## 3 Proposed Work
### State Space Model
The accuracy levels of different classifiers for various neighborhood configurations are compared in Figure 14. Comparing the SOM approach to others, we find it to be very accurate. ### Impact of Feature Selection

Additionally, we aim to introduce a method for selecting significant features while disregarding insignificant ones to minimize the computational load in terms of processing time as well as memory utilization. Our emphasis is on the ability to analyze large-scale 3D scenes with much huge datasets. To achieve this, we once again utilize a SOM classifier and present the outcomes attained by utilizing the seven various neighborhood descriptions and feature sets, resulting in 49 different possible combinations. The mean class recall values (in percentage) for various neighborhood descriptions and classifiers are presented in Table 7.

Decision trees (DTs) use a hierarchical structure of basic tests to make predictions. The construction of a decision tree usually follows a top-down approach. At each step, the best

Figure 14: Comparison of Precision. Figure 13: Neighborhood definitions for various classifiers. parameter for splitting the training examples is selected. The description of the splitting function and the stopping criterion for the recursive partitioning process is crucial to building an accurate decision tree. In this case, standard settings are used for both criteria. The complete feature set achieved a mean class recall of 83.56% and an overall accuracy of 88.76%. The overall accuracy and mean class recall for Feature Set 1 were 88.98% and 84.66%, respectively; for Feature Set 2, these figures were 89.16% and 83.83%. The recall values for different classifiers are displayed in Figure 15. By measuring the natural logarithm of a classifier's accuracy and recall, the F1-score integrates both into a single statistic. It is utilized to compare the performance of two classifiers. For various neighborhood definitions, Figure 16 compares several classifications and their F1-scores. Comparing our SOM approach to others, it has a strong F1-score. Figure 16: Comparison of F1-score. Figure 15: Recall Value for different neighborhood definitions. ## 7 Conclusions

In this paper, a mono-model pre-learning-based SS scheme is proposed for unstructured data and smart environments. The proposed scheme gains insight from PCs through the incorporation of a pre-learning process, which includes the use of Self-Organizing Maps (SOM). The PCs are transformed into 2D frames of images using a mono-model based image registration scheme. We present a novel, fully automated, and adaptable architecture consisting of four sequential components, each equipped with a range of methods, designed to meet the requirements of simplicity, efficiency, and reproducibility. Our work addressed two interconnected issues: selecting the optimal size of individual neighborhoods for feature extraction and choosing a subset of the most applicable characteristics. We evaluated seven different neighborhood definitions, twenty-one different features, seven different feature selection strategies, and ten different classifiers in order to show how using appropriate neighborhood sizes and the benefits of feature selection can significantly improve classification accuracy while decreasing the complexity of computation. We found that the neighborhood selection method depends on decreasing the eigen entropy evaluation over a range of scales, which had a positive effect on classification, regardless of the classifier being used. We also found that feature subsets produced by feature selection methods based on the symmetrical uncertainty measure were the most appropriate as they removed unnecessary features and reduced feature redundancy. Our long-term goal is to employ well-spaced, smooth labelings to dramatically enhance the outcomes of 3D scene analysis in subsequent projects. This can be achieved through the use of smoothing techniques or approaches that utilize contextual data.