Improving air pollution detection accuracy and status monitoring based ‎on supervised learning systems and Internet of Things

In recent decades air pollution and its associated health risks are in growing numbers. Detecting ‎air pollution in the environment and alarming the people may accomplish various advantages ‎among health monitoring, telemedicine, and industrial sectors. A novel method of detecting air ‎pollution using supervised learning models and an alert system using IoT is proposed. The main ‎aim of the research is manifold: a) Air pollution data is preprocessed using the feature scaling ‎method, b) The feature selection and feature extraction process done followed by performing a ‎Recurrent Neural Network and c) The predicted data is stored in the cloud server, and it provides ‎the end-users with an alert when the threshold pollution index exceeds. The proposed RNN ‎reports enhanced performance when tested against traditional machine learning models such as ‎Convolutional Neural Networks (CNN), Deep Neural Networks(DNN), and Artificial Neural ‎Networks(ANN) for parameters such as accuracy, specificity, and sensitivity.‎

Improving air pollution detection accuracy and status monitoring based ‎on supervised learning systems and Internet of Things Keywords:

Internet of Things , Convolutional Neural Networks (CNN) , Deep Neural Networks ‎‎(DNN) , and Artificial Neural Networks (ANN) , Recurrent Neural Network