SHS Web Conf.
Volume 61, 2019Innovative Economic Symposium 2018 - Milestones and Trends of World Economy (IES2018)
|Number of page(s)||11|
|Section||Strategic Partnerships in International Trade|
|Published online||30 January 2019|
Using artificial intelligence to analyse businesses in agriculture industry
University of Žilina, Faculty of Operation and Economics of Transport and Communications, Univerzitná 1, 01026 Žilina, Slovak Republic
* Corresponding author: firstname.lastname@example.org
Artificial intelligence is largely used in many technical applications and allows you to provide various solutions in problem estimation, regression, or optimization. Artificial intelligence, specifically artificial neural networks, extend to the area of economics and finance. They are used primarily for operations that can´t be identified analytically. Neural networks are suitable for modelling very complex strategic decisions, for large sets of data, and so on. The main advantage is the ability to learn and then to capture hidden and strongly non-linear dependencies. In this paper they are used for the analysis of agricultural businesses. The aim is to analyse the state of the agricultural sector through the use of Kohonen networks and then to assess its future development. On the basis of the analysis, significant and large clusters of businesses are depicted, and the most significant clusters are analysed. It is possible to estimate the number of businesses that will be successful, those that will stagnate and those that will fail in the following period. Application of Kohonen networks is rather complex, but they have great potential and the results are very interesting.
Key words: Kohonen networks / Cluster analysis / Prediction / Agricultural businesses / Sector development
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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