## 6 Results and Discussion
### Room Identification Results  
Table 6 shows the confusion matrices for room identification based on the Morse wavelet. Table 6a shows the performance exhibited by the CWT features using CNN with an accuracy of 92.73%. Table 6b shows the proposed augmented approach using CNN with an accuracy of 97.3%. The proposed approach exhibits a superior performance because of the augmented features generated by addition of Gaussian noise to the original RSSI signal before CWT transformation. The results in Table 6a are attributed to a dataset with 255 images, and the augmented dataset whose results are in Table 6b was of size 450 images. Based on the accuracy results, the better performance of the augmented dataset implies that the Gaussian noise introduces more distinguishable features which are desirable for an RSSI classification. Table 6c represents the proposed CWT-PSD without data augmentation using ANN with an accuracy of 88.2%. Table 6d shows the proposed augmented CWT-PSD using ANN with an accuracy of 70.6%. Contrary to the image-based CNN classification in Table 6b, the augmented PSD using ANN did not perform better than the CWT-PSD without augmentation represented by the confusion matrix in Table 6c. This phenomenon reveals that augmented images provide more distinguishable patterns compared to augmented numerical data. Studies such as [35] show that augmentation greatly increases the performance of CNN-based classification. \begin{table}
\begin{tabular}{c c} \hline \hline
**Measure** & **Description** \\ \hline Accuracy & The percentage of correctly predicted samples out of the total number of samples. \\ Precision & is the fraction of relevant instances among the retrieved instances. \\ Recall & is the fraction of the total amount of relevant instances that were actually retrieved \\ Prediction error & A percentage measure of the number of wrongly predicted positions given the total number of positions. \\ Localization error & The Euclidean distance between the actual and predicted position. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics. The dataset size of the proposed CWT-PSD without data augmentation using ANN was 255 vectors, and that of the augmented CWT-PSD was 450. Despite the fact that a huge dataset increases classification performance, a huge dataset can also degrade classification performance, especially if the augmented data cause a huge change in the distribution of the data, causing complexity in separating the data. This analysis implies that the Gaussian noise is not the best for augmenting numeric data in a classification problem. Table 6e shows the performance of the single RSSI vector in [34] with CWT as the feature extraction approach with an accuracy of 95.7%. Table 6f shows the performance of the single rss vector in [34] with FFT as the feature extraction approach with an accuracy of 87.3%. The accuracy results for the CWT features compared to the FFT features in the single RSS vector [34] approach are better. This points to the ability of CWT to better distinguish features in different classes. Generally, the proposed augmented CWT feature set performed better than all the other mentioned feature sets (Table 6). It is also clear that the CNN model outperformed the ANN in classification. The general results in Figure 6 also show that the Morse wavelet provides a better feature set compared to the Morlet wavelet. For the rest of the figures in this article, we use the acronyms \"A, B, C, D, E, F, G\" to represent the CNN-based Morse wavelet-generated feature set, the CNN-based Morlet wavelet-generated feature set, the ANN-based Morse wavelet-generated feature set, the ANN-based Morlet wavelet-generated feature set, the CNN-RSSI [34] Morse-based feature set, the CNN-RSSI [34] Morlet-based feature set, and the CNN-RSSI [34] FFT-based feature set.