时间序列应用分析 深度解读：深度学习在IoT大数据和流分析中的应用(8)

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【11】G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Meghini, and C. Vairo, “Deep learning for decentralized parking lot occupancy detection,” Expert Systems with Applications, 2017.

【12】X. Ma, H. Yu, Y. Wang, and Y. Wang, “Large-scale transportation network congestion evolution prediction using deep learning theory,” PloS one, vol. 10, no. 3, p. e0119044, 2015.

【12】C. Liu, Y. Cao, Y. Luo, G. Chen, V. Vokkarane, and Y. Ma, “Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment,” in International Conference on Smart Homes and Health Telematics. Springer, 2016, pp. 37–48.

【14】C. R. Pereira, D. R. Pereira, J. P. Papa, G. H. Rosa, and X.-S. Yang, “Convolutional neural networks applied for parkinson’s disease identification,” in Machine Learning for Health Informatics. Springer, 2016, pp. 377–390.

【15】Q. Wang, Y. Guo, L. Yu, and P. Li, “Earthquake prediction based on spatio-temporal data mining: An lstm network approach,” IEEE Transactions on Emerging Topics in Computing, 2017.

【16】Y. Liu, E. Racah, J. Correa, A. Khosrowshahi, D. Lavers, K. Kunkel, M. Wehner, and W. Collins, “Application of deep convolutional neural networks for detecting extreme weather in climate datasets,” Int’l Conf. on Advances in Big Data Analytics, 2016.