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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/18/8596 |
| _version_ | 1797520324476010496 |
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| author | Swetha Chittam Balakrishna Gokaraju Zhigang Xu Jagannathan Sankar Kaushik Roy |
| author_facet | Swetha Chittam Balakrishna Gokaraju Zhigang Xu Jagannathan Sankar Kaushik Roy |
| author_sort | Swetha Chittam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:55:09Z |
| format | Article |
| id | doaj.art-70666d519bdd4efda7b5d4df5246abe7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:55:09Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-70666d519bdd4efda7b5d4df5246abe72023-11-22T11:55:14ZengMDPI AGApplied Sciences2076-34172021-09-011118859610.3390/app11188596Big Data Mining and Classification of Intelligent Material Science Data Using Machine LearningSwetha Chittam0Balakrishna Gokaraju1Zhigang Xu2Jagannathan Sankar3Kaushik Roy4Department of Computer Science, College of Engineering, North Carolina A&T University, 1601 E. Market Street, Greensboro, NC 27411, USAEngineering Research Center & Center for Visualization and Computation Advancing Research (ViCAR), Department of Computational Data Science and Engineering, College of Engineering, North Carolina A&T University, 1601 E. Market Street, Greensboro, NC 27411, USADepartment of Mechanical Engineering & Engineering Research Center, College of Engineering, North Carolina A&T University, 1601 E. Market Street, Greensboro, NC 27411, USADepartment of Mechanical Engineering & Engineering Research Center, College of Engineering, North Carolina A&T University, 1601 E. Market Street, Greensboro, NC 27411, USADepartment of Computer Science, College of Engineering, North Carolina A&T University, 1601 E. Market Street, Greensboro, NC 27411, USAThere 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.https://www.mdpi.com/2076-3417/11/18/8596data miningmongodbNo-SQL databaseclassification algorithmslogistic regressionsupport vector machine SVM |
| spellingShingle | Swetha Chittam Balakrishna Gokaraju Zhigang Xu Jagannathan Sankar Kaushik Roy Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning Applied Sciences data mining mongodb No-SQL database classification algorithms logistic regression support vector machine SVM |
| title | Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning |
| title_full | Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning |
| title_fullStr | Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning |
| title_full_unstemmed | Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning |
| title_short | Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning |
| title_sort | big data mining and classification of intelligent material science data using machine learning |
| topic | data mining mongodb No-SQL database classification algorithms logistic regression support vector machine SVM |
| url | https://www.mdpi.com/2076-3417/11/18/8596 |
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