| Air quality prediction in smart cities using machine learning technologies based on sensor data: a review D Iskandaryan, F Ramos, S Trilles Applied Sciences 10 (7), 2401, 2020 | 142 | 2020 |
| New trends in using augmented reality apps for smart city contexts P Yagol, F Ramos, S Trilles, J Torres-Sospedra, FJ Perales ISPRS International Journal of Geo-Information 7 (12), 478, 2018 | 115 | 2018 |
| Promoting pollution-free routes in smart cities using air quality sensor networks F Ramos, S Trilles, A Muñoz, J Huerta Sensors 18 (8), 2507, 2018 | 51 | 2018 |
| Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid D Iskandaryan, F Ramos, S Trilles PloS one 17 (6), e0269295, 2022 | 16 | 2022 |
| Reliability validation of a low-cost particulate matter IoT sensor in indoor and outdoor environments using a reference sampler S Trilles, AB Vicente, P Juan, F Ramos, S Meseguer, L Serra Sustainability 11 (24), 7220, 2019 | 16 | 2019 |
| The effect of weather in soccer results: an approach using machine learning techniques D Iskandaryan, F Ramos, DA Palinggi, S Trilles Applied Sciences 10 (19), 6750, 2020 | 11 | 2020 |
| A Comparative Study in the Standardization of IoT Devices Using Geospatial Web Standards D Marsh-Hunn, S Trilles, A González-Pérez, J Torres-Sospedra, F Ramos IEEE Sensors Journal 21 (4), 5512-5528, 2020 | 7 | 2020 |
| Features exploration from datasets vision in air quality prediction domain D Iskandaryan, F Ramos, S Trilles Atmosphere 12 (3), 312, 2021 | 6 | 2021 |
| Comparison of nitrogen dioxide predictions during a pandemic and non-pandemic scenario in the city of Madrid using a convolutional LSTM network D Iskandaryan, F Ramos, S Trilles International Journal Of Computational Intelligence And Applications 21 (02 …, 2022 | 5 | 2022 |
