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Showing 1 - 20 results of 1,415 for search '(((( element data algorithm ) OR ( recent data algorithm ))) OR ( query processing algorithm ))', query time: 0.33s Refine Results
  1. 1
    ML algorithms in cyber security.

    ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

    Published 2025
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  2. 2
    Comparison of different optimization algorithms.

    Comparison of different optimization algorithms. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  3. 3
    The run time for each algorithm in seconds.

    The run time for each algorithm in seconds. by Edward Antonian (21453161)

    Published 2025
    “…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”
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  4. 4
    The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset.

    The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. by Abdullah Asım Yılmaz (20715311)

    Published 2025
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  5. 5
    Folder with all data and algorithms

    Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

    Published 2025
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  6. 6
    Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is added to the track queries to get noised track queries. For Query Denoising (QD), random noise is added to the ground truth bounding boxes to generate noised track queries. All queries are processed together by the decoder. Attention masks are used to prevent information leakage between the object and track queries, the noised track queries generated from TGD, and the noised track queries generated from QD. The gray boxes indicate information being blocked between sets of queries while colored boxes indicate the flow of information.

    Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is a... by Owen M. O’Connor (11965460)

    Published 2025
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  7. 7
    City driving speed query results.

    City driving speed query results. by Meilin Zhu (688698)

    Published 2025
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  8. 8
    Cells are initially detected in frame t<sub>0</sub> and then tracked to frame t<sub>1</sub>. Object queries are processed in the first layer of the decoder for object detection. In the subsequent decoder layers, track queries are concatenated with the object queries to perform tracking.

    Cells are initially detected in frame t<sub>0</sub> and then tracked to frame t<sub>1</sub>. Object queries are processed in the first layer of the decoder for object detection. In... by Owen M. O’Connor (11965460)

    Published 2025
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  9. 9
    Number of tweets collected per query and type.

    Number of tweets collected per query and type. by Sylvia Iasulaitis (8301189)

    Published 2025
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  10. 10
    Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output alongside the output the from top matching object queries. Extracted from the self-attention mechanism in the decoder, the attention map highlights the strength of interactions among the queries. Lighter colors indicate stronger interactions. The track queries tend to focus on themselves, while the object queries attend to the track query that corresponds most closely in location.

    Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output al... by Owen M. O’Connor (11965460)

    Published 2025
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  11. 11
    Performance of ENQUIRE’s gene normalization algorithm.

    Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

    Published 2025
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  12. 12
    Example of a knowledge query.

    Example of a knowledge query. by Bei Li (90587)

    Published 2025
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  13. 13
    Elements: Kingfisher: Storage Management for Data Federations (CSSI PI Meeting 2025)

    Elements: Kingfisher: Storage Management for Data Federations (CSSI PI Meeting 2025) by Brian Bockelman (5128082)

    Published 2025
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  14. 14
    The 10 features captured by persistence fingerprint.

    The 10 features captured by persistence fingerprint. by Yuxi Long (11024307)

    Published 2025
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  15. 15
    Construction of internuclear persistence contours (IPCs).

    Construction of internuclear persistence contours (IPCs). by Yuxi Long (11024307)

    Published 2025
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  16. 16
    Comparison of proposed model with default ML classifiers and recent existing studies.

    Comparison of proposed model with default ML classifiers and recent existing studies. by Ankur Kumar (1357440)

    Published 2025
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  17. 17
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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  18. 18
    Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer.

    Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer. by Luca Musella (19986312)

    Published 2025
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  19. 19
    Flow diagram of the MAO algorithm.

    Flow diagram of the MAO algorithm. by Mohamed Irhuma (20818274)

    Published 2025
    Subjects:
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  20. 20
    Sanitized databases using MLHProtector algorithm.

    Sanitized databases using MLHProtector algorithm. by Loan T. T. Nguyen (20660789)

    Published 2025
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