Illustration of the unrestricted model and its training algorithm. by Yuru Song (21300760)

Original data for the study "<b>Improving basin-wide flood estimation from a global hydrological model through spatiotemporal-pattern-</b><b>based </b><b>machine learning</b>" by Jiaqing Wang (18814630)

<b>The Negative Digital Root Implemented as a Mathematica Standalone Function</b> by Paul F. Marrero Romero (17450601)

General idea of alternative structures of aptamers binding the same ligand and its specific implementation scheme for the BioRS function. by Alexander V. Spirov (11302085)

Concept of the ISEEML classifier and its main characteristics. by Artem S. Kasianov (8783363)

Subjects:

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

One iteration of the NSGA-II algorithm. by Erick Axel Padilla-García (16839233)

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. …”

Flowchart of MOSRS algorithm. by Vahid Goodarzimehr (21343124)

“…It relies on principles inspired by the theory of special relativity physics, which iteratively refines solutions toward optimality and self-adapts its parameters using these laws. Unlike most algorithms in the literature today, the user sets only the number of iterations and particles (or population). …”

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…More specifically, we plot the <i>R</i>² of the best linear model minus the <i>R</i>² of the best quadratic, where “best” refers to the model identified by the corresponding Metropolis algorithm over its finite runtime (10000 steps). (B) Nevertheless, even when the linear algorithm loses in <i>R</i>², the grouping it identifies can be a better representation of the underlying ground truth. …”

The GOR4 and SOPMA algorithm findings. by Ahmad Abdullah Mahdeen (19704401)

Brief sketch of the quasi-attraction/alignment algorithm. by Takayuki Niizato (162226)

“…(B) A sketch of the cover function, which returns the minimum cap on the interaction sphere , which covers all points <b><i>p</i></b>_<i>i</i> (for the mathematical definition, see the Section 2 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010869#pcbi.1010869.s008" target="_blank">S1 Appendix</a>). …”

The distribution function graphs of prediction errors for CLPREM model and its comparative models in three different environments, as well as the root mean square error of predicti... by Xiaorui Wu (8322705)

Visualizing the scatter plot of the drought (red) versus no-drought (green) and the support vectors (black) using the SVM section 4 classification algorithm. by J. Emmanuel Johnson (9579567)

Schematic of the iterative algorithm. by Pierre Barrat-Charlaix (9556592)

(a) Radar chart of these algorithms (23 Benchmark functions), (b) The sorting diagram of these algorithms (23 Benchmark functions). by Yu Liu (6938)

“…<p>(a) Radar chart of these algorithms (23 Benchmark functions), (b) The sorting diagram of these algorithms (23 Benchmark functions).…”

Iteration Vs fitness function. by Ajmeera Kiran (16819294)

We recorded the distribution function of the one-step prediction time for CLPREM and its comparative models, as well as a curve graph showing the multiple of the comparative model’... by Xiaorui Wu (8322705)

Phase diagram for global archetype model as a function of temperature. by Giulio Ruffini (383994)

Example of a network, its transition function, and its initial and desired states. by Mohammad Moradi (3642007)