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All Journal Bulletin of Electrical Engineering and Informatics Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC)
Serek, Azamat
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Design of QazSL Sign Language Recognition System for Physically Impaired Individuals Zholshiyeva, Lazzat; Zhukabayeva, Tamara; Baumuratova, Dilaram; Serek, Azamat
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.23879

Abstract

Automating real-time sign language translation through deep learning and machine learning techniques can greatly enhance communication between the deaf community and the wider public. This research investigates how these technologies can change the way individuals with speech impairments communicate. Despite advancements, developing accurate models for recognizing both static and dynamic gestures remains challenging due to variations in gesture speed and length, which affect the effectiveness of the models. We introduce a hybrid approach that merges machine learning and deep learning methods for sign language recognition. We provide new model for the recognition of Kazakh Sign Language (QazSL), employing five algorithms: Support Vector Machine (SVM), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN) with VGG19, ResNet-50, and YOLOv5. The models were trained on a QazSL dataset of more than 4,400 photos. Among the assessed models, the GRU attained the highest accuracy of 100%, followed closely by SVM and YOLOv5 at 99.98%, VGG19 at 98.87% for dynamic dactyls, LSTM at 85%, and ResNet-50 at 78.61%. These findings illustrate the comparative efficacy of each method in real-time gesture recognition. The results yield significant insights for enhancing sign language recognition systems, presenting possible advancements in accessibility and communication for those with hearing impairments.
Development of Method to Predict Career Choice of IT Students in Kazakhstan by Applying Machine Learning Methods Berlikozha, Bauyrzhan; Serek, Azamat; Zhukabayeva, Tamara; Zhamanov, Azamat; Dias, Oliver
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25558

Abstract

The growing intricacy of IT education requires resources to aid students in choosing specialized pathways. This study investigates the prediction of specialization preferences among IT students at SDU University in Kazakhstan through the application of machine learning techniques. The research contribution is the development of a predictive model that enhances academic advising by incorporating multiple factors, including academic performance, personality traits, qualifications, and extracurricular involvement. The research examined 692 anonymized student profiles and evaluated the efficacy of five machine learning algorithms: Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting, and Naive Bayes. Stratified 10-fold cross-validation was utilized to reduce the risk of overfitting. Gradient Boosting attained a peak accuracy of 99.10% in validation; however, its performance decreased to 92.16% on an independent test set, suggesting overfitting. Naive Bayes exhibited the lowest accuracy, recorded at 35.26%. Logistic regression analysis indicated a statistically significant correlation (p < 0.05) among academic performance, extracurricular involvement, and specialization selection. Personality traits and certifications significantly influenced the prediction process. The findings suggest that although Gradient Boosting demonstrates high effectiveness, the associated risk of overfitting requires additional refinement for practical application. The notable impact of academic performance and extracurricular activities indicates that educational institutions ought to prioritize these elements in student guidance. The incorporation of machine learning-based recommendations into advising frameworks enhances the precision of specialization predictions, thereby improving student decision-making and career alignment.
Resource-Efficient Sentiment Classification of App Reviews Using a CNN-BiLSTM Hybrid Model Baktibayev, Daulet; Serek, Azamat; Berlikozha, Bauyrzhan; Rustauletov, Babur
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13954

Abstract

This study evaluates the performance of a hybrid convolutional neural network and bidirectional long short-term memory (CNN + BiLSTM) model for sentiment classification on user reviews from the Spotify mobile application. The primary aim is to explore whether competitive results can be achieved without relying on transformer-based architectures, which often require substantial computational resources. The proposed CNN + BiLSTM model combines local feature extraction with sequential context modeling and is benchmarked against traditional machine learning and simpler deep learning models, including a Random Forest classifier enhanced with polarity features, a standalone CNN, and a fully connected DNN. Sentiment labels were binary (positive or negative) and directly provided in the dataset without being inferred from star ratings. The dataset was balanced to avoid class skew. Experimental results indicate that the CNN + BiLSTM model achieves moderate improvements over the baseline models, with an accuracy of 0.8861 and an F1-score of 0.8691. While it does not surpass the highest-performing transformer-based methods reported in the literature, it performs comparably to several of them, despite having a lower computational footprint. Analyses of ROC curves, confusion matrices, and training dynamics further contextualize the model’s performance, showing strengths in classifying negative sentiments and convergence efficiency. To address overfitting, early stopping and dropout layers were employed as regularization techniques. The study contributes to the ongoing discourse on resource-efficient sentiment analysis by showing that hybrid architectures may offer a practical balance between model complexity and performance in specific application domains.
Few-shot brain tumor classification: meta- vs metric-learning comparison Akhmetzhanova, Shynar; Serek, Azamat; Kashayev, Ruslan; Kozhamuratova, Aizhan
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.v14i5.10706

Abstract

Medical imaging requires accurate brain tumor recognition because precise classification is essential for early diagnosis and effective treatment planning. A major challenge in medical applications is that deep learning models typically require extensive amounts of labeled data to perform well. To address this, this research evaluates three few-shot learning (FSL) approaches-prototypical networks, Siamese networks, and model-agnostic meta-learning (MAML)-for brain tumor classification using the Figshare brain tumor dataset. The results show that prototypical networks consistently outperform the other approaches, achieving 89.07% accuracy (95% CI: 88.12–89.96%), 88.73% precision, and 88.67% recall, making them the optimal solution for this task. Siamese networks achieve 83.73% accuracy (95% CI: 82.64–84.76%), while MAML demonstrates significantly reduced performance, with 43.70% accuracy (95% CI: 42.10–45.22%). This study demonstrates that FSL can be applied effectively for medical image classification, with prototypical networks achieving the best performance in brain tumor detection. The inclusion of confidence intervals further validates the robustness and reliability of the results. Future research will focus on improving feature representation and exploring hybrid approaches to better handle rare tumor classes, thereby enhancing the clinical applicability of FSL models.
Accurate Crowd Counting Using an Enhanced LCDANet with Multi-Scale Attention Modules Abeuov, Nurmukhammed; Absatov, Daniyar; Mutaliyev, Yelnur; Serek, Azamat
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14391

Abstract

Accurate crowd counting remains a challenging task due to occlusion, scale variation, and complex scene layouts. This study proposes ME-LCDANet, an enhanced deep learning framework built upon the LCDANet backbone, integrating multi-scale feature extraction via Micro Atrous Spatial Pyramid Pooling (MicroASPP) and attention refinement using CBAMLite modules. A preprocessing pipeline with Gaussian-based density maps, synchronized augmentations, and a dual-objective loss function combining density and count supervision supports effective training and generalization. Experimental evaluation on the ShanghaiTech Part B dataset demonstrates a Mean Absolute Error (MAE) of 11.50 (95% CI: 10.20–12.91) and a Root Mean Squared Error (RMSE) of 11.54 (95% CI: 10.26–12.99). Training dynamics indicate steadily declining loss and reduced validation MAE, while gradient norm analysis suggests reliable convergence. Comparative results show that, although CSRNet and SaNet achieve slightly lower MAE, ME-LCDANet attains a notably reduced RMSE, reflecting robustness against large prediction deviations. While the study focuses on a single benchmark dataset, the proposed architecture offers a promising approach for robust crowd counting in diverse scenarios.
Co-Authors Abeuov, Nurmukhammed Absatov, Daniyar Akhmetzhanova, Shynar Baktibayev, Daulet Baumuratova, Dilaram Berlikozha, Bauyrzhan Dias, Oliver Kashayev, Ruslan Kozhamuratova, Aizhan Mutaliyev, Yelnur Rustauletov, Babur Zhamanov, Azamat Zholshiyeva, Lazzat Zhukabayeva, Tamara