Table 4.

Performance results of the CNN model compared to other conventional classifiers, using random cross validation executed 5 times (we report the mean and standard deviation of all executions).

	Accuracy (%)1	Precision (%)1	Recall (%)1	f1-score (%)1	Number of parameters2	Computational performance
						Train (s)	Test (ms)
Proposed CNN model	$\begin{array}{c}98.43\hfill \\ (\sigma =0.88)\hfill \end{array}$	$\begin{array}{c}98.40\hfill \\ (\sigma =0.89)\hfill \end{array}$	$\begin{array}{c}98.40\hfill \\ (\sigma =0.89)\hfill \end{array}$	$\begin{array}{c}98.40\hfill \\ (\sigma =0.89)\hfill \end{array}$	$\begin{array}{c}2968.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}16.38\hfill \\ (\sigma =1.08)\hfill \end{array}$	$\begin{array}{c}4.32\hfill \\ (\sigma =0.38)\hfill \end{array}$
Multi-Layer Perceptron	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}1527.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}4.08\hfill \\ (\sigma =0.31)\hfill \end{array}$	$\begin{array}{c}2.77\hfill \\ (\sigma =2.19)\hfill \end{array}$
SVC (linear)	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}y114.00\hfill \\ (\sigma =3.74)\hfill \end{array}$	$\begin{array}{c}2.84x{10}^{-3}\hfill \\ (\sigma =1.70x{10}^{-3})\hfill \end{array}$	$\begin{array}{c}0.36\hfill \\ (\sigma =0.16)\hfill \end{array}$
SVC (polynomial)	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}y100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}320.00\hfill \\ (\sigma =19.75)\hfill \end{array}$	$\begin{array}{c}2.09x{10}^{-3}\hfill \\ (\sigma =6.38x{10}^{-4})\hfill \end{array}$	$\begin{array}{c}0.40\hfill \\ (\sigma =0.33)\hfill \end{array}$
SVC (RBF)	$\begin{array}{c}99.22\hfill \\ (\sigma =1.57)\hfill \end{array}$	$\begin{array}{c}99.40\hfill \\ (\sigma =1.20)\hfill \end{array}$	$\begin{array}{c}99.20\hfill \\ (\sigma =1.60)\hfill \end{array}$	$\begin{array}{c}99.20\hfill \\ (\sigma =1.60)\hfill \end{array}$	$\begin{array}{c}332.00\hfill \\ (\sigma =23.37)\hfill \end{array}$	$\begin{array}{c}1.95x{10}^{-3}\hfill \\ (\sigma =3.47x{10}^{-4})\hfill \end{array}$	$\begin{array}{c}0.28\hfill \\ (\sigma =7.56x{10}^{-2})\hfill \end{array}$
RFC	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}100.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}10.00\hfill \\ (\sigma =0.00)\hfill \end{array}$	$\begin{array}{c}1.50x{10}^{-2}\hfill \\ (\sigma =1.31x{10}^{-3})\hfill \end{array}$	$\begin{array}{c}1.92\hfill \\ (\sigma =0.63)\hfill \end{array}$

¹Classification metrics in the test set. In the training set, the classifiers have achieved a score of 100.00% ($\sigma =0.00$) in all metrics.

²For the CNN model and the MLP it is the number of learnable parameters. For SVC's it is the number of coefficients. For the RFC it is the number of trees in the forest.