S1 Dataset - Novel chaotic oppositional fruit fly optimization algorithm for feature selection applied on COVID 19 patients’ health prediction by Nebojsa Bacanin (13932723)

Subjects:

The result of the best fitness value, mean fitness value, standard deviation of fitness value and feature selection ratio of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

Subjects:

CEC 2019 benchmark characteristics. by Nebojsa Bacanin (13932723)

Subjects:

Results of Holm’s step-down procedure. by Nebojsa Bacanin (13932723)

Subjects:

Friedman ranks of tested methods on CEC2019 benchmark functions. by Nebojsa Bacanin (13932723)

Subjects:

V-shaped transfer functions. by Nebojsa Bacanin (13932723)

Subjects:

Resilts of Friedman and Iman-Davenport tests (<i>α</i> = 0.05). by Nebojsa Bacanin (13932723)

Subjects:

Mean fitness and standard deviation results of compared approaches on CEC2019 benchmark functions. by Nebojsa Bacanin (13932723)

Subjects:

Error rate convergence graphs. by Nebojsa Bacanin (13932723)

Subjects:

COVID-19 dataset description. by Nebojsa Bacanin (13932723)

Subjects:

Control parameter settings for comparative feature selection methods. by Nebojsa Bacanin (13932723)

Subjects:

The result of the Wilcoxon test of presented COFFO against compared methods. by Nebojsa Bacanin (13932723)

Subjects:

COFFO control parameters. by Nebojsa Bacanin (13932723)

Subjects:

Convergence graphs for ten CEC 2019 benchmark functions and direct comparison between COFFO and FFO. by Nebojsa Bacanin (13932723)

Subjects:

An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)

Subjects:

The data processing and analysis pipeline. by Xin Zhang (35492)

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

Proposed Algorithm. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

DataSheet1_Improving the convergence of an iterative algorithm for solving arbitrary linear equation systems using classical or quantum binary optimization.pdf by Erick R. Castro (11243697)

“…We transform the linear system into a binary optimization problem, drawing inspiration from the geometry of the original problem and resembling the conjugate gradient method. …”