Article
PREDICTING DRIVER DROWSINESS USING KNN ALGORITHM
For driver state classification, the suggested system employed the k-NN approach. It has not before been explored in the context of a camera-based driver sleepiness detection employing blink features, to the best of our knowledge. Steering behavior, EEG measurements, and facial traits are examples of existing kNN-based 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 k-NN classifier combined with blink-based features. When a highdimensional feature space is available, the k-NN 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.
Full Text Attachment
