Musrrat Ali

Musrrat Ali
King Faisal University · Basic Sciences

PhD

About

55
Publications
7,417
Reads
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1,613
Citations
Introduction
Research Interests: Evolutionary Computation, multi-objective optimization, Image segmentation, digital watermarking
Additional affiliations
September 2018 - present
King Faisal University
Position
  • Professor (Assistant)
September 2015 - August 2018
Glocal University
Position
  • Professor (Assistant)
April 2012 - May 2015
Sungkyunkwan University
Position
  • Professor
Education
January 2007 - July 2011
Indian Institute of Technology Roorkee
Field of study
  • Applied Mathematics
July 2003 - May 2005
Indian Institute of Technology Roorkee
Field of study
  • Applied Mathematics

Publications

Publications (55)
Article
Full-text available
Accurate image moment computation is critical because they are used in a variety of fields, including image reconstruction and object recognition. Orthogonal polynomials are frequently used to compute moments due to their numerous intersecting and important theoretical properties. In polar coordinate systems, orthogonal polynomials such as the Pola...
Article
Full-text available
The key job here in the presented work is to investigate the performance of Generalized Ant Colony Optimizer (GACO) model in order to evolve the shape of three dimensional free-form Non Uniform Rational B-Spline (NURBS) curve using stereo (two) views. GACO model is a blend of two well known meta-heuristic optimization algorithms known as Simple Ant...
Article
Full-text available
Orthogonal polar image moments which are defined over a unit disk, are used often in watermarking applications. One such image moment is polar harmonic Fourier transformation (PHFT). The quality of the extracted watermark depends on the accuracy of the computed image moments. Various methods have already been used for the computation of the moments...
Article
Full-text available
Digital watermarking has become an essential and important tool for copyright protection, authentication, and security of multimedia contents. It is the process of embedding a watermark in the multimedia content and its extraction. Block-based discrete cosine transform (DCT) is a widely used method in digital watermarking. This paper proposes a nov...
Article
Full-text available
An ensemble lossless watermarking scheme is proposed in the present study by integrating different concepts like redistributed invariant wavelet transform, discrete fractional Fourier transform, singular value decomposition (SVD) and visual cryptography within the framework of a single algorithm. The invariant wavelet transform helps to obtain the...
Article
Differential Evolution (DE) algorithm has came out as a robust, effective and well-organized computational technique for solving global optimization problems. However, similar to other evolutionary algorithms of the same genre, DE has some inherent drawbacks like slow/ premature convergence, stagnation of population etc. due to its probabilistic na...
Chapter
Image thresholding is definitely one of the most popular segmentation approaches for extracting objects from the background, or for discriminating objects from objects that have distinct gray-levels. It is typically simple and computationally efficient. It is based on the assumption that the objects can be distinguished by their gray levels.
Conference Paper
In this paper a new variant named IUDE of Differential Evolution (DE) algorithm is presented. IUDE proposed an information utilization selection operation for DE algorithm. In order to check the performance, IUDE is implemented on 3 real life optimization applications, taken from literature. These problems are large scale in nature. The results sho...
Article
Full-text available
Digital image watermarking is the process of concealing secret information in a digital image for protecting its rightful ownership. Most of the existing block based singular value decomposition (SVD) digital watermarking schemes are not robust to geometric distortions, such as rotation in an integer multiple of ninety degree and image flipping, wh...
Article
In the recent paper entitled “A robust logo watermarking technique in divisive normalization transform domain” by Pandey et al. [4], a robust digital image watermarking scheme based on divisive normalization transform, discrete wavelet transform and singular value decomposition is proposed. This comment shows that the scheme has watermark ambiguity...
Article
Digital image watermarking is the process of authenticating a digital image by embedding a watermark into it and thereby protecting the image from copyright infringement. This paper proposes a novel robust image watermarking scheme developed in the wavelet domain based on the singular value decomposition (SVD) and artificial bee colony (ABC) algori...
Article
In the present study, an attempt is made to optimize the electrical performance of the thin polymeric films through optimization techniques. The study is conducted in two phases: (1) laboratory experiments and (2) through numerical optimization. For laboratory analysis, thin and transparent films are prepared using polyethersulfone (PES) as host ma...
Article
This study shows that a recently proposed image watermarking scheme in the paper "Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm" (Agarwal, Mishra, Sharma, & Bedi, 2014) has a fundamental flaw in its design. The extracted watermark is not the embedded watermark, actually it is determined by the reference watermark, whic...
Article
An evolutionary algorithm, called the cuckoo search (CS), is introduced in this paper for finding the optimal scaling factors (SFs) in digital image watermarking to improve robustness and imperceptibility. It is the first application of the CS technique to the image watermarking problem. The basic idea is to treat digital image watermarking as an o...
Article
In this paper, an innovative watermarking scheme based on differential evolution (DE) in the transform domain is proposed. The insertion and extraction of the watermark are performed in discrete wavelet transform-singular value decomposition (DWT–SVD) transform domain. In the embedding process, the host image is transformed into sub-bands of differ...
Article
The multi-level image thresholding is often treated as a problem of optimization. Typically, finding the parameters of these problems leads to a nonlinear optimization problem, for which obtaining the solution is computationally expensive and time-consuming. In this paper a new multi-level image thresholding technique using synergetic differential...
Article
In this paper we applied differential evolution (DE) algorithm to balance the tradeoff between robustness and imperceptibility by exploring multiple scaling factors in image watermarking. First of all, the original image is partitioned into blocks and the blocks are transformed into Discrete Cosine Transform (DCT) domain. The DC coefficients from e...
Article
The performance of differential evolution (DE) algorithm is significantly affected by its parameters setting that are highly problem dependent. In this paper, an optimal discrete wavelet transform-singular value decomposition (DWT-SVD) based image watermarking scheme using self-adaptive differential evolution (SDE) algorithm is presented. SDE adjus...
Chapter
This paper introduces the application of an evolutionary algorithm, called the cuckoo search (CS), in finding the optimal scaling factors in digital image watermarking to improve robustness and imperceptibility. It is the first application of the cuckoo search technique to the image watermarking problem. The basic idea is to treat digital image wat...
Conference Paper
In this paper, an optimal Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) based image watermarking scheme using differential evolution (DE) algorithm is presented. Three-level DWT is applied to the cover image to transform it into sub-bands of different frequencies and then apply the SVD to each sub-band of third level. After appl...
Article
Imperceptibility and robustness are two important issues of watermarking algorithms. A proper balance between these two is possible by finding suitable scaling factor. In this paper we employed differential evolution (DE) algorithm to find optimal multiple scaling factors (SFs). The watermark is divided into two parts and are embedded in singular v...
Article
The crucial role played by the initial population in a population-based heuristic optimization cannot be neglected. It not only affects the search for several iterations but often also has an influence on the final solution. If the initial population itself has some knowledge about the potential regions of the search domain then it is quite likely...
Article
Full-text available
The “trim loss problem” (TLP) is one of the most challenging problems in context of optimization research. It aims at determining the optimal cutting pattern of a number of items of various lengths from a stock of standard size material to meet the customers’ demands that the wastage due to trim loss is minimized. The resulting mathematical model i...
Data
a b s t r a c t In the present study, a modified variant of Differential Evolution (DE) algorithm for solving multi-objec-tive optimization problems is presented. The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an ini-tial population of potent...
Article
Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on the final solution. The conventional method for generating the initial population is the use of comp...
Article
Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving global optimization problems. Practical experiences, however, show that DE is vulnerable to problems like slow and/or premature convergence. In this article we propose a simple and modified DE framework, called MDE, which is a fusion of three recent modifications in...
Article
In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems. In the proposed HDE algorithm, information sharing mechanism of PSO is embedded in the contracted search space obtained by the basic DE algo...
Article
Full-text available
â–º An enhanced DE variant (MODEA) is proposed for solving multi-objective optimization problems (MOPs). â–º It is a fusion of opposition based learning, random localization and a single population DE structure. â–º MODEA introduces a new selection mechanism for generating a well distributed Pareto optimal front. â–º The efficiency of MODEA is vali...
Article
differential evolution (DE) is well known optimization tool for solving global optimization problems. In the present study we present a DE variant called MSDE [22] for solving constrained optimization problems. The proposed algorithm is applied on a set of 6 constrained benchmark problems proposed in CEC 2006 [1]. Numerical results indicate the com...
Article
Full-text available
Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real valued optimization problems. Traditional investigations with DE have used a single mutation operator. Using a variety of mutation operators that can be integrated during evolution could hold the potential to generate a better solution with less computat...
Article
Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real-valued, multimodal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence and/or slow convergence rate resulting in poor solution quality and/or larg...
Article
In the present article we propose two novel variants of Differential Evolution (DE) namely Centroid Differential Evolution (CDE) and Differential Evolution with local Search (DELS) for solving unconstrained, single objective, optimization problems. Both the algorithms make use of geometric centroid to enhance the performance of basic DE in terms of...
Article
Differential evolution (DE) is a reliable and versatile function optimiser especially suited for continuous optimisation problems. Practical experience, however, shows that DE easily looses diversity and is susceptible to premature and/or slow convergence. This paper proposes a modified variant of DE algorithm called improved differential evolution...
Article
Differential Evolution (DE) is a popular metaheuristics for global optimisation, but little research has been done on its initial population generation. The selection of the initial population is important, since it affects the search for several iterations and often has an influence on the final solution. In this study, quadratic interpolation is...
Conference Paper
Full-text available
The parameter estimation or identification problem, which frequently arises, while developing the mathematical models, may be formulated as a nonlinear global optimization problem. Here the objective is to find the set of parameters to minimize the function quantifying the goodness of the fit subject to the system dynamics. The mathematical model o...
Conference Paper
Insertion of a local search technique is often considered an effective mechanism to increase the efficiency of a global optimization algorithm. In this paper we propose and analyze the effect of two local searches namely; Trigonometric Local Search (TLS) and Interpolated Local Search (ILS) on the working of basic Differential Evolution. The corresp...
Conference Paper
In this paper we propose a novel variant of the Differential Evolution (DE) algorithm based on local search. The corresponding algorithm is named as Differential Evolution with Interpolated Local Search (DEILS). In DEILS, the local search operation is applied in an adaptive manner. The adaptive behavior enables the algorithm to search its neighborh...
Article
Full-text available
Differential Evolution (DE) is a stochastic, population based search technique, which can be clas-sified as an Evolutionary Algorithm (EA) using the concepts of selection crossover and reproduction to guide the search. It has emerged as a powerful tool for solving optimization problems in the past few years. How-ever, the convergence rate of DE sti...
Article
Full-text available
Differential evolution (DE) algorithms are commonly used metaheuristics forglobal optimization, but there has been very little research done on the generation of theirinitial population. The selection of the initial population in a population-based heuristicoptimization method is important, since it affects the search for several iterations and oft...
Conference Paper
Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real valued, multi modal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence, slow convergence rate and large computational time for optimizing the com...
Article
Most of the real life problems occurring in various disciplines of science and engineering can be modeled as optimization problems. Also, most of these problems are nonlinear in nature which requires a suitable and efficient optimization algorithm to reach to an optimum value. In the past few years various algorithms has been proposed to deal with...
Article
Differential evolution (DE) is a population based evolutionary search algorithm widely used for solving optimization problems. In the present article we investigate the application of parent-centric approach on the performance of classical DE, without tampering with the basic structure of DE. The parent-centric approach is embedded in the mutation...
Conference Paper
Full-text available
Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real valued optimization problems. Traditional investigations with differential evolution have used a single mutation operator. Using a variety of mutation operators that can be integrated during evolution could hold the potential to generate a better solutio...
Conference Paper
Full-text available
The performance of population based search techniques like Differential Evolution (DE) depends largely on the selection of initial population. A good initialization scheme not only helps in giving a better final solution but also helps in improving the convergence rate of the algorithm. In the present study we propose a novel initialization scheme...
Article
Full-text available
Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real valued, multi modal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence, slow convergence rate and large computational time for optimizing the com...
Conference Paper
Full-text available
Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research intr...
Conference Paper
Optimization problems are ubiquitous and consequential. In fact every sphere of human activity that can be quantified can be formulated as an optimization problem. The focus of this work is on Global Optimization which is not only desirable but also necessary in many cases. In the past few decades several Global optimization algorithms have been su...
Conference Paper
In the present study we propose a new hybrid version of differential evolution (DE) and particle swarm optimization (PSO) algorithms. In the proposed algorithm named as hybrid differential evolution (HDE) a `switchover constant' called ¿ is defined. HDE starts as the basic DE algorithm which switches over to PSO when ¿ is activated. The constant...
Article
Full-text available
In the present study a Modified Differential Evolution (MDE) algorithm is proposed. This algorithm is different in three ways from basic DE. For initialization it utilizes opposition-based learning while in basic DE uniform random numbers serve this task. Secondly, in basic DE mutant individual is random while in MDE it is tournament best and final...
Conference Paper
Differential Evolution (DE) has emerged as a powerful tool for solving optimization problems in the last few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. In order to improve the performance of DE, we propose a modified DE algorithm call...

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