Title
LSTM perfomance analysis for predictive models based on Covid-19 dataset
Date Issued
01 September 2020
Access level
open access
Resource Type
conference paper
Author(s)
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
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.
Language
English
OCDE Knowledge area
Bioinformática
Subjects
Scopus EID
2-s2.0-85095417740
ISBN
9781728193779
Resource of which it is part
Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
ISBN of the container
978-172819377-9
Conference
27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
Sources of information:
Directorio de Producción Científica
Scopus