Mosquito larvae detection using deep learning

Asmai, Siti Azirah and Mohamad Zukhairin, Mohamad Nurallik Daniel and Mohamad Jaya, Abdul Syukor and Abdul Rahman, Ahmad Fadzli Nizam and Abal Abas, Zuraida (2019) Mosquito larvae detection using deep learning. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8 (12). pp. 804-809. ISSN 2278-3075

Text L32131081219.PDF Download (663kB)

Abstract

Dengue cases has become endemic in Malaysia. The cost of operation to exterminate mosquito habitats are also high. To do effective operation, information from community are crucial. But, without knowing the characteristic of Aedes larvae it is hard to recognize the larvae without guide from the expert. The use of deep learning in image classification and recognition is crucial to tackle this problem. The purpose of this project is to conduct a study of characteristics of Aedes larvae and determine the best convolutional neural network model in classifying the mosquito larvae. 3 performance evaluation vector which is accuracy, log-loss and AUC-ROC will be used to measure the model’s individual performance. Then performance category which consist of Accuracy Score, Loss Score, File Size Score and Training Time Score will be used to evaluate which model is the best to be implemented into web application or mobile application. From the score collected for each model, ResNet50 has proved to be the best model in classifying the mosquito larvae species

Item Type:	Article
Uncontrolled Keywords:	Aedes, Dengue, Convolution Neural Network, Deep Learning, Performance Vector, Performance Category
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 May 2022 16:53
Last Modified:	13 May 2022 16:53
URI:	http://eprints.utem.edu.my/id/eprint/24406
Statistic Details:	View Download Statistic

Actions (login required)

View Item