Verification of the in-line PS function. by Raul Castañeda (13932971)

3D visualization of the Rastrigin function from the CEC 2019 benchmark test functions. by Mahmood A. Jumaah (22272371)

Improved A* algorithm flowchart. by Peiying Li (797714)

“…The study conducted algorithm validation on the TurtleBot3 mobile robot, with data sourced from experimental data from a certain college. …”

Predicted body weights (kg) as a function of time (age in weeks) obtained from MARS algorithm and Gompertz model. by Turgay Şengül (12801990)

“…<p>Predicted body weights (kg) as a function of time (age in weeks) obtained from MARS algorithm and Gompertz model.…”

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Two approaches, both leveraging batched dense linear algebra, are found to outperform the others across a suite of molecular benchmarks. We show that batched formation of the XC matrix from the density matrix yields the best performance for large (>O(103) basis functions), sparse systems such as glycine chains and water clusters. …”

Data from: Structural and Functional Analysis of Multi-interface Domains by ZHAO Liang (8657694)

“…This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. …”

Detailed information of benchmark functions. by Guangwei Liu (181992)

“…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Skeletonisation algorithm. by Jordan Hembrow (16908051)

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. …”

Coarse-fine optimization algorithm. by Chao Ma (207385)

“…The improved gradient extraction method combines the Scale Invariant Feature Transformation (SIFT) algorithm to form a new multi-scale image sharpness evaluation function, SIFT Quad-Tenen. …”

Algorithm operation steps. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

“…The Python implementation of the proposed approach is available in this <a href="https://github.com/somnath796/DA_contrast_enhancement" target="_blank">Github repository</a>.…”

Sensitivity analysis of the TV-BayesOpt algorithm performance at tracking a periodic temporal drift when the period of the drift is offset from the covariance function period anticipated by the TV-BayesOpt algorithm. by John E. Fleming (8533956)

“…<p>The performance of a scheduler (green line), the TV-BayesOpt with a periodic temporal covariance function (purple line) and the TV-BayesOpt with a periodic and smooth forgetting covariance algorithm were estimated by calculating the AUC value for the associated cumulative regret plot for each implementation at different offset in the true temporal period.…”

Types of training algorithms. by Caixia Yan (6647072)

HIV exposure risk algorithm. HIV exposure risk algorithm based on available data from the Tonse Padmozi trial. by Anna M. Leone (22486372)

Constructing the dynamical law. by Robert Marsland (8616483)

Flowchart for Algorithm 1. by Sagvan Y. Musa (17797611)