Kottakkaran Sooppy Nisar

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In this paper a new method to construct zero cross correlation code with the help of Pascal's triangle pattern called Pascal's Triangle Matrix Code (PTMC) for Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) system is successfully developed. The advantages of this code are simplicity of code construction, flexibility of choosing(More)
A new generalization of Struve function called generalized Galué type Struve function (GTSF) is defined and the integral operators involving Appell's functions, or Horn's function in the kernel is applied on it. The obtained results are expressed in terms of the Fox-Wright function. As an application of newly defined generalized GTSF, we aim at presenting(More)
In this paper, the (k, s)-fractional integral operator is used to generate new classes of integral inequalities using a family of n positive functions, (n ∈ N). Two classes of integral inequalities involving the (k, s)-fractional integral operator are derived here and these results allow us in particular to generalize some classical inequalities. Certain(More)
Recently, fractional k-integral operators have been investigated in the literature by some authors. Here, we focus to prove some new fractional integral inequalities involving generalized fractional k-integral operator due to Sarikaya et al. for the cases of synchronous functions as well as of functions bounded by integrable functions are considered. c 2016(More)
Twin support vector regression (TSVR) and Lagrangian TSVR (LTSVR) satisfy only empirical risk minimization principle. Moreover, the matrices in their formulations are always positive semi-definite. To overcome these problems, we propose an efficient implicit Lagrangian formulation for the dual regularized twin support vector regression, called IRLTSVR for(More)
2 2 1 1 1 1 nb t nb t t na t na t t u b u b u b y a y a y Abstract: In this paper a new algorithm to identify Auto-Regressive Exogenous Models (ARX) based on Twin Support Vector Machine Regression (TSVR) has been developed. The model is determined by minimizing two ε insensitive loss functions. One of them determines the ε 1-insensitive down bound regressor(More)
approach presented earlier. The new algorithm exploits the properties of generic SVM which LS-SVM based algorithm lacks. These properties are robustness in the presence of outliers and sparseness of solution. The proposed algorithm is reduced to include the least number of quadratic programming problems needed to estimate the system matrices and(More)
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