Aida Orozobekova

@kstu.kg

Department of Applied informatics
KSTU named of I. Razzakov



              

https://researchid.co/aidaorozobekova

RESEARCH INTERESTS

Applied mathematics, mathematical modeling of geomechanical processes, informatics, programming, GIS, databases, geotechnology, geomechanics, artificial intelligence

3

Scopus Publications

Scopus Publications

  • Modeling and forecasting tasks of agriculture based on machine learning
    Baratbek Sabitov, Asel Kartanova, Talant Kurmanbek uulu, Nazgul Seitkazieva, Ainura Dyikanova, and Aida Orozobekova
    EDP Sciences
    Continuous advances in computer technology have provided good support for the expansion of agricultural research using machine learning. This article considered the current problem of yield forecasting using methods and algorithms of machine learning to support management decision-making in the agricultural sector. For a set of data collected from five districts of the Issyk-Kul region, such as weather conditions, soil characteristics and pre-processing of the sowing area, a study of the yield of various crops using advanced machine learning algorithms, such as the support vector method, k-nearest neighbors, variants of gradient boosting and random forest, etc., is demonstrated. To assess the accuracy of the models, a comparative analysis with the results of multiple regression was carried out. It is shown that powerful regression machine learning algorithms like k-nearest neighbors (KNN), random forest (RF), support vector method (SVR) and gradient boosting (GBR) give tangible results in prediction compared to other machine learning methods (MAPE=10%). The calculation results showed the effectiveness of using algorithms with ensemble methods to solve the problems of yield forecasting, and that environmental factors (weather conditions) have a greater impact on yield than soil genotype.


  • Improvement of Computer Recognition of Foam Layer Criteria
    Andrey V. Zatonskiy, Ruslan I. Bazhenov, Aida K. Orozobekova, Saida S. Beknazarova, Tatiana N. Gorbunova, and Irina A. Ledovskikh
    IEEE
    Foam bed flotation processes are common in the poly metallic, potash and food industries. A worker who visually assesses the condition of the foam bed controls the process. This reduces process control and product quality because of the human factor. Using computer vision is based on bubble boundary recognition to control due to poor lighting, low foam contrast, and splash back. Recognition is possible by analyzing the glare from the foam surface. We attempt to improve recognition by accounting for the dark antiglare that takes place due to the bubble deformation. It improved recognition and reduced the noise of the method to a few percents.