Precision-Recall curve. حسب Xiangqian Xu (17310895)

"…Finally, the loss function CIoU of YOLOv7 is optimized to EIoU loss function to accelerate the convergence speed of the model. …"

Comparison experiment results. حسب Xiangqian Xu (17310895)

"…Finally, the loss function CIoU of YOLOv7 is optimized to EIoU loss function to accelerate the convergence speed of the model. …"

Ablation experiment results. حسب Xiangqian Xu (17310895)

"…Finally, the loss function CIoU of YOLOv7 is optimized to EIoU loss function to accelerate the convergence speed of the model. …"

Deepwise separable convolution structure diagram. حسب Xiangqian Xu (17310895)

"…Finally, the loss function CIoU of YOLOv7 is optimized to EIoU loss function to accelerate the convergence speed of the model. …"

Block diagram of 2-DOF PIDA controller. حسب Erdal Eker (19251018)

"…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …"

Zoomed view of Fig 7. حسب Erdal Eker (19251018)

"…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …"

Zoomed view of Fig 10. حسب Erdal Eker (19251018)

"…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …"

Schematic representation of semi-automated algorithm for organoids analyzing. حسب Anna Demchenko (18449439)

Image 7_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png حسب 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 حسب 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 حسب 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 حسب 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 حسب 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 حسب 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 حسب 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 حسب 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. …"

Optimal Timings Codebook.xlsx حسب Emma Spillane (11249121)

Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades. حسب Konstantina Charmpi (9901053)

Optimal Time Intervals of Breech Births Dataset حسب Emma Spillane (11249121)

Description of the real-world dataset. حسب Fadi K. Dib (5204807)

"…<div><p>Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. …"