Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Associating Unemployment with Panic Attack Using Stacked-RNN Model During COVID-19

Version 1 : Received: 17 June 2020 / Approved: 19 June 2020 / Online: 19 June 2020 (12:13:41 CEST)

How to cite: Bandyopadhyay, S.; DUTTA, S. Associating Unemployment with Panic Attack Using Stacked-RNN Model During COVID-19. Preprints 2020, 2020060242 (doi: 10.20944/preprints202006.0242.v1). Bandyopadhyay, S.; DUTTA, S. Associating Unemployment with Panic Attack Using Stacked-RNN Model During COVID-19. Preprints 2020, 2020060242 (doi: 10.20944/preprints202006.0242.v1).

Abstract

Corona Virus Infectious Disease (COVID-19) is newly emerging infectious disease. This disease is known to the globe in early 2019. Poor status of mental health is often caused by unemployment, ongoing socio-economic condition. Poor mental health may even accelerate the process of panic attack. It has been happening rapidly during COVID-19. It has a great effect on human health. This paper utilizes multiple related factors those have impact on causing panic attack. Recurrent Neural Network (RNN) based framework is utilized in this paper that assembles multiple RNN layers along with other parameters into a single platform. This method is implemented by capturing interfering factors and predicts panic attack tendency of people during COVID-19. Early prediction of panic attacks may assist in saving life from unwanted circumstances.

Subject Areas

panic disorder; unemployment rate; RNN; deep learning; mental illness; COVID-19

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.