Feature selection ratio results for tested algorithms. حسب Nebojsa Bacanin (13932723)

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The result of the accuracy of tested algorithms. حسب Nebojsa Bacanin (13932723)

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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)

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Best fitness value results for tested algorithms. حسب Nebojsa Bacanin (13932723)

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Mean fitness value results for tested algorithms. حسب Nebojsa Bacanin (13932723)

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Standard deviation of fitness value results for tested algorithms. حسب Nebojsa Bacanin (13932723)

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a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. حسب Nebojsa Bacanin (13932723)

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Boxplots analysis of the tested algorithms using average error rate across 21 datasets. حسب Nebojsa Bacanin (13932723)

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Algorithm for generating hyperparameter. حسب Balraj Preet Kaur (20370832)

"…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"

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

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Results of machine learning algorithm. حسب Balraj Preet Kaur (20370832)

"…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"

ROC comparison of machine learning algorithm. حسب Balraj Preet Kaur (20370832)

"…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"

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

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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)

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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)

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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)

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

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