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** Authors are invited to submit manuscripts for December 2019 issue.

** November 2019 issue of AARF Journals is officially released.

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S.No Particular Pdf Page No.
1
  • AN INTERVAL-VALUED INTUITIONISTIC FUZZY WEIGHTED ENTROPY METHOD FOR FINDING OPTIMAL SEQUENCE OF FLOW SHOP SEQUENCING PROBLEMS


R. Murugesan

Abstract:
This paper proposes the interval valued intuitionistic fuzzy weighted entropy (IVIFWE) for solving flow shop sequencing problems. The processing time of n jobs and m machines flow shop sequencing problem is modeled as fuzzy numbers by using similarity relation and then, we consider the neighboring points are interval of these fuzzy numbers.


1-8
2
  • EXAMINATION OF MULTIVARIATE MULTIPLE LINEAR REGRESSION ANALYSIS


Nazlı Elif GÜNAŞDI, Mehmet TOPAL

Abstract:
Regression analysis is a statistical method determining the functional relationship between dependent (Y1, Y2, . . . , Yq) and independent (X1, X2, . . . , Xp) variables. In regression model, if there are one dependent (Y1) and one independent (X1) variables, the simple liner regression is used, if there are one dependent variable (Y1) and more than one independent variables (X1, X2, . . . , Xp), multiple linear regression model is used, and if there are more than one dependent (Y1,Y2, . . . , Yq) and more than one independent variables (X1, X2, . . . , Xp) multivariate multiple linear regression model is used.


9-16
3
  • EXAMINATION OF MULTIVARIATE MULTIPLE LINEAR REGRESSION ANALYSIS


Nazlı Elif GÜNAŞDI, Mehmet TOPAL

Abstract:
Regression analysis is a statistical method determining the functional relationship between dependent (Y1, Y2, . . . , Yq) and independent (X1, X2, . . . , Xp) variables. In regression model, if there are one dependent (Y1) and one independent (X1) variables, the simple liner regression is used, if there are one dependent variable (Y1) and more than one independent variables (X1, X2, . . . , Xp), multiple linear regression model is used, and if there are more than one dependent (Y1,Y2, . . . , Yq) and more than one independent variables (X1, X2, . . . , Xp) multivariate multiple linear regression model is used.


9-16
4
  • THE EFFECT OF THERMOPHORESIS ON CONVECTIVE HEAT AND MASS TRANSFER FLOW PAST A STRETCHING SHEET WITH SORET AND DUFOUR EFFECTS


K. Madhu Sudhana, B. Sreenivasa Reddy, M. Sreedhar Babu

Abstract:
Heat and mass transfer characteristics of an unsteady MHD boundary layer flow through porous medium over stretching sheet in the presence of Thermo-Diffusion and Diffusion-Thermo effects with thermophoresis, thermal radiation and non-uniform heat source/sink is discussed in this article.


17-41
5
  • HAAR WAVELET METHOD FOR THE SOLUTION OF TWO-DIMENSIONAL PARABOLIC PARTIAL DIFFERENTIAL EQUATIONS


Sumana R. Shesha, Tejuswini M. and Achala L. Nargund

Abstract:
Parabolic partial differential equations arise in the mathematical modelling of many physical phenomena. In this paper, we use Haar wavelet method for the numerical solution of two-dimensional heat equation.


42-57
6
  • A VARIATION OF NEAREST NEIGHBOUR ALGORITHM TO SOLVE SYMMETRIC TRAVELING SALESMAN PROBLEM


Kanak Chandra Bora

Abstract:
Travelling salesman problem has been occupying as one of the interesting field of research relating to the real life problem which relates Hamiltonian graph. This is world unsolved problem known as NP-Complete problem. In this paper, application of travelling salesman problem in different fields and a variation of nearest neighbour algorithm has been focussed.


58-66
7
  • STEIN-RULE RESTRICTED RIDGE REGRESSION ESTIMATOR


Sadullah Sakallıoğlu, Selahattin Kaçıranlar and Issam Dawoud

Abstract:
Stein-rule and ridge estimators have been extensively used for estimating the coefficient vector in a regression model. These estimators lead to an improvement in the risk properties of the ordinary least squares (OLS) estimator. Instead of using one or the other estimator, both of them may be appropriately combined. We introduce an alternative estimator that combines the approaches followed in obtaining the restricted Stein-rule estimation and the ridge regression estimation. A Monte Carlo simulation is performed to compare the behavior of the proposed estimator.


67-78
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