# Deep Learning Models Applied to Prediction of 5G Technology Adoption
## 4 Presentation of the Results and Discussion  
The deep learning algorithm was implemented on Google Colab using Python libraries. In particular, Keras and TensorFlow were utilised. Several iterations of the algorithms were carried out. The outcomes of the DR, LSMT, and CNN that were proposed were evaluated and compared. Both mean squared error and root mean squared error were used as performance indicators in order to evaluate how well the algorithms performed. Keras and TensorFlow are the software platforms that are used for the library. This is due to the fact that both TensorFlow and Keras are open-source software platforms, and Tensor is written in both C++ and Python. Python, the language in which The Odyssey is written, lends the pair an air of familiarity; in addition to their popularity, both are frequently employed in academic contexts. Keras is a tool that can be used on the TensorFlow backends to easily define deep learning models. Regarding the hardware, the type of CPU used is a GPU. The system's specifications include Windows 11 (64 bits), a 12 Gen Intel(r) CoreTM i5-12500 (18 MB cache, 6 cores, 12 threads, 3.00 GHz to 4.60 GHz Turbo, 65 W), 8 GB, 1 \(\times\) 8 GB, DDR4. The insights associated with why the proposed approach performed much better than the existing methods lie in determining whether or not the algorithms were effective by applying them to the complete datasets. The outcomes were reviewed by contrasting the DR, LSTM, and CNN approaches that were proposed. The following configuration is used by the deep network algorithms: 60 epochs, one with various steps for each set of data for each epoch, and four hidden layers for the DR, CNN, and LSTM models, respectively. The optimal ratio for the data partitioning between training and testing is 70:30. The results of the investigation, which included carrying out the experiment with each of the models, are shown in Table 1, which summarises the findings and indicates how successful the proposed R, LSTM, and CNN models were. Consequently, when compared with other studies [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26], the MSE and RMSE achieved by the proposed R, LSTM, and CNN models were the lowest. This indicates that DR, LSTM, and CNN have the best performance when it comes to predicting and preventing the adoption of 5G technology. Table 1, which includes DR, LSTM, and CNN, shows that the MSE for DR is 0.000064, and the RMSE is 0.0080. CNN has an MSE of 0.000041 and an RMSE of 0.0071, and LSMT has an MSE of 0.000066 and an RMSE of 0.0074. In light of the fact that the mean errors obtained through the application of the cross-validation method in each of the splits--training, validation (dev), and testing--are detailed here, the characteristics of the 5G adoption in the present circumstance as well as the general information of the generated models are also detailed here. This is done on the basis of the fact that the mean errors acquired by utilising the cross-validation technique in each of the splits, specifically, are high (see Table 2). In addition, despite the fact that LSTM models appear to produce accurate findings, the quality of these outcomes is poor. With the use of the RMSE measure, it is possible to demonstrate that the forecast in the validation set, as well as in the test set, has a significant gap in comparison to the training set. Because of this, it is possible to reach the conclusion that the convergence of these models is insufficient due to the presence of overfitting in the findings. Because of this, these models cannot be used to make accurate predictions about how quickly 5G will catch on. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MSE** & **RMSE** \\ \hline DR & 0.000064 & 0.0080 \\ CNN & 0.000041 & 0.0071 \\ LSTM & 0.000066 & 0.0074 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of the first analysis. Finally, the results that the other two models--DR and CNN--produce are extremely satisfactory. These are compatible with every set.