## 6 Results and Discussion
### Room Identification Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**CWT without Augmentation Using CNN**} & \multicolumn{6}{c}{**Proposed Augmented CWT Using CNN**} \\ \hline \multirow{5}{*}{True labels} & R1 & 9 & 0 & 0 & 0 & 0 & R1 & 21 & 0 & 0 & 0 & 0 \\  & R2 & 0 & 11 & 0 & 0 & 0 & R2 & 0 & 22 & 0 & 0 & 0 \\  & R3 & 1 & 0 & 9 & 0 & 0 & R3 & 0 & 0 & 21 & 1 & 0 \\  & R4 & 1 & 0 & 0 & 11 & 0 & R4 & 0 & 0 & 1 & 21 &Figure 7a-d are presented to compare the performance of a deep learning approach used together with a wavelet. This should provide information on which deep learning approach should be used together with a specific wavelet transform to achieve good classification performance in terms of precision and recall. Figure 7a shows that for the augmented dataset, the Morse wavelet-generated features, when used in the CNN deep learning model, generated the best performance in terms of precision. The worst performance in Figure 7a is exhibited by the Morlet wavelet features used in ANN. Figure 7b shows that for the unaugmented dataset, the Morlet wavelet-generated feature set used with the ANN model offered the best precision. Additionally, the worst was the Morse wavelet dataset applied to the ANN model. The ANN-based Morse precision performance for R4 was 0%, which is an indicator that the the time, R4 was not correctly identified. From the original RSSI data, R4's RSSI signal has a huge similarity with all the other rooms' RSSI signals. This gives us the insight that without augmentation, it is highly likely that the classification of R4 would generate a very minimal precision. Figure 7c,d show the recall performance for the augmented and unaugmented datasets respectively. The results still show that the CNN-based Morse wavelet model performed better in the augmented feature set and the ANN-based Morlet wavelet model performed worst in the un augmented feature set. Generally, on average, the CNN-based Morse wavelet dataset performed best for accuracy, recall, and precision for the augmented dataset, and the ANN-based Morlet was preferable for the unaugmented dataset. Figure 6: Accuracy results for room identification. Table 7(a)-d present the confusion matrices for room identification based on the Morlet wavelet for the different feature set formulations mentioned in this study based on the Morlet wavelet. The best accuracy attained was 94.55% attained by the single RSSI Vector [34] using the CNN. Our proposed augmented CWT achieved 89.1%, which is the same as that attained by the unaugmented CWT. The RSSI feature set [34] is not an augmented feature set, yet it performed better than the augmented feature sets, implying that augmentation is not very effective when the Morlet wavelet is used for feature set generation. The augmented CWT-PSD using ANN also exhibited the same accuracy of 58.8% with the unaugmented PSD-CWT using ANN. This still shows the ineffectiveness of using augmentation when using the Morlet wavelet. Figure 7: Room identification precision (**a**,**b**) and recall (**c**,**d**) results for augmented and unaugmented features respectively.