A Hybrid System Combining the Shortest Path Algorithm with and Real-Time VGG19 Convolutional Neural Network

Abstract

Imen Chebbi, Sarra Abidi and Leila Ben Ayed

In the aftermath of catastrophic natural catastrophes such as earthquakes, tsunamis, and explosions, providing immediate help to key areas can mean the difference between life and death for many individuals. To meet this critical demand, we created a hybrid transportation system that harnesses the power of vgg19 and traditional shortest path algorithms. The objective was to create a real-time system that could address these issues and provide a novel viewpoint. The suggested approach can precisely anticipate damaged roads and steer clear of them when determining the shortest way between sites during emergencies or natural disasters by merging VGG19 with the shortest path algorithm. With the potential to save many lives, this creative strategy can assist emergency responders reach vital places swiftly and effectively while also saving important time and resources. The experimental study shows that the proposed model can achieve robust results. In fact, our solution achieves 98% accuracy rate and a 0.972 G-Mean score on the test set.

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