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Synergy of econometric approach and use of neural networks to determine factors of provision of transport and logistics infrastructure in regions of Russia

The article justifies actuality of application of neural network methods for identification of significant predictors of the transport and logistics infrastructure of regions of the Russian Federation. The condition of the logistics industry of the Russian Federation in comparison with foreign countries has been analyzed. It was concluded that it is necessary to increase the accuracy of estimation of indicators of transport and logistics infrastructure of regions in order to identify their impact on the development of logistics. The problem of the traditional methodology of building a model of transport and logistics infrastructure of regions based on the application of mathematical and econometric analysis lies in the inability of the latter to find and accurately describe the non-obvious dependencies in the data. The expediency of sequential coupling of econometric and neural network research tools has been determined. The two-step procedure of identification of factors influencing the logistics development of the Russian Federation has been tested. As a result, it was possible to select the most significant socio-economic (average per capita income of the population, retail trade turnover, imports of the subjects of the Russian Federation) and infrastructure factors (the share of paved roads, the shipment of goods by public rail, the departure of passengers by public rail, the density of public railway) logistics infrastructure on the basis of an econometric approach. In the second step of the study, a neural network model of the remaining factors was developed based on the development of classification trees and a neural network, acting as a kind of computational filter, which allowed solving the problem of attribution of macroeconomic data and achieving a high level of significance of forecasts. The proposed approach of sequential coupling of econometric methods and neural network modelling has universality and practical importance, therefore it is applicable to the study of a wide range of macroeconomic processes. Read more...

Building and analyzing a machine learning model for short-term bitcoin market forecasting based on recurrent neural networks

In this article, the construction and analysis of machine learning models were performed for short-term forecasting in the cryptocurrency market on the example of bitcoin – one of the most popular cryptocurrencies in the world. The initial data for the study leads to the conclusion that over the long period of its existence, bitcoin has shown a high degree of volatility, especially evident in comparison with traditional financial instruments. The article substantiates that this market is influenced by a multitude of factors. No one can say for sure what makes up the value of a particular cryptocurrency, as it involves a range of reasons, which cannot be fully taken into account. To overcome this problem, we have considered the principle of recurrent neural network. It is described why networks with memory are better at making predictions on the time series than conventional autoregressive model and standard forward propagation networks. The initial data processing algorithm and transformation methods are defined. The sample was reduced in order to increase the speed of the network, by reducing the number of recalculations of weights. The algorithm of the family of recurrent neural networks was built and trained to test the hypothesis about their better adaptivity due to short-term and long-term memory. The model is evaluated on the test data representing the bitcoin exchange rate for 2021–2022, since this period is characterized by high volatility. It is concluded that it is reasonable to use a similar type of models for short-term forecasting of cryptocurrency rates. Read more...

The method of preprocessing machine learning data for solving computer vision problems

In the field of machine learning, there is no single methodology for data preprocessing, since all stages of this process are unique for a specific task. However, a specific data type is used in each direction. The research hypothesis assumes that it is possible to clearly structure the sequences and phases of data preparation for text recognition tasks. The article discusses the basic principles of data preprocessing and the allocation of successive stages as a specific technique for the task of recognizing ABC characters. ETL set images were selected as the source data. Preprocessing included the stages of working with images, at each of which changes were made to the source data. The first step was cropping, which allowed to get rid of unnecessary information in the image. Next, the approach of converting the image to the original aspect ratio was considered and the method of converting from shades of gray to black and white format was determined. At the next stage, the character lines were artificially expanded for better recognition of printed alphabets. At the last stage of data preprocessing, augmentation was performed, which made it possible to better recognize ABC characters regardless of their position in space. As a result, the general structure of the data preprocessing methodology for text recognition tasks was built. Read more...

User interface modeling for convolutional neural network for complex character recognition

In this article, we design a user interface for a prototype desktop application using the capabilities of the author’s neural network for recognizing texts in Japanese written by one of the two Japanese alphabets – katakana or hiragana. During the design, the UML notation, a Use-Case Diagram, was used to build scenarios for using the program, and the BPMN notation was used to describe a program’s main algorithm. In the beginning of this article short versions of previous two articles were also given – the basics of proposed method for preprocessing of machine learning data and the main parameters of the proposed convolutional neural network model including its efficiency against reference model EfficientNetB0. In the work, the principles and the tool base for designing the interface of the software solution were defined, the scenarios for using the program, the algorithms of the program were designed, a prototype of the user interface was created. Read more...

Designing an applied software product for emotion recognition and evaluation using a neural network

During the analysis of the most common architectures for building neural networks for image analysis – a direct convolutional layer, a recurrent neural network, a convolutional neural network – the authors determined that the last architecture option for the task is the most suitable. The solutions available on the market, although they allow us to recognize emotions, do not evaluate them, and this is an important result for a wide variety of tasks, because such an assessment allows us to more accurately predict the possible future behavior of both individuals and groups of people. The purpose of this article is to design an application software product (application) and develop a prototype that could perform the functions of analyzing emotions and evaluating them. The objectives of this study include the choice of the architecture of the designed application; the development of an algorithm for the operation of the application; the design of the user interface; a description of the neural network learning process and its results, the model of which was defined in the previous article; demonstration of the prototype (control example). The scientific novelty of the projected application lies in the formation of an assessment of the psycho-emotional state of those people whose images of their faces were evaluated. Such an assessment with recommendations can be widely demanded in various branches of human activity, since it is important to be able to express emotions not only to ordinary people, but also to those whose job is to transfer their emotional state to others: business coaches, journalists, actors, animators, dancers, etc. The presence of emotional intelligence is very important at the moment time. Read more...

Improvement of contactless fare payment by ground urban passenger transport based on geolocation

One of the important social tasks is the task of improving the efficiency of transport service systems. The paper proposes approaches to solving the problem of increasing the efficiency of contactless fare payment on passenger road transport, which are based on geolocation technology. Based on the consideration of fare payment schemes for closed and open cycles, scenarios for the implementation of fare payment using a flat fare system, a zonal fare system and electronic tickets with QR codes are presented. Several payment implementation work schemes have been developed based on the application of the geolocation concept and using various technologies to implement this concept, namely 1) Bluetooth Low Energy, 2) obtaining coordinates from GPS/GLONASS navigation systems, 3) data received through Wi-Fi access points and 4) location data provided by basic stations of GSM networks. A comparative analysis was carried out for each proposed option, a basic set of technical and software support tools was determined, and recommendations were given on the conditions that determine the feasibility of its application. For experimental confirmation of the operability of each of the options for obtaining the estimated values of the main characteristics, operating samples were constructed on which the corresponding measurements were carried out. Read more...