Paper Title
Smart Helmet System with Alcohol Detection and Ml-Based Crash Prediction Using IOT and SMTP Alerts

Abstract
Road safety is a major concern, particularly the traffic-prone area that has a low enforcement of the laws associated with helmet compulsory usage and alcohol consumption. To address these issues, we would recommend the creation of an elaborate smart helmet device with multi-fusion sensors and real-time communications. To ensure safety and only sober individuals can come down the road on the vehicle, the system is set with alcohol recognition sensor (MQ3), which will not allow a vehicle to ignite in case alcohol can be detected in the breath of the rider. Besides the conventional safety checks, we suggest some higher level machine learning models - AdaBoost and Gradient Boosting to anticipate any accidental crashes with the help of the real-time data of the combination of sensors such as accelerator, gyroscope, GPS etc. The patterns are analyzed with the help of these models and indicate the accidents and give predictive warnings with the maximum accurate rates. When crash or a critical anomaly is detected, the system will initiate an SMTP-based notification block which automatically delivers a real-time email notification to emergency contacts including the actual GPS position of the incident. The IoT platform correspondingly integrated will enable the control and monitoring of the helmet (and vehicle status) remotely. The solution is a smart helmet that supplements the key road safety mechanisms and integrates an IoT, smart crash prediction, alcohol and emergency communication in a single small and efficient device - all(erroring to respond to an accident in time and eventually save lives. Keywords - Smart Helmet, Alcohol Detection, Machine learning, Crash Prediction, IoT, Emergency Alerts.