TY - JOUR
T1 - Using cloud-based machine learning technologies in limited funded social research
AU - Romanchukov, S. V.
AU - Berestneva, O. G.
AU - Berezhnova, E. V.
N1 - Funding Information:
The work was supported by the RFBR, and conducted in frames of project 18-37-00344 "Regional Development Social and Economic Parameter's Interdependence Analysis and Modeling" and partially supported by project 18-07-00543 “Intelligent support of managerial decision-making on innovative development of regional scientific medical centers”
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/10
Y1 - 2020/11/10
N2 - This work is devoted to the description of cloud solutions for machine learning, which are already used in the business data analysis and may be applicable in the social sciences. First of all, the article is addressed to specialists in sociology/psychology/economics/gender studies, who need a deep analysis of the accumulated data, but at the same time do not have sufficient expertise in the field of mathematics, machine learning and Big Data processing and/or do not have sufficient funding to support the staff of professional analysts or data scientists. Using as an example one dataset, the size and structure of which are comparable with those for various social studies, we go through all stages of training and testing the model in Google Cloud AI and IBM Watson Auto AI, comparing their advantages and disadvantages.
AB - This work is devoted to the description of cloud solutions for machine learning, which are already used in the business data analysis and may be applicable in the social sciences. First of all, the article is addressed to specialists in sociology/psychology/economics/gender studies, who need a deep analysis of the accumulated data, but at the same time do not have sufficient expertise in the field of mathematics, machine learning and Big Data processing and/or do not have sufficient funding to support the staff of professional analysts or data scientists. Using as an example one dataset, the size and structure of which are comparable with those for various social studies, we go through all stages of training and testing the model in Google Cloud AI and IBM Watson Auto AI, comparing their advantages and disadvantages.
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U2 - 10.1088/1742-6596/1661/1/012076
DO - 10.1088/1742-6596/1661/1/012076
M3 - Conference article
AN - SCOPUS:85096541278
VL - 1661
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012076
T2 - 2020 International Conference on Information Technology in Business and Industry, ITBI 2020
Y2 - 6 April 2020 through 8 April 2020
ER -