The article is devoted to the consideration of topical issues related to the study of the possibility of forecasting the dynamics of stock markets based on neural network models of machine learning. The prospects of applying the neural network approach to building investment forecasts are highlighted. To solve the problem of predicting the dynamics of changes in the value of securities, the problems of training a model on data presented in the form of time series are considered and an approach to the transformation of training data is considered. The method of recursive exclusion of features is described, which is used to identify the most significant parameters that affect price changes in the stock market. An experimental comparison of a number of neural networks was carried out in order to identify the most effective approach to solving the problem of forecasting market dynamics. As a separate example, the implementation of regression based on a radial-basis neural network was considered and an assessment of the quality of the model was presented.

Keywords: stock market, forecast, daily slice, shares, neural network, machine learning, activation function, radial basis function, cross-validation, time series

This article discusses the issue of the features of measuring and predicting changes in the surface electric field strength in the atmosphere. The results of measurements of the atmospheric electric field strength are presented. The possibilities of forecasting changes in the surface electric field strength, including the use of numerical models, as well as the use of measurement results as an indicator of dangerous weather phenomena, are considered. The prospects of using the prediction of variations in the surface electric field intensity to predict adverse weather events and the importance of monitoring the intensity of the atmospheric electric field for understanding global climate change processes and the impact of the electric field on human health and the environment are discussed. For the research, a model was created that allows predicting electric field variations based on meteorological data. The developed neural network has shown good results. It is demonstrated that the use of neural networks can be an effective approach for predicting the parameters of the electric field of the surface layer of the atmosphere. In further research, it is planned to expand the measurement area by including additional parameters such as temperature, pressure and humidity in the analysis, as well as using more complex machine learning models to improve the accuracy of forecasts. In general, the results show that machine learning models can be effective in predicting variations of the electric field in the surface layer of the atmosphere. This can have practical applications in various fields such as aeronautics, meteorology, geology and others. Further research in this area contributes to the development of new methods and technologies in the field of electric power and communications and to improving our knowledge about the nature of the impact of atmospheric electrophysical phenomena on the environment and human health.

Keywords: electric field, surface layer of the atmosphere, measurements, methods, forecasting, modeling of variations in field strength

One of the tasks of data preprocessing is the task of eliminating gaps in the data, i.e. imputation task. The paper proposes algorithms for filling gaps in data based on the method of statistical simulation. The proposed gap filling algorithms include the stages of clustering data by a set of features, classifying an object with a gap, constructing a distribution function for a feature that has gaps for each cluster, recovering missing values ​​using the inverse function method. Computational experiments were carried out on the basis of statistical data on socio-economic indicators for the constituent entities of the Russian Federation for 2022. An analysis of the properties of the proposed imputation algorithms is carried out in comparison with known methods. The efficiency of the proposed algorithms is shown.

Keywords: imputation algorithm, data gaps, statistical modeling, inverse function method, data simulation

The article discusses the use of graph theory to calculate the location of elements and ways of laying information cables in a distributed control system. It describes how the use of graph theory can help improve system performance, reduce maintenance costs, and increase reliability and security. The article presents the general principles of using graph theory to solve problems related to the location of elements and paths for laying information cables in distributed control systems. The authors conclude that the use of graph theory is a powerful tool for solving problems associated with distributed control systems, and can be effectively applied to improve the efficiency of the system, reduce costs and increase reliability and security.

Keywords: graph theory, distributed control system, Python, Matplotlib, production process optimization, automatic analysis, control system, data cable, automation

Software development, namely CRM-systems to improve the efficiency of the company, regardless of the area, is the most effective for business in terms of organizational and managerial activities. An important aspect of the successful implementation and implementation of the system in the company is the principle of developing and building the system architecture at the server level. For users to work in the system, a deep analysis of the company's business processes and the projection of technical requirements, both on the user interface and on the system's performance, are required. The correctness of the system is based on an important factor - further support of the existing code, this requirement is relevant for any project and depends on the initially chosen method of system development and the quality of tasks performed by programmers.

Keywords: CRM-system, BPM, hardcode, development, flexible settings

This article discusses the practical implementation of the self sovereign system based on the technology of a distributed decentralized data registry, also known as blockchain. An implementation of the system based on the Proof of Stake (PoS) consensus-building mechanism is presented, which provides a number of advantages over alternative implementations described in the literature. The results of measuring system performance in comparison with known implementations based on Proof of Work (PoW) are presented, confirming the high efficiency of the proposed solution.

Keywords: decentralized, user-centric, identity-based encryption, blockchain, self Sovereign identity system

The article provides general information about ontologies (including definitions of ontology), its formal (mathematical) model, and also provides a step-by-step process for developing an ontology. The areas of application of ontologies are considered and special attention is paid to the use of ontologies in the field of education. There are some suggestions about using ontologies as a knowledge base for an information security learning system. Also the fragment of a graphical representation of an ontology for biometrics, which is one of the areas of information security, is given. Ontology for biometrics is based on the national standard and developed in the Protege system.

Keywords: biometrics, knowledge, information security, knowledge representation model, learning system, learning, ontology, ontological model, OWL, RDF

This article discusses the forecasting of the collection of payments in post offices, taking into account seasonality and the use of machine learning. An algorithm for constructing a calculation model has been developed, which provides an opportunity for analysts of the Russian Post to make a monthly forecast of the collection of payments for each UFPS (Federal Postal Administration), taking into account seasonality. This model allows you to identify deviations from the norm in matters related to the collection of payments and more accurately adjust the increase in tariffs for services. The SSA algorithm is considered, which consists of 4 steps: embedding, singular decomposition, grouping, diagonal averaging. This information system is implemented in the form of a website using a framework ASP.NET Core and libraries for machine learning ML.NET . Then the forecast is evaluated using various methods.

Keywords: mathematical modeling, seasonally adjusted forecasting, collection of payments, machine learning, neural network

The article discusses correlation methods of image identification. An algorithm of the "rare grid" method has been developed.

Keywords: image identification, algorithm, recognition, cutting, reference frame, element correlations, minimum search

The article discusses the methods and approaches developed by the authors for the recommendation system, which are aimed at improving the quality of rehabilitation of the patient during respiratory training. To describe the training, we developed our own language for a specific subject area, as well as its grammar and syntax analyzer. Thanks to this language, it is possible to build a devereve describing a specific patient's training. Two main methods considered in the article are applied to the resulting tree: "A method for analyzing problem areas during training by patients" and "A method for fuzzy search of similar areas in training". With the help of these methods, it is proposed to analyze the problem areas of patients' training during rehabilitation and look for similar difficult areas of the patient to select similar exercises in order to maintain the level of diversity of tasks and involve the patient in the process.

Keywords: Recommendation system, learning management system, rehabilitation, medicine, respiratory training, marker system, domain-specific language, Levenshtein distance

The paper describes the procedure for conducting a competition for regression models based on statistical data for the East Siberian Railway. At the same time, it is assumed to build a set of additive alternative versions of the model with the subsequent choice of the best option based on the involvement of a number of adequacy criteria. The unloading of wagons is singled out as the output variable of the model, and the input variables are: the average gross weight of a freight train, cases of failures of technical means of the 1st – 2nd category of operational nature, the working fleet of freight wagons. The implementation of the model competition allowed us to build over two hundred alternative options, from which the best alternative was selected using multi-criteria selection methods based in this case on a continuous criterion of consistency of behavior.

Keywords: railway transport, mathematical model, regression analysis, least squares method, model competition, adequacy criteria, multi-criteria selection

Search engine optimization allows a website to rank higher in search engines. Through a lot of manipulations on working with the site, you can achieve good results in increasing the conversion of sites. Modern systems for all kinds of data analysis using neural networks can greatly improve the work on this optimization.

Keywords: website promotion, search engine optimization, neural networks, code optimization, convolutional neural networks

The article discusses the application of machine vision methods for embedded systems using modern microcontrollers. Machine learning methods that are used in embedded systems to solve recognition problems, as well as neural network models, are described. The use of trained models for solving image recognition problems in embedded systems is proposed. The architectures of YOLOv3 and R-CN neural networks are compared. The Jetson TX2 hardware platform is considered. The results of comparing the calculation speed for different modes of the device are presented.

Keywords: machine vision, neural networks, artificial intelligence, embedded systems, pattern recognition, YOLO, RCN, Jetson, Tensorflow

The article considers an approach to estimating the application coefficient of standard control system equipment on a test bench. The relevance of the evaluation task at the design stage of the test bench is shown and a description of the method for solving this problem is given. The proposed approaches can be applied both at the stage of creating a test stand and when upgrading an existing positionю.

Keywords: automatic control system, test bench, analysis of the testing process, experimental testing, standard equipment, centralized control software package, application coefficient of equipment

The development of a decision support system for evaluating a fashionable image of a person is described. This is done by selecting a set of visual attributes from an image and comparing this set with "fashionable" patterns. Fashion patterns are set by the user himself. These are images that are defined in the system as reference images. This paper provides an overview of decision-making methods, analyzes the relevance of decision-making systems in different spheres of society. The algorithm of the program and the tools with which the image is first preprocessed are considered, then the visual attributes are highlighted. The method of making decisions for different types of attributes is given. The comparison of colors in HSL notation is considered.

Keywords: decision support system, decision making methods, machine learning, Python, model learning, image, fashion, information and analytical system, k-means method

Research subject. The influence of the intensity of the light source and the direction of its beam on the process of object recognition by machine vision sensors - visible spectrum cameras and light detection and ranging systems (LiDAR) has been studied. Factors such as the intensity of light, its trajectory and the angle of the beam relative to the horizon are taken into account. Method. The solution to the problem of analyzing the ability of machine vision sensors to recognize the ArUco marker under conditions of various levels of illumination is based on empirical research methods. The main results. During the recognition of objects by a machine vision camera at a high level of illumination, a distortion of the light flux on the camera matrix occurred. Bugs in the operation of LiDAR were also found. Practical significance. Obtaining research results was used to develop a means of assessing a high level of assessment of a high degree of probability of detecting objects of sensory vision.

Keywords: machine vision, LiDAR, influence of light, empirical study

The article discusses the developed model for recognizing a clothing brand by image. The model not only predicts the type and brand of clothing, but can also determine their similarity. At the initial stage, a dataset was collected containing images of clothing from various brands with a total volume of 9,000 images. In this work, the ViT (Vision Transformer) neural network architecture was used, a model for working with images, which was presented by experts from Google Brain. The vit-base-patch16-224 model acted as a representative of the transformer architecture. Before training, all images were converted to black and white, and data augmentation was also used: image rotation by a random angle, mirror transformation. All photos have been normalized – pixel coordinates have been adjusted to the interval [0,1].

Keywords: neural network, model, machine learning, Vision Transformer, fashion industry, clothing brand prediction, clothing type prediction, brand similarity determination

The text describes software tools for analyzing the structure of metallic materials, including ferrous and non-ferrous metals. It presents image processing methods for edge detection and segmentation of structural elements on the metal surface. A Python program is described, which applies watershed algorithms and searches for white and black grains to segment metal images. The program performs analysis of grain sizes and shapes, and the results are presented visually and for further use. This tool is crucial for quality control and optimization of the properties of metallic materials.

Keywords: software tool, metal, quantitative analysis of microstructure, computer program, Python programming language

Large industrial enterprises can be compared to a complex dynamic system in which management decisions are constantly required. One of the main management decisions, on which the main performance indicators of the enterprise depend, is the process of managing the planning of production. In the process of organizing decision-making, information systems can be used, which are based on mathematical and heuristic calculation methods.organizations of the new enterprises, redistribution of investments in interests of the organization and development of new production on available floor spaces. The most important organizational economic targets of a diversification of management are presented by innovative activity of the industrial enterprise.

Keywords: production planning, resource allocation, Kanban

The article analyzes the testing time of the control system on the test bench, identifies the components of the testing time, and provides the calculation procedure for a typical test bench. The results obtained can be used to estimate the time of testing control systems at the design stage of test benches.

Keywords: automatic control system, rocket and space technology, test bench, analysis of the testing process, experimental testing, testing time

In this paper, the importance of transport networks for the development of the country's economy is considered. It is determined that at the same time, a huge number of heterogeneous factors affect the functioning of transport networks. In this case, in order to optimize transportation processes, it is necessary to have information about the state of the road network in advance. Geoinformation systems are an effective tool for presenting data of this kind, which allow storing and processing spatial and related attribute data. The author proposes a technique for geoinformation mapping of road conditions using the open OSM project. Openstreetmap provides access to up-to-date geographical information, as well as software tools for data processing. The paper defines the type and levels of damage to the roadway, a geoinformation project has been developed, which reflects the main types of damage to the roadway of a section of the road network.

Keywords: geoinformation system, geoinformation mapping, road network, OpenStreetMap

Maintaining the optimal age structure of the forestry fund is an important factor in the use of forest resources. The purpose of this study was to analyze the age structure of the forestry fund of exploitation forests of the Republic of Karelia. For this purpose, data on the age structure of the forest fund by species groups was collected for 17 central forest districts of the study region. Data sources were forest planning documents. The results of the study showed that coniferous forests predominate in the Republic of Karelia. Deciduous tree species are more widely represented in the southern part of the study region. Deciduous and coniferous forests have different age structures. Young stock, mature timber and overmature forest predominate. At the same time, Young stocks are predominantly represented by coniferous forests. A small proportion of forest approaching maturity is one of the fundamental problems of the region under study, as it helps to curb the increase in logging volumes.

Keywords: forest resources, logging, age structure, coniferous species, deciduous species, ripening forests

Overview of existing methods for diagnosing faults in synchronous electric motors and methods for their detection. Classification and analysis of existing methods, their applicability in detecting faults, advantages and disadvantages. Three classes of possible faults in synchronous permanent magnet motors are considered and described: electrical faults, mechanical faults, and demagnetization. The article discusses three classes of diagnostic methods: based on the construction of a mathematical model of a real electric motor and modeling its errors, based on processing signals from sensors, and intelligent methods based on processing collected data using artificial intelligence. The following error detection methods based on modeling are considered: detection based on the model of the electrical schematic, based on the analytical model, and based on the digital simulation model. The following frequency-time analysis methods of the obtained signals from the sensors are considered: analysis using fast Fourier transform, short-time Fourier transform, wavelet transform, Hilbert-Huang transform, and Wigner-Ville distribution. The following intelligent diagnostic methods are considered: diagnosis using convolutional neural networks, recurrent neural networks, support vector machines, fuzzy logic, and sparse representation.

Keywords: Synchronous motor with permanent magnets, faults of electric motor, modeling, fast Fourier transform, wavelet transform, Hilbert-Huang transform, Wigner-Ville distribution, neural networks, fuzzy logic, support vector machine, sparse representation.

The article discusses the possibilities of implementing the model of subject-language integrated learning in higher education institutions, and the authors focus on their professional experience in implementing this model in Southern Federal University for undergraduates. The authors managed to highlight in detail the working options of CLIL, taking into account the specifics of the modern labor market. The authors conclude that the presented technology can and should be applied in practice while observing the principle of feasibility in the perception of professional content in a foreign language by the target audience. This approach requires the teacher to be flexible in the development of such disciplines, since their subject content is adapted in accordance with the language level of the students.

Keywords: content and language integrated learning, social order, bilingual education, communicative approach, professionally-oriented content, didactic principles, professional

The paper considers a model of a multi-aperture wave-front sensor for an active laser beam control system based on the iterative image reconstruction algorithms with limitations, particularly, on the Gerchberg-Saxton algorithm. The specifics of these algorithms is the presence of the so-called divergence factor which is characterized by obtaining “successful” and “unsuccessful” solutions, and may be clarified by stagnation conditions available (or by local extrema). The use of global optimization methods allows to avoid this constraint and to build quite an effective strategy for retrieving phase information. An experimental research was conducted to restore phase information using this method. For this purpose, a model of a seven-channel laser system with a different phase shift was developed.

Keywords: multichannel laser systems, wavefront sensor, Gerchberg-Saxton algorithm, physical modeling, image reconstruction, phase retrieval