Prediction in Smart Environments and Administration: Systematic Literature Review
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-28694-0_4
Reference60 articles.
1. Anaya, I.D.P., Simko, V., Bourcier, J., Plouzeau, N., Jézéquel, J.-M.: A prediction-driven adaptation approach for self-adaptive sensor networks. In: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 145–154 (2014)
2. Arabghalizi, T., Labrinidis, A.: Data-driven bus crowding prediction models using context-specific features. ACM Trans. Data Sci. 1(3), 1–33 (2020)
3. Askari, B., Le Quy, T., Ntoutsi, E.: Taxi demand prediction using an LSTM-based deep sequence model and points of interest. In: 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1719–1724. IEEE (2020)
4. Attoui, S.-E., Meddeb, M.: A generic framework for forecasting short-term traffic conditions on urban highways. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10. IEEE (2021)
5. Bhatia, M., Kaur, S., Sood, S.K., Behal, V.: Internet of Things-inspired healthcare system for urine-based diabetes prediction. Artif. Intell. Med. 107, 101913 (2020)
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