## 4 Results
### Performance Evaluation  
A novel method for feature extraction and classification using a bag-of-features (BoF) followed by deep neural network (DNN) framework is proposed in this study. The method is evaluated on a dataset, achieving a classification accuracy of 91.54%. When compared to standalone classifiers, the bidirectional long short-term memory (BiLSTM) achieved the second highest accuracy at 89.72%, while the simple LSTM achieved 89.55%. The support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT) classifiers yielded classification accuracies of 85.02%, 84.85%, and 84.94%, respectively. The confusion matrix generated by each classification method is illustrated in Figure 4. The proposed DNN model, consisting of a layered architecture of LSTM and BiLSTM, achieved a classification accuracy that exceeded that of the standalone LSTM and BiLSTM classifiers by 0.94% and 0.%, respectively. The model demonstrated superior sensitivity, specificity, and precision, achieving a rate of 91.54% when compared to the other methods listed in Table 1. Additionally, the proposed method resulted in a lower fallout rate compared to other classifiers, indicating its ability to accurately detect and classify instances of interest.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & **Performance Metric** & **Second Name** & **DT** & **SVM** & **KNN** & **BiLSTM** & **LSTM** & **Proposed** \\ \hline \multirow{8}{*}{AliExpress} & True positive rate (TPR) & Sensitivity & 84.94 & 85.02 & 84.85 & 89.72 & 89.55 & 91.54 \\  & True negative rate (TNR) & Specificity & 84.94 & 85.02 & 84.85 & 89.72 & 89.55 & 91.54 \\  & Positive predictive value (PPV) & Precision & 84.94 & 85.02 & 84.85 & 89.72 & 89.55 & 91.54 \\  & Negative predictive value (NPV) & - & 84.94 & 85.02 & 84.85 & 89.72 & 89.55 & 91.54 \\  & False positive rate (FPR) & Fallout & 15.06 & 14.98 & 15.15 & 10.28 & 10.45 & 08.46 \\  & False negative rate (FNR) & - & 15.06 & 14.98 & 15.15 & 10.28 & 10.45 & 08.46 \\  & False discovery rate FDR) & - & 15.06 & 14.98 & 15.15 & 10.28 & 10.45 & 08.46 \\  & Overall accuracy (ACC) & - & 84.94 & 85.02 & 84.85 & 89.72 & 89.55 & 91.54 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison with related work on the AliExpress and Amazon datasets.  
Figure 4: Confusion matrix: where the pale rose color denote the false classification, the pinkish red color is the true classifications, white while the black denote overall accuracy of the classification models.  
In Figure 5, the performance metrics of sensitivity, fallout, and accuracy were compared between decision tree, support vector machine, k-nearest neighbor, BiLSTM, LSTM, and the proposed method. The results demonstrated that the proposed method outperformed all the other models in all performance metrics. This suggests that the proposed model may be the most effective option for the task at hand.  
In Table 2, different ratios of the training and testing data size have been used to investigate the performance of the proposed model. The proposed model, when evaluated on both AliExpress and Amazon data with 50% train and 50% test data, provided an accuracy of 74.65% and 70.01%. The accuracy increased to 91.54% and 90.42% when the training data size was increased to 80%, while 20% is utilized for testing.