Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Alimagambetova, Ainagul
Unknown Affiliation
Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : bulletin of electrical engineering and informatics

 1 
Assessing external factors of the agro-industrial complex efficiency based on data Mauina, Gulalem; Aitimova, Ulzada; Kadyrova, Ainagul; Adikanova, Saltanat; Syzdykpayeva, Aigul; Seitakhmetova, Zhanat; Alimagambetova, Ainagul; Shekerbek, Ainur
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10459

Abstract

Modern agriculture faces the challenge of increasing production efficiency in the context of limited resources and variable climatic conditions. This article presents an approach to assessing the impact of various factors on agro-industrial indicators using machine learning methods. The primary focus is on the development and application of a hybrid analysis that includes techniques such as gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE). The study was conducted using data from agro-industrial enterprises in the North Kazakhstan region for the period 2020–2022, encompassing production, climatic, and economic indicators. It was found that crop area, average crop weight, and precipitation are the most significant factors, accounting for up to 93% of the correlation with yield increase. The use of the proposed methods made it possible to reduce forecast uncertainty by 28% and increase the accuracy of key indicator predictions by 15–20%. The results of the analysis, visualized as correlation matrices and feature significance maps, confirm the possibility of applying the proposed approach to optimize the management of agro-industrial production. The application of the developed methodology contributes to the development of strategies aimed at the sustainable development of the agro-industrial complex.
Combined analysis of the importance of factors in agricultural process management tasks Abdikerimova, Gulzira; Yessenova, Moldir; Zharkimbekova, Aizhan; Beldeubayeva, Zhanar; Bayegizova, Aigulim; Uzakkyzy, Nurgul; Alimagambetova, Ainagul; Murzabekova, Gulden
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11206

Abstract

The article presents a combined approach for analyzing the significance of factors in the agro-industrial sector using Shapley additive explanations (SHAP), simple combination, and principal component analysis (PCA)+combination methods. The study addresses the pressing need for efficient agricultural resource management under constrained and changing climatic conditions. The proposed methodology evaluates the impact of various factors on key performance indicators such as yield, income, and operating costs. SHAP analysis identified critical determinants, with "Land Area (ha)" contributing significantly to "Market Capacity" (59.5%) and "Sales Revenue" (57.2%), highlighting the importance of production scale. The simple combination method, integrating gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE) with Lasso, revealed a more balanced factor distribution, assigning 14.5% to "Land Area" and 12.8% and 10.7% to “Seed Use” and “Fertilizer Cost,” respectively. The PCA+combination method emphasized global trends, identifying "Yield per Hectare" (22.5%) and "Field Size" (11.5%) as key contributors to variance. This integrative approach captures localized effects and global interdependencies, offering comprehensive data interpretations. The findings are instrumental in optimizing resource management and strategic planning and enhancing agricultural production efficiency.
Co-Authors Abdikerimova, Gulzira Adikanova, Saltanat Aitimova, Ulzada Bayegizova, Aigulim Beldeubayeva, Zhanar Kadyrova, Ainagul Mauina, Gulalem Murzabekova, Gulden Seitakhmetova, Zhanat Shekerbek, Ainur Syzdykpayeva, Aigul Uzakkyzy, Nurgul Yessenova, Moldir Zharkimbekova, Aizhan