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Your search keyword '"Zhang, Zhixia"' showing total 343 results
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343 results on '"Zhang, Zhixia"'

2. Exosome is a Fancy Mobile Sower of Ferroptosis

Author

Hou, Qin, Ouyang, Siyu, Xie, Zhongcheng, He, Yinling, Deng, Yunong, Guo, Jiamin, Yu, Panpan, Tan, Xiaoqian, Ma, Wentao, Li, Pin, Yu, Jiang, Mo, Qinger, Zhang, Zhixia, Chen, Dandan, Lin, Xiaoyan, Liu, Zhiyang, Chen, Xi, Peng, Tianhong, Li, Liang, and Xie, Wei

Published
2024
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26. Explainable recommender system directed by reconstructed explanatory factors and multi‐modal matrix factorization.

Author

Chang, Teng, Zhang, Zhixia, and Cai, Xingjuan

Subjects
MATRIX decomposition, OPTIMIZATION algorithms, RECOMMENDER systems, DECOMPOSITION method, SENTIMENT analysis, SINGULAR value decomposition
Abstract

Summary: Matrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Federated malware detection based on many‐objective optimization in cross‐architectural IoT.

Author

Zhang, Zhigang, Zhang, Zhixia, and Cui, Zhihua

Subjects
MALWARE, INTERNET of things, BLOOM'S taxonomy, MALWARE prevention, FEDERATED learning
Abstract

Summary: With the rising adoption of the Internet of Things (IoT) across a variety of industries, malware is increasingly targeting the large number of IoT devices that lack adequate protection. Malware hunting is challenging in the IoT due to the variety of instruction set architectures of devices, as shown by the differences in the relevant characteristics of malware on different platforms. There are also serious concerns about resource utilization and privacy leaks in the development of conventional detection models. This study suggests a novel federated malware detection framework based on many‐objective optimization (FMDMO) for the IoT to overcome the problems. First, the framework provides a cross‐platform compatible basis with the federated mechanism as the backbone, while avoiding raw data sharing to improve privacy protection. Second, an intelligent optimization‐based client selection method is designed for four objectives: learning performance, architectural selection deviation, time consumption, and training stability, which leads malware detection to retain a high degree of cross‐architectural generalization while enhancing training efficiency. Based on a large IoT malware dataset we constructed, containing 62,515 malware samples across seven typical architectures, the FMDMO is evaluated comprehensively in three scenarios. The experimental results demonstrate the FMDMO substantially enhances the model's cross‐platform detection performance while preserving effective training and flexibility. [ABSTRACT FROM AUTHOR]

Published
2024
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48. The Influencing Factors of Biochar’s Characteristics and the Development of Carbonization Equipments: A Review

Author

Xia, Lingzhi, Wang, Yonggang, Meng, Jun, Chen, Wenfu, Zhang, Zhixia, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bian, Fuling, editor, and Xie, Yichun, editor

Published
2016
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49. Study of Greenhouse Remote Monitoring System Based on Webaccess

Author

Lyu, Xuepin, Wang, Yonggang, Xu, Tongyu, Zhang, Zhixia, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bian, Fuling, editor, and Xie, Yichun, editor

Published
2016
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