Structure of the Kuhn-Munkres Algorithm. حسب Qingnan Ji (22662198)

"…The algorithm dynamically adjusts weight coefficients based on the importance scores of each modality, while also incorporating a cross-modal correlation matrix as a constraint to improve the robustness of the matching process. …"

Data (3). حسب Qingnan Ji (22662198)

"…The algorithm dynamically adjusts weight coefficients based on the importance scores of each modality, while also incorporating a cross-modal correlation matrix as a constraint to improve the robustness of the matching process. …"

Curve of data size vs. running time. حسب Qingnan Ji (22662198)

"…The algorithm dynamically adjusts weight coefficients based on the importance scores of each modality, while also incorporating a cross-modal correlation matrix as a constraint to improve the robustness of the matching process. …"

Data Sheet 1_Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms.zip حسب Weicheng Pan (12629762)

Data labeling. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Hyperparameter settings. حسب Qingnan Ji (22662198)

"…The algorithm dynamically adjusts weight coefficients based on the importance scores of each modality, while also incorporating a cross-modal correlation matrix as a constraint to improve the robustness of the matching process. …"

Initial weight values and correlation thresholds. حسب Qingnan Ji (22662198)

"…The algorithm dynamically adjusts weight coefficients based on the importance scores of each modality, while also incorporating a cross-modal correlation matrix as a constraint to improve the robustness of the matching process. …"

Ablation experiment results comparison. حسب Qingnan Ji (22662198)

"…The algorithm dynamically adjusts weight coefficients based on the importance scores of each modality, while also incorporating a cross-modal correlation matrix as a constraint to improve the robustness of the matching process. …"

Adjustment step size. حسب Qingnan Ji (22662198)

"…The algorithm dynamically adjusts weight coefficients based on the importance scores of each modality, while also incorporating a cross-modal correlation matrix as a constraint to improve the robustness of the matching process. …"

CNN-LSTM parameters. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Objectives weights. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Comparative analysis of related works. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

GWO parameters. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Confusion matrix. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

PSO parameters. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Comparison with state-of-the-art IDSs. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Feature selection using hybrid GWO-PSO. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Hybrid bio-inspired feature selection. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Feature Selection Parameters for GWO-PSO. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"

Workflow summary: GRN motif, cell signaling model, and inference framework. حسب Michael Alexander Ramirez Sierra (20209354)