## 4 Experimental Results
### Experimental Results and Discussion  
The authors of this study performed two experiments to analyze the performance of the proposed framework. Initially, experiment 1 was carried out to validate the global and local models by measuring the classification evaluation measures before (case 1) and after (case 2) observing new data samples. Later, in experiment 2, the overall classification accuracy of local models (at edges) was measured. These experiments also allowed validating the histogram of clustering distance during edge training and validating the performance (accuracy and loss) during new sample adaptation. 3.1 Experiment 1: Validation of the Global and Local Models by Measuring the Classification Evaluation Measures before (Case 1) and after (Case 2) Observing New Data Samples  
The primary intention of this experiment was to evaluate the performance of the proposed framework in a stable condition. The obtained results were promising with a recorded classification accuracy of 95.6%, loss of 2.50 (as shown in Table 2), and 0.95 precision and recall (as shown in Table 3). This study analyzed the proposed framework's performance with a challenging dataset (complex features), i.e., the ISIC skin disease dataset. Here, the proposed framework was trained using four classes of the skin disease dataset, which were dermatofibroma (DF), vascular lesion (VASC), squamous cell carcinoma (SSC), and unknown (UKN). Despite the complex features and class imbalance problem in the skin disease dataset, the model's performance was satisfactory, even better than highlighted in the literature. In case 2, the proposed model was trained on the first four classes and correctly classified them. Later, the proposed model incorporated four new classes (which were not trained on the proposed framework), with a subsequent degradation in performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Hyper-Parameters** & **Tuning Values** & **Optimized Values** \\ \hline Mini-Batch Size & 16, 32, 64, 128, 256 & 120 \\ Learning Rate & 0.1, 0.01, 0.001 & 0.001 \\ L1 regularization (lambda parameter) & 0.001, 0.0003 & 0.0003 \\ Number of epochs & 10–100 & 100 \\ Optimization function & Adam & Adam \\ Cross-entropy & One-hot encoded & One-hot encoded \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training hyperparameters (tuning values and optimized values). The intent behind this experiment was to determine the accuracy of the proposed model at the cloud level. It was shown that the proposed framework outperformed those in the literature and achieved satisfactory classification accuracy. The reported classification accuracy was 89% (as shown in Table 2). Moreover, a significant level of precision and recall was noted. However, in this case, the loss increased to 3.5 from 2.5 in case 1 (as shown in Table 2). The model's overall performance after adapting new classes was noticeably lesser, because model training in offline mode is always better than that in online mode (online mode creates online training dataset formation, which might contain noisy data). Thus, more advanced techniques are required to overcome these differences. Additionally, all the individual classification accuracies showed good performance after the arrival of new samples, as shown in Figure 10b. This study also generated receiver operating characteristic (ROC) curve plots to demonstrate the tradeoff between sensitivity and specificity. A possible increase in sensitivity, accompanied by a decrease in particularity was observed in the proposed framework before and after new class arrival, as depicted in Figure 8a,b. The ROC curve was closer to the left-hand border and the top edge of the ROC space. This shows the true positive rate vs. the false positive rate. In model1_SD, the obtained ROC curve was desirable in both cases, as shown in Figure 11.