The present work reports a comparative time-series analysis of deep learning techniques (Recurrent Neural Networks with GRU and LSTM cells) and statistical techniques (ARIMA and SARIMA) to forecast the country-wise cumulative confirmed, recovered, and deaths. Wise Memory Optimizer is a smart little tool that can help you to free up the physical memory taken up by some apps to enhance your PC performance. 16.5V 3.65A 60W AC Power Adapter Charger for replacement Apple Macbook pro A1184 A1330 13 1. This work presents an memory-aware deployment topology optimizer for dis. The Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells based on Recurrent Neural Networks (RNN), ARIMA and SARIMA models were trained, tested, and optimized to forecast the trends of the COVID-19.
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Wise the processing takes longer depending on the length of the queue waiting. We deployed python to optimize the parameters of ARIMA which include (p, d, q) representing autoregressive and moving average terms and parameters of SARIMA model include additional seasonal terms which are denoted by (P, D, Q). Similarly, for LSTM and GRU based RNN models’ parameters (number of layers, hidden size, learning rate and number of epochs) are optimized by deploying PyTorch machine learning framework. The best model was chosen based on the lowest Mean Square Error (MSE) and Root Mean Squared Error (RMSE) values. encoding into memory for trials preceded by incongruent, or high conflict trials23. For most of the time-series data of the countries, deep learning-based models LSTM and GRU outperformed statistical ARIMA and SARIMA models, with an RMSE values that are 40 folds less than that of the ARIMA models. threshold P < 0.05, based on an auxiliary uncorrected voxel-wise. Further, we emphasize the importance of various factors such as age, preventive measures and healthcare facilities etc.īut for some countries statistical (ARIMA, SARIMA) models outperformed deep learning models.
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