ПРОГНОЗИРОВАНИЕ РАСПРОСТРАНЕНИЯ БОЛЕЗНЕЙ В ТРОПИЧЕСКИХ ЗО-НАХ С ПОМОЩЬЮ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ

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ПРОГНОЗИРОВАНИЕ РАСПРОСТРАНЕНИЯ БОЛЕЗНЕЙ В ТРОПИЧЕСКИХ ЗО-НАХ С ПОМОЩЬЮ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ. / Kolesnikov, Alexey A.; Kikin, Pavel M.

In: CEUR Workshop Proceedings, Vol. 2534, 12.01.2020, p. 371-376.

Research output: Contribution to journal › Conference article › peer-review

BibTeX

@article{62fa6919aa14422baca4f0fee109d0d0,

title = "ПРОГНОЗИРОВАНИЕ РАСПРОСТРАНЕНИЯ БОЛЕЗНЕЙ В ТРОПИЧЕСКИХ ЗО-НАХ С ПОМОЩЬЮ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ",

abstract = "Infection with tropical parasitic diseases, according to WHO, has a huge impact on the health of more than 40 million people worldwide and is the second leading cause of immunodeficiency. The number of infections is influenced by many factors - climatic, demographic, vegetation cover and a number of others. The article presents a study and an assessment of the degree of influence of each of these factors, as well as a comparison of the quality of forecasting by separate methods of geo-informational analysis and machine learning and the possibility of their ensemble.",

keywords = "Fever, Geostatistics, Machine learning, Neural networks, Tropical diseases",

author = "Kolesnikov, {Alexey A.} and Kikin, {Pavel M.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2019 All-Russian Conference {"}Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes{"}, SDM 2019 ; Conference date: 26-08-2019 Through 30-08-2019",

year = "2020",

month = jan,

day = "12",

language = "русский",

volume = "2534",

pages = "371--376",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "RWTH Aahen University",

}

RIS

TY - JOUR

T1 - ПРОГНОЗИРОВАНИЕ РАСПРОСТРАНЕНИЯ БОЛЕЗНЕЙ В ТРОПИЧЕСКИХ ЗО-НАХ С ПОМОЩЬЮ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ

AU - Kolesnikov, Alexey A.

AU - Kikin, Pavel M.

N1 - Publisher Copyright: Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/1/12

Y1 - 2020/1/12

N2 - Infection with tropical parasitic diseases, according to WHO, has a huge impact on the health of more than 40 million people worldwide and is the second leading cause of immunodeficiency. The number of infections is influenced by many factors - climatic, demographic, vegetation cover and a number of others. The article presents a study and an assessment of the degree of influence of each of these factors, as well as a comparison of the quality of forecasting by separate methods of geo-informational analysis and machine learning and the possibility of their ensemble.

AB - Infection with tropical parasitic diseases, according to WHO, has a huge impact on the health of more than 40 million people worldwide and is the second leading cause of immunodeficiency. The number of infections is influenced by many factors - climatic, demographic, vegetation cover and a number of others. The article presents a study and an assessment of the degree of influence of each of these factors, as well as a comparison of the quality of forecasting by separate methods of geo-informational analysis and machine learning and the possibility of their ensemble.

KW - Fever

KW - Geostatistics

KW - Machine learning

KW - Neural networks

KW - Tropical diseases

UR - http://www.scopus.com/inward/record.url?scp=85078533652&partnerID=8YFLogxK

M3 - статья в журнале по материалам конференции

AN - SCOPUS:85078533652

VL - 2534

SP - 371

EP - 376

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 2019 All-Russian Conference "Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes", SDM 2019

Y2 - 26 August 2019 through 30 August 2019

ER -

ID: 76310193