The use of machine learning when working with text documents significantly increases the efficiency of work and expands the range of tasks to be solved. The paper provides an analysis of the main methods of presenting data in a digital format and machine learning algorithms, and a conclusion is made about the optimal solution for generative and discriminative tasks.

Keywords: machine learning, natural language processing, transformer architecture models, gradient boosting, large language models

Modern web applications are becoming more complex and feature-rich, which creates the need for effective tools for dependency management, optimization, and project assembly. Buider allow you to optimize your code, which directly affects the download and execution speed of applications. The purpose of the work is to conduct a comparative analysis of JavaScript builders: Webpack, Parcel, and Rollup in order to identify their advantages and disadvantages from the point of view of frontend development ergonomics. This includes evaluating the convenience of configuration, resource efficiency, build speed, and other factors that affect developer productivity and the final quality of web applications. Practical testing of the builders was carried out using the example of a standard web project. The ergonomics of working with tools is evaluated: criteria are identified and a comparison is made based on the data obtained. Recommendations have been developed for choosing the optimal tool for various types of projects in front-end development. The research results can be used as a basis for training new specialists, as well as for improving existing practices in developing web applications when making informed decisions on the choice of technologies for long-term projects.

Keywords: web development, development efficiency, ergonomics, frontend development, testing, builder

This paper explores the content-based filtering approach in modern recommender systems, focusing on its key principles, implementation methods, and evaluation metrics. The study highlights the advantages of content-based systems in scenarios that require deep object analysis and user preference modeling, especially when there is a lack of data for collaborative filtering.

Keywords: сontent - oriented filtering, recommendation systems, feature extraction, similarity metrics, personalization

This article presents a comprehensive analysis of Russian-language texts utilizing neural network models based on the Bidirectional Encoder Representations from Transformers (BERT) architecture. The study employs specialized models for the Russian language: RuBERT-tiny, RuBERT-tiny2, and RuBERT-base-cased. The proposed methodology encompasses morphological, syntactic, and semantic levels of analysis, integrating lemmatization, part-of-speech tagging, morphological feature identification, syntactic dependency parsing, semantic role labeling, and relation extraction. The application of BERT-family models achieves accuracy rates exceeding 98% for lemmatization, 97% for part-of-speech tagging and morphological feature identification, 96% for syntactic parsing, and 94% for semantic analysis. The method is suitable for tasks requiring deep text comprehension and can be optimized for processing large corpora.

Keywords: BERT, Russian-language texts, morphological analysis, syntactic analysis, semantic analysis, lemmatization, RuBERT, natural language processing, NLP

Introduction: Mobile Gaming Addiction (MGA) has emerged as a significant public health concern, with the World Health Organization recognizing it as a gaming disorder. Russia, with its growing mobile gaming market, is no exception. Aims and Objectives: This study aims to explore the feasibility of using neural networks for early MGA detection and intervention, with a focus on the Russian context. The primary objective is to develop and evaluate a neural network-based model for identifying behavioral patterns associated with MGA. Methods: A proof of concept study was conducted, employing a simplified neural network architecture and a dataset of 101 observations. The model's performance was evaluated using standard metrics, including accuracy, precision, recall, F1-score, and AUC-ROC score. Results: The study demonstrated the potential of neural networks in detecting MGA, achieving an F1-score of 0.75. However, the relatively low AUC-ROC score (0.58) highlights the need for addressing dataset limitations. Conclusion: This study contributes to the growing body of literature on MGA, emphasizing the importance of considering regional nuances and addressing dataset limitations. The findings suggest promising avenues for future research, including dataset expansion, advanced neural architectures, and region-specific mobile applications.

Keywords: neural networks, neural network architectures, autoencoder, digital addiction, gaming addiction, digital technologies, machine learning, artificial intelligence, mobile game addiction, gaming disorder

The article focuses on the application of machine learning methods for predicting failures in industrial equipment. A review of modern approaches such as Random Forest, SVM, and XGBoost is presented, with emphasis on their accuracy, robustness, and suitability for engineering tasks. Based on the analysis of real-world data (temperature, pressure, vibration, humidity), models were trained and compared, with XGBoost demonstrating the best performance. Key parameters influencing failures were identified, and a recommendation system was proposed, combining statistical analysis and predictive modeling. The developed solution enables timely detection of failure risks and optimization of maintenance processes.

Keywords: machine learning, predictive modeling, equipment management, failure prediction, data analysis

For neural network algorithms to work successfully when processing 3D point clouds, it is necessary to provide a detailed point cloud of the external environment. A similar task arises when a manipulative robot is operating in a new environment, where before processing a cloud of scene points, it is necessary to obtain a detailed representation of the external environment using an RGB-D camera mounted on the end link of the robot. To solve this problem, this study proposes an algorithm for adaptive control of a manipulative robot to build a model of the external environment. By applying an adaptive approach, during the research of the external environment, the manipulative robot moves the RGB-D camera, taking into account the changes in the current environment model introduced by the previous RGB-D image. The results obtained allow us to judge the effectiveness of the proposed approach, showing that due to adaptability, it allows us to achieve high scene coverage rates.

Keywords: environment model, manipulative robot, adaptive control algorithm, surface reconstruction, RGB-D camera, visual information processing, TSDF volume

This paper examines methods for modeling the spread of infectious diseases. It discusses the features of the generalized compartmental approach to epidemic modeling, which divides the population into non-overlapping groups of individuals. The forecast of models built using this approach involves estimating the size of these groups over time. The paper proposes a method for estimating model parameters based on statistical data. It also introduces a method for estimating confidence intervals for the model forecast, based on a series of stochastic model runs. A computational experiment demonstrates the effectiveness of the proposed methods using data on the spread of influenza in European countries. The results show the model's efficiency in predicting the dynamics of the epidemic and estimating confidence intervals for the forecast. The paper also justifies the applicability of the described methods to modeling chronic diseases.

Keywords: epidemic modeling, computer modeling, compartmental models, SIR, stochastic modeling, parameter estimation, confidence interval, forecast, influenza

The paper presents a simulation of flight control of an unmanned aerial vehicle (UAV). A distributed control system is proposed that sequentially includes internal and external circuits to control the state of motion of the aircraft. The control efficiency of a cascade PD controller (proportional-differential) is higher than that of a traditional PID controller (proportional-integral-differentiating). A new cascade control algorithm with a PD controller is proposed. First, the dynamics of the UAV is modeled based on the Newton-Euler method, then the state of motion of the device is controlled by a distributed control system based on cascaded levels of proportional derivatives of the internal and external contours. The simulation results show that the controller, developed on the basis of proportional-derivative control speed of internal and external circuits, is able to achieve fast tracking of the position and orientation of the UAV in case of external disturbances and has good control quality. The developed algorithm has increased the control efficiency by 5-7% compared to the traditional PID algorithm.

Keywords: Unmanned Aerial Vehicle, PID controller, Cascade PD controller, Algorithm Optimization, UAV Control Algorithm

The article discusses the process of developing and modeling an impeller for an unmanned aircraft of the airplane type. Aerodynamic and strength calculations were carried out, key design parameters were determined, including the number of blades, engine power and choice of material. The developed models were created in the CAD system Compass 3D and manufactured by 3D printing using PETG plastic. Impeller thrust tests were carried out depending on engine speed, which allowed the design to be optimized for maximum efficiency.

Keywords: impeller, unmanned aircraft, aerodynamics, 3D modeling, 3D compass, additive technologies, thrust, testing, APM FEM

This article examines the issue of increasing the performance and scalability of transactional systems using the example of a sharded blockchain architecture. Particular attention is paid to the use of a search query—based approach, a model in which the user's transactional intentions are processed asynchronously and aggregatively. This allows you to significantly reduce the load on the network and achieve high throughput without compromising the user experience. The proposed architecture is based on fully controlled smart accounts, embedded wallets, and third-party processing of user search queries through a specialized module. As a result, scalability is achieved that meets the requirements of high-frequency trading and automated decentralized applications. Key performance metrics and application scenarios outside the financial sector are presented.

Keywords: blockchain, distributed ledger, transactional systems, distributed systems

Assessing the technical condition of equipment is an important task for ensuring operational strategy and planning maintenance work at an enterprise. One approach to evaluating equipment condition is the use of a well-known indicator called the 'technical condition index,' the calculation methodology for which has been approved by the Ministry of Energy of the Russian Federation. This methodology also proposes a scale for assessing the level of equipment technical condition. However, the question of the threshold or critical value of this indicator, which can determine the equipment's unsuitability for further operation, remains unresolved. This paper proposes a methodology for determining the threshold value of a modified technical condition index based on the allowable probability of failure-free operation of equipment using statistical methods. The novelty of the work lies in the proposed methodology for determining the threshold value of a modified technical condition index, developed by the author, which uses objective data for evaluation, unlike the subjective assessments of experts in existing methodologies. The proposed method was tested on a set of statistical data on the degradation of turbofan engines from NASA.

Keywords: technical condition index, modified technical condition index, threshold value, probability of equipment failure-free operation, complex technical object

The combination of systems analysis and long-term planning is a crucial factor for ensuring sustainable development and enhancing the competitiveness of enterprises. In this context, the use of the Event Tree Analysis method plays a key role in assessing the achievement of strategic goals, tasks, and identifying potential risks. This study focuses on the development and application of an event tree to analyze various aspects of system operations, including goal setting, strategy development, and task execution. The application of the ETA method not only allows for modeling possible event scenarios but also enables the development of risk mitigation measures, contributing to long-term sustainability and successful system functioning.

Keywords: event tree, system analysis, strategic planning, risk management, threat minimization, sustainable development, enterprise competitiveness, quantitative analysis, qualitative analysis, dependent events, conditional probabilities, protective mechanisms

The issue of developing a prototype for an unmanned aerial vehicle (UAV) and creating a control system based on a computer-aided design (CAD) model as part of a project for inspecting construction sites is under consideration. Special attention has been paid to constructing a computer model of a quadcopter. Based on existing methods, energy calculations have been performed and a process for synthesizing controllers in orientation and positioning control circuits has been proposed, considering the sampling rate of the sensors utilized. The outcomes obtained through modeling confirm the suggested algorithm for adjusting controllers. The solution can be utilized by students and professionals in the development of autonomous UAVs or their computer models.

Keywords: quadcopter, computer modeling, PD controller synthesis, UAV design, stereo camera, room inspection

One of the key parameters used to assess the magnitude of the individual fire risk based on the appropriate calculation methodology is the evacuation start time. To calculate it, there is a need for information about the time of reaching the threshold value of the fire detector, which can be determined on the basis of a fire simulation for the room in which the fire is located. At the same time, it is required to dynamically evaluate the size of the area at each point of which at the height of the location of fire detectors there is an excess of the threshold value of the acting parameter, which is a rather complex task, the solution of which requires the use of automation. This paper proposes a method for automated assessment of the time for reaching the threshold value of the fire detector response based on the results of fire modeling when determining the calculated values of the individual fire risk. Functional model and basic algorithm of the proposed technology are presented. The developed methodology was tested using the example of modeling a fire in a commercial building based on the FDS software kernel for various scenarios. The results of a comparative analysis of solving the problems of estimating the time for reaching the threshold value of fire detectors for various criteria based on the proposed technology and manual method are presented.

Keywords: individual fire risk, fire dynamics simulation, field fire model, automation, algorithm, FDS

The process of ensuring information security is inextricably linked with the assessment of compliance with the requirements. In the field of information protection, this process is called an information security audit. Currently, there are many international and domestic audit standards that describe various processes and methods for assessing compliance with requirements. One of the key drawbacks of these standards is the use of exclusively qualitative assessment without numerical calculations, which in turn does not allow making the procedure the most objective. The use of fuzzy logic allows providing the audit process with an appropriate quantitative assessment, while operating with understandable linguistic variables. The article analyzes existing standards and presents a conceptual model for applying the fuzzy set method in the process of information security audit.

Keywords: information security, information infrastructure, security audit, risk analysis, fuzzy sets, fuzzy logic

The paper discusses the issues of multi-criteria optimization of planning the loading of technological equipment at a machine-building enterprise within a calendar year. Planning and optimizing the loading of technological equipment is one of the key tasks of operational calendar planning at engineering enterprises. The paper presents a model for optimizing the load of technological equipment used in the production process. Within the optimization model, three groups of target indicators were identified: the performance indicator of the group of technological equipment within the calendar year; indicator of uniformity of process equipment group loading within the calendar year; the amount of losses from downtime of a group of process equipment within a calendar year. The paper presents the results of optimizing the load of the fleet of machine tools used within the framework of the machining workshop. Load optimization was carried out for certain groups of technological equipment: a group of lathes, a group of milling machines, a group of grinding machines. Equipment load optimization was carried out by redistributing the total labor intensity of the work performed for the corresponding groups of technological equipment between periods of the calendar year. The Pareto optimization method was used to determine the optimal option for loading groups of process equipment. The following optimization strategy has been defined: minimizing the total amount of losses from downtime of process equipment. The paper presents graphs of Pareto fronts for targets for turning group machines. As a result of optimization, the total amount of losses for certain groups of process equipment resulting from downtime decreased by 6.8% -10.2%. Thus, the use of the developed model to solve the problem of optimizing the load of the fleet of machine-tool equipment made it possible to increase the efficiency of the operational scheduling process at machine-building enterprises.

Keywords: scheduling, multi-criteria optimization, machine stock, targets, losses, process loading

This paper presents an adaptive pipeline architecture designed to enhance both throughput and reduce latency in real-time stream data processing within single- and multi-processor systems. Unlike predominantly conceptual models or narrowly focused algorithms, the practical impact of this architecture is demonstrated by achieving measurable performance gains through reducing redundant data copying and synchronization costs or by providing flexible control over input and output data ordering. The architecture employs shared memory to eliminate buffer duplication, uses data transfer channels that adapt based on the need for order preservation, and supports the replication of processes within or across CPU cores. Experimental results indicate that the proposed architecture delivers both high throughput and low latency while introducing minimal overhead for data transmission and process synchronization. By offering a flexible and scalable foundation, this architecture can be applied to a wide range of real-time applications, from video surveillance and robotics to distributed platforms for processing large data sets. It demonstrates versatility and robustness in adapting to varying computational demands, thereby ensuring both efficiency and reliability in high-performance environments.

Keywords: parallelism, multiprocessor computing, computational pipeline, performance scaling, queues, shared memory

This study is devoted to the analysis of decision-making models in ensuring the protection of public order. The results obtained will allow us to formulate a new mathematical model of decision-making, which will allow us to obtain objective management decisions to ensure the protection of public order in the territory of the Republic of Tajikistan with the possibility of simulation. The object of the study is the process of ensuring the protection of public order. In the scientific literature and in open sources of information, there is a large number of works describing models and algorithms developed on the basis of various mathematical tools. The analysis of a number of papers on this topic will allow us to formulate a new mathematical model of decision-making, which will optimize and improve the quality of prepared decision-making projects while ensuring the protection of public order. The study revealed that the basis for improving the effectiveness of ensuring the safety of citizens during mass events is an effective management decision. 1) Based on this, an analysis of decision-making models is presented, the purpose of which is to determine the need to create a decision-making model while ensuring the protection of public order in the Republic of Tajikistan. 2) A model of decision-making in ensuring the protection of public order in the Republic of Tajikistan is proposed. The model is implemented based on the synthesis of mathematical modeling methods, including cluster analysis, pairwise comparison method and Petri nets. The model allows you to divide committed events, i.e. crimes into clusters according to previously defined criteria. At the final stage, the model allows you to simulate each event, thereby predicting the possible development of the event under study. The presented results of the analysis of decision-making models made it possible to formulate a new mathematical model of decision-making in ensuring the protection of public order in the interests of the Republic of Tajikistan.

Keywords: public order protection, mathematical model, cluster analysis, pairwise comparison method, expert assessments, Petri nets

This article presents an analysis of corporate network traffic over the SMTP protocol to identify malicious traffic. The relevance of the study is driven by the increasing number of email-based attacks, such as the distribution of viruses, spam, and phishing messages. The objective of the work is to develop an algorithm for detecting malicious traffic that combines traditional analysis methods with modern machine learning approaches. The article describes the research stages: data collection, preprocessing, model training, algorithm testing, and effectiveness analysis. The data used were collected with the Wireshark tool and include SMTP logs, message headers, and attachments. The experimental results demonstrated high accuracy in detecting malicious traffic, confirming the potential of the proposed approach.

Keywords: SMTP, malicious traffic, network traffic analysis, email, machine learning, Wireshark, spam, phishing, classification algorithms

Optimization of automated management systems for facility protection complexes remains relevant today. This research paper provides an overview of the tools for implementing separate monitoring processes: device polling, processing of the received data, and transferring data to the graphic user interface. Based on the analysis of the reviewed information, a basis of solutions for developing management system of the technical means complex is planned to be formed. During the research, it was found that the combination of multi-threading architecture and adaptive polling algorithm allows to implement a large-scale polling; the clustering algorithm and special settings of frameworks for processing large-scale datasets can enhance job performance; WebSocket protocol has proved its efficiency for transferring the real-time data. The result of the evaluation of solutions was a set of tools for implementation of a hardware-software complex.

Keywords: sensor, management system, monitoring, SNMP manager, clustering, Hadoop, MapReduce, Spark, Apache Kafka, WebSocket

The paper examines the current state of the industrial Internet of Things market in Russia and around the world, the main areas of its application, as well as the prospects and challenges that businesses and industrial enterprises will face in implementing this technology. Special attention is paid to the advantages of implementing IIoT, such as increased productivity, reduced costs, improved security and transparency of processes. The barriers specific to the Russian market are discussed, including cybersecurity, hardware compatibility, and significant initial costs. Examples of successful implementations of IIoT technologies in various industries such as the oil and gas industry, logistics and chemical production are given. The emphasis is placed on the need for government support and adaptation of the regulatory framework to accelerate implementation. The article highlights the importance of an integrated approach to IIoT implementation, including using international experience and consolidating efforts to develop the digital economy in the face of global and local challenges.

Keywords: industrial Internet of Things, IIoT, industry 4.0, 5G, production automation, digital transformation

The widespread use of social media platforms has led to the accumulation of vast amounts of stored data, enabling the prediction of rare events based on user interaction analysis. This study presents a method for predicting rare events using graph theory, particularly graphlets. The social network VKontakte, with over 90 million users, serves as the data source. The ORCA algorithm is utilized to identify characteristic graph structures within the data. Throughout the study, user interactions were analyzed to identify precursors of rare events and assess prediction accuracy. The results demonstrate the effectiveness of the proposed method, its potential for threat monitoring, and the possibilities for further refinement of graphlet-based prediction models.

Keywords: social media, security event, event prediction, graph theory, graphlet, interaction analysis, time series analysis, correlation analysis, data processing, anomalous activity

In modern conditions of digital transformation, companies are actively implementing customer Relationship Management systems (CRM systems) to manage customer relationships. However, the issues of data protection, confidentiality and transparency of interaction remain critically important. This article explores the possibilities of using blockchain technology to enhance the security of CRM systems and improve trust between businesses and customers. The purpose of the work is to analyze the potential of using blockchain in data protection of CRM systems, as well as to assess its impact on the transparency of customer transactions. The paper examines the main threats to data security in CRM, the principles of blockchain technology and its key advantages in this context, including decentralization, immutability of records and protection from unauthorized access. Based on the analysis, promising areas of blockchain integration into CRM systems have been identified, practical recommendations for its application have been proposed, and the potential effectiveness of this technology has been assessed. The results of the study may be useful to companies interested in strengthening the protection of customer data and increasing the transparency of user interaction processes.

Keywords: blockchain, CRM-system, security, data protection, transparency, customer interaction

The article discusses the use of a recurrent neural network in the task of predicting pollutants in the air based on simulated data in the form of a time series. Neural recurrent network models with long Short-Term Memory (LSTM) are used to build the forecast. Unidirectional LSTM (hereinafter simply LSTM), as well as bidirectional LSTM (Bidirectional LSTM, hereinafter Bi-LSTM). Both algorithms were applied for temperature, humidity, pollutant concentration, and other parameters, taking into account both seasonal and short-term changes. The Bi-LSTM network showed the best performance and the least errors.

Keywords: environmental monitoring, data analysis, forecasting, recurrent neural networks, long-term short-term memory, unidirectional, bidirectional