Tunable parameters in stimuli generation module. ^<i>a</i>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

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Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative “Superbugs”: Six Protein Clusters Amenable for Distinct Scientific Applications by Carlos Sicilia (14228509)

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Great machine learning performance with multiple classification algorithms. by Sydney C. Weiser (15383531)

Enhancement results of Nightstreet. by Hui Huang (4122)

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Fusion results of Nightstreet. by Hui Huang (4122)

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Detail separation layer results of Nato camp. by Hui Huang (4122)

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Example of the algorithm function as an excel template. by Thomas Nickolay (18121412)

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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

Simplified representation of the automated algorithm for tissue differentiation. by Mark T. Scimone (8655114)

(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)

“…<p>The points stretch from the dense region corresponding to the spin glass phase (left top) to the dense region corresponding to the ferromagnetic phase (right bottom), representing the mixed phase region. (b): The dense areas are separated by removing the data less than threshold = 0.5 in the probability density function. …”

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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Functional analysis of anoikis-related genes. by Yufeng He (5673119)

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Immune microenvironment (A) Comparison of immune cell infiltration between the high-risk and low-risk groups using the ssGSEA algorithm. by Chengting Wu (21485612)

“…<p>(B) Comparison of immune function between the high-risk and low-risk groups using the ssGSEA algorithm. …”

Feto-placental vasculatures with non-central (A) and centralised (B) umbilical artery insertion. by Diana C. de Oliveira (9220729)

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Spatial distribution of municipality scores in functional PC1 (A), PC2 (B) and PC3 (C). by Manuel Ribeiro (17889313)

Subjects: “…unsupervised learning algorithms…”

A-B. The boxplots present the distribution of scores for IRF4 and ELANE immune-related functions in low-expression and high-expression groups. by Min-xiao Wang (20868922)

HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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Simulation Results Plot of the SBW System Response: (a) Step response curve of the SBW system; (b) Sine response curve of the SBW system; (c) Steady state error curve of the SBW sy... by Honglei Pang (22693724)

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Simulation Results Plot of the EMB System Response: (a) Step response curve of the EMB system; (b) Sine response curve of the EMB system; (c) Steady state error curve of the EMB sy... by Honglei Pang (22693724)

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HIL test results of low-adhesion road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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