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processes optimization » process optimization (Expand Search), process optimisation (Expand Search), property optimization (Expand Search)
four optimization » fox optimization (Expand Search), after optimization (Expand Search), wolf optimization (Expand Search)
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binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
based four » based food (Expand Search)
processes optimization » process optimization (Expand Search), process optimisation (Expand Search), property optimization (Expand Search)
four optimization » fox optimization (Expand Search), after optimization (Expand Search), wolf optimization (Expand Search)
data processes » damage processes (Expand Search), data processing (Expand Search), state processes (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
based four » based food (Expand Search)
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Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things
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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Proposed Algorithm.
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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Comparisons between ADAM and NADAM optimizers.
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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Medium-scale dataset comparative analysis using the number of features selected.
Published 2023Subjects: -
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Large-scale dataset comparative analysis using the number of features selected.
Published 2023Subjects: -
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Small-scale dataset comparative analysis using the number of features selected.
Published 2023Subjects: