## 4 Results
### Convolution Neural Network for Wildfire Dataset  
The confusion matrix and classification report were calculated and tabulated as standard techniques to verify the accuracy of DL models. Similar analysis methods were also conducted to test the dataset with respect to the CNN or other ML classifiers [43; 44; 45; 46]. In this study, the results were also generated using the scikit-learn library included in the python script (Table 3). The gray color shows the true positive (_TP_) with and without the normalized values, respective to the four labels. The confusion matrix shows that the CNN models were chosen based on the small number of false positive (_FP_) and false negative (_FN_) results: 14 and 25 for ShallowNet, 5 and 17 for LeNet, and 0 and 14 for MiniVGGNet, respectively (Figure 10). Ultimately, the MiniVGGNet model was considered the best CNN network, when compared with LeNet and ShallowNet, with minor type 1 errors (_FP_) and type 2 errors (_FN_). In addition, the classification reports were supported by the confusion matrix results. The pre-trained CNN models of ShallowNet, LeNet, and MiniVGGNet had 95%, 97%, and 98% of the overall accuracy (OA), respectively (Figure 11). The F1 scores of the wildfire label were also analyzed to have a more accurate comparison between these networks. Similar  
Figure 9: Power consumption of KITSUNE deep-learning execution (fourth pass). to the OA results, the F1 scores of MiniVGGNet had the highest score at 97%, followed by 95% for LeNet and 92% for ShallowNet. The MiniVGGNet model used in this study shows significantly higher accuracy compared to other approaches. [PERSON] et al. [33] discussed that applying a Deep Neural Network for cloud detection could achieve 92% accuracy. Meanwhile, utilizing CNN onboard for binary image classification could obtain 90% accuracy, as [PERSON] et al. [45] presented. Therefore, the main contributions are the training dataset and the parameter tuning of the neural network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CNN Models** & **True/Predicted Labels** & **Cloud** & **Land** & **Sea** & **Wildfire** \\ \hline \multirow{8}{*}{ShallowNet} & Cloud & 228 & 4 & 0 & 9 \\  & & 94.6\% & 1.6\% & 0\% & 3.7\% \\  & Land & 2 & 250 & 3 & 2 \\  & & 0.8\% & 97.3\% & 1.2\% & 0.8\% \\ \multirow{2}{*}{ShallowNet} & Sea & 0 & 4 & 260 & 3 \\  & & 0\% & 1.5\% & 97.4\% & 1.1\% \\ \multirow{2}{*}{ShallowNet} & Wildfire & 4 & 21 & 0 & 210 \\  & & 1.7\% & 8.9\% & 0\% & 89.3\% \\ \hline \multirow{8}{*}{LeNet} & Cloud & 236 & 2 & 0 & 3 \\  & & 97.9\% & 0.8\% & 0\% & 1.2\% \\ \multirow{2}{*}{LeNet} & Land & 3 & 252 & 0 & 2 \\  & & 1.2\% & 98.1\% & 0\% & 0.8\% \\ \multirow{2}{*}{LeNet} & Sea & 0 & 1 & 266 & 0 \\  & & 0\% & 0.4\% & 99.6\% & 0\% \\ \multirow{2}{*}{LeNet} & Wildfire & 2 & 15 & 0 & 218 \\  & & 0.9\% & 6.4\% & 0\% & 92.8\% \\ \hline \multirow{8}{*}{MiniVGGNet} & Cloud & 233 & 8 & 0 & 0 \\  & & 96.7\% & 3.3\% & 0\% & 0\% \\ \multirow{2}{*}{MiniVGGNet} & Land & 1 & 256 & 0 & 0 \\  & & 0.4\% & 99.6\% & 0\% & 0\% \\ \multirow{2}{*}{MiniVGGNet} & Sea & 0 & 0 & 267 & 0 \\  & & 0\% & 0\% & 100\% & 0\% \\ \multirow{2}{*}{ShallowNet} & Wildfire & 1 & 13 & 0 & 221 \\  & & 0.4\% & 5.5\% & 0\% & 94.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of ShallowNet, LeNet, and MiniVGGNet (gray color means _TP_ value). Figure 10: The true positive, true negative, false positive, and false negative results corresponding to the models. Furthermore, the training loss and accuracy were also plotted to correlate how the models were trained concerning 100 epochs (Figure 12). MiniVGNet achieved 90% training accuracy at epoch 6 and less than 20% of training loss at epoch 20. It was also noticeable that these three models were trained at different learning rates to gain the highest accuracy possible, 0.05 for MiniVGNet, whereas it was 0.005 for LeNet and ShallowNet. On the other hand, other parameters, like input pixel size and type of optimizers, were measured to see whether they would increase the F1 scores of the CNN models.