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)

Curves of sigmoid functions used in our proposed algorithm. by Binh Thanh Dang (12368831)

Comparison of the methods proposed in this paper in terms of reward value distribution at intersections for different time periods. by Yulin Tian (1457986)

Subjects:

Comparison of edge detector and CHT in detecting droplets. by Manibarathi Vaithiyanathan (6655274)

Algorithm 1. by Jeffrey O. Agushaka (11326679)

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

Python cv coding screenshots of all developed fingerprints on distinct surfaces. by Nisha Yadav (366131)

Comparison of algorithm performance in ZDT1 and ZDT2 function tests. by Tao Dong (15551)

“…<p>Comparison of algorithm performance in ZDT1 and ZDT2 function tests.…”

The parameters of algorithms. by Le Huu Binh (18340233)

Subjects:

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

Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)

Flowchart of the algorithm. by Xiaowen Zhang (83669)

Subjects:

Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art DE variants using CEC2014 functions. by Yang Cao (53545)

Data Sheet 1_IGSA-SAC: a novel approach for intrusion detection using improved gravitational search algorithm and soft actor-critic.docx by Lizhong Jin (20991293)

“…The results demonstrate that IGSA-SAC achieves an accuracy of 84.15% and an F1-score of 84.85% on the NSL-KDD dataset. On the AWID dataset, IGSA-SAC surpasses 98.9% in both accuracy and F1-score, outperforming existing intrusion detection algorithms.…”

Algorithm implemented using SQL with fulltext search feature. by Kathleen M. Akgün (3697702)

Probably pathogenic variants (algorithms). by Felipe Fidalgo (642532)

Compare the test functions. by Yanqiu Li (683621)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

“…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”