Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

“…The experimental observations reveal that the proposed DA-based image contrast enhancement produces high-quality images from its low-contrast counterparts. Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. …”

Flowchart of the clinical decision support system and the interpretable tropical geometry-based fuzzy neural network algorithm. by Yufeng Zhang (195768)

Consistency of model-derived predictive patterns as a function of sample diversity. by Oualid Benkarim (9707986)

(a) Radar chart of 4 algorithms, (b) The sorting diagram of 4 algorithms. by Yu Liu (6938)

Python implementation from Symplectic decomposition from submatrix determinants by Jason L. Pereira (11598632)

“…Python implementation of the algorithm and demonstration of how to use the functions.…”

Algorithm description and the effects of replay and forgetting on model performance. by Georgy Antonov (11938961)

“…(C) Left: without MB forgetting, the algorithm’s estimate of reward obtained for a given move corresponds to the true reward function. …”

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Effects of Nash equilibrium speed changes with the non-sensory motor algorithm on encounters, agent numbers, and payoffs. by Hiroyuki Ichijo (13891153)

Swarm intelligence algorithms for width and length on influence games by Francisco Muñoz (9455441)

“…<br><br><div>usage: Main.py [-h] [-a] [-r] [-d D] [-t T] [-i I] [-q Q] [--shape SHAPE] [--sym] [--folder FOLDER] [--seed SEED] [--sum SUM] [--mh MH] [--tagsfile TAGSFILE] [--notags] [--prune] [--excludenodes EXCLUDENODES]<br><br>Calculates the best Influence Spread set on a Weighted Symmetric Graph using PSO<br></div><div><br></div><div><div>positional arguments:</div><div> file</div><div><br></div><div>optional arguments:</div><div> -h, --help: show this help message and exit</div><div> -a: threat file input contents as an Adjacency Matrix</div><div> -r: reverse order of nodes, from (a,b,w) a -> b will be b -> a</div><div> -d D: line separator to use while parsing</div><div> -t T: number of times to execute</div><div> -i I: number of metaheuristic iterations per execution</div><div> -q Q: fixed quota, use 0 = floor(n/2)+1</div><div> --shape SHAPE: shape functions for binarization - list of implemented shape functions: s2,s2_neg,v2,v4</div><div> --sym: consider graph as symmetric instead of directed</div><div> --folder FOLDER: output folder</div><div> --seed SEED: use custom seed for metaheuristic calcs</div><div> --sum SUM: adds a value to all node labels</div><div> --mh MH: metaheuristic to use - list of implemented metaheuristics: {1: 'Swarm', 2: 'Swarm2', 3: 'Swarm_W', 4:</div><div> 'Swarm_L'}</div><div> --tagsfile TAGSFILE: use first row as node tags instead of using plurality criteria</div><div> --notags: do not use first row as node tags - tags will be calculated</div><div> --prune: nodes with outdegree = 0 and indegree > 0, and with outdegree = 1 and neighbor's outdegree > 0 will be excluded</div><div> --excludenodes EXCLUDENODES: nodes to skip, comma separated</div></div>…”

S1 File - by Yuh-Chin T. Huang (17867207)

“…In this study, we developed a computerized algorithm using the python package (pdfplumber) and validated against clinicians’ interpretation. …”

S1 Dataset - by Yuh-Chin T. Huang (17867207)

“…In this study, we developed a computerized algorithm using the python package (pdfplumber) and validated against clinicians’ interpretation. …”

Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

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

TCGA COAD expression in marker genes from eVIP2-identified pathways. by Alexis M. Thornton (11068403)

A Quadratic Pair Atomic Resolution of the Identity Based SOS-AO-MP2 Algorithm Using Slater Type Orbitals by Arno Förster (8356044)

Available functions in the utility package. by Raul Castañeda (13932971)

Boxplots displaying <i>KL</i> divergence results, indicating variability of key topological metrics obtained after multiple algorithm runs for the generation of chorionic vessels (... by Diana C. de Oliveira (9220729)

metropolis_hastings.py;postprocessing.py;folkman_a_b_c_time.py;figures_Inverse_Proliferation.R;README.md from Bayesian inference of a non-local proliferation model by Zuzanna Szymańska (11679819)

“…;Auxiliary R (version 3.6.2) code to generate figures presenting the results of the random walk Metropolis-Hastings algorithm for the Bayesian inference of a non-local proliferation function.…”

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set? by Minyi Su (1425976)

“…Model scoring functions were derived with these machine-learning algorithms on various training sets selected from over 3700 protein–ligand complexes in the PDBbind refined set (version 2016). …”

Data fitting and inference of the time-dependent parameters by TDINN algorithm for the local outbreaks in Xi’an, Guangzhou, and Yangzhou. by Mengqi He (5753525)