A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series

Ahamd Taheri; Shahriar Ghashghaei; Amin Beheshti; Keyvan RahimiZadeh

doi:10.1109/ICCKE54056.2021.9721510

A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series

Ahamd Taheri, Shahriar Ghashghaei, Amin Beheshti, Keyvan RahimiZadeh

School of Computing

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

2 Citations (Scopus)

Abstract

In this paper, a novel hybrid method called DMHS-GMDH is presented to predict the time series of COVID-19 outbreaks. In this way, a new version of Harmony Search (HS) algorithm, named Double Memory HS (DMHS), is designed to optimize the structure of a Group Method of Data Handling (GMDH) type neural network. We conduct a series of experiments by applying proposed method on real COVID-19 dataset to forecast new cases and deaths of COVID-19. The statistical analysis indicates that the DMHS-GMDH algorithm on average provides better results than other competitors and the results demonstrate how our approach at least improves coefficient of determination and RMSE by 21% and 45%, respectively.

Original language	English
Title of host publication	ICCKE 2021
Subtitle of host publication	11th International Conference on Computer Engineering and Knowledge
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	322-327
Number of pages	6
ISBN (Electronic)	9781665402088
DOIs	https://doi.org/10.1109/ICCKE54056.2021.9721510
Publication status	Published - 2021
Event	11th International Conference on Computer Engineering and Knowledge, ICCKE 2021 - Mashhad, Iran, Islamic Republic of Duration: 28 Oct 2021 → 29 Oct 2021

Conference

Conference	11th International Conference on Computer Engineering and Knowledge, ICCKE 2021
Country/Territory	Iran, Islamic Republic of
City	Mashhad
Period	28/10/21 → 29/10/21

Keywords

COVID-19
harmony search
machine learning
metaheuristic algorithm
pandemic

Access to Document

10.1109/ICCKE54056.2021.9721510

Cite this

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title = "A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series",

abstract = "In this paper, a novel hybrid method called DMHS-GMDH is presented to predict the time series of COVID-19 outbreaks. In this way, a new version of Harmony Search (HS) algorithm, named Double Memory HS (DMHS), is designed to optimize the structure of a Group Method of Data Handling (GMDH) type neural network. We conduct a series of experiments by applying proposed method on real COVID-19 dataset to forecast new cases and deaths of COVID-19. The statistical analysis indicates that the DMHS-GMDH algorithm on average provides better results than other competitors and the results demonstrate how our approach at least improves coefficient of determination and RMSE by 21\% and 45\%, respectively. ",

keywords = "COVID-19, harmony search, machine learning, metaheuristic algorithm, pandemic",

author = "Ahamd Taheri and Shahriar Ghashghaei and Amin Beheshti and Keyvan RahimiZadeh",

year = "2021",

doi = "10.1109/ICCKE54056.2021.9721510",

language = "English",

pages = "322--327",

booktitle = "ICCKE 2021",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "11th International Conference on Computer Engineering and Knowledge, ICCKE 2021 ; Conference date: 28-10-2021 Through 29-10-2021",

}

Taheri, A, Ghashghaei, S, Beheshti, A & RahimiZadeh, K 2021, A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series. in ICCKE 2021: 11th International Conference on Computer Engineering and Knowledge. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 322-327, 11th International Conference on Computer Engineering and Knowledge, ICCKE 2021, Mashhad, Iran, Islamic Republic of, 28/10/21. https://doi.org/10.1109/ICCKE54056.2021.9721510

A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series. / Taheri, Ahamd; Ghashghaei, Shahriar; Beheshti, Amin et al.
ICCKE 2021: 11th International Conference on Computer Engineering and Knowledge. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2021. p. 322-327.

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

TY - GEN

T1 - A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series

AU - Taheri, Ahamd

AU - Ghashghaei, Shahriar

AU - Beheshti, Amin

AU - RahimiZadeh, Keyvan

PY - 2021

Y1 - 2021

N2 - In this paper, a novel hybrid method called DMHS-GMDH is presented to predict the time series of COVID-19 outbreaks. In this way, a new version of Harmony Search (HS) algorithm, named Double Memory HS (DMHS), is designed to optimize the structure of a Group Method of Data Handling (GMDH) type neural network. We conduct a series of experiments by applying proposed method on real COVID-19 dataset to forecast new cases and deaths of COVID-19. The statistical analysis indicates that the DMHS-GMDH algorithm on average provides better results than other competitors and the results demonstrate how our approach at least improves coefficient of determination and RMSE by 21% and 45%, respectively.

AB - In this paper, a novel hybrid method called DMHS-GMDH is presented to predict the time series of COVID-19 outbreaks. In this way, a new version of Harmony Search (HS) algorithm, named Double Memory HS (DMHS), is designed to optimize the structure of a Group Method of Data Handling (GMDH) type neural network. We conduct a series of experiments by applying proposed method on real COVID-19 dataset to forecast new cases and deaths of COVID-19. The statistical analysis indicates that the DMHS-GMDH algorithm on average provides better results than other competitors and the results demonstrate how our approach at least improves coefficient of determination and RMSE by 21% and 45%, respectively.

KW - COVID-19

KW - harmony search

KW - machine learning

KW - metaheuristic algorithm

KW - pandemic

UR - https://www.scopus.com/pages/publications/85127519000

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DO - 10.1109/ICCKE54056.2021.9721510

M3 - Conference proceeding contribution

AN - SCOPUS:85127519000

SP - 322

EP - 327

BT - ICCKE 2021

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 11th International Conference on Computer Engineering and Knowledge, ICCKE 2021

Y2 - 28 October 2021 through 29 October 2021

ER -

A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series

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Keywords

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