## 4 Results and Discussions
### Training Algorithm and Hidden Layers Number Impact  
The learning algorithm and hidden layers number also significantly impact the success of the neural network in coming up with a healthy predictive model. Therefore, deciding on the number of the hidden layer is one of the most important issues that come up while investigating the neural network architecture for predictive modelling and data mining. Using too many hidden layers can result in poor generalization and complex neural network training. According to the work [31], two hidden layers, combined with m output neurons, are adequate for a neural network to learn N data samples and produce negligibly small errors.  
Here, the impact of many learning algorithms has been studied with one, two and three hidden layers numbers. Interestingly, results reveal that the two-layered network is superior to one-layered and three-layered layer network for all the 12 learning algorithms investigated. Interestingly, results show that the neural network architecture trained using lm (i.e., the Levenberg-Marquardt training algorithm) with two hidden-layer sizes and logsig-transit transfer function gave the best performance. Table 5 displays detailed network training/testing error statistics results and hidden layer numbers for each learning algorithm.  
Performance of Grid Search Algorithm and Bayesian Optimisation Search Algorithm for Hyperparameter Tuning  
The hyperparameter tuning process has a weighty influence on neural network learning performance. Given the computational resources requisite, the hyperparameters of high relevance receive superior usage in the hyperparameter tuning process. Hyperparameters with a more robust influence on weights are more effective during neural network training. Thus, the appropriate choice of hyperparameters selected for neural network model training influences the network training and performance.  
As displayed in Table 6, the proposed MLP neural network model results with grid search algorithm-based hyperparameter tuning (optimization) are compared with those obtained using the traditional Bayesian optimization search-based hyperparameter tuning approach for path loss data predictive analysis using location one as a case study. We have reported the results attained for lm and br learning for brevity. While the grid search-based hyperparameter tuning performs an in-depth and comprehensive search on the hyperparameters in a stepwise manner as set specified by users with limited search space, the Bayesian search-based hyperparameter tuning performs a sequential-based search on the hyperparameters via several trials, without the user having preliminary information of  
Figure 8: Root Mean Error performance statistics with one, two and three layers of training.  
the hyper-parameters distribution. From the results in Table 6, it is clear that the proposed MLP neural network model with grid search algorithm-based hyperparameter tuning outperforms the ones obtained using Bayesian Optimization search-based hyperparameter tuning. Next, Section 4.5 is to apply the proposed MLP neural network model with grid search algorithm-based hyperparameter tuning for detailed predictive path loss analysis across the three study locations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Testing**} & \multicolumn{4}{c}{**Overall Performance**} \\ \hline \
\begin{tabular}{c} **Training** \\ **Algorithm** \\ \end{tabular} &
\begin{tabular}{c} **No of Hidden** \\ **Layers** \\ \end{tabular} & **MSE** & **R** & **MSE** & **R** & **MAE** & **STD** & **MSE** & **R** \\ \hline \multirow{3}{*}{lm} & 1 & 7.65 & 0.9359 & 11.55