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Chao Wu
Researcher at Chongqing University
Publications - 17
Citations - 228
Chao Wu is an academic researcher from Chongqing University. The author has contributed to research in topics: Computer science & Sentiment analysis. The author has an hindex of 3, co-authored 13 publications receiving 45 citations. Previous affiliations of Chao Wu include Chinese Ministry of Education.
Papers
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Journal ArticleDOI
A water quality prediction method based on the multi-time scale bidirectional long short-term memory network.
TL;DR: A method for water quality prediction based on the multi-time scale bidirectional LSTM network that improves data integrity and data volume through data preprocessing and adjusts hyper-parameters in the process of modeling is proposed.
Journal ArticleDOI
A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism.
TL;DR: Li et al. as discussed by the authors proposed a water quality prediction model named CNN-LSTM with Attention (CLA) to predict the water quality variables, which is highly capable of resolving nonlinear time series prediction problems.
Proceedings ArticleDOI
Multi-modal cyberbullying detection on social networks
TL;DR: This work proposes a multi-modal cyberbullying detection framework that takes into multi- modal information on social networks, and uses the hierarchical attention networks to capture the session feature in social networks and encode several media information.
Journal ArticleDOI
A method for mixed data classification base on RBF-ELM network
Qiude Li,Qiude Li,Qingyu Xiong,Qingyu Xiong,Shengfen Ji,Yang Yu,Yang Yu,Chao Wu,Chao Wu,Hualing Yi,Hualing Yi +10 more
TL;DR: Zhang et al. as discussed by the authors proposed an extended version of RBF-ELM (Radial Basis Function-Extreme Learning Machine), which can achieve direct, fast, and efficient classification for mixed data.
Journal ArticleDOI
Multiple-element joint detection for Aspect-Based Sentiment Analysis
Chao Wu,Chao Wu,Qingyu Xiong,Qingyu Xiong,Hualing Yi,Hualing Yi,Yang Yu,Yang Yu,Qiwu Zhu,Qiwu Zhu,Min Gao,Min Gao,Jie Chen +12 more
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence.