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 representations.

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 representations.

<p>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 representations.</p>

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Bibliographic Details
Main Author:	Md. Fahim Ul Islam (22470251) (author)
Other Authors:	Shahriar Hossain (17666863) (author), Md. Golam Rabiul Alam (14285073) (author), Nafees Mansoor (16891455) (author), Amitabha Chakrabarty (22470254) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified optimize resource allocation nearest neighbors algorithm gaussian naive bayes deep neural forest certain input characteristics additional methods varies preprocessing initial data data validity assessment shared physical infrastructure shapley additive explanations massive iot deployments including decision trees 69 %), baggingclassifier optimizing network resources network slicing process local interpretable model vae data preprocessing network slicing iot infrastructure reconstructed data including 5g allocate resources agnostic explanations 70 %), work highlights wireless networks various applications variational autoencoder sdn ), rely heavily reliable low provide insight powerful tools offering insights ns ), multilayer perceptrons mlps ). lime ), latency communications intelligent algorithms increasingly applied generation mobile driven solutions dnf ), diverse quality defined networking artificial intelligence 84 %) 55 %).
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