# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 3 Results
### BCI-Based Control  
In order to verify the correctness of the signal classification, five trials were performed in which data with changed learning values were given, and the model was trained anew each time. Training parameters such as batch size and epoch numbers were the same. The batch size was 128 and the epoch number was 50. Training was stopped earlier if trained accuracy was not improved by 0.1% since the previous epoch. The data were given in random layouts and in a random order. A neural network was created to classify the data presented (Figure 11). However, when attempting to classify a larger set, overtraining occurred, and its effectiveness dropped to 0. A unitary approach was therefore tried, so a network was trained for each individual. The data for this network were flattened, and the arrays were merged together. In effect, they were rearranged so that from one part of the action, all the channels were merged into one. To avoid memory overload, the channels were restricted to those in the frontal lobe, i.e., FPZ, FP1, FP2, AF7, and AF3. The highest value achieved was 73.49%. The mean values of this classification were 50.265% \(\pm\) 10.94%, where the minimum was 23.29%. The results of this network are presented in the Figure 12.  
In order to improve performance, a one-dimensional spliced network was created, and the accuracy achieved for 50 data sources is shown in Table 3. It had a better chance of improving classification performance because of its architecture. The shape of the input data was better suited to this type of network, as shown in Figure 13.  
Figure 11: Neural network architecture. Figure 12: Values achieved by neural network. \begin{table}
\begin{tabular}{c c} \hline
**No.** & **Value** \\ \hline
1 & 55.82\% \\
2 & 48.31\% \\
3 & 51.65\% \\
4 & 43.45\% \\
5 & 50.95\% \\ \hline Average & 50.04\% \\ \hline \end{tabular}
\end{table} 
Table 3: Data classification for 50 data sources. Another network (Figure 16) was created to compare another architecture of a similar network. The results are presented as follows (Table 5) for 50 and for 100 data sources (Table 6). The achieved performance was slightly worse than the previous architecture. Another model (Figure 17) was presented to show whether it would do a better job of classifying EEG data. A recurrent neural network was created, which had to have an reduced number of epochs due to its much better learning performance than the others. Figure 16: Another convolutional neural network. \begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Value** \\ \hline
1 & 62.02\% \\
2 & 45.96\% \\
3 & 41.26\% \\
4 & 44.94\% \\
5 & 38.47\% \\ \hline Average & 46.53\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data classification for 50 data sources. Figure 15: Classification loss. Tables 7 and 8 show the effects of learning, network, and classification. Information on learning parameters has also been added. The process was stopped early due to a desire to avoid overtraining the network. For larger data, it was around 10 epochs and for less data, only 3 epochs was enough. Figure 18 shows results of cross-validation for the best solution (recurrent neural networks). \begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Value** \\ \hline
1 & 48.58\% \\
2 & 53.86\% \\
3 & 61.10\% \\
4 & 58.46\% \\
5 & 68.15\% \\ \hline Average & 58.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Data classification for 100 data sources. \begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Value** \\ \hline
1 & 56.39\% \\
2 & 79.69\% \\
3 & 93.79\% \\
4 & 94.19\% \\
5 & 98.82\% \\ \hline Average & 84.58\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Data classification for 50 data sources. Figure 17: Recurrent neural network (GRU). \begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Value** \\ \hline
1 & 78.01\% \\
2 & 95.88\% \\
3 & 91.69\% \\
4 & 94.41\% \\
5 & 96.64\% \\ \hline Average & 91.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Data classification for 100 data sources.