## 7 Conclusions and Future Work  
To comprehend and reasoning for emotions, ideas, etc., it is evident to understand abstract concepts because they are perceived differently from concrete concepts. There have been notable efforts to study Concrete concepts (features like walking or ingredients), but progress in investigating abstract concepts (generic features such as is-moving or recipe) is relatively less. This article proposes an unsupervised computational modeling approach, named Regulated Activation Networks (RANs), that has an evolving topology and learns a representation of abstract concepts. The RAN's methodology was exemplified through a UCI's IRIS dataset, yielding a satisfactory performance evaluation of 95% (ca.) for precision, recall, F1-Score and accuracy metrics, along with an average AUC of 99% (ca.) for all the three classes in the dataset. These evaluation result not only showed the classification capability of RANs but also proved the hypothesis of the experiment i.e., the three newly created nodes in the Layer-1 symbolically represent the three classes of IRIS data as abstract concepts. Another experiment with IRIS data displayed the characteristic of RAN's deep hierarchy generation and independence in choosing the concept Identifier. With the aid of the Concept Hierarchy Creation algorithm (proposed in Section 5.1), the evolving nature of RAN's modeling is shown using the Affinity Propagation clustering algorithm (as an alternate concept Identifier instead of the K-means algorithm as used in modeling with a Toy-data problem). With the generated model it was shown that the model dynamically grew to a depth of six layers and performed with Precision of 94.44% (ca. ), Recall of 93.33% (ca. ), F1-Score of 93.26% (ca.) and Accuracy of 93.33% (ca. ), along with an observed AUC of 100% (ca. ), 92% (ca.) and 94% (ca.) for the three classes of data. This experiment also highlights the application of RANs modeling in data dimension transformation and data visualization. Modeling with UCI's IoT based Home Activity Recognition (UCIHAR) smartphone sensor dataset exhibited the RAN's behavior of natural identification of generic concepts. The experiment hypothesize that six data labels (activity of walking, walking_upstairs, walking_downstairs,  
Figure 12: RANs evaluation metric (precision, recall, F1-Score and accuracy) value behavior with respect to varying test and train data ratio over ten datasets [Mice Protein (MP), Breast Cancer 669 (BC1), Breast Cancer 569 (BC2), Credit Approval (CA), IRIS data (ID), Mamographic Mass (MM), Human Activity Recognition (HAR), Toy-data(TD), Wine Recognition (WR) and Glass Identification (GI)] (**a**) Precision; (**b**) Recall; (**c**) F1-Score; (**d**) Accuracy. sitting, standing and laying) of the dataset are to be identified as mobile (walking, walking_upstairs and walking_downstairs) and immobile (sitting, standing and laying) abstract concepts. This hypothesis was also proven using classification operation, where, the evaluation of the model shown a performance of 99.85% (ca.) for all four metrics and AUC of 99.9% (ca.) for both abstract concepts. The experiment also demonstrates how RAN can be used to model the data from the IoT domain in an unsupervised manner. The proof of concept of RAN's modeling as a Machine Learning classifier was also provided with eight UCI benchmarks. It was identified that RAN's approach performed satisfactorily displaying the best outcome of 98.9% (ca.) with Mice Protein dataset (for all metrics). The comparison of RAN's modeling with five classifiers substantiated the effectiveness of the proposed methodology. We also observed that the RAN's performance remained similar irrespective of the size of train data. RAN was also compared with the five classifiers based upon its features and it was observed that RAN was similar to bio-inspired models. The model presented in this article is capable of modeling data that is convex which limits the RAN's performance with non-convex (or complex) datasets.