Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates
Advent in machine learning is leaving deep impact on various sectors including material science domain. The present paper highlights the application of various supervised machine learning regression algorithms such as polynomial regression, decision tree regression algorithm, random forest algorithm...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2021-10-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
| Subjects: | |
| Online Access: | https://revistas.usal.es/index.php/2255-2863/article/view/26038 |
| _version_ | 1818598073769656320 |
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| author | Akshansh Mishra Devarrishi Dixit |
| author_facet | Akshansh Mishra Devarrishi Dixit |
| author_sort | Akshansh Mishra |
| collection | DOAJ |
| description | Advent in machine learning is leaving deep impact on various sectors including material science domain. The present paper highlights the application of various supervised machine learning regression algorithms such as polynomial regression, decision tree regression algorithm, random forest algorithm, support vector regression algorithm and artificial neural network algorithm to determine the thin film thickness of Polystyrene on the glass substrates. The results showed that polynomial regression machine learning algorithm outperforms all other machine learning models by yielding the coefficient of determination of 0.96 approximately and mean square error of 0.04 respectively. |
| first_indexed | 2024-12-16T11:57:54Z |
| format | Article |
| id | doaj.art-6aded4b9c46242c0b75bbe9d9e9786cf |
| institution | Directory Open Access Journal |
| issn | 2255-2863 |
| language | English |
| last_indexed | 2024-12-16T11:57:54Z |
| publishDate | 2021-10-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj.art-6aded4b9c46242c0b75bbe9d9e9786cf2022-12-21T22:32:31ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632021-10-0110326727910.14201/ADCAIJ202110326727923093Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass SubstratesAkshansh Mishra0Devarrishi Dixit1Centre for Artificial Intelligent Manufacturing Systems, Stir Research TechnologiesDepartment of Materials Science Engineering, Christian Albrechts University zu KielAdvent in machine learning is leaving deep impact on various sectors including material science domain. The present paper highlights the application of various supervised machine learning regression algorithms such as polynomial regression, decision tree regression algorithm, random forest algorithm, support vector regression algorithm and artificial neural network algorithm to determine the thin film thickness of Polystyrene on the glass substrates. The results showed that polynomial regression machine learning algorithm outperforms all other machine learning models by yielding the coefficient of determination of 0.96 approximately and mean square error of 0.04 respectively.https://revistas.usal.es/index.php/2255-2863/article/view/26038thin filmsmachine learningfilm thicknessartificial intelligence |
| spellingShingle | Akshansh Mishra Devarrishi Dixit Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates Advances in Distributed Computing and Artificial Intelligence Journal thin films machine learning film thickness artificial intelligence |
| title | Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates |
| title_full | Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates |
| title_fullStr | Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates |
| title_full_unstemmed | Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates |
| title_short | Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates |
| title_sort | brain inspired computing approach for the optimization of the thin film thickness of polystyrene on the glass substrates |
| topic | thin films machine learning film thickness artificial intelligence |
| url | https://revistas.usal.es/index.php/2255-2863/article/view/26038 |
| work_keys_str_mv | AT akshanshmishra braininspiredcomputingapproachfortheoptimizationofthethinfilmthicknessofpolystyreneontheglasssubstrates AT devarrishidixit braininspiredcomputingapproachfortheoptimizationofthethinfilmthicknessofpolystyreneontheglasssubstrates |