Search alternatives:
process optimization » model optimization (Expand Search)
based optimization » whale optimization (Expand Search)
data process » data processing (Expand Search), damage process (Expand Search), data access (Expand Search)
primary data » primary care (Expand Search)
binary data » dietary data (Expand Search)
data based » data used (Expand Search)
process optimization » model optimization (Expand Search)
based optimization » whale optimization (Expand Search)
data process » data processing (Expand Search), damage process (Expand Search), data access (Expand Search)
primary data » primary care (Expand Search)
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101
Table_9_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx
Published 2022“…Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. …”
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102
Table_7_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx
Published 2022“…Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. …”
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103
Table_4_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx
Published 2022“…Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. …”
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104
Table_2_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx
Published 2022“…Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. …”
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105
Table_6_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx
Published 2022“…Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. …”
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106
Table_10_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx
Published 2022“…Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. …”
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107
Table_3_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx
Published 2022“…Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. …”
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108
Models’ performance without optimization.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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109
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110
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111
RNN performance comparison with/out optimization.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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112
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113
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114
Dynamic resource allocation process.
Published 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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115
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116
An Example of a WPT-MEC Network.
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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117
Related Work Summary.
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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118
Simulation parameters.
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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119
Training losses for N = 10.
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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120
Normalized computation rate for N = 10.
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. …”