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Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques
Machine learning techniques have been employed to predict the glass densities of xBi(2)O(3)–(70 − x)B(2)O(3)–20Li(2)O–5Sb(2)O(3)–5ZnO glasses using a data set of 2000 various B(2)O(3) rich glasses using their chemical composition and ionic radius. The experimental density of present glasses strongly...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763431/ https://www.ncbi.nlm.nih.gov/pubmed/35069058 http://dx.doi.org/10.1007/s10904-021-02183-y |
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author | Ahmed, Shaik Amer Rajiya, Shaik Samee, M. A. Ahmmad, Shaik Kareem Jaleeli, Kaleem Ahmed |
author_facet | Ahmed, Shaik Amer Rajiya, Shaik Samee, M. A. Ahmmad, Shaik Kareem Jaleeli, Kaleem Ahmed |
author_sort | Ahmed, Shaik Amer |
collection | PubMed |
description | Machine learning techniques have been employed to predict the glass densities of xBi(2)O(3)–(70 − x)B(2)O(3)–20Li(2)O–5Sb(2)O(3)–5ZnO glasses using a data set of 2000 various B(2)O(3) rich glasses using their chemical composition and ionic radius. The experimental density of present glasses strongly depends on Bi(2)O(3) content which is increasing with bismuth content. The increasing density in bismuth doped glasses because the BO(3) are converted into BO(4) units, and besides BO(3) units are less heavy than the BO(4) units. The FTIR studies also confirm that the intensity of B–O–B bond decreasing with increasing Bi(2)O(3) content which suggested that B–O–B bond in bond ring isolated to BO(3) units transformed into BO(4) units. In Raman Spectra the stretching vibrations of BO(4) units shifting towards higher wavelengths with increasing Bi(2)O(3) content. This shifting conforms that there is a structural changes in the glass-matrix and borate units converting from BO(3) to BO(4) units. The prepared glasses along with B(2)O(3) rich glass data set train on various AI model such as gradient descent, Random Forest regression and Neural Networks to predict present density of glasses. Among the various models RF regression analysis model is successfully acceptable for the glass data with the highest R(2) value 0.983 which end result conform that the predicted and experimental values correlated. ANNs stood the effective technique in prediction of glass density with the optimum performance resulting with Tanh as the activation function (R(2) = 0.950). The minimum cost 0.018 obtained in the case of gradient decent function which also shows the better performance of regression model. |
format | Online Article Text |
id | pubmed-8763431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-87634312022-01-18 Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques Ahmed, Shaik Amer Rajiya, Shaik Samee, M. A. Ahmmad, Shaik Kareem Jaleeli, Kaleem Ahmed J Inorg Organomet Polym Mater Article Machine learning techniques have been employed to predict the glass densities of xBi(2)O(3)–(70 − x)B(2)O(3)–20Li(2)O–5Sb(2)O(3)–5ZnO glasses using a data set of 2000 various B(2)O(3) rich glasses using their chemical composition and ionic radius. The experimental density of present glasses strongly depends on Bi(2)O(3) content which is increasing with bismuth content. The increasing density in bismuth doped glasses because the BO(3) are converted into BO(4) units, and besides BO(3) units are less heavy than the BO(4) units. The FTIR studies also confirm that the intensity of B–O–B bond decreasing with increasing Bi(2)O(3) content which suggested that B–O–B bond in bond ring isolated to BO(3) units transformed into BO(4) units. In Raman Spectra the stretching vibrations of BO(4) units shifting towards higher wavelengths with increasing Bi(2)O(3) content. This shifting conforms that there is a structural changes in the glass-matrix and borate units converting from BO(3) to BO(4) units. The prepared glasses along with B(2)O(3) rich glass data set train on various AI model such as gradient descent, Random Forest regression and Neural Networks to predict present density of glasses. Among the various models RF regression analysis model is successfully acceptable for the glass data with the highest R(2) value 0.983 which end result conform that the predicted and experimental values correlated. ANNs stood the effective technique in prediction of glass density with the optimum performance resulting with Tanh as the activation function (R(2) = 0.950). The minimum cost 0.018 obtained in the case of gradient decent function which also shows the better performance of regression model. Springer US 2022-01-18 2022 /pmc/articles/PMC8763431/ /pubmed/35069058 http://dx.doi.org/10.1007/s10904-021-02183-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Ahmed, Shaik Amer Rajiya, Shaik Samee, M. A. Ahmmad, Shaik Kareem Jaleeli, Kaleem Ahmed Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques |
title | Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques |
title_full | Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques |
title_fullStr | Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques |
title_full_unstemmed | Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques |
title_short | Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques |
title_sort | density of bismuth boro zinc glasses using machine learning techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763431/ https://www.ncbi.nlm.nih.gov/pubmed/35069058 http://dx.doi.org/10.1007/s10904-021-02183-y |
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