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

"…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i>_<i>δ</i>) based on this metric. <b>(E)</b> Starting from the interaction at the top of <i>I</i>_<i>δ</i>, <i>hi</i>PRS constructs <i>I</i>_<i>K</i>, selecting <i>K</i> (where <i>K</i> is user-specified) terms through the greedy optimization of the ratio between MI (<i>relevance</i>) and a suitable measure of similarity for interactions (<i>redundancy)</i> (cf. …"

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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DE algorithm flow. حسب Ling Zhao (111365)

"…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …"

Test results of different algorithms. حسب Ling Zhao (111365)

"…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …"

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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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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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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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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