Deep recurrent neural network-based Hadoop framework for COVID prediction with applications to big data in cloud computing

This paper proposes a particle squirrel search optimisation-based deep recurrent neural network (PSSO-based DRNN) to predict the coronavirus epidemic (COVID). Here, the cloud-based Hadoop framework is used to perform the prediction process by involving the mapper and reducer phases. Initially, the technical indicators are extracted from the time series data. Then, the deep belief network (DBN) is employed for feature selection from the technical indicators. After that, the COVID prediction is done by the DRNN classifier trained using the PSSO algorithm. The PSSO is developed by the integration of particle swam optimisation (PSO) and squirrel search algorithm (SSA). The PSSO-based DRNN is compared with existing methods and obtained minimal MSE and RMSE of 0.0523, and 0.2287 by considering affected cases. By considering death cases, the proposed method achieved minimal MSE and RMSE of 0.0010, and 0.0323 and measured minimum MSE of 0.0049 and minimum RMSE of 0.0702 for recovered cases.

Automated summary

This paper proposes a particle squirrel search optimisation-based deep recurrent neural network (PSSO-based DRNN) to predict the coronavirus epidemic (COVID). The cloud-based Hadoop framework is used for the prediction process, involving the mapper and reducer phases. Technical indicators are extracted from the time series data, and feature selection is done using the deep belief network (DBN). The COVID prediction is then made by the DRNN classifier trained using the PSSO algorithm. The proposed method achieves minimal MSE and RMSE for affected, death, and recovered cases.