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AEDES CLASSIFIER

Theme -- HEALTHCARE

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About Aedes Mosquito

  • Aedes aegypti the yellow fever mosquito, spread dengue fever, chikungunya, Zika fever, Mayaro and yellow fever viruses, and other disease agents.
  • No particular medicine for the treatment of dengue fever.
  • Endemic in more than 128 countries, with about 3.8 million people at risk.
  • There has been a more than 300% hike in dengue cases since 2009, and even the total number of deaths in 2017 was the highest.

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FEATURES

COLOUR

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SHAPE

Area,Perimeter,size,Length

WING BEAT FREQUENCY

MALE - 512 - 832 Hz

FEMALE- 412 - 560 Hz

standard deviation - 9hz(M),15(F)

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BLACK AND WHITE on legs (at least 2)

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WINGSCALE

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CYLPEUS

Head of Insect

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CONCEPT :

It uses digital image processing and support vector machine (SVM) to detect aedes aegypti mosquito. Aedes aegypti mosquito is a carrier of dengue virus which causes the problem that is called dengue hemorrhagic fever. As of today there is still no particular medicine for the treatment of such. As a result the study proposed a way on how to detect if a mosquito is an aedes aegypti or not using SVM in Matlab. First, it was trained by feeding images of aedes aegypti and not aedes aegypti such as aedes albopictus and culex that were taken at different views, highlighting their unique features. In the image processing, the features were extracted and tllen classified to aedes ae~ypti or not aedes ae~ypti. Confusion matrix was used to show tile accuracy of tile system in detecting tile Aedes Aegypti mosquito

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Overview

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System Flowchart:

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SVM

Support Vector Machine is a supervised learning machine that is used for classification analysis. SVM has its learning algorithm for it to be able to identify and analyze patterns. Its main concept is about hyperplane which separates different classification data for different objects. The hyperplane is also defined as the decision boundary for SVM. The figure below shows how the classification of the SVM. The 'x' and '0' are the different data for an object and the line drawn between them is the hyperplane.

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MODULES REQUIRED :

  • Ultrasound system to identify wing beat frequency .
  • Blue Light.
  • Camera for identification.
  • Raspberry -Pi.
  • Decentralized Network.

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APPLICATIONS :

  • Software can be mounted on drone which themselves take survellance of area.

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  • CCTV cameras with microscopic lens.

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  • Used in home’s CCTV cameras with microscopic lens.

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ADVANTAGES :

  • Keeps aware whether area is dengue affected.

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  • Alert about area whether it is dengue affected or not.