Algorithm. by Daniel Sanchez-Taltavull (11654630)

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Comparison of deconvolution and optimization algorithms on a batch of data. by Ali-Kemal Aydin (10968731)

“…Both experimental data have been resampled at 50ms and used to compute a set of TFs (in orange) either with direct deconvolution approaches (Fourier or Toeplitz methods, middle-upper panel TFs) or with 1-Γ function optimization performed by 3 different algorithms (middle-lower panel TFs). …”

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

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Multi-functional liquid crystal devices based on random binary matrix algorithm by Tibin Zeng (10890697)

“…<p>Multi-functional diffraction gratings have found broad applications in laser processing, medicine, beam manipulation, etc. …”

Automatic cropping algorithm. by Thomas W Georgi (12427189)

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

Fuzzy membership functions for yaw with Δ<i>ρ</i>_<i>u</i>. by Yasir Mehmood (744452)

Fuzzy membership functions for longitudinal velocity with Δ<i>ρ</i>_<i>θ</i>. by Yasir Mehmood (744452)

-value on 23 benchmark functions (dim = 30). by Yuqi Xiong (12343771)

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Flowchart of system algorithm. by Syed Zahurul Islam (9635163)

Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

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Overview of the Cell2Spatial algorithm. by Huamei Li (8815955)

Hybrid feature selection algorithm of CSCO-ROA. by Afnan M. Alhassan (18349378)

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Average accuracy (across all 12 subject) as a function of the number of eigenvectors obtained with different dimensionality reduction techniques for the best classifier—k-NN (see a... by Elnaz Lashgari (11278808)

As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algor... by Daniela Huppenkothen (9174507)

“…We note that the middle row is a special case: here, <i>f</i>_target only corresponds to the proportions of the embedded cohort, while “success” for these two panels is defined as recovering maximally diverse cohorts, as this particular algorithm is designed to do. …”

Membership function of output for tank 1. by Tamiru Demelash Kassie (20678515)

Comparison of optimization performance of six ABC algorithms. by Shuhui Yu (1535500)

Comprehensive Re, F1 and Pre values of FcaNet-CycleGAN model. by Xiaojun Wang (50332)

Membership function of error E1 for tank 1. by Tamiru Demelash Kassie (20678515)