Network slicing architecture. by Md. Fahim Ul Islam (22470251)

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Graphical representation of our proposed INBSI system. by Md. Fahim Ul Islam (22470251)

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

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

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Anomaly detection of the constructed data. by Md. Fahim Ul Islam (22470251)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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