## 3 Results  
The insect dataset was generated after three months of field data collection via a digital camera and web-based image collection using images.google.com as the key search engine. A total of 400 images of mosquitoes (200), flies (100) and bees (100) were collected. Among the 400 images, 100 images of mosquitoes, 50 images of flies and another 50 images of bees were taken for training the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **SVM Module** & **Kernel** & **Mathematical Expression for Kernel** \\ \hline SVM I & C-SVC & Linear & \(K(x_{1},x_{2})=x_{1}Tx_{2}\) \\ SVM II & C-SVC & Radial Basis Function & \(K(x_{1},x_{2})=\exp{(-\frac{|x_{1}-x_{2}|^{2}}{2\sigma^{2}})}\) \\ SVM III & nu-SVC & Radial Basis Function & \(K(x_{1},x_{2})=\exp{(-\frac{|x_{1}-x_{2}|^{2}}{2\sigma^{2}})}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three types of support vector machine (SVM) and corresponding kernel functions used for the classification process, C-SVC: C-Support Vector Classification, nu-SVC: nu-Support Vector Classification.  
Remaining images from each set were used for the validation of the system. Figure 6 demonstrates the results from classified images from our training experiments using the SVM. We use the SVM by invoking LibSVM library that is included in the OpenCV package. Figure 7 illustrates the sample images correctly and incorrectly classified from our test. Table 2 shows the experimental results of accuracy analysis in terms of mosquito identification, while SVM I is used for classification. The experiments show accuracies of 65.6% and 76.0% in identifying the other bugs namely bees and flies. The experiments show a recall percentage of 82%--the proportion of positive images that are returned in the case of mosquito identification--and a recall percentage of 57% in identifying other bugs.  
The results convey that the usage of SVM with linear kernel function is not effective for classifying mosquitoes and other bugs. The linear basis function generates a simple 2D hyperplane to decimate the data vectors into two classes. If the data points are clustered in an overlapped fashion in the multidimensional space, the probability of error in the classification using SVM with linear kernel function will be higher. In the above-mentioned case, a complex hyperplane can classify the data points effectively, whereas the RBF kernel generates a complex hyperplane in the multi-dimensional space. Tables 3 and 4 shows the experimental results of accuracy analysis in terms of the mosquito identification where SVM II and SVM III are used. The results show the accuracies of 85.2% and 98.9% when using SVM II and SVM III for identifying mosquitoes, respectively. Moreover, the classifications using SVM II and SVM III show 97.6% and 92.2% accuracy in identifying the other bugs, namely bees and flies. The results with SVM II show a recall percentage of 98%, but in the case of SVM III the recall percentage is 92%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Insect** & **Mosquitoes** & **Other Bugs** & **Accuracy \%** \\ \hline
**Mosquitoes** & 82 & 43 & 65.6 \\
**Others** & 18 & 57 & 76.0 \\
**Recall \%** & 82.0 & 57.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The result of the classification of mosquitoes and others bugs using SVM I.  
Figure 7: Sample classification results. A correctly classified mosquito (**A**) and a correctly classified bug (**B**); (**C,D**) shows wrongly classified bug and mosquito.