Journal of Machine and Computing


Adaptive Deep Learning Strategies for Formaldehyde Monitoring in Industrial Air Quality



Journal of Machine and Computing

Received On : 19 March 2025

Revised On : 10 April 2025

Accepted On : 09 May 2025

Published On : 05 July 2025

Volume 05, Issue 03

Pages : 1712-1724


Abstract


Inhaling formaldehyde a chemical that is widely used in many different industries can have serious health consequences. In order to precisely detect formaldehyde levels in industrial air quality environments, this study makes use of deep learning techniques. Using sensor data gathered from high-risk industrial areas the study focuses on variables like air quality index temperature and humidity. The data is processed by Convolutional Neural Networks (CNNs), which identify trends linked to increases in formaldehyde concentrations. To improve model accuracy preprocessing of the data is done including feature scaling and outlier elimination. The model's performance is assessed using evaluation metrics like Mean Squared Error (MSE), sensitivity, specificity, and prediction accuracy. Results show that when compared to conventional regression models the CNN-based model considerably lowers false positives while achieving a high prediction accuracy. Rapid reaction to hazardous formaldehyde levels is made possible by the deep learning frameworks' real-time monitoring capability which lowers possible health hazards. To improve long-term prediction accuracy and trend identification future research will investigate the use of recurrent neural networks (RNN) for time-series analysis.


Keywords


Formaldehyde, Deep Learning, Air Quality Monitoring, CNN, Industrial Safety, Health Risks.


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CRediT Author Statement


The authors confirm contribution to the paper as follows:

Conceptualization: Kishore Kunal, Leena Nesamani S, Kathiravan M, Parthasarthy K and Vairavel Madeshwaren; Writing- Original Draft Preparation: Kishore Kunal, Pillalamarri Lavanya, Leena Nesamani S, Kathiravan M, Parthasarthy K and Vairavel Madeshwaren; Visualization: Kathiravan M, Parthasarthy K and Vairavel Madeshwaren; Investigation: Kishore Kunal, Pillalamarri Lavanya and Leena Nesamani S; Supervision: Kathiravan M, Parthasarthy K and Vairavel Madeshwaren; Validation: Kishore Kunal, Pillalamarri Lavanya and Leena Nesamani S; Writing- Reviewing and Editing: Kishore Kunal, Pillalamarri Lavanya, Leena Nesamani S, Kathiravan M, Parthasarthy K and Vairavel Madeshwaren; All authors reviewed the results and approved the final version of the manuscript.


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The authors would like to thank to the reviewers for nice comments on the manuscript.


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Cite this article


Kishore Kunal, Pillalamarri Lavanya, Leena Nesamani S, Kathiravan M, Parthasarthy K and Vairavel Madeshwaren, “Adaptive Deep Learning Strategies for Formaldehyde Monitoring in Industrial Air Quality”, Journal of Machine and Computing, vol.5, no.3, pp. 1712-1724, July 2025, doi: 10.53759/7669/jmc202505135.


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© 2025 Kishore Kunal, Pillalamarri Lavanya, Leena Nesamani S, Kathiravan M, Parthasarthy K and Vairavel Madeshwaren. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.