Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers. by Solomon Shiferaw Beyene (9143178)

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

ddPCR mtDNA amplicon concentration as a function of distance. by Patrick Kurtz (21772385)

The SCOPer algorithm can be run efficiently on multiple cores. by Nima Nouri (2183760)

Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)

Power flow and cost optimization with APSO variant-16 of APSO algorithm (b). by Muhammad Ahmad Iqbal (11851338)

(a) Flow chart for PSO algorithm (b) position and velocity update of particles (c) convergence of PSO. by Waqas Anjum (8415186)

Constructing the dynamical law. by Robert Marsland (8616483)

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

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)

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

Convergence curves of different algorithms on the benchmark functions. by Aihua Guo (13267712)

Algorithm results based on experimental likelihood functions. by Nicolas Herzig (9230425)

BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

“…(C) Schematic representation of the implementation of the genetic algorithm (GA) using the metabolic network presented in B. …”

Approximate energy consumed per particles in <i>nano Joules</i> (<i>nJ</i>) for various computation platforms and algorithms (A: first algorithm, B: second algorithm, C: third algo... by Abedalmuhdi Almomany (18547669)

Conservation and functional variation across TcdB subtypes. by Michael J. Mansfield (9922934)

Summary of greedy algorithms presented in this section. Details of approximation factors for FastGreedy and UnitGreedy may be found in [26] and [25], respectively. by Jeffrey Keithley (14626551)

Subjects:

Results of searching performance of the improved NAG algorithm on Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)