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  • Open Access

    ARTICLE

    Payload Capacity Scheme for Quran Text Watermarking Based on Vowels with Kashida

    Ali A.R. Alkhafaji1,*, Nilam Nur Amir Sjarif1, M.A Shahidan1, Nurulhuda Firdaus Mohd Azmi1, Haslina Md Sarkan1, Suriayati Chuprat1, Osamah Ibrahim Khalaf2, Ehab Nabiel Al-Khanak3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3865-3885, 2021, DOI:10.32604/cmc.2021.015803

    Abstract The most sensitive Arabic text available online is the digital Holy Quran. This sacred Islamic religious book is recited by all Muslims worldwide including non-Arabs as part of their worship needs. Thus, it should be protected from any kind of tampering to keep its invaluable meaning intact. Different characteristics of Arabic letters like the vowels (), Kashida (extended letters), and other symbols in the Holy Quran must be secured from alterations. The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio (PSNR) and Embedding Ratio (ER).… More >

  • Open Access

    ARTICLE

    Decision Support System Tool for Arabic Text Recognition

    Fatmah Baothman*, Sarah Alssagaff, Bayan Ashmeel

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 519-531, 2021, DOI:10.32604/iasc.2021.014828

    Abstract The National Center for Education Statistics study reported that 80% of students change their major or institution at least once before getting a degree, which requires a course equivalency process. This error-prone process varies among disciplines, institutions, regions, and countries and requires effort and time. Therefore, this study aims to overcome these issues by developing a decision support tool called TiMELY for automatic Arabic text recognition using artificial intelligence techniques. The developed tool can process a complete document analysis for several course descriptions in multiple file formats, such as Word, Text, Pages, JPEG, GIF, and JPG. We applied a comparative… More >

  • Open Access

    ARTICLE

    Tampering Detection Approach of Arabic-Text Based on Contents Interrelationship

    Fahd N. Al-Wesabi1, Abdelzahir Abdelmaboud2,*, Adnan A. Zain3, Mohammed M. Almazah4, Ammar Zahary5

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 483-498, 2021, DOI:10.32604/iasc.2021.014322

    Abstract Text information depends primarily on natural languages processing. Improving the security and usability of text information shared through the public internet has therefore been the most demanding problem facing researchers. In contact and knowledge sharing through the Internet, the authentication of content and the identification of digital content are becoming a key problem. Therefore, in this paper, a fragile approach of zero-watermarking based on natural language processing has been developed for authentication of content and prevention of misuse of Arabic texts distributed over the Internet. According to the proposed approach, watermark embedding, and identification was technically carried out such that… More >

  • Open Access

    ARTICLE

    Recognition of Offline Handwritten Arabic Words Using a Few Structural Features

    Abderrahmane Saidi*, Abdelmouneim Moulay Lakhdar, Mohammed Beladgham

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2875-2889, 2021, DOI:10.32604/cmc.2021.013744

    Abstract Handwriting recognition is one of the most significant problems in pattern recognition, many studies have been proposed to improve this recognition of handwritten text for different languages. Yet, Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts. The present paper suggests a feature extraction technique for offline Arabic handwriting recognition. A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function (RBF) neural… More >

  • Open Access

    ARTICLE

    A Hybrid Intelligent Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet

    Fahd N. Al-Wesabi1,2,*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 195-211, 2021, DOI:10.32604/cmc.2020.012088

    Abstract In this paper, a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents. The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach (SAMMZWA). Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach. The SAMMZWA approach embeds and detects the watermark logically without altering the original text document. The extracted… More >

  • Open Access

    ARTICLE

    Highly Accurate Recognition of Handwritten Arabic Decimal Numbers Based on a Self-Organizing Maps Approach

    Amin Alqudah1,2, Hussein R. Al-Zoubi2, Mahmood A. Al-Khassaweneh2,3, Mohammed Al-Qodah1

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 493-505, 2018, DOI:10.31209/2018.100000005

    Abstract Handwritten numeral recognition is one of the most popular fields of research in automation because it is used in many applications. Indeed, automation has continually received substantial attention from researchers. Therefore, great efforts have been made to devise accurate recognition methods with high recognition ratios. In this paper, we propose a method for integrating the correlation coefficient with a Self-Organizing Maps (SOM)-based technique to recognize offline handwritten Arabic decimal digits. The simulation results show very high recognition rates compared with the rates achieved by other existing methods. More >

  • Open Access

    ARTICLE

    SVM Model Selection Using PSO for Learning Handwritten Arabic Characters

    Mamouni El Mamoun1,*, Zennaki Mahmoud1, Sadouni Kaddour1

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 995-1008, 2019, DOI:10.32604/cmc.2019.08081

    Abstract Using Support Vector Machine (SVM) requires the selection of several parameters such as multi-class strategy type (one-against-all or one-against-one), the regularization parameter C, kernel function and their parameters. The choice of these parameters has a great influence on the performance of the final classifier. This paper considers the grid search method and the particle swarm optimization (PSO) technique that have allowed to quickly select and scan a large space of SVM parameters. A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model. SVM is applied to handwritten Arabic characters… More >

  • Open Access

    ARTICLE

    Gene expression of 49 kDa apyrase, cytoskeletal proteins, ATPase, ADPase and amino acid contents of Pisum sativum (L.) cells germinated in Euryops arabicus (Steud. ex Jaub. & Spach) water extract

    Mahmoud MOUSTAFA1,2, Saad ALAMRI1, Hoida ZAKI2, Naglaa LOUTFY2, Tarek TAHA3, Ali SHATI1, Mohamed ALKAHTANI1, Sajda SIDDIQI1

    BIOCELL, Vol.43, No.2, pp. 81-88, 2019, DOI:10.32604/biocell.2019.06231

    Abstract The present research reports of quick and marked changes induced by plant extract of Euryops arabicus in the gene expression of 49-kDa apyrases, cytoskeletal proteins, ATPases, ADPase and amount of amino acid of pea (Pisum sativum L. var. Alaska). Pellets of cytoskeletals proteins (27000 xg) were probed with anti-apyrase antibody, biotinylated anti-rat, actin and alpha and beta-tubulin for Western blotting. ATPase and ADPase activities were determined based on the hydrolytic efficacy of adenine triphosphate and adenine diphosphate. By 72 hours, the abundance of apyrases, cytoskeletal proteins and amount of amino acid in pellets of 27000 xg of germinated pea seeds… More >

  • Open Access

    ARTICLE

    A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

    Maha Al-Yahya1, *

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 421-433, 2019, DOI:10.32604/cmc.2019.06209

    Abstract The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of this work lies in the… More >

  • Open Access

    ARTICLE

    Efficient Analysis of Vertical Projection Histogram to Segment Arabic Handwritten Characters

    Mamouni El Mamoun1,*, Zennaki Mahmoud1, Sadouni Kaddour1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 55-66, 2019, DOI:10.32604/cmc.2019.06444

    Abstract The paper discusses the segmentation of words into characters, which is an essential task in the development process of character recognition systems, as poorly segmented characters will automatically be unrecognized. The segmentation of offline handwritten Arabic text poses a greater challenge because of its cursive nature and different writing styles. In this article, we propose a new approach to segment handwritten Arabic characters using an efficient analysis of the vertical projection histogram. Our approach was tested using a set of handwritten Arabic words from the IFN/ENIT database, and promising results were obtained. More >

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