## 5 Results
### Accuracy  
Figure 8 represents results of accuracy using raw data with machine learning models and the log distance path loss model (LDPL) [43; 44]. The LDPL is a common method to measure distance from the signal transmitters with their RSSI value. As a result, machine learning models have better accuracy than the LDPL model. This is because LDPL is based on the assumption that RSSI values decrease with increasing distance. Because the sources that interfere with the signal propagation make sure that the RSSI value may not decrease as the distance increases, a decrease in the accuracy of the LDPL model occurs.  
Figure 8: Accuracy for each classification type when using raw data according to door material.  
Figure 7: Confusion matrix when using MLP and KFAE + DAE data from wood door. The red rectangles indicate that the accuracy using pre-processing has improved compared to the accuracy using raw data at specific proximity levels.  
Deep learning models such as MLP and 1D-CNN generally have higher accuracy than SVM. Additionally, it is confirmed that the accuracy varies depending on the material of the door. Next, we show the accuracy of proximity detection using the proposed pre-processing and ensemble.  
Figures 9a and 10a show proximity accuracy using the ensemble and the error reduction rate compared with using raw data when the steel door is between the scanner and the advertiser. As a result, MLP and 1D-CNN have better classification performance than SVM. Also, ensembles can improve accuracy. It was seen that the ensemble can improve accuracy regardless of the type of classifier. Likewise, Figures 9b and 10b represent the accuracy and the error reduction rate when the wood door is between the scanner and the advertiser. Also, MLP and 1D-CNN show better classification performance than SVM. In the case of MLP and 1D-CNN, the ensemble can improve accuracy. However, in the case of SVM, the accuracy was seen to decrease. The glass door between the scanner and the advertiser produces similar results as the wood door. Figures 9c and 10c represent the accuracy and the error reduction rate when the wood door is between the scanner and the advertiser. In this case, MLP and 1D-CNN show higher accuracy than SVM, and it was confirmed that accuracy improved when the ensemble was used. However, the accuracy of SVM is decreased using the ensemble.  
As an aspect of accuracy, deep learning models have better performance than machine learning models, regardless of the environment. Additionally, it has been confirmed that ensemble provides good effects in deep learning models such as MLP and 1D-CNN. Since the ensemble process uses the results of eight classifiers, the performance of the classifiers is also important. Although the accuracy of a classifier using pre-processed data is lower than that of a classifier using raw data, we confirmed in the previous section that the accuracy of classifying a specific distance can be high.  
Figure 10: Error reduction rate of ensemble compared with the model using raw data. (**a**) Steel door. (**b**) Wood door. (**c**) Glass door.  
Figure 9: Proximity detection accuracy applied by ensemble for each classification model types. (**a**) Steel door. (**b**) Wood door. (**c**) Glass door.