Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2
Search results for: R. Mallika
2 Optimization of Hydraulic Fluid Parameters in Automotive Torque Converters
Authors: S. Venkateswaran, C. Mallika Parveen
Abstract:
The fluid flow and the properties of the hydraulic fluid inside a torque converter are the main topics of interest in this research. The primary goal is to investigate the applicability of various viscous fluids inside the torque converter. The Taguchi optimization method is adopted to analyse the fluid flow in a torque converter from a design perspective. Calculations are conducted in maximizing the pressure since greater the pressure, greater the torque developed. Using the values of the S/N ratios obtained, graphs are plotted. Computational Fluid Dynamics (CFD) analysis is also conducted.Keywords: Hydraulic fluid, Taguchi's method, optimization, pressure, torque.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30791 An SVM based Classification Method for Cancer Data using Minimum Microarray Gene Expressions
Authors: R. Mallika, V. Saravanan
Abstract:
This paper gives a novel method for improving classification performance for cancer classification with very few microarray Gene expression data. The method employs classification with individual gene ranking and gene subset ranking. For selection and classification, the proposed method uses the same classifier. The method is applied to three publicly available cancer gene expression datasets from Lymphoma, Liver and Leukaemia datasets. Three different classifiers namely Support vector machines-one against all (SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant analysis (LDA) were tested and the results indicate the improvement in performance of SVM-OAA classifier with satisfactory results on all the three datasets when compared with the other two classifiers.Keywords: Support vector machines-one against all, cancerclassification, Linear Discriminant analysis, K nearest neighbour, microarray gene expression, gene pair ranking.
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