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Showing 1 - 20 results of 169 for search 'multiple machine detection algorithm*', query time: 0.22s Refine Results
  1. 1
    Comparison of Multiple Runs of Same Algorithm on the Same Dataset (NSL-KDD).

    Comparison of Multiple Runs of Same Algorithm on the Same Dataset (NSL-KDD). by Muhammad Rehan Naeem (21432852)

    Published 2025
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  2. 2
    Comparison of Multiple Runs of Same Algorithm on the Same Dataset (Kaggle-RL).

    Comparison of Multiple Runs of Same Algorithm on the Same Dataset (Kaggle-RL). by Muhammad Rehan Naeem (21432852)

    Published 2025
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  3. 3
    Comparison of Multiple Runs of Same Algorithm on the Same Dataset (UNSW-NB15).

    Comparison of Multiple Runs of Same Algorithm on the Same Dataset (UNSW-NB15). by Muhammad Rehan Naeem (21432852)

    Published 2025
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  4. 4
    Multiple Sequence Alignment of IgV domains in fasta format.

    Multiple Sequence Alignment of IgV domains in fasta format. by Caesar Tawfeeq (21087272)

    Published 2025
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  5. 5
    Multiple Sequence Alignment of IgI domains in fasta format.

    Multiple Sequence Alignment of IgI domains in fasta format. by Caesar Tawfeeq (21087272)

    Published 2025
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  6. 6
    Multiple Sequence Alignment of IgCad (Cadherin) domains in fasta format.

    Multiple Sequence Alignment of IgCad (Cadherin) domains in fasta format. by Caesar Tawfeeq (21087272)

    Published 2025
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  7. 7
    Multiple Sequence Alignment of IgC1 domains in fasta format.

    Multiple Sequence Alignment of IgC1 domains in fasta format. by Caesar Tawfeeq (21087272)

    Published 2025
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  8. 8
    Multiple Sequence Alignment of IgFN3 domains in fasta format.

    Multiple Sequence Alignment of IgFN3 domains in fasta format. by Caesar Tawfeeq (21087272)

    Published 2025
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  9. 9
    Multiple Sequence Alignment of IgC2 domains in fasta format.

    Multiple Sequence Alignment of IgC2 domains in fasta format. by Caesar Tawfeeq (21087272)

    Published 2025
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  10. 10
    Table 1_A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms.docx

    Table 1_A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms.docx by Yi-xiang Zhang (22563236)

    Published 2025
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  11. 11
    Underlying Data for '<b>Detection of False Position Attacks in VANETs through Bagging Ensemble Learning</b>'

    Underlying Data for '<b>Detection of False Position Attacks in VANETs through Bagging Ensemble Learning</b>' by Bekan Mekonen (21544637)

    Published 2025
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  12. 12
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  13. 13
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  14. 14
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  15. 15
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  16. 16
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  17. 17
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  18. 18
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  19. 19
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  20. 20
    Rapid Detection of Physicochemical Indicators of Tobacco Flavorings Using Fourier-Transform Near Infrared Spectroscopy with Chemometrics and Machine Learning

    Rapid Detection of Physicochemical Indicators of Tobacco Flavorings Using Fourier-Transform Near Infrared Spectroscopy with Chemometrics and Machine Learning by Qinlin Xiao (14813476)

    Published 2025
    “…The least angle regression (LAR), successive projection algorithm (SPA), and random frog (RF) were used to select characteristic wavelengths. …”
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