Research on educational resource recommendation system based on MRLG Rec

[1] Ocepek U, Bosnić Z, Šerbec I N, et al. Exploring the relation between learning style models and preferred multimedia types[J]. Computers & Education, 2013, 69: 343-355. Search in Google Scholar

[2] Mampadi F, Chen S Y, Ghinea G, et al. Design of adaptive hypermedia learning systems: A cognitive style approach[J]. Computers & Education, 2011,56 (4):1003-1011. Search in Google Scholar

[3] Salton G, Buckley C. Term-weighting approaches in automatic text retrieval[J]. Information Processing & Management, 1988, 24(5):513-523. Search in Google Scholar

[4] Sutton RS, Barto A G. Reinforcement learning: An introduction[M]. Cambridge: MIT Press, 1998. Search in Google Scholar

[5] Puterman M. Markov decision process [J]. Statistica Neerlandica, 1985, 39(2):219-233. Search in Google Scholar

[6] Wu Y, Shen T. Policy Iteration algorithm for optimal control of stochastic logical dynamical systems [J]. IEEE Transactions on Neural Networks &. Learning Systems, 2017, 28(99):1-6. Search in Google Scholar

[7] Wei Q, Liu D, Lin H. Value iteration adaptive dynamic pro-gramming for optimal control of discrete-time nonlinear systems[J]. IEEE Transactions on Cybernetics, 2016, 46(3):840-853. Search in Google Scholar

[8] Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network [C]//Proceedings of the 30th IEEE Conference on ComputerVi-sion and Pattern Recognition. Hawai: IEEE, 2017:105-114. Search in Google Scholar

[9] Cao ZY, Niu S Z, Zhang J W. Masked image inpainting algorithm based on generative adversarial networks[J]. Journal of Beijing University of Posts and Telecom, 2018, 41(3):81-86.(in Chinese) Search in Google Scholar

[10] Zhang YZ, Gan Z, Carin L. Generating text via adversarial training[C]/∥ Proceedings of the 30th Conference on Neural In-formation Processing Systems. Barcelona: MIT Press, 2016:1543-1551. Search in Google Scholar

[11] Reed S, Akata Z, Yan X C, et al. Generative adver-sarial text to image synthesis[C]//Proceedings of the 33rd International Conference on Machine Learning. New York: ACM, 2016:1060-1069. Search in Google Scholar

[12] Mirza M, Osindero S. Conditional generative adversarial nets[J].Computer Science, 2014, 8(13):2672-2680. Search in Google Scholar

[13] Arjvsky M, Chintala S, Bottou L. Wasserstein genrative adversarial networks[C]//Proceedings of the 34th Inter-national Conference on Machine Learning. Sydney: ACM, 2017:214-223. Search in Google Scholar

[14] Samaila, M.G., et al., Performance evaluation of the SRE and SBPG components of the IoT hardware platform security advisor framework. Computer Networks, 2021. 199: p. 108496. Search in Google Scholar

[15] Kamalraj, R., et al., Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm. Measurement, 2021. 183: p. 109804. Search in Google Scholar

[16] Chen, M., et al., Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization. Computers in Industry, 2022. 134: p. 103551. Search in Google Scholar

[17] Zheng, X. and W. Chen, An Attention-based Bi-LSTM Method for Visual Object Classification via EEG. Biomedical Signal Processing and Control, 2021. 63: p. 102174. Search in Google Scholar

[18] Su, J., et al., BERT-hLSTMs: BERT and hierarchical LSTMs for visual storytelling. Computer Speech & Language, 2021. 67: p. 101169. Search in Google Scholar

[19] Xiao, X., et al., Battery-free wireless moisture sensor system for fruit monitoring. Results in Engineering, 2022. 14: p. 100420. Search in Google Scholar

[20] Hazzaa, F., et al., Security Scheme Enhancement for Voice over Wireless Networks. Journal of Information Security and Applications, 2021. 58: p. 102798. Search in Google Scholar

[21] Cirne, A., et al., IoT security certifications: Challenges and potential approaches. Computers & Security, 2022. 116: p. 102669. Search in Google Scholar

[22] Zhang Y, Qian T, Tang W. Buildings-to-distribution-network integration considering power transformer loading capability and distribution network reconfiguration[J]. Energy, 2022, 244. Search in Google Scholar

[23] T. Qian, Xingyu Chen, Yanli Xin, W. H. Tang, Lixiao Wang. Resilient Decentralized Optimization of Chance Constrained Electricity-gas Systems over Lossy Communication Networks [J]. Energy, 2022, 239, 122158. Search in Google Scholar

[24] Baining Zhao, Tong Qian, Wenhu Tang, Qiheng, Liang. A Data-enhanced Distributionally Robust Optimization Method for Economic Dispatch of Integrated Electricity and Natural Gas Systems with Wind Uncertainty[J] Energy, 2022, Energy, 2022:123113. Search in Google Scholar

[25] T. Qian, Y. Liu, W. H Zhang, W. H. Tang, M. Shahidehpour. Event-Triggered Updating Method in Centralized and Distributed Secondary Controls for Islanded Microgrid Restoration[J]. IEEE Transactions on Smart Gird, 2020, 11(2): 1387-1395. Search in Google Scholar