Home > Published Issues > 2023 > Volume 14, No. 2, 2023 >
JAIT 2023 Vol.14(2): 373-383
doi: 10.12720/jait.14.2.373-383

Development of an Electronic Nose for Harmful Gases with Prediction Modeling Using Machine Learning

Ana Antoniette C. Illahi1,* , Argel A. Bandala1 , Edwin Sybingco1 , Elmer P. Dadios1 , Ryan Rhay P. Vicerra1 , Ronnie Concepcion II1 , Laurence A. Gan Lim1 , Raouf Naguib2,*

  • 1Department of Electronics and Computer Engineering, De La Salle University, Manila, Philippines
  • 2School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, United Kingdom

* Corresponding author

  • Manuscript receivedOctober 11, 2022
  • revisedDecember 23, 2022
  • acceptedFebruary 14, 2023
  • publishedApril 26, 2023

Abstract

Harmful gases can inflict damage, accident, or even death on a human being. The safety and security of human beings are essential to the community. This study aims to develop an electronic nose with prediction modeling using machine learning. With the use of the sensor substitution technique, the system can predict the gas by utilizing a minimal number of sensors. The system, which is designed to predict Ammonia, Ethanol, and Isobutylene, relies on Support Vector Machine (SVM), Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN) to ensure the prediction models are accurate. As a result, for Ammonia, Ethanol, and Isobutylene, using SVM, GPR, and FFNN model attain value of 1.00 for R2. The RMSE and MAE has a low result value indicates that the SVM, GPR, and FFNN model is performing well and can be used to make decisions regarding the concentration levels of harmful gases. This study shows that the system can predict the presence of gases within it using machine learning.

Keywords

  • machine learning
  • e-nose
  • prediction model
  • support vector machine
  • gaussian process regression
  • feed forward neural network
JAIT-V14N2-373

How to Cite

Copied

Ana Antoniette C. Illahi, Argel A. Bandala, Edwin Sybingco, Elmer P. Dadios, Ryan Rhay P. Vicerra, Ronnie Concepcion II, Laurence A. Gan Lim, and Raouf Naguib, "Development of an Electronic Nose for Harmful Gases with Prediction Modeling Using Machine Learning," Journal of Advances in Information Technology, vol. 14, no. 2, pp. 373-383, 2023. https://doi.org/10.12720/jait.14.2.373-383

Copyright & License

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Article Metrics in Dimensions

PREVIOUS PAPER: Breast Cancer Classification Using an Extreme Gradient Boosting Model with F-Score Feature Selection Technique NEXT PAPER: Chronic Kidney Disease Prediction Using Machine Learning

Copyright © 2010-2026. Journal of Advances in Information Technology. Unless otherwise stated.

​Published by