Classification of hydrogen concentrations based on TIO₂ gas sensor responses using artificial neural network

Mohd Chachuli, Siti Amaniah and Hj Abdullah Pirus, Ahmad Irfan and Hamidon, Mohd Nizar and Shamsudin, Nur Hazahsha and Che Aziz, Siti Asma (2025) Classification of hydrogen concentrations based on TIO₂ gas sensor responses using artificial neural network. ASEAN Engineering Journal, 15 (4). pp. 67-74. ISSN 2586-9159

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Abstract

In this research endeavor, a TiO2 gas sensor was employed to discern the TiO2 gas sensor response to varying hydrogen gas concentrations across three distinct temperature settings: 150℃, 200℃, and 250℃. The concentration levels spanned from 100 to 1000 ppm. The primary objective of this investigation was twofold: firstly, to eliminate the noise from the captured response, thereby clustering the gas sensor response at various hydrogen concentrations using principal component analysis, and secondly, to classify the hydrogen concentration using an artificial neural network. Five distinct hydrogen concentration values were extracted from each set of samples, in the range of 100 to 1000 ppm. All the values were acquired at different operational temperatures. The ensuing analytical phase utilizes the Principal Component Analysis (PCA) method in conjunction with an Artificial Neural Network (ANN). Remarkably, classification accuracy achieved a median testing accuracy of 88.8% in 70% of the training data and 15% of the testing strategy.

Item Type:	Article
Uncontrolled Keywords:	TiO2 gas sensor, Principal component analysis, Artificial neural network, Gas classification
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 May 2026 02:00
Last Modified:	13 May 2026 02:00
URI:	http://eprints.utem.edu.my/id/eprint/29712
Statistic Details:	View Download Statistic

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