Issue |
SHS Web Conf.
Volume 107, 2021
9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2021)
|
|
---|---|---|
Article Number | 05005 | |
Number of page(s) | 8 | |
Section | Information Systems and Technologies in Economics | |
DOI | https://doi.org/10.1051/shsconf/202110705005 | |
Published online | 24 May 2021 |
A flexible evolutionary model of machine learning of the most successful strategies of human capital development
Classical private university, 70B Zhukovskoho Str., Zaporizhzhia, 69002, Ukraine
* e-mail: penev.vladislav@gmail.com
As a result of research, the concept of a flexible evolutionary model is proposed, which with the help of machine learning allows obtaining the most successful strategy for the development of human capital. The proposed conceptual and methodological approach to machine learning of the process of assessing human capital of enterprises, taking into account the cognitive psychology of man and reflective attitudes in the human environment, can increase the effectiveness of decision-making in the field of human capital development management. The training involves indicators of return on investment in the individual, in the types of components of human capital, which are characterized by properties (creativity, competence, purposefulness, communication, motivation), where between their varieties there are appropriate reflective relationships. The main difficulty of this approach to the choice of alternative solutions for finding options for the use of human capital is the correct selection of indicators of significance (return) of contributions to the development of types of human capital, on the basis of which cycles occur of systemic learning. This approach can simplify the search for and developments of human capital development strategies, present alternative ways, and simplify management decisions.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.