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


  • Ahmad Hasham MNS-University of Agriculture, Multan
  • Ayesha Hakim Muhammad Nawaz Sharif University of Agriculture, Multan
  • Javeria Jabeen MNS-University of Agriculture, Multan
  • Samra Naseem MNS-University of Agriculture, Multan



Aedes Aegypti, CNN, Mel-frequency, Mobile application, Sequential model


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.


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Author Biography

Ayesha Hakim, Muhammad Nawaz Sharif University of Agriculture, Multan

Dr. Ayesha Hakim has a PhD in Computer Science from Massey University, New Zealand. She joined University of Auckland as Research Specialist and then joined Waikato Institute of Technology (WINTEC) as a Lecturer in Computer Science. She moved to Pakistan in 2018 where she is currently an Assistant Professor of Computer Science at MNS-University of Agriculture, Multan.  She is academic lead and focal person of a tech startup SmarTrapS that is an intelligence system for flying insect surveillance. She is IEEE counsellor and focal person of NGIRI ignite, ICT academy, blended learning and LMS management. Her research interests include data science, precision agriculture, shape and audio analysis.