## 5 Simulation Result and Discussion
### The Comparison of Several Methods' Performances  
In Table 3, the performance of the proposed decision fusion model is compared with the performances of other CNN-based classification models and conventional machine learning models. The decision fusion between EfficientNet and ShuffleNet is indicated by ESNet. We compared the proposed model and the ESNet with Cruz-vega architecture [7], average pooling approach [48], ResNet101 and MobileNetV2, and ShuffleNet combination that was fused using Max-Pooling probability (MS+MP). The proposed model and the ESNet achieved a score of 1.0 for all evaluation metrics, i.e, accuracy, sensitivity or recall, precision, and F-Measure, which are the highest values compared with those of other methods. The proposed model and the ESNet have higher accuracy, sensitivity, precision,  
Figure 19: The activation function result of MobileNetV2 and ShuffleNet.  
Figure 18: Confusion matrix of: (**a**) MobileNetV2; (**b**) ShuffleNet; (**c**) the proposed method.  
and F-measure compared with all CNN-based classification methods, except for Cruz-Vega, with a precision of 1.0. Furthermore, the proposed framework shows a better performance compared with those of the conventional machine-learning-based classification methods. The combination of local binary pattern (LBP) and a support vector machine (SVM) resulted in noticeable performance, so did the combination of LBP and random forest (RF). The LBP+SVM and LBP+RF methods obtained an accuracy of 0.880, a recall of 0.917, a precision of 0.917, and an F-Measure of 0.917. LBP combined with Naive Bayes (NB) result in the poorest performance compared with those of deep learning and traditional machine-learning-based methods, with an accuracy of 0.710, a recall of 0.890, a precision of 0.670, and an FM of 0.760.  
Table 3 also shows a comparison of the learning parameters used in the deep learning models, including in the proposed model. The proposed model is composed of MobileNetV2 and ShuffleNet, which means that it has the learning parameters of both architectures. The proposed model has 3.08 M learning parameters, while the ESNet has 4.88 M. The reason why the proposed model has fewer learning parameters is because it is composed of MobileNetV2, with fewer learning parameters than the EfficientNet that composed the ESNet.  
Cruz-Vega [7] has fewer learning parameters than the proposed DF; however, Cruz-Vega shows a poorer performance. The lower the learning parameters, the faster the computation process. Due to the availability of computer resources, the number of learning parameters is no longer an issue. Despite having fewer learning parameters compared with other CNN models, the proposed DF and ESNet achieve better performances. Here, it was shown that the accuracy is not determined by the number of learning parameters. The proposed model results in a higher accuracy rate (1.0) compared with that of ResNet (0.9). Similarly, the proposed model achieves a higher accuracy value (1.0) compared with those of the conventional machine learning (0.88). Based on the comparison result, the proposed model achieved the best accuracy result (1.0) with only 3.08 M learning parameters. Only ESNet achieve a similar accuracy of 63%.