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applications algorithm » approximation algorithm (Expand Search), location algorithm (Expand Search)
learning applications » sensing applications (Expand Search)
bayesian optimization » based optimization (Expand Search)
data learning » meta learning (Expand Search), deep learning (Expand Search), a learning (Expand Search)
amp bayesian » a bayesian (Expand Search), art bayesian (Expand Search), task bayesian (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
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The Pseudo-Code of the IRBMO Algorithm.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things
Published 2025“…In general, BRBPNN does not show any optimization adaption methods to determine the optimal parameter for appropriate detection. 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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IRBMO vs. meta-heuristic algorithms boxplot.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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IRBMO vs. feature selection algorithm boxplot.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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Related studies on IDS using deep learning.
Published 2024“…This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. …”
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The architecture of the BI-LSTM model.
Published 2024“…This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. …”
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Comparison of accuracy and DR on UNSW-NB15.
Published 2024“…This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. …”
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Comparison of DR and FPR of UNSW-NB15.
Published 2024“…This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. …”
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