Three machine learning algorithms were further employed to select candidate key hub genes. by Zhifeng Wu (303016)

Values misclassified by the CART algorithm. by Anne Wojtanowski (17507992)

“…A multivariate logistic regression model and a classification and regression tree algorithm were used to evaluate the HRV variables’ ability to detect “BIS^TM≥60”. …”

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

Study algorithm. Participants were divided into two age groups (8–12 and 13–17 years) and underwent baseline assessment (T0) at the time of recruitment.... by Carlos González-Pérez (20990602)

“…<p>Study algorithm. Participants were divided into two age groups (8–12 and 13–17 years) and underwent baseline assessment (T0) at the time of recruitment. …”

Functional enrichment analysis of periodontitis-related DEGs in AAA and identification of candidate key hub genes through Protein-Protein Interaction (PPI) network were conducted. by Zhifeng Wu (303016)

Stages of conducting the research. by Mohammadmahdi Taheri (21722285)

Running time of four algorithms. by Jirong Zeng (20308604)

“…The recall curve area and F1 value of the improved algorithm were 0.95 and 0.9789, respectively. In the detection of basketball, volleyball, and calibration columns, the average classification accuracy of the improved model was 95.87%, and the average calibration box accuracy was 97.05%. …”

mAP results of five algorithms. by Jirong Zeng (20308604)

“…The recall curve area and F1 value of the improved algorithm were 0.95 and 0.9789, respectively. In the detection of basketball, volleyball, and calibration columns, the average classification accuracy of the improved model was 95.87%, and the average calibration box accuracy was 97.05%. …”

PR curve and F1 value results of four algorithms. by Jirong Zeng (20308604)

“…The recall curve area and F1 value of the improved algorithm were 0.95 and 0.9789, respectively. In the detection of basketball, volleyball, and calibration columns, the average classification accuracy of the improved model was 95.87%, and the average calibration box accuracy was 97.05%. …”

The selection algorithm flowchart. by Shiyu Wang (813972)

“…A total of 10 (19.23% of total) positive signals were obtained using the above four algorithms. Median age was 71 years, time to onset was within 30 days, and hospitalization rates of acute renal failure outcomes were 14.33% (narrow SMQs) and 30.69% (broad SMQs).…”

Genetic algorithm flowchart. by Junjie He (700642)

“…To validate the effectiveness of the proposed model and algorithm, numerical experiments were performed, which took an urban area in China as an example. …”

Details of the metaheuristic algorithms. by Yanzhao Gu (21192659)

“…It may also fall into local optima in high-dimensional cases. Several experiments were conducted to verify the effectiveness of GWOA. …”

Participant recruitment algorithm. by Priscilla Nkonde Gardner (21169103)

Patient selection algorithm. by Dwi Kisworo Setyowireni (22656144)

Algorithm flow chart. by Yun Qi (560401)

“…These enhancements are compared against single-strategy improved WOAs.Subsequently, path planning simulations were conducted using several extracted algorithms and grid-based methods. …”

Algorithm iteration graph. by Yun Qi (560401)

“…These enhancements are compared against single-strategy improved WOAs.Subsequently, path planning simulations were conducted using several extracted algorithms and grid-based methods. …”

Flowchart of proposed algorithm. by Tibebu Legesse (21030014)

“…Moreover, the proposed algorithm significantly extends network lifetime, with a <b>3.5%</b> and <b>7.5%</b> improvement over EAPS-AODV and AODV. …”

Significantly enriched Gene Ontology (GO) terms among the 512 genes showing significant results in the all ancestry TWAS. Enrichment analyses were conducted using the multi-query in g:Profiler [16].... by Jenna C. Carlson (3950024)

“…<p>Significantly enriched Gene Ontology (GO) terms among the 512 genes showing significant results in the all ancestry TWAS. Enrichment analyses were conducted using the multi-query in g:Profiler [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1011581#pgen.1011581.ref016" target="_blank">16</a>]. …”

Python-Based Algorithm for Calculating Physical Properties of Aqueous Mixtures Composed of Substances Not Available in Databases by Jina Lee (3138492)

“…To validate the accuracy of the model, the results obtained from the proposed algorithm were compared to experimental data for 37 binary aqueous mixture systems covering properties such as density, heat capacity, viscosity, and thermal conductivity. …”

Python-Based Algorithm for Calculating Physical Properties of Aqueous Mixtures Composed of Substances Not Available in Databases by Jina Lee (3138492)

“…To validate the accuracy of the model, the results obtained from the proposed algorithm were compared to experimental data for 37 binary aqueous mixture systems covering properties such as density, heat capacity, viscosity, and thermal conductivity. …”