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Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID’19

  • Satish Chander EMAIL logo , Vijaya Padmanabha and Joseph Mani
Published/Copyright: November 13, 2020
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Bio-Algorithms and Med-Systems
From the journal Bio-Algorithms and Med-Systems Volume 16 Issue 4

Abstract

COVID’19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID’19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID’19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID’19 prediction method uses the COVID’19 data, which is the trending domain of research at the current era of fighting the COVID’19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID’19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID’19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID’19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China.

Keywords: COVID’19; deep convolutional long short-term classifier; exponential moving average (EMA); Jaya algorithm; Spider monkey Optimization
Corresponding author: Satish Chander, Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India, E-mail:

  1. Research funding: None.

  2. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  3. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

  4. Ethical approval: The conducted research is not related to either human or animal use.

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Received: 2020-05-30
Accepted: 2020-10-06
Published Online: 2020-11-13

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bams-2020-0030
Keywords for this article
COVID’19; deep convolutional long short-term classifier; exponential moving average (EMA); Jaya algorithm; Spider monkey Optimization

Articles in the same Issue

  1. Research Article
  2. Robust controller for cancer chemotherapy dosage using nonlinear kernel-based error function
  3. Review
  4. Deep learning of the role of interleukin IL-17 and its action in promoting cancer
  5. Research Articles
  6. An empirical survey of electroencephalography-based brain-computer interfaces
  7. Analysis of brain waves changes in stressful situations based on horror game with the implementation of virtual reality and brain-computer interface system: a case study
  8. Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID’19
  9. Feature engineering combined with 1-D convolutional neural network for improved mortality prediction
  10. On the mutation model used in the fingerprinting DNA
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