بدائل البحث:
where optimization » whale optimization (توسيع البحث), phase optimization (توسيع البحث), other optimization (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (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)
data where » data were (توسيع البحث), dataset where (توسيع البحث)
where optimization » whale optimization (توسيع البحث), phase optimization (توسيع البحث), other optimization (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (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)
data where » data were (توسيع البحث), dataset where (توسيع البحث)
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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. …"
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Simulation parameters.
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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Training losses for N = 10.
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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Normalized computation rate for N = 10.
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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Summary of Notations Used in this paper.
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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Flowchart scheme of the ML-based model.
منشور في 2024"…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …"
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Generalized Tensor Decomposition With Features on Multiple Modes
منشور في 2021"…An efficient alternating optimization algorithm with provable spectral initialization is further developed. …"
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Presentation_1_Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.PPTX
منشور في 2021"…Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. …"
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DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx
منشور في 2021"…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …"
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Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf
منشور في 2024"…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …"
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DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx
منشور في 2024"…Logistic regression emerged as the optimal machine learning algorithm for both DLR models. …"
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PathOlOgics_RBCs Python Scripts.zip
منشور في 2023"…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …"
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Seed mix selection model
منشور في 2022"…The genetic algorithm then operated over 1000 iterations, applying crossover and mutation processes to optimize bee richness. …"
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Flow diagram of the automatic animal detection and background reconstruction.
منشور في 2020"…(H) The region of interest is the area where movement has been detected. The algorithm will now restrict the search for the animal to this region. …"