Flowchart of improved FOX optimization algorithm. by Mahmood A. Jumaah (22272371)

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Summary of recent and state-of-the-art optimization algorithms. by Mahmood A. Jumaah (22272371)

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Flowchart of a single epoch in the FOX optimization algorithm [10]. by Mahmood A. Jumaah (22272371)

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Adaptive exploration and exploitation method of Improved FOX optimization algorithm. by Mahmood A. Jumaah (22272371)

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Summary of classical and CEC-based benchmark test functions used in this study. by Mahmood A. Jumaah (22272371)

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Friedman test average rankings of IFOX and competing optimization algorithms across CL, C17, C19, C21, C22, and RWPs. The “Position” column indicates the overall rank based on the... by Mahmood A. Jumaah (22272371)

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Wilcoxon signed-rank test results comparing IFOX with other optimization algorithms across CL, C17, C19, C21, C22, and RWPs. by Mahmood A. Jumaah (22272371)

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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

Algorithm for generating hyperparameter. by 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. …”

Average Friedman test ranks (left) and pairwise average Wilcoxon signed-rank test p-values (right) for all compared optimization algorithms across CL, C17, C19, C21, C22, and RWPs. by Mahmood A. Jumaah (22272371)

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Results of machine learning algorithm. by 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. …”

Convergence curves on the functions C19F2, C19F3, C19F5, C19F6, C19F10, C21F2, C21F3, C21F9, and C21F10. by Mahmood A. Jumaah (22272371)

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General parameters setting. by Mahmood A. Jumaah (22272371)

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Convergence curves on the functions C17F5, C17F7, C17F8, C17F9, C17F11, C17F19, C17F20, C17F22, and C17F27. by Mahmood A. Jumaah (22272371)

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Raw results. by Mahmood A. Jumaah (22272371)

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Scalability of the proposed IFOX on C17F8 with the following metrics: Runtime (seconds), Memory usage (MB), and Relative error across problem dimensions (10, 30, 50, 100) and epoch... by Mahmood A. Jumaah (22272371)

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The performance results on benchmark test functions and real-world problems. by Mahmood A. Jumaah (22272371)

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3D visualization of the Rastrigin function from the CEC 2019 benchmark test functions. by Mahmood A. Jumaah (22272371)

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Convergence curves on the functions CL4, CL11, CL13, CL14, CL18, CL19, C17F1, C17F2, and C17F4. by Mahmood A. Jumaah (22272371)

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Scalability of IFOX on C17F8: (a) average memory usage, (b) average runtime, and (c) average relative error across epochs (50–1000) for dimensions 10, 30, 50, and 100. by Mahmood A. Jumaah (22272371)

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