# Simplified Machine Learning Model as an Intelligent Support for Safe Urban Cycling
## 5 Discussion  
The results presented in Tables 5-8 allow us to observe the comparison of accuracies in the average classification, which includes the models selected in the state of the art and the proposed SML model. Models using very similar feature extractors and datasets were explored and considered, allowing us to evaluate our SML model results with classification accuracy in both modes (one-shot and five-shot) at a 95% trustworthiness. Training for the SML model was performed with the MiniImageNet dataset and the ResNet-18 and EfficientNet-B0 CNNs as feature extractors. Graphically, in Figure 14a, it can be seen that the proposed SML model for the MiniImageNet dataset in one-shot mode and as an EfficientNet-B0 feature extractor performs better than all models except RENet, which was better by 1.04%. Similarly, in performance for five-shot mode (Figure 14a), the model RENet was also 1.06% better than the SML model. Therefore, RENet was the model that presented the best result for both modes (one-shot and five-shot) of the comparative models using the MiniImageNet dataset. Similarly, in Figure 15a,b it can be observed that for the CUB-200-2011 dataset, the SML model in both modes (one-shot and five-shot) did not have an average precision as close to the RENet model, since the latter was better by 6.93% and 7.92% for the one-shot and five-shot modes, respectively. Continuing with the results obtained, Figure 16a,b show us how the SML model using the CIFAR-100 dataset, and when compared with the state-of-the-art models, surpassed the Dual Trinet model on average in accuracy by 12.24% in one-shot mode and by 3.43% in five-shot mode to the model, whose results had been the best as observed in Table 7.  
Finally, for the DroNet dataset, as can be seen in Figure 17a, the SML model using the one-shot mode outperforms the Dual Trinet model by only 0.91%, the latter being the model that presented the best average accuracy of the analyzed models for that particular mode. Similarly, the Dual Trinet model for the five-shot mode was outperformed by our SML model in average accuracy but only by 0.40% as seen in Figure 17b. As shown in this previous section, in the experiments carried out with the described datasets and the CNN networks, the proposed Siamese network model was able to perform better in the one-shot and five-shot learning methods. This is because the ResNet-18 CNN learns from residuals, and as shown in [35], it is a practical feature extractor for  
Figure 14: Performance between the state-of-the-art models and the proposed model (SML) using the MiniImageNet dataset, in one-shot (**a**) and five-shot mode (**b**). one-shot learning tasks. It could also be seen that its demonstrated classification accuracy was very close to EfficientNet-B0 and was consistent with the CNNs that have been used for comparison. It can also be observed that when classifying new data with the experimental models, the accuracy within the classification decreases, and this is due to the change in the distribution of the data present in the dataset. Although the data used in one-shot learning com from disjoint classes, they all present and come from the same data distribution. Likewise, based on the work presented in [31], we present the accuracy demonstrated in the classification of our model using the CNNs ResNet-18 CNN and EfficientNet-B0, which have been trained with the MiniImageNet dataset and validated with the CIFAR-100 and DroNet datasets, and the classification accuracy is presented in Tables 7 and 8 with 95% confidence. Our experimental phase with the datasets and CNN networks resulted in our proposed model performing better in classification accuracy than other one-shot learning methods that use the cosine function as a similarity layer. As shown in particular, our Siamese network model when using the ResNet-18 feature extractor had better performance than when using EfficientNet-B0 for the MiniImageNet dataset. Architectures with fewer parameters were used, similar to those used in [23; 35].