Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM)

Journal of Big Data

Table 4 Formulations description

\(Accuracy = \frac{{{ }TP + TN}}{{TP + {\text{T}}N + FP + FN}}\)	This was the most important criterion for determining the performance of a classification algorithm, which showed the percentage of the proper classification of the total set of the experimental record
\({\text{Rcall}} = \frac{TP}{{TP + FN}}\)	It showed the ability of the algorithm to accurately detect delay
\(Specificity = \frac{TN}{{FP + TN{ }}}\)	It demonstrated the efficiency of the classifier in the accurate prediction of the lack of delay
\({\text{Precision}} = \frac{TP}{{TP + FP{ }}}\)	It demonstrated the ability of the algorithm to detect the positive categories (i.e., delay)
\({\text{F - measure}} = \frac{{2RecallPrecision{ }}}{{Precision + Recall{ }}}\)	It showed the harmonic mean between accuracy and recall
\(R{\text{MSE}}\sqrt {\mathop \sum \limits_{{{\text{t}} = 1}}^{{\text{n}}} \left( {{\text{y}} - {\text{y}}} \right)^{2} /{\text{n}}}\)	Measuring the accuracy of the predicted rates compared to the correct rates
\({\text{MSE}} = \mathop \sum \limits_{{{\text{t}} = 1}}^{{\text{n}}} \left( {{\text{y}} - {\text{y}}} \right)^{2} /{\text{n}}\)	It was a statistical tool to determine the predictive accuracy in modeling
Balanced_Acc_Test	If the distribution of two datasets in a dataset was not the same, this criterion was used to calculate the accuracy of the introduced method