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Showing 1 - 20 results of 169 for search 'multiple machine detection algorithm', query time: 0.23s 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
    Trainable parameters for different algorithms.

    Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

    Published 2024
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  5. 5
    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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  6. 6
    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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  7. 7
    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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  8. 8
    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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  9. 9
    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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  10. 10
    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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  11. 11
    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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  12. 12
    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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  13. 13
    Control Parameters of IRSA Algorithm.

    Control Parameters of IRSA Algorithm. by Wan-Hua Zhang (21601139)

    Published 2025
    “…This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. …”
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  14. 14
    Table 1_Predicting financial distress in TSX-listed firms using machine learning algorithms.docx

    Table 1_Predicting financial distress in TSX-listed firms using machine learning algorithms.docx by Mark Eshwar Lokanan (20344707)

    Published 2024
    “…Introduction<p>This study investigates the application of machine learning (ML) algorithms, a subset of artificial intelligence (AI), to predict financial distress in companies. …”
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  15. 15
    Distribution of discriminatory score identified using decision-tree algorithms for four antibiotics; amikacin (A), linezolid (B), clarithromycin (C), and cefoxitin (D).

    Distribution of discriminatory score identified using decision-tree algorithms for four antibiotics; amikacin (A), linezolid (B), clarithromycin (C), and cefoxitin (D). by Tran Duong Thai (20939598)

    Published 2025
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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
    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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