Conceptual framework for the implementation of the hybrid model.
<p>Conceptual framework for the implementation of the hybrid model.</p>
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1852025798270124032 |
|---|---|
| author | Micanaldo Ernesto Francisco (18723706) |
| author2 | Thaddeus M. Carvajal (8796821) Kozo Watanabe (544664) |
| author2_role | author author |
| author_facet | Micanaldo Ernesto Francisco (18723706) Thaddeus M. Carvajal (8796821) Kozo Watanabe (544664) |
| author_role | author |
| dc.creator.none.fl_str_mv | Micanaldo Ernesto Francisco (18723706) Thaddeus M. Carvajal (8796821) Kozo Watanabe (544664) |
| dc.date.none.fl_str_mv | 2024-10-21T17:43:32Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pntd.0012599.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Conceptual_framework_for_the_implementation_of_the_hybrid_model_/27272902 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Molecular Biology Biotechnology Ecology Cancer Infectious Diseases Plant Biology Virology Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines extreme gradient boosting conditional inference forest artificial neural networks impending dengue outbreaks spatiotemporal resolution differs fine spatiotemporal scales predicted dengue incidence dengue incidence may dengue incidence due generalized additive models dengue incidence prediction spatiotemporal dengue prediction quantitative pattern extraction six spatiotemporal resolutions six ml algorithms novel hybrid approach frequent zero values stage quantitative prediction dengue incidence data inflated models cannot enhance prediction accuracy qualitative dengue variables dengue incidence spatiotemporal resolutions hybrid approach quantitative prediction qualitative models prediction accuracy higher resolutions environmental variables binary variables qualitative predictions combining qualitative xlink "> study aimed rare phenomena random forests predicting zero model validation inflated data data distribution control strategies alternative solution |
| dc.title.none.fl_str_mv | Conceptual framework for the implementation of the hybrid model. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Conceptual framework for the implementation of the hybrid model.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_85228b09be3e2ff307b17b6ccbd41c7b |
| identifier_str_mv | 10.1371/journal.pntd.0012599.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27272902 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Conceptual framework for the implementation of the hybrid model.Micanaldo Ernesto Francisco (18723706)Thaddeus M. Carvajal (8796821)Kozo Watanabe (544664)BiochemistryMolecular BiologyBiotechnologyEcologyCancerInfectious DiseasesPlant BiologyVirologyMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsupport vector machinesextreme gradient boostingconditional inference forestartificial neural networksimpending dengue outbreaksspatiotemporal resolution differsfine spatiotemporal scalespredicted dengue incidencedengue incidence maydengue incidence duegeneralized additive modelsdengue incidence predictionspatiotemporal dengue predictionquantitative pattern extractionsix spatiotemporal resolutionssix ml algorithmsnovel hybrid approachfrequent zero valuesstage quantitative predictiondengue incidence datainflated models cannotenhance prediction accuracyqualitative dengue variablesdengue incidencespatiotemporal resolutionshybrid approachquantitative predictionqualitative modelsprediction accuracyhigher resolutionsenvironmental variablesbinary variablesqualitative predictionscombining qualitativexlink ">study aimedrare phenomenarandom forestspredicting zeromodel validationinflated datadata distributioncontrol strategiesalternative solution<p>Conceptual framework for the implementation of the hybrid model.</p>2024-10-21T17:43:32ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pntd.0012599.g001https://figshare.com/articles/figure/Conceptual_framework_for_the_implementation_of_the_hybrid_model_/27272902CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272729022024-10-21T17:43:32Z |
| spellingShingle | Conceptual framework for the implementation of the hybrid model. Micanaldo Ernesto Francisco (18723706) Biochemistry Molecular Biology Biotechnology Ecology Cancer Infectious Diseases Plant Biology Virology Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines extreme gradient boosting conditional inference forest artificial neural networks impending dengue outbreaks spatiotemporal resolution differs fine spatiotemporal scales predicted dengue incidence dengue incidence may dengue incidence due generalized additive models dengue incidence prediction spatiotemporal dengue prediction quantitative pattern extraction six spatiotemporal resolutions six ml algorithms novel hybrid approach frequent zero values stage quantitative prediction dengue incidence data inflated models cannot enhance prediction accuracy qualitative dengue variables dengue incidence spatiotemporal resolutions hybrid approach quantitative prediction qualitative models prediction accuracy higher resolutions environmental variables binary variables qualitative predictions combining qualitative xlink "> study aimed rare phenomena random forests predicting zero model validation inflated data data distribution control strategies alternative solution |
| status_str | publishedVersion |
| title | Conceptual framework for the implementation of the hybrid model. |
| title_full | Conceptual framework for the implementation of the hybrid model. |
| title_fullStr | Conceptual framework for the implementation of the hybrid model. |
| title_full_unstemmed | Conceptual framework for the implementation of the hybrid model. |
| title_short | Conceptual framework for the implementation of the hybrid model. |
| title_sort | Conceptual framework for the implementation of the hybrid model. |
| topic | Biochemistry Molecular Biology Biotechnology Ecology Cancer Infectious Diseases Plant Biology Virology Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines extreme gradient boosting conditional inference forest artificial neural networks impending dengue outbreaks spatiotemporal resolution differs fine spatiotemporal scales predicted dengue incidence dengue incidence may dengue incidence due generalized additive models dengue incidence prediction spatiotemporal dengue prediction quantitative pattern extraction six spatiotemporal resolutions six ml algorithms novel hybrid approach frequent zero values stage quantitative prediction dengue incidence data inflated models cannot enhance prediction accuracy qualitative dengue variables dengue incidence spatiotemporal resolutions hybrid approach quantitative prediction qualitative models prediction accuracy higher resolutions environmental variables binary variables qualitative predictions combining qualitative xlink "> study aimed rare phenomena random forests predicting zero model validation inflated data data distribution control strategies alternative solution |