Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Proposed Method Using Deep Learning from Unseen to Seen Anxieties of Children during COVID-19

Version 1 : Received: 13 September 2020 / Approved: 15 September 2020 / Online: 15 September 2020 (02:56:33 CEST)

How to cite: Bandyopadhyay, S.K.; Dutta, S.; Goyel, V. A Proposed Method Using Deep Learning from Unseen to Seen Anxieties of Children during COVID-19. Preprints 2020, 2020090323 (doi: 10.20944/preprints202009.0323.v1). Bandyopadhyay, S.K.; Dutta, S.; Goyel, V. A Proposed Method Using Deep Learning from Unseen to Seen Anxieties of Children during COVID-19. Preprints 2020, 2020090323 (doi: 10.20944/preprints202009.0323.v1).

Abstract

COVID-19 and new concept, lockdown, change social life of all classes of humans. Children partially feel the changes of daily life and this situation has been children’s free mind. Children are under a new type of restriction imposed on them by their parents. Normally they prefer play with their their friends than study and always waiting for holidays. They heard a new jargon i.e. lockdown where everything stands still. Very often they see peoples in the roads and few vehicles are moving in the roads. However, a peculiar thing happens now that they sit in front of computer to hear the virtual classes that are taken by the teachers. This also happens when there is no lockdown since COVID-19 still affects people. The environment is totally changed and they do not find any proper answers from the parents about the scenario.This study has been made an attempt to carry out the mental affairs of children in West Bengal, India. Several families are surveyed for collecting responses mostly from rural areas as well as urban areas for the time-period from April, 2020 to July, 2020. An effort has been given in this paper to predict the stress, depression and anxiety faced by children during the COVID-19. A Deep Learning Neural Network (DLNN) based method is applied to understand the stress level, depression level and anxiety level amongst the children. A hybrid DLNN has been presented in this research that combines both Convolutional Layer and Gated-Recurrent Unit (GRU) for obtaining the prediction of the mental health of children. The model obtains an accuracy of 89.57% for defeminizing mental anxiety of children.

Subject Areas

COVID-19; lockdown; CNN; DLNN; GRU; mental anxiety; hybrid approach

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