metadata of articles for the last 2 years
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

metadata of articles for the last 2 years

Detection of depression features with user data from social network using neural network

2025. T.13. № 1. id 1810
Solokhov T.D.  Kochkarov A.A. 

DOI: 10.26102/2310-6018/2025.48.1.020

The article studies the problem of identifying signs of depression based on user data from social networks using machine learning methods and network analysis. The study includes the development of a model for detecting users with signs of depression, which relies on text analysis of their social network posts and profile metadata. Neural networks were used as algorithms in the study, showing high classification accuracy. Network analysis was implemented to examine the influence of users with signs of depression and it shows that such users have low centrality and do not form dense clusters, indicating their social isolation. The hypothesis of depression spreading through social connections was not confirmed, suggesting minimal impact of depressive users on others. The research results can be utilized to develop systems for early detection of depression. Special attention is given to the study's limitations, including the use of data from a single social network and the complexity of processing textual data. The article proposes directions for further research aimed at expanding methods for analyzing the spread of depressive behavior in social networks.

Keywords: forecasting, depression, psychological disorder, classification, social network, machine learning, neural network, network analysis

Real-time monitoring of communication networks based on cloud computing

2025. T.13. № 1. id 1809
Amoa K.  Sidorenko E.V.  Ryndin N.A. 

DOI: 10.26102/2310-6018/2025.48.1.014

When creating a communication network, various obstacles inevitably arise that negatively affect its effectiveness. The lack of measures to eliminate such interference makes it difficult to optimize the network. Among the problems caused by interference, the problem of blocking them is one of the most significant. This unresolved issue may make successful network design impossible. In order to solve the problems that the traditional method has a long response time to monitor the congestion of the communication network and the detection effect is not ideal, a real-time monitoring method based on cloud computing for blocking the communication network is proposed. Firstly, a communication network monitoring point is established, and the receiver completes the communication data collection process. Based on the collected data, continuous traffic calculation is performed to determine whether there is an emergency blocking state in the communication network channel and determine the exact location of the blocking point. In this way, the information generates an alarm message to obtain the monitoring results. The real-time running time and the accuracy of the monitoring method are experimentally analyzed. It is found that the monitoring method can control the delay time within 0.2 s, and the monitoring error rate is low.

Keywords: cloud computing, telecommunications, network congestion, real-time monitoring, monitoring point, system management, blocking

Sensor data integration system in onboard control systems of unmanned aerial systems

2025. T.13. № 1. id 1806
Guliutin N.N.  Ermienko N.A.  Antamoshkin O.A. 

DOI: 10.26102/2310-6018/2025.48.1.019

Modern unmanned aerial systems (UAS) play a key role in various industries, including environmental monitoring, geodesy, agriculture, and forestry. One of the most critical factors for their successful application is the integration of data from various sensors, such as global navigation satellite systems, inertial navigation systems, lidars, cameras, and thermal imagers. Sensor data fusion significantly enhances the accuracy, reliability, and functionality of control systems. This paper explores data integration methods, including traditional algorithms like Kalman filters and their extended versions, as well as modern approaches based on deep learning models, such as FusionNet and Deep Sensor Fusion. Experimental studies have shown that learning-based models outperform traditional algorithms, achieving up to a 40 % improvement in navigation accuracy and enhanced resilience to noise and external disturbances. The proposed approaches demonstrate the potential to expand UAS applications in autonomous navigation, cartography, and monitoring, particularly in challenging operational environments. Future development prospects include the implementation of hyperspectral sensors and the development of adaptive data integration methods to further improve the efficiency and effectiveness of unmanned systems.

Keywords: sensor data integration, unmanned aerial systems, kalman filter, fusionNet, deep Sensor Fusion, autonomous navigation, resilience to disturbances

Development of API rate limiting methods based on consumer classes

2025. T.13. № 1. id 1803
Seleznev R.M. 

DOI: 10.26102/2310-6018/2025.48.1.013

Rate limiting is a crucial aspect of managing the availability and reliability of APIs. Today, there are several approaches to implementing rate limiting mechanisms, each based on specific algorithms or their combinations. However, existing methods often treat all consumers as a homogeneous group, hindering the creation of flexible resource management strategies in modern distributed architectures. In this article, the author proposes two new methods for rate limiting based on the token bucket algorithm. The first method involves using a shared token bucket with different minimum fill requirements depending on the consumer class. The second method suggests using separate token buckets for each consumer class with individual parameter values but a common limit. Simulation results confirmed that both methods enable efficient API request limitation, though disparities emerged regarding resource distribution patterns across diverse consumer classes. These findings have practical implications for developers of information systems and services who need to maintain high availability while ensuring access guarantees for various consumer categories.

Keywords: rate limiting, token bucket algorithm, software interface, consumer class, quota, threshold, burst traffic

Automated user segmentation using RFM analysis in marketing strategies

2025. T.13. № 1. id 1798
Svyatov R.S. 

DOI: 10.26102/2310-6018/2025.48.1.018

The relevance of the study is determined by the need to enhance the effectiveness of marketing strategies through automated and customizable customer segmentation. This work proposes a universal customer data management system based on RFM segmentation with the ability to configure flexible logic, as well as the capability to integrate with various external systems. Traditional CRM systems and manual RFM segmentation methods are limited in functionality and do not always meet the business needs for flexibility and integration with various data sources. The study identifies the shortcomings of traditional CRM systems and suggests points for improvement in the described system. Additionally, an experiment was conducted comparing the RFM segments generated using the proposed architecture with Yandex's auto-strategies in the Yandex.Direct advertising platform. The application of the system showed significant advantages over auto-strategies, including a 30.71% increase in purchases in the case of a clothing store. The results confirm the practical value of the system for optimizing marketing campaigns and improving conversion. The results are of practical importance for companies in need of customized solutions and integrations. Further development is proposed, focusing on improving the RFM segmentation method by implementing machine learning algorithms and exploring additional effective channels for utilizing the generated segments.

Keywords: RFM analysis, marketing automation, customer loyalty, user segmentation, e-commerce, advertising strategy optimization

System analysis and modeling of the profitability of the energy service contract based on the digital ruble

2025. T.13. № 1. id 1797
Kaziev V.M.  Kazieva B.V. 

DOI: 10.26102/2310-6018/2025.48.1.015

Enterprises participating in housing and communal services need market energy viability and competitiveness, attractiveness for consumers. For Russian companies, it is important to adhere to relatively "soft" (flexible) tariffs and energy supply strategies. It is necessary to find effective solutions, for example, investment and reducing uncertainties such as "white noise" in the energy system. The purpose of the study is a systematic analysis of the potential of a smart contract, a digital ruble and digital payments in energy service contracts. The possibilities of energy contracts and services, as well as the content and features of such contracts, measures for sustainable energy conservation with a certain profitability and optimization of energy resources were studied by methods of system analysis and modeling. Therefore, it is necessary to identify the parameters and features of the contract and simulate the processes of energy supply. The results of the study are: 1) a systematic analysis of standard forms of contracts and a description of a set of energy-saving key procedures of the enterprise; 2) analysis of the potential of the digital ruble and its "energy capabilities"; 3) model of dynamics of management of an energy service enterprise based on diffusion of digital services and its research. The results of the work will expand the possibilities of concluding and developing energy service contracts in practice, as well as build flexible models and algorithms for energy supply.

Keywords: system analysis, smart contract, energy consumption, energy service contract, modeling

A system for analyzing images of nucleated bone marrow cells for the formation of a diagnostic conclusion in oncohematology

2025. T.13. № 1. id 1796
Polyakov E.V.  Popov V.V.  Dmitrieva V.V. 

DOI: 10.26102/2310-6018/2025.48.1.016

The paper presents a system for analyzing images of nucleated bone marrow cells to form a diagnostic conclusion in oncohematology, aimed at solving the problem of constructing a data processing pipeline in automatic analyzers of biomedical images. The relevance of the study is due to the need to improve the reliability of theof automatic microscopic analysis of biomedical samples, which is aa difficult task due to high variability and morphological complexity of the investigated objects. One solution to this problem is to develop a web service that uploads, processes and describes images, then classifies them into categories of confirmed and unconfirmed cases. This web service provides cross-platform and accessibility, builds an open database of verified images and providestools for processing and analyzing images, as well as tools for correcting by the physician of the processing results. The system does not prescribe treatment and does not make diagnoses in dependently, but serves as an intelligent tool for processing, analyzing and transmission of research results in real time. The testing results showed high accuracy of the system: 91% for neural network methods and up to 97% for classical algorithms. The developed system allows for the analysis of data processing modules for computer microscopy systems.

Keywords: analysis of biomedical images, selection of objects, classification of nucleated cells, pattern recognition, oncohematology

Application of elitist ant system and Max-Min ant system algorithms for path optimization in quantum key distribution networks

2025. T.13. № 1. id 1792
Razdyakonov E.S.  Korchagin S.A.  Timoshenko A.V.  Bulatov M.F. 

DOI: 10.26102/2310-6018/2025.48.1.012

This study focuses on route optimization in quantum key distribution (QKD) networks, whose features are a number of physical constraints and strong topology dependence. This paper examines the application of two variations of the ant colony algorithm, the elitist ant system (EAS) and Max-Min ant system (MMAS) algorithms, to construct optimal routes in QKD networks. A metric for the communication efficiency of a route in QKD networks has been presented to evaluate the quality of a route according to given capacity and security requirements. The peculiarity of this metric is its non-additive capacity component, which depends on the minimum link efficiency in the route. A series of experiments were conducted on a randomly generated planar graph for long and short routes with EAS and MMAS algorithms, which resulted in MMAS being significantly more efficient for long routes, but in the case of short routes, EAS found the route faster without significant loss in solution quality. The results obtained in this study can be applied in solving problems of dynamic routing, as well as optimization of the topology of quantum key distribution networks.

Keywords: quantum key distribution, metaheuristics, ant algorithm, elitist ant system, max-Min ant system, pathfinding

Simplified physical models of the mediastinum of newborns for electrical impedance tomography

2025. T.13. № 1. id 1791
Konko M.A.  Aleksanyan G.K.  Gorbatenko N.I.  Elkin N.S.  Temnyakov N.S. 

DOI: 10.26102/2310-6018/2025.48.1.011

The article presents the results of the development and experimental study of two simplified physical models of the neonatal mediastinum for electrical impedance tomography. The created models are based on spiral computed tomography data and take into account the anatomical features of the infant chest organs. The designs were implemented using 3D printing technologies, which made it possible to achieve high accuracy of geometric parameters. The models are equipped with a controlled air filling system for the lungs and three rows of electrodes, which makes it possible to conduct experiments on modeling global and regional ventilation. Experimental studies have demonstrated that the developed models make it possible to record respiratory volumes in the range from 2 to 120 ml, which corresponds to the physiological parameters of newborn breathing. The data obtained confirmed the operability of the models, their sensitivity to changes in air volumes, as well as their suitability for research and testing of new algorithms and methods in the field of electrical impedance tomography. It was found that the proposed models provide adequate reproduction of ventilation processes and can be used to develop diagnostic solutions in the field of neonatology. The results of the work are of practical value for scientific research aimed at improving methods for diagnosing respiratory disorders in newborns, and can be used in educational practice.

Keywords: simplified physical model of mediastinum, electrical impedance tomography, newborns, process of global and regional ventilation, lungs

Determination of the optimal geometric parameters of the metal seal of the tubing hanger

2025. T.13. № 1. id 1789
Timofeev E.  Godenko A.  Tarasova I. 

DOI: 10.26102/2310-6018/2025.48.1.017

Operation of underwater complex hydrocarbon production systems is accompanied by increased risks of emergency situations, in particular, leaks and emissions due to loss of sealing between the underwater fountain fittings and the tubing hanger. Emissions and leaks during operation of underwater wells can lead to such irreversible consequences as loss of produced products and harm to the environment, as well as damage to expensive equipment, which requires expensive and technically complex repairs. For this reason, in the process of experimental design work on the design of this type of equipment, it is necessary to carry out high-quality and effective calculation support for the developed metal seals, allowing for determining their optimal geometry. The authors have developed a mathematical model for determining the stress-strain state of a metal seal of the tubing hanger, taking into account its rigidity characteristics. To determine the optimal geometry of a metal seal, the theory of qualities was used, based on which the overall quality of a metal seal was assessed by its strength and tightness. For the tightness and strength of a metal seal, particular quality functions were constructed, which are combined into an objective function using the Kolmagorov-Nagumo functional average. The results of optimizing the standard geometric parameters of a metal seal of a prototype of a tubing hanger according to the proposed method are presented. The information contained in this publication is useful to engineering and scientific professionals involved in the development and research of methods for ensuring the tightness of underwater connections using metallic seals.

Keywords: subsea production system, metal seal, stress-strain state, tubing hanger, underwater fountain fittings, contact pressure, quality theory, optimization

Biotechnical system of personalized rehabilitation of patients with limited motor functions

2025. T.13. № 1. id 1787
Filist S.A.  Petrunina E.V.  Pshenichny A.E.  Ermakov D.A.  Krupchatnikov R.A.  Serebrovskiy V.V. 

DOI: 10.26102/2310-6018/2025.48.1.002

The article considers a rehabilitation biotechnical system with an adaptable virtual reality intended for rehabilitation of patients with impaired motor functions of the lower limbs in rehabilitation complexes with combined feedback. The biotechnical system has the following functional modules: formation of controlled effects on the patient, control of controlled effects, rehabilitation management and information support. During rehabilitation, the patient's muscle fatigue and its dynamics are monitored. This made it possible to make adjustments to the rehabilitation block program during a rehabilitation session and manage the procedure for adapting virtual reality to the patient's functional state, as well as to carry out mathematical modeling of rehabilitation course scenarios. A model for planning a rehabilitation course using biofeedback intended for a biotechnical system with virtual reality is proposed. An experimental group was formed to assess the effectiveness of rehabilitation of post-stroke patients with paretic lower limbs. The rehabilitation results in this group showed that the choice of virtual reality content adapted to the patient allows increasing the effectiveness of rehabilitation according to the LEFS scale by 11%. Experimental studies of the effectiveness of muscle fatigue control during rehabilitation have been conducted. It is confirmed by testing the statolocomotor sphere according to the Tinetti scale, the indicators of which, on average, exceeded the indicators in the comparison group by 10%. Inclusion of adaptive virtual reality and muscle fatigue monitoring in the rehabilitation process leads to earlier restoration of impaired balance function, motor activity and social rehabilitation.

Keywords: persons with limited mobility, biotechnical system, impaired mobility, virtual reality, biofeedback, muscle fatigue

Structural modeling for management in organizational systems with a heterogeneous structure of spatial elements

2024. T.12. № 4. id 1785
Linkina A.V. 

DOI: 10.26102/2310-6018/2024.47.4.041

The article discusses the process of structural modeling in management in organizational systems with a heterogeneous structure of spatial elements. It is shown that the objects under study belong to territorially distributed organizational systems. Their peculiarity is the variety of spatial elements within a limited territory, which significantly influence the indicators of the effective functioning of the activity environment. Three classes of structural models are identified: object, system and process levels. The processes of structural modeling of the organizational system, its management, and management decision-making are examined in detail. It has been determined that the structural model of the organizational system reflects the interaction of the control center, objects with a heterogeneous structure of spatial elements, and a geographically distributed environment that unites objects into an organizational whole. The structural model of the management system is focused on the balanced interaction of the traditional management subsystem based on expert opinions and the decision support subsystem based on optimization of resource distribution, transformation and variation processes. The structural model of the process level is formed as an invariant sequence of algorithmic actions when making management decisions. It is substantiated that in order to meet the requirements of the control center it is necessary to combine algorithmic actions within the following stages: identification of classification signs of heterogeneity in the structure of spatial elements of objects, expert assessment of quantitative parameters for the formation of a multivariate choice model, generation of options for management decisions, expert selection on the generated set.

Keywords: organizational system, management, heterogeneity of the structure of spatial elements, structural modeling, expert optimization modeling

Optimization of corporate learning process management: a mathematical model and its application

2025. T.13. № 1. id 1782
Kharitonov I.A. 

DOI: 10.26102/2310-6018/2025.48.1.006

This article considers the problem of optimization of the employee training process control at an enterprise, including the distribution of teachers, students, lessons and training rooms under multiple constraints. The relevance of the work is determined by the need for effective management of training processes in organizations, considering the qualifications of teachers, skills of employees, time constraints and the sequence of skill acquisition. To formalize the problem, a mathematical model was developed that allows for a linear description of the key aspects of training. The model includes multidimensional constraints such as training time, teacher employment, availability of training places and the sequence of skill acquisition. However, due to the combinatorial nature of the problem and discrete variables, its solution requires the use of specialized methods. To solve the problem, optimization approaches are used that include: formalization of the problem in a linear manner to identify subtasks that can be solved separately (for example, determining available classes for teachers); application of heuristic methods and dynamic programming for the final distribution of classes and resources. The proposed model demonstrates its effectiveness in personnel training management scenarios where both cost minimization and fulfillment of all specified constraints are important. Despite the limitations of the linear description, the model provides a simplification of the solution due to the structured approach to resource allocation. This makes it a universal tool applicable to the management of employee training in companies of various types. The materials of the article can be useful for the development of adaptive learning management systems, as well as for further research aimed at improving resource allocation algorithms.

Keywords: in-house training, mentoring, training optimization, resource planning, skills sequencing, human resources management, employee qualifications, training simulation

Mathematical models and software complex for intelligent analysis and forecasting the performance of government contracts

2025. T.13. № 1. id 1778
Rubtsov D.Y. 

DOI: 10.26102/2310-6018/2025.48.1.010

The paper proposes mathematical models and a software package for intellectual analysis and forecasting of the execution of government contracts, based on a neural network and classical machine learning methods trained on a retrospective database of counterparties and contracts. A set of mathematical models and programs allows you to calculate the probabilities and risks of non-fulfillment of government contracts, thereby reducing budget losses and positively influencing the stability of the real sector of the economy. A comparative analysis of machine learning methods was carried out: logistic regression, decision tree, support vector machine and neural network model. A model has been developed that allows forecasting with an accuracy of 97.89%. For each mathematical model, a separate module has been developed, which together constitute a software package. The neural network model showed a result of 87.65%, which is associated with a relatively small set of data for training; however, this model allows us to reveal the further potential of the system in connection with continuous training in real time on new contracts, for the evaluation of which the proposed software package will be used. The results of the study can be used to further improve decision support systems in the field of procurement and its application in order to improve the overall quality of analysis and forecasting of the implementation of government contracts.

Keywords: mathematical modeling, software package, data analysis, government contracts, machine learning, intelligent system, forecasting

Dynamic model of coordinateometry of nearby signal sources using the super-resolution algorithm MUSIC

2025. T.13. № 1. id 1775
Glushankov E.I.  Kondrshov Z.K.  Syrovetnik D.S.  Rylov E.A. 

DOI: 10.26102/2310-6018/2025.48.1.004

In recent years, the problem of radar and radio direction finding has become very relevant due to the rapid development of microelectronics for small-sized unmanned aerial vehicles and communication terminals. The article is devoted to determining the coordinates of signal sources. In particular, closely spaced, uncorrelated emitters and receiving antenna arrays on several mobile devices spaced apart in space are investigated. To resolve such signal sources, the MUSIC super-resolution algorithm is used, followed by solving the coordinate measurement problem using the least squares method. A software model was created in the MATLAB environment, implementing a dynamic system in which each radio device has its own trajectories and speed indicators. A comparative analysis of the accuracy of the results obtained in various situations from the point of view of geometry and dynamics was carried out. It was found that the algorithm works most effectively in the case of targets located inside the zone formed by the scanning objects. In this case, the accuracy of determining coordinates is comparable to the distance between the signal sources. Based on the obtained results, it is possible to construct radar receivers for direction finding of nearby signal sources in the main lobe of the directional pattern of the receiving antenna of a location station, which is required when solving a number of location and monitoring problems in a complex electronic environment.

Keywords: direction finding, coordinateometry, super-resolution, MUSIC, MATLAB

A comprehensive analysis of the impact of G1 Garbage Collector parameters on JVM performance and stability

2024. T.12. № 4. id 1774
Zolotukhina D.Y. 

DOI: 10.26102/2310-6018/2024.47.4.039

The relevance of thе study is determined by the necessity of improving memory management efficiency in high-load Java applications, where minimizing garbage collection pauses and maintaining high throughput are critically important tasks. This article aims to systematically investigate the parameter space of the G1 Garbage Collector (G1 GC) and develop practical recommendations for its optimization under high-load conditions. The primary research method is an empirical approach, which involves the development of a multithreaded Java application capable of generating sustained memory and CPU loads. The study utilized a control and six experimental G1 GC configurations, differing in parameters such as heap region size, heap occupancy threshold, maximum pause duration, young region size, the number of GC threads, and the activation of Periodic GC. Key metrics, including pause durations, GC frequency, throughput, and freed memory volume, were measured, visualized, and systematically analyzed using the GCViewer tool. The article presents recommendations for G1 GC optimization, highlights the advantages of reallocating memory toward young regions and enabling Periodic GC, and identifies the limitations of the MaxGCPauseMillis parameter in aggressive configurations. The findings have practical value for developers of high-load applications requiring low latency and high system stability. The conclusions contribute to a deeper understanding of G1 GC functionality and can serve as a foundation for further research in JVM memory management.

Keywords: g1 garbage collector, memory management, jvm optimization, high-load applications, gc parameter tuning, throughput, pause duration, garbage collection, young memory regions, java application performance

The role of machine learning algorithms in optimizing agricultural production: a review of international experience and adaptation to Ethiopian conditions

2025. T.13. № 1. id 1773
Mekecha B.  Gorbatov A.V. 

DOI: 10.26102/2310-6018/2025.48.1.005

The relevance of this study is driven by the need to enhance the efficiency of agricultural production in response to the growing demand for food security, particularly in economically underdeveloped countries such as Ethiopia. The main objective of the research is to explore the potential application of machine learning algorithms to optimize agricultural processes and adapt international practices to the specific conditions of Ethiopia. The methodological approach includes an analysis of contemporary scientific literature on the use of machine learning in agriculture and the systematization of successful practices involving algorithms such as CNN, LSTM, RNN, and Q-Learning. The study investigates the characteristics of Ethiopia's agricultural sector, including existing barriers to the adoption of advanced technologies. The results highlight that machine learning algorithms hold significant potential for increasing crop yields, improving soil and crop monitoring, and forecasting climate risks. Specifically, utilizing data from drones and sensors enables the creation of precise models for managing agricultural processes. However, key challenges such as insufficient funding, a lack of specialized data processing infrastructure, and limited access to technology have been identified. The study concludes by emphasizing the importance of attracting governmental and international investments, developing tailored databases, and creating models that account for local conditions. The findings provide practical value for developing strategies to digitize agriculture and prevent food crises in countries facing similar challenges.

Keywords: machine learning, artificial intelligence, agricultural production, precision agriculture technologies, ethiopian conditions

Method of intelligent decision support for forming tutor’s assistant group for checking free response works in online learning management

2024. T.12. № 4. id 1769
Latypova V.A. 

DOI: 10.26102/2310-6018/2024.47.4.031

Despite the attractiveness of massive online courses for learners, only a small part of the latter reaches the finish line. This situation arises due to the effects of different adverse factors on the learning process. Additional staff and “smart” assistants (educational chatbots) are used for reducing the impact of these factors by helping tutors in online learning management. Tutor’s assistants are engaged to perform checking free response works and identifying course problems connected with its content, and educational chatbots to guide students through a course and organize interactions between them. On every launch of the online course, a tutor faces a choice of a group of the most suitable assistants. In the existing research, different assistants’ features such as: scores, motivation, way of communicating, etc. are taken into consideration in this selection. However, in the former, the ability of assistants to properly assess and comment on the performance of free response works are not taken into account. To close the gap, a method of intelligent decision support for forming tutor’s assistant group for checking such works is proposed in the paper. The method was tested on one of the laboratory works on the course “Modeling” and allowed to form a group of assistants capable of assessing the work correctly.

Keywords: intelligent decision support, tutor’s assistant, feedback mining, online learning management, free response work, online course

About a decentralized solution to the problem of simultaneous arrival of autonomous mobile objects to the final point using large data analysis

2024. T.12. № 4. id 1768
Chernoivanenko I.A.  Kravets O.Y. 

DOI: 10.26102/2310-6018/2024.43.4.036

The need to switch to more advanced control methods when using a conventional autonomous mobile facility (AMO) to control simultaneous arrivals arises due to excessive deviation. An innovative solution to this problem is the use of a decentralized management method to control the simultaneous arrival of the AMO to the final point, which is based on the analysis of big data. A solution was proposed to combine decentralized information through the use of filtering, on the basis of which the decentralized coordination of formations is managed. The article presents the main characteristics of AMO, shows the parameters of combining information about AMO, describes decentralized coordination management of formation and calculates the optimal path and convergence rate for decentralized management, and also takes into account restrictions on communication delay. An experimental study of errors in the x direction by the proposed method was carried out and compared with errors in the experiment without using this control method. Graphs comparing the convergence rate are also presented. The results of the experiment showed that the decentralized management method has a significant impact on the definition of the aim of AMO and the convergence of errors. Thanks to the proposed approach, it was possible to increase the efficiency of management and reduce errors, thereby proving the expediency of using this management method.

Keywords: large data analysis, autonomous mobile objects, decentralized management, information filtering, coordination management of formation

The role of user identification in predicting target actions on a website

2024. T.12. № 4. id 1767
Svyatov R.S. 

DOI: 10.26102/2310-6018/2024.47.4.037

The relevance of study lies in the need to improve the accuracy of predicting users' target actions on websites, which is a key aspect of optimizing marketing strategies and personalizing user experiences. The complexity of the task is exacerbated by the lack of stable identifiers, leading to data fragmentation and reduced prediction accuracy. This paper aims to analyze the impact of user identification methods and develop approaches to segmentation, which will help to eliminate existing gaps in this area. The primary research method involves applying machine learning algorithms to evaluate the influence of different identifiers, such as client_id and user_id, on prediction accuracy. Segmentation of users was carried out based on the gradient boosting method, as well as an analysis of the effectiveness of retargeting campaigns in the Yandex.Direct system based on conversion rates, customer acquisition costs, and the share of advertising expenses using the example of a client specializing in the sale of e-books. The findings reveal that utilizing the user_id identifier improves purchase prediction accuracy by 8%, recall by 6%, and the F1-score by 7%. Segmenting users into targeted groups demonstrated a 67% reduction in customer acquisition cost, a decrease in advertising expense share to 5.87% compared to Yandex auto-strategies, and an increase in conversion rate to 34%. The article's materials are of significance for specialists in the field of e-commerce and marketing, providing a scientific basis for the implementation of personalized advertising campaigns. The proposed methods also offer potential for further enhancement of analytics and data integration in multichannel environments.

Keywords: machine learning, user behavior analysis, user identification, user segmentation, e-commerce, target action prediction

Systematization of CT reconstruction filters for artificial intelligence technologies: a retrospective study for chest and brain

2025. T.13. № 1. id 1766
Vasilev Y.A.  Blokhin I.A.  Gonchar A.P.  Kodenko M.R.  Reshetnikov R.V.  Arzamasov K.M.  Omelanskaya O.V. 

DOI: 10.26102/2310-6018/2025.48.1.003

The selected convolution kernel in computed tomography (CT) directly affects the results of artificial intelligence (AI) algorithms. The formation of uniform requirements for this parameter is complicated by the fact that such filters are unique to equipment developers. The aim of the work is to create a table of correspondence of reconstruction filters between different equipment manufacturers to direct to the AI algorithms the series of images on which, in CT of the chest organs and the brain, the quantitative analysis will be most reproducible. DICOM tags 0018,1210 (Convolution Kernel), 0008,0070 (Manufacturer), 0018,0050 (Slice Thickness) of CT images from the Unified Radiology Information Service of Moscow were downloaded and analyzed. Inclusion criteria: age older than 18 years; slice thickness ≤ 3 mm. The data analysis is presented in the form of summary tables comparing reconstruction filters from different manufacturers for chest and brain CT, a number of clinical tasks, as well as descriptive statistics of their distribution by scanning area and manufacturer. 1905 chest ("CHEST" and "LUNG") and brain ("HEAD", "BRAIN") CT studies were included in the analysis. In chest CT, reconstructions to evaluate pulmonary parenchyma and mediastinal structures were common. Reconstructions for brain parenchyma and bone structures were common in brain CT. Systematization of reconstruction filters for chest and brain CT was performed. The obtained data will allow correct image series selection for quantitative AI analysis.

Keywords: reconstruction filters, computed tomography, artificial intelligence, chest organs, brain, data systematization

Analysis of the stability of water-saturated soils under cyclic influence: mathematical models and forecasts

2025. T.13. № 1. id 1765
Tishin N.R.  Ozmidov O.R.  Proletarsky A.V. 

DOI: 10.26102/2310-6018/2025.48.1.001

The article discusses the mathematical modeling of soil liquefaction under the influence of dynamic loads, such as seismic, storm, or technogenic cyclic impacts. The liquefaction process, in which soil loses strength and bearing capacity, is critical for assessing the safety of construction objects, especially in areas with increased seismic activity or water-saturated soils. Several approaches were used for modeling, including the following functions: the exponential function from the work of H. Bilge et al. (2009), the logarithmic function from the work of V. Lentini et al. (2018), the power function (polynomial) proposed by C. Guoxing et al. (2018), an additional logarithmic function from the study of E. Meziane et al. (2021), and a hyperbolic function proposed by the authors of this article, which approximated the soil's resistance to cyclic impacts. The study analyzed laboratory test data for various soil types, combined into engineering-geological elements. Each function was analyzed in terms of approximation accuracy using the least squares method, which minimized the deviations between experimental and theoretical values. When evaluating the functions, consideration was given to how each behaves under a large number of loading cycles, which is important for predicting liquefaction under intense and prolonged loads. The selection of the optimal function was made by comparing the MSE and R2 metrics presented in the results tables. The application of the research results has practical significance in geotechnical design, especially for calculating foundations and underground structures in conditions of potentially liquefiable soils. Choosing the most suitable function for modeling soil liquefaction allows predicting soil stability under long-term and intense cyclic loads, minimizing the risk of deformation and destruction of structures.

Keywords: soil liquefaction, mathematical modeling, geotechnical engineering, dynamic loads, soil liquefaction function, liquefaction potential

Evaluation of the quality of intelligent text paraphrasing in Russian

2024. T.12. № 4. id 1763
Dagaev A.E.  Popov D.I. 

DOI: 10.26102/2310-6018/2024.47.4.038

The study focuses on the development of an integral metric for evaluating the quality of text paraphrasing models, addressing the pressing need for comprehensive and objective evaluation methods. Unlike previous research, which predominantly focuses on English-language datasets, this study emphasizes Russian-language datasets, which have remained underexplored until now. The inclusion of datasets such as Gazeta, XL-Sum, and WikiLingua (for Russian) as well as CNN Dailymail and XSum (for English) ensures the multilingual applicability of the proposed approach. The proposed metric combines lexical (ROUGE, BLEU), structural (ROUGE-L), and semantic (BERTScore, METEOR, BLEURT) evaluation criteria, with weights assigned based on the importance of each metric. The results highlight the superiority of ChatGPT-4 on Russian datasets and GigaChat on English datasets, whereas models such as Gemini and YouChat exhibit limited capabilities in achieving semantic accuracy regardless of the dataset language. The originality of this research lies in the integration of multiple metrics into a unified system, enabling more objective and comprehensive comparisons of language models. The study contributes to the field of natural language processing by providing a tool for assessing the quality of language models.

Keywords: natural language processing, text paraphrasing, gigaChat, yandexGPT 2, chatGPT-3.5, chatGPT-4, gemini, bing AI, youChat, mistral Large

Dynamic pricing for real estate

2025. T.13. № 1. id 1761
Razumovskiy L.G.  Gerasimova M.A.  Karenin N.E. 

DOI: 10.26102/2310-6018/2025.48.1.008

The paper considers a mathematical model for dynamic price adjustment in real estate. The model is characterized by a finite number of real estate objects, a fixed sales horizon, and the presence of intermediate goals for sales and revenue. The model developed in this work addresses the case of variable total demand, incorporating the time value of money and the increase in real estate objects value as construction progresses. The general structure of pricing policy is studied, and an algorithm for determining prices under variable total demand is presented. Similar constructions are carried out for a model that accounts for the time value of money and the rising property value during construction. The case of a linear elasticity function is also examined as a basic but widely used practical scenario. Rigorous mathematical proofs of the results are provided, along with numerical simulations based on real estate data from a specific city over 3.5 years to compare different approaches to pricing policy formulation. The obtained results can be applied to effectively manage real estate pricing.

Keywords: dynamic pricing, real estate, price adjustment, variable total demand, cost of money, price increase, stages of construction

Multi criteria decision-making with the use of ELECTRE methods group

2024. T.12. № 4. id 1760
Latypova V.A. 

DOI: 10.26102/2310-6018/2024.47.4.030

Solving semi-structured problems is an essential part of the organizational system management. To simplify addressing these problems, different methods of multi-criteria decision-making are used. Among the basic widespread methods can be distinguished ELECTRE family methods. A large number of scientific works are devoted to the latter, but nevertheless they do not give enough coverage to the following problem: when using different ELECTRE methods for solving the same task, you can get an unequal result. The reason is that these methods possess their own specific features along with the common basis. A method of multi criteria decision-making with the use of a group of ELECTRE methods: ELECTRE I, ELECTRE Iv, ELECTRE Is, ELECTRE II, ELECTRE III, ELECTRE IV; considering results of each method and applying integral scores of alternatives in defining an overall task solution, is suggested in the paper to eliminate the problem. The proposed method has been validated on a test case of multi-criteria selection of a candidate for a vacant position in the hiring process in human resource management. The former allowed to smooth out the discrepancies in the results each of the methods of the group and identify a comprehensive solution.

Keywords: decision-making, ELECTRE, multicriteria choice, integral score, expert assessment

Mathematical formalization of agent conflict in achieving local goals

2024. T.12. № 4. id 1757
Rossikhina L.V.  Toropov B.A.  Makarov V.F.  Ovchinsky A.S. 

DOI: 10.26102/2310-6018/2024.47.4.035

The article presents a mathematical formalization of the conflict interaction of active agents focused on achieving their local goals in the process of achieving the common goal of the organizational system. The conflict is considered as a clash of active agents over a single resource, the possession of which will allow achieving a local goal. Three types of relations of an active agent to a given resource (possession, non-distinction, opposition) are presented, taking into account their usefulness in achieving a local goal. Mathematically, the conflict between agents is determined by the establishment of links between the elements of the set of active agents with the elements of the set of resources that caused the conflict. An algorithm for evaluating the mutual impact of active agents due to a resource in the core of the conflict is proposed, based on the construction of a bipartite graph "active agent - resource" and a graph of conflict in the organizational system. The weights of the arcs of a bipartite graph are defined as the values of the utility functions of the resource that caused the conflict in achieving local goals by active agents. The implementation of the algorithm allows to obtain an assessment of the degree of collision of active agents due to a single resource and an assessment of the interaction of active agents in the core of the conflict. An example of the algorithm execution is given.

Keywords: agent, conflict, resource, conflict core, local target, graph, graph weight matrix, organizational system

Using neural networks to determine dust pollution near open-pit coal mining areas based on Earth remote sensing data

2025. T.13. № 1. id 1756
Ozaryan Y.A.  Kozhevnikova T.V.  Tsygulev K.S.  Okladnikov V.Y. 

DOI: 10.26102/2310-6018/2025.48.1.007

The article examines the use of neural networks for detecting dust pollution near open-pit coal mining areas based on remote sensing data. The study involved coal mining sites located in various regions of the Russian Federation. Satellite images from the Sentinel-2 mission served as the primary data source and were processed using Quantum GIS software. An algorithm for forming the training dataset was developed, utilizing the visible and near-infrared spectral bands from the satellite imagery. The mask creation technology in the developed algorithm is based on the Enhanced Coal Dust Index and its subsequent clustering. U-Net is used as a neural network model. The trained model was tested on a validation dataset. The recognition accuracy was 59.3% for the Intersection over Union metric, 78.9% for the Precision metric, 80.6% for the F1 metric, and 95.5% for the Accuracy metric. This level of accuracy is attributed to the limited volume of training data. The potential for improving accuracy through increasing the sample size in conjunction with optimizing the parameters of the neural network is discussed. The results obtained provide a basis for assessing the environmental impacts of coal mining activities and for developing measures to ensure ecological safety based on these findings.

Keywords: dust pollution, earth remote sensing, machine learning, clustering, neural network

Model of stochastic electrical load in the residential sector using the Weibull probability density function

2024. T.12. № 4. id 1755
Borovskiy A.V.  Yumenchuk A.A. 

DOI: 10.26102/2310-6018/2024.47.4.034

The article proposes a method for simulating daily schedules of electrical loads in the residential sector based on convolution theory. The authors consider models using the Weibull probability density and the probability density of the normal distribution for shifts in the time of switching on household appliances. The goal is to select a model, the results of which most accurately correspond to the actual energy consumption in the residential sector. First, the energy consumption of household appliances is considered, and the results are compared without a shift and with a shift in the Weibull probability density. The correct variant of comparing the results of simulation modeling using the Weibull probability density with the results of modeling using the probability density of the normal distribution is determined. Next, the energy consumption of households in rural areas is considered, which takes into account the use of electric heating devices by the population. This makes it possible to carry out simulation modeling of energy consumption of settlements or their individual areas. The results are compared with real data on the energy consumption of the village. Based on the results of the work, a model was selected that most accurately reflects the real dynamics of changes in energy consumption levels in the residential sector. The reasons why the choice was made in favor of one of the models are described. Sufficient accuracy of simulation modeling using the selected model has been demonstrated.

Keywords: stochastic energy consumption models, simulation modeling, daily energy consumption schedule, weibull probability density, normal distribution

Influence of the TensorFlow library’s version on the quality of code generation from an image

2024. T.12. № 4. id 1754
Nikitin I.V. 

DOI: 10.26102/2310-6018/2024.47.4.040

This study compares the efficiency of training models that implement two different approaches: complicating the original neural network architecture, or maintaining the architecture while improving the tools used in the training framework. Attempts to complicate the architecture of the solution for generating source code based on an image lead to solutions that might be difficult to support in the future. At the same time, such improvements do not use more modern tools and libraries that such systems are built upon. The relevance of the study is due to the lack of attempts to use more modern and relevant libraries. In this regard, during the experiment to compare the indicators of models of three versions of image-based source code generation systems: the original pix2code system, its complex version, and the version with a modern version of the TensorFlow library - in the process of their training, it was revealed that approaches with a complex architecture and the current TensorFlow have the same indicators, higher quality than the original pix2code. Based on the experiment, we can conclude that updating the TensorFlow library can provide an additional increase in the quality of results that the image-based source code generation system can predict.

Keywords: code generation, image, machine learning, tensorFlow, keras, domain-specific language

Integration of the processes of digital transformation of enterprises and training of specialists in the field of automation of industrial and agricultural production

2024. T.12. № 4. id 1753
Lishilin M.V.  Pryakhin V.N.  Karapetyan M.A. 

DOI: 10.26102/2310-6018/2024.47.4.032

Digital transformation in industry and agriculture is aimed at driving intensive economic growth and revolutionizing project management at the state level. It involves changing management strategies and operational models through the integration of information systems. Key challenges include increasing the digital maturity of agricultural enterprises, involving stakeholders in the digital transformation process, improving access to technological information, and expanding the use of technologies like the Industrial Internet of Things and digital twins. To achieve strategic goals, it is crucial not only to enhance the digital maturity of enterprises but also to prepare qualified personnel. The integration of university and enterprise information systems, the use of virtual computer labs in education, and data-driven management are essential elements of successful digital transformation. A critical factor is the systematic use of tools such as virtual computer labs as a technological foundation for integrating production data into the educational process. However, further development of methodological and regulatory frameworks for university and enterprise information systems is needed. This will improve the quality of specialist training and actively involve universities in the processes of industrial digital transformation.

Keywords: digital transformation, virtual computer lab, information technology, training of personnel, knowledge management