## 4 Experimental Assessment
### DL-Based Diagnostic Assessments
For each of the models summarized in Table 1, each of the different input/source data channels were deployed for a training-validation process over a 5-fold cross-validation over 50 epochs on the training data (with 20% validation set included). Overall, eight (8) different input/source data channels were acquired, and they include raw spindle only (\(SP_{R}\)), critical spindle only (\(SP_{C}\)), raw frame only (\(FR_{R}\)), critical frame only (\(SP_{C}\)), raw spindle and raw frame (\(SP_{-}FR_{C}\)), raw spindle and critical frame (\(SP_{R}FR_{C}\)), and critical spindle and raw frame (\(SP_{C}FR_{R}\)). Figure 9 shows each of the models' training history over the 50 epochs on the different input/source data channels. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Architecture** & **Hyperparameters/Description** \\ \hline \multirow{4}{*}{\(CNN64\)} & Conv1D, & Filter1 = 64, kernel1\_size = 3, \\  & GlobalAveragePooling1D, & activation\_Conv1D = ReLU, \\  & Dense\_output & activation\_Dense\_output = Softmax, \\  & Dense\_output & optimizer = adam, \\  & & Loss = categorical\_crossentropy \\ \hline \multirow{4}{*}{\(CNN64\_64\)} & Conv1D—Conv1D, & Filter1 = Filter2 = 64, \\  & GlobalAveragePooling1D, & kernel1\_size = 8, kernel2\_size = 5, \\  & Dense\_output & activation\_Conv1D = ReLU, \\  & Dense\_output & activation\_Dense\_output = Softmax, \\  & & optimizer = adam, \\  & Loss = categorical\_crossentropy \\ \hline \multirow{4}{*}{\(CNN64\_Dense100\)} & Conv1D, & Filter1 = 64, activation\_Conv1D = ReLU, kernel1\_size = 3, \\  & GlobalAveragePooling1D, & activation\_Dense\_100 = ReLU, \\  & Dense\_100, Dense\_output & activation\_Dense\_output = Softmax, \\  & & Optimizer = adam, \\  & & Loss = categorical\_crossentropy \\ \hline \multirow{4}{*}{\(FCN\)[33]} & Conv1D+Batch\_Norm, & Filter1 = 128, Filter2 = 256, \\  & Conv1D+Batch\_Norm, & Filter3 = 128, activation\_Conv1D = ReLU, kernel1\_size = 8, \\  & Conv1D+Batch\_Norm, & kernel2\_size = 5, kernel3\_size = 3, \\  & GlobalAveragePooling1D, & activation\_Dense\_output = Softmax, \\  & Dense\_output & optimizer = adam, \\  & Dense\_output & Loss = categorical\_crossentropy \\ \hline \multirow{4}{*}{\(DNN64\)} & \multirow{4}{*}{Dense\_64—Dense\_output} & MLP: nodes in Dense\_64 = 64, \\  & Dense\_128—Dense\_64— & activation\_Dense\_64 = ReLU, \\  & Dense\_output & activation\_Dense\_output = Softmax, \\  & & optimizer = adam, \\  & & Loss = categorical\_crossentropy \\ \hline \multirow{4}{*}{\(DNN128\_64\)} & \multirow{4}{*}{Dense\_128—Dense\_64—} & MLP: nodes in Dense\_128 = 128, \\  & Dense\_128—Dense\_64— & activation\_Dense\_64 = ReLU, \\  & Dense\_output & activation\_Dense\_output = Softmax, \\  & & optimizer = adam, \\  & & Loss = categorical\_crossentropy \\ \hline \multirow{4}{*}{\(DNN100\_150\)} & \multirow{4}{*}{Dense\_100—Dense\_150—} & MLP: nodes in Dense\_100 = 100, nodes in Dense\_150 = 150, nodes in Dense\_50 = 50, \\  & Dense\_50—Dense\_output & activation\_Dense\_100 = \\  & Dense\_50—Dense\_output & activation\_Dense\_50 = ReLU, \\  & & activation\_Dense\_output = Softmax, \\  & & optimizer = adam, \\  & & Loss = categorical\_crossentropy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The different DNN models employed and their architectures. As shown, the respective models have zero training and validation losses over the first few iterations (as shown in the red-dotted and yellow lines, respectively). These are also accompanied by a 100% training and validation accuracy from each of the models (as shown in the black-dotted and blue lines, respectively). These only validate that the models are quite efficient for learning the discriminative information stored in the training data and cannot form generalization criteria for assessing the models. Similarly, a test set was deployed for the models in an unsupervised manner, and their respective test scores were recorded. Table 2 provides the dimensions of the different input/source data channels and the test scores of the different models, respectively.