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LSTM for Rabies Outbreak Prediction by ahoora.sh.77 is a document available to read on EtoBox.

This study evaluates the performance of the Long Short-Term Memory (LSTM) model for predicting rabies outbreaks, demonstrating its effectiveness compared to the traditional ARIMA model. The LSTM achieved an accuracy of 97.10% and a Root Mean Square Error (RMSE) of 2.04, significantly outperforming the ARIMA model, which had an accuracy of 72.10% and an RMSE of 3.12. The findings suggest that LSTM is a powerful tool for epidemic prediction, which can aid public health interventions by providing timely foreca

Author

ahoora.sh.77

Language

EN