Feature selection using the Boruta algorithm. by Guang Tu (22054865)

“…We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. …”

Means plots of genetic algorithm calibration parameters. by Junjie He (700642)

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The hyperparameter selection methodology derived from sensitivity analysis. by Wenxing Wu (16853002)

Parameters selected. by Ali Abbas Abbod (22386097)

Non-dominated flood management scenarios identified in the multi-objective decision space. by Ghazi Al-Rawas (16857943)

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Relationship between NPV and main optimization objectives: cost of designing, SUFRI, and overflow. by Ghazi Al-Rawas (16857943)

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Demonstration process of the robotic arm transporting an object: (A) starting position; (B) via position 1; (C) via position 2; (D) target position. by Jingnan Yan (20727331)

The process of the robotic arm autonomously transporting an object: (A) starting position; (B) via position 1; (C) via position 2; (D) target position. by Jingnan Yan (20727331)

Decision maker preference and scheme selection. by Yuhang Wang (332111)

“…Based on this, a case study is conducted using a chemical industrial park in China as an example, generating 41 sets of weights covering extreme preferences, two-objective balance, and three-objective balance. Decision-makers screen solutions based on loss tolerance thresholds and select the optimal solution using a composite score of comprehensive weighted losses. …”

Q-Learning state definition based on objective medians. by Zhongfeng Li (3965060)

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Natural selection diagram. by Yanchun Li (248761)

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Selection of crossing point. by Yanchun Li (248761)

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Comparative analysis of second-phase objective values under varied and . by Xuejiao Zhang (3089274)

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HV comparison of QLNSGA-II and baseline algorithms on real-world multi-objective problems (RWMOP). The best value in each row is highlighted in bold. by Zhongfeng Li (3965060)

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Scenarios for algorithms using IEEE 33 bus. by Bachirou Bogno (20485724)

Scenarios for algorithms using IEEE 69 bus. by Bachirou Bogno (20485724)

Summary of optimization algorithms for coal blending in thermal power plants. by Zhongfeng Li (3965060)

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The variation of the objective function with the number of iterations for the 118-bus. by Ahmed A. Zaki Diab (12757190)

Analyzing misclassification in ImageNet 1K dataset: Insights from 20 randomly selected errors. by Ebrahim Parcham (20700216)

Comparison of classification accuracy across different machine learning models using the objective function metric. by Amal H. Alharbi (21755906)