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

S. Prabhu, P. Kalpana, Vijayakumar Polepally, Dattaraj J. Rao
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Abstract

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.
基于深度递归神经网络的Hadoop新冠肺炎预测框架及其在云计算大数据中的应用
提出了一种基于粒子松鼠搜索优化的深度递归神经网络(PSSO-based DRNN)预测冠状病毒流行(COVID)。在这里,基于云的Hadoop框架通过涉及映射器和reducer阶段来执行预测过程。首先,从时间序列数据中提取技术指标。然后,利用深度信念网络(DBN)对技术指标进行特征选择。之后,使用PSSO算法训练的DRNN分类器完成COVID预测。该算法是将粒子游优化算法(PSO)与松鼠搜索算法(SSA)相结合而开发的。将基于psso的DRNN与已有方法进行比较,得到最小MSE为0.0523,考虑影响案例的RMSE为0.2287。考虑死亡病例,该方法对恢复病例的最小MSE和RMSE为0.0010,最小MSE为0.0323,实测最小MSE为0.0049,最小RMSE为0.0702。
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