Author: Podolchak, N.; Tsygylyk, N.; Martyniuk, V.; Sokil, O.
Title: Predicting Human Resource Losses due to the COVID-19 Pandemic in the Context of Personnel Security of Organizations Cord-id: lc325fer Document date: 2021_1_1
ID: lc325fer
Snippet: To ensure the economic security of enterprises during the Covid-19 pandemic, it is extremely important to ensure their personnel security, namely to take a set of necessary measures in a timely manner. This is not possible without accurately predicting the staff turnover situation that is directly affected by the pandemic. With this in mind, the paper proposes a mechanism for highly accurate forecasting (error 0.1-0.2%) of staff turnover, taking into account the state of the labor market in the
Document: To ensure the economic security of enterprises during the Covid-19 pandemic, it is extremely important to ensure their personnel security, namely to take a set of necessary measures in a timely manner. This is not possible without accurately predicting the staff turnover situation that is directly affected by the pandemic. With this in mind, the paper proposes a mechanism for highly accurate forecasting (error 0.1-0.2%) of staff turnover, taking into account the state of the labor market in the country, which clearly depends on Covid-19. For this purpose, the possibilities of artificial neural networks with radial-basic transmission functions are used. © 2021 IEEE.
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