This project aims to develop an innovative anomaly detection system using advanced data mining and deep learning techniques to accurately identify and localize defects in manufacturing components, thereby enhancing quality control processes and reducing production losses.

Teleoperated vehicles over 5G face reliability issues due to reactive handovers. We propose a deep learning framework using time-series radio data to predict handover onset, target cells, and link quality. These forecasts enable proactive actions like bandwidth scaling or control rate adjustment to ensure stability.

The AQI Prediction project uses ML to forecast air pollution based on environmental data. It leverages pollutant levels (PM2.5, PM10, NO2, CO, SO2, O3), temperature, humidity, and wind speed to predict AQI. Using linear regression and deep learning, it aids proactive air quality management and pollution control.