# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 4 Experimental Scenarios
### Scenario II: Experimental Results  
In this section, cross-comparison between the proposed stranded-NN model and the LSTM model are described. Since MLP performed significantly worse than both the other two models in the previous experiment, it is not taken into consideration. Tables 9 and 10 present the performances of two representative parametrizations of the LSTM model and three of the proposed models, respectively, for real-time and close-to-real-time cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Real-Time**} & \multicolumn{2}{c}{**Close-to-Real-Time**} \\ \hline
**Model** & **Loss** & **Accuracy** & **Loss** & **Accuracy** \\ \hline Stranded-NN & 0.128 & **0.954** & 0.311 & 0.892 \\ LSTM & **0.116** & 0.952 & **0.124** & **0.940** \\ MLP & 4.53 & 0.753 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of the trained stranded-NN, LSTM, and MLP models on temperature data for both real-time and close-to-real-time cases. In the case of the MLP and stranded-NN models, the best performances are selected, according to Tables 4 and 6, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Description** & **Number of Samples** \\ \hline
0 & Normal behavior & 250,000 \\
1 & 6 g RFC & 250,000 \\
2 & 10 g RFC & 250,000 \\
3 & 20 g RFC & 250,000 \\
4 & 30, 35 g RFC & 500,000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Number of vibration samples per class.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Real-Time**} & \multicolumn{2}{c}{**Close-to-Real-Time**} \\ \hline
**Memory** & **Timestep** & **Loss** & **Accuracy** & **Loss** & **Accuracy** \\
**Size (\(\textit{nc}\times\textit{2}\))** & **Timestep** & **Loss** & **Accuracy** & **Loss** & **Accuracy** \\ \hline
12 \(\times\) 2 & 200 & 0.419 & 0.790 & - & - \\
16 \(\times\) 2 & 200 & **0.306** & **0.839** & - & - \\
60 \(\times\) 2 & 1000 & - & - & 1.593 & 0.280 \\
150 \(\times\) 2 & 1000 & - & - & **1.187** & **0.397** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation of different memory sizes (\(\textit{nc}\times\textit{2}\)) for the LSTM model on MaFaulDa vibration data [(42)] for the real-time and close-to-real-time cases, where \(\textit{nc}\) is the number of units per LSTM layer.  
Table 11 summarizes the obtained results, signifying the absolute superiority of the proposed stranded-NN model against the corresponding ones of the LSTM model in both real-time and close-to-real-time cases. As shown in the summary Table for the real-time case (see Table 11), of stranded-NN with batch size 60, the stranded-NN slightly outperforms the LSTM (16 \(\times\) 2) real-time model by 2.32% in terms of accuracy, even if LSTM maintains a slightly smaller loss. For close-to-real-time cases, the representative stranded-NN model with a batch input size of 150 values significantly outperforms the LSTM (150 \(\times\) 2) model by 39% in terms of accuracy and 27.7% in terms of loss. It also outperforms the corresponding close to real time LSTM (60 \(\times\) 2 \(\times\) 1000) model in terms of size and parameters (see Tables A1 and A2 in the Appendix D, total trainable parameters and sizes of LSTM and stranded-NN models), by 57% in terms of accuracy and 46% in terms of loss.