## 6 Results and Discussion  
_Metrics and Evaluation of Performance_  
Python was used to create deep learning models, along with libraries like TensorFlow 2. Three measures--_MSE_, root _MSE_ (_RMSE_), and mean absolute error (_MAE_)--were used to calculate the difference between the estimated location and the actual value in order to evaluate the model's performance. According to Section 4, the localization error and prediction accuracy of the model on the datasets were assessed. By averaging the absolute values of the errors, _MAE_ was calculated. \[MAE=\frac{1}{\eta}{\sum_{i=1}^{n}}|PS_{i}-SA_{i}| \tag{13}\] \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{**Virtual Locations: Experiment**} \\ \hline  & Zone 1 = (0.75, 2.25) & \multicolumn{2}{c}{Zone 2 = (2.25, 2.25)} \\ \hline \(P_{1,1}\) & \((x_{1,1},\ y_{1,1})=(0.375,2.625)\) & \(P_{2,1}\) & \((x_{2,1},\ y_{2,1})=(1.875,2.625)\) \\ \(P_{1,2}\) & \((x_{1,2},\ y_{1,2})=(1.125,2.625)\) & \(P_{2,2}\) & \((x_{2,2},\ y_{2,2})=(2.625,2.625)\) \\ \(P_{1,3}\) & \((x_{1,3},\ y_{1,3})=(0.375,1.875)\) & \(P_{2,3}\) & \((x_{2,3},\ y_{2,3})=(1.875,1.875)\) \\ \(P_{1,4}\) & \((x_{1,4},\ y_{1,4})=(1.125,1.875)\) & \(P_{2,4}\) & \((x_{2,4},\ y_{2,4})=(2.625,1.875)\) \\ \hline  & Zone 3 = (0.75, 0.75) & \multicolumn{2}{c}{Zone 4 = (2.25, 0.75)} \\ \hline \(P_{3,1}\) & \((x_{3,1},\ y_{3,1})=(0.375,1.125)\) & \(P_{4,1}\) & \((x_{4,1},\ y_{4,1})=(1.875,1.125)\) \\ \(P_{3,2}\) & \((x_{3,2},\ y_{3,2})=(1.125,1.125)\) & \(P_{4,2}\) & \((x_{4,2},\ y_{4,2})=(2.625,1.125)\) \\ \(P_{3,3}\) & \((x_{3,3},\ y_{3,3})=(0.375,0.375)\) & \(P_{4,3}\) & \((x_{4,3},\ y_{4,3})=(1.875,0.375)\) \\ \(P_{3,4}\) & \((x_{3,4},\ y_{3,4})=(1.125,0.375)\) & \(P_{4,4}\) & \((x_{4,4},\ y_{4,4})=(2.625,0.375)\) \\ \hline \end{tabular}
\end{table} 
Table 2: Virtual Locations for the Experiment. Figure 6: Correlation between RSSI and log(distance). The dash line is a linear regression of RSSI against log(distance). According to Equation (14), \(MSE\) is the average of the squared differences between the estimated and actual values. The average size of the prediction mistakes is shown by the following equation:  
\[MSE=\frac{1}{n}{\sum_{i=1}^{\Pi}}{(PS_{i}-SA_{i})}^{2} \tag{14}\]  
The average difference between the expected and actual values is quantified by the statistic known as \(RMSE\), which can be used to assess the accuracy of a model's predictions. This value gives a thorough evaluation of the model's performance and is obtained by calculating the square root of the residual. In several domains, \(RMSE\) is frequently used to evaluate the difference between expected and actual results. \[RMSE=\sqrt{\frac{\sum_{i=1}^{n}{(PS_{i}-SA_{i})}^{2}}{n}} \tag{15}\]  
The number of experiments performed is represented by \(n\) in the equation. The prediction of the final location by the proposed algorithm is denoted as \(PS_{i}\), while the actual final location in the i-th experiment is referred to as \(SA_{i}\). The performance of the best-fitting model was evaluated by incorporating batch normalization into the MLP regression algorithm and analyzing the impact of various numbers of hidden layers. The accuracy of the model predictions was determined by computing the ratio of the correct predictions to the total predictions. The optimization process was performed using the Adam optimizer, and all activation functions were performed using it. The results indicated that the SELU activation function had a lower location error (0.65 m compared to the other activation functions (ReLU, Softplus, and ELU)), thus demonstrating its superior performance. The decay curves of the loss function for various epochs of the training and validation datasets are shown in Figure 7. The stability of the curve was observed after 60 epochs, but the observations were extended to 140 epochs for further confirmation of the results. The proposed model was used to determine the quadrants in sequential steps and the error distance for the target coordinates.