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Deep Convolution Network for Covid-19 Death Rate Prediction

Pokkuluri Kiran Sree*, SSSN Usha Devi N.**

* Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, Andhra Pradesh, India.

** Department of Computer Science and Engineering, University College of Engineering, JNTU Kakinada, Andhra Pradesh, India.

Periodicity:December - February'2020
DOI : https://doi.org/10.26634/jit.9.1.17254

Abstract

COVID-19 is a new virus that originated in China, which is one of the dangerous and widespread infectious diseases. More than one lakh deaths have been reported to date, and the death toll continues to rise. This is a comprehensive survey of what aspects need to be considered for developing an accurate classifier to predict the variations in death rate. After this step, a novel and robust classifier with a Deep Convolution Network augmented with Hybrid Non-Linear Cellular Automata rules(DCNHCAC) was proposed to predict the rate, in which the deaths are varying. Eighty thousand plus datasets are collected from Kaggle, data world for training and testing the proposed classifier. The proposed classifier is compared with the existing methods such as SVM, Regression, K-Means, Adaboost, SVR. DCNAHNLCA has reported an accuracy of 86.7%, precision of 0.86, and recall of 0.82, which is the best available at this moment.

Keywords

Convolution Neural Networks, COVID-19, Cellular Automata, Deep Learning.

How to Cite this Article?

Sree, P. K., and Devi, S. U. N. (2020). Deep Convolution Network for Covid-19 Death Rate Prediction. i-manager's Journal on Information Technology, 9(1), 1-5. https://doi.org/10.26634/jit.9.1.17254

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