Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет
Постоянный URI для этого сообществаhttps://cris.qyzpu.kz/handle/123456789/1
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- Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Modern programming technologies in the tasks of identification and classification of military aircraft using machine learning algorithms(2024-12-16) - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, A Systematic Review of Computer Vision-Based Approaches in Educational Contexts(Springer Science+Business Media, 2026-07-17); ; ; - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities(Science and Information Organization, 2023-01-01); Цитирования (OpenAlex) 24Цитирования (OpenCitations) 16 - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, ANALYSIS OF THE EFFECTIVENESS OF OBJECT RECOGNITION METHODS IN IMAGESThis paper considers the problem of object recognition in images, which is one of the key problems of computer vision. The relevance of the research is due to the wide application of object recognition systems in such areas as security, medicine, robotics, automotive industry and quality control. The research analyses existing recognition methods, including traditional approaches and modern deep learning methods. Their advantages, disadvantages and effectiveness in different environments are evaluated. On the basis of experimental data, the most effective algorithms for application in recognition systems were selected. The results of the work allowed us to propose recommendations for the selection and improvement of methods of object recognition, which helps to improve the accuracy and reliability of such systems. The obtained conclusions can be useful for specialists in the field of computer vision and developers of applications that use recognition technologies. - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, СҰЙЫҚТЫҚ АҒЫНЫН ЕСЕПТЕУДЕ САНДЫҚ ӘДІСТЕР МЕН ЖАСАНДЫ ИНТЕЛЛЕКТТІ ҚОЛДАНУ(2024-12-01); - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Integrated AI-based Auxiliary Device to Improve Power Quality and Increase Output Capacity in Renewable Power Plants(2025-12-22); Yuri SITNIKOVThis paper presents the design and implementation of an integrated AI-based auxiliary device to enhance power quality and increase output capacity in renewable power plants. Specifically, a deep learning (DL) algorithm is developed to stabilize the DC-link voltage of a multifunction inverter operating within a solar and wind-based microgrid system. The proposed controller dynamically adapts to fluctuations in renewable power generation and varying load demands, ensuring stable system operation and improved power quality under diverse and unpredictable conditions. MATLAB/Simulink simulations were performed to validate system performance. Results show that the controller reduced the Total Harmonic Distortion (THD) of the grid current from 28.13% to 2.3%, meeting IEEE 519 standards. The controller also maintained DC-link voltage stability and managed fluctuating PV power outputs (ranging from 0 to 8000 W) and battery charging levels (0.1 to 1.1 units) under dynamic scenarios. These outcomes demonstrate the superior effectiveness of the proposed AI-based controller compared to conventional approaches, particularly in scenarios characterized by uneven renewable energy generation, sudden load changes, and power quality disturbances. The findings highlight the potential of deep learning techniques in addressing critical challenges in modern renewable energy systems, paving the way for more reliable and efficient microgrid operations. - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Electrocardiographic Signatures of Dysglycaemia: Mechanistic Foundations, Digital Biomarkers, and Artificial Intelligence for Non-Invasive Diabetes Risk Stratification(Multidisciplinary Digital Publishing Institute, 2026-03-18); Цитирования (OpenAlex) 1 - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, STUDENTS’ LEARNING APPROACHES AND TEACHING QUALITY IN CHEMISTRY TEACHER TRAINING PROGRAM: A COMPARATIVE PERSPECTIVE OF STUDENTS AND TEACHERSThis study comprehensively analyzed the learning approachesof future chemistry teachers and the quality of chemistry teaching, and identified their relationship. The data for the study were collected through an online survey. 179 students from 8 higher education institutions and 29 teachers from 6 higher education institutions in Kazakhstan participated in the research. The data were collected on the basis of a Likert scale and processed using t-test for independent samples, correlation and multiple regression analysis. The results showed that strategic and deep learning approaches are relatively dominant among students, while the surfacelearning approach is weakly observed. It was also found that teachers rate the quality of lectures and laboratory classes higher than students, which is due to the fact that teachers prioritize the scientific validity and structure of the content, and students pay more attention to learning activity, the ability to participate in classes and the availability of feedback.Correlation and regression analyses showed that the quality of lectures, seminars and especially laboratory classes is positively related to students' deep and strategic learning approaches. The quality of laboratory classes was the strongest predictor of the deep learning approach. The results of the study indicate the need to strengthen the content connection between lectures, seminars, and laboratory classes, and to systematically introduce interactive teaching methods and feedback in the chemistry teachertraining program.