Classification of containers with Aedes aegypti pupae using a Neural Networks model

This paper discusses the presence of Aedes aegypti pupae in different types of containers considering: volume, pH of the container, among other variables.

A nonlinear method for selection was applied, based on Mutual Information, by placing in order of importance the most appropriate variables for identifying containers with and without Aedes aegypti pupae. Such variables were used for input into a Neural Network in layers for classification.

Among the experiments carried out, the best result obtained used the first eight variables selected by order of importance. The percentage of hits for containers which had no Aedes aegypti pupae was 73.3%, and 80.9% for those which did have Aedes aegypti pupae in the containers. This Neural Network method, a model with the capacity to emulate non-linear data, got better results in comparison with the discriminant power of the Logistic Regression model. Thus, the outcomes of using the Neural Networks method achieved better separability in classifying the containers with pupae and those with no pupae.

This type of analysis will aid in the efforts to design an efficient program to control Aedes aegypti that can concentrate principally on containers which present the greatest productivity.