Information, Computing and Intelligent systems

Environment for Tuning Parameters of a Multithreaded Program Developed Using a Dependency Graph

Kostiantyn Nesterenko — 2025-09-19

With the advent of multi-core central processors, multithreading has become the most widespread practice for improving program execution performance. However, the development of a multithreaded program remains a rather complex process. To simplify this process and enhance the performance of the resulting program, various methods for managing thread-based execution are often employed.

One such method is the method of managing the execution of tasks of a multithreaded program according to a given dependency graph. This method significantly reduces the resource intensity of program development and increases program performance by employing a lockless approach to multithreaded programming.

Nevertheless, the challenge of efficient utilization of computational resources remains relevant and can only be addressed through the careful design of parallel computations. In particular, identifying the configuration parameters for a multithreaded program that ensure optimal resource utilization is a resource-intensive and complex task, even for highly qualified specialists.

This study examines existing approaches to tuning the parameters of multithreaded programs to achieve the most efficient execution. It proposes the use of an environment for tuning multithreaded program parameters based on the method of managing the execution of tasks of a multithreaded program according to a given dependency graph. The accuracy of the resource efficiency metrics obtained through this environment was experimentally validated. A practical example demonstrates the application of the environment in the development of a multithreaded program. The use of the environment also facilitates the configuration process of multithreaded program parameters.

Method for On-line Acceleration of Dependent Operation Chains Using Redundant Code on FPGA with System of Linear Equations Example

Illya Verbovskyi — 2025-09-19

This study examines methods for accelerating the execution of dependent operation chains in on-line mode through parallel processing of operands at the bit level in redundant code on field-programmable gate arrays (FPGA). The object of research is the hardware implementation of the Thomas algorithm for solving systems of linear equations with tridiagonal matrices on FPGA platforms. The aim is to develop a method for accelerating dependent operation chains in on-line mode using redundant code with minimization of pin count requirements. The methodology employs algorithmic analysis, hardware modeling using Active HDL, performance evaluation based on timing characteristics and resource utilization on Altera Cyclone III EP3C5E144 platform, with verification performed using Quartus.

The results reveal bottlenecks in traditional FPGA implementations of the Thomas algorithm and demonstrate that the proposed optimized method provides over threefold performance improvement while maintaining constant pin count regardless of operand bit depth. The developed computing module architecture enables bit-wise parallel data processing and supports a modified version of the Thomas algorithm adapted for on-line operation. The scientific novelty lies in combining redundant code with on-line computation techniques to simultaneously achieve computational acceleration and hardware implementation simplification. The practical value is determined by the applicability of the proposed approach to resource-constrained FPGA platforms, ensuring efficient implementation of computationally intensive algorithms with dependent operation chains.

Method for Software Pipelining on Graphical Processing Units

Artemii Vinokurov — 2025-09-19

Graphics Processing Units (GPUs) play a significant role in high-end computations, including artificial intelligence. However, the GPU hardware is often underloaded. This forces an increased volume of GPU hardware to maintain a high throughput for task execution. The low loading of the GPU resources remains an actual problem and it needs to be solved now. Therefore, it is essential to seek methods that enhance GPU loading.

The research object is computational processes in modern processors, especially in GPUs. The purpose of this study is to review the software pipelining approach, its advantages and disadvantages, the techniques that can be used in it, including both instruction-level and decoupled versions, and to assess the effectiveness of this approach for the GPU.

To satisfy the requirements, different analysis methods were used. First, the architectural requirements to apply software pipelining were reviewed. Second, the original formulation and historical development of the approach were examined. Third, different levels of parallelisation to implement software pipelining were explored. Finally, C-slowing was proposed as an optimisation technique to overcome the adversities of the underutilisation of computational resources.

The research has revealed the abundance of proper software pipelining for GPU implementations. Whereas existing works review the possibilities of this technique, they are often overlooked in contrast to simpler multi-threading techniques. However, investigated researchers have defined the crucial limiting factor to computational resources as a constraint by memory overloading, specifically the pipelining registers. To address this, the C-slowing approach was suggested and theoretically evaluated. It demonstrated a possible increase of over 30% in GPU loading for the analysed algorithm, proving its applicability.

In conclusion, the software pipelining approach shows decent potential to optimise GPU algorithms, requiring further investigation. C-slowing could be utilised to handle the problem of underutilisation of computation.

A Multimodal Retrieval-Augmented Generation System with ReAct Agent Logic for Multi-Hop Reasoning

Denys Yuvzhenko — 2025-09-19

The rapid advancement of generative artificial intelligence models significantly influences modern methods of information processing and user interactions with information systems. One of the promising areas in this domain is Retrieval-Augmented Generation (RAG), which combines generative models with information retrieval methods to enhance the accuracy and relevance of responses. However, most existing RAG systems primarily focus on textual data, which does not meet contemporary needs for multimodal information processing (text, images, tables).

The research object of this work is a multimodal RAG system based on ReAct agent logic, capable of multi-hop reasoning. The main emphasis is placed on integrating textual, graphical, and tabular information to generate accurate, complete, and relevant responses. The system's implementation utilized the ChromaDB vector storage, the OpenAI embedding generation model (text-embedding-ada-002), and the GPT-4 language model.

The purpose of the study is the development, deployment, and empirical evaluation of the proposed multimodal RAG system based on the ReAct agent approach, capable of effectively integrating diverse knowledge sources into a unified informational context.

The experimental evaluation utilized the Global Tuberculosis Report 2024 by the World Health Organization, containing various textual, graphical, and tabular data. A specialized test set of 50 queries (30 textual, 10 tabular, 10 graphical) was created for empirical analysis, allowing comprehensive testing of all aspects of multimodal integration.

The research employed methods such as semantic vector search, multi-hop agent-based planning with ReAct logic, and evaluations of answer accuracy, answer recall, and response latency. Additionally, an analysis of response speed dependence on query volume was conducted.

The obtained results confirmed the high efficiency of the proposed approach. The system demonstrated an answer accuracy of 92%, answer recall of 89%, and ensured complete (100%) coverage of all data types. The average response time was approximately 5 seconds, meeting interactive system requirements. Optimal parameters were experimentally determined (for example, parameter k = 6, classification threshold 0.35, and up to three reasoning iterations), ensuring the best balance among completeness, speed, and operational efficiency.

The study's findings highlighted significant advantages of the multimodal agent-based approach compared to traditional textual RAG solutions, confirming the promising direction for further research.

Hexacopter-Based Cyber-Physical System for Water Sampling with Adaptive Path Planning and Multi-Drone Coordination

Andrii Pysarenko — 2025-09-19

The object of this study is a hexacopter-based cyber-physical system designed for autonomous water sampling to support environmental monitoring, addressing the problem of inefficient control under dynamic conditions. The subject focuses on integrating physical flight control and water sampling operations with cyber supervisory functions, including real-time waypoint navigation, task scheduling, and multi-drone coordination, validated as a current system component. The research investigates the system’s performance under payload variations and wind disturbances, ensuring robustness and precision in adverse environments. The purpose is to improve efficiency of water sampling through this CPS, achieving enhanced flight stability and positioning accuracy via a cascade PID control system, optimizing mission planning with adaptive cyber strategies, and increasing scalability through multi-drone operations. This approach aims to surpass traditional UAV systems by using physical-cyber integration for precise, robust, and scalable water quality assessment.

The methodology combines simulation-based and analytical techniques to develop and assess the hexacopter CPS. A 6-degree-of-freedom mathematical model, based on Newton-Euler equations, was constructed in MATLAB/Simulink to simulate hexacopter dynamics, incorporating payload and wind effects. The cascade PID control system was tuned using the Ziegler-Nichols method, with iterative optimization to reduce overshoot and settling time across three scenarios: 1 kg static payload, 1.5 kg dynamic payload, and 5 m/s wind. The cyber supervisory system, implemented in ROS 2, employs graph-based algorithms (Dijkstra’s for waypoint navigation, list-scheduling for task allocation) and a consensus protocol for multi-drone coordination, tested in a 500x500 m² environment. Performance metrics, such as position root mean square error (RMSE) and attitude errors, were analyzed to evaluate system effectiveness.

Results demonstrate significant improvements in water sampling capabilities. The cascade control system achieved a 40–50% reduction in position RMSE and maintained attitude errors
within ±0.8° to ±1.2°, ensuring stable flight. The cyber-physical framework reduced mission time
by 15% through adaptive path optimization, while multi-drone coordination increased sampling coverage by 20%, enhancing scalability. These outcomes reflect the system’s precision and robustness that highlight novel control and coordination strategies with practical value for environmental monitoring. The study provides a foundation for future ecological applications.

Detection Method of Fraudulent Payment Transaction Based on C-Score Metric

Dmytro Korynetskyi — 2025-09-19

Fraud detection for payment transactions is a cost-sensitive task, as the costs associated with misclassification – such as missing a fraudulent transaction or incorrectly blocking a legitimate one – can vary significantly depending on business priorities. Traditional evaluation metrics, particularly the F1-score, ignore this asymmetry, creating a need for more flexible approaches. This research focuses on developing a method for building adaptive, cost-sensitive fraud detection systems. The aim is to develop a method that enables the practical application of the cost-sensitive C-score metric to configure a multi-level decision logic. The paper also presents a possible software architecture for its implementation.

The proposed two-phase method (offline calibration and online scoring) uses the C-score metric to determine multiple decision thresholds corresponding to different business scenarios. Its validation was conducted on the public “Credit Card Fraud Detection” dataset using the XGBoost algorithm. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to overcome the severe class imbalance in the data, and a comparison was made against the traditional F1-score-based approach.

The experimental results showed that the proposed approach allows for the identification of two distinct thresholds from a single classifier. The first threshold ensures high precision, making it suitable for automated blocking of payment transactions with minimal false positives. The second threshold, focused on high recall, enables the selection of suspicious payment transactions for subsequent manual review. It was also confirmed that the SMOTE significantly contributed the model's class separation ability, thereby increasing the reliability of calibrating these thresholds. Based on the method, a practical blueprint for a service-oriented architecture is proposed for creating flexible and configurable anti-fraud systems.

Hybrid Path Planning Method for Unmanned Ground Vehicles Swarm in Dynamic Environments

Myroslav Rudnytskyi — 2025-09-19

Unmanned ground vehicles (UGVs) have significant potential across various applications. These include automation of the agricultural tasks, inspection and maintenance within construction and industrial sectors, automation of complex assembly processes and infrastructure repairs, explosives disposal, automation of logistical operations, search-and-rescue missions, and expeditions to hard-to-reach or hazardous areas. However, a key challenge limiting their widespread deployment is autonomous navigation, which remains a significant problem due to dynamic environments characterized by constantly changing obstacle configurations, unpredictable scenarios, and the need for rapid real-time decision-making to ensure safe and stable movement.

The object of this paper is a hybrid path planning for the autonomous navigation of unmanned ground vehicles swarm within a simulated environment. The research aims to develop autonomous navigation method for the unmanned ground vehicles swarm by employing a hybrid approach designed to enhance the efficiency of obstacle avoidance and improve the adaptability to dynamic environments.

To achieve this goal, a novel autonomous swarm navigation method based on a hybrid approach is proposed. This approach differs from existing solutions by employing the A* path planning algorithm with incorporated traversal costs on the map for global-level navigation and the artificial potential field (APF) algorithm, that supports linear and V-shaped formations for local-level navigation.

The research findings indicate that the proposed method allows the swarm to perform optimal path planning, considering traversal costs, and effectively avoid local minimum problems that are inherent to the artificial potential field method. The successful performance of the method within the simulated environment demonstrates its potential for future validation in real-world scenarios and practical applications involving swarms of unmanned ground vehicles operating in challenging environments. At the same time, the study identified challenges related to swarm size scalability in narrow spaces, defining directions for further improvements.

Vision-Based Neighbor Selection Method for Occlusion-Resilient Uncrewed Aerial Vehicle Swarm Coordination in Three-Dimensional Environments

Oleksii Smovzhenko — 2025-09-19

Uncrewed aerial vehicle (UAV) swarms provide superior scalability, reliability, and efficiency compared to individual UAVs, enabling transformative applications in search and rescue, precision agriculture, environmental monitoring, and urban surveillance. However, their dependence on Global Navigation Satellite Systems (GNSS) and wireless communication introduces vulnerabilities like signal loss, jamming, and scalability constraints, particularly in GNSS-denied environments. This study advances swarm robotics by developing a novel neighbor selection method for occlusion-resilient, vision-based coordination of UAV swarms in three-dimensional (3D) environments, addressing the problem of visual occlusions that disrupt decentralized flocking. Unlike prior research focusing on planar settings or communication-dependent systems, we model swarm coordination as an artificial potential field problem. Additionally, we evaluate performance through metrics like minimum nearest neighbor distance (collision avoidance), alignment (velocity synchronization), and union (cohesion). Using simulations in point mass and realistic quadcopter dynamics (Gazebo with PX4) environments, we assess swarm behavior across dense, default, and sparse configurations. Our findings reveal that occlusions degrade alignment (below 0.9) and distances (below 0.5 m) in dense swarms exceeding 70 agents, increasing collision risks. Our novel method, incorporating metric, topographic, and Delaunay strategies, mitigates these effects. Topographic selection achieves high alignment (above 0.9) in small swarms (up to 50 agents), while Delaunay ensures perfect cohesion (union = 1) and robust alignment across all swarm sizes. Validation in simulations confirms these results. Furthermore, our method enables communication-free coordination that matches or surpasses communication-enabled performance, with topographic selection outperforming (alignment above 0.9 vs. 0.85) in small swarms and Delaunay excelling in larger ones. This result eliminates the need for inter-agent communication, enhancing resilience and bandwidth efficiency. These findings establish a scalable, infrastructure-independent framework for UAV swarms, with practical value for autonomous operations in complex, occlusion-prone environments.

Statistical Evaluation of Parameters in Nonlinear Models Using Integral-Form of Least Squares Method and Differential Non-Taylor Transformations

Oleksii Pysarchuk — 2025-09-19

The article presents the method for statistical leaning of nonlinear model parameters, its principle and efficiency of application. It proposes a solution to the shortcomings of using the differential spectra balance approach through the integral form of least squares in the scheme of differential non-Taylor transformations.

The object of the study is the process of statistical learning of nonlinear model parameters. The purpose of this paper is to formulate the statistical learning method for evaluation of nonlinear model parameters using LSM in integral form and differential non-Taylor transformations. It is relevant in many areas of modern activity and is necessary for applying the statistical training methodology to a more complex time series, as well as increasing the accuracy of the received expectations.

To achieve this goal the statistical learning methodology was proposed, which is based on the creation of process model in integral form of least squares with simplification using non-Taylor transformations. It differs from existing approaches by incorporating all the available differential discretes in the created model, which allows for better predictability and circumvents the problem of unequal count of discretes inside the models, which allows for better application of the method for different model forms. The algorithm for the process was formed, using which it can be applied different models. In the paper several experiments were conducted to verify the efficacy of the proposed method in different situations. These experiments use generated datasets that are polluted with stochastic errors to better simulate real data.

The results of modeling are shown, and the statistical characteristics of the obtained expectation are compared with the results of the application of the statistical training methodology using the differential spectra balance. During the study, features of the technique were found that must be considered for its application to data with a high number of stochastic deviations. Based on the obtained metrics, a conclusion is made about the effectiveness of using the technique relative to time series reflecting processes of different nature.

Waste Management Model with Timed Colored Petri Nets

Hryhorii Rozhkov — 2025-09-19

In Progress

Drone Swarm Control Model Based on High-Level Petri Nets

Valentyn Ivankov — 2025-09-19

The rapid growth of unmanned aerial vehicle (UAV) applications in the modern world imposes significant demands on the reliability of control logic. An error in the sequence of stages can lead at best to inefficient battery usage or violations of airspace regulations, and at worst to an accident with loss of the vehicle and potential harm. Control is usually implemented using scripts or behavior trees, which complicates maintenance. The reason is that the size of the source files quickly increases, and when it becomes necessary to add new functionality or modify existing logic, there is a risk of introducing vulnerabilities by failing to account for all possible situations. This is why High-Level Petri Nets (HLPN) were chosen, as this method addresses the problem of formally describing the control system and allows the system to be easily scaled or modified in any way.

The aim of the study is to develop and validate a model based on HLPN that will serve as the single source of truth for UAV swarm control. In the proposed model, the places correspond to flight stages, and the tokens carry numerical parameters such as battery charge, coordinates, and telemetry. Thus, a single scheme simultaneously describes discrete events and constraints. For each transition, conditions are formalized to verify the possibility of its execution, such as checking the minimum required battery level or verifying location.

The methodology includes several stages. First, the network structure is formally defined. Then, based on this structure, a Python model is built that implements the developed network, controls movement between states, and ensures the correct sequence of transition firings. After developing the model, testing and analysis of the obtained results are performed.

The results show that using HLPNs to build a model for verifying commands in a discrete mode indeed ensures a correct description of transitions between states and increases the reliability and survivability of the developed control system model, while also significantly reducing maintenance efforts. The developed model is easily adaptable to route changes, addition of sensors, or functional expansion.

Decentralized Task Allocation Method in Hierarchical IoT Systems Using Fuzzy Logic

Oleksandr Rolik — 2025-09-19

The use of fog and edge computing extends the computational capabilities of IoT systems to the network edge, contributing to the minimization of delays during task execution. Osmotic computing complements distributed computing by providing seamless integration between computational environments through dynamic migration of micro-elements across different hierarchy tiers according to current load conditions and resource availability. However, within the concept of osmotic computing, a key challenge remains the effective management of task allocation under conditions of uncertainty, dynamism, and heterogeneity of the IoT environment. The aim of this study is to improve the efficiency of resource utilization and task allocation in hierarchical IoT systems based on osmotic computing under uncertain and dynamically changing environmental conditions. The object of the study is the process of task allocation in multi-tier IoT systems that include cloud, fog, and edge computing. The subject of the study is methods and models for task allocation and computing resource management in IoT systems using the osmotic computing paradigm.

The paper presents a three-tier hierarchical management model built on cloud, fog, and edge environments, which implements a centralized-decentralized management approach. Each tier is represented by a set of computing nodes and a management system that performs local task allocation, resource state monitoring, and micro-element management. The management system of the lower tier is subordinate to the higher-tier management system in the hierarchy. A method for decentralized task allocation in hierarchical IoT systems using fuzzy logic has been developed. The allocation method includes two decision-making stages using a fuzzy inference system: determining the direction of task allocation and selecting the optimal computing node for its execution. The determination of task allocation direction is carried out based on task characteristics, and the suitability rating of computing nodes is determined considering task execution latency, resource utilization efficiency, and load balancing. The task is assigned to the node with the maximum rating. The use of fuzzy logic ensures rational decision-making under conditions of uncertainty in real-time, which is characteristic of highly heterogeneous and dynamic IoT environments.

Experimental modeling and investigation of the method were carried out using the iFogSim simulation environment. The research results show that the percentage of locally executed tasks remains virtually unchanged with different numbers of tasks, indicating stability in decision-making. Increasing the intensity of task generation leads to an increase in task computation latency due to increased load on computing nodes, while task assignment latency and response latency remain unchanged. The method demonstrated adaptability in task allocation for different types of tasks.

Hybrid Voting Model for Decentralized Autonomous Organizations with Dynamic Quorum

Roman Serebriakov — 2025-09-19

The article examines the problem of balancing security and flexibility in decision-making mechanisms within decentralized autonomous organizations (DAOs), which operate without centralized control through the use of smart contracts. To this end, two main voting models employed in DAOs are analyzed: the conjunctive model, which requires unanimous approval of a proposal by all participant groups, and the disjunctive model, where approval from a single group is sufficient. Both models have significant advantages and drawbacks: the former ensures a high level of security and protection of all parties’ interests but considerably slows down the decision-making process, while the latter provides speed and scalability but introduces risks of centralized influence.

In response to these challenges, a hybrid voting model is proposed, in which the type of logic is determined by the nature of the proposal. Specifically, critical changes, such as updates to governance rules or quorum parameters, must involve all groups, whereas routine operational matters can be decided through a simplified disjunctive procedure. The implemented smart contract architecture supports both mechanisms and enables DAOs to dynamically adjust quorum thresholds through separate governance proposals.

To evaluate the effectiveness of the model, a simulation of 1,000 voting processes was conducted under four different scenarios of participant activity: balanced, one-sided, and low overall participation. The results showed a reduction in the probability of deadlock situations and an increase in the share of successful votes when hybrid logic was applied, particularly under conditions of low or asymmetric participation. In addition, special attention was given to gas cost optimization: the disjunctive approach allows vote counting to be stopped once a quorum is reached by one group, thus reducing overall computational expenses.

Therefore, the proposed solution appears promising for both financial DAOs and decentralized infrastructures, particularly the Internet of Things, where speed, scalability, and secure coordination are especially important.

Method for Detecting Anomalous Bearing Measurements Based on the Analysis of Distribution Density Histogram

Illia Starodubtsev — 2025-09-19

Subject of the research. Statistical models and algorithmic techniques for processing bearing-measurement datasets with the goal of detecting and filtering anomalous values. Object of the research. Methods for handling outliers in bearing-measurement datasets. Purpose of the work. To develop a method that improves the reliability of determining the direction to a radio-emission source in a single-station radio direction-finding system.

The article proposes a method for detecting anomalous measurements by analysing the histogram of their probability-density distribution. Propagation characteristics of very-high-frequency radio waves complicate radio direction finder operation, producing both random and systematic anomalies. The approach examines bearing-density histogram intervals rather than individual measurements, enabling an integrated assessment of sample structure and anomaly identification.

The method’s performance was assessed on bearing datasets of 100, 500 and 1000 measurements at confidence levels of 80–95 %. The evaluation of the method’s robustness was made for small and large arrays. The study analysed the influence of systematic and random anomalies on histogram construction and confidence-interval determination. Key metrics were bearing-estimation accuracy, root-mean-square error and method stability across varying confidence levels.

Results show the method minimises the impact of anomalous measurements at the tails and within the main cluster. Experiments demonstrate a reduced root-mean-square error and a consistent rise in direction-finding accuracy after anomaly removal. These findings confirm the technique’s potential for further development and integration into synthesised radio direction-finding systems, where bearing reliability and precision are critical.

Automatic Network Reconfiguration Method with Dynamic IP Address Management

Anatolii Haidai — 2025-09-19

In the context of growing cyber threats, systems capable not only of detecting anomalies in the operation of network infrastructure but also of promptly responding to them without administrator intervention are becoming increasingly relevant. This paper proposes a method for automatic network reconfiguration based on the integration of the Zabbix monitoring system with the pfSense network gateway functionality. Such a system enables centralized control of the operating system status, resource usage, and network activity, while also allowing for automatic changes to host IP addresses, routing adaptation, and connection restrictions according to defined security policies.

The aim of the study is to develop a method for automatic network monitoring and reconfiguration with dynamic IP address changes to improve the effectiveness of cyber threat mitigation. The object of the study is the processes of information security management in computer networks. The subject of the study includes methods of anomaly detection and automatic response through modification of network parameters using Zabbix and pfSense.

In the context of automatic response to detected threats, the method of comprehensive monitoring of client host operating systems has been formalized, including subsequent analysis of logs, user actions, resource load, network port usage, and interaction with external services. A methodology for network reconfiguration after anomaly detection has been developed and implemented: in particular, changing the IP address while maintaining functionality in a minimal network access configuration and isolating the node using pfSense. Scripts for Windows client OS were employed, interacting with the Zabbix and pfSense APIs, thus ensuring dynamic and fully automated operation.

Testing results of the proposed system in a simulated environment confirm its effectiveness. Compared to manual or partially automated solutions, incident response time was reduced, and the risk of attack propagation within the network was minimized.

Mathematical Model of Clustering of Informational Messages with Indicators of Activity for the Information Content by Tone and Areas of Society Activity

Danylo Baran — 2025-09-19

The mathematical model of clustering of information messages has been further developed, which is based on the frequency analysis of their tonality using Natural Language Processing methodologies with the support of large language models; OLAP visualization of clustering results and is distinguished by an established system of indicators of information content activity by areas of society activity with hierarchical compression of incoming Big Data arrays, which determines the database model for their storage. This provides an improvement to the analysis of information messages in global information networks by taking into account many factors in the areas of society activity.

The main idea and goal of the mathematical model for clustering information messages is to implement a sequence of preparation stages for detecting critical activity of the information content in global media. In practice, this is the establishment of a list and the determination of indicator values that measure content activity in primary messages, followed by their transformation into a time series – a systematized dataset. In the conditions of high density of the flow of occurrence, dynamics of development, and transformation of information content, a Big Data structure of information messages is taken into account. Therefore, the clustering model, apart from division by informational features, should provide the hierarchical compression of incoming Big Data arrays.

Research objective: development of a mathematical model of clustering information messages with indicators of information content activity by tone and spheres of activity of society. Research subject: methods of clustering information messages. Research object: process of clustering information messages.