Journal of Machine and Computing


Machine Learning Powered Asbestos Exposure Modeling Using Feature Extraction from IoT Based Sensor Data



Journal of Machine and Computing

Received On : 19 March 2025

Revised On : 08 April 2025

Accepted On : 09 May 2025

Published On : 05 July 2025

Volume 05, Issue 03

Pages : 1673-1684


Abstract


Asbestos, a dangerous substance commonly used in buildings, continues to present serious risks in urban areas, because of outdated infrastructure and inappropriate disposal methods. The goal of this study is to help with proactive public health measures by utilizing machine learning algorithms to predict asbestos exposure levels. An IoT-based environmental sensor dataset that tracks temperature humidity and air quality is presented in this study. Random Forest, Support Vector Machines (SVM), and Neural Networks are three machine-learning techniques used to create predictive models that can estimate asbestos concentrations under different conditions. Data preprocessing includes feature extraction and normalization to improve prediction accuracy. Performance metrics such as F1 score, accuracy, sensitivity, and specificity are used to compare the models. Additionally, certain environmental factors that influence asbestos dispersion are identified by the Random Forest feature importance analysis. Moreover, the IoT-based environmental sensor dataset used in this study is derived from real-world deployed sensors installed in high-risk industrial zones. These sensors continuously monitor environmental parameters such as formaldehyde concentration, temperature, humidity, and AQI, ensuring that the data reflects authentic field conditions for reliable model training and evaluation. These findings demonstrate how real-time asbestos exposure prediction using machine learning enables timely interventions. Future studies aim to increase accuracy and computational efficiency, future enhancements may incorporate techniques such as Long Short-Term Memory (LSTM) networks for temporal modeling, CNN pruning for model optimization, and feature selection methods to reduce dimensionality and processing time.


Keywords


Asbestos, Urban Environments, Neural Networks, Machine Learning, Public Health, Predictive Models.


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CRediT Author Statement


The authors confirm contribution to the paper as follows:

Conceptualization: Banushri Annamalai, Kishore Kunal, Vairavel Madeshwaren, Kathiravan M, Goli Ramkrishna and Neha Sharma; Methodology: Banushri Annamalai and Kishore Kunal; Software: Vairavel Madeshwaren, Kathiravan M, Goli Ramkrishna and Neha Sharma; Data Curation: Banushri Annamalai and Kishore Kunal; Writing- Original Draft Preparation: Banushri Annamalai, Kishore Kunal, Vairavel Madeshwaren, Kathiravan M, Goli Ramkrishna and Neha Sharma; Visualization: Vairavel Madeshwaren, Kathiravan M, Goli Ramkrishna and Neha Sharma; Investigation: Banushri Annamalai and Kishore Kunal; Supervision: Vairavel Madeshwaren, Kathiravan M, Goli Ramkrishna and Neha Sharma; Validation: Banushri Annamalai and Kishore Kunal; Writing- Reviewing and Editing: Banushri Annamalai, Kishore Kunal, Vairavel Madeshwaren, Kathiravan M, Goli Ramkrishna and Neha Sharma; All authors reviewed the results and approved the final version of the manuscript.


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Cite this article


Banushri Annamalai, Kishore Kunal, Vairavel Madeshwaren, Kathiravan M, Goli Ramkrishna and Neha Sharma, “Machine Learning Powered Asbestos Exposure Modeling Using Feature Extraction from IoT Based Sensor Data”, Journal of Machine and Computing, vol.5, no.3, pp. 1673-1684, July 2025, doi: 10.53759/7669/jmc202505132.


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© 2025 Banushri Annamalai, Kishore Kunal, Vairavel Madeshwaren, Kathiravan M, Goli Ramkrishna and Neha Sharma. 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.