Data and code resources. by Sam Hall-McMaster (10343795)

Subjects:

Performance of machine learning algorithms. by Wenxue Liu (6877754)

Convergence curve of the DBO algorithm. by Ma Haohao (22177538)

“…The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. …”

Top-rated mode of transport based on the SEU algorithm (SEU scores from 0 to 40). by Johannes Weyer (13392175)

Risk element category diagram. by Yao Hu (3479972)

Subjects:

, MSE, and MAE of the optimised algorithms. by Itxasne Antúnez Sáenz (22794696)

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

The genome coding scheme. by Wenbing Shi (5806160)

Subjects:

Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. by Daniel Suárez-Barrera (22676654)

Subjects:

Ablation experiment for the micro-localization algorithm model. by Zhongjian Xie (4633099)

Ablation experiment for the macro-detection algorithm model. by Zhongjian Xie (4633099)

Comparison of results based on CycleGAN method. by Xiaojun Wang (50332)

Subjects:

Identification of CRG based on WGCNA algorithm. by Lin Feng (84785)

Algorithm flow based on CL-EPIDTN. by Fang Yang (123514)

Ablation experiment results table. by Xiaozhou Feng (2918222)

“…Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …”

Dynamic window based median filtering algorithm. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. …”

Comparison of mAP curves in ablation experiments. by Xiaozhou Feng (2918222)

“…Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …”

Algorithm parameter settings employed in the experiments on simulated data. by Liyan Sun (760586)

The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

Subjects:

The errors of the predictions based on different algorithm methods. by Zhang Wei (317854)