A newer version of the Gradio SDK is available:
6.6.0
metadata
title: SONAR-AI
emoji: π±
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: true
license: mit
short_description: X-Ray Cargo Analysis + 2128 HS Codes - 96% Accuracy
π Model Performance
| Metric | Value |
|---|---|
| Accuracy | 96.00% |
| Match Recall | 100% |
| Mismatch Precision | 100% |
| F1-Score | 0.89 |
| Parameters | 5.67M |
π§ Architecture: Deep-SOSUFS
Deep-SOSUFS (Self-Organizing Sonar Unsupervised Feature Selection):
- β EfficientNet-B0 backbone
- β Self-organizing clustering layers
- β Learnable feature importance
- β Attention mechanisms
π Dataset
| Info | Value |
|---|---|
| Total Images | 125 |
| Match | 112 (89.6%) |
| Mismatch | 13 (10.4%) |
| Source | Iraqi Customs |
π― Classification
- β Match: Cargo matches declaration
- β Mismatch: Cargo does NOT match declaration
π¨βπ¬ Author
Dr. Abbas Fadel Jassim Al-Jubouri
- ποΈ Iraqi General Customs Authority
- π Universiti Kebangsaan Malaysia (UKM)
- π 2025-2026
π Citation
@article{aljubouri2026deepsosufs,
title={Deep-SOSUFS: Self-Organizing Feature Selection for X-Ray Cargo Analysis},
author={Al-Jubouri, Abbas Fadel Jassim},
year={2026},
institution={Universiti Kebangsaan Malaysia}
}
Β© 2026 Dr. Abbas Al-Jubouri | UKM Malaysia