# Deep Learning Models Applied to Prediction of 5G Technology Adoption
## 4 Presentation of the Results and Discussion
The CNN model stands out because it is able to extract patterns from convolutions, which enables it to produce results that are consistent and accurate. Therefore, it is possible to assert that this model is the most accurate one for estimating the uptake of 5G. Both DR and CNN are modified, but based on the outcomes of the metrics that are presented, it is not possible to determine which of the two is superior. Adoption of 5G may occur despite the fact that erroneous outcomes are possible, despite the fact that models may or may not be forecasting exactly properly. In order to evaluate the models' accuracy in the relevant time periods, the results of the models are presented in Table 2 with no regard assigned to night-time throughput. Table 3 confirms the prior decisions made using Table 3. CNN is the worst model, the DR and LSTM models are overfitting; CNN is the best model; DR and LSTM models are good but not as good as CNN. If one looks at Table 3, they will notice that the CNN model consistently produces better outcomes than the DR model. As a result, the DR model will provide more convincing fits during non-normal times. The results are also unaffected by the fact that we are considering the entire time span. When all factors are taken into account, modelling can be made more resistant to data changes as a result of continuity and expertise. According to the decision made, DR and CNN are the models that are best suited to projecting 5G uptake. Since the models continue to produce the same results regardless of whether the throughput is taken into account, the variations between the two approaches do not produce significant a priori distinctions. Despite the fact that it demonstrates the positive changes that were produced by the DR, CNN, and LSTM models during the training split for both situations, the SVR model is the one that has periods with better accuracy during the times where 5G adoption can be observed, where the mean errors are 0.91% in DR, 0.93 in CNN, and 0.91 in LSTM. This is shown by the fact that the DR model has mean errors of 0.91%, while CNN and LSTM both have mean errors of 0.91. In this section of the study, the researchers present the characteristics of the implementation of 5G in this environment. They also provide general information on the models that were developed, such as the mean errors that were obtained by using the cross-validation method during each of the stages of training, validation, and testing. In order to find the optimal solution, the cross-validation method is used to evaluate and contrast the mean errors of each of the sub-splits. Specifically, this is due to the common mistakes that are made (see Table 4). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Partition** & **Metric** & **DR** & **LSTM** & **CNN** \\ \hline \multirow{3}{*}{Train} & MSE & 0.03 & 0.06 & 0.06 \\  & RMSE & 0.173 & 0.245 & 0.245 \\ \multirow{2}{*}{Test} & MSE & 0.04 & 0.07 & 0.05 \\  & RMSE & 0.2 & 0.264 & 0.22 \\ \multirow{2}{*}{Train} & R\({}^{2}\) & 0.91 & 0.93 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average error obtained with the first cross-validation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Partition** & **Metric** & **DR** & **LSTM** & **CNN** \\ \hline \multirow{3}{*}{Train} & MSE & 0.006 & 0.0042 & 0.0068 \\  & RMSE & 0.0775 & 0.0649 & 0.0825 \\ \multirow{2}{*}{Test} & MSE & 0.016 & 0.0042 & 0.0068 \\  & RMSE & 0.126 & 0.065 & 0.0825 \\ \multirow{2}{*}{Train} & R\({}^{2}\) & 0.87 & 0.72 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Averaged error obtained with the second cross-validation. Despite the fact that LSTM models appear to have a high degree of precision, the quality of the data they generate is quite low. Using the root-mean-squared error (RMSE) measure, it is possible to demonstrate that the forecasts for the validation set and test set are significantly different from the forecasts for the training set. When compared side-by-side, the validation set and the test set reveal a number of intriguing discrepancies.