An IOT-based bus safety system using ESP32, Flask, MQTT, and sensors to enhance women's safety through smart entry, panic alerts, and alcohol detection.
This project aims to detect heavy drinking risk using smartphone accelerometer time-series data and machine learning. It combines classical ML models (RandomForest, XGBoost, etc.) and deep learning (1D CNN) with an interactive Streamlit dashboard for risk analysis and participant comparison, trained on the UCI Bar Crawl dataset.
Detect alcohol levels and control vehicle speed.
This is a thesis project in Polytechnic University of the Philippines for alcohol and drowsiness detection system used in PUJ and this contains not optimized system but working
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