Abstract
Life is the most precious asset, and thousands of times due to terrible car accidents lives are lost. However, real-time sleep detectors used in cars can significantly prevent these accidents and save precious lives around the world. The main reason is the driver's inattention, which is mainly called driver drowsiness. The driver’s drowsiness monitoring system is used in conjunction with a high-frequency detection system. The system uses the input video stream from the driver to target the driver’s natural visual changes, such as constantly closing the eyes and using artificial intelligence and scientific drowsiness to slow down the rate of changes in facial expressions and detection measures. The proposed work focuses on the different monitoring systems used in drowsiness detection and the process of the detection system. It is recommended to use a driver drowsiness detector connected to a key predictive analytics system. This research work focuses on the analysis of various sleepiness systems in order to perform a better predictive analysis. Machine learning is a very important example of predictive analytics, so this article focuses on various machine learning techniques and their effectiveness in detecting drowsiness in various systems.
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Siddiqi, A.S., Zafar, S., Alam, M.A., Khan, S., Iftekhar, N., Biswas, S.S. (2022). Applying Predictive Analysis Methods for Detection of Driver Drowsiness. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P., Goel, L. (eds) Proceedings of International Conference on Recent Trends in Computing . Lecture Notes in Networks and Systems, vol 341. Springer, Singapore. https://doi.org/10.1007/978-981-16-7118-0_2
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DOI: https://doi.org/10.1007/978-981-16-7118-0_2
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