AI based Intelligent Mosquito Trap to Control Vector Borne Diseases
Team Members: Dr. P. Subashini, Dept of Computer Science
Dr. M. Krishnaveni, Assistant Professor (SG), Dept of Computer Science
Dr. T.T. Dhivyaprabha, Research Associate, DST-CURIE-AI
Ms. B. Gayathre -19PCA001, II MCA, Department of Computer Science
Project Summary: Vector-borne diseases are the most harmful and threat to human beings health, affecting nearly seven hundred million people every year and causing one million deaths annually. Information on mosquito species' population and spatial distribution is essential in identifying vector-borne diseases. Mosquito prevention and monitoring programs are established by public health departments with mosquito traps. Many monitoring systems have already been implemented concerning the worldwide spreading of mosquitoes and mosquito-borne infections, although mosquito population monitoring is inadequate and time[1]consuming in order to identify mosquito species and diseases. Aedes aegypti, Aedes albopictus, Anopheles gambiae, Anopheles arabiensis, Culex pipiens, and Culex quinquefasciatus are the six primary mosquito species prevalent in India that inflict vector[1]borne diseases. It aims to construct an IoT-based mosquito-based disease identification system using machine learning algorithms. The proposed methodology is described as follows. It collects the mosquito's wingbeat audio from the Kaggle website, then eliminates noise from the wingbeat audio file using the Butterworth pre-processing algorithm. After pre[1]processing, wingbeat is subjected to feature extraction for frequency using the Fast Fourier Transform algorithm, followed by classification using the Decision Tree algorithm to classify mosquito wingbeat signals. In the experimental findings and analysis, the accuracy of the constructed system is compared with and without pre-processing approaches. The system enables monitoring of the mosquito population and epidemic through automation, which delivers correct output in a defined time frame without human intervention.
Experimental Testing Kit Methodology of IoT Integrated with ML Phase
