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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Mukhanova, Ayagoz
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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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Autism detection using facial and motor analysis using machine learning Amirbay, Aizat; Baigabylov, Nurlan; Mukhanova, Ayagoz; Mukhambetova, Kuralay; Zaitov, Elyor; Burganova, Roza; Khusanova, Khayriniso; Akhmedova, Feruza
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10319

Abstract

This paper proposes a method for detecting autism spectrum disorders (ASD) through the analysis of facial and motor features using machine learning. The aim is to develop an algorithm for automatic ASD diagnosis based on spatiotemporal behavioral patterns. Traditional diagnostic methods rely on subjective expert observations, often delaying intervention. To address this, a hybrid convolutional neural network and long short-term memory (CNN+LSTM) model was employed. Convolutional layers extracted spatial features from video frames, while recurrent layers tracked temporal dynamics. Using MediaPipe face mesh, pose, and hands models, 1,639 parameters were obtained, including facial and pose coordinates, hand landmarks, mouth aspect ratio (MAR), and motion energy. The dataset comprised 100 children, aged 5–9 years (50 with ASD, 50 typically developing (TD)). Stratified cross-validation was applied to ensure subject-independent evaluation. Results showed 90% accuracy on the training set, 85–90% on validation, and an area under the curve (AUC) greater than 0.90, confirming model stability. Data visualization highlighted significant differences in motor activity and emotional expression between groups. The proposed approach demonstrates the potential for robust and objective ASD detection. It can be applied in clinical and educational contexts to improve early diagnosis and timely intervention.
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 Akhmedova, Feruza Akynbekova, Ayman Alimova, Zhanar Amirbay, Aizat Amirov, Azamat Baibulova, Makbal Baigabylov, Nurlan Baigusheva, Kanagat Baitemirov, Madiyar Bapiyev, Ideyat Bayegizova, Aigulim Burganova, Roza Eslyamov, Serik Ignatovich, Artyom Kaipova, Assemgul Kaldarova, Mira Khusanova, Khayriniso Kudabekov, Medet Lamasheva, Zhanar Makhatova, Valentina Makhazhanova, Ulzhan Mukhambetova, Kuralay Mukhamedrakhimova, Galiya Mukhomedyarova, Ainagul Muratkhan, Raikhan Ospanova, Tleugaisha Santeyeva, Saya Smailova, Ulmeken Tanirbergenov, Adilbek Tasbolatuly, Nurbolat Tassuov, Bolat Tynykulov, Marat Tynykulova, Assemgul Yussupova, Gulbakhar Zaitov, Elyor