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

• Autorzy: Chander Satish , Padmanabha Vijaya , Mani Joseph

Identyfikatory

DOI

10.1515/bams-2020-0030

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

COVID-19; deep convolutional long short-term classifier; exponential moving average; EMA; Jaya algorithm; Spider Monkey Optimization

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2020
• Tom: Vol. 16, no. 4
• Strony: art. no. 20200030
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Chander Satish

• Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India

autor

Padmanabha Vijaya

• Department of Mathematics and Computer Science, Modern College of Business and Science, Muscat, Sultanate of Oman

autor

Mani Joseph

• Department of Mathematics and Computer Science, Modern College of Business and Science, Muscat, Sultanate of Oman

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).