LSTM perfomance analysis for predictive models based on Covid-19 dataset

Isac Cruz-Mendoza; Jonathan Quevedo-Pulido; Luz Adanaque-Infante

doi:10.1109/INTERCON50315.2020.9220248

LSTM perfomance analysis for predictive models based on Covid-19 dataset

Isac Cruz-Mendoza
, Jonathan Quevedo-Pulido
, Luz Adanaque-Infante

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781728193779
DOI	https://doi.org/10.1109/INTERCON50315.2020.9220248
Estado	Publicada - set. 2020
Publicado de forma externa	Sí
Evento	27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 - Virtual, Lima, Perú Duración: 3 set. 2020 → 5 set. 2020

Serie de la publicación

Nombre	Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

Conferencia

Conferencia	27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
País/Territorio	Perú
Ciudad	Virtual, Lima
Período	3/09/20 → 5/09/20

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10.1109/INTERCON50315.2020.9220248

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Cruz-Mendoza, I., Quevedo-Pulido, J., & Adanaque-Infante, L. (2020). LSTM perfomance analysis for predictive models based on Covid-19 dataset. En Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 Artículo 9220248 (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

Cruz-Mendoza, Isac ; Quevedo-Pulido, Jonathan ; Adanaque-Infante, Luz. / LSTM perfomance analysis for predictive models based on Covid-19 dataset. Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020).

@inproceedings{b815eefdf2c54821a23eb8cbb84ddadd,

title = "LSTM perfomance analysis for predictive models based on Covid-19 dataset",

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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author = "Isac Cruz-Mendoza and Jonathan Quevedo-Pulido and Luz Adanaque-Infante",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 ; Conference date: 03-09-2020 Through 05-09-2020",

year = "2020",

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doi = "10.1109/INTERCON50315.2020.9220248",

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series = "Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020",

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Cruz-Mendoza, I, Quevedo-Pulido, J & Adanaque-Infante, L 2020, LSTM perfomance analysis for predictive models based on Covid-19 dataset. En Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020., 9220248, Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020, Institute of Electrical and Electronics Engineers Inc., 27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020, Virtual, Lima, Perú, 3/09/20. https://doi.org/10.1109/INTERCON50315.2020.9220248

LSTM perfomance analysis for predictive models based on Covid-19 dataset. / Cruz-Mendoza, Isac; Quevedo-Pulido, Jonathan; Adanaque-Infante, Luz.
Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9220248 (Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - LSTM perfomance analysis for predictive models based on Covid-19 dataset

AU - Cruz-Mendoza, Isac

AU - Quevedo-Pulido, Jonathan

AU - Adanaque-Infante, Luz

PY - 2020/9

Y1 - 2020/9

N2 - 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.

AB - 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.

KW - Colab

KW - LSTM

KW - MATLAB

KW - Neural Networks

KW - Optimization

KW - Performance

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DO - 10.1109/INTERCON50315.2020.9220248

M3 - Conference contribution

AN - SCOPUS:85095417740

T3 - Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

BT - Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Cruz-Mendoza I, Quevedo-Pulido J, Adanaque-Infante L. LSTM perfomance analysis for predictive models based on Covid-19 dataset. En Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9220248. (Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020). doi: 10.1109/INTERCON50315.2020.9220248

LSTM perfomance analysis for predictive models based on Covid-19 dataset

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