The quality of training of incompletely connected neural networks based on decision's roots is discussed. Using the example of limited data on patients with clinically diagnosed Alzheimer's disease and conditionally healthy patients, a decision's root and the corresponding neural network structure are found by preprocessing the data. The results of training an incompletely connected artificial neural network of this type are demonstrated for the first time. The results of training of this type of neural network allowed us to find a neural network with an acceptable level of accuracy for the practical application of the obtained neural network to support medical decision making - in the considered example for the diagnosis of Alzheimer's disease.

Keywords: neural networks, complex assessment mechanisms; decision roots, criteria trees, convolution matrices, data preprocessing

The article presents the state of technology of websites for designing robots with artificial intelligence. The image of a modern technical site-book as a place for the development of artificial intelligence applications is considered, the possibility of executing algorithms from the page to ensure the connection of robots with real and virtual objects is shown.

Keywords: mathematical network, technical website-book, artificial intelligence, algorithms executed on the website-book, network development of robots

The article is devoted to the development of a mathematical model and a software package designed to automate scientific research in the field of financial industry news analysis. The authors propose an approach based on the use of graph theory methods to identify the most significant scientific hypotheses, the methods used, as well as the obtained qualitative and quantitative results of the scientific community in this field. The proposed model and software package make it possible to automate the process of scientific research, which contributes to a more effective analysis of it. The research results can be useful both for professional participants in financial markets and for the academic community, since the identification of the most cited and fundamental works serves as the starting point of any scientific work.

Keywords: software package, modeling, graph theory, news streams, Russian stock market, stocks, citation graph

The paper presents a brief overview of publications describing the experience of using mathematical modeling methods to solve various problems. A multivariate piecewise linear regression model of a steel company was built using the continuous form of the maximum consistency method. To assess the adequacy of the model, the following criteria were used: average relative error of approximation, continuous criterion of consistency of behavior, sum of modules of approximation errors. It is concluded that the resulting model has sufficient accuracy and can be used for forecasting.

Keywords: mathematical modeling, piecewise linear regression, least modulus method, continuous form of maximum consistency method, steel company

The features of the (m,m) implementation scheme of visual cryptography are considered, which differs from the existing ones by the formation of shadow images (shares) of an image containing a secret. The proposed approach is based not on the decomposition of the secret image into shares, but on their step-by-step transformation by multiplication by orthogonal Hadamard matrices. The images obtained during each transformation of the stock are noise-resistant in the data transmission channel.

Keywords: image with a secret, image decomposition, image transformation, orthogonal Hadamard matrices, two-way matrix multiplication, noise-resistant image encoding

One of the directions that makes it possible to increase the efficiency of low-orbit satellite Internet in conditions of destructive influences is the use of OFDM systems that support the frequency hopping mode. It is obvious that the effectiveness of countering interference generated by electronic warfare (EW) is largely determined by the algorithm for selecting operating frequencies. In this paper, it is proposed to implement a block of SSF based on the SPN cipher "Grasshopper", which provides high resistance to the selection of the operating frequency by the SREB. However, in the event of failures and failures in the operation of such a unit, the transmitter and receiver operating in the microwave mode will not be able to establish information transmission. To solve this problem, the article proposes to use polynomial modular residue class codes (PMCCS). However, the analysis of the well-known error correction algorithms in PMCS has shown that they cannot be used to increase the reliability of the SPN-based CCF unit.

Keywords: Keywords: OFDM systems supporting frequency hopping, pseudorandom number generation methods, Grasshopper SPN cipher, polynomial modular residue class codes, error correction algorithm

Fifth-generation networks are of great interest for various studies. One of the most important and relevant technologies for efficient use of resources in fifth-generation networks is Network Slicing technology. The main purpose of the work is to simulate the probabilistic characteristics of blocking requests for access to wireless network radio resources. The main task is to analyze one of the options for implementing a two–service model of a wireless network radio access scheme with two slices and BG traffic. In the course of the work, the dependence of the probability of blocking a request depending on the intensity of receipt of applications of various types was considered. It turned out that the probability of blocking a type i application has the form of an exponential function. According to the results of the analysis, request blocking occurs predictably, taking into account the nature of incoming traffic. Previously, there are no significant drawbacks in the considered model. The developed model is of great interest for future, deeper and long-term research, for example, using simulation modeling, with the choice of optimal network parameters.

Keywords: queuing system, 5G, two - service queuing system, resource allocation, Network Slicing, elastic traffic, minimum guaranteed bitrate

In the context of rapid urbanization of society, modeling the processes of sustainable urban development has attracted considerable attention from scientists. This paper presents a study of fuzzy cognitive maps (FCMs) as an interdisciplinary model for simulating urban development processes. This highlights the versatility of FCM in integrating expertise and quantifying the impact of indicators that shape urban space, from infrastructure and housing to environmental sustainability and community well-being. The study uses a synthesis of an extensive literature review and expert opinions to create and refine a cognitive map tailored for municipal development. The methodology outlined formulates a systematic approach to selecting concepts, assigning weights, and validating the model. Through collaboration with cross-disciplinary experts, the study confirms the value of FCM for identifying cascading effects in the decision-making process when shaping urban development strategies. Recognizing the limitations of expert methods and the fuzzy nature of data, the article argues for the effectiveness of FCM in not only identifying but also addressing emerging urbanization problems. Ultimately, this article contributes a nuanced perspective to strategic planning discourse by advocating for the use of NCC as a management decision support tool that can assist policymakers in achieving a sustainable and equitable urban future.

Keywords: fuzzy cognitive maps, urban development, urban planning, sustainable urbanization, expert systems, social well-being

Stepper motors are often used in automated laser cutting systems. The control circuit of a stepper motor requires a special electronic device - a driver, which receives logical signals as input and changes the current in the motor windings to provide motion parameters. This research study evaluated stepper motor drivers to determine the feasibility of their use - PLDS880, OSM-42RA, OSM-88RA. To control the system, software code was written, which was connected to the controller via a link board. With each driver, in different modes, optimal parameters were selected (initial speed, final speed and acceleration), that is, the movement of the carriage without stalling for ten passes with a minimum travel time. The results of the experiments are presented in the form of tables.

Keywords: laser, laser cutting, automation, technological process, stepper motor, performance, driver, controller, control circuit, optimal parameters

In this paper, we reviewed and analyzed various time series forecasting models using data collected from IoT mobile devices. The main attention is paid to models describing the behavior of traffic in telecommunication systems. Forecasting methods such as exponential smoothing, linear regression, autoregressive integrated moving average (ARIMA), and N-BEATS, which uses fully connected neural network layers to forecast univariate time series, are covered. The article briefly describes the features of each model, examines the process of their training, and conducts a comparative analysis of the quality of training. Based on data analysis, it was noted that for the UDP protocol, the ARIMA model has the best learning quality, for the TCP protocol - linear regression, and for the HTTPS protocol - ARIMA.

Keywords: telecommunication systems, traffic analysis, forecasting models, QoS, artificial intelligence, linear regression, ARIMA, Theta, N-BEATS

This article explores the introduction and implementation of neural network models in the field of agriculture, with an emphasis on their use in smart greenhouses. Smart greenhouses are innovative systems for controlling the microclimate and other factors affecting plant growth. Using neural networks trained on data on soil moisture, temperature, illumination and other parameters, it is possible to predict future indicators with high accuracy. The article discusses the stages of data collection and preparation, the learning process of neural networks, as well as the practical implementation of this approach. The results of the study highlight the prospects for the introduction of neural networks in the agricultural sector and their important role in optimizing plant growth processes and increasing the productivity of agricultural enterprises.

Keywords: neural network, predicting indicators, smart greenhouse, artificial intelligence, data modeling, microclimate

The paper presents a refined regression model of water level dynamics in the Siberian river Iya, which includes six natural factors on the right side (the number of days with precipitation in the Sayan Mountains, average day and night temperatures for the month, the amount of precipitation, snow depth, average atmospheric pressure for the month ) taking into account the delay, as well as a specially generated seasonal variable. The high adequacy of the model is indicated by the values ​​of the criteria of multiple determination, Fisher, and the average relative error of approximation. The constructed model can be effectively used to solve a wide range of forecasting problems.

Keywords: regression model, river water level, lag time, seasonal variable, forecast

This paper examines and compares two neural networks, U-Net-Attention and SegGPT, which use different attention mechanisms to find relationships between different parts of the input and output data. The U-Net-Attention architecture is a dual-layer attention U-Net neural network, an efficient neural network for image segmentation. It has an encoder and decoder, combined connections between layers and connections that pass through hidden layers, which allows information about the local properties of feature maps to be conveyed. To improve the quality of segmentation, the original U-Net architecture includes an attention layer, which helps to enhance the search for the image features we need. The SegGPT model is based on the Visual Transformers architecture and also uses an attention mechanism. Both models focus attention on important aspects of a problem and can be effective in solving a variety of problems. In this work, we compared their work on segmenting cracks in road surface images to further classify the condition of the road surface as a whole. An analysis and conclusions are also made about the possibilities of using architectural transformers to solve a wide range of problems.

Keywords: machine learning, Transformer neural networks, U-Net-Attention, SegGPT, roadway condition analysis, computer vision

The article is devoted to the problems of managing the implementation of multi-scenario, multi-stage projects under conditions of uncertainty. The proposed approach is based on representing the project model in the form of a scenario network. The developed fuzzy linguistic model of a project stage is a set of linguistic variables corresponding to the stage indicators and external factors influencing the subsequent implementation of the project. The decisive rules for choosing the arc of transition to the next stage are constructed in the form of fuzzy products, the left parts of which are fuzzy statements regarding the preference of possible options. The constructed decision support procedure is based on the use of the Mamdani fuzzy inference algorithm, which has high interpretability. The proposed approach allows for multi-scenario planning and adaptability of management of the implementation of multi-stage projects.

Keywords: multi-scenario multi-stage projects, adaptive project management, scenario network, decision support, linguistic variable, fuzzy inference

The article is the result of an analytical study of the development of structures of medium and small businesses in the engineering implementation of the stages of survey, preparation in the production of building materials, semi-finished products, sections of projects, as well as participants in the commissioning of facilities for 2012-2022. During this period, the number of small and medium enterprises in the territory of the Russian Federation increased by 224 thousand units. In the Central Federal District (which includes the Tula Region), the increase was 31.8%. At the same time, their growth in construction amounted to 6.39%. However, the trend has changed from 2019 to 2022. the number of entrepreneurs significantly decreased by 457 thousand. In this regard, the authors in their studies solved the problem of analyzing the state, dynamics of changes in the number and content of the activities of structures of medium and small businesses in construction; developing proposals to improve development efficiency. The main attention is paid to specialization, the reasons for curbing the growth of business services and the economic results of their work.

Keywords: business planning, specialization, planning, project management, building complex

The paper presents an overview of the task of automatic text summarization. The formulation of the problem of automatic text summarization is carried out. The classification of algorithms for automatic text summarization by the type of the resulting summary and by the approach to solving the problem is carried out. Some existing problems in the field of automatic text summarization and disadvantages of certain classes of algorithms are described. The concepts of quality and information completeness of the summary are defined. The most popular approaches to the assessment of the information completeness of the summary and their classification in accordance with the methodology used are considered. The metrics of the ROUGE family are considered in relation to the task of automatic text summarization. Special attention is paid to the evaluation of the information completeness of the summary using such metrics of information proximity as the Kulback-Leibler divergence, the Jensen-Shannon divergence and the cosine distance (similarity). The metrics mentioned above can be applied to the text vector representations of the initial text and summary. The text vector representation in question can be performed using such methods like frequency vectorization, TF-IDF, static vectorizers and so on.

Keywords: automatic summarization, summary, information completeness, ROUGE, vectorization, TF-IDF, static vectorizer, Kullback-Leibler divergence, Jensen-Shannon divergence, cosine distance

The historical aspects of the emergence of the problem of noise-resistant image encoding are considered using the example of delivering photographs of the surface of Mars to Earth. Using the example of generalization of orthogonal matrices by quasi-orthogonal ones, the expansion of the number of matrices for use in image conversion for transmission in noise communication channels is shown.

Keywords: Hadamard matrices, Hadamard coding, Reed-Solomon codes, orthogonal matrices, quasi-orthogonal matrices, noise-resistant image encoding

The issue of using the screen of an aircraft's collimator system as a means of providing a help to the pilot about the vertical profile of the flight path in poor visibility conditions at low and extremely low piloting altitudes is being considered.

Keywords: low flight altitude, extremely low flight altitude, threat of collision, collimator, virtual elevation map, virtual reality, augmented reality, artificial intelligence, data fusion, pilot assistance system

This article analyzes and reviews modern methods and technologies used in anti-plagiarism systems, with an emphasis on the Russian market. The purpose of considering all of the above is to choose a suitable anti-plagiarism system for integration. The article presents the most popular Russian services for detecting borrowings, their business models, algorithms of operation, as well as a general description of the principles and mechanisms underlying these algorithms. It was determined that the most universal and effective system for finding loans is the service Antiplagiat.ru , since it has the possibility of integration via the API, as well as 34 additional modules that provide the opportunity to adapt the functionality of the system to individual needs.

Keywords: antiplagiarism, text analysis, text processing algorithms, semantic analysis, stylistic analysis

This article discusses the basic principles and design patterns of an application for collecting data from third-party sources. Research has been carried out on various methods of obtaining data, including web scraping, using APIs and file parsing. It also describes various approaches to extracting information from structured and unstructured sources.

Keywords: internet sources, API, parsing, web, headless browser, scraping, etag, data collection

Road surface quality assessment is one of the most urgent tasks in the world. To solve it, there are many systems that mainly interact with images of the roadway. They work on the basis of both traditional methods (machine learning is not used) and machine learning algorithms. Traditional approaches, for example, include methods for edge detection in images that are the object of this study. However, each of the algorithms has certain features. For example, some of them allow to get a processed version of the original photo faster. The following methods were selected for analysis: "Canny algorithm", "Kirsch operator", "Laplace Operator", "Marr-Hildreth algorithm", "Prewitt operator" and "Sobel Operator". The main indicator of effectiveness in the study is the average time to receive the processed photo. The initial material of the experiment is 10 different images of the road surface in 5 sizes (1000x1000, 894x894, 775x775, 632x632, 447x447) in bmp, jpg, png formats. The study found that the "Kirsch operator", "Laplace Operator" and "Prewitt Operator" and "Sobel operator" have a linear dependence of O(n), the "Canny algorithm" and the "Marr-Hildreth algorithm" have a quadratic character of O(n2). The best results are demonstrated by the "Prewitt Operator" and the "Sobel Operator".

Keywords: comparison, effectiveness, method, edge detection, image, photo, road surface, dependence, size, format

Unintentional errors occur in all data transmission channels. The standard way to deal with them is to use noise-resistant codecs based on the use of algebraic error correction codes. There are transmission channels in which a special type of error occurs – erasures, i.e. a type of error in which the location of the error is known, but its value is not known. Coding theory claims that error-control methods can be applied to protect data from erasure, however, these statements are not accompanied by details. This work fills this gap. Algorithms for correcting erasures using arbitrary decoders for error correcting codes are constructed. Lemmas about the correctness of the constructed algorithms are formulated, some estimates of the probability of successful decoding are obtained.

Keywords: channels with erasures, noise-resistant code, algebraic code, error correction code decoder, erasure correction algorithm

The problem of vulnerabilities in the Robot Operating System (ROS) operating system when implementing a multi-agent system based on the Turtlebot3 robot is considered. ROS provides powerful tools for communication and data exchange between various components of the system. However, when exchanging data between Turtlebot3 robots, vulnerabilities may arise that can be used by attackers for unauthorized access or attacks on the system. One of the possible vulnerabilities is the interception and substitution of data between robots. An attacker can intercept the data, change it and resend it, which can lead to unpredictable consequences. Another possible vulnerability is unauthorized access to the commands and control of Turtlebot3 robots, which can lead to loss of control over the system. To solve these vulnerabilities, methods of protection against possible security threats arising during the operation of these systems have been developed and presented.

Keywords: Robotic operating system (ROS), multi-agent system, system packages, encryption, SSL, TLS, authentication and authorization system, communication channel, access restriction, threat analysis, Turtlebot3

The work is devoted to the problem of providing electrical energy to remote production enterprises in the absence of a centralized power supply. The purpose of the work is to develop decision support tools for choosing autonomous power generation projects from a large number of possible alternatives. To achieve this purpose, a hierarchy of criteria was constructed and a comparative analysis of existing technical and economic solutions in the field of small-scale autonomous energy was carried out. It is shown that when choosing a power generation project for a particular enterprise, there is a fairly large number of alternatives, which makes the use of commonly used decision support procedures based on the hierarchy analysis method/analytical network method (in the classical version) ineffective. An iterative procedure with dynamic changes in feedback between criteria and alternatives is proposed, which makes it possible to reduce the dimension of the supermatrix during the calculation process and, thereby, reduce the time complexity of the algorithms. The effectiveness of the proposed modification of the analytical network method is confirmed by calculations. The constructed procedure for selecting an autonomous power generation project makes it possible to increase the level of scientific validity of technical and economic decisions when expanding the production activities of small enterprises in remote and sparsely populated areas.

Keywords: autonomous power system, decision support, analytical network method

Among the vast range of tasks that modern advanced video surveillance systems face, the dominant position is occupied by the task of tracing various objects in the video stream, which is one of the fundamental problems in the field of video analytics. Numerous studies have shown that, despite the dynamism of processes in the field of information technology and the introduction of various tools and methods, the task of object maintenance still remains relevant and requires further improvement of previously developed algorithms in order to eliminate some inherent disadvantages of these algorithms, systematization of techniques and methods and the development of new systems and approaches. The presented article describes the process of step-by-step development of an algorithm for tracking human movements in a video stream based on the analysis of color groups. The key stages of this algorithm are: the selection of certain frames when dividing the video stream, the selection of the object under study, which is further subjected to a digital processing procedure, the basis of which is to obtain information about color groups, their average values and percentages of their occupancy relative to the object under study. This information is used for the procedure of searching, detecting and recognizing the selected object with an additional function of predicting the direction of movement on video frames, the result of which is the formation of the entire picture of the movement of the person under study. The materials presented in this paper may be of interest to specialists whose research focuses on issues related to the automated acquisition of certain data in the analysis of various images and videos.

Keywords: surveillance cameras, u2– net neural network, rembg library, pattern recognition, clothing recognition, delta E, tracing, direction prediction, object detection, tracking, mathematical statistics, predicted area, RGB pixels