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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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Journal : Bulletin of Electrical Engineering and Informatics

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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.
Combined analysis of the importance of factors in agricultural process management tasks Abdikerimova, Gulzira; Yessenova, Moldir; Zharkimbekova, Aizhan; Beldeubayeva, Zhanar; Bayegizova, Aigulim; Uzakkyzy, Nurgul; Alimagambetova, Ainagul; Murzabekova, Gulden
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.v15i2.11206

Abstract

The article presents a combined approach for analyzing the significance of factors in the agro-industrial sector using Shapley additive explanations (SHAP), simple combination, and principal component analysis (PCA)+combination methods. The study addresses the pressing need for efficient agricultural resource management under constrained and changing climatic conditions. The proposed methodology evaluates the impact of various factors on key performance indicators such as yield, income, and operating costs. SHAP analysis identified critical determinants, with "Land Area (ha)" contributing significantly to "Market Capacity" (59.5%) and "Sales Revenue" (57.2%), highlighting the importance of production scale. The simple combination method, integrating gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE) with Lasso, revealed a more balanced factor distribution, assigning 14.5% to "Land Area" and 12.8% and 10.7% to “Seed Use” and “Fertilizer Cost,” respectively. The PCA+combination method emphasized global trends, identifying "Yield per Hectare" (22.5%) and "Field Size" (11.5%) as key contributors to variance. This integrative approach captures localized effects and global interdependencies, offering comprehensive data interpretations. The findings are instrumental in optimizing resource management and strategic planning and enhancing agricultural production efficiency.
Co-Authors Abdikerimova, Gulzira Aldashova, Madina Alimagambetova, Ainagul Aziyeva, Nurgul Baktiyarova, Nazira Bayegizova, Aigulim Bazarova, Madina Beldeubayeva, Zhanar 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 Uzakkyzy, Nurgul Yerbayev, Yerbol Yessenova, Moldir Zharkimbekova, Aizhan