Showing 1 - 20 results of 111 for search '(( binary data processing reflection algorithm ) OR ( binary data task optimization algorithm ))', query time: 1.03s Refine Results
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    The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

    Published 2025
    “…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”
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    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
    “…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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    IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

    Published 2025
    “…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”
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    IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

    Published 2025
    “…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”
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    Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

    Published 2025
    “…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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    fdata-02-00029_Reflections on Gender Analyses of Bibliographic Corpora.pdf by Helena Mihaljević (8541108)

    Published 2020
    “…An author's gender is typically inferred from their name, further reduced to a binary feature by an algorithmic procedure. This and subsequent data processing steps introduce biases whose effects are hard to estimate. …”
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    fdata-02-00029_Reflections on Gender Analyses of Bibliographic Corpora.xml by Helena Mihaljević (8541108)

    Published 2020
    “…An author's gender is typically inferred from their name, further reduced to a binary feature by an algorithmic procedure. This and subsequent data processing steps introduce biases whose effects are hard to estimate. …”
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    IRBMO vs. variant comparison adaptation data. by Chenyi Zhu (9383370)

    Published 2025
    “…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”
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    Pseudo Code of RBMO. by Chenyi Zhu (9383370)

    Published 2025
    “…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”
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    P-value on CEC-2017(Dim = 30). by Chenyi Zhu (9383370)

    Published 2025
    “…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”