# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 5 Results and Discussion
A low classification accuracy of 90.11% is obtained when FA is utilized with the ELM classifier. On examining Table 6, it is evident that a high classification accuracy of 98.93% is obtained when EFA is classified with the arc-cosine ELM classifier. A low classification accuracy of 98.99% is obtained when EFA is utilized with the arc-cosine ELM classifier. A low classification accuracy of 98.23% is obtained when the BTSA is classified with the ELM classifier. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SVR** & **Ensemble Models** & **NBC** & **SVM** & **Adaboost** & **RF** & **DT** \\ \hline Gabor dictionary + CCF & 96.76 & 97.64 & 93.45 & 94.56 & 93.25 & 94.37 & 93.45 \\ \hline Gabor dictionary + PCCF & 97.21 & 98.03 & 94.35 & 95.32 & 94.24 & 95.21 & 94.22 \\ \hline Gabor dictionary + LASSO & 96.24 & 97.99 & 95.21 & 94.34 & 94.92 & 96.01 & 92.05 \\ \hline Gabor dictionary + elastic net & 95.77 & 98.78 & 97.09 & 97.22 & 94.67 & 95.78 & 93.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proposed work of robust models with Gabor dictionary analysis and machine learning. On examining Figure 4, it is evident that a high classification accuracy is obtained for the Gabor dictionary and elastic net combination classified with the ensemble models. On examining Figure 5, it is evident that a high classification accuracy is obtained for the TQWT concept with AF analysis and the arc-cosine ELM classifier. On examining Figure 6, it is evident that a high classification accuracy is obtained from the DCC concept with EFA analysis and classified with the arc-cosine ELM classifier. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **ELM** & **Kernel ELM** & **Arc-Cosine ELM** & **RSO-ELM** & **SVM** & **Adaboost** & **NBC** \\ \hline BTSA & 92.23 & 95.09 & 97.11 & 98.22 & 95.11 & 95.09 & 95.22 \\ \hline AF & 94.46 & 94.77 & 98.24 & 98.36 & 97.23 & 94.97 & 95.24 \\ \hline FA & 95.77 & 97.67 & 98.75 & 97.87 & 96.78 & 95.56 & 96.68 \\ \hline EFA & 95.89 & 95.83 & 98.93 & 97.98 & 95.99 & 93.88 & 95.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Proposed model of DCC, feature selection and classification for audio cough classification. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **ELM** & **Kernel ELM** & **Arc-Cosine ELM** & **RSO-ELM** & **SVM** & **Adaboost** & **NBC** \\ \hline BTSA & 89.45 & 92.35 & 96.89 & 97.44 & 95.34 & 93.23 & 90.12 \\ \hline AF & 91.03 & 93.45 & 98.34 & 98.23 & 96.34 & 92.24 & 91.22 \\ \hline FA & 90.31 & 94.21 & 97.34 & 98.01 & 95.45 & 93.45 & 92.01 \\ \hline EFA & 92.34 & 94.99 & 96.23 & 98.11 & 96.22 & 91.45 & 90.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Proposed model of TQWT, feature selection and classification for audio cough classification. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ELM** & **Kernel ELM** & **Arc-Cosine ELM** & **RSO-ELM** & **SVM** & **Adaboost** & **NBC** \\ \hline BTSA & 90.23 & 93.33 & 97.11 & 98.03 & 97.23 & 95.34 & 94.22 \\ \hline AF & 92.34 & 94.21 & 98.99 & 98.22 & 96.23 & 94.56 & 93.45 \\ \hline FA & 91.23 & 95.11 & 98.02 & 98.01 & 95.35 & 93.56 & 94.09 \\ \hline EFA & 93.23 & 95.01 & 97.34 & 97.99 & 97.01 & 95.01 & 93.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Proposed model of sparse TQWT, feature selection and classification for audio cough classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **AE** & **CAE** & **SCAE** \\ \hline Accuracy & 91.02 & 93.45 & 96.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Proposed stacked CAE for audio cough classification.