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design optimization » bayesian optimization (Expand Search)
work optimization » wolf optimization (Expand Search), swarm optimization (Expand Search), dose optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary past » binary mask (Expand Search), binary pairs (Expand Search), binary plddt (Expand Search)
design optimization » bayesian optimization (Expand Search)
work optimization » wolf optimization (Expand Search), swarm optimization (Expand Search), dose optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary past » binary mask (Expand Search), binary pairs (Expand Search), binary plddt (Expand Search)
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Medium-scale dataset comparative analysis using the number of features selected.
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Large-scale dataset comparative analysis using the number of features selected.
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Small-scale dataset comparative analysis using the number of features selected.
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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. …”