Жарияланымдар / Публикации

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A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities
(Science and Information Organization, 2023-01-01)
Bakһytzһan Omarov
;
N.K. Nurmash
;
Bauyrzhan Doskarayev
;
Nagashbek Zhilisbaev
;
Maxat Dairabayev
In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.
Цитирования (OpenAlex) 24Цитирования (OpenCitations) 16
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Automatic Classification of Scanned Electronic University Documents using Deep Neural Networks with Conv2D Layers
(Science and Information Organization, 2023-01-01)
Aigerim S. Baimakhanova
;
Аinur Zhumadillayeva
;
Sailaugul Avdarsol
;
Yermakhan Zhabayev
;
Makhabbat Revshenova
This paper proposes a novel approach for scanned document categorization using a deep neural network architecture. The proposed approach leverages the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract features from the scanned documents and model the dependencies between words in the documents. The pre-processed documents are first fed into a CNN, which learns and extracts features from the images. The extracted features are then passed through an RNN, which models the sequential nature of the text. The RNN produces a probability distribution over the predefined categories, and the document is classified into the category with the highest probability. The proposed approach is evaluated on a dataset of scanned documents, where each document is categorized into one of four predefined categories. The experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods. The proposed approach achieves an overall accuracy of 97.3%, which is significantly higher than the existing methods' accuracy. Additionally, the proposed approach's performance was robust to variations in the quality of the scanned documents and the OCR accuracy. The contributions of this paper are twofold. Firstly, it proposes a novel approach for scanned document categorization using deep neural networks that leverages the strengths of CNNs and RNNs. Secondly; it demonstrates the effectiveness of the proposed approach on a dataset of scanned documents, highlighting its potential applications in various domains, such as information retrieval, data mining, and document management. The proposed approach can help organizations manage and analyze large volumes of data efficiently.
Цитирования (OpenAlex) 2Цитирования (OpenCitations) 1
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НЕЙРОННЫЕ АРХИТЕКТУРЫ ДЛЯ ОПРЕДЕЛЕНИЯ ПОЛА ИИДЕНТИФИКАЦИИ ГОВОРЯЩЕГО
(2024-06-01)
N. Mekebayev
;
D. Darkenbayev
;
A. Altybay
В этой статье мы исследуем две нейронные архитектуры для задач определения пола и идентификации говорящего, используя функции мелкочастотных кепстральных коэффициентов (MFCC), которые не охватывают характеристики, связанные с голосом. Одна из наших целей –сравнить различные нейронные архитектуры, многослойный персептрон (MLP) и сверточные нейронные сети(CNNS) для обеих задач с различными настройками и автоматически изучить особенности, характерные для пола/ говорящего. Экспериментальные результаты показывают, что модели, использующие z-оценку и преобразование матрицы Грамиана, дают лучшие результаты, чем модели, использующие только максимальную-минимальную нормализацию MFCC. С точки зрения времени обучения, MLP требует больших периодов обучения для сходимости, чем CNN. Другие экспериментальные результаты показывают, что MLP превосходят CNN в решении обеих задач с точки зрения ошибок обобщения.
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Wearable ECG Device and Machine Learning for Heart Monitoring
(Multidisciplinary Digital Publishing Institute, 2024-06-28)
Zhadyra N. Alimbayeva
;
Chingiz A. Alimbayev
;
Кassymbek Ozhikenov
;
Nurlan A. Bayanbay
;
Aiman K. Ozhikenova
With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing.
Цитирования (OpenAlex) 55Цитирования (OpenCitations) 30
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МАШИНАЛЫҚ ОҚЫТУ АЛГОРИТМДЕРІН ҚОЛДАНА ОТЫРЫП ДАУЫСТЫ ТАНУ
(Mukhametzhan Tynyshbayev ALT University, 2024-09-17)
Nurbapa Mekebayev
;
Dauren Darkenbayev
;
Arshyn Altybay
Біз мақалада біз дауысты автоматты түрде тану жүйесінің жұмысын жақсартатын алгоритмін анықтау үшін дауыстың әртүрлі MFCC мүмкіндіктерін және дауысты тану үшін машиналық оқыту алгоритмдерін қарастыратын боламыз. Біздер дауысты тану үшін машиналық оқыту алгоритмдерінің ішінен нейрондық желі архитектурасын салыстырып қайсысы жақсы нәтиже беретінін анықтаймыз. Біздің мақсатымыз нейрондық желі архитектурасының көп қабатты перцептронды (MLP) және конволюциялық нейрондық желілерді (CNN) дауысты тануға тән ерекшеліктерді автоматты түрде зерттеу. Эксперимент барысында сөйлеушінің дауысын тану, анықтау алгоритмі мен моделі құрылып, дауысты тану екі кезеңде жүзеге асырылды: оқыту және тестілеу.

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