Multi-criteria model of rational management of information flows in a digital organizational system

2026. T.14. № 7. id 2380

Mikheev V.S.

DOI: 10.26102/2310-6018/2026.58.7.006

The article proposes a formalized model for the rational management of information flows within a digital organizational system. The aim of the study is to develop a procedure for selecting a control action in which the information flow is treated as an independent object of management, characterized by a measurable state, defined constraints, and a set of admissible alternatives. The methodological basis of the study includes a systemic description of the digital organizational system, a six-parameter model of the flow state, normalization of criteria with different units of measurement, weighted aggregation of indicators, and verification of threshold constraints. The state of the information flow is described through a set of parameters: intensity, information transmission delay, data quality, availability, maintenance cost, and integral risk. As a result, a multicriteria model is developed that makes it possible to compare organizational, software-related, and infrastructural control actions using a unified integral rationality index. The study demonstrates that a rational decision is determined not by maximizing a single indicator, but by achieving a coordinated balance between throughput, timeliness, quality, availability, costs, and risk level. To assess the applicability of the model, the article presents a numerical example involving four management alternatives. The results demonstrate the possibility of selecting a compromise control action under conditions of multiple interrelated criteria. The scientific novelty of the proposed approach lies in integrating the information flow state model and the control action selection procedure within a single computational framework. The proposed approach can be applied in the design of digital management loops, corporate monitoring systems, service platforms, and decision support systems.

Keywords: information flow, digital organizational system, rational management, multi-criteria choice, data quality, information availability, integrated risk, control action, rationality index

A mathematical model of the process of automated construction of review texts based on the use of quotation-aware summarization

2026. T.14. № 6. id 2375

Kuznetsov I.I.

DOI: 10.26102/2310-6018/2026.57.6.020

This paper examines the automated generation of scientific review texts based on the analysis of a corpus of scientific publications. A mathematical model has been developed that describes an approach based on the use of citation-aware summarization with preliminary selection of citation fragments and formalizes the full data processing cycle, from publication selection and extraction of citing fragments to the generation of individual summaries and the final review text. The model defines a unified formal description of the sequence of data transformation operators and includes a system of quality criteria that ensure control of the plausibility, coverage, and factual consistency of results at all stages of processing. A software system in the form of a modular pipeline is implemented based on the developed model. Experimental studies using the developed model were conducted on the SurGE dataset, which includes 114 topics, over 7,000 cited and over 73,000 citing publications. It is shown that the use of a citation-aware approach with preliminary fragment selection improves the quality of summary generation compared to alternative methods. The following quality criteria were achieved for the resulting review texts: credibility – 0.8744, coverage – 0.9356, factual reliability – 0.9713, and LLM score – 0.9232, which outperforms the results of generation based on the full text of sources (by 7.67 % for credibility, 4.63 % for coverage, 9.22 % for factual consistency, and 4.42 % for LLM score). The obtained results confirm the effectiveness of the proposed approach and the developed model and their applicability for the automated generation of reliable scientific reviews.

Keywords: citation-aware summarization, scientific reviews, large language models, text information analysis, scientific publications, citation, citation extraction

Fake news detection in low-resource languages with LLMs

2026. T.14. № 6. id 2368

Kabir A. Khan S. Kharlamov A.A. Voronkov I.M.

DOI: 10.26102/2310-6018/2026.57.6.009

The proliferation of fake news is a global challenge to tackle in the digital era of information availability. The resourceful languages are tackling this issue through enormous research works whereas the low-resource languages are left behind to address the issue adequately. Bangla is one of the low-resource languages in computation despite being in the top ten most spoken languages in the world. To contribute in the field and address the issue of fake news, this research work focuses on the fake news detection in Bangla language leveraging large recent advancement of language models using cross-lingual prompting techniques for better response from the large language models. We leverage the open source models for resource accessibility and utilize DeepSeek-R1, Llama 3.2 and Qwen 2.5 large language models in our experiments and extensively analyze the fake news detection capacity of each model in Bangla language. We find that Qwen 2.5 outperforms the other models in this specific task achieving a maximum accuracy of 97.5 while it also reports no inconclusive response.

Keywords: fake news, bangla, large language models, low-resource language, cross-lingual prompting

Evaluation of the effectiveness of the hybrid RLS algorithm with α-β pre-filtering in the suppression of diverse interference in antenna arrays

2026. T.14. № 7. id 2366

Glushankov E.I. Ageychik N.O.

DOI: 10.26102/2310-6018/2026.58.7.004

The relevance of this study stems from the widespread use of antenna arrays in radar, navigation, and communication systems, where interference suppression efficiency critically depends on adaptive algorithms. The classical RLS algorithm loses stability in the face of pulsed and non-stationary interference, which poses challenges for operation in real-world conditions (moving objects, the Doppler effect, and intentional jamming). The objective of this study is a comparative analysis of the classical RLS and a hybrid RLS algorithm with adaptive α-β pre-filtering under the influence of 11 types of heterogeneous interference. The study was based on simulation modeling implemented in the MATLAB environment. For each of the 11 types of interference, the following parameters were varied: the interference-to-signal power ratio (ΔP, which ranged from 1 to 11), the angular misalignment between the signal and interference arrival directions (range from 0° to 9°), and the presence of the Doppler effect (carrier frequency shift of 0 % and 10 %). A total of 24,200 computational experiments were conducted, processing over 193 million data samples. The classical RLS algorithm demonstrates acceptable performance only under stationary conditions. When exposed to pulsed, semi-periodic, and combined interference, its output signal-to-noise ratio decreases to negative values. In contrast, the hybrid RLS+αβ algorithm provides a stable output signal-to-noise ratio level above 20 dB in all considered scenarios, and in the worst-case scenarios, it outperforms classical RLS by 20–25 dB. The Doppler effect has virtually no effect on the performance of the hybrid algorithm, whereas for classical RLS, it leads to divergence of weighting coefficients and loss of performance. The proposed hybrid algorithm is a universal and robust solution for antenna arrays operating in complex, non-stationary interference environments. The results can be used in the design of radar systems, unmanned aerial vehicles navigation, and electronic warfare systems.

Keywords: adaptive antenna arrays, hybrid RLS algorithm, α-β filter, spatial filtering, noise immunity, impulse noise, combined noise, signal-to-noise-plus-interference ratio, doppler effect, non-stationary noise

Optimizing the operation of a digitized organizational system through proactive management of information environment parameters

2026. T.14. № 6. id 2362

Gotishan A.A. Lvovich Y.Y.

DOI: 10.26102/2310-6018/2026.57.6.013

The paper considers the problem of optimizing the functioning of a digitalized organizational system in a dynamically changing information environment. It is shown that traditional reactive control methods do not provide the required level of reliability and efficiency under high load variability. The necessity of transition to proactive management based on anticipatory changes in the parameters of the information environment is substantiated. A formalization of the optimization problem is proposed, taking into account the total costs of system operation and probabilistic reliability requirements. A method for selecting optimal control parameters is developed, based on the introduction of a generalized functional that combines the efficiency criterion and penalty constraints, as well as an iterative procedure for parameter correction using pseudo-random initialization, an adaptive step, and a mechanism for updating penalty coefficients. A feature of the method is taking into account predicted changes in the state of the system, which makes it possible to implement a proactive control mechanism. It is shown that the proposed approach provides cost reduction, increased operational stability and prevention of critical system states. The limitations of the method (sensitivity to the choice of hyperparameters, dependence on the quality of predictive models) and directions for its further development are identified. The practical significance of the work lies in the possibility of applying the proposed approach in resource management systems for cloud platforms, container orchestrators and other digitalized organizational systems with dynamically changing loads.

Keywords: digitalized organizational system, proactive management, parameter optimization, information environment, reliability, costs, iterative algorithm, pseudo-random initialization, adaptive step, time series forecasting

Automated monitoring and control system for the parameters of an ionized steam environment

2026. T.14. № 6. id 2351

Stepulev A.I. Dulskiy E.Y. Ivanov P.Y. Kovshin A.S. Medvedev V.P.

DOI: 10.26102/2310-6018/2026.57.6.019

This paper addresses the development of an automated system for monitoring and controlling the parameters of an ionized vapor medium. The relevance of the work stems from the need to ensure stable physicochemical characteristics of such media in industrial and laboratory applications. It is shown that the formation of an ionized vapor medium should be considered a controllable process, whose state is determined by a set of measurable physicochemical parameters. A structural and functional scheme of the system is proposed, comprising a control plant, a measurement circuit, a data processing unit, and an actuating circuit that implements mode correction via closed-loop feedback. The system operates by continuously monitoring key parameters and automatically adjusting vapor generation modes to maintain specified characteristics. To formalize the state of the medium, a vector of controlled parameters is introduced, including oxidation-reduction potential (ORP), temperature, pH, and electrical conductivity, along with an integral criterion for deviation from the target regime. As a first research stage, initial validation of the ORP measurement channel was performed. Experiments were conducted on a laboratory bench simulating real vapor generation conditions. It was found that during intensive vapor generation, negative ORP values are recorded at all control points, and this parameter demonstrates spatiotemporal sensitivity to the vapor formation regime. The obtained results confirm the feasibility of using ORP as a basic feedback parameter for the subsequent construction of a multichannel automated system for monitoring and controlling ionized vapor medium parameters. Future research will focus on integrating all measurement channels and developing multiparameter control algorithms.

Keywords: ionized steam environment, automated monitoring, automated control, oxidation-reduction potential, feedback, environment state vector, structural and functional diagram, control algorithm

A method for network traffic anomaly detection in small information systems based on behavioral microprofiles

2026. T.14. № 6. id 2348

Albekova Z.M. Okolelova A.F. Shapetin M.A. Egorov P.V. Bakunov A.A.

DOI: 10.26102/2310-6018/2026.57.6.006

Small information systems – corporate networks of small and medium-sized enterprises, departmental local area networks, and specialized automated control systems – are vulnerable to automated credential brute-force attacks over FTP and SSH protocols, as they possess limited computational resources and personnel capacity to deploy full-scale security solutions. This paper proposes a semi-supervised network traffic anomaly detection method with minimal labelling requirements, which models normal user behavior through behavioral microprofiles – robust statistical descriptions of typical network activity modes derived by adaptive K-Means clustering of TCP flows. Each profile is defined by a median and scaled median absolute deviation pair, while the anomaly score of a new flow is computed as a weighted Z-score relative to the profile of its nearest cluster. Feature weights are determined using the Kolmogorov–Smirnov statistic, and the number of clusters is selected by a ROC-curve area saturation criterion. Experimental evaluation on the publicly available CICIDS2017 dataset for FTP-Patator and SSH-Patator attacks demonstrated that the proposed method substantially outperforms classical unsupervised detectors – Isolation Forest, Local Outlier Factor, and One-Class SVM – both in ranking ability and in the proportion of true alarms. The key practical finding is the method's effectiveness in a deployment mode that requires no labelling on the target system: feature selection is performed once using publicly available attack data, after which profile construction and threshold calibration proceed without any labels. Under these conditions, the method detects more than three quarters of credential brute-force attempts, whereas competing methods under identical conditions produce virtually no detections.

Keywords: anomaly detection, network traffic, behavioral microprofiles, MAD statistics, k-Means, brute-force attacks, CICIDS2017, small information systems

The probability density of switching on household appliances and its approximations by the Weibull, Gauss and Lorentz functions

2026. T.14. № 6. id 2346

Borovskiy A.V. Yumenchuk A.A.

DOI: 10.26102/2310-6018/2026.57.6.021

Data on stochastic energy consumption in the residential sector and in the village for 500 households are analyzed. The analyzed energy consumption logs from two foreign databases UK DALE and REFIT contain information about the time of switching on and off of household appliances and the power they consume. The number of recorded inclusions of household appliances in the energy consumption logs was ~30,000. It is demonstrated that the schedules of electric energy consumption in the public sector are characterized by morning and evening consumption peaks. To obtain sufficiently accurate simulation results of daily electrical load schedules, it is necessary to determine the switch-on densities of electrical appliances, i.e. the number of switch-on devices per unit of time. The article analyzes which probability density function best approximates experimental data. The approximation of the moment densities of household appliances is performed using the following functions: Weibull, Gauss, Lorentz. The calculation of the distribution parameters is justified. The search for the most appropriate type of approximation is based on comparing the standard deviation between experimental points and the theoretical function. It is shown that it is preferable to use the Weibull probability density.

Keywords: stochastic modeling, weibull approximation, household electrical load, daily load profiles, probability density function

Application of uplift modeling to enhance the effectiveness of targeted marketing campaigns in retail

2026. T.14. № 6. id 2345

Azarnova T.V. Rebrova J.I.

DOI: 10.26102/2310-6018/2026.57.6.018

The growing competition in the retail and e-commerce markets requires companies to adopt more precise approaches to planning marketing campaigns and personalizing customer communications. Existing response prediction models fail to isolate the effect of marketing interventions from natural purchasing behavior, leading to irrational budget spending and complicating the objective evaluation of campaigns. Uplift modeling emerges as a solution – an approach that enables causal effect assessment at the individual consumer level and identifies audience segments most responsive to communications. This article presents a comparative analysis of uplift modeling methods to select the most effective one for evaluating marketing impact. The study examines five methods (S-Learner, T-Learner, Class Transformation, X-Learner, and R-Learner) using the open Lenta Uplift Modeling Dataset provided by the Lenta retail chain during the BigTarget hackathon in collaboration with Microsoft. Model performance was evaluated using specialized metrics (Uplift@k, Qini AUC, Uplift AUC, Weighted Average Uplift, Average Squared Deviation). The analysis reveals the strengths and weaknesses of each approach and identifies the top-performing method for this dataset.

Keywords: uplift modeling, machine learning, treatment effect evaluation, targeted marketing, communication personalization, uplift model quality metrics

Development of a lexicographic system for Bengali hearing-impaired learners

2026. T.14. № 9. id 2344

Ashrafi A. Mokhnachev V.S.

DOI: 10.26102/2310-6018/2026.60.9.012

This article presents automated methods and tools for lexicographic research and dictionary development for Bengali Sign Language (BdSL). The research addresses the challenge of documenting BdSL, an under-resourced sign language primarily used in Bangladesh and Indian Bengali-speaking regions. Established lexicographic methods require extensive manual effort to capture the multidimensional features of signs, including handshape, location, movement, orientation, and non-manual markers. The proposed system integrates computer vision-based feature extraction, machine learning-assisted annotation, and a structured database for lexical entries. The web-based platform enables lexicographers to collect, annotate, and validate sign language entries efficiently. Evaluation with 35 participants, including lexicography experts, BdSL linguists, and deaf community members, showed a 67 % reduction in annotation time while maintaining high inter-annotator agreement (κ = 0.82). During a six-month period, participants documented 1,847 BdSL signs, identified 127 regional variants, and created 456 dictionary records at different stages of completion. These records included entries containing core phonological annotations as well as entries that had undergone additional linguistic and expert validation, providing a foundation for the development of a future learner-facing dictionary for Bengali students with hearing impairments. The scientific novelty lies in applying automated methods specifically to BdSL lexicography and establishing foundational tools for systematic dictionary development.

Keywords: sign language recognition, bengali Sign Language, lexicographic system, sign language documentation, educational technology, multilingual translation, computer-assisted lexicography

Architectural model of the audio module of the Tundra Nenets online dictionary

2026. T.14. № 6. id 2342

Shnyakov P.Y. Kokanova E.S.

DOI: 10.26102/2310-6018/2026.57.6.001

The digitalization of language resources for low-resource languages requires a formal organization of audio data collection, description, quality control, and publication. In this context, the study aims to develop a data model for the audio module of the Tundra Nenets online dictionary, i.e. the Nenets-Russian and Russian-Nenets online dictionary, and a decision support framework for selecting lexical units for recording, post-processing audio materials, and integrating them into the dictionary system. The empirical base includes corpus and dictionary resources, educational and thematic materials, previously created audio resources, and the results of fieldwork conducted in Naryan-Mar in December 2025. The methodological framework combines systems analysis, formalization of information flows, multicriteria prioritization of lexical items, and a reproducible workflow for processing audio materials. The study identifies the core entities of the audio module data model, the quality control framework, and the decision support framework for expanding the dictionary’s audio coverage. A list of 542 units was profiled by unit type, part of speech, theme, and microtheme; the paper also characterizes the composition of informants, the structure of audio materials, file naming conventions, and quality control statuses. The proposed solution can be applied to the development of digital dictionaries and speech resources for low-resource languages.

Keywords: information system, online dictionary, audio module, decision support, metadata, low-resource language, tundra Nenets language

Dynamic visualization of MCTS-Tree construction in the GGP Base Package

2026. T.14. № 7. id 2340

Litovkin D.V. Roldugin O.D. Yakimov G.A. Nikolaenko S.D. Smutin D.A.

DOI: 10.26102/2310-6018/2026.58.7.001

The paper presents a dynamic visualization system for the search tree of the Monte Carlo Tree Search (MCTS) algorithm implemented in the General Game Playing Base Package (GGP Base Package). The main limitation of existing approaches is the lack of tools for observing the evolution of the search tree during its construction, which complicates debugging and analysis of MCTS agents’ behaviour. The methodology includes: a systematic evaluation of eight visualization tools against 12 criteria (universality, dynamism, interactivity, scalability, performance, etc.); the design of a four‑layer architecture (Java/GGP Base Package → Redis → ASP.NET Core → React + D3.js); and the implementation of an interactive mechanism for replaying tree evolution with step‑by‑step analysis. The comparative evaluation shows that the proposed system achieves an integral score of 0.752 according to the defined criteria and, among the considered tools, offers the most favourable combination of key properties relevant for debugging MCTS agents. Experimental studies on the ConnectFour (6×8) game demonstrate that the system provides smooth visualization (>50 FPS) for MCTS trees with up to 1000 nodes, supports arbitrary games in GDL format and, according to expert judgment, enables differences in the behaviour of MCTS algorithm modifications to be identified within seconds of visual inspection, whereas without visualization this would require time‑consuming manual comparison of final statistics. The results confirm that dynamic visualization of intermediate MCTS tree states offers additional opportunities for detecting hidden implementation defects and non‑trivial behavioural patterns of the MCTS algorithm that remain unobvious when only the final tree state is analysed. The proposed tool may be of practical interest to researchers, developers and educators in the field of General Game Playing.

Keywords: monte Carlo Tree Search, general Game Playing, search tree visualization, interactive playback, search algorithm debugging, MCTS algorithm visualization, evolution playback

Вiomechanical study of the condition of the hip joint structures in children with various degrees of hip joint dysplasia

2026. T.14. № 6. id 2339

Tikhonenkova O.V. Nikolaeva E.A. Sergeev T.V.

DOI: 10.26102/2310-6018/2026.57.6.002

Hip dysplasia leads to a deterioration in the patient's physical condition due to the disruption of biomechanics in the affected joint and the musculoskeletal system as a whole, and is also one of the leading causes of coxarthrosis. There are various methods for correcting hip dysplasia, and the direction and degree of multi-plane correction have a significant impact on the outcome of surgery. The relevance of this study lies in the need to develop a biomechanical approach for assessing the condition of the hip joint, which will help identify the key factors for stability during surgical correction, thereby reducing the occurrence of hypo- and hypercorrection in clinical practice. The leading approach to studying this problem is to determine the actual stresses in the hip joint and identify the areas of under- and over-stress, which will influence the subsequent selection of screw configurations to achieve maximum stability. Based on the conducted research, a method was developed for selecting the optimal amount of correction of the acetabulum position during its reorientation during surgical operation. The method includes an algorithm for constructing computer models of the patient's hip joint based on computed tomography and analyzing the stress-strain state in the hip joint, depending on the congruence of the acetabulum and the femoral head, as well as depending on the configuration of the screws after surgical correction. The results of the study of hip joint models allow us to analyze the nature of changes in the stresses acting in the hip joint and to calculate the change in the contact area under different configurations of the installation of osteotomy screws. The materials of the article are of practical value for surgeons, as well as for specialists in the field of biomechanics.

Keywords: hip joint dysplasia, osteotomy, computer modeling, biomechanical analysis, finite element analysis

Automated control of a roller mill

2026. T.14. № 6. id 2337

Kudriavtsev A.V. Antsupov E.E.

DOI: 10.26102/2310-6018/2026.57.6.003

The relevance of the study is driven by the need to develop and implement competitive domestic automation solutions in technological processes, as well as by the difficulties in solving this problem in certain mature industries, such as the flour milling industry. In this context, this paper examines a roller mill to model it as a control plant and to study the parameters of various types of control devices. In this study, the roller mill is identified as a control plant, its mathematical model is developed, various types of controllers are considered, and their characteristics for controlling the selected plant are investigated using simulation methods. The parameters of controllability and stability are determined, and the most preferable approaches for controlling the mill are identified. The conclusions are as follows: correct identification of the mill is important; although a PI controller has better performance for controlling a single mill, a fuzzy logic controller may be more effective for controlling a multi mill system. The results obtained in this work can be used both for modernizing existing production facilities and for manufacturing new domestic equipment, as well as for further research.

Keywords: roller mill, automatic control system, grinding gap adjustment, modeling in MATLAB, ABCD representation