بدائل البحث:
resource optimization » resource utilization (توسيع البحث), resource utilisation (توسيع البحث), resource limitations (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
data resource » data resources (توسيع البحث), data source (توسيع البحث), water resource (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
image model » damage model (توسيع البحث), primate model (توسيع البحث), climate model (توسيع البحث)
resource optimization » resource utilization (توسيع البحث), resource utilisation (توسيع البحث), resource limitations (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
data resource » data resources (توسيع البحث), data source (توسيع البحث), water resource (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
image model » damage model (توسيع البحث), primate model (توسيع البحث), climate model (توسيع البحث)
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Proposed Algorithm.
منشور في 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.
منشور في 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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ROC curve for binary classification.
منشور في 2024"…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …"
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Confusion matrix for binary classification.
منشور في 2024"…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …"
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A* Path-Finding Algorithm to Determine Cell Connections
منشور في 2025"…Future work aims to generalize this algorithm for broader biological applications by training additional Cellpose models and adapting the A* framework.…"
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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm
منشور في 2025"…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …"
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Dynamic resource allocation process.
منشور في 2025"…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …"
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Summary of existing CNN models.
منشور في 2024"…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …"
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An Example of a WPT-MEC Network.
منشور في 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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Related Work Summary.
منشور في 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. …"