Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines
The Covid19 has significantly changed the global landscape in every aspect including economy, social life, and many others. After almost two years of living with the pandemic, new challenges are faced by the research community. It may take some time before the world can be declared as totally safe f...
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| Format: | Article |
| Language: | English |
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Sciendo
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33077/ http://umpir.ump.edu.my/id/eprint/33077/1/Covid-19%20confirmed%20cases%20prediction%20in%20china%20based%20on%20barnacles%20mating%20optimizer-least%20squares%20support%20vector%20machines.pdf |
| _version_ | 1848824169373892608 |
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| author | Zuriani, Mustaffa Mohd Herwan, Sulaiman |
| author_facet | Zuriani, Mustaffa Mohd Herwan, Sulaiman |
| author_sort | Zuriani, Mustaffa |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The Covid19 has significantly changed the global landscape in every aspect including economy, social life, and many others. After almost two years of living with the pandemic, new challenges are faced by the research community. It may take some time before the world can be declared as totally safe from the virus. Therefore, prediction of Covid19 confirmed cases is vital for the sake of proper prevention and precaution steps. In this study, a hybrid Barnacles Mating Optimizer with Least Square Support Vector Machines (BMO-LSSVM) is proposed for prediction of Covid19 confirmed cases. The employed data are the Covid19 cases in China which are defined in daily periodicity. The BMO was utilized to obtain optimal values of LSSVM hyper-parameters. Later, with the optimized values of the hyper-parameters, the prediction task will be executed by LSSVM. Through the experiments, the study recommends the superiority of BMO-LSSVM over the other identified hybrid algorithms. |
| first_indexed | 2025-11-15T03:08:45Z |
| format | Article |
| id | ump-33077 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:08:45Z |
| publishDate | 2021 |
| publisher | Sciendo |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-330772022-05-31T01:39:37Z http://umpir.ump.edu.my/id/eprint/33077/ Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines Zuriani, Mustaffa Mohd Herwan, Sulaiman QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering The Covid19 has significantly changed the global landscape in every aspect including economy, social life, and many others. After almost two years of living with the pandemic, new challenges are faced by the research community. It may take some time before the world can be declared as totally safe from the virus. Therefore, prediction of Covid19 confirmed cases is vital for the sake of proper prevention and precaution steps. In this study, a hybrid Barnacles Mating Optimizer with Least Square Support Vector Machines (BMO-LSSVM) is proposed for prediction of Covid19 confirmed cases. The employed data are the Covid19 cases in China which are defined in daily periodicity. The BMO was utilized to obtain optimal values of LSSVM hyper-parameters. Later, with the optimized values of the hyper-parameters, the prediction task will be executed by LSSVM. Through the experiments, the study recommends the superiority of BMO-LSSVM over the other identified hybrid algorithms. Sciendo 2021-12-01 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/33077/1/Covid-19%20confirmed%20cases%20prediction%20in%20china%20based%20on%20barnacles%20mating%20optimizer-least%20squares%20support%20vector%20machines.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman (2021) Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines. Cybernetics and Information Technologies, 21 (4). pp. 62-76. ISSN 1311-9702. (Published) https://doi.org/10.2478/cait-2021-0043 https://doi.org/10.2478/cait-2021-0043 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Zuriani, Mustaffa Mohd Herwan, Sulaiman Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines |
| title | Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines |
| title_full | Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines |
| title_fullStr | Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines |
| title_full_unstemmed | Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines |
| title_short | Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines |
| title_sort | covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33077/ http://umpir.ump.edu.my/id/eprint/33077/ http://umpir.ump.edu.my/id/eprint/33077/ http://umpir.ump.edu.my/id/eprint/33077/1/Covid-19%20confirmed%20cases%20prediction%20in%20china%20based%20on%20barnacles%20mating%20optimizer-least%20squares%20support%20vector%20machines.pdf |