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  • Open Access


    An Enhanced Graphical Authentication Scheme Using Multiple-Image Steganography

    Khalil Hamdi Ateyeh Al-Shqeerat*

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

    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 it must have a colorful… More >

  • Open Access


    Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies

    Nasir Mahmud Khokhar1, Muhammad Nadeem Majeed2, Syed Muslim Shah3,*

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

    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 random sleep strategy is followed… More >

  • Open Access


    Generalized Class of Mean Estimators with Known Measures for Outliers Treatment

    Ibrahim M. Almanjahie1,2, Amer Ibrahim Al-Omari3,*, Emmanuel J. Ekpenyong4, Mir Subzar5

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

    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 ancillary information of non-conventional measures… More >

  • Open Access


    New Improved Ranked Set Sampling Designs with an Application to Real Data

    Amer Ibrahim Al-Omari1, Ibrahim M. Almanjahie2,3,*

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

    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. The suggested mean estimators under… More >

  • Open Access


    Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring

    Mohammed Omari1,*, 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 compressed based on dividing… More >

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