## 6 RANs Applicability and Observations
The evaluation plots of Glass Identification (GI), Wine Recognition (WR), Mammographic Mass (MM), Breast cancer 569 (BC2) and Mice Protein (MP) datasets show a minimal decline in observations w.r.t RD-1 and RD-9 Research Design. On the contrary, results  
Figure 11: RANs performance with eight datasets using Precision, Recall, F1-Score and Accuracy along with ROC-AUC analysis with Eight benchmark datasets [Mice Protein (MP), Breast Cancer 669 (BC1), Breast Cancer 569 (BC2), Credit Approval (CA), IRIS data (ID), Mammographic Mass (MM), Wine Recognition (WR) and Glass Identification (GI)]. (b) shows the plot of percentage AUC for classes 1 to 8. For each dataset class labels of the graph is serially mapped as: _Mice protein_ (_c-CS_s [Class-1], _c-CS_-m [Class-2], _c-SC_-s [Class-3], _c-SC_-m [Class-4], _t-CS_-s [Class-5], _t-CS_-m [Class-6], _t-SC_s [Class-7] and _t-SC_-m [Class-8]); _Mammographic Mass_ (_Benign [Class-1] and Malignant [Class-2]); _Credit Approval_ (_Positive [Class-1] and Negative [Class-2]); _IRIS_) (_Setosa [Class-1], Versicular [Class-2] and Virginia [Class-3]); _Breast Cancer 569_ (_Benign [Class-1] and Malignant [Class-2]); _Breast Cancer 669_ (_Benign [Class-1] and Malignant [Class-2]), Wine Recognition_ (_Class-1, Class-2 and Class-3) _Glass Identification_ (_Window Glass [Class-1] and Non-Window Glass [Class-2]). (a) RANs performance with eight different datasets depicting RANs appositeness with data belonging to distinct domains; (b) Observed Area Under Curve (AUC) while performing ROC curve analysis for RANs model generated with eight different datasets. from IRIS Data (ID) and Credit Approval (CA) dataset depicted a higher value while comparing the evaluation of RD-1 with RD-9 Research Designs of these data sets. Principally, the results of all four metrics of evaluation obtained similar results (with marginal variation) irrespective of the Test and Train data ratio. This is a notable observation because it shows that the RAN's approach obtains a satisfactory result even when trained with a small amount of data. Besides classification comparison, the RAN's modeling is compared with the five classifiers based upon seven features: (1) Whether the modeling in graph-based; (2) whether the modeling has a dynamic topology; (3) and (4) whether modeling can reduce or expand the dimension of the data; (5) whether modeling can perform classification; and (7) whether modeling is biologically inspired or not. Table 7 details this comparative study. It can be observed from this table that RAN is closely related to the models that are biologically inspired i.e., RBM and MLP. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Features\textbackslash\{}Models** & **RBM** & **K-NN** & **LR** & **MLP** & **RANs** & **SGD** \\ \hline Graph-Based & Yes & No & No & Yes & Yes & No \\ Dynamic Topology & No & No & No & No & Yes & No \\ Dimension Reduction & Yes & Yes & No & Yes & Yes & No \\ Dimension Expansion & May be & No & No & May be & Yes & No \\ Unisupervised & Yes & No & No & No & Yes & No \\ Supports Classification & Yes & Yes & Yes & Yes & Yes & Yes \\ Bio-inspired & Yes & No & No & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Feature based comparative study of RANs with five modeling techniques.