Location prediction: a deep spatiotemporal learning from external sensors data

Author:

Cruz Lívia AlmadaORCID,Zeitouni Karine,da Silva Ticiana Linhares Coelho,de Macedo José Antonio Fernandes,Silva José Soares da

Publisher

Springer Science and Business Media LLC

Subject

Information Systems and Management,Hardware and Architecture,Information Systems,Software

Reference23 articles.

1. Alhasoun, F., Alhazzani, M., Aleissa, F., Alnasser, R., González, M.: City scale next place prediction from sparse data through similar strangers. In: Proceedings of ACM KDD Workshop, Halifax, Canada (2017)

2. Bucher, D.: Vision paper: Using volunteered geographic information to improve mobility prediction. In: Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility, PredictGIS’17, pp. 2:1–2:4. ACM, New York, NY, USA (2017). https://doi.org/10.1145/3152341.3152344

3. Chollet, F., et al.: Keras. https://keras.io (2015)

4. Cruz, L.A., Zeitouni, K., de Macedo, J.A.F.: Trajectory prediction from a mass of sparse and missing external sensor data. In: 2019 20th IEEE International Conference on Mobile Data Management (MDM), pp. 310–319. IEEE (2019)

5. Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96, 226–231 (1996)

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