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Materials and Manufacturing Processes Volume 24, 2008 - Issue 1
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Original Articles

Analyzing Sparse Data for Nitride Spinels Using Data Mining, Neural Networks, and Multiobjective Genetic Algorithms

Frank Pettersson Faculty of Technology, Heat Engineering Laboratory, Åbo Akademi University, Åbo, Finland
,
Changwon Suh Department of Materials Science and Engineering, Iowa State University, Ames, Iowa, USA
,
Henrik Saxén Faculty of Technology, Heat Engineering Laboratory, Åbo Akademi University, Åbo, Finland
,
Krishna Rajan Department of Materials Science and Engineering, Iowa State University, Ames, Iowa, USA
&
Nirupam Chakraborti Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, IndiaCorrespondence[email protected]
Pages 2-9 | Received 08 Jan 2008, Accepted 30 Apr 2008, Published online: 02 Mar 2009

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J. L. Smith. (2020) Advances in neural networks and potential for their application to steel metallurgy. Materials Science and Technology 36:17, pages 1805-1819.
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S Datta & P P Chattopadhyay. (2013) Soft computing techniques in advancement of structural metals. International Materials Reviews 58:8, pages 475-504.
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Erol Kilickap & Mesut Huseyinoglu. (2010) Selection of Optimum Drilling Parameters on Burr Height Using Response Surface Methodology and Genetic Algorithm in Drilling of AISI 304 Stainless Steel. Materials and Manufacturing Processes 25:10, pages 1068-1076.
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A. Agarwal, U. Tewary, F. Pettersson, S. Das, H. Saxén & N. Chakraborti. (2010) Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithms. Ironmaking & Steelmaking 37:5, pages 353-359.
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Akash Agarwal, Frank Pettersson, Arunima Singh, Chang Sun Kong, Henrik Saxén, Krishna Rajan, Shuichi Iwata & Nirupam Chakraborti. (2009) Identification and Optimization of AB2 Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms. Materials and Manufacturing Processes 24:3, pages 274-281.
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Anton O. Oliynyk & Arthur Mar. (2017) Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches. Accounts of Chemical Research 51:1, pages 59-68.
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Anton O. Oliynyk, Lawrence A. Adutwum, James J. Harynuk & Arthur Mar. (2016) Classifying Crystal Structures of Binary Compounds AB through Cluster Resolution Feature Selection and Support Vector Machine Analysis. Chemistry of Materials 28:18, pages 6672-6681.
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Duane D. Johnson. 2013. Informatics for Materials Science and Engineering. Informatics for Materials Science and Engineering 349 364 .
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Wei-Chiang Hong, Yucheng Dong, Li-Yueh Chen & Shih-Yung Wei. (2011) SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Computing 11:2, pages 1881-1890.
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Henrik Saxén & Frank Pettersson. 2010. Simulated Evolution and Learning. Simulated Evolution and Learning 687 695 .
Baidurya Bhattacharya, G.R. Dinesh Kumar, Akash Agarwal, Şakir Erkoç, Arunima Singh & Nirupam Chakraborti. (2009) Analyzing Fe–Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms. Computational Materials Science 46:4, pages 821-827.
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N. Chakraborti. (2009) How multi-objective genetic algorithms handle lack of data, sparse data and excess data: evaluation of some recent case studies in the materials domain. Statistical Analysis and Data Mining 1:5, pages 322-328.
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