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Neal N. Xiong
Researcher at Northeastern State University
Publications - 208
Citations - 4896
Neal N. Xiong is an academic researcher from Northeastern State University. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 28, co-authored 185 publications receiving 2643 citations. Previous affiliations of Neal N. Xiong include Xi'an University of Architecture and Technology & Southwestern Oklahoma State University.
Papers
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EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing
TL;DR: A scheme that supports CBIR over the encrypted images without revealing the sensitive information to the cloud server is proposed and the security and efficiency of the proposed scheme are shown.
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Robust Cyber-Physical Systems
Fei Hu,Yu Lu,Athanasios V. Vasilakos,Qi Hao,Rui Ma,Yogendra Patil,Ting Zhang,Jiang Lu,Xin Li,Neal N. Xiong +9 more
TL;DR: This paper comprehensively survey the concept and strategies for building a resilient and integrated cyber-physical system (CPS) and uses two detailed examples from achieved projects to explain how to achieve a robust, systematic CPS design.
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PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities
Prabhat Kumar,Randhir Kumar,Gautam Srivastava,Govind P. Gupta,Rakesh Tripathi,Thippa Reddy Gadekallu,Neal N. Xiong +6 more
TL;DR: Experimental results demonstrate the superiority of the PPSF framework over some recent approaches in blockchain and non-blockchain systems.
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Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
TL;DR: The integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection and empirical results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.
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Learning Knowledge Graph Embedding With Heterogeneous Relation Attention Networks.
TL;DR: Wang et al. as discussed by the authors proposed a heterogeneous GNNs framework based on attention mechanism, where the neighbor features of an entity are first aggregated under each relation-path, and then the importance of different relationpaths is learned through the relation features.