TY  - JOUR
T1  - An Innovative Optimization Technique for Drowsiness Detection Based on Feature Extraction Capitalizing Neural Network and Sparse Classifiers
AU - Daphne, Reena AU - Raj, A. Albert 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 14
SP  - 2399
EP  - 2410
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.2399.2410
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.2399.2410
KW  - Electroencephalogram (EEG)
KW  -ROC
KW  -drowsiness detection
KW  -feature extraction
KW  -WPT
KW  -bootsrapping
KW  -Neural Network (NN) classifier
KW  -FrFT
KW  -ABC
KW  -Sparse classifier
AB  - Drowsiness is considered as a significant risk factor that contributes to large number of accidents. This study, focuses on methodologies developed for counteracting its effects with very accurate classification techniques categorizing the different drowsy states and alerting the person at definite instants. An optimal Bootstrap technique is applied to features extracted by Daubechies Wavelet Transform (DWT) and the drowsy states are classified using Neural Network (NN) classifier. The Receiver Operating Characteristics (ROC) curve shows the classification accuracy and the computation time is also calculated. In order to improve the efficiency of the proposed method, Fractional Fourier Transform (FrFT) based feature extraction is implemented with ABC (Artificial Bee Colony) for optimization and classification done using NN and Sparse classifiers. The three methods exhibit high efficiency in improving the system’s performance in terms of accuracy F1 score and computation time. A comparative study of the three methods is also done with the latter showing better results.
ER  -