Abstract
Within the large amount of data that can be processed with Neural Networks (NN), COVID-19 is leaving us a lot of information that is susceptible to be treated and set trends regarding the development of the disease in the country. The present work shows the implementation and the optimization of a Long Short-Term Memory (LSTM) Neural Network in two different simulation environments, with a dataset related to the number of infected people by COVID-19 in Peru, in order to optimize the prediction level on the number of infected people on following days.
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CITATION STYLE
Cruz-Mendoza, I., Quevedo-Pulido, J., & Adanaque-Infante, L. (2020). LSTM perfomance analysis for predictive models based on Covid-19 dataset. In Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/INTERCON50315.2020.9220248
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