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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Yussupova, Gulbakhar
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Computer Science & IT Electrical & Electronics Engineering

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Using deep learning to diagnose retinal diseases through medical image analysis Azhibekova, Zhanar; Bekbayeva, Roza; Yussupova, Gulbakhar; Kaibassova, Dinara; Ostretsova, Idiya; Muratbekova, Svetlana; Kakabayev, Anuarbek; Sultanova, Zhanylsyn
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6455-6465

Abstract

The scientific article focuses on the application of deep learning through simple U-Net, attention U-Net, residual U-Net, and residual attention U-Net models for diagnosing retinal diseases based on medical image analysis. The work includes a thorough analysis of each model's ability to detect retinal pathologies, taking into account their unique characteristics such as attention mechanisms and residual connections. The obtained experimental results confirm the high accuracy and reliability of the proposed models, emphasizing their potential as effective tools for automated diagnosis of retinal diseases based on medical images. This approach opens up new prospects for improving diagnostic procedures and increasing the efficiency of medical practice. The authors of the article propose an innovative method that can significantly facilitate the process of identifying retinal diseases, which is critical for early diagnosis and timely treatment. The results of the study support the prospect of using these models in clinical practice, highlighting their ability to accurately analyze medical images and improve the quality of eye health care.
Hybrid analytical framework for evaluating socio-economic factors in regional development Akynbekova, Ayman; Muratkhan, Raikhan; Lamasheva, Zhanar; Mukhanova, Ayagoz; Yussupova, Gulbakhar; Eslyamov, Serik; Santeyeva, Saya; Abdrakhmanova, Alfiya
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.v15i2.11380

Abstract

This study aims to develop and validate a hybrid analytical framework for evaluating the influence of socio-economic factors on regional development. The framework combines correlation analysis, principal component analysis (PCA), and fuzzy inference modeling into a unified approach, applied to 2023 data from the city of Taraz, Kazakhstan, covering 16 socio-economic indicators across demographic, economic, social, and industrial domains. The findings reveal that investments in fixed assets (r=0.8963 and q=0.000010), average monthly salary (r=0.8907 and q=0.000010), and retail trade (r=0.8885 and q=0.000010) exert the strongest positive influence, while migration balance and manufacturing show weak or negative effects. The results demonstrate that the hybrid model offers more comprehensive insights compared to single-method approaches, validating its effectiveness in capturing complex and uncertain dependencies. Practically, the model provides policymakers with a robust decision-support tool for identifying priority areas, designing targeted strategies, and ensuring sustainable regional growth, with adaptability to other regions and datasets.
Co-Authors Abdrakhmanova, Alfiya Akynbekova, Ayman Azhibekova, Zhanar Bekbayeva, Roza Eslyamov, Serik Kaibassova, Dinara Kakabayev, Anuarbek Lamasheva, Zhanar Mukhanova, Ayagoz Muratbekova, Svetlana Muratkhan, Raikhan Ostretsova, Idiya Santeyeva, Saya Sultanova, Zhanylsyn