Confusion Matrix and Classification report for DL models and Proposed CNN Model. حسب Md. Fahim Ul Islam (22470251)

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Comparison of the models. حسب Md. Fahim Ul Islam (22470251)

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LIME interpretability with decision tree. حسب Md. Fahim Ul Islam (22470251)

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VAE performance baseline. حسب Md. Fahim Ul Islam (22470251)

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Achieved scores of these models based on performance metrics. حسب Md. Fahim Ul Islam (22470251)

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The proposed Model Architecture using NADAM Opitmizer. حسب Md. Fahim Ul Islam (22470251)

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3D PCA and t-SNE plots demonstrating well-separated clusters for network slices, further validating the INBSI model’s ability to learn meaningful, discriminative feature representa... حسب Md. Fahim Ul Islam (22470251)

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Model comparison used. حسب Md. Fahim Ul Islam (22470251)

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Statistical Analysis of Model Performance. حسب Md. Fahim Ul Islam (22470251)

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Our INBSI model for 5G network slicing, utilizing VAE for anomaly detection, CNN with NADAM for slice prediction, and SHAP/LIME for interpretability to ensure efficient resource al... حسب Md. Fahim Ul Islam (22470251)

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Confusion Matrices for the ML models. حسب Md. Fahim Ul Islam (22470251)

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Learning curves for the ML models. حسب Md. Fahim Ul Islam (22470251)

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Comparison of 5G and other networks. حسب Md. Fahim Ul Islam (22470251)

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The tree-structured output of the XGBoost classifier on the SHAP model. حسب Md. Fahim Ul Islam (22470251)

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Comparison of the models. حسب Md. Fahim Ul Islam (22470251)

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SHAP feature importance and summary plot of proposed CNN. حسب Md. Fahim Ul Islam (22470251)

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Inference Time Comparison of Models (Lower is Better). حسب Md. Fahim Ul Islam (22470251)

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LIME interpretability with our proposed CNN. حسب Md. Fahim Ul Islam (22470251)

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Variational autoencoder architecture. حسب Md. Fahim Ul Islam (22470251)

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Classification Report for the ML models. حسب Md. Fahim Ul Islam (22470251)

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