This research addresses excessive false alarms in hospital medical devices, which burden staff and distress patients. By detecting and filtering noisy data, the proposed system prevents false alerts while preserving true ones. Early results show complete removal of false alarms, improving efficiency, patient experience, and clinical response in healthcare settings.

This research improves drone-based search and rescue by creating networks of communicating drones that optimize data routing. Inspired by traffic flow, it minimizes delays by avoiding congested paths. Faster data transmission enables quicker detection and response, allowing larger areas to be searched efficiently and increasing the chances of saving lives.

This research develops drones with soft robotic arms capable of safely grasping and transporting objects in challenging environments. By combining predictive modelling with visual feedback, it overcomes control challenges associated with soft materials. The work advances intelligent, adaptive aerial robotics for applications such as emergency delivery and hazardous environments.