Dengue vector surveillance using acoustic signals through sequential model of convolutional neural networks

Ahmad Hasham; Ayesha Hakim; Javeria Jabeen; Samra Naseem

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Dengue vector surveillance using acoustic signals through sequential model of convolutional neural networks

Author(s)

Ahmad Hasham Department of Computer Science, MNS-University of Agriculture,Multan,Pakistan

Ayesha Hakim Department of Computer Science, MNS-University of Agriculture,Multan,Pakistan

Javeria Jabeen Department of Computer Science, MNS-University of Agriculture,Multan,Pakistan

Samra Naseem Department of Statistics, MNS-University of Agriculture, Multan, Pakistan.

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 in 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 the wingbeat of Aedes Aegypti using a sequential model of convolutional neural network. Mel-frequency cepstrum coefficients generated from the audio signal are given as an input feature to the sequential model that significantly improves classification performance up to 93% accuracy. The model has been trained on low-dimensional Mel-frequency cepstrum coefficients by executing discrete cosine transform to improve the efficiency of the system. The system generates notifications through a specially designed android application to alert detected Aedes Aegypti mosquitoes in the region. It is helpful in continuous monitoring of dengue vectors to take early precautionary measures for effective control and prevention.

Publication Details

Page(s) 42-48

DOI DOI not available

Published Journal: Sukkur IBA Journal of Computing and Mathematical Sciences, Volume: 6, Issue: 1, Year: 2022

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Keywords

Aedes aegypti, android application, transform, Mel-frequency

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