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» Research » Student research projects » Machine learning model(s) for identifying mosquitoes from their sound wavesMachine learning model(s) for identifying mosquitoes from their sound waves
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Description
Mosquitoes are widely dispersed worldwide and serve as causal agent of several diseases like yellow fever, Dengue fever, Zika, Chikungunya etc. Typically, both male and female mosquitoes feed on nectar and plant juices, but female mosquitoes possess specialized mouthparts, designed to suck bloods – crucial for their egg development. Beside this sex-biasness, species differentiation is also observed in type of diseases. While yellow-fever-mosquito (Aedes aegypti) causes viral diseases (yellow fever, Dengue fever), Anopheles mosquito serve as a “vector” for Malaria. Besides morphological features, a good-curated wing beating data is available publicly for 6 different species of mosquitoes.
Goals
The project aims to:
- Develop machine learning algorithm to distinguish sex and species of different mosquitoes, based on wing-beating sound wave.
- Validate data using phenotypic and genotypic (DNA and protein) data, available on databases (e.g. NCBI, Vectorbase)
The data generated and trained from this project will be utilized for mosquito surveillance and implemented on control strategies for mosquito-borne diseases.
Background Literature
- I Potamitis and I Rigakis (2016). Large aperture optoelectronic devices to record and time-stamp insects’ wingbeats. IEEE Sensors Journal, 16(15):6053-6061.
