Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{155372,
        author = {DR. UDAI BHAN TRIVEDI},
        title = {Comparative analysis of various classification Machine Learning Algorithm on Driver Drowsiness Dataset},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {10},
        pages = {37-48},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155372},
        abstract = {Real Time Drowsiness behaviours of a driver related to fatigue are in the form of eye closing, head nodding. The focus of this research paper is on the detection of blinks of eye by estimating the EAR (Eye aspect Ratio). This is achieved by monitoring the eyes of the driver throughout the entire video sequence. An IR camera will be used for capturing live video of driver eyes in all light conditions and frames will extracted for image processing scheme of video capturing. The Various Binary classifying algorithm will be applied on Driver Drossiness Dataset and feature like sagging leaning of driver’s head and open/closed state of eyes will determine the state of the driver. The focus of this research paper is to classify the driver Drowsiness into two classes: 1. Open/Alert 2. Close/ Drowsiness   through various classifying machine learning algorithms and determine the best performing algorithm for this purpose.},
        keywords = {Classification, Machine Learning, Driver Drowsiness, EAR (Eye Aspect Ratio)},
        month = {},
        }