Cloud-Driven Autonomous Drones for Dynamic Emergency Medical Response Using Convolutional Neural Networks
P. Karthikeyani, D. David Neels Ponkumar, S. Hemalatha, M. Tamilselvam, M. Muthulekshmi, C. Dhanesh · 2024
This research presents an innovative method for improving emergency medical response by using autonomous drones powered by Convolutional Neural Networks (CNNs) and controlled by cloud computing. The proposed system incorporates advanced technology to speed up help delivery in emergency conditions requiring rapid medical intervention. Using CNNs, drones can detect and recognize objects in real-time, vital for quickly diagnosing life-threatening medical emergencies. Drones can make quick decisions and navigate themselves using the vast computer capacity made available by cloud computing. The system uses dynamic routing algorithms to maximize drone deployment and respond quickly to changing emergency dynamics. The experimental results demonstrate the framework's effectiveness and scalability, highlighting its capacity to transform emergency medical care. The system's seamless integration image processing shows an enormous step forward in enhancing emergency response capabilities. Drones may now effectively provide lifesaving help by navigating complicated situations autonomously, made possible by the combination of advanced technology. It presents a viable route for improving outcomes in critical conditions by employing AI-driven drones for dynamic emergency medical intervention.