ДАУКЕЕВА»
ISSN 2790-0886
В Е С Т Н И К
АЛМАТИНСКОГО УНИВЕРСИТЕТА ЭНЕРГЕТИКИ И СВЯЗИ
Учрежден в июне 2008 года
Тематическая направленность: энергетика и энергетическое машиностроение, информационные, телекоммуникационные и космические технологии
2 (61) 2023
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Алматы
о постановке на переучет периодического печатного издания, информационного агентства и сетевого издания
№ KZ14VPY00024997 выдано
Министерством информации и общественного развития Республики Казахстан
Подписной индекс – 74108
Бас редакторы – главный редактор Стояк В.В.
к.т.н., профессор
Заместитель главного редактора Жауыт Алгазы, доктор PhD Ответственный секретарь Шуебаева Д.А., магистр
Редакция алқасы – Редакционная коллегия
Главный редактор Стояк В.В., кандидат технических наук, профессор Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан;
Заместитель главного редактора Жауыт А., доктор PhD, ассоциированный профессор Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан;
Сагинтаева С.С., доктор экономических наук, кандидат физико-математических наук, профессор математики, академик МАИН;
Ревалде Г., доктор PhD, член-корреспондент Академии наук, директор Национального Совета науки, Рига, Латвия;
Илиев И.К., доктор технических наук, Русенский университет, Болгария;
Белоев К., доктор технических наук, профессор Русенского университета, Болгария;
Обозов А.Д., доктор технических наук, НАН Кыргызской Республики, заведующий Лабораторией «Возобновляемые источники энергии», Кыргызская Республика;
Кузнецов А.А., доктор технических наук, профессор Омского государственного технического университета, ОмГУПС, Российская Федерация, г. Омск;
Алипбаев К.А., PhD, доцент Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан;
Зверева Э.Р., доктор технических наук, профессор Казанского государственного энергетического университета, Российская Федерация, г. Казань;
Лахно В.А., доктор технических наук, профессор Национального университета биоресурсов и природопользования Украины, кафедра компьютерных систем, сетей и кибербезопасности, Украина, Киев;
Омаров Ч.Т., кандидат физико-математических наук, директор Астрофизического института имени В.Г. Фесенкова, Казахстан;
Коньшин С.В., кандидат технических наук, профессор Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан;
Тынымбаев С.Т., кандидат технических наук, профессор Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан.
За достоверность материалов ответственность несут авторы.
При использовании материалов журнала ссылка на «Вестник АУЭС» обязательна.
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ИНФОРМАЦИОННЫЕ,
ТЕЛЕКОММУНИКАЦИОННЫЕ И КОСМИЧЕСКИЕ ТЕХНОЛОГИИ МРНТИ 28.23.37 https://doi.org/10.51775/2790-0886_2023_61_2_106
RESEARCH ON MACHINE LEARNING METHODS FOR RECOGNITION AND CLASSIFICATION OF CARDIOVASCULAR PATHOLOGIES
S.B. Rakhmetulayeva1, B.M. Ukibassov2, Zh.O. Zhanabekov3, A.K. Mukasheva4*
1,2,3
International Information Technology University, Almaty, Kazakhstan
4Non-profit JSC “Almaty University of Power Engineering and Telecommunicationsnamed after Gumarbek Daukeyev”, Almaty, Kazakhstan
E-mail: ssrakhmetulayeva@gmail.com, a.mukasheva@aues.kz, ukibas.b@gmail.com, zzhanabekov@gmail.com
Abstract. This research describes machine learning methods and algorithms for early diagnosis of cardiovascular diseases. In a comparative analysis of the noninvasive diagnostic methods used, echocardiography and electrocardiography are the most common. This article explores new methods of automatic augmentation and correction of echocardiogram and electrocardiogram results using machine learning methods and algorithms. The article develops a system that minimizes both medical and hardware errors in the interpretation of echocardiography and electrocardiography using neural networks and machine learning methods. The scientific novelty of the study is that machine learning methods can reduce image analysis time, accelerate clinical decision making, and provide feedback to less experienced clinicians. The experiment, by training models of pathology recognition and classification, gave a clear idea of how to create the same model for other, more serious diseases, such as cancer and its various types. The purpose of this article is to analyze new methods for automatically adding and correcting echocardiogram and electrocardiogram results. The practical relevance of this study is to use computational resources to improve subsequent interpretation using machine learning algorithms.
Keywords: Research, Machine Learning, Neural Networks, Echocardiogram, Electrocardiography, Diagnostic System, Pattern Recognition.
Introduction
Cardiovascular disease (CVD) is the leading cause of death worldwide. According to the World Health Organization, annually, because of CVD, 17.1 million people die [1].
The most effective way to prevent CVD is through early and timely diagnosis. Among the applicable non-invasive diagnostic methods, echocardiography (echo-CG, heart ultrasound) and electrocardiography (ECG) are the most common ones. The methods have no side effects, are not invasive and do not need additional preparation before the study. Also, they can be applied in stationary conditions almost instantly.
Echocardiography (echo-CG) and electrocardiography (ECG) play a crucial role in the diagnosis of cardiovascular diseases and is the only imaging modality that allows obtaining images of the heart in real- time, allowing to detect various violations immediately. The great advantage of echocardiography and electrocardiography methods is not only high available information content but also the possibility of repeated use both before and after the surgery as well as at the open-heart stages.
For a reliable diagnosis of the heart's state, the echocardiography results should be interpreted and analyzed. Unfortunately, exact decoding of the echo-CG image is not always possible for the following reasons:
1. Incorrect positioning on the screen with reference to actual physical dimensions;
2. Incorrect calculations and measurements;
3. Artifacts and interference with the image:
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An ultrasound image is often accompanied by many artifacts that create a visual picture that does not match the reality of the displayed object. Some artifacts worsen the quality of the echographic picture and complicate the interpretation according to Figure 1.
Figure 1. Reverb artifacts are better reflected in the grayscale image (A) and can be skipped with color Doppler (B). They become again apparent on the SR color-coded display as parallel yellow and blue high- intensity lines (C). If we take into account only the reconstructed time curve of such an area (D), erroneous artifacts of pathological curves that simulate “systolic lengthening” or “post systolic shortening” (red arrows)
may appear [2]
1. Abnormal structure of the body or pathology such as transposition of the great arteries, coarctation of the aorta/interruption of the aortic arch, aortic stenosis and atresia, left heart hypoplasia syndrome (HLHS), right heart hypoplasia syndrome (HRHS), atresia of the pulmonary artery, etc.;
2. Insufficient qualifications for decryption;
3. Subjective errors.
If any of these barriers occur, the echocardiography procedure should be repeated, with the possible consultation of a specialist, which slows down the timely diagnosis. An error in the ultrasound protocol leads to a mistake by the cardiologist. Further, a cardiologist's mistake can become a cardiac surgeon's mistake and lead to severe consequences.
Image and video modality.
Treating the echo-CG as an image is straightforward and arguably the most common representation form. It allows to utilize and tap into the vast amount of computer vision and image processing literature and, in particular, the convolutional neural networks, which show the state-of-the-art performance on large-scale image recognition [3] and segmentation tasks [4]. With commonly used networks as ResNet-s [5], UNet-s [6], R-CNN [7], etc. Zhang et al. [8] the UNet model can be used to segment the heart's echo-CG images into four regions (A4c, A2c, PLAX, and PSAX) in order to identify weak areas and to detect diseases such as hypertrophic cardiomyopathy and cardiac amyloidosis. The follow-up work of Zhang et al. [9] expands the model to have multiple views and improve the overall performance. Similarly, Madani et al. [10] use short video clips recorded during the echo and apply a convolutional neural network to understand which parts of the heart are being diagnosed, e.g., subcostal 4-chamber or subcostal inferior vena cava. Lu et al. [11]
proposed a deep regression neural network to identify heart anomalies from echocardiography images.
Ouyang et al. [12] presented a model named EchoNet-Dynamic, which segments echo images into structural parts and can predict ejection fraction and cardiomyopathy through a video-based echocardiogram. Similar work has been done by Ghorbani et al. [13]. Besides segmentation and detection, neural network-based approaches are used to register and track cardiac blood flow [14].
Based on a thorough quantitative analysis of sound and ECG symptoms using machine learning methods, new approaches to the diagnosis of coronary heart disease, heart defects, and pulmonary hypertension can be proposed [15].
Audio modality.
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The useful modality of the echocardiogram is being explored too. Dissanayake et al. [16] use sound segmentation results in combination with image-view to better classify anomalies in heart sound detection.
Unnikrishnan et al. [17] use heart sounds with generative-adversarial networks [18] to improve the model performance.
There is a strong trend of combining machine learning (ML) models with actual medical tasks for the public good. In Kazakhstani research, several groups use ML for medical research: Amirgaliyev et al. [19]
use ML tools for kidney disease diagnosis, and colleagues from Nazarbayev University use ML for brain- machine interaction research [20]. We are not aware of any research group from Kazakhstan working on improving the quality of the automatic interpretation of echocardiography images.
2 Materials and methods
The population's most promising and efficient part is subject to cardiovascular pathologies. Regional features of the prevalence of this group of diseases depend on the level of organization of perinatal diagnostics, the state of pediatric and cardiological care, the availability of modern equipment, specialists, and the awareness of neonatologists and pediatricians about the forms and characteristics of the course of congenital heart defects at different periods of a child's development [21].
Automatic interpretation of the result of EchoCG, ECG, and heart function tests using artificial intelligence can provide an accurate differentiated diagnosis of the related pathologies. The machine has also been used for forced vibration testing, heart rate analysis, computerized heart sound analysis, and telemedicine with promising results, but only in small studies. Therefore, more extensive studies are needed to confirm the current results and to stimulate the adoption of machine learning by the medical community [22].
Artificial intelligence (AI) has been successfully applied for medical purposes, thus being one of the major trends in health care. The capabilities of AI can be applied in the development of a system for diagnosing various diseases. Unfortunately, the success of these undertakings cannot be unambiguously translated into the use of AI in echo cardio diagnostics in Kazakhstani medicine for the following reasons:
1) The main factor in the success of neural networks and deep learning is the extensive collection of high-quality labeled data. For example, 30000 labeled mammograms were used in [23]. Foreign medical organizations' labeled data are unavailable due to the confidentiality of personal information and/or the commercial component. Therefore, one of the main tasks of this project is to create a new Kazakhstani dataset of labeled echocardiograms. Please pay attention to the difference between the availability of data and its quality markup, and the latter involves the work of a highly qualified doctor to interpret the result, which can take up to 10 to 30 minutes for one echocardiogram and up to 5 to 20 minutes for one echocardiogram.
2) One of the fundamental unresolved issues of echocardiograms is the question of representation – in what form is it best to represent the echocardiogram: as an image, video, or audio signal? Using the correct representation of data and models that can improve this representation has been the main pathway to progress in Deep Learning [24]. We set ourselves the task of studying the possible representation of echocardiography for the qualitative improvement of modern models for a more accurate diagnosis of cardiovascular diseases [25-26].
3) The final product of our project is not a single model but a combination of models and software for the fastest and most convenient use in medical practice. At the moment, there are no complete solutions ready to be deployed in actual medical settings – all research is limited to highly specialized models (see 3.4.1) where the system does only one specific task (i.e., determine pathology A or B).
As mentioned above, the project proposes the solution to three main problems using methods of machine learning and computer vision:
1. Determination of the quantity and quality of artifacts in the resulting image. This problem can be modeled as a multiclass classification task for the presence of one of the types of artifacts or as a task of detecting (labeling) groups of pixels on the images that are artifacts. Both models will use a multilayer convolutional neural network (CNNs). The determining factors, in this case, will be:
- Collection of necessary tagged and labeled data for different artifacts in the pictures;
- Data preprocessing, with important decision points of what view and form the image data will be fed to the neural network;
- Setting hyperparameters, such as the number of layers, filters, activation, and loss functions. The parameters will be determined based on the type of input data, as well as the types of objects (such as vessels) that are contained in the objects of study;
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The final output of this stage will be probabilistic quantities that determine whether the image and / or group of pixels (i.e., superpixels) belongs to one of the types of artifacts.
2. Automatic segmentation of structural and non-structural parts (objects of research) on the echo-CG data (images and video) and ECG data (sounds).
The main models for solving this problem are segmentation networks from the UNet or Mask-RCNN family. Models like UNet and Res-UNet are widely used in segmentation problems in the medical field.
However, networks such as Mask-RCNN, which are more popular and apply to many problems, will also be tested.
As was discussed in the previous stage, the main tasks here will be data pre-preparation and the selection of hyper-network parameters. In addition, the quality of data labeling will play a significant role and is the final metric that determines the segmentation quality. Traditionally, the primary metrics for these tasks are IoU (Intersection of Union, aka Jaccard index) and Dice Coefficient (aka F1-score).
3. The variance analysis method will verify the parameters and characteristics of the neural network before machine learning starts. When studying the EchoCG and ECG dependencies, one of the methods of correlation analysis will be applied (measurement errors are measured through the method of least squares and regression analysis). The results of the experimental tests will be marked with a partial order scale, and the statistical data will be converted to the corresponding test ones through the interval scale. Both datasets will be analyzed using a relationship scale.
4. Anomaly detection in segmented objects.
The problems of detecting anomalies are one of the fundamental tasks of machine learning and statistics, with many practical approaches. The classical solution is to model the data distribution function in the frequentist interpretation and use the statistical tests or use the Bayesian interpretation and consider the probability of the input data sample under the modeled pdf. We intend to combine classical methods with deep learning for modeling the data distribution function in images using deep variational learning methods or modeling the data distribution directly as it is done in generative-adversarial networks.
Alternatively, classical machine learning methods can be used on extracted features: for example, the measured vessel thickness or other parameters of heart structure. In the real world, the interpreter uses tools such as rulers and pointers built into the software to measure objects on the screen. A software algorithm can also cope with this task. The main methods that will be applied at this stage are the selection of borders (for a more precise display), the adjustment of contrast, brightness, and other image parameters.
3 Research results
Generation of synthetic echocardiograms
Generation of synthetic echocardiographic data for segmentation of interheart partition defect [27]
using CycleGAN [28] and CUT (Contrastive Unpaired Translation) [29] architectures. The ultimate goal is to saturate the natural data set with the above synthetic data and to describe the technical process of training generative models.
The NVIDIA A100 cluster was used for this experiment, with 80 gigabytes of graphical memory designed for the CUDA 11.4 programming environment and above, as shown in the following figure 2.
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Figure 2. Computing cluster characteristics
The experiment used computing resources of one terabyte of hard memory, 128 gigabytes of RAM and 2 processors of 32 physical cores. Virtual environments with packages containing the PyTorch framework, CUDA 11.8 with the corresponding torchvision package, tensorboard 2.12.0 were also used.
according to figure 3.
Figure 3. Data processing libraries
The experiment used generative neural networks such as CycleGAN] and CUT. Public 3D models collected in «.vtk» format «placeholder reference» were used as input data. As a result of the experiment, images were obtained using computing resources after processing 200 epochs.
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Figure 4. Drawings after 200 epochs
Then the data was processed after 2000 epochs, and the results were obtained as shown in Figure 5.
Figure 5. Drawings after 2000 epochs
This study considers the study of new methods of automatic filling and correction of echocardiograms, electrocardiograms, as well as the development of comprehensive software to improve subsequent interpretation using ML methods and algorithms. After analyzing the existing solutions, the main stages of achieving the goals were determined. It was decided that it was necessary to conduct an experimental comparison of existing machine learning methods and algorithms to complement and visualize echocardiography images and to study the applicability for the diagnosis of cardiovascular diseases with the involvement of cardiologists. It is also necessary to collect data, generate and tag EchoCG data. The authors concluded that the data should be divided into training and test sets in a ratio of 70/30.
Conclusion
Methods of automatic annotation and decoding of echocardiography based on the collected labeled data were investigated to determine the maximum possible set of image anomalies and the physical condition of the heart using the CNN algorithm. The study demonstrated the results obtained using complex software to improve subsequent interpretation using machine learning algorithms. Development of a qualitative assessment of the echo-CG image with the possibility of automatic correction of image artifacts using the CNN algorithm.
As a result of the conducted experiment on saturation of atrial septal defect data with successful generation of synthetic data, new vectors were formed to investigate how applicable they are for a sample of Kazakhstani patients. In order to prove or disprove their applicability, an additional study will be conducted on a controlled comparative analysis of the generated images.
In conclusion, based on the above, one of the observed trends in machine learning is the search and solution of new, medical, tasks for the common social good.
Acknowledgements
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This research has been funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP13068032).
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ЖҮРЕК-ҚАН ТАМЫРЛАРЫ ЖҮЙЕСІНІҢ ПАТОЛОГИЯСЫН ТАНУ ЖӘНЕ ЖІКТЕУ ҮШІН МАШИНАЛЫҚ ОҚЫТУ ӘДІСТЕРІН ЗЕРТТЕУ
С.Б. Рахметулаева1, Б.M. Укибасов2, Ж.О. Жанабеков3, A.К. Мукашева4*
1,2,3Халықаралық Ақпараттық Технологиялар Университеті, Алматы, Қазақстан
4 «Ғұмарбек Дәукеев атындағы Алматы энергетика және байланыс университеті» КЕАҚ, Алматы, Қазақстан
E-mail: ssrakhmetulayeva@gmail.com, ukibas.b@gmail.com, zzhanabekov@gmail.com, a.mukasheva@aues.kz
Аңдатпа. Бұл зерттеуде жүрек-қан тамырлары ауруларына ерте диагностика жасау үшін машиналық оқыту әдістері мен алгоритмдері сипатталады. Қолданылатын инвазивті емес диагностикалық әдістерді салыстырмалы талдау барысында эхокардиография және электрокардиография кең
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таралғандығы айқындалды. Бұл мақалада машиналық оқыту әдістері мен алгоритмдерін пайдалана отырып, эхокардиограммалар мен электрокардиограммалардың нәтижелерін автоматты түрде қосу мен түзетудің жаңа әдістері зерттелді. Бұл мақалада нейрондық желілер мен машиналық оқыту әдістерін пайдалана отырып, эхокардиография мен электрокардиографияны түсіндірудегі медициналық және аппараттық қателерді барынша азайтатын жүйе әзірленді. Бұл зерттеудің ғылыми жаңалығы - машиналық оқыту әдістерінің кескінді талдау уақытын қысқартуы, клиникалық шешім қабылдауды тездетуі және тәжірибесі аз клиникалар үшін қайталанатын кері байланысты қамтамасыз етуі болып табылады. Эксперимент нәтижесінде патологияларды тану және жіктеу модельдерін үйрету арқылы қатерлі ісік пен оның әртүрлі түрлері үшін бірдей үлгіні қалай жасау керектігі туралы нақты түсініктер алынды. Мақаланың мақсаты эхокардиограмма мен электрокардиограмма нәтижелерін автоматты түрде қосу мен түзетудің жаңа әдістерін талдау болып табылады. Бұл зерттеудің практикалық маңыздылығы машиналық оқыту алгоритмдерін пайдалана отырып, кейінгі түсіндірулер үшін күрделі бағдарламалық жасақтаманы пайдалануда жатыр.
Түйін сөздер: машиналық оқыту, нейрондық желілер, эхокардиограмма, электрокардиография, диагностикалық жүйе, үлгіні тану.
ИССЛЕДОВАНИЕ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ ДЛЯ РАСПОЗНАВАНИЯ И КЛАССИФИКАЦИИ ПАТОЛОГИЙ СЕРДЕЧНО-
СОСУДИСТЫХ СИСТЕМ
С.Б. Рахметулаева1, Б.М. Укибасов2, Ж.О. Жанабеков3, A.К. Мукашева4*
1,2,3Международный Университет Информационных Технологии, Алматы, Казахстан
4НАО «Алматинский университет энергетики и связи имени Гумарбека Даукеева», Алматы, Казахстан
E-mail: ssrakhmetulayeva@gmail.com, ukibas.b@gmail.com, zzhanabekov@gmail.com, a.mukasheva@aues.kz
Аннотация. В данном исследовании описываются методы машинного обучения и алгоритмы для ранней диагностики сердечно-сосудистых заболеваний. При сравнительном анализе применяемых неинвазивных методов диагностики наиболее распространенными стали эхокардиография и электрокардиография. В статье исследуются новые методы автоматического добавления и коррекции результатов эхокардиограмм и электрокардиограмм с использованием методов и алгоритмов машинного обучения. В статье разработана система, которая минимизирует как медицинские, так и аппаратные ошибки при расшифровке эхокардиографии и электрокардиографии с использованием нейронных сетей и методов машинного обучения. Научная новизна данного исследования заключается в том, что методы машинного обучения могут сократить время анализа изображений, ускорить принятие клинических решений и обеспечить повторную обратную связь для менее опытных клиницистов. В результате эксперимента обучив модели распознаванию и классификации патологий, были получены четкие представления о том, как создать такую же модель для других, более тяжелых заболеваний, таких как рак и его различные типы.
Целью статьи является анализ новых методов автоматического добавления и коррекции результатов эхокардиограмм и электрокардиограмм. Практическая значимость данного исследования заключается в использовании сложного программного обеспечения для улучшения последующей интерпретации с использованием алгоритмов машинного обучения.
Ключевые слова: машинное обучение, нейронные сети, эхокардиограмма, электрокардиография, диагностическая система, распознавание образов.
Басылымның шығыс деректері
Мерзімді баспасөз басылымының атауы «Алматы энергетика және байланыс университетінің Хабаршысы» ғылыми- техникалық журналы
Мерзімді баспасөз басылымының меншік иесі «Ғұмарбек Дәукеев атындағы Алматы энергетика және байланыс университеті»
коммерциялық емес акционерлік қоғамы, Алматы, Қазақстан
Бас редактор Профессор, т.ғ.к., В.В. Стояк
Қайта есепке қою туралы куәліктің нөмірі мен күні және берген органның атауы
№ KZ14VPY00024997, күні 17.07.2020,
Қазақстан Республикасының Ақпарат және қоғамдық даму министрлігі
Мерзімділігі Жылына 4 рет (тоқсан сайын)
Мерзімді баспасөз басылымының реттік нөмірі және жарыққа шыққан күні
Жалпы нөмір 61, 2-басылым, 2023 жылғы 30 маусым
Басылым индексі 74108
Басылым таралымы 200 дана
Баға Келісілген
Баспахана атауы, оның мекен-жайы «Ғұмарбек Дәукеев атындағы Алматы энергетика және байланыс университеті»
КЕАҚ баспаханасы, Байтұрсынұлы көшесі, 126/1 үй, А120 каб.
Редакцияның мекен-жайы 0 5 0 0 1 3 , Алм а т ы қ. , «Ғ ұ м а р бе к Дә ук е ев а т ы н да ғы А л м а т ы эн ер г ет и ка ж ә н е ба й ла н ы с ун и в ер с и т ет і » К ЕА Қ, Б а й т ұ р с ы н ұ лы к- с і , 1 2 6 / 1 ү й , ка б. А 2 2 4 , т е л. : 8 ( 7 2 7 ) 2 9 2 5 8 4 8 , 7 08 8 8 0 7 7 9 9 , e - m a i l : v e s t n i k @ a u e s . k z
Выходные данные
Название периодического печатного издания Научно-технический журнал «Вестник Алматинского университета энергетики и связи»
Собственник периодического печатного издания
Некоммерческое акционерное общество «Алматинский университет энергетики и связи имени Гумарбека Даукеева», Алматы, Казахстан
Главный редактор Профессор, к.т.н., Стояк В.В.
Номер и дата свидетельства о постановке на переучет и наименование выдавшего органа
№ KZ14VPY00024997 от 17.07.2020
Министерство информации и общественного развития Республики Казахстан
Периодичность 4 раза в год (ежеквартально)
Порядковый номер и дата выхода в свет
периодического печатного издания Валовый номер 61, выпуск 2, 30 июня 2023
Подписной индекс 74108
Тираж выпуска 200 экз.
Цена Договорная
Наименование типографии, ее адрес Типография НАО «Алматинский университет энергетики и связи имени Гумарбека Даукеева», ул. Байтурсынулы, дом 126/1, каб. А 120
Адрес редакции 050013, г. Алматы, НАО «Алматинский у ниверситет э нергетики и с вязи имени Гумарбека Даукеева», ул. Байтурсынулы, дом 126/1, каб. А 224, т ел.: 8 (727) 292 58 48, 708 880 77 99, e-mail: vestnik@aues.kz
Issue output
Name of the periodical printed publication Scientific and technical journal "Bulletin of the Almaty University of Power Engineering and Telecommunications"
Owner of the periodical printed publication Non-profit joint-stock company "Almaty University of Power Enginnering and Telecommunications named after Gumarbek Daukeyev", Almaty, Kazakhstan
Chief Editor Professor, candidate of technical sciences Stoyak V.V.
Number and date of the registration certificate and the name of the issuing authority
№ KZ14VPY00024997 from 17.07.2020
Ministry of Information and Social Development of the Republic of Kazakhstan
Periodicity 4 times a year (quarterly)
Serial number and date of publication of a periodical printed publication
Number 61, edition 2, June 30, 2023
Subscription index 74108
Circulation of the issue 200 copies
Price Negotiable
The name of the printing house, its address Printing house of Non-profit joint-stock company "Almaty University of Power Enginnering and Telecommunications named after Gumarbek Daukeyev", 126/1 Baitursynuly str., office A 120, Almaty, Republic of Kazakhstan
Editorial office address 050013, Non-profit joint-stock company "Almaty University of Power Enginnering and Telecommunications named after Gumarbek Daukeyev",
A 2 2 4 , t e l .: 8 (727) 292 58 48, 708 880 77 99, e-mail: vestnik@aues.kz
