Table 2.

Evaluation criteria used in the papers on deep learning and neural network

Formula	Method Name	Evaluation Method
$\frac{\mathit{TP}+\mathit{TN}}{\mathit{TP}+\mathit{TN}+\mathit{FP}+\mathit{FN}}$	Accuracy(ACC)	- The ability of a measurement to match the actual (true) value of the quantity being measured [42].
$\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}}$	Precision(P)	Precision is the most critical performance measure in this field to evaluate the model that focuses on false positives (FP(or positive) prediction [42].
$\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}}$	Recall or Sensitivity	- The recall usually utilized to evaluate the model focuses on false negatives (FN). It shows how many samples are truly positive among all the positive examples.
$\mathrm{F}1=2\ast \frac{\text{precision}\ast \text{recall}}{\text{precision}+\text{recall}}$	F1 score or F-measure	Harmonic average of two criteria of precision and recall The best state is one, and the worst condition is zero.
Curve	AUC and ROC(Receiver Operating Characteristic)	- Based on the ROC, the curve area under the curve AUC (Area under the curve) can calculate the overall performance measures, And gives us information about the model’s ability to detect classes. The model will be better if the AUC is higher [63].
$\mathit{\text{MAPE}}=\frac{1}{N}\sum _{K=1}^{K=N}{\mathit{AP}}_K$	Map(Mean Average Precision)	A score widely used in ranking problems [44] AP is class K, and N is the number of classes [1].
$\mathit{MRR}=\frac{1}{N}\sum _{I=1}^N\frac{1}{\mathit{rank}i}$	Mean Reciprocal Rank (MRR)	Mean Reciprocal Rank is a statistical criterion for evaluating each process that provides a list of possible answers to a sample of questions sorted by probability of accuracy [76].
LR	Learning Rate	The learning rate is a configurable hyperparameter used in training neural networks with a positive value, mostly between 0.0 and 1.0 [46].
MSE=$\frac{1}{N}\sum {\left(Y-\widehat{Y}\right)}^2$	mean squared error (MSE)	- In statistics, the MSE estimate measures the average of the squares of the errors, that is, the average squared difference between the estimated values and the actual value [16, 20].
$\mathit{\text{RMSE}}=\sqrt{\frac{1}{N}\sum {\left(Y-\widehat{Y}\right)}^2}$	root mean squared error (RMSE),	Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors) [1, 16].
$\mathit{MRR}=\frac{1}{N}\sqrt{\mid Y-\widehat{Y}}\mid$	mean absolute error (MAE)	Mean Absolute Error (MAE) is a public metric used to amount accuracy for continuous variables [43].