Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning

There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from s...

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Vydané v:	Applied sciences Ročník 11; číslo 18; s. 8596
Hlavní autori:	Chittam, Swetha, Gokaraju, Balakrishna, Xu, Zhigang, Sankar, Jagannathan, Roy, Kaushik
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.09.2021
Predmet:	Algorithms Alloys Automation Big Data Classification classification algorithms Data analysis Data mining Data models Datasets Ductility Electronic health records Literature reviews logistic regression Machine learning Metals mongodb No-SQL database Science Structured Query Language-SQL support vector machine SVM Support vector machines Tensile strength Variables Yield stress
ISSN:	2076-3417, 2076-3417
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Abstract	There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively.
AbstractList	There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively.
Author	Gokaraju, Balakrishna Roy, Kaushik Xu, Zhigang Chittam, Swetha Sankar, Jagannathan
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SubjectTerms	Algorithms Alloys Automation Big Data Classification classification algorithms Data analysis Data mining Data models Datasets Ductility Electronic health records Literature reviews logistic regression Machine learning Metals mongodb No-SQL database Science Structured Query Language-SQL support vector machine SVM Support vector machines Tensile strength Variables Yield stress
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Title	Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning
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