The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

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

“…Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. …”

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

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Elite search behavior. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Calibration curves for the test set of four models. by Meng Cao (105914)

Subjects: “…retirement longitudinal survey…”

SHAP bar plot. by Meng Cao (105914)

Subjects: “…retirement longitudinal survey…”

ROC curves for the test set of four models. by Meng Cao (105914)

Subjects: “…retirement longitudinal survey…”

Display of the web prediction interface. by Meng Cao (105914)

Subjects: “…retirement longitudinal survey…”

Sample screening flowchart. by Meng Cao (105914)

Subjects: “…retirement longitudinal survey…”

Descriptive statistics for variables. by Meng Cao (105914)

Subjects: “…retirement longitudinal survey…”

SHAP summary plot. by Meng Cao (105914)

Subjects: “…retirement longitudinal survey…”

Performance metrics of the models on the training and test set. by Meng Cao (105914)

Subjects: “…retirement longitudinal survey…”

Comparison in terms of the sensitivity. by Ying Li (38224)

“…Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. …”

Parameter sensitivity of BIMGO. by Ying Li (38224)

“…Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. …”

Details of the medical datasets. by Ying Li (38224)

“…Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. …”

The flowchart of IMGO. by Ying Li (38224)

“…Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. …”

Comparison in terms of the selected features. by Ying Li (38224)

“…Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. …”