e-ISSN:0976-5166
p-ISSN:2231-3850


INDIAN JOURNAL OF COMPUTER SCIENCE AND ENGINEERING

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Publication: 20 Oct 2023

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ABSTRACT

Title : Comparison of Linear Regression and Simple Linear Regression for critical temperature of semiconductor
Authors : Dr.R.Venkatesh Babu, Dr.G.Ayyappan, Dr.A. Kumaravel
Keywords : Conductivity, Simple Linear Regression, Atomic Radius, Linear Regression, valence, Electron affinity, Correlation Coefficient and Atomic Mass.
Issue Date : Dec 2019-Jan 2020
Abstract :
The regression analysis plays a vital role in forecasting, estimating and predicting the material science domain. In this research work measures a statistical model to estimate the critical temperature of superconductor. This critical temperature formulated by using the superconductorís chemical formula. The statistical model has given several measurements like Correlation Co efficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSR), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). These measurements extracted based on atomic mass (AM), atomic radius (AR), valence(V), thermal conductivity (TC), and electron affinity (EA) contribute the most to the modelís predictive accuracy. This research work focuses the comparisons of various measurements namely Correlation Co efficient, Mean absolute error, Root Mean squared error, Relative absolute error, Root relative squared error and also time taken to build the model of leading regression algorithms like Linear and Simple Linear regression models for superconductor.
Page(s) : 177-183
ISSN : 0976-5166
Source : Vol. 10, No.6
PDF : Download
DOI : 10.21817/indjcse/2019/v10i6/191006050