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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One advantage of our algorithm is that it is not binary in nature. حسب Yong Kuk Kim (13955214)

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

DataSheet1_Improving the convergence of an iterative algorithm for solving arbitrary linear equation systems using classical or quantum binary optimization.pdf حسب Erick R. Castro (11243697)

Binary contingency table. حسب Lu Xiao (314030)

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. …"

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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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. …"

Parameter settings of the comparison algorithms. حسب Ying Li (38224)

"…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"

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

Top-performing model configuration and performance by algorithm type. حسب Uğur Ejder (22683228)

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Large-scale dataset comparative analysis using computation resources. حسب Olaide N. Oyelade (14047002)

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Medium-scale dataset comparative analysis using computation resources. حسب Olaide N. Oyelade (14047002)

الموضوعات:

Small-scale dataset comparative analysis using computation resources. حسب Olaide N. Oyelade (14047002)

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Medium-scale dataset comparative analysis using the number of features selected. حسب Olaide N. Oyelade (14047002)

الموضوعات:

Large-scale dataset comparative analysis using the number of features selected. حسب Olaide N. Oyelade (14047002)

الموضوعات:

Small-scale dataset comparative analysis using the number of features selected. حسب Olaide N. Oyelade (14047002)

الموضوعات:

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. …"

Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. حسب Olaide N. Oyelade (14047002)

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Medium-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. حسب Olaide N. Oyelade (14047002)

الموضوعات: