L
Li Daming
Researcher at City University of Macau
Publications - 42
Citations - 887
Li Daming is an academic researcher from City University of Macau. The author has contributed to research in topics: Smart city & Computer science. The author has an hindex of 9, co-authored 42 publications receiving 501 citations.
Papers
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A novel CNN based security guaranteed image watermarking generation scenario for smart city applications
TL;DR: A novel algorithm using synergetic neural networks for robustness and security of digital image watermarking is proposed, which obtains an optimal Peak Signal-to-noise ratio (PSNR) and can complete certain image processing operations with improved performance.
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IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning
TL;DR: An IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model, which combines deep learning model with intrusion detection technology is proposed.
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RETRACTED ARTICLE: Mobile network intrusion detection for IoT system based on transfer learning algorithm
TL;DR: In this article, the authors analyze the characteristics of network security and security problems, and discuss the system framework of Internet security and some key security technologies, including key management, authentication and access control, routing security, privacy protection, intrusion detection and fault tolerance and intrusion etc.
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Information security model of block chain based on intrusion sensing in the IoT environment
TL;DR: In this paper, intrusion detection technology is applied to block chain information security model, and the results show that proposed model has higher detection efficiency and fault tolerance.
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Intelligent Transportation System in Macao Based on Deep Self-Coding Learning
TL;DR: This paper introduces the deep code learning technique and applies it to the Macao intelligent system, and combines the deep belief network model and support vector regression classifier as the prediction model, and uses the deep believe network model to learn traffic flow characteristics.