The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. حسب Olaide N. Oyelade (14047002)

الموضوعات:

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification حسب Vaibhavi Patel (19843282)

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things حسب Ashok Kumar K (21441108)

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

الموضوعات:

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. حسب Nebojsa Bacanin (13932723)

الموضوعات:

CEC 2019 benchmark characteristics. حسب Nebojsa Bacanin (13932723)

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

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

الموضوعات:

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

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

الموضوعات:

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

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Error rate convergence graphs. حسب Nebojsa Bacanin (13932723)

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

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

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

الموضوعات:

COFFO control parameters. حسب Nebojsa Bacanin (13932723)

الموضوعات:

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

الموضوعات:

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

Parameter settings of the comparison algorithms. حسب Ying Li (38224)

"…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"

Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF حسب Maya Khatun (7437011)