## 4 Comparative Analysis of RPLC with CNN through Experimental Validation
### Benchmarking Analysis
The CNN was trained for two different image sizes, i.e., 100 by 100, and 640 by 640, the resulting accuracies turned out to be 60% accuracy and 73% validation accuracy for image size 100 by 100. Whereas, for image sizes 640 by 640 the obtained accuracy is up to 66% and validation accuracy is 68.8%. The associated losses in both the scenarios are still very high. The accuracy plots are further discussed for detailed analysis in Figure 7. The resultant accuracies of SVM are higher this time, it retains accuracy up to 85% for both the scenarios involving different image sizes. The ML classifiers (CNN and SVM) used for the experimentations are highly dependent on the annotated data and training phase is slow [39]. For the addition of even a single class in these models entire retraining is required [42]. However, for RPLC does not impose prior restrictions for classification. The proposed algorithm RPLC keeps its accuracy intact and drops by a smaller margin and still retains accuracy up to 75.7%. The performance \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Scenario** & & & **Batch** & & **Dataset** & & **Accuracy** & & **Loss** \\
**ID** & **Classifier** & **Epochs** & **Size** & **Image Size** & **Size** & **Optimizer/Kernel** & & **Accuracy** & & **Loss** \\ \hline \multirow{2}{*}{\(1\)} & \multirow{2}{*}{Basic-CNN} & \multirow{2}{*}{60} & \multirow{2}{*}{15} & \multirow{2}{*}{100 \(\times\) 100} & \multirow{2}{*}{300} & \multirow{2}{*}{adagrad} & Acc & Val. Acc & Loss & Val. Loss \\  & & & & & & & 73.3\% & 64.4\% & 56\% & 100\% \\ \hline \multirow{2}{*}{\(2\)} & \multirow{2}{*}{Basic-CNN} & \multirow{2}{*}{60} & \multirow{2}{*}{15} & \multirow{2}{*}{640 \(\times\) 640} & \multirow{2}{*}{300} & \multirow{2}{*}{adagrad} & Acc & Val. Acc & Loss & Val. Loss \\  & & & & & & 53\% & 71.1 & 83\% & 100\% \\ \hline \multirow{2}{*}{\(3\)} & \multirow{2}{*}{SVM} & \multirow{2}{*}{} & \multirow{2}{*}{100 \(\times\) 100} & \multirow{2}{*}{300} & \multirow{2}{*}{Linear} & \multirow{2}{*}{72.5\%} & \multirow{2}{*}{-} \\  & & & & & 640 \(\times\) 640 & 300 & & & 73.3\% & - \\ \hline \multirow{2}{*}{\(4\)} & \multirow{2}{*}{SVM} & \multirow{2}{*}{} & \multirow{2}{*}{640 \(\times\) 640} & \multirow{2}{*}{300} & \multirow{2}{*}{Linear} & \multirow{2}{*}{73.3\%} & \multirow{2}{*}{-} \\  & & & & & & & 78\% & & - \\ \hline \multirow{2}{*}{\(5\)} & \multirow{2}{*}{RPLC} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{100 \(\times\) 100} & \multirow{2}{*}{300} & \multirow{2}{*}{-} & \multirow{2}{*}{78\%} & \multirow{2}{*}{-} \\  & & & & & & & & \\ \hline \multirow{2}{*}{\(6\)} & \multirow{2}{*}{RPLC} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & Actual & \multirow{2}{*}{300} & \multirow{2}{*}{-} & \multirow{2}{*}{77\%} & \multirow{2}{*}{-} \\  & & & & image size & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance Comparison of RPLC. For the experimental setup and CNN architecture construction the python library that has been used is Keras and Tensorflow. Generally, for assessing the network performance the criteria are with increasing epochs the accuracy should be increasing whereas the loss is expected to decrease to ensure that network is working optimally. All the accuracy plots presented in Figure 7 shows differing facts about the network performance. The network performance is fluctuating with the increasing epochs and the losses reach to 100%, as mentioned in Tables 2 and 3. This means the network is not performing as desired and for such situations the network is usually overfitted, and it is highly likely that such networks result in misclassified results when any new data is classified. Table 4 shows some of the detailed results of images along with the inferences as acquired by the network and their actual or correct label as annotated by the human annotator. Table 4 shows the experimental results of CNN and with increasing resolution similar images are witnessed to get correct classification results by the CNN.