In proposed method-2, the big value of absolute error between precise & predicted information has been notated between 75th and 203rd day of prediction whereas the mannequin is tested. These two extreme output values have been in contrast with the actual knowledge to check whether or not the LSTM layer output had captured the pattern current in the precise data. To differentiate the heatmaps, the utmost & minimal output worth from LSTM layer for every hidden unit is extracted. It’s difficult to differentiate the heatmaps of proposed strategy-1&2 visually. So, the output value of LSTM layer from 75th to 203rd day of prediction is considered for additional study of proposed approach-1&2.
The proposed strategy-1 has given the higher outcomes as in comparison with proposed strategy-2. The forecasting of lively circumstances helps the Indian authorities to implement the mandatory restrictions & carry out the covid-19 testing in airports, bus & railway stations. The pretrained LSTM model is used to forecast the energetic cases in India from 16th December, 2021 to fifth March, 2022. The forecasted knowledge had proven that there shall be a emergence of third wave by January, 2022 in India. From the visualization, it is evident that the proposed approach-1 had succeeded to generalize the mannequin inorder to seize the completely different dynamics of transmission. This kind of downside may be overcomed by proposed method-1. For Lakshadweep, the primary case was reported on 18th January, 2021. It was notated that quantity of training days has to be elevated in proposed strategy-2 as there’s negligible dynamics in information. The examine had additionally focused on the visualization of maximum & minimum output worth from LSTM layer for few states which embrace Kerala, Chhattisgarh, Gujarat, Karnataka & Tamil Nadu.
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From the visualization of maximum & minimum output of LSTM layer for Gujarat in Fig. 14, it is evident that there is correct drift in the sample captured in proposed strategy-2 as in comparison with method-1. The right drift within the trend of predicted value is notated for proposed approach-2 in Fig 10. For Karnataka, the development in precise knowledge is captured in both proposed approach-1 & 2. There’s a slight deviation at the peak worth of most & minimum output from LSTM layer in proposed approach-2 as proven in Fig. 15. For Tamil Nadu, there is a deviation in the predicted knowledge after the peak worth for proposed method-2 as shown in Fig. 12. The same deviation of trend has been notated in most and minimal activation output in proposed strategy-2 with respect to proposed method-1 and actual trend of test knowledge.
For the prediction of 203 days in check knowledge, there shall be 203 x 150 dimensional matrix on the output of the LSTM layer. The heat map for 203 x one hundred fifty dimensional matrix at the output of the LSTM layer for proposed approach-1 & 2 are proven in Fig. 5 & 7 respectively. The comparison between precise & predicted knowledge for Kerala in proposed method-1 & 2 is shown in Fig. 4 & 6 respectively. There is slight variation within the coloration depth of heatmap for proposed approach-1 & 2. The output from the LSTM layer is used to get the prediction on the output of the dense layer within the proposed LSTM mannequin.
