The objective is to create an AI bot that monitors exam halls in real time, detects suspicious movements and sounds, and sends instant alerts to invigilators to prevent malpractice and ensure exam fairness.
This project presents an AI-driven disaster forecasting and response system using Arduino, DHT11, MQ135 gas sensor, pressure sensor, rain drop sensor, LCD display, and buzzer. The sensors continuously monitor environmental conditions such as temperature, humidity, air quality, atmospheric pressure, and rainfall. The collected data is transmitted to a computer, where a CNN-based prediction model analyzes the sensor readings to forecast potential disaster situations. The monitored data and prediction results are displayed on the LCD, while the buzzer provides alerts whenever abnormal conditions are detected. The system offers a low-cost and intelligent solution for real-time environmental monitoring, disaster prediction, and early warning generation.
Keywords: Arduino, CNN, Disaster Forecasting, Environmental Monitoring, Early Warning System.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Hardware components:
Software requirements: