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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Abdrakhmanova, Alfiya
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Computer Science & IT Electrical & Electronics Engineering

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Application of machine learning methods to analysis and evaluation of distance education Mukhiyadin, Ainur; Mukasheva, Manargul; Makhazhanova, Ulzhan; Kassekeyeva, Aislu; Azieva, Gulmira; Kenzhebayeva, Zhanat; Abdrakhmanova, Alfiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2172-2180

Abstract

In recent decades, distance learning has become an essential component of the modern educational system, providing students with flexibility and access to knowledge regardless of location. This paper discusses creating a hybrid machine-learning model for assessing the quality of distance learning based on survey data. The model combines two feature extraction methods: Term frequency-inverse document frequency (TF-IDF) and Word2Vec. Combining these methods allows for a more complete and accurate representation of text data, improving the quality of machine learning models. The study aims to develop and evaluate the effectiveness of the proposed hybrid model for analyzing survey data and assessing the quality of distance learning. The paper considers the tasks of collecting and preprocessing text data, experimentally comparing various feature extraction methods and their combinations, training and evaluating a machine learning model based on a combination of TF-IDF and Word2Vec features, as well as analyzing the results and assessing the effectiveness of the proposed model using various metrics. In conclusion, the prospects for further development and application of the proposed model in educational institutions to improve the quality of distance learning are discussed.
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 Akynbekova, Ayman Azieva, Gulmira Eslyamov, Serik Kassekeyeva, Aislu Kenzhebayeva, Zhanat Lamasheva, Zhanar Makhazhanova, Ulzhan Mukasheva, Manargul Mukhanova, Ayagoz Mukhiyadin, Ainur Muratkhan, Raikhan Santeyeva, Saya Yussupova, Gulbakhar