An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 5 | Issue - 4 | december 2023
Published
22 January, 2024
Addressing the critical issue of air quality in the Coimbatore region, this study introduces a novel approach for continuous monitoring and forecasting of air pollution. By utilizing the Internet of Things (IoT) technology integrated with Artificial Intelligence (AI) methods, this research focuses on monitoring and forecasting three major pollutants such as Ozone (O3), Ammonia (NH3), and Carbon Monoxide (CO). The proposed IoT-based sensor nodes collect the real-time data and give the resultant data as an input to the Naive Bayes (NB) for classification and Auto-Regression Integrating Moving Average (ARIMA) for optimization. The optimized model parameters are obtained and then validated by using performance metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Deploying a machine learning algorithm on a Raspberry Pi-3, the proposed system ensures efficient monitoring and forecasting of air pollutants 24/7 through an online open-source dashboard.
KeywordsInternet of Things (IoT) Artificial Intelligence (AI) Naive Bayes (NB) Auto-Regression Integrating Moving Average (ARIMA) Raspberry Pi-3 Sensor Nodes Air Pollutants
Full Article PDF