## 6 Results and Discussion
### Comparison with the State of the Art Approaches
All other algorithms included  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**bWSO-****log**} & \multicolumn{2}{c}{**bWSO-****bWSO-****s1**} & \multicolumn{2}{c}{**bWSO-****s2**} & \multicolumn{2}{c}{**bWSO-****s3**} & \multicolumn{2}{c}{**bWSO-****s4**} & \multicolumn{2}{c}{**bWSO-****bWSO-****v3**} & \multicolumn{2}{c}{**bWSO-****v4**} \\ \hline \multirow{4}{*}{N10} & \(\uparrow\)mean & 0.785 & 0.780 & 0.782 & **0.794** & 0.785 & 0.791 & 0.753 & 0.782 & 0.757 & 0.734 \\  & \(\pm\)std & 0.088 & 0.092 & 0.097 & 0.080 & 0.107 & 0.102 & 0.096 & 0.099 & **0.063** & 0.113 \\  & \(\uparrow\)best & 0.933 & 0.954 & 0.947 & 0.937 & **0.969** & 0.954 & 0.902 & 0.913 & 0.876 & 0.933 \\  & \(\uparrow\)worst & **0.607** & **0.607** & 0.606 & 0.606 & 0.463 & 0.541 & 0.519 & 0.450 & 0.606 & 0.478 \\ \hline \multirow{4}{*}{N11} & \(\uparrow\)mean & **0.930** & 0.920 & 0.928 & 0.923 & 0.927 & 0.924 & 0.923 & 0.911 & 0.916 & 0.918 \\  & \(\pm\)std & 0.026 & 0.045 & **0.021** & 0.027 & 0.025 & 0.029 & 0.028 & 0.027 & 0.027 & 0.022 \\  & \(\uparrow\)best & **0.990** & 0.971 & 0.981 & 0.973 & 0.972 & 0.981 & 0.973 & 0.963 & 0.962 & 0.962 \\  & \(\uparrow\)worst & 0.853 & 0.728 & **0.887** & 0.842 & 0.862 & 0.841 & 0.861 & 0.870 & 0.847 & 0.884 \\ \hline \multirow{4}{*}{N12} & \(\uparrow\)mean & 0.820 & 0.843 & 0.787 & 0.803 & 0.816 & 0.812 & 0.846 & 0.859 & **0.868** & 0.840 \\  & \(\pm\)std & 0.041 & 0.055 & 0.052 & 0.070 & 0.047 & 0.064 & 0.052 & 0.056 & **0.041** & 0.059 \\  & \(\uparrow\)best & 0.917 & **0.984** & 0.880 & 0.928 & 0.912 & 0.939 & 0.937 & 0.955 & 0.939 & 0.930 \\  & \(\uparrow\)worst & 0.743 & 0.735 & 0.658 & 0.602 & 0.709 & 0.700 & 0.743 & 0.743 & **0.797** & 0.683 \\ \hline \multirow{4}{*}{N13} & \(\uparrow\)mean & 0.917 & 0.921 & **0.949** & 0.937 & 0.937 & 0.922 & 0.902 & 0.866 & 0.904 & 0.895 \\  & \(\pm\)std & 0.068 & 0.043 & 0.043 & 0.042 & **0.032** & 0.037 & 0.047 & 0.061 & 0.063 & 0.050 \\  & \(\uparrow\)best & 0.986 & **1.000** & **1.000** & **1.000** & 0.989 & 0.985 & 0.969 & 0.978 & **1.000** & 0.980 \\  & \(\uparrow\)worst & 0.730 & 0.820 & 0.815 & 0.819 & **0.851** & 0.846 & 0.801 & 0.747 & 0.737 & 0.771 \\ \hline \multirow{4}{*}{N14} & \(\uparrow\)mean & 0.903 & 0.831 & 0.937 & **0.947** & 0.941 & 0.931 & 0.844 & 0.839 & 0.910 & 0.822 \\  & \(\pm\)std & 0.139 & 0.193 & 0.111 & **0.105** & 0.126 & 0.126 & 0.151 & 0.171 & 0.132 & 0.209 \\  & \(\uparrow\)best & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** \\  & \(\uparrow\)worst & 0.546 & 0.321 & 0.583 & **0.631** & 0.567 & 0.593 & 0.600 & 0.500 & 0.622 & 0.050 \\ \hline \multirow{4}{*}{N15} & \(\uparrow\)mean & 0.813 & 0.818 & **0.822** & 0.820 & 0.817 & 0.812 & 0.819 & 0.807 & 0.807 & 0.817 \\  & \(\pm\)std & 0.024 & 0.030 & 0.027 & 0.023 & **0.020** & 0.029 & 0.027 & 0.026 & 0.030 & 0.030 \\  & \(\uparrow\)best & 0.844 & 0.883 & 0.866 & 0.860 & **0.898** & 0.867 & 0.857 & 0.876 & 0.860 \\  & \(\uparrow\)worst & 0.751 & 0.758 & 0.765 & 0.773 & **0.780** & 0.762 & 0.750 & 0.766 & 0.754 & 0.744 \\ \hline \multirow{4}{*}{N16} & \(\uparrow\)mean & **0.536** & 0.323 & 0.443 & 0.514 & 0.442 & 0.424 & 0.385 & 0.373 & 0.381 & 0.335 \\  & \(\pm\)std & 0.195 & 0.179 & 0.202 & **0.175** & 0.250 & 0.219 & 0.189 & 0.177 & 0.192 & 0.204 \\  & \(\uparrow\)best & 0.867 & 0.867 & 0.822 & 0.886 & **1.000** & 0.778 & 0.722 & 0.722 & 0.750 & 0.822 \\  & \(\uparrow\)worst & 0.111 & 0.083 & 0.000 & **0.148** & 0.000 & 0.000 & 0.083 & 0.095 & 0.083 & 0.000 \\ \hline \multirow{4}{*}{N17} & \(\uparrow\)mean & **0.914** & 0.911 & **0.914** & 0.909 & 0.907 & 0.908 & 0.909 & 0.912 & 0.910 & 0.910 \\  & \(\pm\)std & 0.008 & 0.011 & 0.010 & 0.010 & 0.013 & 0.009 & 0.012 & **0.008** & 0.009 & 0.011 \\  & \(\uparrow\)best & 0.928 & 0.934 & **0.938** & 0.934 & 0.931 & 0.928 & 0.928 & 0.926 & 0.930 & 0.931 \\  & \(\uparrow\)worst & **0.902** & 0.892 & 0.897 & 0.888 & 0.862 & 0.894 & 0.869 & 0.898 & 0.889 & 0.885 \\ \hline \multirow{4}{*}{N18} & \(\uparrow\)mean & **0.790** & 0.761 & 0.772 & 0.790 & 0.774 & 0.783 & 0.766 & 0.757 & 0.766 & 0.770 \\  & \(\pm\)std &in the comparison were discretized using the S1 transfer function, which is frequently preferred in the literature.