Shawkh Ibne Rashid
Papers
1
Total Citations
50
H-Index
1
About
Shawkh Ibne Rashid is a rising researcher at the intersection of artificial intelligence, disaster management, and remote sensing. His work focuses on leveraging deep learning and computer vision to solve pressing humanitarian challenges, particularly in flood-prone regions. Rashid’s most cited paper, “An integrated convolutional neural network and sorting algorithm for image classification for efficient flood disaster management” (2023, 50 citations), introduces a novel framework that combines CNNs with sorting algorithms to analyze drone-captured imagery. This system autonomously classifies flood-affected areas and prioritizes relief delivery, addressing critical gaps in accessibility and technology during disasters. By enabling drones to make real-time, intelligent decisions, Rashid’s contribution enhances the speed and efficiency of post-flood response, potentially saving lives and resources. His work exemplifies how machine learning can be directly applied to environmental monitoring and humanitarian aid, marking him as a promising voice in applied AI for social good. With growing recognition in the field, Rashid continues to push the boundaries of autonomous systems for disaster resilience.
Research Focus
Key Achievements
Top Papers
- 1