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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Aitimova, Ulzada
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Computer Science & IT Electrical & Electronics Engineering

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Noisy image enhancements using deep learning techniques Daurenbekov, Kuanysh; Aitimova, Ulzada; Dauitbayeva, Aigul; Sankibayev, Arman; Tulegenova, Elmira; Yerzhan, Assel; Yerzhanova, Akbota; Mukhamedrakhimova, Galiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp811-818

Abstract

This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.
Data generation using generative adversarial networks to increase data volume Aitimova, Ulzada; Aitimov, Murat; Mukhametzhanova, Bigul; Issakulova, Zhanat; Kassymova, Akmaral; Ismailova, Aisulu; Kadirkulov, Kuanysh; Zhumabayeva, Assel
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2369-2376

Abstract

The article is an in-depth analysis of two leading approaches in the field of generative modeling: generative adversarial networks (GANs) and the pixel-to-pixel (Pix2Pix) image translation model. Given the growing interest in automation and improved image processing, the authors focus on the key operating principles of each model, analyzing their unique characteristics and features. The article also explores in detail the various applications of these approaches, highlighting their impact on modern research in computer vision and artificial intelligence. The purpose of the study is to provide readers with a scientific understanding of the effectiveness and potential of each of the models, and to highlight the opportunities and limitations of their application. The authors strive not only to cover the technical aspects of the models, but also to provide a broad overview of their impact on various industries, including medicine, the arts, and solving real-world problems in image processing. In addition, we have identified prospects for the use of these technologies in various fields, such as medicine, design, art, entertainment, and in unmanned aerial vehicle systems. The ability of GANs and Pix2Pix to adapt to a variety of tasks and produce high-quality results opens up broad prospects for industry and research.
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.
Co-Authors Adikanova, Saltanat Aitimov, Murat Alimagambetova, Ainagul Dauitbayeva, Aigul Daurenbekov, Kuanysh Ismailova, Aisulu Issakulova, Zhanat Kadirkulov, Kuanysh Kadyrova, Ainagul Kassymova, Akmaral Mauina, Gulalem Mukhamedrakhimova, Galiya Mukhametzhanova, Bigul Sankibayev, Arman Seitakhmetova, Zhanat Shekerbek, Ainur Syzdykpayeva, Aigul Tulegenova, Elmira Yerzhan, Assel Yerzhanova, Akbota Zhumabayeva, Assel