Details

Autor(en) / Beteiligte

• AM, Arul Raj
• R, Sugumar

Titel

A Deep Learning-Based Preventive Measures of COVID-19 in a crowd using Reinforcement Model over GAN for Enhanced efficiency

Ist Teil von

• 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2023, p.1-8

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• The fast human-to-human spread of COVID-19 has caused significant lifestyle changes for many individuals. At the end of January 2020, the pandemic began, and many nations responded with varying degrees of testing, sanitation, lockdown, and quarantine centers. New normals of testing, sanitization, social separation, and lockdown are being implemented, and people are gradually returning to work and other daily routines. The COVID-19 infected population is monitored by testing individuals regularly. But it's a resource-heavy endeavor to test everyone without good reason. An optimum strategy is required to efficiently identify persons who are most likely to test positive for COVID-19. Sanitation is utilized for both persons and public spaces to eliminate germs. However, the disruption of governmental operations and economic development makes the use of lockdown and quarantine centers a resource-intensive endeavor. Conversely, it degrades the standard of living across a society. Furthermore, keeping people inside their houses or quarantine centers for an unlimited amount of time would not allow the government to care for everyone. These variables impact virus propagation, human health and happiness, available resources, and the economy's health, making their management resource-intensive. counting and density estimation are both attempts to create clever and efficient algorithms that can interpret the data provided by images to carry out Efficiency. GANs have been proven to have promising applications in overcoming the data dearth problem in COVID-19 lung image analysis. The Convolutional Neural Network (CNN) models built for the diagnosis of COVID-19 have benefited from the GAN-generated data used to refine their training. Moreover, GANs have helped improve the performance of CNNs by super-resolving pictures and performing segmentation. This work highlights the Reinforcement deep learning model over the fundamental constraints of the possible transformation of GANs-based approaches. This work proposes the model be developed with a new intelligent approach using RL to quantify these different types of testing considered for social distancing, face mask detection, limiting the gathering, and locking the location using the Q Learning technique. Different RL algorithms are implemented, and agents are equipped with these algorithms so that they may interact with the environment and learn the optimum method for doing so.