BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique

Ana Antoniette C. Illahi; Elmer P. Dadios; Ronnie S. Concepcion II; Argel A. B; ala; Ryan Rhay P. Vicerra; Edwin Sybingco; Laurence A. Gan Lim; Kate Francisco

doi:10.20965/jaciii.2022.p0834

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JACIII Vol.26 No.5 pp. 834-841

doi: 10.20965/jaciii.2022.p0834

(2022)

Paper:

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BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique

Ana Antoniette C. Illahi^1,†, Elmer P. Dadios^2,3, Ronnie S. Concepcion II^2,3, Argel A. Bandala^1, Ryan Rhay P. Vicerra^2, Edwin Sybingco^1, Laurence A. Gan Lim^4, and Kate Francisco^2,*3

^*1Department of Electronics and Computer Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

^*2Department of Manufacturing Engineering and Management, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

^*3Center for Engineering and Sustainable Development Research, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

^*4Department of Mechanical Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

^†Corresponding author

Received:

May 3, 2022

Accepted:

July 15, 2022

Published:

September 20, 2022

Keywords:

computational intelligence, machine learning, harmful gas

Abstract

The safety and security of an individual is important in our society. Bombing attacks can cause significant destruction and death. Energy efficient and compact bomb removal robots are challenging to develop because these typically involved a large array of sensors individually acquiring gas data. This study addresses this challenge by developing a multiple bomb-related gas prediction model using machine learning and the electronic nose sensor substitution technique. Three models can predict gasses such as ammonia, ethanol, and isobutylene using only carbon monoxide, toluene, and methane sensors. The feedforward artificial neural network (FFNN) with three hidden layers was optimized for the regression of each target gas. Consequently, ammonia, ethanol, and isobutylene predictions achieved R² values of 1, 1, and 1 as well as MSE values of 0.35696, 0.052995, and 0.0022953, respectively. This study demonstrates that the sensor substitution model (BombNose) is highly reliable and appropriately sensitive in the field of bomb detection.

Multiple bomb-related gas prediction model

Cite this article as:

A. Illahi, E. Dadios, R. Concepcion II, A. Bandala, R. Vicerra, E. Sybingco, L. Lim, and K. Francisco, “BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.5, pp. 834-841, 2022.

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References

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[2] GOV.UK, “Foreign travel advice Philipines, Terrorism,” https://www.gov.uk/foreign-travel-advice/philippines/terrorism [accessed July 11, 2022]
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[5] S. Gadre and S. Joshi, “E-nose system using artificial neural networks (ANN) to detect pollutant gases,” 2nd IEEE Int. Conf. on Recent Trends in Electronics, Information and Communication Technol. (RTEICT), pp. 121-125, 2017.
[6] A. A. C. Illahi et al., “Electronic Nose Technology and Application: A Review,” IEEE 13th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM54116.2021.9731890, 2021.
[7] Y. Thazin et al., “Formalin Adulteration Detection in Food Using E-Nose Based on Nanocomposite Gas Sensors,” IEEE Int. Conf. on Consumer Electronics-Asia (ICCE-Asia), pp. 64-67, 2019.
[8] D. Macasaet et al., “Development of an Electronic Nose for Smell Categorization Using Artificial Neural Network,” J. of Advances in Information Technol., Vol.12, No.1, pp. 36-44, 2021.
[9] A. A. C. Illahi et al., “Automatic Harmful Gas Detection Using Electronic Nose Technology,” IEEE 13th Int. Conf. on HNICEM, doi: 10.1109/HNICEM54116.2021.9732049, 2021.
[10] A. A. C. Illahi, A. Bandala, and E. Dadios, “Detection of Gas Harmful Effect Using Fuzzy Logic System,” IEEE 11th Int. Conf. on HNICEM, doi: 10.1109/HNICEM48295.2019.9073560, 2019.
[11] D. Macasaet et al., “Hazard Classification of Toluene, Methane and Carbon Dioxide for Bomb Detection Using Fuzzy Logic,” IEEE 11th Int. Conf. on HNICEM, doi: 10.1109/HNICEM48295.2019.9073559, 2019.
[12] M. Adib and M. Sommer, “UV excited SnO2 nanowire based printed e-Nose: Potential application as burning smell detector and explosive detector,” 2016 IEEE Sensors, doi: 10.1109/ICSENS.2016.7808805, 2017.
[13] L. Trizio et al., “Application of Artificial Neural Networks to a Gas Sensor-Array Database for Environmental Monitoring,” A. D’Amico et al. (Eds.), “Sensors and Microsystems, Lecture Notes in Electrical Engineering,” Vol.109, pp. 139-144, Springer, 2012.
[14] D. Wang et al., “Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine,” Meat Science, Vol.90, No.2, pp. 373-377, 2012.
[15] O. Gualdrón et al., “Variable selection for support vector machine based multisensor systems,” Sensors and Actuators B: Chemical, Vol.122, No.1, pp. 259-268, 2007.
[16] C. Rupprecht et al., “Sensor substitution for video-based action recognition,” IEEE Int. Conf. on Intelligent Robots and Systems (IROS), pp. 5230-5237, doi: 10.1109/IROS.2016.7759769, 2016.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] D. Fisher et al., “Bomb swab: Can trace explosive particle sampling and detection be improved?,” Talanta, Vol.174, pp. 92-99, doi: 10.1016/j.talanta.2017.05.085, 2017.

[2] [2] GOV.UK, “Foreign travel advice Philipines, Terrorism,” https://www.gov.uk/foreign-travel-advice/philippines/terrorism [accessed July 11, 2022]

[3] [3] LANDMINE and CLUSTER MUNITION MONITOR, “Philippines, Mine Action,” http://www.the-monitor.org/en-gb/reports/2021/philippines/mine-action.aspx [accessed July 11, 2022]

[4] [4] R. Laref et al., “A comparison between SVM and PLS for E-nose based gas concentration monitoring,” Proc. of the IEEE Int. Conf. on Industrial Technol. (ICIT), pp. 1335-1339, doi: 10.1109/ICIT.2018.8352372, 2018.

[5] [5] S. Gadre and S. Joshi, “E-nose system using artificial neural networks (ANN) to detect pollutant gases,” 2nd IEEE Int. Conf. on Recent Trends in Electronics, Information and Communication Technol. (RTEICT), pp. 121-125, 2017.

[6] [6] A. A. C. Illahi et al., “Electronic Nose Technology and Application: A Review,” IEEE 13th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM54116.2021.9731890, 2021.

[7] [7] Y. Thazin et al., “Formalin Adulteration Detection in Food Using E-Nose Based on Nanocomposite Gas Sensors,” IEEE Int. Conf. on Consumer Electronics-Asia (ICCE-Asia), pp. 64-67, 2019.

[8] [8] D. Macasaet et al., “Development of an Electronic Nose for Smell Categorization Using Artificial Neural Network,” J. of Advances in Information Technol., Vol.12, No.1, pp. 36-44, 2021.

[9] [9] A. A. C. Illahi et al., “Automatic Harmful Gas Detection Using Electronic Nose Technology,” IEEE 13th Int. Conf. on HNICEM, doi: 10.1109/HNICEM54116.2021.9732049, 2021.

[10] [10] A. A. C. Illahi, A. Bandala, and E. Dadios, “Detection of Gas Harmful Effect Using Fuzzy Logic System,” IEEE 11th Int. Conf. on HNICEM, doi: 10.1109/HNICEM48295.2019.9073560, 2019.

[11] [11] D. Macasaet et al., “Hazard Classification of Toluene, Methane and Carbon Dioxide for Bomb Detection Using Fuzzy Logic,” IEEE 11th Int. Conf. on HNICEM, doi: 10.1109/HNICEM48295.2019.9073559, 2019.

[12] [12] M. Adib and M. Sommer, “UV excited SnO2 nanowire based printed e-Nose: Potential application as burning smell detector and explosive detector,” 2016 IEEE Sensors, doi: 10.1109/ICSENS.2016.7808805, 2017.

[13] [13] L. Trizio et al., “Application of Artificial Neural Networks to a Gas Sensor-Array Database for Environmental Monitoring,” A. D’Amico et al. (Eds.), “Sensors and Microsystems, Lecture Notes in Electrical Engineering,” Vol.109, pp. 139-144, Springer, 2012.

[14] [14] D. Wang et al., “Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine,” Meat Science, Vol.90, No.2, pp. 373-377, 2012.

[15] [15] O. Gualdrón et al., “Variable selection for support vector machine based multisensor systems,” Sensors and Actuators B: Chemical, Vol.122, No.1, pp. 259-268, 2007.

[16] [16] C. Rupprecht et al., “Sensor substitution for video-based action recognition,” IEEE Int. Conf. on Intelligent Robots and Systems (IROS), pp. 5230-5237, doi: 10.1109/IROS.2016.7759769, 2016.

BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique

Ana Antoniette C. Illahi*1,†, Elmer P. Dadios*2,*3, Ronnie S. Concepcion II*2,*3, Argel A. Bandala*1, Ryan Rhay P. Vicerra*2, Edwin Sybingco*1, Laurence A. Gan Lim*4, and Kate Francisco*2,*3

Ana Antoniette C. Illahi^1,†, Elmer P. Dadios^2,3, Ronnie S. Concepcion II^2,3, Argel A. Bandala^1, Ryan Rhay P. Vicerra^2, Edwin Sybingco^1, Laurence A. Gan Lim^4, and Kate Francisco^2,*3