Benchmark functions. by Mingjun Li (178925)

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Result of benchmark functions. by Mingjun Li (178925)

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The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

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

The probability density function of points in feature space of variance versus mean of the local field for (0,3] and (0,3] displayed as a contour-plot. by Ali Talebi (7164203)

Table_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.DOCX by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

The control algorithm of MPC for SSI. by Ahmed Abdelaleem (19066115)

(a): These systems were simulated for (0,3] and (0,3] without the prior knowledge about different phases, and the probability density function of points in feature space illustrate... by Ali Talebi (7164203)

“…(b): The dense areas are separated by removing the data less than threshold = 0.5 in the probability density function. (c): The centroid of each cluster is determined by the K-means algorithm.…”

Data_Sheet_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.PDF by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…<p dir="ltr"><b>Opti2Phase: Python Scripts for Two-Stage Focal Reducer Design</b></p><p dir="ltr">The folder <b>Opti2Phase</b> contains the Python scripts used to generate the results presented in the manuscript. …”

Phase stability at interfaces (dataset) by J Pitfield (21962924)

Comparison of classification accuracy across different machine learning models using the objective function metric. by Amal H. Alharbi (21755906)

Analysis of ABIDE dataset: Conventional regression frameworks for optimized algorithmic value. by Alexandria M. Jensen (6494957)

Subjects: “…community detection algorithms…”

TV-BayesOpt algorithm performance for tracking a gradual drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

TV-BayesOpt algorithm performance for tracking a periodic drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches by Andrés Halabi Diaz (20798460)

“…This study integrates conceptual density functional theory (CDFT) descriptors with explainable no-code machine learning (ML) models to predict NA mutagenicity based on Ames test results. …”

Multidimensional test results of BWEMFO and MFO on IEEE CEC 2017 test functions. by Jingjing Ma (419752)

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TV-BayesOpt algorithm performance for tracking a superimposed (gradual and periodic) drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

Taming Negative Ion Resonances Using Nonlocal Exchange-Correlation Functionals by Abhisek Ghosal (762577)

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