## 3 Proposed Work
### State Space Model
The training data comprise a set X of training samples, each consisting of a class label and a feature vector in a feature space with d dimensions. Only feature vectors in the d-dimensional feature space can be used in the test set Y, which contains new data that need to be categorized. We prioritize individual point classification because it offers simplicity, efficiency, and reproducibility, as well as the availability of numerous suitable algorithms in different software. In order to establish a correlation between feature vectors and class labels during the training phase, we use various classifiers and provide a brief overview of the most important concepts in the following sections. On the other hand, an uneven distribution of training examples by class in the training set can frequently be detrimental to the training procedure. We use class rebalancing to address this, which entails randomly choosing the same number of testing samples for each class, which results in a smaller testing set. In this manner, end users will not only comprehend how each method functions but will also be able to compare them in a knowledgeable manner. Self-Organizing Maps (SOM)

In \"Self-Organizing Maps (SOM)\", the pre-learned principal component data are taken into consideration and trained. The input is chosen from the similar workspace. First, the building is selected as the input of the ROI. Then, ten classes are considered, and the specific data are trained. In the building class, two distinct sub-labels are referred to as \"Block 4\" and \"Block 6\". The Weight Distance (WD) and the number of hits for the building are utilized. SOM differs from other Artificial Neural Networks (ANN) as it implements an efficient learning mechanism for error correction. The Weight Vectors (WVs) are initialized. From a randomly selected sample vector, the WV is analyzed to identify the weight that represents the sample. Each WV has neighboring weights that are close. The weight is adapted to resemble the selected vector. Figure 5 illustrates the SOM framework. The modification of weights and adjustments in the surrounding area are illustrated below. \[\mathrm{P}(m/z_{1:t})=\frac{p(\mathrm{m}/z_{1:t-1})\mathrm{p}(Z_{t}/m)}{P(Z_{t} /Z_{1:t-1})} \tag{3}\]1

where \(W\) is Weight vector, \(W_{ij}(t)\) is Connection Weight, \(t\) is Current iteration, \(\alpha_{i(t)}\) is Learning Rate, \(x(t)\) is Input Vector. ## 5 Experiments

This section includes a description of the experiment's methodology and a data summary. The equipment and sensors used in this section are normally described. Here, we explain the steps used to gather the data. If the experiment is complicated, a separate section could be dedicated to the technique. We employed a challenges being faced that was very comparable to the two datasets in order to test our claim. In the upcoming sections, we concentrate on evaluating the efficiency of our framework. Initially, in Section 5.1, we introduce two pertinent benchmark datasets, which are publicly accessible, and we describe their real-time application. We then elaborate on the experiments conducted in Section 5.2. Later, in Section 6, we provide the outcomes of the analysis of selecting the ideal neighborhood size, comparing them with conventional neighborhood definitions and focusing on evaluating individual techniques. \[\mathrm{P}(m/z_{1:t})=\frac{p(\mathrm{m}/z_{1:t-1})\mathrm{p}(Z_{t}/m)}{P(Z_{t} /Z_{1:t-1})} \tag{3}\]2 SOM Framework. ### Data Set

We aim to facilitate an unbiased comparison with other approaches and prioritize the ease of applying the procedures and reproducibility of results. As a result, we assess the effectiveness of our framework by utilizing two labeled 3D PC datasets that are publicly available. These datasets are elaborated on in the subsequent sections. The implementation of these procedures is also performed in real-time data. #### 5.1.1 Oakland (3D PC Dataset)

The Oakland 3D PC Dataset is among the most extensively employed MLS datasets.