# Deep Learning Models Applied to Prediction of 5G Technology Adoption
## 4 Presentation of the Results and Discussion
Every model demonstrates some degree of predictive power, as indicated by the AUC values of the area, which are (0.986) and (0.927) for training and validation, respectively. On the other hand, the performance can be considered to be the best possible. When validating a model, the AUC value calculated based on the training dataset cannot be considered separately because this is the case. The AUC values for the validation dataset were not used in the construction of the model; however, they ought to be taken into consideration while verifying the model. The findings of the ROC curve study show that the model with two parameters also has the greatest AUC value (0.867) with a high weight, followed by the model with a combination of all the parameters. This was discovered through more research (0.851). That is, as shown in Figure 5, there is a decline in the ROC curve and the AUC value based on the training dataset, and Figure 6 shows a similar decrease for the validation dataset. In other words, the value of the ROC curve has decreased. Figure 1: The ROC area of the first trained data: AUC (0.954). Figure 2: The ROC area of the validation data: AUC (0.989). Figure 4: The ROC area of validation data: AUC (0.927). Figure 5: The ROC area of all the modified trained data: AUC (0.851). Figure 3: The ROC area of all the trained data: AUC (0.986). The findings of this study provide credence to the contention that it is critical to emphasize prediction on the manufacturing of 5G networks as well. This is the situation because it is extremely important to anticipate the adoption of 5G technology, but relatively few of the recommended solutions have been put into practice [46; 47; 48; 49; 50]. In the present investigation, a deep learning method is utilised to investigate the numerous facets of 5G technology and to estimate the degree to which it will be generally accepted. Some of the components that factor into this are channel measurements, context metrics, cellular metrics, and throughput data. Indicators that these regions are the most likely to adopt 5G can be found there. Deep reinforcement (DR), long short-term memory (LSTM), and a convolutional neural network (CNN) were the three distinct deep learning models that were put into use. Deep learning has been completely ignored in virtually all of the earlier research that has been carried out on the subject of 5G rollout. On the other hand, they have placed a higher priority on technical research, while others have stressed the significance of estimating the size of the new 5G mobile market and analysing the reliability of forecasts in the 5G service sector. The vast majority of the earlier studies that were conducted on the topic of the installation of 5G did not make use of deep learning, which is the key reason why this is the case. The findings of the prediction indicate that although LSTM models may give the impression of accuracy, the quality of the data that they supply is in fact rather poor. This is the case, in spite of the fact that the models provide the impression of being quite precise. An examination of the root-mean-squared error (RMSE) revealed that the visual appeal of results generated by the DR model and the CNN model are on par with one another. The findings of this study lend support to the notion that it is essential to provide equal weight to different projections on the expansion of 5G networks. This is due to the fact that accurate forecasts regarding the adoption of 5G technology are required, but either very few or none of the strategies that are advised for achieving this goal have been put into practice. This was spurred by the requirement to produce projections regarding the expansion of 5G networks.