Ziping He
Hebei University of Technology
5 Papers
8 Citations
Ziping He is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Computer science & Local optimum. The author has an hindex of 2, co-authored 5 publications.
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Papers
Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection
TL;DR: The experimental results have proved that the established AdaBoost-ISSA-S4VM classification model has good performance on labeled and unlabeled lung CT images.
Unpaired Image Denoising via Wasserstein GAN in Low-Dose CT Image with Multi-Perceptual Loss and Fidelity Loss
TL;DR: Wang et al. as discussed by the authors proposed a generative adversarial network combining multi-perceptual loss and fidelity loss to achieve the purpose of noise suppression by minimizing the difference between the LDCT image and the normal-dose computed tomography (NDCT) image.
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A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification
TL;DR: Wang et al. as mentioned in this paper proposed a graph-based semi-supervised algorithm combined with particle cooperation and competition, which can improve the model performance effectively by using unlabeled samples.
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Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model.
TL;DR: A dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies to enhance the randomness of the foraging behavior's movement and can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.
Semisupervised SVM by Hybrid Whale Optimization Algorithm and Its Application in Oil Layer Recognition
TL;DR: A new semisupervised SVM by hybrid whale optimization algorithm (HWOA-S3VM) is proposed in this paper, and it is shown that HWOA has a higher convergence speed and better global searchability than other algorithms.