يعرض 1 - 20 نتائج من 70 نتيجة بحث عن '(( elements could algorithm ) OR ((( faces using algorithm ) OR ( neural coding algorithm ))))', وقت الاستعلام: 0.13s تنقيح النتائج
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    Faces in the crowd حسب Abu-Khzam, Faisal N.

    منشور في 2017
    "…This is a considerable improvement over the best previously-published face cover algorithms, which rely instead on reductions to planar dominating set.…"
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    conferenceObject
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    Developing a framework for using face recognition in transit payment transactions حسب HABEH, ORABI MOHAMMAD ABDULLAH

    منشور في 2021
    "…On the other hand, a tremendous advancement has been noticed in the human face detection and recognition technology which mainly used to authenticate and identify person face from a group of people through detecting a unique feature of the face and ignore the background image then compare the outcomes with the registered faces in the database to identify the person. …"
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    Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering حسب Abu Zitar, Raed

    منشور في 2022
    "…This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. …"
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    Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm حسب Noor Habib Khan (22224775)

    منشور في 2024
    "…To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. …"
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    Predicting Dropouts among a Homogeneous Population using a Data Mining Approach حسب BILQUISE, GHAZALA

    منشور في 2019
    "…Our research reveals that the Gradient Boosted Trees is a robust algorithm that predicts dropouts with an accuracy of 79.31% and AUC of 88.4% using only pre-enrollment data. …"
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