Research experience
-
Dec 2008–
Feb 2012Research: Scientific Knowledge Discovery Systems (SKDS) For Advanced Engineering Materials Design Applications
Mangalore University · Dept of Computer Science · Mangalore UniversityMangaloreData mining , Engineering Materials, Knowledge Discovery.
Education
-
Sep 2009–
Sep 2012Mangalore University
Data Mining · Ph.DIndia · Mangalore
Other
-
LanguagesEnglish,Kannada,Hindi,Tamil and telegu.
Publications (2) View all
-
Article: Performance Evaluation of Predictive Classifiers For Knowledge Discovery From Engineering Materials Data Sets
[show abstract] [hide abstract]
ABSTRACT: In this paper, naive Bayesian and C4.5 Decision Tree Classifiers(DTC) are successively applied in materials informatics to classify the engineering materials into different classes for the selection of materials that suit the input design specifications. Here, the classifiers are analyzed individually and their performance evaluation is analyzed with confusion matrix predictive parameters and standard measures, the classification results are analyzed on different class of materials. Comparison of classifiers has found that naive Bayesian classifier is more accurate and better than the C4.5 DTC. The knowledge discovered by the naive Bayesian classifier can be employed for decision making in materials selection in manufacturing industriesCiiT International Journal Of Artificial Intelligent systems and Machine Learning. 01/2011; 3(3):162-168. -
Article: Hybrid Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets
[show abstract] [hide abstract]
ABSTRACT: Studying materials informatics from a data mining perspective can be beneficial for manufacturing andother industrial engineering applications. Predictive data mining technique and machine learningalgorithm are combined to design a knowledge discovery system for the selection of engineering materialsthat meet the design specifications. Predictive method-Naive Bayesian classifier and Machine learningAlgorithm - Pearson correlation coefficient method were implemented respectively for materialsclassification and selection. The knowledge extracted from the engineering materials data sets is proposedfor effective decision making in advanced engineering materials design applications.International Journal of Database Management Systems. 01/2011;
