Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites
Abstract This study applies a hybridized wavelet transform-artificial neural network (WT-ANN) model to simulate the acetone detecting ability of the Indium oxide/Iron oxide (In2O3/Fe2O3) nanocomposite sensors. The WT-ANN has been constructed to extract the sensor resistance ratio (SRR) in the air wi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29898-x |
| _version_ | 1797630214195380224 |
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| author | Reza Iranmanesh Afham Pourahmad Danial Soltani Shabestani Seyed Sajjad Jazayeri Hamed Sadeqi Javid Akhavan Abdelouahed Tounsi |
| author_facet | Reza Iranmanesh Afham Pourahmad Danial Soltani Shabestani Seyed Sajjad Jazayeri Hamed Sadeqi Javid Akhavan Abdelouahed Tounsi |
| author_sort | Reza Iranmanesh |
| collection | DOAJ |
| description | Abstract This study applies a hybridized wavelet transform-artificial neural network (WT-ANN) model to simulate the acetone detecting ability of the Indium oxide/Iron oxide (In2O3/Fe2O3) nanocomposite sensors. The WT-ANN has been constructed to extract the sensor resistance ratio (SRR) in the air with respect to the acetone from the nanocomposite chemistry, operating temperature, and acetone concentration. The performed sensitivity analyses demonstrate that a single hidden layer WT-ANN with nine nodes is the highest accurate model for automating the acetone-detecting ability of the In2O3/Fe2O3 sensors. Furthermore, the genetic algorithm has fine-tuned the shape-related parameters of the B-spline wavelet transfer function. This model accurately predicts the SRR of the 119 nanocomposite sensors with a mean absolute error of 0.7, absolute average relative deviation of 10.12%, root mean squared error of 1.14, and correlation coefficient of 0.95813. The In2O3-based nanocomposite with a 15 mol percent of Fe2O3 is the best sensor for detecting acetone at wide temperatures and concentration ranges. This type of reliable estimator is a step toward fully automating the gas-detecting ability of In2O3/Fe2O3 nanocomposite sensors. |
| first_indexed | 2024-03-11T11:05:01Z |
| format | Article |
| id | doaj.art-124652b9e1384a3ba1995d9228a14de7 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-11T11:05:01Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-124652b9e1384a3ba1995d9228a14de72023-11-12T12:14:25ZengNature PortfolioScientific Reports2045-23222023-03-0113111110.1038/s41598-023-29898-xWavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocompositesReza Iranmanesh0Afham Pourahmad1Danial Soltani Shabestani2Seyed Sajjad Jazayeri3Hamed Sadeqi4Javid Akhavan5Abdelouahed Tounsi6Faculty of Civil Engineering, K.N. Toosi University of TechnologyDepartment of Polymer Engineering, Amirkabir University of TechnologyDepartment of Chemistry, Mashhad Branch, Islamic Azad UniversityDepartment of Chemical Engineering, Abadan Azad UniversityDepartment of Internet and Wide Network, Iran Industrial Training Center Branch, University of Applied Science and TechnologyMechanical Engineering Department, Stevens Institute of TechnologyMaterial and Hydrology Laboratory, Civil Engineering Department, Faculty of Technology, University of Sidi Bel AbbesAbstract This study applies a hybridized wavelet transform-artificial neural network (WT-ANN) model to simulate the acetone detecting ability of the Indium oxide/Iron oxide (In2O3/Fe2O3) nanocomposite sensors. The WT-ANN has been constructed to extract the sensor resistance ratio (SRR) in the air with respect to the acetone from the nanocomposite chemistry, operating temperature, and acetone concentration. The performed sensitivity analyses demonstrate that a single hidden layer WT-ANN with nine nodes is the highest accurate model for automating the acetone-detecting ability of the In2O3/Fe2O3 sensors. Furthermore, the genetic algorithm has fine-tuned the shape-related parameters of the B-spline wavelet transfer function. This model accurately predicts the SRR of the 119 nanocomposite sensors with a mean absolute error of 0.7, absolute average relative deviation of 10.12%, root mean squared error of 1.14, and correlation coefficient of 0.95813. The In2O3-based nanocomposite with a 15 mol percent of Fe2O3 is the best sensor for detecting acetone at wide temperatures and concentration ranges. This type of reliable estimator is a step toward fully automating the gas-detecting ability of In2O3/Fe2O3 nanocomposite sensors.https://doi.org/10.1038/s41598-023-29898-x |
| spellingShingle | Reza Iranmanesh Afham Pourahmad Danial Soltani Shabestani Seyed Sajjad Jazayeri Hamed Sadeqi Javid Akhavan Abdelouahed Tounsi Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites Scientific Reports |
| title | Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites |
| title_full | Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites |
| title_fullStr | Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites |
| title_full_unstemmed | Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites |
| title_short | Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites |
| title_sort | wavelet artificial neural network to predict the acetone sensing by indium oxide iron oxide nanocomposites |
| url | https://doi.org/10.1038/s41598-023-29898-x |
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