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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Uzakkyzy, Nurgul
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Computer Science & IT Electrical & Electronics Engineering

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Image noise reduction by deep learning methods Uzakkyzy, Nurgul; Ismailova, Aisulu; Ayazbaev, Talgatbek; Beldeubayeva, Zhanar; Kodanova, Shynar; Utenova, Balbupe; Satybaldiyeva, Aizhan; Kaldarova, Mira
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6855-6861

Abstract

Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.
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 Alimagambetova, Ainagul Ayazbaev, Talgatbek Bayegizova, Aigulim Beldeubayeva, Zhanar Ismailova, Aisulu Kaldarova, Mira Kodanova, Shynar Murzabekova, Gulden Satybaldiyeva, Aizhan Utenova, Balbupe Yessenova, Moldir Zharkimbekova, Aizhan