A Hybrid Deep Learning Model for Multi Source Disaster Recognition Using ResNet-50 and Efficient Net

Abstract
The proposed system is designed to classify disasters using heterogeneous visual images. The framework integrated two state-of-the-heart convolutional neural networks: ResNet-50, which provides robust hierarchical feature extraction through residual learning, and EfficientNet, which achieves high accuracy with optimized computational efficiency using compound scaling.
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