Algorithms for improving the process of modeling complex systems based on big data: on the example of regional agricultural production

dc.contributor.authorF D Juraev
dc.contributor.authorG M Aralov
dc.contributor.authorB Sh Ibragimov
dc.contributor.authorI Ibragimov
dc.contributor.authorA R Mallaev
dc.date.accessioned2025-11-15T06:49:52Z
dc.date.available2025-11-15T06:49:52Z
dc.date.issued2023-02-09
dc.description.abstractAbstract. 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.en_US
dc.identifier.urihttps://dspace.kstu.uz/xmlui/handle/123456789/1819
dc.language.isoenen_US
dc.publisherE3S Web of Conferences 392, 01050 (2023)en_US
dc.titleAlgorithms for improving the process of modeling complex systems based on big data: on the example of regional agricultural productionen_US
dc.typeArticleen_US

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