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. …”

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

Description of the real-world dataset. by 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. …”

Screenshot of our visualization tool MGDrawVis. by 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. …”

DataSheet_1_Stronger wind, smaller tree: Testing tree growth plasticity through a modeling approach.docx by Haoyu Wang (429641)

“…The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to maximize the multi-objective function (stem biomass and tree height) by determining the key parameter value controlling the biomass allocation to the secondary growth. …”

Using BART to Perform Pareto Optimization and Quantify its Uncertainties by Akira Horiguchi (11768593)

“…The performance of our BART-based method is compared to a GP-based method using analytic test functions, demonstrating convincing advantages. …”

Warning dialog box of proposed NIDS. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Feature extraction of proposed NIDS. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Performance comparison analysis. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Trained dataset after preprocessing. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Environmental setup. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Data repository. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Proposed architecture of fast R–CNN. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Test dataset after preprocessing. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Accuracy comparison with various datasets. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

“…Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”

Fig 5 - by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”