Please use this identifier to cite or link to this item:
https://rda.sliit.lk/handle/123456789/1374
Title: | DenGue CarB: Mosquito Identification and Classification using Machine Learning |
Authors: | Mohommed, M. Rajakaruna, P. Kehelpannala, N. Perera, A. Abeysiri, L. |
Keywords: | Classification SVM KNN CNN Prediction Mosquito classification Image processing Web Application |
Issue Date: | 10-Dec-2020 |
Publisher: | 2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT |
Series/Report no.: | Vol.1; |
Abstract: | This 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. |
URI: | http://rda.sliit.lk/handle/123456789/1374 |
ISBN: | 978-1-7281-8412-8 |
Appears in Collections: | 2nd International Conference on Advancements in Computing (ICAC) | 2020 Department of Computer Systems Engineering-Scopes Department of Information Technology-Scopes |
Files in This Item:
File | Description | Size | Format | |
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DenGue_CarB_Mosquito_Identification_and_Classification_using_Machine_Learning.pdf Until 2050-12-31 | 602.67 kB | Adobe PDF | View/Open Request a copy |
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