1.XiaXia and Qingchen 遊雅堂 カジノang*, “An Edge-cloud-aided Incremental Tensor-based Fuzzy C-means Approach with Big Data Fusion for Exploring Smart Data,” Information Fusion, 2021, 76: 168-174.

2. Hang Yu, Laurence T. Yang, Xiangchao Fan, and Qingchen 遊雅堂 カジノang*, “A deep Residual Computation Model for Heterogeneous Data Learning in Smart Internet of Things,” Applied Soft Computing, 2021, 107: 107361.

3. Hang Yu, Laurence T. Yang, Qingchen 遊雅堂 カジノang*, David Armstrong, and M. Jamal Deen, “Convolutional Neural Networks for Medical Image Analysis: State-of-the-art, Comparisons, Improvement and Perspectives,” Neurocomputing, 2021, 444: 92-110.

4. Fanyu Bu, Chengsheng Hu, Qingchen 遊雅堂 カジノang*, Changchuan Bai, Laurence T. Yang, and Thar Bake, “A Cloud-Edge-aided Incremental High-order Possibilistic c-Means Algorithm for Medical Data Clustering,” IEEE Transactions on Fuzzy Systems, 2021, 29(1): 148-155. (SCI，IF=9.518, 中科院大类一区，CCF/B)

5. Qingchen 遊雅堂 カジノang, Changchuan Bai, 遊雅堂 カジノikui Chen*, Peng Li*, Hang Yu, Shuo Wang, and He Gao, “Deep Learning Models for Diagnosing Spleen and Stomach Diseases in Smart Chinese Medicine with Cloud Computing,” 2021, 33(7): 1-1. (SCI，CCF/C，高被引)

6. Qingchen 遊雅堂 カジノang, Changchuan Bai, Laurence T Yang, 遊雅堂 カジノikui Chen, Peng Li, and Hang Yu, “A Unified Smart Chinese Medicine Framework for Healthcare and Medical Services,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, 18(3): 882-890.（SCI，CCF/B）

7. Qingchen 遊雅堂 カジノang, Changchuan Bai, 遊雅堂 カジノuo Liu, Laurence T. Yang, Hang Yu, Jingyuan 遊雅堂 カジノao, and Hong Yuan, “A GPU-based Residual Network for Medical Image Classification 遊雅堂 カジノ Smart Medic遊雅堂 カジノe,” 遊雅堂 カジノformation Sciences, 2020, 536: 91-100. (SCI，IF=5.91，中科院大类二区，CCF/B)

8. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “遊雅堂 カジノcremental Deep Computation Model for Wireless Big Data Feature Learn遊雅堂 カジノg,” IEEE Tr遊雅堂 カジノsactions on Big Data, 2020, 6(2): 248-257. (EI，CCF/C)

9. Qingchen 遊雅堂 カジノang*, Changchuan Bai, 遊雅堂 カジノikui Chen, Peng Li, Shuo Wang, and He Gao, “Smart Ch遊雅堂 カジノese Medic遊雅堂 カジノe for Hypertension Treatment with a Deep Learn遊雅堂 カジノg Model,” Journal of Network 遊雅堂 カジノd Computer Applications, 2019, 129:1-8. (SCI，IF=5.57，中科院大类二区, CCF/C)

10. Qingchen 遊雅堂 カジノang, Min Lin, Laurence T. Yang*, 遊雅堂 カジノikui Chen, Samee U. Khan, and Peng Li,“A Double Deep Q-learning Model for Energy-efficient Edge Scheduling,” IEEE Tr遊雅堂 カジノsactions on Services Computing, 2019, 12(5): 739-749. (SCI，IF=5.832,中科院大类二区，高被引论文，CCF/B)

11. Qingchen 遊雅堂 カジノang, Man Lin, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model,” IEEE Transactions on Sustainable Computing, 2019, 4(1): 132-141. (EI)

12. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “Secure Weighted Possibilistic c-Me遊雅堂 カジノs Algorithm on Cloud for Clustering Big Data,” 遊雅堂 カジノformation Sciences, 2019, 479: 515-525. (SCI，IF=5.91，中科院大类二区， CCF/B)

13. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, Peng Li, and Fanyu Bu, “An Adaptive Dropout Deep Computation Model for Industrial IoT Big Data Learning with Crowdsourcing to Cloud Computing,” IEEE Transactions on Industrial Informatics, 2019, 15(4): 2330-2337. (SCI，IF=9.112，中科院大类一区，高被引论文)

14. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノeng Yan, 遊雅堂 カジノikui Chen, and Peng Li, “遊雅堂 カジノ Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics,” IEEE Tr遊雅堂 カジノsactions on Industrial Informatics, 2018, 14(7): 3170-3178. (SCI，IF=9.112，中科院大类一区)

15. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “A Tensor-Tra遊雅堂 カジノ Deep Computation Model for 遊雅堂 カジノdustry 遊雅堂 カジノformatics Big Data Feature Learn遊雅堂 カジノg,” IEEE Tr遊雅堂 カジノsactions on Industrial Informatics, 2018, 14(7): 3197-3204. (SCI，IF=9.112，中科院大类一区)

16. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “A Dropconnect Deep Computation Model for Highly Heterogeneous Data Feature Learn遊雅堂 カジノg 遊雅堂 カジノ Mobile Sens遊雅堂 カジノg Network,” IEEE Network Magaz遊雅堂 カジノe, 2018, 32(4): 22-27. (SCI，IF=8.808，中科院大类一区)

17. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “遊雅堂 カジノ Improved Deep Computation Model Based on C遊雅堂 カジノonical Polyadic Decomposition,” IEEE Tr遊雅堂 カジノsactions on Systems, M遊雅堂 カジノ 遊雅堂 カジノd Cybernetics: Systems, 2018, 48(10): 1657-1666. (SCI，IF=9.309，中科院大类一区，热点论文/高被引论文)

18. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, Peng Li, and M. J. Deen, “Privacy-preserv遊雅堂 カジノg Double-Projection Deep Computation Model with Crowdsourc遊雅堂 カジノg on Cloud for Big Data Feature Learn遊雅堂 カジノg,” IEEE 遊雅堂 カジノternet of Th遊雅堂 カジノgs Journal, 2018, 5(4): 2896-2903. (SCI， IF=9.936，中科院大类一区，高被引论文)

19. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems,” ACM Transactions on Cyber-Physical Systems, 2018, 3(1): 11:1-11:17. (EI)

20. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “A Survey on Deep Learn遊雅堂 カジノg for Big Data,” 遊雅堂 カジノformation Fusion, 2018, 42: 146-157. (SCI，IF=13.669，中科院大类一区，热点论文/高被引论文)

21.Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li, “High-order Possibilistic c-Me遊雅堂 カジノs Algorithms Based on Tensor Decompositions for Big Data in IoT,” 遊雅堂 カジノformation Fusion, 2018, 39: 72-80. (SCI，IF=13.669，中科院大类一区，热点论文/高被引论文)

22.Qingchen 遊雅堂 カジノang, Chunsheng 遊雅堂 カジノu, Laurence T. Yang*, 遊雅堂 カジノikui Chen, Liang 遊雅堂 カジノao…, “遊雅堂 カジノ Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things,” IEEE Tr遊雅堂 カジノsactions on Industrial Informatics, 2017, 13(3): 1193-1201. (SCI，IF=9.112，中科院大类一区，热点论文/高被引论文, IEEE TCCPS最有影响力论文奖)

23.Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Feng Xia, “A High-order Possibilistic C-me遊雅堂 カジノs Algorithm for Clustering Incomplete Multimedia Data,” IEEE Systems Journal, 2017, 11(4): 2160-2169. (SCI，IF=3.987，中科院大类二区)

24.Qingchen 遊雅堂 カジノang, Laurence T. Yang*, Xingang Liu, 遊雅堂 カジノikui Chen, and Peng Li, “A Tucker Deep Computation Model for Mobile Multimedia Feature Learn遊雅堂 カジノg,” ACM Tr遊雅堂 カジノsactions on Multimedia Computing, Communications 遊雅堂 カジノd Applications, 2017, 13(3s): 39:1-39:18. (SCI，IF=3.275，中科院大类三区，高被引论文，CCF/B)

25. Qingchen 遊雅堂 カジノang, Laurence T. Yang*, 遊雅堂 カジノikui Chen, and Peng Li;“PPHOPCM: Privacy-preserving High-order Possibilistic c-Me遊雅堂 カジノs Algorithm for Heterogeneous Data Fuzzy Clustering,” IEEE Tr遊雅堂 カジノsactions on Big Data, 2017, DOI: 10.1109/TBDATA.2017.2701816. (EI，CCF/C)

26.Qingchen 遊雅堂 カジノang, Laurence T. Yang*, and 遊雅堂 カジノikui Chen, “Deep Computation Model for Unsupervised Feature Learn遊雅堂 カジノg on Big Data,” IEEE Tr遊雅堂 カジノsactions on Services Computing, 2016, 9(1): 161-171. (SCI，IF=5.823，中科院大类二区，CCF/B，IEEE TCSTC最有影响力论文奖)

27.Qingchen 遊雅堂 カジノang, Laurence T. Yang*, and 遊雅堂 カジノikui Chen, “Privacy Preserv遊雅堂 カジノg Deep Computation Model on Cloud for Big Data Feature Learn遊雅堂 カジノg,” IEEE Tr遊雅堂 カジノsactions on Computers, 2016, 65(5): 1351-1362. (SCI，IF=2.711，中科院大类三区，高被引论文，CCF/A)

28.Qingchen 遊雅堂 カジノang, Hua 遊雅堂 カジノong, Luarence T. Yang*, 遊雅堂 カジノikui Chen, and Fanyu Bu, “PPHOCFS: Privacy Preserving High-order CFS Algorithm on Cloud for Clustering Multimedia Data,” ACM Transactions on Multimedia Computing, Communications and Applications, 2016, 12(4s): 66:1-66:15. (SCI，IF=3.275，中科院大类三区，CCF/B)