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A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, develo...

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Main Authors: Ahmed, Marzia, Ahmad Johari, Mohamad, Rahman, Mostafijur, Mohd Herwan, Sulaiman, Abul Kashem, Mohammod
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2023
Subjects:
RA Public aspects of medicine
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/37861/1/A%20novel%20hybrid%20evolutionary%20mating%20algorithm%20for%20Covid19%20.pdf
http://umpir.ump.edu.my/id/eprint/37861/2/A%20Novel%20Hybrid%20Evolutionary%20Mating%20Algorithm_FULL.pdf
http://umpir.ump.edu.my/id/eprint/37861/
https://doi.org/10.1109/NCIM59001.2023.10212867
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record_format eprints
spelling my.ump.umpir.378612023-11-08T03:27:39Z http://umpir.ump.edu.my/id/eprint/37861/ A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination Ahmed, Marzia Ahmad Johari, Mohamad Rahman, Mostafijur Mohd Herwan, Sulaiman Abul Kashem, Mohammod RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics. IEEE 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37861/1/A%20novel%20hybrid%20evolutionary%20mating%20algorithm%20for%20Covid19%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/37861/2/A%20Novel%20Hybrid%20Evolutionary%20Mating%20Algorithm_FULL.pdf Ahmed, Marzia and Ahmad Johari, Mohamad and Rahman, Mostafijur and Mohd Herwan, Sulaiman and Abul Kashem, Mohammod (2023) A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination. In: 2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023, 16 - 17 June 2023 , Gazipur, Bangladesh. pp. 1-6.. ISBN 979-8-3503-1600-1 https://doi.org/10.1109/NCIM59001.2023.10212867
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
Ahmad Johari, Mohamad
Rahman, Mostafijur
Mohd Herwan, Sulaiman
Abul Kashem, Mohammod
A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination
description Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics.
format Conference or Workshop Item
author Ahmed, Marzia
Ahmad Johari, Mohamad
Rahman, Mostafijur
Mohd Herwan, Sulaiman
Abul Kashem, Mohammod
author_facet Ahmed, Marzia
Ahmad Johari, Mohamad
Rahman, Mostafijur
Mohd Herwan, Sulaiman
Abul Kashem, Mohammod
author_sort Ahmed, Marzia
title A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination
title_short A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination
title_full A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination
title_fullStr A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination
title_full_unstemmed A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination
title_sort novel hybrid evolutionary mating algorithm for covid19 confirmed cases prediction based on vaccination
publisher IEEE
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/37861/1/A%20novel%20hybrid%20evolutionary%20mating%20algorithm%20for%20Covid19%20.pdf
http://umpir.ump.edu.my/id/eprint/37861/2/A%20Novel%20Hybrid%20Evolutionary%20Mating%20Algorithm_FULL.pdf
http://umpir.ump.edu.my/id/eprint/37861/
https://doi.org/10.1109/NCIM59001.2023.10212867
_version_ 1822923534064680960
score 13.232414

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