Algorithm pseudocode. by Cheng-jie Chen (22272090)

“…On the basis of the Retinex model, multi-level decomposition of brightness is carried out, and different filtering parameters are set to obtain multiple illumination and reflection images with different scale information; Using exponential function and Sigmoid function to process reflection images and illumination images separately, reducing external interference on images of different scales, and solving the difficulty of enhancing images with uneven illumination, high noise, low illumination, and loss of details. …”

Benchmarking functions. by Guilin Yang (583364)

“…To evaluate the performance of the proposed algorithm, 27 well-known test reference functions were selected for experimentation, which showed significant advantages compared to other algorithms. …”

Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

“…We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. …”

An Efficient Algorithm for Minimizing Multi Non-Smooth Component Functions by Minh Pham (2458399)

“…<p>Many problems in statistics and machine learning can be formulated as an optimization problem of a finite sum of nonsmooth convex functions. We propose an algorithm to minimize this type of objective functions based on the idea of alternating linearization. …”

Mean and standard deviation of RBF classifier performance across different training algorithms with cross-validation. by Mohammad Hossein Hadizadeh Isfahani (22634016)

Mean and standard deviation of MLP classifier performance across different training algorithms with cross-validation. by Mohammad Hossein Hadizadeh Isfahani (22634016)

Auscultation locations for the CirCor DigiScope dataset [6], which was used for the Challenge: Pulmonary valve (PV), aortic valve (AV), mitral valve (MV), and tricuspid valve (TV). by Matthew A. Reyna (16965038)

Bioinformatics pipeline for circadian function. by Patrick B. Schwartz (14782608)

“…CYCLOPS (cyclic ordering by periodic structure) is an algorithm that can identify rhythmic (longitudinal) data from population data where sample time acquisition is unknown. …”

Retinal thickness analysis algorithm in which mean thicknesses (in micrometers) are provided for each of 9 areas defined by an Early Treatment Diabetic Retinopathy Study (ETDRS). by Adriano Magli (5782706)

Three examples for which the match between experimental data and simulations is challenging. by Robin Dirk (17296179)

Data_Sheet_1_Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms.pdf by Alejandro O. Blenkmann (13914651)

“…The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. …”

Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO₂ Capture: Machine Learning and DFT Calculation Approaches by Mohammad Rahimi (1715590)

“…A diverse training algorithm was applied, in which <i>trainbr</i> illustrated the lowest mean absolute percent error (MAPE) of <3.5%. …”

Function convergence curve. by Guilin Yang (583364)

“…To evaluate the performance of the proposed algorithm, 27 well-known test reference functions were selected for experimentation, which showed significant advantages compared to other algorithms. …”

Mean RMSE and R² for the training and test phases of RBF by different training algorithms for temperature and humidity prediction. by Caixia Yan (6647072)

Mean RMSE and R² for the training and test phases of MLP by different training algorithms for temperature and humidity prediction. by Caixia Yan (6647072)

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

“…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …”

Pyrho Validation - Check re-gridded periodic data by Jimmy Shen (12664675)

23 benchmark functions. by Yu Liu (6938)

“…<div><p>The competition of tribes and cooperation of members algorithm (CTCM) is a novel swarm intelligence algorithm, which increases the diversity of the population to a certain extent through tribal competition and member cooperation mechanisms. …”

Theoretical models used in this study and the aspects in which they were applied. by Jennifer Hornung Garvin (20765727)

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)