## 4 The Proposed Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Classifier**} & \multicolumn{6}{c}{**Feature Extracted Dimension**} \\ \cline{2-7}  & \multicolumn{2}{c}{**FEF\_1000F**} & \multicolumn{2}{c}{**SEF\_50F**} & \multicolumn{2}{c}{**SEF\_500F**} \\ \cline{2-7}  & **Accuracy Rate (\%)** & **Error Rate (\%)** & **Accuracy Rate (\%)** & **Error Rate (\%)** & **Accuracy Rate (\%)** & **Error Rate (\%)** \\ \hline GDA & 94.24 & 5.76 & 96.20 & 3.80 & 93.73 & 6.27 \\ \hline KNN & 94.88 & 5.12 & 96.67 & 3.33 & 95.13 & 4.87 \\ \hline LOG & 94.75 & 5.25 & 96.54 & 3.46 & 89.76 & 10.24 \\ \hline SVM & 95.65 & 4.35 & 96.05 & 3.95 & 94.88 & 5.12 \\ \hline RF & 95.03 & 4.97 & 96.16 & 3.84 & 95.77 & 4.23 \\ \hline ELM & 93.60 & 6.40 & 95.01 & 4.99 & 94.18 & 5.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation measure results of SqueezeNet features and a number of different classifiers over the Malimg dataset. Figure 12: Accuracy percentage for various ResNet50 feature dimensions under the Malimg dataset. to the ResNet50, DenseNet201, GoogLeNet, AlexNet, and SqueezeNet features, as can be seen in Figure 13a. It was discovered that the average error values for KNN, GDA, LOG, SVM, RF, and ELM are 3.46, 3.46, 3.74, 4.71, 4.02, and 5.79, respectively. When compared to all of the classifiers, the KNN classifier was able to obtain the minimum error rate in regard to the ResNet50, DenseNet201, GoogLeNet, AlexNet, and SqueezeNet features, as can be seen in Figure 13b. When compared to the other classifiers, the KNN classifier demonstrated superior performance in terms of both accuracy and error. Then, the GDA classifier, on the other hand, is ranked in the second position, while the ELM classifier is ranked at last place and generates the lowest possible results. The KNN classifier produced the highest results over two assessment criteria, as indicated by the previous results obtained from a number of other classifiers that were applied to the Malimg dataset. The KNN classifier was therefore taken into consideration for the purpose of carrying out more tests in this paper. Performance Analysis of Each Feature

An analysis of the findings in Tables 5-9 will make selecting the optimal feature possible. With the purpose of applying the assessment metrics, a comparison across all features will be carried out, including accuracy and error. One of the things that we are carrying out right now is determining the overall mean for every metric for all of the classifier for each feature that we are currently testing. Figure 13: Comparative analysis of several classifiers evaluation metrics using the Malimg dataset: (**a**) accuracy; (**b**) error. It was discovered that the average accuracy values for ResNet50, DenseNet201, GoogLeNet, AlexNet, and SqueezeNet features are 96.17, 96.073, 96.071, 94.56, and 96.10, respectively. When compared to all of the models' features, the DenseNet201 feature model was able to obtain the maximum accuracy rate in regard to the KNN, GDA, LOG, SVM, RF, and ELM classifiers, as can be seen in Figure 14a. It was discovered that the average error values for ResNet50, DenseNet201, GoogLeNet, AlexNet, and SqueezeNet features are 3.79, 3.99, 3.92, 5.38, and 3.89, respectively. When compared to all of the models' features, the DenseNet201 feature model was able to obtain the minimum error rate in regard to the KNN, GDA, LOG, SVM, RF, and ELM classifiers, as can be seen in Figure 14b. When compared to the other models' features, the DenseNet201 feature demonstrated superior performance in terms of both accuracy and error. Then, the GoogLeNet feature model, on the other hand, ranks in the second position, while the SqueezeNet feature ranks last and generates the lowest possible results. DenseNet201 produced the highest results over two assessment criteria, as indicated by the previous results obtained from a number of other model features that were applied to the Malimg dataset.