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Title: Accident Mitigation System with Drowsiness Detection: A Machine Learning and Iot with Hybrid Approach
Authors: Suhail Razeeth, M.S.
Kariapper, R.K.A.R.
Sabraz Nawaz, S.
Keywords: Wireless sensor networks
Machine learning algorithms
Velocity control
Issue Date: 14-Jul-2021
Publisher: Institute of Electrical and Electronics Engineers Inc
Citation: International Conference on Information Technology, ICIT 2021 - Proceedings; Al-Zaytoonah University of Jordan (ZUJ)Amman;Article number 9491646; pp: 462-465
Abstract: Accidents are unavoidable with population growth around the world. There have been numerous researches conducted to preserve both life and morals. Drowsiness and fatigue have been consistently identified as significant causes of accidents. Instead of relying on limited methods to detect drowsiness and tiredness, this study incorporates deep learning in conjunction with IoT. This study focuses on developing a prototype to minimize road accidents due to drowsiness, fatigue, carelessness, and other reasons. The CNN algorithm handled drowsiness detection; drivers will be notified as soon as they fall asleep. This study takes a novel approach by combining machine learning with drunk avoidance, direction control, speed control, and distance preservation. When paired with proper guidance, the said hybrid approach would produce the best solution to the accident issues without suspects.
ISBN: 978-1-6654-2870-5 (Electronic)
Appears in Collections:Research Articles

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