Search alternatives:
required optimization » guided optimization (Expand Search), resource optimization (Expand Search), feature optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
task required » task requiring (Expand Search), time required (Expand Search), also required (Expand Search)
primary data » primary care (Expand Search)
binary task » binary mask (Expand Search)
data model » data models (Expand Search)
required optimization » guided optimization (Expand Search), resource optimization (Expand Search), feature optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
task required » task requiring (Expand Search), time required (Expand Search), also required (Expand Search)
primary data » primary care (Expand Search)
binary task » binary mask (Expand Search)
data model » data models (Expand Search)
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Features selected by optimization algorithms.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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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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Models’ performance without optimization.
Published 2024“…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”
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