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)

C-level road irregularity sample. by Jianwei Liu (163716)

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

Multi objective optimization design process. by Xueyong Pan (20390363)

“…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …”

Optimal Latin square sampling distribution. by Xueyong Pan (20390363)

“…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …”

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. …”

PANet network design. by Mengqi Yuan (4852555)

“…Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. …”

IRBMO vs. meta-heuristic algorithms boxplot. 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. …”

IRBMO vs. feature selection algorithm boxplot. 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. …”

BiFPN network design. by Mengqi Yuan (4852555)

“…Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. …”

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

“…To solve this optimization model, a multi-level optimization algorithm is designed. …”

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…This strategy </p><p dir="ltr">not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, </p><p dir="ltr">leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection </p><p dir="ltr">process and CNNs for image classification, the proposed approach reduces computational costs while increasing </p><p dir="ltr">classification accuracy. …”

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

Design variables and range of values. by Xueyong Pan (20390363)

“…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …”

Feasibility diagram of design points. by Xueyong Pan (20390363)

“…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …”