## 4 Experimental Results
### Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Configuration** & **Classification Accuracy (\%)** & \multicolumn{2}{c}{**Loss**} \\ \hline  & **Case 1** & **Case 2** & **Case 1** & **Case 2** \\ \hline
**Model1\_SD** & 95.6 & 89.0 & 2.50 & 3.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy and loss for ISIC 2019 (skin disease) streams in different cases. Figure 10: Confusion matrix: (**a**) confusion matrix before adaptation (dermatofibroma (DF), vascular lesion (VASC), squamous cell carcinoma (SSC), and unknown (UKN) ISIC dataset classes); (**b**) confusion matrix after adaptation (class 1: melanoma (MEL), class 2: melanocytic nevus NV, class 3: basal cell carcinoma (BCC), and class 4: actinic keratosis (AK); ISIC dataset classes). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Configuration** & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} \\ \hline  & **Case 1** & **Case 2** & **Case 1** & **Case 2** \\ \hline
**Model1\_SD** & 0.95 & 0.90 & 0.95 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision and recall for ISIC skin disease data streams in different cases. 3.2 Experiment 2: Local Model Overall Classification Accuracy Performance, the Histogram of Clustering Distance during Edge Training and Testing, and Validation of Performance with New Samples  
The intent behind this experiment was to test the overall classification accuracies of our deployed model, as well as apply its feature extraction technique to prepare for online training dataset formation. Lastly, the updated training and validation accuracies and loss are presented to validate the model's successful incorporation of the new dataset at the edges and on the cloud server. Overall Classification Performance of the Cloud Models  
Figure 12 shows the confusion matrix for all trained eight classes. It can be noted that the global model performed well in the stable scenario, with classification accuracy above 90%. Simultaneously, some classes performed exceptionally, such as class 5 and class 1, with maximum correct predictions of 4005 and 3900 samples, respectively. Histogram of Clustering Distance during Edge Model Training for New Samples  
The histogram of clustering distances identifies how correctly the new samples are extracted and clustered for new dataset formation. New dataset formation is an essential procedure for training edges with newly collected samples. Table 4 represents the four classes of updated samples that were collected and clustered after the feature extraction process. The feature extraction process was done using the pretrained network. Later, the extracted features were clustered using the k-means clustering algorithm. In Table 4, it can be noted that class 6 means clustered well, showing a mean values of 99.85, variance of 160.55, and standard deviation of 12.6. Class 4 showed worse performance with a mean value of 78.5, variance of 144.34, and standard deviation of 12.10. Well-clustered features ensure better training and validation accuracies when updating the edge models. Furthermore, they reduce the overfitting issue by neglecting the condition of bias toward a particular class. Figure 11: Receiver operating characteristic (ROC) curve with sensitivity on the \(x\)-axis (true positive rate) and specificity on the \(y\)-axis (false positive rate): (**a**) before adaptation; (**b**) after adaptation. Figure 13 represents each prediction input used for computing the cluster distance, assigned into histogram bins. Here, the \(y\)-axis is the number of predictions and the \(x\)-axis represents the cluster distance. Notably, the histogram represents how frequently the prediction/classification falls within a particular range of cluster distances.