Choice of metadata Статьи
Page 1, Results: 4
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1.
Подробнее
26.8
A98
Ayzel, G. V.
Forecasting of the state of the Small Aral Sea based on open data sources. [Текст] / G. V. Ayzel, A. C. Izhitskiy, A. K. Kurbaniyazov // Reports of the National Academy of Sciences of the Republic of Kazakhstan. - 2020. - №4. - P. 57-64
ББК 26.8
Рубрики: geography
Кл.слова (ненормированные):
Small Aral Sea -- sea level -- machine learning river discharge -- basins -- Syrdarya -- hydrodynamic modeling
Аннотация: The study of the dynamics of the level and volume of water in the Aral Sea is urgent scientific task due to the need to understand the mechanisms of natural and anthropogenic processes that have induced a radical change in its water and salt balance over the past 60 years.
Держатели документа:
WKU
Доп.точки доступа:
Izhitskiy, A.C.
Kurbaniyazov, A.K.
A98
Ayzel, G. V.
Forecasting of the state of the Small Aral Sea based on open data sources. [Текст] / G. V. Ayzel, A. C. Izhitskiy, A. K. Kurbaniyazov // Reports of the National Academy of Sciences of the Republic of Kazakhstan. - 2020. - №4. - P. 57-64
Рубрики: geography
Кл.слова (ненормированные):
Small Aral Sea -- sea level -- machine learning river discharge -- basins -- Syrdarya -- hydrodynamic modeling
Аннотация: The study of the dynamics of the level and volume of water in the Aral Sea is urgent scientific task due to the need to understand the mechanisms of natural and anthropogenic processes that have induced a radical change in its water and salt balance over the past 60 years.
Держатели документа:
WKU
Доп.точки доступа:
Izhitskiy, A.C.
Kurbaniyazov, A.K.
2.
Подробнее
22.3
M99
Myrzatay, A. A.
The effect of the amount of data array on the results of forecasting network equipment failures. [Текст] / A. A. Myrzatay, L. G. Rzayeva, G. A. Uskenbayeva, A. K. Shukirova, G. Abitova // News of national academy of sciences of the republic of Kazakhstan. . - 2021. - №6. - P. 28-36
ББК 22.3
Рубрики: physics
Кл.слова (ненормированные):
machine learning methods -- modeling of machine learning method -- network equipment failure forecasts -- LAN
Аннотация: The article discusses three methods for predicting network equipment failures and the impact of the data array of input controllers. The purpose of the article is to reveal the relevance of the approach proposed by the authors to the use of large-amount of data in the chosen method of machine learning and to make a comparative analysis of the final values with the works of other world researchers. In the first section, the authors analyze the work of scientists from the Beijing University of Post and Telecommunications, noting the strengths and weaknesses of their method. In Section 2, the authors analyze the Holt-Winters method for developing algorithms for analyzing network traffic, which was applied by researchers from the Tomsk State University of Control Systems and Radio Electronics. In section 3, the authors applied the method of random trees in the modeling of machine learning methods of Rapid Miner Studio. The authors have carried out work with a large amount of data, and a comparative analysis of the results of modeling the method. The importance of using large amounts of information to train a model of network equipment failure forecasts is proved. In the final section, the authors highlight the need to improve forecasting models for its further implementation in the working environment. Also, the authors emphasize that the two articles considered by international researchers are a special case, as well as their chosen method for predicting failures in the LAN system.
Держатели документа:
WKU
Доп.точки доступа:
Rzayeva, L.G.
Uskenbayeva, G.A.
Shukirova, A.K.
Abitova, G.
M99
Myrzatay, A. A.
The effect of the amount of data array on the results of forecasting network equipment failures. [Текст] / A. A. Myrzatay, L. G. Rzayeva, G. A. Uskenbayeva, A. K. Shukirova, G. Abitova // News of national academy of sciences of the republic of Kazakhstan. . - 2021. - №6. - P. 28-36
Рубрики: physics
Кл.слова (ненормированные):
machine learning methods -- modeling of machine learning method -- network equipment failure forecasts -- LAN
Аннотация: The article discusses three methods for predicting network equipment failures and the impact of the data array of input controllers. The purpose of the article is to reveal the relevance of the approach proposed by the authors to the use of large-amount of data in the chosen method of machine learning and to make a comparative analysis of the final values with the works of other world researchers. In the first section, the authors analyze the work of scientists from the Beijing University of Post and Telecommunications, noting the strengths and weaknesses of their method. In Section 2, the authors analyze the Holt-Winters method for developing algorithms for analyzing network traffic, which was applied by researchers from the Tomsk State University of Control Systems and Radio Electronics. In section 3, the authors applied the method of random trees in the modeling of machine learning methods of Rapid Miner Studio. The authors have carried out work with a large amount of data, and a comparative analysis of the results of modeling the method. The importance of using large amounts of information to train a model of network equipment failure forecasts is proved. In the final section, the authors highlight the need to improve forecasting models for its further implementation in the working environment. Also, the authors emphasize that the two articles considered by international researchers are a special case, as well as their chosen method for predicting failures in the LAN system.
Держатели документа:
WKU
Доп.точки доступа:
Rzayeva, L.G.
Uskenbayeva, G.A.
Shukirova, A.K.
Abitova, G.
3.
Подробнее
3
B 96
Buribayev, ZH. A.
Tomato maturity recognition using yolo 5 machine learning [Текст] / ZH. A. Buribayev, ZH. Amirgaliyeva [и др.] // Вестник национальной инженерной академии Республики Казахстан. - 2021. - №4. - с. 49-60
ББК 3
Рубрики: Техника. Технические науки
Кл.слова (ненормированные):
machine learning -- tomato recognition -- robotic systems -- agricultural robot -- HSV segmentation -- YOLO
Аннотация: The implementation of robotic systems and digitalization in agriculture are important tasks today. In this paper, the possibility of using pattern recognition and machine learning methods as a computer model of an agricultural robot for harvesting is considered.
Держатели документа:
ЗКУ им М. Утемисова
Доп.точки доступа:
Amirgaliyeva, ZH.
Joldasbayev, S. K.
Zhassuzak, M. S.
Turegali, A. S.
Dauletiya, D. D.
B 96
Buribayev, ZH. A.
Tomato maturity recognition using yolo 5 machine learning [Текст] / ZH. A. Buribayev, ZH. Amirgaliyeva [и др.] // Вестник национальной инженерной академии Республики Казахстан. - 2021. - №4. - с. 49-60
Рубрики: Техника. Технические науки
Кл.слова (ненормированные):
machine learning -- tomato recognition -- robotic systems -- agricultural robot -- HSV segmentation -- YOLO
Аннотация: The implementation of robotic systems and digitalization in agriculture are important tasks today. In this paper, the possibility of using pattern recognition and machine learning methods as a computer model of an agricultural robot for harvesting is considered.
Держатели документа:
ЗКУ им М. Утемисова
Доп.точки доступа:
Amirgaliyeva, ZH.
Joldasbayev, S. K.
Zhassuzak, M. S.
Turegali, A. S.
Dauletiya, D. D.
4.
Подробнее
3
M 26
Mansurova, M. E.
Monitoring system for young diabetes patients [Текст] / M. E. Mansurova, O. A. Postolache [et al.] // Вестник Национальной инженерной академии Республики Казахстан. - 2021. - №3. - с. 99-105
ББК 3
Рубрики: Техника. Технические науки
Кл.слова (ненормированные):
diabetes mellitus -- glucose control -- lnretnet of Things -- machine learning
Аннотация: The article describes the structures for monitoring the health status of patients with diabetes mellitus.
Держатели документа:
ЗКУ им М. Утемисова
Доп.точки доступа:
Postolache, O. A.
Zholdas, N. A.
Sarsembaeva, T. S.
Ixanov, S. SH.
M 26
Mansurova, M. E.
Monitoring system for young diabetes patients [Текст] / M. E. Mansurova, O. A. Postolache [et al.] // Вестник Национальной инженерной академии Республики Казахстан. - 2021. - №3. - с. 99-105
Рубрики: Техника. Технические науки
Кл.слова (ненормированные):
diabetes mellitus -- glucose control -- lnretnet of Things -- machine learning
Аннотация: The article describes the structures for monitoring the health status of patients with diabetes mellitus.
Держатели документа:
ЗКУ им М. Утемисова
Доп.точки доступа:
Postolache, O. A.
Zholdas, N. A.
Sarsembaeva, T. S.
Ixanov, S. SH.
Page 1, Results: 4