## 5 Results and Discussions  
In this section, we conduct simulations in both classification and regression schemes to analyze the performance of the proposed algorithm. The simulation is done using real data collected from a wireless environment. In the classification scheme, we compared MLP performances with SVM and KNN algorithms in different scenarios; more specifically, we used original datasets (Scenario 1), reduced feature only (Scenario 2), and reduced data having appended 5-BSSID features (Scenario 3). In the regression scheme, testing is performed in similar scenarios to prove the effectiveness of the proposed approach. Table 2 shows the localization performance in the classification scheme at different scenarios. The result shows that localization accuracies in Scenario 1 are 82.10%, 90.11%, and 93.67% using KNN, SVM-polynomial (SVM-poly), and MLP classifiers, respectively. The localization accuracies in Scenario 2 are 92.17%, 95.63%, and 98.00% using KNN, SVM-poly, and MLP, respectively. The whole algorithms' localization accuracies are improved in Scenario 2 than in Scenario 1, since LDA resolved outliers and removed noise to localize accurately. In Scenario 3, the localization accuracy of MLP is highly improved, with splendid accuracy. MLP offers 99.15% localization accuracy. Thus, the location errors are improved on the projected data vector with appended BSSIDs. The performance of SVM-poly is also improved, while it is lower compared to MLP. The reason MLP outperforms in each scenario is due to the high learning endurances in large and complex datasets than others. Moreover, MLP outperforms with better localization accuracy than KNN and SVM-poly because it has a nonlinear activation function to easily operate and understand nonlinear datasets. Therefore, the results in Table 2 show that the proposed approach has better localization accuracy, as it integrates reduced data and selected BSSIDs since LDA helps to remove noise and other irrelevant information, and makes the complex datasets simpler to operate. Additionally, BSSIDs give additional information about each class to enhance the localization accuracy. BSSIDs help to easily differentiate each class whenever there will be similar signal distribution in different locations. Generally, MLP offers maximum accuracy compared to KNN and SVM-poly in the proposed data types, because MLP is more flexible with respect to accurate localization of datasets with complex natures. Table 3 shows the computational complexity comparisons of classier algorithms in different scenarios. The results clearly show that it is possible to improve computational time complexity of MLP using the LDA algorithm. The computational times of MLP in each scenario are better than other algorithms, since the data nature is not linear, so that computing localization using KNN and SVM-poly is more difficult than using the MLP algorithm. After LDA is applied, the computational times are improved in each algorithm. In Table 4, the MLP performances in three scenarios are presented. The MLP performance shows that there are variations of accuracies in various scenarios. The accuracies are evaluated in the range performances at each scenario. The MLP achieves 51.00%, 59.72%, and 93.10% for errors less than 0.90 m in Scenario 1, Scenario 2, and Scenario 3, respectively. Their localization accuracies also achieve 48.61%, 38.89, and 75% for errors less than 0.75 m in Scenario 1, Scenario 2, and Scenario 3, respectively. For errors less than 0.5 m, the proposed system localizes 23.61% in Scenario 1, 19.44% in Scenario 2, and 50% in Scenario 3. MLP performs 2.75% in Scenario 1, 11.11% in Scenario 2, and 29.17% in Scenario 3 for estimation errors less than 0.25 m. These differences in the results show that the system learning