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

“…<b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”

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

“…After <i>K</i> − 2 = 2 steps, the resulting tree is binary and the algorithm stops.</p>…”

Application of ABC SMC to the fitting of the binary flip with cusp model. by Elena Camacho-Aguilar (10906327)

Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

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

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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Simulation parameters. by Hend Bayoumi (22693738)

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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Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.6). 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 (0.3). by Yi-Wen Hsiao (13767916)

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