Dengue Vector Surveillance using Acoustic Signals through Sequential Model of Convolutional Neural Networks

Abstract

Dengue fever is among the most dangerous infectious viral diseases transmitted through the bite of infected Aedes Aegypti mosquitoes. One way to decline the spread of dengue is by raising awareness to the community about mosquito habitats through continuous surveillance. The traditional surveillance techniques of Aedes Aegypti are difficult, time taking, and can lead to severe health risks. This paper presents a possible way of dengue vector surveillance through acoustic signals generated by wingbeat of Aedes Aegypti using the sequential model of convolutional neural network. Mel-frequency spectrum is given as an input feature to the sequential model that significantly improves classification performance up to 93% accuracy. The system generates notification through a specially designed mobile application to alert detected dengue vectors in the region. It is helpful in continuous monitoring of dengue vectors to take early precautionary measures for effective control and prevention.