## 4 Results and Discussion
Figure 15: Average regression performance of the LM for Monday. As can be seen from Tables 5 and 6, the average correlation performance ranged from 0.826 to 0.982 for LM, while ranging between 0.854 to 0.982 for the BR method. The higher and closer the respective R values are to 1, the better the fit between the actual data and the predicted data; both methods have similar regression performances. The highest average R values for all data were obtained for Monday for both methods, and the lowest was seen for Sunday. The fitting performances for these days are shown in Figure 16a,b, where the data represent target data for Monday and Sunday. As can be concluded from the figures, the reason for the best fitting of Monday's data is the smooth fluctuations in the measured RF-EMF levels, whereas the deep fluctuations yield lower fitting performance for Sunday. When all of the data was analyzed, the developed LM and BR models showed similar fitting performances and could accurately predict the RF-EMF levels in the medium. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Days**} & \multicolumn{3}{c}{**R Value \({}^{*}\)**} \\ \cline{2-4}  & **Training** & **Testing** & **All** \\ \hline Mon. & 0.982 & 0.978 & 0.981 \\ Tue. & 0.975 & 0.954 & 0.972 \\ Wed. & 0.973 & 0.965 & 0.972 \\ Thu. & 0.973 & 0.962 & 0.971 \\ Fri. & 0.968 & 0.956 & 0.966 \\ Sat. & 0.929 & 0.944 & 0.932 \\ Sun. & 0.863 & 0.854 & 0.860 \\ \hline \hline \multicolumn{4}{l}{\({}^{*}\)Average values.} \\ \end{tabular}
\end{table} 
Table 6: R values for the BR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Days**} & \multicolumn{3}{c}{**MSE \({}^{*}\)**} & \multirow{2}{*}{**Epoch \({}^{*}\)**} & \multirow{2}{*}{**Performance \({}^{*}\)**} \\ \cline{2-2} \cline{4-5}  & **Training** & **Testing** & & \\ \hline Mon. & \(2.28\times 10^{-3}\) & \(4.51\times 10^{-3}\) & 473.18 & \(2.95\times 10^{-3}\) \\ Tue. & \(3.41\times 10^{-3}\) & \(5.72\times 10^{-3}\) & 154.34 & \(4.11\times 10^{-3}\) \\ Wed. & \(3.04\times 10^{-3}\) & \(5.63\times 10^{-3}\) & 289.98 & \(3.82\times 10^{-3}\) \\ Thu. & \(3.66\times 10^{-3}\) & \(4.02\times 10^{-3}\) & 120.10 & \(3.77\times 10^{-3}\) \\ Fri. & \(2.60\times 10^{-3}\) & \(4.94\times 10^{-3}\) & 344.52 & \(3.30\times 10^{-3}\) \\ Sat. & \(3.19\times 10^{-3}\) & \(5.73\times 10^{-3}\) & 277.84 & \(3.96\times 10^{-3}\) \\ Sun. & \(2.82\times 10^{-3}\) & \(3.76\times 10^{-3}\) & 335.80 & \(3.10\times 10^{-3}\) \\ \hline \hline \multicolumn{4}{l}{\({}^{*}\)Average values.} \\ \end{tabular}
\end{table} 
Table 4: Performance evaluation of the BR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Days**} & \multicolumn{3}{c}{**R Value \({}^{*}\)**} \\ \cline{2-5}  & **Training** & **Validation** & **Testing** & **All** \\ \hline Mon. & 0.981 & 0.982 & 0.977 & 0.980 \\ Tue. & 0.982 & 0.976 & 0.943 & 0.975 \\ Wed. & 0.978 & 0.979 & 0.941 & 0.974 \\ Thu. & 0.975 & 0.976 & 0.960 & 0.972 \\ Fri. & 0.977 & 0.962 & 0.959 & 0.971 \\ Sat. & 0.939 & 0.881 & 0.894 & 0.972 \\ Sun. & 0.885 & 0.826 & 0.853 & 0.868 \\ \hline \hline \multicolumn{4}{l}{\({}^{*}\)Average values.} \\ \end{tabular}
\end{table} 
Table 5: R values for the LM. The prediction performance of the LM model for Monday's data was calculated, and the MSE was obtained as 1.86 \(\times\) 10\({}^{-5}\). The RMSE is 4.30 \(\times\) 10\({}^{-3}\), and the accuracy percentage is 99.6%. For the BR model, the corresponding values are 1.53 \(\times\) 10\({}^{-4}\), 12.3 \(\times\) 10\({}^{-3}\), and 98.8%, respectively. A similar assessment was performed on Sunday's data, and the results were 7.25 \(\times\) 10\({}^{-4}\), 26.9 \(\times\) 10\({}^{-3}\), 93.8% for the LM, respectively, and 8.83 \(\times\) 10\({}^{-4}\), 29.7 \(\times\) 10\({}^{-3}\), and 93.2% for the BR model, respectively. It can be concluded from the results that the proposed ANN models' prediction accuracy is significantly better than similar works [17; 25; 31; 32; 33] in terms of the MSE, RMSE, and accuracy percentage.