Showing 1 - 20 results of 144 for search '(( primary risk based optimization algorithm ) OR ( binary rate feature optimization algorithm ))', query time: 0.56s Refine Results
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    Feature selection results. by Doaa Sami Khafaga (21463870)

    Published 2025
    “…Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
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    Routing policy based on path satisfaction. by Yang Yu (4292)

    Published 2025
    “…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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    Flowchart of simple ant colony algorithm. by Yang Yu (4292)

    Published 2025
    “…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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    Changes of risk value under different parameters. by Yang Yu (4292)

    Published 2025
    “…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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    Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

    Published 2025
    “…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”
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    Classification baseline performance. by Doaa Sami Khafaga (21463870)

    Published 2025
    “…Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
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    ANOVA test result. by Doaa Sami Khafaga (21463870)

    Published 2025
    “…Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
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    Summary of literature review. by Doaa Sami Khafaga (21463870)

    Published 2025
    “…Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
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