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nr gnb  (NextGen Sciences)

 
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    Structured Review

    NextGen Sciences nr gnb
    Nr Gnb, supplied by NextGen Sciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gnb-a/nr+gnb/us12342407-278-3-7
    Average 90 stars, based on 1 article reviews
    nr gnb - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: Methods for integrity protection of user plane data
    Article Snippet: TABLE 1 Architecture options for Xn-handover in NextGen Source Target Options node node Core network gNB-A and gNB-B connected to same NGC gNB-A gNB-B NextGen core (in option 2 and option 4) gNB and LTE eNB connected to same NGC gNB LTE NextGen core (option 2 combined with option 5) eNB (option 2 combined with option 7/7A) LTE eNB-A and LTE eNB-B connected to same LTE LTE NextGen core NGC eNB-A eNB-B (in options 5 and option 7/7A) LTE eNB and gNB connected to same NGC LTE gNB NextGen core (option 5 combined with option 2) eNB (option 7/7A combined with option 5) S1-Handover 4G (EPC/LTE) At handover from a source eNB to a target eNB over S1 (possibly including an MME change and hence a transfer of the UE security capabilities, and the UP integrity protection mode, and optionally the UE capability for the maximum data rate for integrity protection of UP from source MME to target MME), the target MME shall send the UE EPS security capabilities, and the UP integrity protection mode, and optionally the UE capability for the maximum data rate for integrity protection of UP to the target eNB in the 51 AP HANDOVER REQUEST message.



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    Image Search Results


    Results of classifiers (in %) without feature selection.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) without feature selection.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques: Selection

    Results of classifiers (in %) with Information Gain.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Information Gain.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with Chi-Square Test.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Chi-Square Test.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with FDA.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with FDA.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with Variance Threshold.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Variance Threshold.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with MAD.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with MAD.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with Dispersion Ratio.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Dispersion Ratio.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques: Dispersion

    Results of classifiers (in %) with Relief.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Relief.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with Lasso.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Lasso.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with RF Importance.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with RF Importance.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with LDA.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with LDA.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with PCA.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with PCA.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Performance of proposed model and state-of-the-art models.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Performance of proposed model and state-of-the-art models.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques: