A) Average error of all classifiers and descriptors, B) ROC curves for Classification using AdaBoost and Bagging with intensity and CMYK&Hb&Lb&HSV&Lab colour model combination.
Statistical significance of differences in performance of the different classifiers at loss and recovery of consciousness (LOC and ROC respectively). Classifiers: linear (SVML) and nonlinear (SVMNL) S
All data were randomly selected from the CSE-CIC-IDS2018 dataset. The data fields were censored after going through the analysis and 64 valid features were retained.There are 5 types of data, the