FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3183793485> ?p ?o ?g. }

• W3183793485 endingPage "375" @default.
• W3183793485 startingPage "357" @default.
• W3183793485 abstract "The aim of this work is to examine the appropriate predictive model for COVID-19 epidemiological data in the Indian population using Computational Intelligence based Hyper parameter Tuned Regression Techniques (CI-HTR). The development of the COVID-19 Epidemiological Data Prediction (EDP) model for India is proposed by using CI-HTR such as Gaussian process regression (GPR) model and shallow neural network models. For that purpose, this analysis uses a time series data set that includes daily cumulative COVID-19 epidemiological data. In order to increase forecast accuracy, hybrid models are constructed by merging Hyper parameter tuned regression models with Non-Linear Auto Regressive (NAR) neural network. Experiential research in this work reveals significant differences between the different types of CI-HTR models for the COVID-19 fatality prediction. The performance metrics like Root Mean Squared Error (RMSE), and the correlation factor called ‘R-Value’, are used to estimate the proposed methodology for COVID-19 dataset. It has been found that the hybrid GPR prediction model is the superior to the other models for the COVID-19 EDP." @default.
• W3183793485 created "2021-08-02" @default.
• W3183793485 creator A5021606393 @default.
• W3183793485 creator A5045590843 @default.
• W3183793485 date "2021-07-28" @default.
• W3183793485 modified "2023-09-27" @default.
• W3183793485 title "Computational Intelligence Based Hybrid Hyperparameter Tuned Prediction Techniques for COVID-19 Epidemiological Data" @default.
• W3183793485 cites W1502922572 @default.
• W3183793485 cites W1608714496 @default.
• W3183793485 cites W2051588691 @default.
• W3183793485 cites W2069670593 @default.
• W3183793485 cites W2091651498 @default.
• W3183793485 cites W2124609748 @default.
• W3183793485 cites W2293522866 @default.
• W3183793485 cites W2593566717 @default.
• W3183793485 cites W2782891976 @default.
• W3183793485 cites W2888771060 @default.
• W3183793485 cites W2894665398 @default.
• W3183793485 cites W2901950602 @default.
• W3183793485 cites W2903453060 @default.
• W3183793485 cites W2904939573 @default.
• W3183793485 cites W2936437846 @default.
• W3183793485 cites W2949044262 @default.
• W3183793485 cites W2962717448 @default.
• W3183793485 cites W2965459005 @default.
• W3183793485 cites W2971238384 @default.
• W3183793485 cites W2981407587 @default.
• W3183793485 cites W2981625275 @default.
• W3183793485 cites W2981915280 @default.
• W3183793485 cites W2982175401 @default.
• W3183793485 cites W2987729123 @default.
• W3183793485 cites W2998318498 @default.
• W3183793485 cites W3005515137 @default.
• W3183793485 cites W3007586552 @default.
• W3183793485 cites W3013601031 @default.
• W3183793485 cites W3016236951 @default.
• W3183793485 cites W3022600262 @default.
• W3183793485 cites W3030109869 @default.
• W3183793485 cites W3031443331 @default.
• W3183793485 cites W3031525229 @default.
• W3183793485 cites W3033836482 @default.
• W3183793485 cites W3034114235 @default.
• W3183793485 cites W3034529070 @default.
• W3183793485 cites W3035537071 @default.
• W3183793485 cites W3036259678 @default.
• W3183793485 cites W3038780555 @default.
• W3183793485 cites W3041383483 @default.
• W3183793485 cites W3080537550 @default.
• W3183793485 cites W3081923509 @default.
• W3183793485 cites W3091887274 @default.
• W3183793485 cites W3127055724 @default.
• W3183793485 cites W3132322827 @default.
• W3183793485 cites W3143351795 @default.
• W3183793485 cites W3155878780 @default.
• W3183793485 doi "https://doi.org/10.1007/978-3-030-74761-9_16" @default.
• W3183793485 hasPublicationYear "2021" @default.
• W3183793485 type Work @default.
• W3183793485 sameAs 3183793485 @default.
• W3183793485 citedByCount "1" @default.
• W3183793485 countsByYear W31837934852023 @default.
• W3183793485 crossrefType "book-chapter" @default.
• W3183793485 hasAuthorship W3183793485A5021606393 @default.
• W3183793485 hasAuthorship W3183793485A5045590843 @default.
• W3183793485 hasConcept C105795698 @default.
• W3183793485 hasConcept C119857082 @default.
• W3183793485 hasConcept C124101348 @default.
• W3183793485 hasConcept C139945424 @default.
• W3183793485 hasConcept C144024400 @default.
• W3183793485 hasConcept C149923435 @default.
• W3183793485 hasConcept C152877465 @default.
• W3183793485 hasConcept C154945302 @default.
• W3183793485 hasConcept C2908647359 @default.
• W3183793485 hasConcept C33923547 @default.
• W3183793485 hasConcept C41008148 @default.
• W3183793485 hasConcept C45804977 @default.
• W3183793485 hasConcept C50644808 @default.
• W3183793485 hasConcept C58489278 @default.
• W3183793485 hasConcept C8642999 @default.
• W3183793485 hasConceptScore W3183793485C105795698 @default.
• W3183793485 hasConceptScore W3183793485C119857082 @default.
• W3183793485 hasConceptScore W3183793485C124101348 @default.
• W3183793485 hasConceptScore W3183793485C139945424 @default.
• W3183793485 hasConceptScore W3183793485C144024400 @default.
• W3183793485 hasConceptScore W3183793485C149923435 @default.
• W3183793485 hasConceptScore W3183793485C152877465 @default.
• W3183793485 hasConceptScore W3183793485C154945302 @default.
• W3183793485 hasConceptScore W3183793485C2908647359 @default.
• W3183793485 hasConceptScore W3183793485C33923547 @default.
• W3183793485 hasConceptScore W3183793485C41008148 @default.
• W3183793485 hasConceptScore W3183793485C45804977 @default.
• W3183793485 hasConceptScore W3183793485C50644808 @default.
• W3183793485 hasConceptScore W3183793485C58489278 @default.
• W3183793485 hasConceptScore W3183793485C8642999 @default.
• W3183793485 hasLocation W31837934851 @default.
• W3183793485 hasOpenAccess W3183793485 @default.
• W3183793485 hasPrimaryLocation W31837934851 @default.
• W3183793485 hasRelatedWork W2995227436 @default.
• W3183793485 hasRelatedWork W3014815208 @default.

• next