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Evaluating the driver behavior and car movement and assessment of third party insurance using novel AdaBoost classifier over SVM

Publication Type : Conference Paper

Publisher : AIP Publishing

Source : AIP Conference Proceedings

Url : https://doi.org/10.1063/5.0198483

Campus : Chennai

School : School of Computing

Department : Computer Science and Engineering

Year : 2024

Abstract :

The Novel Ada boost machine learning algorithm can forecast the Sensitivity % of driver sleepiness behaviour, which may assist avoid accidents, injuries, and even fatalities due to driving tiredness. For the purpose of forecasting the sensitivity % of driver sleepiness behaviour, the NovelAda boost machine learning method for Blink frequency with sample size = 100 was iterated many times. Adaboost's sensitivity is much higher. Our Novel AdaBoost algorithm outperforms SVM (83.50 percent) in terms of blink frequency (75.60 percent). AdaBoost and SVM both performed better than random guessing (p = 0.002). Adaboost increases the predictive accuracy of sensitivity tests for driver fatigue. The programme will determine whether the driver is asleep and activate the alert if necessary. 

Cite this Research Publication : Kongara Bhargav Naidu, S. Udhayakumar, Evaluating the driver behavior and car movement and assessment of third party insurance using novel AdaBoost classifier over SVM, AIP Conference Proceedings, AIP Publishing, 2024, https://doi.org/10.1063/5.0198483

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