Browsing Institute of New Imaging Technologies (INIT) by Author "cac0971b-0621-482f-9265-39333edfa0e5"
Now showing items 1-9 of 9
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Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review
Iskandaryan, Ditsuhi; Ramos, Jose Francisco; Trilles, Sergio MDPI (2020-04-01)The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies ... -
An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment
Hammad, Sahibzada Saadoon; Iskandaryan, Ditsuhi; Trilles, Sergio Elsevier (2023-07-08)Artificial Intelligence of Things (AIoT) is an emerging area of interest, and this can be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) devices. IoT data are prone to anomalies due ... -
Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid
Iskandaryan, Ditsuhi; Ramos, Jose Francisco; Trilles, Sergio PLoS (2022-06-01)Nitrogen dioxide is one of the pollutants with the most significant health effects. Advanced information on its concentration in the air can help to monitor and control further consequences more effectively, while also ... -
Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network
Iskandaryan, Ditsuhi; Ramos, Jose Francisco; Trilles, Sergio World Scientific (2022-06-22)Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution ... -
Features Exploration from Datasets Vision in Air Quality Prediction Domain
Iskandaryan, Ditsuhi; Ramos, Jose Francisco; Trilles, Sergio MDPI (2021-02-28)Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To ... -
Graph Neural Network for Air Quality Prediction: A Case Study in Madrid
Iskandaryan, Ditsuhi; Ramos Romero, José Francisco; Trilles, Sergio IEEE (2023)Air quality monitoring, modelling and forecasting are considered pressing and challenging topics for citizens and decision-makers, including the government. The tools used to achieve the above goals vary depending on the ... -
Reconstructing Secondary Data based on Air Quality, Meteorological and Traffic Data Considering Spatiotemporal Components
Iskandaryan, Ditsuhi; Ramos, Jose Francisco; Trilles, Sergio Elsevier (2023-02-08)This paper introduces the reconstructed dataset along with procedures to implement air quality prediction, which consists of air quality, meteorological and traffic data over time, and their monitoring stations and measurement ... -
Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks
Iskandaryan, Ditsuhi; Ramos, Francisco; Trilles, Sergio Springer (2022-11-10)Air quality prediction, especially spatiotemporal prediction, is still a challenging issue. Considering the impact of numerous factors on air quality causes difficulties in integrating these factors in a spatiotemporal ... -
The Effect of Weather in Soccer Results: An Approach Using Machine Learning Techniques
Iskandaryan, Ditsuhi; Ramos, Jose Francisco; Palinggi, Denny Asarias; Trilles, Sergio MDPI (2020-09-26)The growing popularity of soccer has led to the prediction of match results becoming of interest to the research community. The aim of this research is to detect the effects of weather on the result of matches by implementing ...