Average accuracy of removing redundant content. by Guilian Feng (18530806)

“…In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. …”

Run chart of multi feature construction method. by Guilian Feng (18530806)

“…In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. …”

Comparison of literature content. by Guilian Feng (18530806)

“…In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. …”

Run chart of multi feature construction method. by Guilian Feng (18530806)

“…In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. …”

Parametric environment. by Guilian Feng (18530806)

“…In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. …”

Data Sheet 1_Robust multi-objective optimization framework for performance-based seismic design of steel frame with energy dissipation system.docx by Yuting Cheng (11954209)

“…The core contribution lies in integrating these three metrics (FVD cost, repair cost, and a robustness measure) into an integrated optimization process using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). …”

Video1_Reducing the muscle activity of walking using a portable hip exoskeleton based on human-in-the-loop optimization.AVI by Linghui Xu (6295400)

“…However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. …”

Image 7_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…</p>Methods<p>The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Image 2_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…</p>Methods<p>The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Table 1_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.docx by Yihang Wang (4731429)

“…</p>Methods<p>The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Image 4_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…</p>Methods<p>The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Image 6_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…</p>Methods<p>The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Image 5_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…</p>Methods<p>The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Image 1_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…</p>Methods<p>The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Image 3_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…</p>Methods<p>The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Online sextuplet mining for SNAP-loss. by Muhammad Usman Tariq (11022141)

Table_1_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. …”

Table_2_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. …”

SBH17: Benchmark Database of Barrier Heights for Dissociative Chemisorption on Transition Metal Surfaces by T. Tchakoua (7202804)

“…The medium algorithm does require metal lattice constants and interlayer distances that are optimized separately for each functional. …”

Data_Sheet_2_Assessing the impact of forest structure disturbances on the arboreal movement and energetics of orangutans—An agent-based modeling approach.PDF by Kirana Widyastuti (13848250)

“…In order to parameterize the energy related processes of the orangutans described in the model, we applied a computationally intensive evolutionary algorithm and evaluated the simulation results against observed behavioral patterns of orangutans. …”