Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

Study proposed algorithm. by Ainhoa Pérez-Guerrero (21377457)

Subjects: “…assess microvascular function…”

Algorithm comparison experiment on the RSOD dataset. by Pingping Yan (462509)

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Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

“…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …”

Classification algorithm implemented in SPARTA. by Baptiste Ruiz (20285217)

Algorithm comparison experiment on the NWPU VHR-10 dataset. by Pingping Yan (462509)

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Distribution of cross correlations in functional connectivity in ABIDE sample. by Alexandria M. Jensen (6494957)

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Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Experiment 1b: Evaluating modularity-based consensus clustering pipelines on the algorithm design datasets as a function of the mixing parameter. by Yasamin Tabatabaee (18095491)

Cleavage time as a function of scaled fiber diameter. by Roukayatou R. Ouedraogo (20460980)

Working time of different processes (<i>h</i>). by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”

Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<p>Among 46 DEGs whose expression significantly changed in A549 and BEAS-2B cell lines, the core genes were selected using three algorithms: MCC, MNC, and DEGREE. <b>(A)</b> The top 10 genes were prioritized using MCC analysis, which identifies the largest clique within the PPI network and selects the central proteins within the clique. …”

Image 1_Construction of a right ventricular function assessment model in patients undergoing invasive mechanical ventilation based on VExUS grading and the classification and regression tree algorithm.pdf by Jun Gao (203098)

“…Objective<p>Investigate the correlation between right ventricular function ultrasound indicators and the Venous Excess Ultrasound (VExUS) grading system in patients undergoing invasive mechanical ventilation (IMV), and develop a right ventricular function assessment model using VExUS grading and the Classification and Regression Tree (CART) algorithm.…”

Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

Boxplot of PLNMFG and other eight state-of-the-art algorithms measured by (a) ACC and (b) AMI on six real datasets. by Hui Yuan (402180)

Sensitivity analysis of parameters <i>α</i> and <i>β</i>. by Haoran Cui (7235234)

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Multimodal reference functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Logistic kernel: Power and Type I error for cluster evaluation metric simulations. by Alexandria M. Jensen (6494957)

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Linear kernel: Power and Type I error for cluster evaluation metric simulations. by Alexandria M. Jensen (6494957)

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Trajectory comparison for system 1: (A) starting point within collection range; (B) starting point outside collection range. by Jingnan Yan (20727331)

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