# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 4 Experimental Scenarios
### Scenario I: Experimental Results  
In order to compare the performance of the proposed stranded-NN model against a real-time model and a close-to-real-time model, the MLP Classifier and the LSTM model were selected, respectively. In the case of the MLP classifier, several hidden layers along with a set of number of units per hidden layer were tested and the most representative models are presented in Table 4. Additionally, the LBFGS optimizer was used with a parameter \(alpha=10^{-5}\). The maximum number of iterations was set equal to 10,000. From the experimental results, it is obvious that the MLP classifier presents a maximum accuracy of 0.753 at its deep MLP (100-layers, 20-perceptrons) representative model, with a significant loss value of 4.53. It is also worth mentioning that an MLP (10-layers, 4-perceptrons) model is outperformed in terms of 3.9% accuracy by the MLP (100, 20) model. Figure 2: Temperature sensors grouped by alert thresholds. Sets 3 and 7 contain 8 sensors each, which are related to _alert_ = 60 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Number of Samples** \\ \hline
0 & 50,248 \\
1 & 3,077,880 \\
2 & 4,291,731 \\
3 & 19,725 \\
4 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of temperature samples per class. The architecture of the LSTM model consists of two LSTM layers with an equal number of units (\(nc\)), while it produces an output layer for the class prediction, as is depicted in Figure A5. Table A2 in Appendix D highlights the number of units per LSTM layer, along with the number of trainable parameters and the corresponding model sizes in KB. From Table A2, the 16-16 and 60-60 LSTM models were selected as representatives of real-time and close-to-real-time cases, based on the number of trainable parameters with respect to the stranded-NN model (see Table A1 in the Appendix D). Table 5 presents the results in terms of accuracy and loss of the most representative LSTM variations depending on the underlying memory size (\(nc\times 2\)) and the Timestep resulting in their corresponding losses and accuracies. Table 6 presents an extensive search for the best parametrization of the proposed stranded-NN model, with and without dropout layers. Lastly, Table 7 summarizes the performance of the three compared models, resulting into a proof that the proposed model performs fairly close to the LSTM performance, with a slight superiority of the LSTM model in the close-to-real-time cases, while both models outperform the MLP. Taking into consideration the fact that the collected temperature measurements lack a high jitter value and are of uniform variation, it is unquestionable that a more complicated dataset must be chosen to further investigate the performance of stranded-NN and LSTM; thus, their performance on the MaFaultda vibration dataset is presented in the next experimental section. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Real-Time**} & \multicolumn{2}{c}{**Close-to-Real-Time**} \\ \hline
**Input Size** & **Loss** & **Accuracy** & **Loss** & **Accuracy** \\ \hline
16 & 0.295 (\(\mathbf{0.353}\)) & 0.929 (\(\mathbf{0.887}\)) & - & - \\
48 & 0.174 (0.566) & 0.951 (0.807) & - & - \\
80 & \(\mathbf{0.128}\) (0.377) & \(\mathbf{0.954}\) (0.848) & - & - \\
160 & - & - & **0.311** (\(\mathbf{0.365}\)) & **0.892** (\(\mathbf{0.862}\)) \\
480 & - & - & 0.671 (0.771) & 0.866 (0.826) \\
960 & - & - & 1.81 (3.299) & 0.386 (0.583) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of different input sizes for the stranded-NN model on temperature data for the real-time and close-to-real-time cases and with dropout layers (in parentheses).