Developed a deep learning-based medical image analysis system for early lung cancer prediction using YOLO object detection and Sybil risk assessment models. The solution processes CT scan images, identifies suspicious lung regions, and assists in estimating cancer risk through automated image analysis. This project highlights the application of Artificial Intelligence, Computer Vision, and Medical Imaging techniques to support faster and more accurate healthcare decision-making.

Developed a disaster-response robotic system capable of autonomous navigation, obstacle detection, and environmental monitoring in hazardous environments. The bot utilizes Arduino-based control systems, ultrasonic sensors, GPS, Bluetooth communication, and motor driver modules to navigate disaster zones and assist rescue teams. Designed to improve rescue efficiency and reduce risks to human responders, the project demonstrates practical applications of robotics, embedded systems, sensor integration, and real-time monitoring.