## 6 RANs Applicability and Observations  
This section highlights the scope of RANs modeling as a classifier with respect to distinct domains. To support this ambit of RANs usability, experimental results are reported using eight datasets concerning different areas. A comparative study was also carried out using these datasets to match RANs classification ability with five different classifiers. Table 16 in Appendix A.5 shows configurations of all the models for all the experiments. Table 17 in Appendix A.4 provide the details about the all the datasets used in this article. Among the eight datasets (Appendix A.4 lists the description of all the datasets used in the article), the Mice Protein [62], _Mammographic Mass_[63], Breast Cancer 569 and 669 [64; 65] data pertain to the medical field, Glass Identification [66] data representing forensic science, Credit Approval [67] represents economic data, Iris [68] is a botanical data set, and Wine Recognition [69] is a data set for chemical composition analysis. The experiments performed with these datasets were akin to the investigations done with Toy-data (in Section 4), and UCIHAR data (in Section 5.2), i.e., K-means algorithm used as concept identifier, where 'K' is the number of class labels of each dataset, the hierarchy is set to have a depth of two layers (one Input and one abstract concept layer). For every dataset, models were generated using thirty iterations in nine Research Designs (RDs) (refer the Table 18 in Appendix A.2). In every RD 10-Fold cross-validation was applied to determine the performance of the models. An aggregate of precision, recall, F1-Score, and accuracy of all folds in all RDs was calculated for all the datasets, as shown in Figure 10(a). From the Figure 10(a) it can be observed that with _Mice Protein_ data RANs scores 99.99% (ca.) for all evaluation metric, whereas for _Iris, Glass Identification, Breast Cancer, and Wine Recognitions_ the observations were convincing, i.e., above 89.00% (ca.). In all the folds of nine RDs ROC curves were also plotted for each class label of the eight datasets, the mean AUC for each class of the datasets is shown in Figure 10(b). The evaluation metrics and ROC-AUC analysis (Figure 10(a),b respectively) display the RAN's capability in machine learning tasks with different kind of datasets. The same procedure was applied to obtain average Precision, Recall, F1-Score and Accuracy for all the datasets with five other classifiers (i.e., _RBM+, KNN, LR, MLP, and SGD_). Table 6 shows the overall comparison. It is worth noting that being dynamic and unsupervised RANs modeling performed quite satisfactorily especially with Mice Protein data, where it outperformed SGD and RBM+, was found competent with LR, KNN and MLP classifiers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline RBM & 99.68 \(\pm\) 0.14 & 99.68 \(\pm\) 0.14 & 99.68 \(\pm\)0.14 & 99.68 \(\pm\) 0.14 \\ K-NN & 99.96 \(\pm\) 0.02 & 99.96 \(\pm\) 0.02 & 99.96 \(\pm\) 0.02 & 99.96 \(\pm\) 0.02 \\ LR & 99.97 \(\pm\) 0.02 & 99.97 \(\pm\) 0.02 & 99.97 \(\pm\) 0.02 & 99.97 \(\pm\) 0.02 \\ MLP & 99.96 \(\pm\) 0.02 & 99.96 \(\pm\) 0.02 & 99.96 \(\pm\) 0.02 & 99.96 \(\pm\) 0.02 \\ RANs & 99.85 \(\pm\) 0.01 & 99.85 \(\pm\) 0.01 & 99.85 \(\pm\) 0.01 & 99.85 \(\pm\) 0.01 \\ SGD & 99.98 \(\pm\) 0.01 & 99.98 \(\pm\) 0.01 & 99.98 \(\pm\) 0.01 & 99.98 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RAN’s Comparative Study for UCIHAR dataset. Figure 12 shows four graphs depicting RANs performance with different benchmark data sets. These graphs display an important aspect of RANs modeling and its performance behavior when evaluated to different research design Figure 12. The precision, recall, F1-Score, and accuracy trajectories of Human Activity Recognition (HAR), Breast Cancer 669 (BC1), Toy-data (TD) and Mice Protein (MP) Data is almost straight.