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ORCIDFeng Zhou 0018
Person information
- affiliation: Guangdong University of Finance and Economics, Guangzhou, China
Other persons with the same name
- Feng Zhou — disambiguation page
- Feng Zhou 0001
![[0000-0002-1514-7393]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-1514-7393")
— Xidian University, Ministry Key Laboratory of Electronic Information Countermeasure and Simulation, Xi'an, China - Feng Zhou 0002 — Baidu Research, Sunnyvale, CA, USA (and 2 more)
- Feng Zhou 0003 — University of Michigan at Dearborn, Industrial and Manufacturing Systems Engineering, MI, USA (and 2 more)
- Feng Zhou 0004 — Peking University, College of Environmental Sciences and Engineering, Beijing, China
- Feng Zhou 0005 — Central South University China, School of Information Science and Engineering, Changsha, China
- Feng Zhou 0006 — Nanjing University of Information Science and Technology, School of Information and Control, Jiangsu Key Laboratory of Big Data Analysis Technology, China
- Feng Zhou 0007 — Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Beijing, China
- Feng Zhou 0008 — UISEE (Shanghai) Automotive Technologies Ltd, China
- Feng Zhou 0009 — Army Engineering University of PLA, College of Communications Engineering, Nanjing, China
- Feng Zhou 0010 — Yancheng Institute of Technology, School of Information Technology, China
- Feng Zhou 0011 — University of New South Wales, Sydney, NSW, Australia (and 1 more)
- Feng Zhou 0012 — Harbin Engineering University, Acoustic Science and Technology Laboratory, College of Underwater Acoustic Engineering, China
- Feng Zhou 0013 — Suzhou University of Science and Technology, Suzhou, China
- Feng Zhou 0014 — Shanghai Normal University, Shanghai, China
- Feng Zhou 0015 — Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, Zhejiang, China
- Feng Zhou 0016 — Huazhong University of Science and Technology, Wuhan, Hubei, China
- Feng Zhou 0017 — University of Cambridge, Cambridge, UK
- Feng Zhou 0019 — China Academy of Information and Communications Technology, Beijing, Chin
- Feng Zhou 0020 — University of Nottingham, Nottingham, UK
- Feng Zhou 0021 — Xiamen University, Amoy, China
- Feng Zhou 0022 — China Electric Power Research Institute, Wuhan, China
- Feng Zhou 0023 — China Electric Power Research Institute, Wuhan, China
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2020 – today
- 2024
[j16]Feng Zhou, Antonio Cicone, Haomin Zhou:
IRCNN: A novel signal decomposition approach based on iterative residue convolutional neural network. Pattern Recognit. 155: 110670 (2024)- 2023
- [j15]Lijun Yang, Xiaoge Wei, Fengrui Liu, Xiangru Zhu, Feng Zhou:
Automatic feature learning model combining functional connectivity network and graph regularization for depression detection. Biomed. Signal Process. Control. 82: 104520 (2023) - [j14]Feng Zhou, Qun Zhang, Yuan Zhu, Tian Li:
T2V_TF: An adaptive timing encoding mechanism based Transformer with multi-source heterogeneous information fusion for portfolio management: A case of the Chinese A50 stocks. Expert Syst. Appl. 213(Part): 119020 (2023) - [j13]Linyan Gu, Lihua Yang, Feng Zhou:
Refinements of Approximation Results of Conditional Restricted Boltzmann Machines. IEEE Trans. Neural Networks Learn. Syst. 34(3): 1228-1242 (2023) - [i2]Feng Zhou, Antonio Cicone, Haomin Zhou:
RRCNN: A novel signal decomposition approach based on recurrent residue convolutional neural network. CoRR abs/2307.01725 (2023) - [i1]Feng Zhou, Antonio Cicone, Haomin Zhou:
RRCNN+: An Enhanced Residual Recursive Convolutional Neural Network for Non-stationary Signal Decomposition. CoRR abs/2309.04782 (2023) - 2022
- [j12]Qun Zhang, Peihui Zhang, Feng Zhou:
Intraday and interday features in the high-frequency data: Pre- and post-Crisis evidence in China's stock market. Expert Syst. Appl. 209: 118321 (2022) - [j11]Linyan Gu, Lihua Yang, Feng Zhou:
Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks. Neural Networks 153: 49-63 (2022) - 2021
- [j10]Feng Zhou, Haomin Zhou, Zhihua Yang, Linyan Gu:
IF2CNN: Towards non-stationary time series feature extraction by integrating iterative filtering and convolutional neural networks. Expert Syst. Appl. 170: 114527 (2021) - [j9]Qun Zhang, Lijun Yang, Feng Zhou:
Attention enhanced long short-term memory network with multi-source heterogeneous information fusion: An application to BGI Genomics. Inf. Sci. 553: 305-330 (2021) - 2020
- [j8]Zhihua Yang, Qian Zhang, Feng Zhou:
A New Illumination-Rotation-Invariance Texture Feature Based on Quasi-Periodic Signal Analysis. IEEE Access 8: 29901-29911 (2020) - [j7]Xiangxia Li, Bin Li, Fang Liu, Hua Yin, Feng Zhou:
Segmentation of Pulmonary Nodules Using a GMM Fuzzy C-Means Algorithm. IEEE Access 8: 37541-37556 (2020) - [j6]Zhihua Yang, Feng Zhou, Lihua Yang, Qian Zhang:
A new prediction method for recommendation system based on sampling reconstruction of signal on graph. Expert Syst. Appl. 159: 113587 (2020) - [c1]Zhihua Yang, Qian Zhang, Feng Zhou, Lihua Yang:
A New Credit Scoring Model Based on Prediction of Signal on Graph. ICPRAI 2020: 228-237
2010 – 2019
- 2019
- [j5]Feng Zhou, Qun Zhang, Didier Sornette, Liu Jiang:
Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices. Appl. Soft Comput. 84 (2019) - [j4]Feng Zhou, Haomin Zhou, Zhihua Yang, Lihua Yang:
EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction. Expert Syst. Appl. 115: 136-151 (2019) - [j3]Feng Zhou, Haomin Zhou, Zhihua Yang, Lihua Yang:
A 2-Stage Strategy for Non-Stationary Signal Prediction and Recovery Using Iterative Filtering and Neural Network. J. Comput. Sci. Technol. 34(2): 318-338 (2019) - 2018
- [j2]Zhihua Yang, Qian Zhang, Feng Zhou, Lihua Yang:
Hilbert spectrum analysis of piecewise stationary signals and its application to texture classification. Digit. Signal Process. 82: 1-10 (2018) - 2016
- [j1]Feng Zhou, Lijun Yang, Haomin Zhou, Lihua Yang:
Optimal averages for nonlinear signal decompositions - Another alternative for empirical mode decomposition. Signal Process. 121: 17-29 (2016)
Coauthor Index
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