2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.

<p>2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Md. Fahim Ul Islam (22470251) (author)
مؤلفون آخرون:	Shahriar Hossain (17666863) (author), Md. Golam Rabiul Alam (14285073) (author), Nafees Mansoor (16891455) (author), Amitabha Chakrabarty (22470254) (author)
منشور في:	2025
الموضوعات:	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 %).
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

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author	Md. Fahim Ul Islam (22470251)
author2	Shahriar Hossain (17666863) Md. Golam Rabiul Alam (14285073) Nafees Mansoor (16891455) Amitabha Chakrabarty (22470254)
author2_role	author author author author
author_facet	Md. Fahim Ul Islam (22470251) Shahriar Hossain (17666863) Md. Golam Rabiul Alam (14285073) Nafees Mansoor (16891455) Amitabha Chakrabarty (22470254)
author_role	author
dc.creator.none.fl_str_mv	Md. Fahim Ul Islam (22470251) Shahriar Hossain (17666863) Md. Golam Rabiul Alam (14285073) Nafees Mansoor (16891455) Amitabha Chakrabarty (22470254)
dc.date.none.fl_str_mv	2025-10-21T17:38:41Z
dc.identifier.none.fl_str_mv	10.1371/journal.pone.0333286.g017
dc.relation.none.fl_str_mv	https://figshare.com/articles/figure/2D_PCA_and_t-SNE_plots_showing_distinct_clusters_for_network_slices_indicating_effective_feature_extraction_and_slice_differentiation_by_the_INBSI_model_/30409904
dc.rights.none.fl_str_mv	CC BY 4.0 info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv	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 %).
dc.title.none.fl_str_mv	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.
dc.type.none.fl_str_mv	Image Figure info:eu-repo/semantics/publishedVersion image
description	<p>2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.</p>
eu_rights_str_mv	openAccess
id	Manara_28c0dc6fd42a0ca44f019c9b4ff6c8a7
identifier_str_mv	10.1371/journal.pone.0333286.g017
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/30409904
publishDate	2025
repository.mail.fl_str_mv
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repository_id_str
rights_invalid_str_mv	CC BY 4.0
spelling	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.Md. Fahim Ul Islam (22470251)Shahriar Hossain (17666863)Md. Golam Rabiul Alam (14285073)Nafees Mansoor (16891455)Amitabha Chakrabarty (22470254)Space ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedoptimize resource allocationnearest neighbors algorithmgaussian naive bayesdeep neural forestcertain input characteristicsadditional methods variespreprocessing initial datadata validity assessmentshared physical infrastructureshapley additive explanationsmassive iot deploymentsincluding decision trees69 %), baggingclassifieroptimizing network resourcesnetwork slicing processlocal interpretable modelvae data preprocessingnetwork slicingiot infrastructurereconstructed dataincluding 5gallocate resourcesagnostic explanations70 %),work highlightswireless networksvarious applicationsvariational autoencodersdn ),rely heavilyreliable lowprovide insightpowerful toolsoffering insightsns ),multilayer perceptronsmlps ).lime ),latency communicationsintelligent algorithmsincreasingly appliedgeneration mobiledriven solutionsdnf ),diverse qualitydefined networkingartificial intelligence84 %)55 %).<p>2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.</p>2025-10-21T17:38:41ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0333286.g017https://figshare.com/articles/figure/2D_PCA_and_t-SNE_plots_showing_distinct_clusters_for_network_slices_indicating_effective_feature_extraction_and_slice_differentiation_by_the_INBSI_model_/30409904CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304099042025-10-21T17:38:41Z
spellingShingle	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model. Md. Fahim Ul Islam (22470251) 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 %).
status_str	publishedVersion
title	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.
title_full	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.
title_fullStr	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.
title_full_unstemmed	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.
title_short	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.
title_sort	2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.
topic	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 %).

2D PCA and t-SNE plots showing distinct clusters for network slices, indicating effective feature extraction and slice differentiation by the INBSI model.

مواد مشابهة