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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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)

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COFFO control parameters. حسب Nebojsa Bacanin (13932723)

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

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

tUbenet Foundation Model Weights حسب Natalie Holroyd (7099391)

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

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

"…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …"

Proposed Algorithm. حسب Hend Bayoumi (22693738)

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Comparisons between ADAM and NADAM optimizers. حسب Hend Bayoumi (22693738)

"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Fig 1 - حسب Jakub Stoklosa (3315042)