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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728193779
DOIs	https://doi.org/10.1109/INTERCON50315.2020.9220248
State	Published - Sep 2020
Externally published	Yes
Event	27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 - Virtual, Lima, Peru Duration: 3 Sep 2020 → 5 Sep 2020

Publication series

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

Conference

Conference	27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
Country/Territory	Peru
City	Virtual, Lima
Period	3/09/20 → 5/09/20

Keywords

Colab
LSTM
MATLAB
Neural Networks
Optimization
Performance

Access to Document

10.1109/INTERCON50315.2020.9220248

Cite this

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 Article 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.",

keywords = "Colab, LSTM, MATLAB, Neural Networks, Optimization, Performance",

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",

month = sep,

doi = "10.1109/INTERCON50315.2020.9220248",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "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. in 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, Peru, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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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U2 - 10.1109/INTERCON50315.2020.9220248

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.

T2 - 27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

Y2 - 3 September 2020 through 5 September 2020

ER -

Cruz-Mendoza I, Quevedo-Pulido J, Adanaque-Infante L. 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. 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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