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All Journal International Journal of Electrical and Computer Engineering
Rysbekkyzy, Bakhytgul
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Computer Science & IT Electrical & Electronics Engineering

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Deep neural networks for removing clouds and nebulae from satellite images Glazyrina, Natalya; Muratkhan, Raikhan; Eslyamov, Serik; Murzabekova, Gulden; Aziyeva, Nurgul; Rysbekkyzy, Bakhytgul; Orynbayeva, Ainur; Baktiyarova, Nazira
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.pp5390-5399

Abstract

This research paper delves into contemporary methodologies for eradicating clouds and nebulae from space images utilizing advanced deep learning technologies such as conditional generative adversarial networks (conditional GAN), cyclic generative adversarial networks (CycleGAN), and space-attention generative adversarial networks (space-attention GAN). Cloud cover presents a significant obstacle in remote sensing, impeding accurate data analysis across various domains including environmental monitoring and natural resource management. The proposed techniques offer novel solutions by leveraging spatial attention mechanisms to identify and subsequently eliminate clouds from images, thus uncovering previously concealed information and enhancing the quality of space data. The study emphasizes the necessity for further research aimed at refining cloud removal algorithms to accommodate diverse detection conditions and enhancing the overall efficiency of deep learning in satellite image processing. By highlighting potential benefits and advocating for ongoing exploration, the paper underscores the importance of advancing cloud removal techniques to improve data quality and unlock new applications in Earth remote sensing. In conclusion, the proposed approaches hold promise in addressing the persistent challenge of cloud cover in space imagery, paving the way for more accurate data analysis and future advancements in remote sensing technologies.
Detection of lung pathology using the fractal method Abdikerimova, Gulzira; Shekerbek, Ainur; Tulenbayev, Murat; Sultanova, Bakhyt; Beglerova, Svetlana; Dzhaulybaeva, Elvira; Zhumakanova, Kamshat; Rysbekkyzy, Bakhytgul
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.pp6778-6786

Abstract

Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis. 
Co-Authors Abdikerimova, Gulzira Aziyeva, Nurgul Baktiyarova, Nazira Beglerova, Svetlana Dzhaulybaeva, Elvira Eslyamov, Serik Glazyrina, Natalya Muratkhan, Raikhan Murzabekova, Gulden Orynbayeva, Ainur Shekerbek, Ainur Sultanova, Bakhyt Tulenbayev, Murat Zhumakanova, Kamshat