… data enables the metropolitans to analyze the current traffic situation … Besides the health
problems related to air pollution, it also … of land-use regression models used to predict air quality …

… Internet of Things (IoT) is nowadays finding profound use in each and every sector and
plays a key role in our air quality monitoring … It represents Linear Regression applied on the …

… it is worth saying that modern approaches that combine machine learning methods with the
utilize of air pollution information to get it … model for predicting NO2 concentrations from traffic …

… The proposed system is analogous to Google traffic or the navigation application … IoT
mobile–air pollution detection application. In Section II, existing monitoring systems for air pollution …

… those who have attempted to improve it have not taken air pollution into account. Along with
… air quality which is done by controlling traffic congestion. An ordinal regression model was …

… its chemical mixing transportation of atmospheric gases with … it was quite interesting to know
that a fair number of articles (5) have applied classic statistical regression model for pollution …

… regression analysis, Multiple discriminant analysis and … traffic congestion and clogging
growing, urban transport system overwhelmed, the consequent environmental noise, air pollution, …

… Regression is a supervised learning model for forecasting distinct values. Its same important
framework Support Vector Regression because it … IoT-enabled air quality predictive models …

… used for measuring personal exposure to traffic pollution. This device contains six low-cost …
, a non-linear regression model is employed in the next section to test how it performs in …

… regression models to forecast the Air Quality Index (AQI). The utilization of regression
methodologies, including Support Vector Regression (SVR) and multiple linear regression models, …