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

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dc.contributor.authorKamalesh Navaneethakumar 1NH22CS103
dc.contributor.authorKamil Nissar 1NH22CS104
dc.contributor.authorFaheera Kounain 1NH23CS404
dc.contributor.authorPooja Prakash Janagouda 1NH23CS411
dc.date.accessioned2026-02-04T07:14:16Z
dc.date.available2026-02-04T07:14:16Z
dc.date.issued2026-02-04
dc.description.abstractThe 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.
dc.identifier.urihttp://192.168.75.5:4000/handle/123456789/20872
dc.titleA Hybrid Deep Learning Model for Multi Source Disaster Recognition Using ResNet-50 and Efficient Net
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