Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1374
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dc.contributor.authorMohommed, M.-
dc.contributor.authorRajakaruna, P.-
dc.contributor.authorKehelpannala, N.-
dc.contributor.authorPerera, A.-
dc.contributor.authorAbeysiri, L.-
dc.date.accessioned2022-02-23T08:47:34Z-
dc.date.available2022-02-23T08:47:34Z-
dc.date.issued2020-12-10-
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1374-
dc.description.abstractThis research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.en_US
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.relation.ispartofseriesVol.1;-
dc.subjectClassificationen_US
dc.subjectSVMen_US
dc.subjectKNNen_US
dc.subjectCNNen_US
dc.subjectPredictionen_US
dc.subjectMosquito classificationen_US
dc.subjectImage processingen_US
dc.subjectWeb Applicationen_US
dc.titleDenGue CarB: Mosquito Identification and Classification using Machine Learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357133en_US
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020
Department of Computer Systems Engineering-Scopes
Department of Information Technology-Scopes

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