FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

الموضوعات:

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

الموضوعات:

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

الموضوعات:

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

الموضوعات:

Zimbabwe model outputs on actual and optimized 2016 spending and impact by intervention. حسب Nicole Fraser-Hurt (2636833)

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"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …"

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"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …"

Simulation parameters. حسب Hend Bayoumi (22693738)

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …"

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

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …"

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

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …"

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"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …"

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A portfolio selection model based on the knapsack problem under uncertainty حسب Fereshteh Vaezi (6655028)

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"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

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"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

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