Fei Long
Guizhou University
22 Papers
21 Citations
Fei Long is an academic researcher from Guizhou University. The author has contributed to research in topics: Computer science & Nonlinear system. The author has an hindex of 4, co-authored 15 publications.
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Papers
Sentiment Analysis of Text Based on Bidirectional LSTM With Multi-Head Attention
TL;DR: This article aims to investigate the sentiment analysis of social media Chinese text by combining Bidirectional Long-Short Term Memory (BiLSTM) networks with a Multi-head Attention (MHAT) mechanism in order to overcome the deficiency of Sentiment Analysis that is performed with traditional machine learning.
106
Signal detection of MIMO-OFDM system based on auto encoder and extreme learning machine
Xin Yan,Fei Long,Jingshuai Wang,Na Fu,Weihua Ou,Bin Liu +5 more
- 01 May 2017
TL;DR: Together AE network with ELM, a novel signal detection scheme for MIMO-OFDM system is proposed that outperforms that of many detection schemes such as zero-forcing (ZF), ZF-SIC, minimum-mean-square-error (MMSE), MMSE-S IC, and reaches the similar bit-error-rate (BER) performance of MLD and QGA-RBF with much lower complexity.
47
Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning
TL;DR: This work proposes a co-regularized multiview nonnegative matrix factorization method with correlation constraint for nonnegative representation learning, which jointly exploits consistent and complementary information across different views to accommodate the presence of noisy views.
Bilingual attention based neural machine translation
TL;DR: A novel bilingual attention based NMT, where its bilingual attention mechanism exploits decoding history and enables the NMT model to better dynamically select and exploit source side and target side information.
22
CNN-based color image encryption algorithm using DNA sequence operations
Jingshuai Wang,Fei Long,Weihua Ou +2 more
- 01 Dec 2017
TL;DR: Simulation results and security analysis show that the encryption effect of this paper is not only better than traditional encryption algorithms but also has excellent ability to hold back familiar attacks.
15