## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classified** & \multicolumn{8}{c}{**Real SCT**} \\ \hline
**SCT** & **0** & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline
0 & 1.0 & 0.0317 & 0 & 0 & 0 & 0 & 0 \\
5 & 0 & 0.9384 & 0.0013 & 0 & 0 & 0 & 0 \\
10 & 0 & 0.0300 & 0.9987 & 0 & 0 & 0 & 0 \\
15 & 0 & 0 & 0 & 1.0 & 0 & 0 & 0 \\
20 & 0 & 0 & 0 & 0 & 1.0 & 0.0034 & 0 \\
25 & 0 & 0 & 0 & 0 & 0 & 0.9966 & 0.0044 \\
30 & 0 & 0 & 0 & 0 & 0 & 0 & 0.9956 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & _100_ & _500_ & _1000_ & _5000_ & _8000_ \\ \cline{2-6}
**SCTs** & _Mean (min,max)_ & _Mean (min,max)_ & _Mean (min,max)_ & _Mean (min,max)_ & _Mean (min,max)_ \\ \hline
0 & 0.9889 (0.94, 1.0) & 0.9998 (0.99, 1.0) & 1.0 (1.0, 1.0) & 1.0 (1.0, 1.0) & 1.0 (1.0, 1.0) \\
5 & 0.8758 (0.78, 0.93) & 0.9310 (0.75, 1.0) & 0.9460 (0.77, 1.0) & 0.9439 (0.65, 1.0) & 0.9632 (0.82, 1.0) \\
10 & 0.9811 (0.91, 1.0) & 0.9987 (0.99, 1.0) & 0.9990 (0.99, 1.0) & 0.9996 (0.99, 1.0) & 0.9999 (0.99, 1.0) \\
15 & 0.9997 (0.99, 1.0) & 1.0 (1.0, 1.0) & 1.0 (1.0, 1.0) & 1.0 (1.0, 1.0) & 1.0 (1.0, 1.0) \\
20 & 0.9980 (0.99, 1.0) & 1.0 (1.0, 1.0) & 1.0 (1.0, 1.0) & 1.0 (1.0, 1.0) & 1.0 (1.0, 1.0) \\
25 & 0.9918 (0.97, 1.0) & 0.9947 (0.98, 1.0) & 0.9958 (0.98, 1.0) & 0.9974 (0.97, 1.0) & 0.9983 (0.98, 1.0) \\
30 & 0.9929 (0.96, 1.0) & 0.9969 (0.98, 1.0) & 0.9977 (0.98, 1.0) & 0.9988 (0.99, 1.0) & 0.9995 (0.99, 1.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean, minimum, and maximum accuracy values of SCT classification with various data window sizes. The box and whisker plots shown in Figure 8 show the comparison among the mean accuracies in the classification of SCTs using data window sizes of 100, 500, 1000 and 5000 samples. These plots show the distribution of the obtained accuracies. For instance, the higher accuracy distribution observed for the condition of 5 SCTs indicates that the obtained values are more dispersed and, consequently, more difficult to classify in the system since their range of possibilities is bigger. This issue can be seen in the plot for the case of 100 samples, since some outliers (red marks) for the 10 SCT condition overlap with the range of the 5 SCTs. The 8000 and 5000 window sample plots are similar. The QSA method is implemented using a feedforward and fully connected NN classification to compare it with the results obtained using KNN. The NN is applied with Bayesian optimization, 30 objective function evaluations, an activation function for classification by sigmoid function, and 10 values in each dimension. The results are presented in Table 4, where the KNN method shows higher accuracy than the NN method for all data window sizes and classifications. KNN uses fewer resources than NN; hence, the QSA method using KNN is the best option with high accuracy, low window samples, and low processing. In the same way, the DT classification is applied to the QSA method. It presents similar accuracies with a low classification process as is shown in Table 5, however, there are cases where the DT method presents the best results as in the case of 5000 samples. The KNN classification method presents the best accuracy using 100, 500, and 1000 window