| 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 | 158 | 2020 |
| Graph neural network for air quality prediction: A case study in madrid D Iskandaryan, F Ramos, S Trilles IEEE Access 11, 2729-2742, 2023 | 27 | 2023 |
| Anomaly detection based on artificial intelligence of things: A systematic literature mapping S Trilles, SS Hammad, D Iskandaryan Internet of Things, 101063, 2024 | 21 | 2024 |
| 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 | 18 | 2022 |
| An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment SS Hammad, D Iskandaryan, S Trilles Internet of Things 23, 100848, 2023 | 16 | 2023 |
| 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 | 13 | 2020 |
| Features exploration from datasets vision in air quality prediction domain D Iskandaryan, F Ramos, S Trilles Atmosphere 12 (3), 312, 2021 | 6 | 2021 |
| Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal components D Iskandaryan, F Ramos, S Trilles Data in Brief 47, 108957, 2023 | 5 | 2023 |
| 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 |
| Exploratory analysis and feature selection for the prediction of nitrogen dioxide D Iskandaryan, S Di Sabatino, F Ramos, S Trilles AGILE: GIScience Series 3, 6, 2022 | 4 | 2022 |
| A set of deep learning algorithms for air quality prediction applications D Iskandaryan, F Ramos, S Trilles Software Impacts 17, 100562, 2023 | 2 | 2023 |
| Spatiotemporal prediction of nitrogen dioxide based on graph neural networks D Iskandaryan, F Ramos, S Trilles Environmental Informatics, 111-128, 2022 | 2 | 2022 |
| Application of deep learning and machine learning in air quality modeling D Iskandaryan, F Ramos, S Trilles Current Trends and Advances in Computer-Aided Intelligent Environmental Data …, 2022 | 2 | 2022 |
| Visualization and visual analytics of geospatial data for psychological treatment D Iskandaryan Universitat Jaume I, 2018 | 1 | 2018 |
| Open data and disaster management D Iskandaryan | 1 | 2017 |
| Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components D Iskandaryan Universitat Jaume I, 2023 | | 2023 |
| Data Visualisation For Teachers: How To Read, Interpret And Show Data Correctly JF Ramos, D Iskandaryan, I Koribska International Academy of Technology, Education and Development (IATED), 2022 | | 2022 |
| Dataset for prediction of Nitrogen Dioxide in Madrid city D Iskandaryan, F Ramos, S Trilles | | 2021 |
| IMPROVING TEACHERS VISUAL PRESENTATIONS WITH SIMPLICITY, CLARITY AND BREVITY F Ramos, D Iskandaryan, A Gomez-Cambronero EDULEARN19 Proceedings, 6218-6218, 2019 | | 2019 |
