Aedes Mosquito Larvae Recognition With A Mobile App

Asmai, Siti Azirah and Mohd Ali, Muhammad Hafizi and Zainal Abidin, Zaheera and Abdul Rahman, Ahmad Fadzli Nizam and Abal Abas, Zuraida (2020) Aedes Mosquito Larvae Recognition With A Mobile App. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4). pp. 5059-5065. ISSN 2278-3091

Text AEDES-MOSQUITO-LARVAE-RECOGNITION-WITH-A-MOBILE-APP2020INTERNATIONAL-JOURNAL-OF-ADVANCED-TRENDS-IN-COMPUTER-SCIENCE-AND-ENGINEERING.PDF Download (357kB)

Abstract

In the era of industrial revolution, mobile application becomes the heart of the intelligent system that integrates Artificial Intelligent (AI) system for autonomous and internet-of-things (IoT). Smartphone acts as an IoT and ubiquitous gadget to perform data analytics for fast detection or prediction. Therefore, the use of the technology is to overcome the problem of increasing number of dengue cases in Malaysia, which the Intelligent Mosquito Larvae Detection Mobile Application (iMOLAP) is proposed in this study. The purpose of iMOLAP is to help the community to responsive about the dengue larvae spotted in their area by using their smartphone and also can be used to classify the species of mosquito larvae. The mobile application uses one of the Convolutional Neural Network (CNN) techniques, which is the Inception V3 model. The new mobile application learns and classify the species of mosquito larvae by referring to a pre-set collection of mosquito larvae species image. The image captured is compared with pre-set image collection to measure the accuracy. As the results, the accuracy shows 92.8% after the image is captured using the mobile application. Finally, iMOLAP successfully analyze and able to classify the aedes species of mosquito larvae from the image taken and detect the affected area of location. The impact of iMOLAP performs fast response in mosquito larvae detection and an awareness tool for the community in combating dengue cases in Malaysia

Item Type:	Article
Uncontrolled Keywords:	Convolutional Neural Network, Inception V3, Industrial Revolution 4.0, Mosquito Larvae. Aedes Mosquito Larvae Mobile Application
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	24 Feb 2021 22:27
Last Modified:	01 Mar 2021 09:49
URI:	http://eprints.utem.edu.my/id/eprint/24905
Statistic Details:	View Download Statistic

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