Results of Lightbgm. حسب Balraj Preet Kaur (20370832)

Results of Lightbgm. حسب Balraj Preet Kaur (20370832)

Feature selection process. حسب Balraj Preet Kaur (20370832)

Results of KNN. حسب Balraj Preet Kaur (20370832)

After upsampling. حسب Balraj Preet Kaur (20370832)

Results of Extra tree. حسب Balraj Preet Kaur (20370832)

Gradient boosting classifier results. حسب Balraj Preet Kaur (20370832)

Calibration curves for the test set of four models. حسب Meng Cao (105914)

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ROC curves for the test set of four models. حسب Meng Cao (105914)

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Performance metrics of the models on the training and test set. حسب Meng Cao (105914)

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An Example of a WPT-MEC Network. حسب Hend Bayoumi (22693738)

"…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. …"

Related Work Summary. حسب Hend Bayoumi (22693738)

"…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. …"

Simulation parameters. حسب Hend Bayoumi (22693738)

"…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. …"

Training losses for N = 10. حسب Hend Bayoumi (22693738)

"…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. …"

Normalized computation rate for N = 10. حسب Hend Bayoumi (22693738)

"…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. …"

Summary of Notations Used in this paper. حسب Hend Bayoumi (22693738)

"…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. …"

SHAP bar plot. حسب Meng Cao (105914)

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Display of the web prediction interface. حسب Meng Cao (105914)

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Sample screening flowchart. حسب Meng Cao (105914)

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Descriptive statistics for variables. حسب Meng Cao (105914)

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