Forecasting of mobile network traffic and spatio–temporal analysis using modLSTM-Reference-Cited by-同舟云学术

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Forecasting of mobile network traffic and spatio–temporal analysis using modLSTM

Published:2023-11-30 Issue: Volume: Page:
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Aski Vidyadhar J.,Chavan Rugved Sanjay,Dhaka Vijaypal Singh,Rani Geeta,Zumpano Ester,Vocaturo Eugenio

Funder

Progetto di Ricerca POR SIMPATICO: Sistema Informativo Medico PATologIe COmplesse 3D

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s10994-023-06471-1.pdf

Reference28 articles.

1. Barlacchi, G. (2015). A multi$$-$$source dataset of urban life in the city of Milan and the province of Trentino. Scientific Data, 2, 1–15.

2. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. Hoboken: Wiley.

3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

4. Chavan, R. S., Srivastava, G., & Pradhan, N. (2023). Advance plant health monitoring and forecasting system using edge-fog-cloud computing and lstm networks. In Mathur, G., Bundele, M., Tripathi, A., Paprzycki, M. (eds.) Proceedings of 3rd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore . https://doi.org/10.1007/978-981-19-7041-2_26.

5. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y. (2014). Learning phrase representations using rnn encoder$$-$$decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

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