P.Usha Sree, M.Vasuki Deepika, J.Srividya 02 10 For driver state classification the suggested system employed the kNN approach It has not before been explored in the context of a camerabased driver sleepiness detection employing blink features to the best of our knowledge Steering behavior EEG measurements and facial traits are examples of existing kNNbased techniques The research looks into the viability of a drowsiness classification system based on blink features collected using an EOG The author attained a promising classification accuracy demonstrating the utility of a kNN classifier combined with blinkbased features When a highdimensional feature space is available the kNN model requires a set of acceptable features as a basis for classification The accessible data becomes scarce as the number of alternative configurations increases according to the curse of dimensionality phenomenon grows As a result one goal of this research is to discover an appropriate set of significant traits Wrapper approaches are the most commonly utilized feature selection strategies in this work Wrapper approaches choose feature subsets based on their predictive value during the classification phase As a result because it directly evaluates classification performance this method can take into account dependencies between the feature subset and the classifier Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026