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. Preprints2020, 2020060242. https://doi.org/10.20944/preprints202006.0242.v1
Bandyopadhyay, S.; DUTTA, S. Associating Unemployment with Panic Attack Using Stacked-RNN Model During COVID-19. Preprints 2020, 2020060242. https://doi.org/10.20944/preprints202006.0242.v1
Bandyopadhyay, S.; DUTTA, S. Associating Unemployment with Panic Attack Using Stacked-RNN Model During COVID-19. Preprints2020, 2020060242. https://doi.org/10.20944/preprints202006.0242.v1
APA Style
Bandyopadhyay, S., & DUTTA, S. (2020). <strong>Associating Unemployment with Panic Attack Using Stacked-RNN Model During COVID-19</strong>. Preprints. https://doi.org/10.20944/preprints202006.0242.v1
Chicago/Turabian Style
Bandyopadhyay, S. and SHAWNI DUTTA. 2020 "<strong>Associating Unemployment with Panic Attack Using Stacked-RNN Model During COVID-19</strong>" Preprints. https://doi.org/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.
Keywords
panic disorder; unemployment rate; RNN; deep learning; mental illness; COVID-19
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
