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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37861/ 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 |
| _version_ | 1848825365047279616 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:27:46Z |
| format | Conference or Workshop Item |
| id | ump-37861 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:27:46Z |
| publishDate | 2023 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) https://doi.org/10.1109/NCIM59001.2023.10212867 |
| 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 |
| title | 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_short | 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 |
| topic | RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/37861/ http://umpir.ump.edu.my/id/eprint/37861/ 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 |