Article: Deep recurrent neural network-based Hadoop framework for COVID prediction with applications to big data in cloud computing Journal: International Journal of Bio-Inspired Computation (IJBIC) 2023 Vol.21 No.1 pp.36 - 47 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: D.B. Jagannadha Rao; Vijayakumar Polepally; S. Nagendra Prabhu; Parsi Kalpana

Addresses: Department of Computer Science and Engineering (Data Science), Malla Reddy University, Hyderabad, Telangana, India ' Department of Computer Science and Engineering, Kakatiya Institute of Technology and Science, Warangal, Telangana, India ' Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamilnadu, India ' Department of Computer Science and Engineering, Vasavi College of Engineering Hyderabad, Telangana, India

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.

Keywords: COVID-19; MapReduce; cloud; deep belief network; DBN; deep recurrent neural network.

DOI: 10.1504/IJBIC.2023.130022

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.1, pp.36 - 47

Received: 03 Sep 2021
Received in revised form: 09 Jun 2022
Accepted: 19 Jun 2022
Published online: 04 Apr 2023 *

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

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