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dc.contributor.authorCutad, Reir Erlinda E.
dc.contributor.authorGerardo, Bobby D.
dc.date.accessioned2024-05-06T06:09:09Z
dc.date.available2024-05-06T06:09:09Z
dc.date.issued2019
dc.identifier.citationCutad, R. E. E., & Gerardo, B. D. (2019). A Prediction-based Curriculum Analysis using the Modified Artificial Bee Colony Algorithm. International Journal of Advanced Computer Science and Applications, 10(10), 117–123. https://doi.org/10.14569/ijacsa.2019.0101017en
dc.identifier.issn2158107X
dc.identifier.urihttps://hdl.handle.net/20.500.14353/417
dc.description.abstractDue to the vast amount of students' information and the need of quick retrieval, establishing databases is one of the top lists of the IT infrastructure in learning institutions. However, most of these institutions do not utilize them for knowledge discovery which can aid in informed decision-making, investigation of teaching and learning outcomes, and development of prediction models in particular. Prediction models have been utilized in almost all areas and improving the accuracy of the model is sought- after this study. Thus, the study presents a Scoutless Rule-driven binary Artificial Bee Colony (SRABC) as a searching strategy to enhance the accuracy of the prediction model for curriculum analysis. Experimental verification revealed that SRABC paired with K-Nearest Neighbor (KNN) increases the prediction accuracy from 94.14% to 97.59% than paired with Support Vector Machine (SVM) and Logistic Regression (LR). SRABC is efficient in selecting 14 out of 60 variables through majority voting scheme using the data of the BSIT students of Davao Del Norte State College (DNSC), Davao del Norte, Philippines. © 2019 International Journal of Advanced Computer Science and Applications.en
dc.language.isoenen
dc.publisherScience and Information Organizationen
dc.relation.urihttps://www.researchgate.net/profile/Reir-Cutad/publication/337018477_A_Prediction-based_Curriculum_Analysis_using_the_Modified_Artificial_Bee_Colony_Algorithm/links/5deca6d1a6fdcc28370f0169/A-Prediction-based-Curriculum-Analysis-using-the-Modified-Artificial-Bee-Colony-Algorithm.pdfen
dc.subjectBinary artificial bee colonyen
dc.subjectCurriculum analysisen
dc.subjectPrediction modelen
dc.subjectRule-driven mechanismen
dc.subjectScoutless rule-driven binary artificial bee colonyen
dc.subjectSearching strategyen
dc.subjectCurriculum analysisen
dc.subjectAlgorithmic knowledge discoveryen
dc.subjectKnowledge discovery in databasesen
dc.subjectAcademic dataen
dc.subjectVariable selectionen
dc.subjectSwarm-based intelligenceen
dc.subjectSwarm-based algorithmsen
dc.subjectParticle Swarm Optimizationen
dc.subjectAnt colony optimizationen
dc.subjectArtificial bee economyen
dc.subjectNovel binary ABC algorithmen
dc.subjectK-nearest Neighboren
dc.subjectSupport vector machineen
dc.subjectLogistic regressionen
dc.subject.lcshDecision makingen
dc.subject.lcshForecastingen
dc.subject.lcshSupport vector machinesen
dc.subject.lcshBee cultureen
dc.subject.lcshData miningen
dc.subject.lcshKnowledge discoveren
dc.subject.lcshArtificial intelligenceen
dc.titleA prediction-based curriculum analysis using the modified artificial bee colony algorithmen
dcterms.accessRightsOpen accessen
dc.identifier.doi10.14569/ijacsa.2019.0101017


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