Khalil Hamdi Ateyeh Al-Shqeerat^*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2095-2107, 2023, DOI:10.32604/csse.2023.028975 - 01 August 2022

Abstract Most remote systems require user authentication to access resources. Text-based passwords are still widely used as a standard method of user authentication. Although conventional text-based passwords are rather hard to remember, users often write their passwords down in order to compromise security. One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users. Graphical user authentication methods have recently been proposed to verify the user identity. However, the fundamental limitation of a graphical password is that… More >

Nasir Mahmud Khokhar¹, Muhammad Nadeem Majeed², Syed Muslim Shah^3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2209-2224, 2022, DOI:10.32604/cmc.2022.019120 - 27 September 2021

Abstract In this study, it is proposed that the diffusion least mean square (LMS) algorithm can be improved by applying the fractional order signal processing methodologies. Application of Caputo’s fractional derivatives are considered in the optimization of cost function. It is suggested to derive a fractional order variant of the diffusion LMS algorithm. The applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless medium. The topology of the network is selected such that a smaller number of nodes are informed. In the network, a… More >

Ibrahim M. Almanjahie^1,2, Amer Ibrahim Al-Omari^3,*, Emmanuel J. Ekpenyong⁴, Mir Subzar⁵

Computer Systems Science and Engineering, Vol.38, No.1, pp. 1-15, 2021, DOI:10.32604/csse.2021.015933 - 01 April 2021

Abstract In estimation theory, the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares (OLS) method or robust regression techniques for estimating regression coefficients. But when the correlation is negative and the outliers are presented, the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased estimates. Hence, this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation method. Precisely, we have proposed generalized estimators by using the… More >

Amer Ibrahim Al-Omari¹, Ibrahim M. Almanjahie^2,3,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1503-1522, 2021, DOI:10.32604/cmc.2021.015047 - 05 February 2021

Abstract This article proposes two new Ranked Set Sampling (RSS) designs for estimating the population parameters: Simple Z Ranked Set Sampling (SZRSS) and Generalized Z Ranked Set Sampling (GZRSS). These designs provide unbiased estimators for the mean of symmetric distributions. It is shown that for non-uniform symmetric distributions, the estimators of the mean under the suggested designs are more efficient than those obtained by RSS, Simple Random Sampling (SRS), extreme RSS and truncation based RSS designs. Also, the proposed RSS schemes outperform other RSS schemes and provide more efficient estimates than their competitors under imperfect rankings. More >

Mohammed Omari^1,*, Souleymane Ouled Jaafri¹

CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 753-775, 2019, DOI:10.32604/cmc.2019.06576

Abstract Image compression is a process based on reducing the redundancy of the image to be stored or transmitted in an efficient form. In this work, a new idea is proposed, where we take advantage of the redundancy that appears in a group of images to be all compressed together, instead of compressing each image by itself. In our proposed technique, a classification process is applied, where the set of the input images are classified into groups based on existing technique like L₁ and L₂ norms, color histograms. All images that belong to the same group are More >