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Pervasive computing in the context of COVID-19 prediction with AI-based algorithms

Magesh S. (Maruthi Technocrat E Services, Chennai, India)
Niveditha V.R. (Department of Computer Science and Engineering, Dr M.G.R Educational and Research Institute, Chennai, India)
Rajakumar P.S. (Department of Computer Science and Engineering, Dr M.G.R Educational and Research Institute, Chennai, India)
Radha RamMohan S. (Department of Computer Applications, Dr. M.G.R Educational and Research Institute, Chennai, India)
Natrayan L. (School of Mechanical Engineering, VIT University – Chennai Campus, Chennai, India)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 10 August 2020

Issue publication date: 5 October 2020

579

Abstract

Purpose

The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.

Design/methodology/approach

For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.

Findings

Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.

Originality/value

The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.

Keywords

Citation

S., M., V.R., N., P.S., R., S., R.R. and L., N. (2020), "Pervasive computing in the context of COVID-19 prediction with AI-based algorithms", International Journal of Pervasive Computing and Communications, Vol. 16 No. 5, pp. 477-487. https://doi.org/10.1108/IJPCC-07-2020-0082

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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