A TIME SERIES PREDICTION USING LSTM NETWORKS FOR PREDICTION OF COVID-19 DATA
Author(s):
K.Sreelatha, V. Nikhila, Sk. Sameer, D.Srihitha
Keywords:
Machine learning algorithms, Coronavirus, long short term memory networks, training and testing data.
Abstract
The new episode of Coronavirus sickness 2019 (COVID-19), which gets brought about by extreme intense respiratory condition (SARS) Covid 2 (SARS-CoV-2), has been answerable for the passings of more than 3,00,000 individuals and simultaneously has contaminated over 4.7 million individuals in the entire world as of mid-May, 2020. There has been more that 1.8 million recuperations during this period as well. It gets basic for Governments to know about the circumstance and to have the option to foresee the future number of patients so availability as far as medical care and arranging of other fundamental activities can be kept up. Utilizing this as a main impetus, a model for expectation of the quantity of COVID–19 patients has been created utilizing the Long-Sort Term Memory (LSTM) organization and afterward utilized it for estimating future cases. The cases India are considered. The investigation tracks down that the LSTM network created in this paper performs better compared to different organizations and subsequently can be a helpful contender for expectation of future number of patients of COVID–19.
Article Details
Unique Paper ID: 152552

Publication Volume & Issue: Volume 8, Issue 3

Page(s): 1128 - 1133
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