The Impact of the Covid-19 Pandemic on Key Indicators of Personnel Security: A Study with Neural Network Technologies
Ładowanie...
Data
2021
Inny tytuł
Typ
Artykuł recenzyjny
Redaktor
dc.contributor.advisor
Dyscyplina PBN
Czasopismo lub seria
EUROPEAN RESEARCH STUDIES JOURNAL
ISSN
1108-2976
ISBN
DOI
10.35808/ersj/2197
Strona internetowa
Wydawca
Wydawca
Wydanie
Numer
Strony od-do
Tytuł monografii
item.page.defence
Tytuł tomu
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Abstrakt (en)
Purpose: The aim of this paper is to analyze the conceptual foundations of the use of
artificial neural networks for highly accurate prediction of personnel security, construction
of a mathematical model and building network architecture to solve the problem, as well as
providing an example of forecasting and interpretation of results.
Design/Methodology/Approach: Assessing the impact of the coronavirus disease (COVID 19) pandemic caused by SARS-CoV-2 on all aspects of human civilization is an urgent
scientific challenge today. However, it is already clear that it is human potential that will
be impacted most by the pandemic. Using artificial neural networks with radial basis
functions, the article predicts the influence of the COVID-19 pandemic on staff turnover,
which is one of the most important indicators of personnel security.
Findings: The network architecture is built and its mathematical description is made. The
main factors influencing staff turnover as one of the main components of personnel security
have been defined. Staff turnover in the EU in 2020 and its dependence on the GDP change
value has been analyzed.
Practical Implications: Personnel security of enterprises and organizations is the basis of
economic security nationwide. Nowadays, the Covid-19 pandemic first and foremost hits
staff, especially their mental and physical health, thus having a direct impact on the level of
personnel security. That is why, in order to effectively prevent a decline in the level of
economic security, the impact of the pandemic on key personnel security indicators should
be monitored in a timely manner. This is possible then using our metod.
Originality/Value: The value of the research is to test the adequacy of the artificial neural
network with RBF in predicting the impact of the COVID-19 pandemic on personnel
security. It also offers testing the prognostic properties of this type of ANN and considers
the possibility of their use for analysis, evaluation and forecasting of socio-economic
phenomena and processes.