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Hence, an effective and optimal prediction of COVID\u201919 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\u201919 prediction method uses the COVID\u201919 data, which is the trending domain of research at the current era of fighting the COVID\u201919 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\u201919 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\u201919 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\u201919 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.<\/jats:p>","DOI":"10.1515\/bams-2020-0030","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T08:09:05Z","timestamp":1605600545000},"source":"Crossref","is-referenced-by-count":11,"title":["Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID\u201919"],"prefix":"10.5604","volume":"16","author":[{"given":"Satish","family":"Chander","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering , Birla Institute of Technology , Mesra , Ranchi , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vijaya","family":"Padmanabha","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science , Modern College of Business and Science , Muscat , Sultanate of Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Mani","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science , Modern College of Business and Science , Muscat , Sultanate of Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3689","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"2023010916551160983_j_bams-2020-0030_ref_001_w2aab3b7c75b1b6b1ab2b1b1Aa","unstructured":"Za, ZhiZhonghua, Liu, Xing Bing, Xue. 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