Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Barlybayev, Alibek
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Development of system for generating questions, answers, distractors using transformers Barlybayev, Alibek; Matkarimov, Bakhyt
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1851-1863

Abstract

The goal of this article is to develop a multiple-choice questions generation system that has a number of advantages, including quick scoring, consistent grading, and a short exam period. To overcome this difficulty, we suggest treating the problem of question creation as a sequence-to-sequence learning problem, where a sentence from a text passage can directly mapped to a question. Our approach is data-driven, which eliminates the need for manual rule implementation. This strategy is more effective and gets rid of potential errors that could result from incorrect human input. Our work on question generation, particularly the usage of the transformer model, has been impacted by recent developments in a number of domains, including neural machine translation, generalization, and picture captioning.
Machine learning for real estate valuation: Astana, Kazakhstan case Barlybayev, Alibek; Sankibayev, Arman; Niyazova, Rozamgul; Akimbekova, Gulnara
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1110-1121

Abstract

Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.
Combined-adaptive image preprocessing method based on noise detection Shamshanovna, Razakhova Bibigul; Amangeldy, Nurzada; Kassymova, Akmaral; Kudubayeva, Saule; Kurmetbek, Bekbolat; Barlybayev, Alibek; Gazizova, Nazerke; Buribayeva, Aigerim
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.pp1584-1592

Abstract

The image processing method involves several critical steps, with image preprocessing being particularly significant. Segmentation and contour extraction on digital images are essential in fields ranging from image recognition to image enhancement in various recording devices, such as photo and video cameras. This research identifies and analyzes the main drawbacks of existing segmentation and contour extraction methods, focusing on object recognition. Not all filters effectively remove noise; some may clear areas of interest, affecting gesture recognition accuracy. Therefore, studying the impact of image preprocessing on gesture recognition outcomes is crucial for improving pattern recognition performance through more efficient preprocessing methods. This study seeks to find an optimal solution by detecting specific features during the preprocessing stage that directly influence gesture recognition accuracy. This research is a key component of the AP19175452 project, funded by the ministry of science and higher education. The project aims to create automated interpretation systems for Kazakh sign language, promoting inclusivity and technological innovation in communication aids. By addressing these challenges, the study contributes to the development of more robust and adaptive image preprocessing techniques for gesture recognition systems.
Formalization of Morphological Rules for Kazakh Nouns in the New Latin Alphabet Zhetkenbay, Lena; Sharipbay, Altynbek; Razakhova, Bibigul; Bekmanova, Gulmira; Barlybayev, Alibek; Nazyrova, Aizhan; Yergesh, Banu
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.820

Abstract

This study presents a hybrid computational model for formalizing and predicting morphological inflections of Kazakh nouns written in the new Latin alphabet. The motivation stems from limitations in previous systems based on Cyrillic orthography, which often misrepresented key phonological features such as vowel harmony and consonant assimilation. The main objective is to develop a linguistically informed and computationally efficient system to support Natural Language Processing (NLP) for Kazakh during its transition to Latin script. The methodology combines rule-based grammar formalization with a machine learning approach, specifically a Bayesian Regulation Backpropagation Neural Network (BR-BPNN). A manually curated dataset of 1,000 Latin-script Kazakh nouns was annotated for various morphological forms. Each word was encoded at the character level using a custom dictionary (kazlat_dict), capturing the final four letters as feature vectors. Formal logic and regular expressions were used to model morphological rules such as pluralization and case endings, incorporating vowel harmony, consonant softness, and sonority. These rules provided the training labels for the BR-BPNN model. The trained model achieved 91.5% accuracy, 89.4% precision, and a correlation coefficient (R) above 0.98, confirming the effectiveness of the hybrid system. A user interface prototype was developed to demonstrate practical utility, enabling users to input root nouns and receive suffix predictions with confidence scores and linguistic explanations. The novelty of this work lies in integrating linguistic theory with machine learning for a low-resource Turkic language. It offers a foundation for intelligent Kazakh language tools including spell checkers, grammar correctors, and educational platforms. Future work will extend the system to other parts of speech and explore contextual modeling to improve handling of ambiguous or irregular forms.
Co-Authors Akimbekova, Gulnara Amangeldy, Nurzada Bekmanova, Gulmira Buribayeva, Aigerim Gazizova, Nazerke Kassymova, Akmaral Kudubayeva, Saule Kurmetbek, Bekbolat Matkarimov, Bakhyt Nazyrova, Aizhan Niyazova, Rozamgul Razakhova, Bibigul Sankibayev, Arman Shamshanovna, Razakhova Bibigul Sharipbay, Altynbek Yergesh, Banu Zhetkenbay, Lena