Curves of sigmoid functions used in our proposed algorithm. by Binh Thanh Dang (12368831)

“…<p>Curves of sigmoid functions used in our proposed algorithm.</p>…”

Algorithm for constructing Boot-t CIs. by Qin Gong (118801)

Subjects: “…lindley approximation algorithm…”

Comparison of results of different algorithms. by Zhongzhong Guo (12133402)

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The used test functions in the evaluation. by Xuan Chen (158617)

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Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

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Algorithm pseudocode. by Cheng-jie Chen (22272090)

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Algorithm flow of the CNN + LSTM model. by Zhongzhong Guo (12133402)

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DMTD algorithm. by Yunhu Huang (21402795)

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Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

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The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Penalty functions and estimators for some of the regularized regression methods used in this paper. by Dongsheng Li (222712)

ROC curves of CNN + LSTM model and traditional algorithms. by Zhongzhong Guo (12133402)

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Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

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Sensitivity results from the Morris screening algorithm. by Mitchel J. Colebank (7390403)

Search Algorithms and Loss Functions for Bayesian Clustering by David B. Dahl (11761055)

“…<p>We propose a randomized greedy search algorithm to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. …”

Distribution of DLCO in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians. by Yuh-Chin T. Huang (17867207)

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A heatmap of the examined values for Jaya algorithm parameters. by Fadi K. Dib (5204807)

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Scheduling time of five algorithms. by Huichao Guo (14515171)

“…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”