Жарияланымдар / Публикации

Постоянный URI для этого сообществаhttps://cris.qyzpu.kz/handle/123456789/2

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Вестник ЗКУ
(2026-07-24)
Е.А.Тулегенов
;
Н.О.Мекебаев
;
Ж.А. Орынтаева
This study comprehensively examines contemporary issues in predicting topographic elevation through the integration of Geographic Information Systems (GIS) and Machine Learning algorithms. Currently, the development of high-precision Digital Elevation Models (DEM) for the territory of Kazakhstan is of paramount importance. This is primarily due to the unique geomorphological complexity of the country; the Kazakh landscape, ranging from the Caspian depressions to the high-altitude systems of the Tien Shan and Altai, poses a significantly challenge for any mathematical model. From a practical standpoint, such natural heterogeneity significantly reduces the effectiveness of classical geostatistical interpolation methods, such as Kriging or Inverse Distance Weighting (IDW). Standard approaches often fail to account for sharp topographic gradients, leading to substantial errors. To address these shortcomings, this research employs modern data-driven approaches. The subject of the study is the process of regression forecasting of elevation points by integrating spatial data from GIS platforms into machine learning models. The primary objective is to identify the most optimal model adapted to the specific characteristics of the local terrain. To this end, three different architectures were tested: Random Forest, Gradient Boosting, and Artificial Neural Networks (ANN). In the initial stage, GIS data underwent preprocessing and normalizations, after which the predictive capacity of each model was compared against actual measurement points. The results indicated that machine learning methods provide 20-35% higher accuracy compared to traditional techniques. During the comparative analysis, the Random Forest model datasets. This model allowed for a more accurate description of local terrain fluctuations. In conclusion, the proposed methodology paves the way for elevating digital cartography in Kazakhstan to a qualitatively new level. It is recommended to implement these research findings in geodetic design, hydrological modeling, environmental monitoring, and state systems for transformation aimed at the efficient use of the country`s land resources.
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Spatiotemporal analysis and identifying the driving forces of land use change in the Abay district (Karagandy Region, Kazakhstan)
(EDP Sciences, 2024-01-01)
Onggarbek Alipbeki
;
Pavel Grossul
;
Daniyar Rakhimov
;
Erassyl Aliken
;
Kairat Augambayev
Land use and cover change (LUCC) affects the nature of human activities in a particular area. Therefore, the manifestation of the driving forces of these changes plays a decisive role. This paper analyses the LULC dynamics of the Abay district of Karagandy oblast from 2016 to 2023. The study’s main objective is to find the driving forces of land use based on the integrated assessment of spatio-temporal data (STD) and socio-economic, climatic and environmental indicators (SECEI). Classification of Sentinel- 2 images into LULC classes is carried out using the Random Forest (RF) algorithm on the Google Earth Engine (GEE) platform. The driving factors were assessed using principal component analysis (PCA) and linear regression (LR). The results obtained can be used to guide the development planning of the territory.
Цитирования (OpenAlex) 1
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МАШИНАЛЫҚ ОҚЫТУ ӘДІСІНІҢ КӨМЕГІМЕН ЖҮРЕК АУРУЫН БОЛЖАУ
(Mukhametzhan Tynyshbayev ALT University, 2022-10-28)
Zhadyra N. Alimbayeva
;
Кassymbek Ozhikenov
;
Айдана Еркебай
;
Chingiz A. Alimbayev
;
Aiman K. Ozhikenova
Мүгедектік пен өлім деңгейінің жоғары болуына байланысты жүрек-қан тамырлары ауруларын дер кезінде анықтау маңызды мәселе болып отыр. Бұл мақалада машиналық оқыту алгоритмдерін қолданып электрокардиосигналды өңдеу арқылы жүрек ауруын болжауға зерттеу жүргізілген. Жалпы, зерттеу барысында әртүрлі машиналық оқыту алгоритмдерімен 50000-нан астам сандық эксперимент жүргізілді. Салыстырмалы талдау негізінде машиналық оқытудың үлгілері мен әдістері таңдалып, ең жоғары жақсы көрсеткіш нәтиже берді. Зерттеуге келесі әдістер қолданылды: тірек векторлық машинасы, шешімдер ағашы, Байес классификаторы, логистикалық регрессия, k-жақын көршілер, кездейсоқ орман, терең нейрондық желілер. Таңдалған модельдер параметрлерді анықтау және тиімді қолдану үшін ұсынылды.
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Deep Convolutional Neural Network for Accurate Prediction of Seismic Events
(Science and Information Organization, 2023-01-01)
Assem Turarbek
;
Maktagali A. Bektemesov
;
А. Д. Онгарбаева
;
Assel Orazbayeva
;
Aizhan Koishybekova
In recent years, the realm of seismology has witnessed an increased integration of advanced computational techniques, seeking to enhance the precision and timeliness of earthquake predictions. The paper titled "Deep Convolutional Neural Network and Machine Learning Enabled Framework for Analysis and Prediction of Seismic Events" embarks on an ambitious exploration of this interstice, marrying the formidable prowess of Deep Convolutional Neural Networks (CNNs) with an array of machine learning algorithms. At the forefront of our investigation is the Deep CNN, known for its unparalleled capability to process spatial hierarchies and multi-dimensional seismic data. Accompanying this neural behemoth is LightGBM, a gradient boosting framework that offers superior speed and performance, especially with voluminous datasets. Additionally, conventional neural networks, noted for their adeptness in pattern recognition, offer a robust method to gauge the intricacies of seismic data. Our exploration doesn't halt here; the research delves deeper with Random Forest and Support Vector Machines (SVM), both renowned for their resilient performance in classification tasks. By amalgamating these diverse methodologies, this research crafts a multifaceted and synergistic framework. The culmination is a sophisticated tool poised to not only discern the minutiae of seismic activities with heightened accuracy but to predict forthcoming events with a degree of certainty previously deemed elusive. In this era of escalating seismic activities, our research offers a timely beacon, heralding a future where communities are better equipped to respond to the Earth's capricious tremors.
Цитирования (OpenAlex) 3Цитирования (OpenCitations) 1
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PHONEME (VOWEL SOUND) RECOGNITION USING MACHINE LEARNING
(2025-12-25)
Nurbapa Mekebayev
;
Dauren Darkenbayev
;
Г.З. Зиятбекова
;
Орынтаева Ж.А.
;
Ғ.М. Сулейменов
Қазіргі таңда сөйлеуді автоматты түрде тану (ASR) жүйелері жасанды интеллект пен машиналық оқытудың дамуына байланысты өзекті бағытқа айналды. Қазақ тіліндегі фонемаларды, әсіресе дауысты дыбыстарды тану саласының жеткілікті дамымауы және цифрлық ресурстардың аздығы зерттеу өзектілігін айқындайды. Зерттеу мақсаты – қазақ тіліндегі дауысты дыбыстарды жоғары дәлдікпен танитын машиналық оқыту үлгісін жасау. Бұл үшін сөйлеу сигналдарын алдын ала өңдеу, MFCC арқылы ерекшелік белгілерін алу және Random Forest, SVM, ANN алгоритмдерін салыстыру жүргізілді. Нәтижесінде ANN моделі ең жоғары дәлдік көрсетті. Алынған нәтижелер қазақ тіліндегі сөйлеуді тану жүйелерінің сапасын арттыруға және болашақта дауыс биометриясы мен дыбыстық интерфейстерде қолдануға мүмкіндік береді.
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Prediction of Percutaneous Coronary Intervention from Clinical and ECG Data Using Machine Learning: A Retrospective Single-Center Observational Study
(Multidisciplinary Digital Publishing Institute, 2026-05-06)
Zhadyra N. Alimbayeva
;
Chingiz A. Alimbayev
;
Kassymbek Ozhikenov
;
Kairat Karibayev
;
Aiman K. Ozhikenova
The aim of this study was to evaluate the feasibility of predicting percutaneous coronary intervention (PCI) based on clinical, laboratory, and electrocardiographic data available at various stages of hospitalization. A retrospective single-center study was conducted, including 137 patients with suspected coronary artery disease. The fact that PCI was performed during the current hospitalization was considered as the endpoint. Taking into account the temporary availability of data, three sets of signs were formed: basic (SAFE), including indicators available at admission; clinical (CLINICAL); and extended (EXTENDED), supplemented with glycemic parameters. Logistic regression, random forest, and gradient boosting were used to build the models. The assessment was carried out using repeated stratified cross-validation (5 × 10). The main metrics were ROC-AUC, PR-AUC, accuracy and F1-measure. The models demonstrated moderate predictive ability. The basic model (SAFE) showed a ROC-AUC of 0.734 ± 0.092, while the best results were achieved using an extended model based on a random forest (ROC-AUC 0.755 ± 0.079). The addition of glycemic parameters provided a moderate improvement in prediction quality. In the logistic regression, the most significant predictor was the presence of type 2 diabetes mellitus (OR = 7.36; p < 0.001). The results indicate the potential for using non-invasive data to assess the likelihood of PCI in the early stages of hospitalization. However, the models show moderate accuracy and require further validation on larger and more independent samples.
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Interpretable Machine Learning Approach for Diabetes Classification in Patients with Cardiovascular Disease
(Multidisciplinary Digital Publishing Institute, 2026-07-04)
Chingiz A. Alimbayev
;
Zhadyra N. Alimbayeva
;
Kassymbek Ozhikenov
;
Kairat Karibayev
;
Zhanat Abuova
Diabetes mellitus is strongly associated with cardiovascular dysfunction and remains one of the leading contributors to morbidity and mortality worldwide. Early identification of diabetes-related cardiovascular alterations is essential for timely risk stratification and personalized clinical management. In the present study, an interpretable machine learning framework for diabetes classification in patients with cardiovascular disease was developed using routinely available clinical, biochemical, renal, and echocardiographic parameters. A retrospective dataset consisting of 131 cardiovascular patients was included in the final analysis, comprising 65 patients with diabetes mellitus and 66 patients without diabetes. Demographic, metabolic, renal, and cardiovascular variables, including age, body mass index (BMI), glycated hemoglobin (HbA1c), glucose concentration, estimated glomerular filtration rate (eGFR), troponin level, heart rate, and left ventricular ejection fraction (EF), were included in the analysis. Multiple supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, and Random Forest, were implemented and compared using repeated stratified cross-validation. Among the evaluated models, Random Forest demonstrated the highest classification performance, achieving a mean ROC AUC of 0.880 ± 0.050. Statistical analysis revealed significantly elevated HbA1c, glucose, and troponin levels together with reduced ejection fraction values in diabetic patients. Explainable artificial intelligence analysis using SHAP and partial dependence plots identified glucose concentration, HbA1c, age, and renal function as the dominant contributors to diabetes classification. Nonlinear relationships between metabolic and cardiovascular variables were additionally observed. The obtained findings demonstrate that interpretable machine learning approaches can provide effective discrimination between diabetic and non-diabetic cardiovascular patients while maintaining clinically meaningful interpretability. The proposed framework may contribute to future intelligent clinical decision-support systems and personalized cardiovascular risk assessment strategies.

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