International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Intelligent Visual Analytics for Monitoring Air Quality with AI |
|---|---|
| ABSTRACT | The IoT-based device was integrated with the thinger.io platform, which has proven stability and effectiveness. This platform enabled stable, real-time data storage and transmission, providing cost-effective and reliable environmental monitoring. The system’s robustness and affordability present significant advantages, making it scalable for wider use in similar regions with budget constraints. Monthly data collection at one-minute intervals provided overall insight into the pollution levels, alongside humidity and temperature data. The dataset was classified using five machine learning algorithms, leading to important findings. The Gradient Boosting (GD) model reached the best precision, closely followed by the Random Forest (RF) model. The Support Vector Classifier (SVC) has proven to have a strong performance, while K-Nearest neighbors (KNN) achieved an acceptable accuracy. Logistic Regression (LR) exhibited relatively lower accuracy. Notably, the region recorded high pollution levels, with predominant classifications falling into the higher risk categories, from unhealthy and very unhealthy to hazardous. The results highlight the ability of machine learning (ML) techniques to accurately classify AQ data. The successful integration of IoT platforms with ML models demonstrates the potential for developing low-cost, scalable, and stable systems for real-time AQ monitoring. This hybrid approach is crucial, especially in developing countries with limited research on pollution and budget constraints, as it may help improve environmental management in highly populated and industrialized regions. |
| AUTHOR | RAMYA V, SUJITH.S, SUJITHKUMAR.V, SWARAJ.S, SETHUPATHY.D Assistant Professor, Department of Computer Science and Engineering, The Kavery Engineering College, Mecheri, Salem, India Department of Computer Science and Engineering, The Kavery Engineering College, Mecheri, Salem, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404068 |
| pdf/68_Intelligent Visual Analytics for Monitoring Air Quality with AI.pdf | |
| KEYWORDS | |
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