## 4 Results
#### Sensor Data Fusion
KMA performed a maximum of 300 iterations of the data after column normalisation in the K-Means toolkit. The output from the K-Means toolkit was used to train DM models such as KNN, NN, SGD, and SVM based on a 66% training-set size. The evaluation result from the analysis based on a 10-fold cross-validation [42] are presented in Table 2.  
In Table 2, an average accuracy of more than 95% was obtained in all the parameters evaluated. The parameters included AUC, CA, F1, Specificity, Precision, and Recall. Specificity has the highest average accuracy of 99.8%, followed by an AUC of 99.1%. CA and F1 had the least accuracy (in Table 2) as 95.0%. A closer look at each model indicated that KNN has the least accuracies in CA, F1, Precision, and Recall. Although the pooled average in KMCM was the same as PCA's, they cannot be directly compared because different models were used in their analysis. KMCM, however, presented a very useful and explanatory analysis of the datasets compared with HCA. Another KMCM-based analysis was performed to evaluate the models and parameters for T1, T2, and fused (F1) datasets. Data from T1 and T2 were analysed separately for the four models: KNN, SGD, NN, and SVM. The evaluation results are presented in Tables 3 and 4 for T1 and T2, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **AUC (\%)** & **CA (\%)** & **F1 (\%)** & **Precision (\%)** & **Recall (\%)** & **LogLoss (\%)** & **Specificity (\%)** \\ \hline KNN & 98.8 & 91.8 & 91.9 & 92.0 & 91.8 & 0.1 & 99.6 \\ SGD & 97.6 & 95.5 & 95.5 & 95.5 & 95.5 & 0.0 & 99.8 \\ NN & 99.9 & 96.7 & 96.7 & 96.7 & 96.7 & 1.5 & 99.8 \\ SVM & 99.9 & 96.0 & 96.0 & 96.0 & 96.0 & 0.1 & 99.8 \\ Average & 99.1 & 95.0 & 95.0 & 95.1 & 95.0 & 0.4 & 99.8 \\ \hline Legend.: KNN = K-Nearest Neighbours, NN = Neural Network, SGD = Stochastic Gradient & Descent, SVM = Support Vector Machine, CA = Classification Accuracy, and AUC = Area Under the Curve. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: K-Means evaluation results for fused datasets (F1) using data mining models such as KNN, SGD, NN, and SVM. Figure 12: Simplified data fusion architecture based on the K-Means Clustering Method (KMCM). In Table 3, AUC and Specificity's average accuracy are obtained as 99.6% and 99.7%, respectively. Comparing these values to those in Table 4 (98.3% and 99.3%), AUC and Specificity had their highest accuracies in Table 3. Additionally, the metrics (in Table 3), namely, CA, F1, Precision and Recall obtained accuracies that were 4.1% higher than those in Table 4. A combination of the averages of all the metrics (excluding LogLoss) in Tables 2-4 is presented in Table 5.  
In Table 5, a combination of the parameters, AUC, CA, Precision, F1, Recall, and Specificity, indicated that T1 has the highest accuracy in all the models compared with those from T2 and F1 datasets. In addition, the Mean accuracy for all the models indicated 98.0%, 95.0%, and 96.5% for T1, T2, and F1 datasets, respectively. This implied that T1 obtained the highest Mean accuracy, followed by F1 and then T2. An interval plot can further illustrate the Mean accuracy of T1, T2, and F1 datasets, as presented in Figure 13. It should be noted that the intervals were calculated using the pooled Standard Deviation (SD). Nevertheless, although previous analysis indicated a higher Mean average in favour of T1, one-way ANOVA of the models in the T1, T2, and F1 datasets using Welch's Test at 95% Confidence Interval indicated that there was no significant difference (\(p\) = 0.105) between the average values of the parameters. In addition, a Two-Sample T-Test between T1 and F1, T2, and F1 indicated no significant difference between the fused data and those from individual SSs, \(p\) = 0.08 and 0.156, respectively. Further analysis with Grubbs' Test on T1, T2, and F1 datasets at a 5% significant level indicated no outlier in the Mean values of the datasets.