XGBoost-Forget applies machine unlearning to XGBoost on IoT-23 and GeNIS network intrusion datasets, achieving faster forgetting with maintained predictive performance.
In: 2024 IEEE 3rd International Conf
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DenseNet201 CNN reaches 99% accuracy on teaLeafBD dataset for classifying tea leaf diseases, with added Grad-CAM explainability and adversarial robustness.
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Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
XGBoost-Forget applies machine unlearning to XGBoost on IoT-23 and GeNIS network intrusion datasets, achieving faster forgetting with maintained predictive performance.
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TeaLeafVision: An Explainable and Robust Deep Learning Framework for Tea Leaf Disease Classification
DenseNet201 CNN reaches 99% accuracy on teaLeafBD dataset for classifying tea leaf diseases, with added Grad-CAM explainability and adversarial robustness.