Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Algorithm for optimal decision determination in sequential decision-making. by Mariko I. Ito (5007323)

Latin sampling. by Honghua Liu (15307022)

AUW-CE Mining Algorithms & Dataset Hub by Shuaikang Yuan (21091847)

“…Moreover, in response to the limitations of conventional cross-entropy methods for HUCPM, four core optimization strategies are designed: optimization of the initial probability distribution to guide the search direction, enhancement of sample diversity to prevent local convergence, dynamic adjustment of sample size to reduce redundant calculations, and incorporation of utility weights to improve the accuracy of probability updates. …”

NucB sampling: by Carlos A. Gomez-Uribe (21492788)

Alpha amylase sampling: by Carlos A. Gomez-Uribe (21492788)

PANet network design. by Mengqi Yuan (4852555)

“…Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. …”

BiFPN network design. by Mengqi Yuan (4852555)

“…Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. …”

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization by Yingying Ma (3617810)

“…Notably, the <i>D</i> of three candidates have shown significant improvements compared to the samples with similar <i>H</i> in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. …”

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization by Yingying Ma (3617810)

“…Notably, the <i>D</i> of three candidates have shown significant improvements compared to the samples with similar <i>H</i> in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. …”

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Performance of the optimal classifiers on each feature list. by Wenwei Wu (9352541)

Data_Sheet_1_Critical Evaluation of Metaheuristic Algorithms for Weight Minimization of Truss Structures.docx by Aristotelis E. Charalampakis (7454258)

Three sensor sample data with noise data. by Tang Ruipeng (20141334)

Third setting: batch optimization. by Camille Gontier (9442304)