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.‎