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An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach

SARS-CoV-2 is a multi-organ disease characterized by a wide range of symptoms, which also causes severe acute respiratory syndrome. When it initially began, it rapidly spread from its origin to adjacent nations, infecting millions of people around the globe. In order to take appropriate preventative...

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Main Authors: Ahmed, Marzia, Sulaiman, M. H., Mohamad, A. J., Rahman, Md. Mostafijur
Format: Conference or Workshop Item
Language:English
English
Published: Springer 2023
Subjects:
RA Public aspects of medicine
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/36824/1/An%20improved%20optimization%20algorithm-based%20prediction%20approach%20.pdf
http://umpir.ump.edu.my/id/eprint/36824/2/An%20Improved%20Optimization%20Algorithm-based%20Prediction_FULL.pdf
http://umpir.ump.edu.my/id/eprint/36824/
https://doi.org/10.1007/978-981-19-9483-8_18
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spelling my.ump.umpir.368242023-06-23T07:47:54Z http://umpir.ump.edu.my/id/eprint/36824/ An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach Ahmed, Marzia Sulaiman, M. H. Mohamad, A. J. Rahman, Md. Mostafijur RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering SARS-CoV-2 is a multi-organ disease characterized by a wide range of symptoms, which also causes severe acute respiratory syndrome. When it initially began, it rapidly spread from its origin to adjacent nations, infecting millions of people around the globe. In order to take appropriate preventative and precautionary actions, it is necessary to anticipate positive Covid19 instances in order to better comprehend future risk. Therefore, it is vital to build mathematical models that are resilient and have as few prediction mistakes as feasible. This research recommends an optimization based Least Square Support Vector Machines (LSSVM) for forecasting Covid19 confirmed cases along with the daily total vaccination frequency. In this work, a novel hybrid Barnacle Mating Optimizer (BMO) via Gauss Distribution is combined with Least Squares Support Vector Machines algorithm for time series forecasting. The data source consists of the daily occurrences of cases and frequency of total vaccination since 24 February,2021 to 27 July,2022 in Malaysia. LSSVM will thereafter conduct the prediction job with the optimized hyper-parameter values using BMO via gauss distribution. This study concludes, based on its experimental findings, that hybrid IBMOLSSVM outperforms cross validations, original BMO, ANN and few other hybrid approaches with optimally optimized parameters. Springer 2023-05 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/36824/1/An%20improved%20optimization%20algorithm-based%20prediction%20approach%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/36824/2/An%20Improved%20Optimization%20Algorithm-based%20Prediction_FULL.pdf Ahmed, Marzia and Sulaiman, M. H. and Mohamad, A. J. and Rahman, Md. Mostafijur (2023) An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach. In: Lecture Notes in Networks and Systems; 4th International Conference on Trends in Computational and Cognitive Engineering 2022 (TCCE-2022), 18 - 19 December 2022 , Mawlana Bhashani Science and Technology University, Bangladesh. pp. 209-223., 618. ISBN 978-981-19-9483-8 https://doi.org/10.1007/978-981-19-9483-8_18
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic RA Public aspects of medicine
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle RA Public aspects of medicine
TK Electrical engineering. Electronics Nuclear engineering
Ahmed, Marzia
Sulaiman, M. H.
Mohamad, A. J.
Rahman, Md. Mostafijur
An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach
description SARS-CoV-2 is a multi-organ disease characterized by a wide range of symptoms, which also causes severe acute respiratory syndrome. When it initially began, it rapidly spread from its origin to adjacent nations, infecting millions of people around the globe. In order to take appropriate preventative and precautionary actions, it is necessary to anticipate positive Covid19 instances in order to better comprehend future risk. Therefore, it is vital to build mathematical models that are resilient and have as few prediction mistakes as feasible. This research recommends an optimization based Least Square Support Vector Machines (LSSVM) for forecasting Covid19 confirmed cases along with the daily total vaccination frequency. In this work, a novel hybrid Barnacle Mating Optimizer (BMO) via Gauss Distribution is combined with Least Squares Support Vector Machines algorithm for time series forecasting. The data source consists of the daily occurrences of cases and frequency of total vaccination since 24 February,2021 to 27 July,2022 in Malaysia. LSSVM will thereafter conduct the prediction job with the optimized hyper-parameter values using BMO via gauss distribution. This study concludes, based on its experimental findings, that hybrid IBMOLSSVM outperforms cross validations, original BMO, ANN and few other hybrid approaches with optimally optimized parameters.
format Conference or Workshop Item
author Ahmed, Marzia
Sulaiman, M. H.
Mohamad, A. J.
Rahman, Md. Mostafijur
author_facet Ahmed, Marzia
Sulaiman, M. H.
Mohamad, A. J.
Rahman, Md. Mostafijur
author_sort Ahmed, Marzia
title An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach
title_short An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach
title_full An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach
title_fullStr An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach
title_full_unstemmed An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach
title_sort improved optimization algorithm-based prediction approach for the weekly trend of covid-19 considering the total vaccination in malaysia: a novel hybrid machine learning approach
publisher Springer
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/36824/1/An%20improved%20optimization%20algorithm-based%20prediction%20approach%20.pdf
http://umpir.ump.edu.my/id/eprint/36824/2/An%20Improved%20Optimization%20Algorithm-based%20Prediction_FULL.pdf
http://umpir.ump.edu.my/id/eprint/36824/
https://doi.org/10.1007/978-981-19-9483-8_18
_version_ 1822923287730061312
score 13.232414

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