Improved Artificial Neural Network with High Precision for Predicting Burnout among Managers and Employees of Start-Ups during COVID-19 Pandemic

Notwithstanding the impact that the Coronavirus pandemic has had on the physical and psychological wellness of people, it has also caused a change in the psychological conditions of many employees, particularly among organizations and privately owned businesses, which confronted numerous limitations because of the unique states of the pandemic. Accordingly, the current review expected to implement an RBF neural network to dissect the connection between demographic variables, resilience, Coronavirus, and burnout in start-ups. The examination technique was quantitative. The statistical populace of the review is directors and representatives of start-ups. In view of the statistical sample size of the limitless community, 384 of them were investigated. For information gathering, standard polls incorporating MBI-GS and BRCS and specialist-made surveys of pressure brought about by Coronavirus were utilized. The validity of the polls was affirmed by a board of specialists and their reliability was affirmed by Cronbach's alpha coefficient. The designed network structure had ten neurons in the input layer, forty neurons in the hidden layer, and one neuron in the output layer. The amount of training and test data were 70% and 30%, respectively. The output of the neural network and the collected results were compared with each other, and the designed network was able to classify all the data correctly. Using the method presented in this research can greatly help the sustainability of companies.

Automated summary

Despite the impact of the Coronavirus pandemic on people's physical and psychological well-being, it has also affected the psychological conditions of many employees, particularly in organizations and privately owned businesses facing pandemic-related restrictions. This study aimed to analyze the relationship between demographic variables, resilience, Coronavirus, and burnout in start-ups using an RBF neural network. The study employed a quantitative research method with a sample population of start-up managers and employees. Standard surveys and specially designed questionnaires were used to collect data, and their validity and reliability were confirmed. The designed network structure had ten neurons in the input layer, forty neurons in the hidden layer, and one neuron in the output layer. The training and test data were divided into 70% and 30% respectively. The results showed that the designed network was able to accurately classify all the data, and the method presented in this research can greatly contribute to the sustainability of companies.