BIO Web Conf.
Volume 17, 2020International Scientific-Practical Conference “Agriculture and Food Security: Technology, Innovation, Markets, Human Resources” (FIES 2019)
|Number of page(s)||6|
|Published online||28 February 2020|
Automated system-cognitive analysis of the dependence of export and import of agricultural machinery on its production (the case of Russia)
Kuban state agrarian University named after I. T. Trubilin, Krasnodar 350004, Russia
* Corresponding author: firstname.lastname@example.org
The issue raised in the article is the study of the dependence of export and import of agricultural machinery on its production in Russia. The Russian agricultural machinery market is influenced by three groups of factors: 1) own production of agricultural machinery; 2) export of agricultural machinery of Russian production; 3) import of agricultural machinery of foreign production. Traditionally, such problems are solved using multivariate analysis. However, in this case, the use of this method is problematic for a number of reasons: the source data is dimensional and measured in different units, the number of observations is less than the number of factors, the factors depend on each other, and the number of factors is too large. These restrictions are proposed to be overcome by applying automated system-cognitive analysis and its software tools of the intellectual system “Eidos”. For this purpose the following tasks were solved: 1) formulation of the idea and the concept of problem solving; 2) justification of the choice of the method and tool for solving the problem; 3) application of the selected method and tool to solve the problem; 4) evaluation of the effectiveness of the proposed solution of the problem; 5) consideration of the restrictions and disadvantages of the proposed solution of the problem and the prospects for its development by overcoming these restrictions and disadvantages. Some results of solving these problems are briefly summarized in this article.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.