Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. by Tehnan I. A. Mohamed (16846175)

“…<p>Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms.…”

Algorithm implemented using SQL with fulltext search feature. by Kathleen M. Akgün (3697702)

Distribution of total lung capacity (TLC) in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians. by Yuh-Chin T. Huang (17867207)

Subjects:

The algorithm is designed to identify patterns in the treatment dataset. by Belén Otero-Carrasco (21262360)

“…<p>This process involves two key functions. First, from lines 1 to 8, the algorithm identifies amino acids that meet the occurrence threshold, starting with patterns of length 1. …”

Visualization of the RITSS algorithm. by Julian Hecker (758791)

Characteristics of patients identified by the ILD-algorithm. by Erica Farrand (14774297)

Subjects:

ILD diagnoses identified by the ILD algorithm. by Erica Farrand (14774297)

Subjects:

Specific information on the 7 benchmark functions. by M. A. JiMing (19207527)

Procedure of the DCT-based EIT algorithm. by Rongqing Chen (249906)

Subjects:

Results of 7 benchmarking functions in 50 dimensions. by M. A. JiMing (19207527)

Results of 7 benchmarking functions in 30 dimensions. by M. A. JiMing (19207527)

Impact of breath-detection algorithms on main MBW outcomes. by Marc-Alexander Oestreich (13961375)

The gene set related to the “Cytotoxicity” and “Progenitor exhaustion” function of T cells. by Yanfeng Xue (465345)

Subjects:

What constitutes an obstructive ventilatory impairment in a pediatric population? A comparative study of five definitions by scholarly societies by Mariem Abdesalem (22677712)

Subjects:

Image1_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.JPEG by Haomin Zhang (4427188)

Table3_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Table2_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Image2_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.JPEG by Haomin Zhang (4427188)

Table1_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Comparison of best, worst, mean, median and standard deviation values for ABC, WOA, BOA PSO, EOSA, DE, GA, HGSO, SOA, and BMO metaheuristic algorithms using the classical benchmark functions over 500 runs and 100 population size. by Tehnan I. A. Mohamed (16846175)

“…<p>Comparison of best, worst, mean, median and standard deviation values for ABC, WOA, BOA PSO, EOSA, DE, GA, HGSO, SOA, and BMO metaheuristic algorithms using the classical benchmark functions over 500 runs and 100 population size.…”