## 4 Comparative Analysis of RPLC with CNN through Experimental Validation
### Benchmarking Analysis
Table 7 represents the stats of accuracy gains of RPLC over SVM. For S_ID 3 \(v/s\) S_ID 5 and S_ID 4 \(v/s\) S_ID 6, RPLC retains positive gain margins whereas for the latter one gains hits negative numbers. However, RPLC's simplicity, extensibility, speed and consistent performance with minimum prior knowledge for deployment remains a plus point. The extensive experimentation along with the gain analysis of RPLC over the state-of-the-art ML approaches shows that for accurate functionality of these methods massive amount of annotated data covering the problem domain is required. Since synthetic augmentation of the labelled images fails to add to the performance accuracy and has \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Comparisons of** \\ **Scenarios (S\_ID)** \\ \end{tabular} & \begin{tabular}{c} **Image Size** \\ \end{tabular} & \begin{tabular}{c} **Dataset Size** \\ \end{tabular} & \begin{tabular}{c} **Luminance-Based** \\ **Classification** \\ \end{tabular} & \
\begin{tabular}{c} **CNN-Based Classification** \\ \end{tabular} &
\begin{tabular}{c} **Gain in \% Age of** \\ **Luminance-Based v/s** \\ **CNN-Based Classification** \\ \end{tabular} \\ \hline S\_ID 1\(v/s\)\_S\_ID 5 & \(100\times 100\) & 300 & 78\% & Acc & Val\_Acc & Acc & Val\_Acc \\ S\_ID 2\(v/s\)\_S\_ID 6 & \(640\times 640\)\(v/s\) Actual Image Size & 300 & 77\% & Acc & Val\_Acc & Acc & Val\_Acc \\ S\_ID 7\(v/s\)\_S\_ID 11 & \(100\times 100\) & 900 & 75.7\% & Acc & Val\_Acc & Val\_Acc \\ S\_ID 8\(v/s\)\_S\_ID 12 & \(640\times 640\)\(v/s\) Actual Image Size & 900 & 75.6\% & Acc & Val\_Acc & Val\_Acc \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Gain analysis of RPLC algorithm over CNN based classification. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Comparisons of** \\ **Scenarios (S\_ID)** \\ \end{tabular} & \begin{tabular}{c} **Image Size** \\ \end{tabular} & \begin{tabular}{c} **Dataset Size** \\ \end{tabular} & \begin{tabular}{c} **Luminance-Based** \\ **Classification** \\ \end{tabular} & \
\begin{tabular}{c} **SVM-Based** \\ **Classification** \\ \end{tabular} &
\begin{tabular}{c} **Gain in \% Age of** \\ **Luminance-Based v/s** \\ **SVM-Based** \\ **Classification** \\ \end{tabular} \\ \hline S\_ID 3\(v/s\) S\_ID 5 & \(100\times 100\) & 300 & 78\% & 72.5\% & 7.5\% \\ S\_ID 4\(v/s\) S\_ID 6 & \(640\times 640\)\(v/s\) Actual Image Size & 300 & 77\% & 73.3\% & 5.04\% \\ S\_ID 9\(v/s\) S\_ID 11 & \(100\times 100\) & 900 & 75.7\% & 85\% & \(-10.09\%\) \\ S\_ID 10\(v/s\) S\_ID 12 & \(640\times 640\)\(v/s\) Actual Image Size & 900 & 75.6\% & 89\% & \(-11\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Gain Analysis of RPLC Algorithm over SVM Classification. its own drawbacks, such as overfitting (for detailed read over of the associated risks of augmentation refer to studies [56; 57]), therefore, as a mitigation strategy to deal with annotation problem of the images RPLC can be used to annotate the data using the proposed thresholds. For extended research, a novel CNN network can also be created, incorporating the information of RPLC and its thresholds in the network parameters to create hybrid approach. Finally, the overall change in performance for classification of images can be examined.