## 5 Results and Discussion
While the best ACC value has been achieved to be 0.86 for the classification carried out with 32 \(\times\) 25 data matrices, a 0.95 ACC value has been obtained for 32 \(\times\) 2. In the classifications made with 32 \(\times\) 25 data matrices, the \(\kappa\) value was obtained as greater than 0.6 for 7 conditions; this value was obtained for 27 conditions in 32 \(\times\) 2 data matrices. Both of those results reveal a significant increase in classification success after reducing the matrix dimensions. If there is a comparison made between in the mean values of TP, FP, TN, FN, ACC, and \(\kappa\), then the best results have been obtained for 32 \(\times\) 2 data matrices. This situation explains that the dimension reduction is essential to get best kNN classification. Classification times have been determined to be 42 ms and 3.62 ms for the groups with the best results: the data matrices of 32 \(\times\) 25 and 32 \(\times\) 2, respectively. This means that 23.80 and 276.2 datasets can be classified in one second for 32 \(\times\) 25 and 32 \(\times\) 2 data matrices, respectively. By subjecting 32 \(\times\) 25 and 32 \(\times\) 2  
Figure 12: The x–z projection plots for 60 mm Mortar Ammunition, M16 Anti-Personal (AP) Mine, Sample 3, and Sample 11.  
data matrices to the kNN classification algorithm, samples having explosive properties have been distinguished successfully from the non-explosives materials by 86% and 95%, respectively. There have been significant improvements in the speed and in the number of datasets processed in one second in the classification made for 32 \(\times\) 2 data matrices. In the conventional buried object and explosive detection systems, FAR is 50% (50% classification success). In our study, FAR was calculated as 5.31% for selected samples, and this ratio indicates a significant improvement. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{k\(\rightarrow\)3} & \multirow{2}{*}{\(\kappa\)} & \multirow{2}{*}{0.82} & \multirow{2}{*}{0.67} & \multirow{2}{*}{0.71} & \multirow{2}{*}{0.8} & \multirow{2}{*}{0.75} & \multirow{2}{*}{0.81} & \multirow{2}{*}{0.87} & \multirow{2}{*}{0.67} & \multirow{2}{*}{0.85} & \multirow{2}{*}{0.89} & \multirow{2}{*}{0.83} \\ \cline{2-2} \cline{6-11}  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{k\(\rightarrow\)3} & \multirow{2}{*}{\(\kappa\)} & \multirow{2}{*}{0.82} & \multirow{2}{*}{0.67} & \multirow{2}{*}{0.71} & \multirow{2}{*}{0.8} & \multirow{2}{*}{0.75} & \multirow{2}{*}{0.81} & \multirow{2}{*}{0.87} & \multirow{2}{*}{0.67} & \multirow{2}{*}{0.85} & \multirow{2}{*}{0.89} & \multirow{2}{*}{0.83} \\ \cline{2-2} \cline{6-11}  & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{k\(\rightarrow\)5} & \multirow{2}{*}{\(\kappa\)} & \multirow{2}{*}{0.67} & \multirow{2}{*}{0.71} & \multirow{2}{*}{0.78} & \multirow{2}{*}{0.77} & \multirow{2}{*}{0.73} & \multirow{2}{*}{0.75} & \multirow{2}{*}{0.75} & \multirow{2}{*}{0.72} & \multirow{2}{*}{0.82} & \multirow{2}{*}{0.76} \\ \cline{2-2} \cline{6-11}  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{k\(\rightarrow\)7} & \multirow{2}{*}{\(\kappa\)} & \multirow{2}{*}{0.71} & \multirow{2}{*}{0.53} & \multirow{2}{*}{0.52} & \multirow{2}{*}{0.69} & \multirow{2}{*}{0.72} & \multirow{2}{*}{0.58} & \multirow{2}{*}{0.64} & \multirow{2}{*}{0.48} & \multirow{2}{*}{0.7} & \multirow{2}{*}{0.79} & \multirow{2}{*}{0.