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JACIII Vol.26 No.5 pp. 834-841
doi: 10.20965/jaciii.2022.p0834
(2022)

Paper:

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

Multiple bomb-related gas prediction model

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.

Cite this article as:
A. Illahi, E. Dadios, R. 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.
Data files:
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Last updated on Sep. 22, 2022