<i>hi</i>PRS algorithm process flow. حسب Michela C. Massi (14599915)

"…The sequences can include from a single SNP-allele pair up to a maximum number of pairs defined by the user (<i>l</i>_max). <b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …"

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

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

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

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

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

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Confusion matrix for a binary outcome (adapted from Fawcett [30]). حسب João Albuquerque (12936572)

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

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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