## 2 Materials and Methods
### Evaluating Metrics  
Valid metrics assessment should be implemented for managing the performance of artificial intelligence models, whether controlled manually by a user or automatically (in problems governed by optimization algorithms). Accuracy, precision, recall, F1-Score, and root mean squared error can be mentioned as well-known examples.  
Before delving into the analysis of these metrics and presenting the corresponding equations, it is crucial to establish a clear understanding of certain fundamental concepts. Let us consider a scenario where the objective entails the classification of shapes into two distinct categories: \"circle\" and \"cube\".  
The term \"true positive\" (TP) refers to the count of instances that belong to the positive class (i.e., circles) and have been accurately predicted as such. Conversely, the designation \"true negative\" (TN) encompasses the instances belonging to the negative class (i.e., cubes) that have been correctly identified as such in the predictions.  
Conversely, \"false positive\" (FP) signifies the instances that pertain to the positive class (circles) but have been erroneously predicted as belonging to the negative class (cubes). On  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Option** & **Value** & **Option** & **Value** \\ \hline Mini batch size & 12 & Learning rate & \(1\times 10^{-4}\) \\ Maximum epoch & 500 & Validation frequency & 30 \\ Number of hidden layers & 512 & Shuffle & Every epoch \\ \hline \hline \end{tabular}
\end{table}
Table 2: The LSTM network’s hyperparameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Option** & **Value** & **Option** & **Value** \\ \hline Kernel type & Quadratic & Box constraint level & 1 \\ Kernel scale & Auto & Standardize data & True \\ Multiclass method & One-vs-One & Cross-validation folds & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The SVM network’s hyperparameters.  
the other hand, \"false negative\" (FN) denotes the instances that belong to the negative class (cubes) but have been incorrectly identified as members of the positive class (circles).  
Accuracy can be defined as the ratio of the correctly classified samples to the overall number of instances and normally is stated as a percentage; precision, on the other hand, measures the percentage of correctly predicted positive instances among all positive instances that were predicted. It focuses on the reliability of optimistic forecasts and evaluates how well the model does at reducing false positives. The proportion of correctly predicted positive instances among all actual positive instances is measured by recall (also known as sensitivity or true positive rate). It is helpful in applications where false negatives should be minimized because it concentrates on correctly identifying all positive instances.  
The harmonic means of recall and precision is known as the F1-Score. It offers a fair evaluation of both recall and precision, giving each metric the same weight [62].  
Based on the values of TP, TN, FP, and FN, the following formulas provide a mathematical definition for the stated metrics.  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{3}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{4}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{5}\]  
\[\text{F1-Score}=2\times(\text{Precision}\times\text{Recall})/(\text{Precision}+\text{Recall}) \tag{6}\]  
The mentioned metrics will be utilized further in the Results section to represent the designed classification models along with the feature extraction procedure.