E. Fields-report, , p.25, 2020.

J. Wiart, Radio-Frequency Human Exposure Assessment: From Deterministic to Stochastic Methods
URL : https://hal.archives-ouvertes.fr/hal-02287674

, Etude de l'exposition du Public aux Ondes Radioélectriques, p.25, 2020.

P. Gaj?ek, P. Ravazzani, J. Wiart, J. Grellier, T. Samaras et al., Electromagnetic field exposure assessment in Europe radiofrequency fields (10 MHz-6 GHz), J. Expo. Sci. Environ. Epidemiol, vol.25, pp.37-44, 2015.

M. Tesanovic, E. Conil, A. De-domenico, R. Aguero, F. Freudenstein et al., The LEXNET project: Wireless networks and EMF: Paving the way for low-EMF networks of the future, IEEE Veh. Technol. Mag, vol.9, pp.20-28, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02287683

L. Diez, R. Agüero, and L. Muñoz, Electromagnetic Field Assessment as a Smart City Service: The Smartsantander Use-Case, vol.17, 1250.

P. Philippe, T. Pascal, P. Yannick, O. ;. Lamine, and . Js-ursi-france, Observatoire des Ondes, une Réponse au Débat Sociétal EMF Observatory, an Answer to the Societal Debate, p.25, 2020.

O. Observatoire-des, , 2020.

Y. Huang and J. Wiart, Simplified assessment method for population RF exposure induced by a 4G network
URL : https://hal.archives-ouvertes.fr/hal-02100947

I. J. Electromagn, Microwaves Med. Biol, pp.34-40, 2017.

P. Joshi, D. Colombi, B. Thors, L. Larsson, E. Törnevik et al., Output power levels of 4G user equipment and implications on realistic RF EMF exposure assessments, IEEE Access, vol.5, pp.4545-4550, 2017.

S. Aerts, D. Deschrijver, L. Verloock, T. Dhaene, L. Martens et al., Assessment of outdoor radiofrequency electromagnetic field exposure through hotspot localization using kriging-based sequential sampling, Environ. Res, vol.126, pp.184-191, 2013.

T. Lemaire, J. Wiart, and P. De-doncker, Variographic Analysis of Public Exposure to Electromagnetic Radiation Due to Cellular base Stations, Bioelectromagnetics, vol.37, pp.557-562, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02100964

K. N. Prachi and G. Matta, Artificial neural network applications in air quality monitoring and management, Int. J. Environ. Rehabil. Conserv, vol.2, pp.30-64, 2011.

K. P. Singh, A. Basant, A. Malik, and G. Jain, Artificial neural network modeling of the river water quality-A case study, Ecol. Model, vol.220, pp.888-895, 2009.

O. Rozenblit, Y. Haddad, Y. Mirsky, and R. Azoulay, Machine learning methods for SIR prediction in cellular networks, Phys. Commun, vol.31, pp.239-253, 2018.

A. Zappone, M. Di-renzo, M. Debbah, T. T. Lam, and X. Qian, Model-aided wireless artificial intelligence: Embedding expert knowledge in deep neural networks for wireless system optimization, IEEE Veh. Technol. Mag, vol.14, pp.60-69, 2019.

E. Ostlin, H. Zepernick, and H. Suzuki, Macrocell path-loss prediction using artificial neural networks, IEEE Trans. Veh. Technol, vol.59, pp.2735-2747, 2010.

S. I. Popoola, E. Adetiba, A. Atayero, N. Faruk, and C. T. Calafate, Optimal model for path loss predictions using feed-forward neural networks, Cogent Eng, vol.5, 2018.

J. M. Mom, C. O. Mgbe, and G. A. Igwue, Application of artificial neural network for path loss prediction in urban macrocellular environment, Am. J. Eng. Res, vol.3, pp.270-275, 2014.

S. Aerts, Y. Huang, L. Martens, W. Joseph, and J. Wiart, Use of artificial intelligence to model exposure to radiofrequency electromagnetic fields based on sensor network measurements, Proceedings of the 4th Workshop on Uncertainty Modeling for Engineering Applications (UMEMA 2018), pp.10-11, 2018.

. Anfr-cartoradio, , p.25, 2020.

H. T. Friis, A note on a simple transmission formula, Proc. IRE IEEE 1946, vol.34, pp.254-256

V. Erceg, L. J. Greenstein, S. Y. Tjandra, S. R. Parkoff, A. Gupta et al., An empirically based path loss model for wireless channels in suburban environments, IEEE J. Sel. Areas Commun, vol.17, pp.1205-1211, 1999.

B. Fu, G. Bernáth, B. Steichen, and S. Weber, Wireless background noise in the Wi-Fi spectrum, Proceedings of the IEEE 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp.1-7, 2008.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, 2013.

Y. Bengio, Practical Recommendations for Gradient-Based Training of Deep Architectures, Neural Networks: Tricks of the Trade, pp.437-478, 2012.

S. Bazrafkan, H. Javidnia, J. Lemley, and P. Corcoran, Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture, First Application on Depth from Monocular Camera, J. Electron. Imaging, 2018.

C. Systems, , p.25, 2020.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, pp.249-256, 2010.