Issue |
E3S Web Conf.
Volume 392, 2023
II International Conference on Agriculture, Earth Remote Sensing and Environment (RSE-II-2023)
|
|
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Article Number | 01050 | |
Number of page(s) | 6 | |
Section | Issues of Sustainable Development of Agriculture | |
DOI | https://doi.org/10.1051/e3sconf/202339201050 | |
Published online | 06 June 2023 |
Algorithms for improving the process of modeling complex systems based on big data: On the example of regional agricultural production
Karshi Engineering and Economics Institute, Karshi, 180100, Uzbekistan
* Corresponding author: koinot_30@mail.ru
The article analyzes the sources of the development of specific mechanisms, assessing the future problems of regional agro-industry development. Agro-industry as a production facility forms a complex system. In this case, as the main elements, it is suggested that the criterion of non-loss of characteristics as a result of processing information about these elements is primary. It is justified that the information system of the Big Data type, the models that describe the laws, the mechanism that enables the improvement of the models, and the algorithms that activate the mechanism are of particular importance as these sources. Researchers estimate that the mechanism that allows for the improvement of system research models is artificial intelligence, and the main problem here is the obstacles in the development of algorithms that determine the trajectory of its activity. These obstacles are explained by the complexity of the system, the lack of sufficient information to assess the importance levels of its elements, low accuracy, and the fact that the level of importance is presented differently in different sources. Taking into account that agro-industry is a complex enough system, the need to research the laws of this network requires a big data type information system, and algorithms for using artificial intelligence-based mechanisms are proposed.
© The Authors, published by EDP Sciences, 2023
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.
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