Simulation parameters. by Hend Bayoumi (22693738)

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

Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

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<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

Flowchart of the general algorithm for one trial. by Junying Wang (509009)

Proposed Algorithm. by Hend Bayoumi (22693738)

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

Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

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

Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. by Tristan Mary-Huard (3864)

The performance of our proposed method in four scenarios (I to IV) uses simulated data that mimics real-world data distribution with the complete version. by Yi-Wen Hsiao (13767916)

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Performance of our proposed method in four scenarios (I to IV) using naïve simulated data with the complete version. by Yi-Wen Hsiao (13767916)

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The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…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. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…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. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…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. …”

Joint Detection of Change Points in Multichannel Single-Molecule Measurements by Hugh Wilson (11797444)

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RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)

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Performance of the ceRNAR package using synthetic data with randomly selected correlation values from 0.8 to 0.9. by Yi-Wen Hsiao (13767916)

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Performance comparisons between ceRNIA, JAMI, SPONGE, GDCRNATools and ceRNAR packages using synthetic data that mimic the real-world case. by Yi-Wen Hsiao (13767916)

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Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.9). by Yi-Wen Hsiao (13767916)

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Performance of the ceRNAR package in five scenarios under the fast version using synthetic data with a fixed correlation value (0.9). by Yi-Wen Hsiao (13767916)

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