Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Mukhamedrakhimova, Galiya
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Development of a decision-making module in the field of real estate rental using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Ignatovich, Artyom; Bayegizova, Aigulim; Tanirbergenov, Adilbek; Tynykulova, Assemgul; Bapiyev, Ideyat; Mukhamedrakhimova, Galiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5430-5442

Abstract

The research is aimed at developing a prototype of a decision support information system for managers of a company operating in the real estate rental industry. The system provides tools for data analysis, the use of mathematical models and expert knowledge to solve complex problems. The work analyzes the practical aspects of the design and use of decision support systems and formulates the requirements for the functionality of the system being developed. The Python programming language was used for implementation. The prototype includes machine learning models, expert systems, user interface and reports. Linear regression, data clustering density-based spatial clustering of applications with noise (DBSCAN) and backpropagation methods were implemented to train the classifying perceptron. The developed tool represents a significant contribution to the field of decision support, providing unique analysis and forecasting capabilities in the dynamic real estate rental environment. This prototype is an innovative solution that promotes effective management and strategic decision making in complex real estate business scenarios.
The extraction of a brief summary from scientific documents using machine learning methods Murzabekova, Gulden; Mukhamedrakhimova, Galiya; Taszhurekova, Zhazira; Yerbayev, Yerbol; Doumcharieva, Zhanagul; Makhatova, Valentina; Tolganbaeva, Moldir; Serikbayeva, Sandugash
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10660

Abstract

This study proposes a machine learning-based approach for automatic summarization of scientific documents using a fine-tuned DistilBART model a lightweight and efficient version of the bidirectional and auto-regressive transformers (BART) architecture. The model was trained on a large corpus of 12,540 scientific articles (2015–2023) collected from the arXiv repository, enabling it to effectively capture domain-specific terminology and structural patterns. The proposed pipeline integrates advanced text preprocessing techniques, including tokenization, stopword removal, and stemming, to enhance the quality of semantic representation. Experimental evaluation demonstrates that the fine-tuned DistilBART achieves high summarization performance, with ROUGE-2=0.472 and ROUGE-L=0.602, outperforming baseline transformer-based models. Unlike conventional approaches, the method shows strong applicability beyond academic research, including automated indexing of technical documentation, metadata extraction in digital libraries, and real-time text processing in embedded natural language processing (NLP) systems. The results highlight the potential of transformer-based summarization to accelerate scientific knowledge discovery and improve the efficiency of information retrieval across various domains.
Co-Authors Aitimova, Ulzada Baitemirov, Madiyar Bapiyev, Ideyat Bayegizova, Aigulim Dauitbayeva, Aigul Daurenbekov, Kuanysh Doumcharieva, Zhanagul Ignatovich, Artyom Makhatova, Valentina Mukhanova, Ayagoz Murzabekova, Gulden Sankibayev, Arman Serikbayeva, Sandugash Tanirbergenov, Adilbek Taszhurekova, Zhazira Tolganbaeva, Moldir Tulegenova, Elmira Tynykulova, Assemgul Yerbayev, Yerbol Yerzhan, Assel Yerzhanova, Akbota