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Xiaokang Zhou
Researcher at Shiga University
Publications - 122
Citations - 2599
Xiaokang Zhou is an academic researcher from Shiga University. The author has contributed to research in topics: Computer science & The Internet. The author has an hindex of 16, co-authored 85 publications receiving 889 citations. Previous affiliations of Xiaokang Zhou include Waseda University.
Papers
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Journal ArticleDOI
CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support
Xiaokang Zhou,Yue Li,Wei Liang +2 more
TL;DR: A so-called DP-CRNN algorithm is developed with a newly designed neural network structure, to extract and highlight the combination of semantic and sequential features in terms of patient's inquiries in order to deal with the situation that patients’ online inquiries are usually not very long.
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Variational LSTM Enhanced Anomaly Detection for Industrial Big Data
TL;DR: Experiments demonstrate that the proposed VLSTM model can efficiently cope with imbalance and high-dimensional issues, and significantly improve the accuracy and reduce the false rate in anomaly detection for IBD according to F1, area under curve (AUC), and false alarm rate (FAR).
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Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things
TL;DR: This article focuses on the deep-learning-enhanced HAR in IoHT environments, and a semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model.
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Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems
TL;DR: A few-shot learning model with Siamese convolutional neural network (FSL-SCNN) is proposed, to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS.
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Academic Influence Aware and Multidimensional Network Analysis for Research Collaboration Navigation Based on Scholarly Big Data
TL;DR: An improved Random Walk with Restart (RWR) based algorithm is developed, in which the time-varying academic influence is newly defined and measured in a certain social context, to provide researchers with research collaboration navigation for their future works.