Kuru Ratnavelu

University of Malaya, Malaysia, Www.math.um.edu.my, Faculty Member

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Address: Kuala Lumpur, Kuala Lumpur, Malaysia

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Lancaster University

Claus Emmeche

University of Copenhagen

Dawn Bennett

Bond University

Ana Vitoria Baptista

Imperial College London

rudolph bauer

Washington Center for Consciousness Studies

Tony Waters

Leuphana University

Madhusudan Subedi

Patan academy of health sciences

Romulo Pinheiro

University of Agder

venkat P Bakthavatchaalam

Cardiff University

Caspian Journal of Scientometrics

Babol University Of Medical Sience

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$Research paper thumbnail of Solution of fuzzy fractional differential equations using homotopy analysis method$

Solution of fuzzy fractional differential equations using homotopy analysis method

Mathematika, 2016

This paper presents an efficient analytical solution for fuzzy fractional differential equations ... more This paper presents an efficient analytical solution for fuzzy fractional differential equations (FDEs) using homotopy analysis method. HAM is the generalization of various methods like homotopy perturbation method and Adomian decomposition method. The main advantage is its rapid convergence to the solution without using perturbation or restrictive assumptions. Illustrative numerical example are presented for the proposed method.

Combined higher order non-convex total variation with overlapping group sparsity for impulse noise removal

Multimedia Tools and Applications, 2021

A typical approach to eliminate impulse noise is to use the l1-norm for both the data fidelity te... more A typical approach to eliminate impulse noise is to use the l1-norm for both the data fidelity term and the regularization terms. However, the l1-norm tends to over penalize signal entries which is one of its underpinnings. Hence, we propose a variational model that uses the non-convex lp-norm, 0 < p < 1 for both the data fidelity and a second-order total variation regularization term combined with an overlapping group sparse regularizer. Specifically, to robustly eliminate impulse noise, the proposed method uses a non-convex data fidelity term. The hybrid combination of a second-order non-convex total variation and an overlapping group sparse regularization term is used to eliminate the remaining staircase artifacts while maintaining a sharp restored image. A mathematical formulation is derived and to implement it, the iterative re-weighted l1 (IRL1) based alternating direction method of multipliers (ADMM) is used to solve the constraints and the subproblems. Experimental results for image denoising and deblurring on several widely used standard images demonstrate that the proposed method performed better when compared to the l1-norm total variation (TV), total generalized variation (TGV) model, and the non-convex lp-norm TV-based data fidelity model in terms of peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM).

Polynomial mixture method of solving ordinary differential equations

AIP Conference Proceedings, 2017

Double-Key Secure for N-1-N Sound Record Data (SRD) by the Drive-Response of BAM NNs

Neural Processing Letters, 2019

In this work, the problem of N sound record data (audio) encryption based on double-key secure is... more In this work, the problem of N sound record data (audio) encryption based on double-key secure is proposed. The first key is the math tricks to cumulate N audio files in a single file. The second key is the values of the parameters A, B, D,Ã,B,D, of the constructed drive-response bidirectional associative memory neural networks to be found by suitable Lyapunov-Krasovskii functional and satisfying the linear matrix inequality to obtain the dynamical signal (irregular), which are used to encrypt an audio file. Further, the key sensitivity of 1e − 10 of the proposed method are large adequate key space to make hacker's attack infeasible. Numerical simulations, cryptanalysis of the proposed scheme are provided to show the best performance. Keywords Bidirectional associative memory • Dynamical signal • Neural networks • Secure • Sound record data Mathematics Subject Classification 92B20 • 37B55 • 34D06 • 68P25 1 Introduction Nowadays, people desire privacy and security for their sending data, especially for sound record data (SRD). So, the information can be coverted from others by converting electronic information or signals into the letters, numbers, symbols or audio (secret code), in which the B K. Ratnavelu

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