The COVID-19 pandemic has been one of the most defining factor of the recent decade due to its extreme impact on the world and the people within it. The coronavirus caused a lot of problems at the personal level as well as the economic level. There have been massive losses of life and the governments across the world have been hit very badly and are yet to recover from this pandemic. The main problem that has led to the escalation of the pandemic has been the fact that it was unexpected. The lack of highly accurate prediction approaches led to the medical infrastructure not being able to handle the load of patients. Therefore, there is a need for a methodology that can predict the COVID patient count to adequately prepare the healthcare professionals in advance. The proposed methodology utilizes deep learning in the form of Convolutional Neural Networks and Long Short Term Memory to achieve the prediction of COVID-19 infection count. This approach will be elaborated further in the upcoming editions of this research.
Convolutional Neural Networks-Long Short-Term Memory, Decision Tree.
Maithali S. Patil , Prof. Bhagyashree Dhakulkar "An In-Depth Review on COVID-19 Infection Count Prediction Using Deep Learning" Iconic Research And Engineering Journals Volume 6 Issue 6 2022 Page 262-267
Maithali S. Patil , Prof. Bhagyashree Dhakulkar "An In-Depth Review on COVID-19 Infection Count Prediction Using Deep Learning" Iconic Research And Engineering Journals, 6(6)