The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

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The flowchart of Algorithm 2. by Jing Xu (15337)

“…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …”

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

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

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

Algorithm flow chart. by Zhiqiang Chen (218692)

Subjects:

Binary contingency table. by Lu Xiao (314030)

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by 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. …”

Framework of the multilabel classification algorithm. by Zhiqiang Chen (218692)

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The final delivery plan of <i>r</i>₁ and <i>r</i>₂. by Yanju Zhang (437336)

Experimental comparisons of single category indexes under different algorithms. by Zhiqiang Chen (218692)

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Comparison of annotation precision for single category labels using different algorithms. by Zhiqiang Chen (218692)

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Algorithms runtime comparison. by Meilin Zhu (688698)

“…Finally, the correctness of the model is analyzed through numerical experiments, and the effectiveness of the proposed algorithm, the joint distribution strategy, and the disruption event processing idea of combining immediate processing and scheduled batch processing is analyzed. …”

Train stopping plan. by Jing Xu (15337)

“…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …”

Major notations. by Jing Xu (15337)

“…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …”

S1 File - by Jing Xu (15337)

“…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …”

Parameters of joint significance test. by Yixuan Lu (9987980)

Solution results of different algorithms. by Meilin Zhu (688698)

“…Finally, the correctness of the model is analyzed through numerical experiments, and the effectiveness of the proposed algorithm, the joint distribution strategy, and the disruption event processing idea of combining immediate processing and scheduled batch processing is analyzed. …”

Proposed Algorithm. by 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. …”

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