Search alternatives:
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
based optimization » whale optimization (Expand Search)
task learning » based learning (Expand Search)
binary score » binary scoring (Expand Search), injury score (Expand Search)
binary task » binary mask (Expand Search)
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
based optimization » whale optimization (Expand Search)
task learning » based learning (Expand Search)
binary score » binary scoring (Expand Search), injury score (Expand Search)
binary task » binary mask (Expand Search)
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CDF of task latency, approximated as the inverse of the achieved computation rate.
Published 2025Subjects: -
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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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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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Algorithm for generating hyperparameter.
Published 2024“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30.
Published 2025Subjects: -
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Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30.
Published 2025Subjects: -
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Results of machine learning algorithm.
Published 2024“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”