AI- based crowd density for railway stations

The proposed system focuses on evaluating deep learning and machine learning models for heart disease prediction in an IoT-enabled healthcare environment. The system integrates multiple hardware components such as Arduino for sensor control, NodeMCU for cloud communication, and an LCD for real-time display of patient parameters. Physiological data including heart rate and body temperature are collected using a heartbeat sensor and temperature sensor, respectively. Additionally, a webcam is incorporated to monitor visual cues that may indicate abnormal health conditions. The collected data is transmitted to an IoT platform via NodeMCU for remote monitoring and storage. A Random Forest algorithm is employed to analyze the sensor data and predict the likelihood of heart disease. In case of abnormal readings or high-risk predictions, a buzzer alert system is activated to provide immediate warning. This integrated system enables continuous health monitoring, early detection, and timely intervention, making it suitable for smart healthcare applications.