# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 5 Discussion of the Results  
The proposed stranded-NN model can carry several strands and selectively train and predict using one of its strands, as mentioned. The strand \(n\) parameter is mainly determined by the number of time-sequential measurements used by the strand as data inputs, mentioned as input batch size. The \(n\) value also defines the number of hidden layers and parameters created for each strand. Based on this \(n\) value, the authors can differentiate strands for real-time detection (small \(n\) values), close-to-real-time detection (medium \(n\) values), and periodic maintenance detection (high \(n\) values). For long strands, the stranded-NN model may face gradient elimination issues. A series of batch normalization layers need to be introduced to deal with such issues. The authors also identified the maximum number of layers for their stranded model where such a normalization process requires \(l\) = 10-12 layers. The experimental results have shown that the stranded-NN model significantly outperforms the best MLP (100, 20) model of 100 layers and 20 perceptrons/layer. That is at least double the number of layers and parameters than the real-time NN strands. Moreover, for real-time cases, stranded-NN outperforms the MLP (100, 20) model by 15-21% in terms of accuracy and by at least 10-14% for close-to-real-time events. That is, when building very deep MLP NNs, the vanishing gradients problem starts to appear. Furthermore, the use of random dropout layers at the stranded-NN model reduces, as expected, the model's accuracy by 2-8%. However, it still maintains a better performance footprint than its MLP counterpart. Dropout layers in the stranded-NN models are recommended only in cases of over-fitting or due to limited training datasets. LSTM models with a small number of \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
12 & 0.425 & 0.792 & - & - \\
30 & 0.396 & 0.823 & - & - \\
60 & **0.310** & **0.859** & - & - \\
150 & - & - & **0.858** & **0.652** \\
300 & - & - & 0.917 & 0.640 \\
600 & - & - & 0.898 & 0.647 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation of different input sizes for the stranded-NN model on vibration data for the 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 & 0.310 & **0.859** & **0.858** & **0.652** \\ LSTM & **0.306** & 0.839 & 1.187 & 0.397 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary evaluation table of the trained stranded-NN and LSTM models on vibration data for real-time and close-to-real-time cases. cells \((2,4,6)\) were not taken into account for real-time cases. Only LSTMs with 12-16 cells were considered real-time models, because they maintain the same number of trainable parameters with their corresponding real-time strands of the stranded-NN model (see Appendix D, Tables A1 and A2). Taking the previous into account, for the close-to-real-time cases, the LSTM models with 42-80 cells were considered. Therefore, post-processing periodic maintenance processes can utilize LSTMs of 80 cells and above. The experimental results of the stranded-NN model and LSTM model for real-time results have shown that the stranded-NN models slightly outperform the corresponding LSTM models by 2-3%. Nevertheless, for close-to-real-time cases, the corresponding LSTM models fail significantly, by at least 39% in terms of accuracy (39-57%) and 27% in terms of loss (27-46%). The use of high periodic intervals in the LSTM models, especially for sensory measurements that vary over time, causes LSTM models to fail significantly as predictors or classifiers. Even if more cells are used, the outcome results are still disappointing.