## 5 Results and Discussion  
Within the scope of two different classification studies, TP, FP, TN, FN, ACC, SNS, SPC, and \(\kappa\) values were calculated, and different k values were selected. In the first classification study, 32 \(\times\) 25 datasets were used, and the kNN classification algorithms were operated for the feature groups in Table 1 and for different k values. And then, TP, FP, TN, FN, ACC, and \(\kappa\) values were calculated using the numerical values obtained. The results of the first classification process are given in Table 2. The conditions for the \(\kappa\) being greater than 0.6 correspond to the \"good reliability\" for the chosen conditions in the classification [50]. When the classification results are examined, it can be easily seen that the best ACC value was obtained in the Group 11 while \(k=3\). \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & **Max** & **Min** & \(\sigma\) & **KC** & **AM** \\ \hline Group 1 & ✓ & ✓ & & & & ✓ & & \\ Group 2 & ✓ & ✓ & ✓ & & & & & \\ Group 3 & ✓ & ✓ & & & & & ✓ & \\ Group 4 & ✓ & ✓ & & & ✓ & ✓ & & ✓ \\ Group 5 & ✓ & ✓ & ✓ & ✓ & ✓ & & \\ Group 6 & ✓ & ✓ & ✓ & ✓ & & ✓ & ✓ \\ Group 7 & & & & ✓ & ✓ & ✓ & ✓ & \\ Group 8 & & & & ✓ & ✓ & & ✓ & \\ Group 9 & & & & & ✓ & ✓ & ✓ & \\ Group 10 & & & & ✓ & & & ✓ & \\ Group 11 & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature groups and elements. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{k\(\rightarrow\)3} & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \cline{2-11}  & \(\kappa\) & 0.5 & 0.55 & 0.55 & 0.64 & 0.56 & 0.59 & 0.64 & 0.56 & 0.57 & 0.53 & 0.7 \\ \cline{2-11}  & TP & 66 & 67 & 65 & 70 & 70 & 65 & 66 & 69 & 67 & 67 & 74 \\ \cline{2-11}  & FP & 14 & 13 & 15 & 10 & 10 & 15 & 14 During the sample scan, x-z projection plots were also obtained. Figure 12 shows the x-z projection plots of 60 mm Mortar Ammunition, M16 AP (Anti-Personnel) Mine, Sample 3, and Sample 11 scanned at 5 cm distance. These plots includes different unique properties for each sample. Therefore, instead of using 32 \(\times\) 25 data matrices directly, it was thought that the use of data matrices obtained from x-z projection plots would provide significant improvements in system and classification performance. For this purpose, new 32 \(\times\) 2 data matrices having same properties were created by reducing the dimensions of 32 \(\times\) 25 data matrices. Thus, 32 \(\times\) 25 matrices, which had800 independent data points, were reduced into 32 \(\times\) 2 matrices, which had 64 independent data points. While generating 32 \(\times\) 2 data matrices, the maximum and minimum values for each column of 32 \(\times\) 25 data matrices were determined. Then, the maximum and minimum values of each 32 \(\times\) 25 matrix were placed in the first and second rows of 32 \(\times\) 2 data matrices, respectively. A schematic diagram of how these processes were performed is given in Figure 13.  
In the second classification study, 32 \(\times\) 2 datasets were used, and the kNN classification algorithms were operated for the feature groups in Table 1 and for different k values. And then, TP, FP, TN, ANC, and \(\kappa\) values were calculated using the numerical values obtained. The results of the second classification process are given in Table 3. The conditions for the \(\kappa\) being greater than 0.6 correspond to the \"good reliability\" for the chosen conditions in the classification. When the classification results are examined, it can be easily seen that the best ACC value was obtained in the Group 10 while \(k=3\). In this study, explosive materials have been successfully distinguished from non-explosive ones by using reduced data matrices and the features that have been defined. The best result has been obtained for the comparison based on the standard deviation and arithmetic mean.