Matches in SemOpenAlex for { <https://semopenalex.org/work/W4289130147> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W4289130147 endingPage "2093" @default.
- W4289130147 startingPage "2081" @default.
- W4289130147 abstract "This paper presents an efficient prediction model for a good learning environment using Random Forest (RF) classifier. It consists of a series of modules; data preprocessing, data normalization, data split and finally classification or prediction by the RF classifier. The preprocessed data is normalized using min-max normalization often used before model fitting. As the input data or variables are measured at different scales, it is necessary to normalize them to contribute equally to the model fitting. Then, the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation (k = 10) is used to validate the model. The proposed Prediction Model for Course Selection (PMCS) system is considered a multi-class problem that predicts the course for a particular learner with three complexity levels, namely low, medium and high. It is operated under two modes; locally and globally. The former considers the gender of the learner and the later does not consider the gender of the learner. The database comprises the learner opinions from 75 males and 75 females per category (low, medium and high). Thus the system uses a total of 450 samples to evaluate the performance of the PMCS system. Results show that the system’s performance, while using locally i.e., gender-wise has slightly higher performance than the global system. The RF classifier with 75 decision trees in the global system provides an average accuracy of 97.6%, whereas in the local system it is 97% (male) and 97.6% (female). The overall performance of the RF classifier with 75 trees is better than 25, 50 and 100 decision trees in both local and global systems." @default.
- W4289130147 created "2022-08-01" @default.
- W4289130147 creator A5016948052 @default.
- W4289130147 creator A5027285117 @default.
- W4289130147 date "2023-01-01" @default.
- W4289130147 modified "2023-09-26" @default.
- W4289130147 title "Prediction Model for a Good Learning Environment Using an Ensemble Approach" @default.
- W4289130147 cites W2063793405 @default.
- W4289130147 cites W2101900104 @default.
- W4289130147 cites W2119372911 @default.
- W4289130147 cites W2343874041 @default.
- W4289130147 cites W2465628989 @default.
- W4289130147 cites W2800700858 @default.
- W4289130147 cites W2895615678 @default.
- W4289130147 cites W2968982710 @default.
- W4289130147 doi "https://doi.org/10.32604/csse.2023.028451" @default.
- W4289130147 hasPublicationYear "2023" @default.
- W4289130147 type Work @default.
- W4289130147 citedByCount "0" @default.
- W4289130147 crossrefType "journal-article" @default.
- W4289130147 hasAuthorship W4289130147A5016948052 @default.
- W4289130147 hasAuthorship W4289130147A5027285117 @default.
- W4289130147 hasBestOaLocation W42891301471 @default.
- W4289130147 hasConcept C10551718 @default.
- W4289130147 hasConcept C119857082 @default.
- W4289130147 hasConcept C124101348 @default.
- W4289130147 hasConcept C136886441 @default.
- W4289130147 hasConcept C144024400 @default.
- W4289130147 hasConcept C153180895 @default.
- W4289130147 hasConcept C154945302 @default.
- W4289130147 hasConcept C169258074 @default.
- W4289130147 hasConcept C19165224 @default.
- W4289130147 hasConcept C34736171 @default.
- W4289130147 hasConcept C41008148 @default.
- W4289130147 hasConcept C45942800 @default.
- W4289130147 hasConcept C95623464 @default.
- W4289130147 hasConceptScore W4289130147C10551718 @default.
- W4289130147 hasConceptScore W4289130147C119857082 @default.
- W4289130147 hasConceptScore W4289130147C124101348 @default.
- W4289130147 hasConceptScore W4289130147C136886441 @default.
- W4289130147 hasConceptScore W4289130147C144024400 @default.
- W4289130147 hasConceptScore W4289130147C153180895 @default.
- W4289130147 hasConceptScore W4289130147C154945302 @default.
- W4289130147 hasConceptScore W4289130147C169258074 @default.
- W4289130147 hasConceptScore W4289130147C19165224 @default.
- W4289130147 hasConceptScore W4289130147C34736171 @default.
- W4289130147 hasConceptScore W4289130147C41008148 @default.
- W4289130147 hasConceptScore W4289130147C45942800 @default.
- W4289130147 hasConceptScore W4289130147C95623464 @default.
- W4289130147 hasIssue "3" @default.
- W4289130147 hasLocation W42891301471 @default.
- W4289130147 hasOpenAccess W4289130147 @default.
- W4289130147 hasPrimaryLocation W42891301471 @default.
- W4289130147 hasRelatedWork W3008615541 @default.
- W4289130147 hasRelatedWork W4200420744 @default.
- W4289130147 hasRelatedWork W4230140792 @default.
- W4289130147 hasRelatedWork W4239030218 @default.
- W4289130147 hasRelatedWork W4281560664 @default.
- W4289130147 hasRelatedWork W4312632137 @default.
- W4289130147 hasRelatedWork W4313270526 @default.
- W4289130147 hasRelatedWork W4318350883 @default.
- W4289130147 hasRelatedWork W4322744035 @default.
- W4289130147 hasRelatedWork W4375930479 @default.
- W4289130147 hasVolume "44" @default.
- W4289130147 isParatext "false" @default.
- W4289130147 isRetracted "false" @default.
- W4289130147 workType "article" @default.