بدائل البحث:
where optimization » whale optimization (توسيع البحث), phase optimization (توسيع البحث), other optimization (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
data where » data were (توسيع البحث), dataset where (توسيع البحث)
binary a » binary _ (توسيع البحث), binary b (توسيع البحث), hilary a (توسيع البحث)
a based » ai based (توسيع البحث), _ based (توسيع البحث), 1 based (توسيع البحث)
where optimization » whale optimization (توسيع البحث), phase optimization (توسيع البحث), other optimization (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
data where » data were (توسيع البحث), dataset where (توسيع البحث)
binary a » binary _ (توسيع البحث), binary b (توسيع البحث), hilary a (توسيع البحث)
a based » ai based (توسيع البحث), _ based (توسيع البحث), 1 based (توسيع البحث)
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Proposed Algorithm.
منشور في 2025"…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"…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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Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization
منشور في 2025"…The performance of the proposed LEGAN-BEPO-BCMANET technique attains 29.786%, 19.25%, 22.93%, 27.21%, 31.02%, 26.91%, and 25.61% greater throughput, compared to existing methods like Blockchain-based BATMAN protocol utilizing MANET with an ensemble algorithm (BATMAN-MANET), Block chain-based trusted distributed routing scheme with optimized dropout ensemble extreme learning neural network in MANET (DEELNN-MANET), A secured trusted routing utilizing structure of a new directed acyclic graph-blockchain in MANET internet of things environment (DAG-MANET), An Optimized Link State Routing Protocol with Blockchain Framework for Efficient Video-Packet Transmission and Security over MANET (OLSRP-MANET), Auto-metric Graph Neural Network based Blockchain Technology for Protected Dynamic Optimum Routing in MANET (AGNN-MANET) and Data security-based routing in MANETs under key management process (DSR-MANET) respectively.…"
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<i>hi</i>PRS algorithm process flow.
منشور في 2023"…<b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …"
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An Example of a WPT-MEC Network.
منشور في 2025"…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"…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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Simulation parameters.
منشور في 2025"…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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Training losses for N = 10.
منشور في 2025"…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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Normalized computation rate for N = 10.
منشور في 2025"…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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Summary of Notations Used in this paper.
منشور في 2025"…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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Seed mix selection model
منشور في 2022"…The model thus requires three types of data presented as matrices in order to calculate the maximum number of bee species supported by a given seed mix: 1) adult phenology of each bee species, where each cell represents whether or not that bee species was observed in the data during a given time period, 2) flowering phenology of plants, where each cell represents whether or not a bee was collected from that plant species during a given time period, and 3) pairwise interactions between plant species and bee species, where each cell represents whether each plant-bee species pair was observed interacting in the data.…"
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Flow diagram of the automatic animal detection and background reconstruction.
منشور في 2020"…All data used to create these plots are available from <a href="https://doi.org/10.25349/D9ZK50" target="_blank">https://doi.org/10.25349/D9ZK50</a>.…"
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Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
منشور في 2025"…</p><p dir="ltr">IC50 (µg/mL): The concentration at which 50% of cells are inhibited, used as a toxicity threshold.</p><p dir="ltr">These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. …"