Dengue outbreak prediction: hybrid meta-heuristic model
Parameter tuning of Leas Squares Support Vector Machines (LSSVM) hyper-parameters, namely regularization parameter and kernel parameters plays a crucial role in obtaining a promising result in prediction task. Any improper values setting of the said hyper-parameters would demote the generalization o...
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2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/22085/1/17.%20Dengue%20outbreak%20prediction%20-hybrid%20meta-heuristic%20model.pdf http://umpir.ump.edu.my/id/eprint/22085/2/17.1%20Dengue%20outbreak%20prediction%20-hybrid%20meta-heuristic%20model.pdf http://umpir.ump.edu.my/id/eprint/22085/ |
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| author | Zuriani, Mustaffa Mohd Herwan, Sulaiman Ernawan, Ferda Yuhanis, Yusof Mohamad Farhan, Mohamad Mohsin |
| author_facet | Zuriani, Mustaffa Mohd Herwan, Sulaiman Ernawan, Ferda Yuhanis, Yusof Mohamad Farhan, Mohamad Mohsin |
| author_sort | Zuriani, Mustaffa |
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| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Parameter tuning of Leas Squares Support Vector Machines (LSSVM) hyper-parameters, namely regularization parameter and kernel parameters plays a crucial role in obtaining a promising result in prediction task. Any improper values setting of the said hyper-parameters would demote the generalization of LSSVM. Concerning that matter, in this study, Flower Pollination Algorithm (PA), which is relatively new optimization algorithm is hybrid with LSSVM. Here, the FPA is served as an optimization algorithm for LSSVM. The hybrid FPA-LSSVM is later realized for prediction of dengue outbreak in Yogyakarta, Indonesia. Since it was first recognized, until now Dengue Fever (DF) remains as a major concern of public health in community, specifically during the massive outbreaks. A serious infection of dengue can progress into a more critical condition namely Dengue Hemorrhagic Fever (DHF). Therefore, a good prediction model is vital to predict the dengue outbreak cases. By using monthly disease surveillance and meteorological data, the performance of the prediction model is guided by Mean Square Error (MSE) and Root Mean Square Percentage Error (RMSPE). Findings of the study demonstrate that FPA-LSSVM is able to produce lower error rate compared to the other identified algorithms. |
| format | Conference or Workshop Item |
| id | my.ump.umpir.22085 |
| institution | Universiti Malaysia Pahang |
| language | en en |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | eprints |
| spelling | my.ump.umpir.220852018-11-15T08:35:24Z http://umpir.ump.edu.my/id/eprint/22085/ Dengue outbreak prediction: hybrid meta-heuristic model Zuriani, Mustaffa Mohd Herwan, Sulaiman Ernawan, Ferda Yuhanis, Yusof Mohamad Farhan, Mohamad Mohsin QA Mathematics QA76 Computer software Parameter tuning of Leas Squares Support Vector Machines (LSSVM) hyper-parameters, namely regularization parameter and kernel parameters plays a crucial role in obtaining a promising result in prediction task. Any improper values setting of the said hyper-parameters would demote the generalization of LSSVM. Concerning that matter, in this study, Flower Pollination Algorithm (PA), which is relatively new optimization algorithm is hybrid with LSSVM. Here, the FPA is served as an optimization algorithm for LSSVM. The hybrid FPA-LSSVM is later realized for prediction of dengue outbreak in Yogyakarta, Indonesia. Since it was first recognized, until now Dengue Fever (DF) remains as a major concern of public health in community, specifically during the massive outbreaks. A serious infection of dengue can progress into a more critical condition namely Dengue Hemorrhagic Fever (DHF). Therefore, a good prediction model is vital to predict the dengue outbreak cases. By using monthly disease surveillance and meteorological data, the performance of the prediction model is guided by Mean Square Error (MSE) and Root Mean Square Percentage Error (RMSPE). Findings of the study demonstrate that FPA-LSSVM is able to produce lower error rate compared to the other identified algorithms. Institute of Electrical and Electronics Engineers Inc. 2018-08 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22085/1/17.%20Dengue%20outbreak%20prediction%20-hybrid%20meta-heuristic%20model.pdf pdf en http://umpir.ump.edu.my/id/eprint/22085/2/17.1%20Dengue%20outbreak%20prediction%20-hybrid%20meta-heuristic%20model.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman and Ernawan, Ferda and Yuhanis, Yusof and Mohamad Farhan, Mohamad Mohsin (2018) Dengue outbreak prediction: hybrid meta-heuristic model. In: 19th IEE/ACIS International Conference On Software Engineering, Artificial Intelligence,Networking And Parallel/Distributed Computing (SNPD 2018) , 27 - 29 June 2018 , Busan, Korea. pp. 1-4.. ISBN 978-153865889-5 (Published) |
| spellingShingle | QA Mathematics QA76 Computer software Zuriani, Mustaffa Mohd Herwan, Sulaiman Ernawan, Ferda Yuhanis, Yusof Mohamad Farhan, Mohamad Mohsin Dengue outbreak prediction: hybrid meta-heuristic model |
| title | Dengue outbreak prediction: hybrid meta-heuristic model |
| title_full | Dengue outbreak prediction: hybrid meta-heuristic model |
| title_fullStr | Dengue outbreak prediction: hybrid meta-heuristic model |
| title_full_unstemmed | Dengue outbreak prediction: hybrid meta-heuristic model |
| title_short | Dengue outbreak prediction: hybrid meta-heuristic model |
| title_sort | dengue outbreak prediction: hybrid meta-heuristic model |
| topic | QA Mathematics QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/22085/1/17.%20Dengue%20outbreak%20prediction%20-hybrid%20meta-heuristic%20model.pdf http://umpir.ump.edu.my/id/eprint/22085/2/17.1%20Dengue%20outbreak%20prediction%20-hybrid%20meta-heuristic%20model.pdf http://umpir.ump.edu.my/id/eprint/22085/ |
| url_provider | http://umpir.ump.edu.my/ |