J
Jianqiang Li
Researcher at Beijing University of Technology
Publications - 395
Citations - 8358
Jianqiang Li is an academic researcher from Beijing University of Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 36, co-authored 300 publications receiving 5155 citations. Previous affiliations of Jianqiang Li include Shenzhen University & University of Science and Technology Beijing.
Papers
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Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges
TL;DR: This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoSDoS attacks, which are important for the smooth evolution ofSDN-based cloud without the distraction ofDDoS attacks.
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Predicting miRNA-disease association based on inductive matrix completion.
TL;DR: A novel model of Inductive Matrix Completion for MiRNA‐Disease Association prediction (IMCMDA) to complete the missing miRNA‐disease association based on the known associations and the integrated miRNA similarity and disease similarity.
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Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges
TL;DR: The 5C architecture that is widely adopted to characterize the Industrial Internet systems is presented and the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management are investigated.
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A hybrid solution for privacy preserving medical data sharing in the cloud environment
TL;DR: This paper proposes a practical solution for privacy preserving medical record sharing for cloud computing, where the statistical analysis and cryptography are innovatively combined together to provide multiple paradigms of balance between medical data utilization and privacy protection.
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A Hybrid Path Planning Method in Unmanned Air/Ground Vehicle (UAV/UGV) Cooperative Systems
TL;DR: The evaluation results show that the proposed approach can obtain a much less costly path compared to the traditional path planning algorithms such as the genetic algorithm and the A-star algorithm and can run in real-time to support the UAV/UGV systems.