Fitting results of the different optimization algorithms applied. by Sonja Langthaler (6069149)

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Functional annotations of peaks. by Azusa Tanaka (540528)

The signal detection algorithm for constructing a neurometric function (the probability of segregation as a function of time) generates acceptable buildup fits at <i>DF</i> = 1, 3, 6, 9. by Quynh-Anh Nguyen (847240)

“…Lower panel: The signal detection algorithm constructs neurometric functions using numerical data from all <i>N</i>_<i>in</i> neuronal units. …”

Convergence curves of the mean function error values for six test functions at <i>D</i> = 30. by Xuxu Zhong (10099054)

Example performance of the smooth-index algorithm. by Alexander Borst (32619)

“…<b>D</b> Different cost functions as the algorithm performs the gradient descent. …”

Algorithm of genetic workup. by Idoia Martinez de Lapiscina (16528261)

MOEA/D-ND algorithm flowchart. by Jun Sun (48981)

“…The experimental outcomes denote that on the logic function with Zener diode transistor, the proposed algorithm has a mean generation distance index of 5.03E-3, which is lower than most algorithms. …”

MSX-60 module: (a) <i>I</i>-<i>U</i> and (b) <i>P-U</i> characteristics. by Ziad M. Ali (12516532)

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. …”

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density. by Hassan Mehboob (8960273)

“…<p>A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density.…”

RTC-F solar cell: (a) <i>I</i>-<i>U</i> and (b) <i>P</i>-<i>U</i> characteristics. by Ziad M. Ali (12516532)

PWP201 solar module: (a) <i>I</i>-<i>U</i> and (b) <i>P</i>-<i>U</i> characteristics. by Ziad M. Ali (12516532)

Demonstration of the combinatorial Hill functions (A and B), and the regulation function (C and D) under the regulation of an activator and a repressor. by Ruihao Li (2908991)

Functional enrichment and correlation analysis of hub DE-SRGs. by Xihao Shen (20347942)

Subjects:

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