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dc.contributor.authorSrivastava, Harshit-
dc.contributor.authorBansal, Kailash-
dc.contributor.authorDas, Santos Kumar-
dc.contributor.authorSarkar, Santanu-
dc.identifier.citationInternational Conference on Devices, Intelligent Systems & Communications (DISC), JBIET, Hyderabad, 09-10 January 2020en_US
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractAir pollution occurs when the environmental gases such as CO2, NH3 etc. concentration levels go above the optimum level. As the AQI is being calculated and as per the Central Pollution Control Board (CPCB), there is standard level of ranges for pollution level. This paper presents about monitoring the pollution level using Raspberry Pi 3 based on IoT technology. Here, the temperature, humidity, due point and wind speed parameters are also monitored and use of these parameters as data sets for prediction of pollution forecasting. Then, the target of this project is applying the deep learning concept for the prediction and analysis of gas sensors pollution level so that we can analyze the pollution level due to the pollutant gases based on pre-diction analysis. Various experiments being performed validation of the development of the system for real-time monitoring. Here, We are dis-cussing about the different methods used in deep learning i.e. Artificial Neural Networks (ANN), Multilayer Perceptron (MLP) and Recurrent Neural Networks (RNN) using LSTM model to analyze and predict the multivariate time series forecasting.en_US
dc.subjectInternet of Things (IoT)en_US
dc.subjectRaspberry Pi 3en_US
dc.subjectRecurrent Neural Networks (RNN)en_US
dc.subjectLong Short Term Memory (LSTM)en_US
dc.subjectAir Quality Index (AQI)en_US
dc.titleAn IoT Based Air Quality Monitoring with Deep Learning Model Systemen_US
Appears in Collections:Conference Papers

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