Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf by Mikhail Ya Yadgarov (18543769)

Predictive variables for channel-averaged complexity measures obtained from the whole signal. by Lorenzo Semeia (11849033)

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Participant characteristics. by Lorenzo Semeia (11849033)

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Complexity measures during bursts. by Lorenzo Semeia (11849033)

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Complexity measures during interburst periods (IBIs). by Lorenzo Semeia (11849033)

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Comparisons of complexity between burst and interburst periods. by Lorenzo Semeia (11849033)

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Predictive variables and complexity measures computed on the whole signal, during bursts, or during interburst periods. by Lorenzo Semeia (11849033)

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Element model generation method with geometric distribution errors by Yiqi Liu (21357815)

“…The product surface geometric distribution error is directly attached to the element nodes of the product ideal element model using the error surface reconstruction method and the replacement algorithm of the element node vector height based on the product’s point cloud data. …”

UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. by Fatemeh Keikha (20917848)

A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... by Zachary P. Neal (9946331)

Video 1_A hybrid elastic-hyperelastic approach for simulating soft tactile sensors.mp4 by Berith Atemoztli De la Cruz Sánchez (21758708)

“…A significant challenge for simulating tactile sensors is balancing the trade-off between accuracy and processing time in simulation algorithms and models. To address this, we propose a hybrid approach that combines elastic and hyperelastic finite element simulations, complemented by convolutional neural networks (CNNs), to generate synthetic tactile maps of a soft capacitive tactile sensor. …”

Performance metrics of various algorithms. by Pritam Chakraborty (9261302)

“…Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. …”

Algorithm comparison - top performances. by Pritam Chakraborty (9261302)

“…Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. …”

Ranking of features for each algorithm. by Pritam Chakraborty (9261302)

“…Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. …”

Data Sheet 1_Fast forward modeling and response analysis of extra-deep azimuthal resistivity measurements in complex model.docx by Pan Zhang (206408)

Training, validation, and test mean squared error across 26 epochs, with the best validation performance at epoch 26. by Quxi Kuang (22493270)

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ROC curves for the training, validation, test, and combined datasets, illustrating the model’s classification performance. by Quxi Kuang (22493270)

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Error distribution of the proposed model for training, validation, and test datasets with zero error. by Quxi Kuang (22493270)

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Correlation between variables and cyber attack (label). by Quxi Kuang (22493270)

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The folder containing the code is provided as supportive documentation associated with this paper. by Quxi Kuang (22493270)

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