Kun Zhu
Orcid: 0000-0002-5773-5089Affiliations:
- Wuhan University, School of Computer Science, Wuhan, China
According to our database1,
Kun Zhu
authored at least 13 papers
between 2020 and 2024.
Collaborative distances:
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Bibliography
2024
From Discrete Representation to Continuous Modeling: A Novel Audio-Visual Saliency Prediction Model With Implicit Neural Representations.
IEEE Trans. Emerg. Top. Comput. Intell., December, 2024
An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection.
IEEE Trans. Artif. Intell., August, 2024
MTCAM: A Novel Weakly-Supervised Audio-Visual Saliency Prediction Model With Multi-Modal Transformer.
IEEE Trans. Emerg. Top. Comput. Intell., April, 2024
IMDAC: A robust intelligent software defect prediction model via multi-objective optimization and end-to-end hybrid deep learning networks.
Softw. Pract. Exp., February, 2024
WSBCV: A data-driven cross-version defect model via multi-objective optimization and incremental representation learning.
Inf. Sci., 2024
IAPCP: An Effective Cross-Project Defect Prediction Model via Intra-Domain Alignment and Programming-Based Distribution Adaptation.
IET Softw., 2024
Proceedings of the Pattern Recognition - 27th International Conference, 2024
E<sup>2DAS</sup>: An Efficient Equivariant Dynamic Aggregation Saliency Model for Omnidirectional Images.
Proceedings of the Pattern Recognition - 27th International Conference, 2024
2022
IVKMP: A robust data-driven heterogeneous defect model based on deep representation optimization learning.
Inf. Sci., 2022
Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine.
IET Softw., 2022
2021
Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network.
J. Syst. Softw., 2021
WGNCS: A robust hybrid cross-version defect model via multi-objective optimization and deep enhanced feature representation.
Inf. Sci., 2021
2020
Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network.
IET Softw., 2020