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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Murzabekova, Gulden
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Computer Science & IT Electrical & Electronics Engineering

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Using deep learning algorithms to classify crop diseases Murzabekova, Gulden; Glazyrina, Natalya; Nekessova, Anargul; Ismailova, Aisulu; Bazarova, Madina; Kashkimbayeva, Nurzhamal; Mukhametzhanova, Bigul; Aldashova, Madina
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.pp6737-6744

Abstract

The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in detecting pests and crop diseases. The advantages and disadvantages of each model considered during training were taken into account, and the Inception V3 algorithm was incorporated into the application. They can monitor the condition of crops on a daily basis with the help of new technology-applications on gadgets. Aerial photographs used by research institutes and agricultural grain centers do not show the changes that occur in agricultural grains, that is, diseases and pests. Therefore, the method proposed in this paper determines the types of diseases and pests of cereals through a mobile application and suggests ways to deal with them.
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.
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 Aldashova, Madina Aziyeva, Nurgul Baktiyarova, Nazira Bazarova, Madina Doumcharieva, Zhanagul Eslyamov, Serik Glazyrina, Natalya Ismailova, Aisulu Kashkimbayeva, Nurzhamal Makhatova, Valentina Mukhamedrakhimova, Galiya Mukhametzhanova, Bigul Muratkhan, Raikhan Nekessova, Anargul Orynbayeva, Ainur Rysbekkyzy, Bakhytgul Serikbayeva, Sandugash Taszhurekova, Zhazira Tolganbaeva, Moldir Yerbayev, Yerbol