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orcid org 0000 0001 7842 5496 ruipeng li national synchrotron light source ii nsls ii  (Brookhaven Instruments)

 
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    Brookhaven Instruments orcid org 0000 0001 7842 5496 ruipeng li national synchrotron light source ii nsls ii
    Orcid Org 0000 0001 7842 5496 Ruipeng Li National Synchrotron Light Source Ii Nsls Ii, supplied by Brookhaven Instruments, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/nsls/pm42263146-184-227-238?v=Brookhaven+Instruments
    Average 86 stars, based on 1 article reviews
    orcid org 0000 0001 7842 5496 ruipeng li national synchrotron light source ii nsls ii - by Bioz Stars, 2026-07
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    Fractional order ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} ) impact analysis on CFDNN performance for (a) NSL-KDD and (b) CIC-IDS2018 datasets. The highlighted region ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.2 \le \alpha \le 1.8$$\end{document} ) represents the optimal performance range.

    Journal: Scientific Reports

    Article Title: Conformable Fractional Deep Neural Networks (CFDNN) for high-speed cyber-attack detection

    doi: 10.1038/s41598-026-45213-w

    Figure Lengend Snippet: Fractional order ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} ) impact analysis on CFDNN performance for (a) NSL-KDD and (b) CIC-IDS2018 datasets. The highlighted region ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.2 \le \alpha \le 1.8$$\end{document} ) represents the optimal performance range.

    Article Snippet: The raw data utilized in this study are derived from the publicly available NSL-KDD and CIC-IDS2018 datasets, both accessible through the University of New Brunswick (UNB) repository.

    Techniques:

    Multi-dimensional performance analysis: (a) NSL-KDD performance radar, (b) CIC-IDS2018 performance radar, (c) Optimal performance comparison at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =1.8$$\end{document} , (d) Normalized computational time analysis.

    Journal: Scientific Reports

    Article Title: Conformable Fractional Deep Neural Networks (CFDNN) for high-speed cyber-attack detection

    doi: 10.1038/s41598-026-45213-w

    Figure Lengend Snippet: Multi-dimensional performance analysis: (a) NSL-KDD performance radar, (b) CIC-IDS2018 performance radar, (c) Optimal performance comparison at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =1.8$$\end{document} , (d) Normalized computational time analysis.

    Article Snippet: The raw data utilized in this study are derived from the publicly available NSL-KDD and CIC-IDS2018 datasets, both accessible through the University of New Brunswick (UNB) repository.

    Techniques: Comparison