## 5 Behavioral Demonstration of RANs
### Experiment with Human Activity Recognition Data  
This experiment aims to show the ability of the RAN's approach to building the representation of generic concepts. The experiment uses UCIHAR [61] dataset for home activity recognition using the smartphone, obtained from the UCI machine learning repository. The data captured six activities: walking, walking_upstairs, walking_downstairs, sitting, standing, and laying. The hypothesis of this experiment is that the labels walking, walking_upstairs, walking_downstairs are identified by an abstract concept (say) mobile and the other three labels sitting, standing, and laying by abstract concept (say) immobile. In this experiment also classification operation can be used to prove the hypothesis. The UCIHAR dataset was normalized and a header was attached. In CHC algorithm K-means is chosen as a concept identifier and the parameter desired-depth was set to 1 so that model has only two layers. The K-means was configured with K = 2 because the model was hypothesized to have 2 abstract concepts at Layer-1. Having fulfilled the initialization part of the CHC algorithm modeling is performed, generating a two-layered model as depicted in Figure 9. In Figure 9 Layer-0 shows input-layer and Layer-1 corresponds to the abstract concept layer where both nodes (N\({}_{1}\), and N\({}_{2}\)) represents either of the two abstract concepts (i.e., mobile and immobile abstract concepts). Among captured six activities (walking, walking_upstairs, walking_downstairs,sitting, standing and laying), walking, walking_upstairs, and walking_downstairs are the actions of motion, whereas the remaining three represent static states. Based upon these two facts, we expect that one of the abstract nodes in Layer-1 conjointly represents walking, walking_upstairs and walking_downstairs as one class. The other node in Layer-1 stages the other three categories (i.e., sitting, standing and laying) together. Upon performing the labeling of nodes at Layer-1 through ACL procedure (see Appendix A.3 for ACL process elaboration), it was observed that walking, walking_upstairs, and walking_downstairs \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Support** \\ \hline Setosa & 100 & 100 & 100 & 5 \\ Versicolour & 83.33 & 100 & 90.91 & 5 \\ Virginica & 100 & 80 & 88.89 & 5 \\ \hline Avg/Total & 94.44 & 93.33 & 93.26 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of RANs Model generated through IRIS data. Figure 8: Evaluation with IRIS data. (**a**) Confusion Matrix generated to validate Regulated Activated Networks (RANs) model with IRIS data (having \(9:1\) train and test data ratio) for Class-0 (Setosa), Class-1 (Verisicolour), and Class-2 (Virginica); (**b**) Receiver Operating Characteristics (ROC) curve analysis with IRIS dataset (having \(9:1\)_train, and test_ data ratio), for Class-0 (Setosa), Class-1 (Verisicolour), and Class-2 (Virginica). classes were mapped to one node of Layer-1. Whereas, the labels sitting, standing and laying traced to the other node in Layer-1. Interestingly, this outcome commensurate with the expectations from this experiment and shows the RANs capability to identify abstract concepts in an unsupervised manner naturally. The True-label and Test-label obtained through ACL operation were used to form the confusion matrix, which is later referred to calculate precision, recall, F1-score, and accuracy for evaluating the generated model. Node-wise binary labels and confidence scores were determined (as described in Appendix A.5) for both abstract nodes at Layer-1. Figure 10 shows the Area Under the Curve (AUC) observed during the ROC curve analysis of all 10-folds in different research designs. With both these evaluations it is deduced that, apart from building the representation of abstract concepts, the model generated with RANs performed satisfactorily. The RANs modeling was compared with five different types of approaches based upon their classification operation.