# Deep Learning Models Applied to Prediction of 5G Technology Adoption
## 3 Methodology
### Performance Metrics  
The success of implementing 5G using deep learning must be measured across a number of dimensions (including channel metrics, context metrics, cell metrics, and throughput data). Performance metrics are used to evaluate the efficacy of a deep learning algorithm or model. Several performance measures can be used to learn about the efficiency of the models, methods, or processes. By comparing a proposed method or algorithm to existing ones, flaws may be revealed. To evaluate the relative merits of various 5G implementations based on deep learning, we must first dissect each component individually (channel metrics, context metrics, cell metrics, and throughput data). Despite the fact that there are numerous success indicators, the current study used the most widely used performance indicators in the academic research community. As a result, this study explores many performance metrics to evaluate the performance of the experimental analysis carried out. However, since the deep learning algorithms utilised need to be compared in order to find the difference among them, the measurement of error metrics is the crucial measurement considered. Despite the number of measures computed in order to evaluate the performance of various deep learning models, mean squared error (MSE) is adopted to measure how far the estimated value deviates, on average, from the actual value. The predicted value of the squared error is represented by Equation (16):  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^{2} \tag{16}\]  
Root mean squared error (RMSE), with the residuals of the prediction errors, measures how, and shows how closely, the data gravitate toward the optimal line of fit and measure by Equation (17):  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^ {2}} \tag{17}\]  
Furthermore, while determining how closely the predicted values fall along the optimal line of fits at the target values, the coefficient of determination (R\({}^{2}\)) measured by Equation (18):  
\[\text{R}^{2}=\frac{\sum_{i=1}^{n}\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^{2}}{\sum_{ i=1}^{n}\bigl{(}Y_{i}-Y\bigr{)}^{2}} \tag{18}\]  
A confusion matrix provides a concise summary of the prediction output of a technique in relation to specific test data. It is a matrix with two dimensions; the first dimension is indexed by the entity's actual class and the second dimension is indexed by the class that the algorithm assigns to the entity. In this particular use of the uncertainty matrix, there are two classes: one is referred to as the positive class, and the other is referred to as the negative class. According to this interpretation, the four cells that make up the matrix are referred to as true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN), as outlined in the table that follows. Furthermore, accuracy is critical in the evaluation of a deep learning algorithm; one can say that a measurement or calculated value is accurate if and only if it agrees with the real value or meets some other criterion. The extent to which experimental values approximate genuine value is what is meant by \"accuracy\". The accuracy (Acc) is provided as \(\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{T}+\text{TN}}\), while the sensitivity is the \"True positive rate\" which can be referred to as \"Recall (R)\" provided as \(\text{R}=\text{TP}/(\text{TP}+\text{FN})\), and the specificity (S), referred to \"True negative rate\", where \(\text{R}=\text{TN}/(\text{TN}+\text{FP})\). The receiver operating characteristic (ROC) curve has been established as a reliable method for evaluating the performance of both deterministic and probabilistic models [45]. The ROC curve is an all-inclusive and visually appealing summary of prediction accuracy. Its usefulness extends to a variety of contexts and prediction methods. Plotting the model's \"sensitivity\" (X axis) against its \"specificity\" (Y axis) generates the ROC curve.