ANN Based Dengue Mosquito Zap

Dengue fever, a significant global health burden, is transmitted by the Aedes Aegypti mosquito. This research presents a novel system to combat dengue by targeting Aedes aegypti in high-risk areas. An electric fence equipped with UV LED lights and generation of a specific wing-beat frequency attracts mosquitoes, while a camera and artificial neural network (ANN) enable real-time species identification and targeted elimination. The advancement in machine learning techniques, like artificial neural network enabled for the accurate prediction of aedes aegypti mosquito. The datasets were processed to extract features like contrast, correlation, energy and homogeneity. The samples were then trained under feed forward neural network and finally tested to evaluate the accuracy in predicting aedes aegypti mosquito. By accurately detecting Aedes aegypti and selectively applying an electrical charge, this system aims to reduce dengue transmission. Rigorous testing will evaluate the system’s performance, including detection accuracy and false positive/negative rates. This paper demonstrates the efficiency of ANN model in predicting the Aedes aegypti mosquito. The proposed model has the potential to significantly improve public health outcomes.