Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (5)
  • Open Access


    Entropy-Based Watermarking Approach for Sensitive Tamper Detection of Arabic Text

    Fahd N. Al-Wesabi1,2,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3635-3648, 2021, DOI:10.32604/cmc.2021.015865

    Abstract The digital text media is the most common media transferred via the internet for various purposes and is very sensitive to transfer online with the possibility to be tampered illegally by the tampering attacks. Therefore, improving the security and authenticity of the text when it is transferred via the internet has become one of the most difficult challenges that researchers face today. Arabic text is more sensitive than other languages due to Harakat’s existence in Arabic diacritics such as Kasra, and Damma in which making basic changes such as modifying diacritic arrangements can lead to change the text meaning. In… More >

  • Open Access


    Proposing a High-Robust Approach for Detecting the Tampering Attacks on English Text Transmitted via Internet

    Fahd N. Al-Wesabi1,*, Huda G. Iskandar2, Mohammad Alamgeer3, Mokhtar M. Ghilan2

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1267-1283, 2020, DOI:10.32604/iasc.2020.013782

    Abstract In this paper, a robust approach INLPETWA (an Intelligent Natural Language Processing and English Text Watermarking Approach) is proposed to tampering detection of English text by integrating zero text watermarking and hidden Markov model as a soft computing and natural language processing techniques. In the INLPETWA approach, embedding and detecting the watermark key logically conducted without altering the plain text. Second-gram and word mechanism of hidden Markov model is used as a natural text analysis technique to extracts English text features and use them as a watermark key and embed them logically and validates them during detection process to detect… More >

  • Open Access


    Spam Detection in Reviews Using LSTM-Based Multi-Entity Temporal Features

    Lingyun Xiang1,2,3, Guoqing Guo2, Qian Li4, Chengzhang Zhu5,*, Jiuren Chen6, Haoliang Ma2

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1375-1390, 2020, DOI:10.32604/iasc.2020.013382

    Abstract Current works on spam detection in product reviews tend to ignore the temporal relevance among reviews in the user or product entity, resulting in poor detection performance. To address this issue, the present paper proposes a spam detection method that jointly learns comprehensive temporal features from both behavioral and text features in user and product entities. We first extract the behavioral features of a single review, then employ a convolutional neural network (CNN) to learn the text features of this review. We next combine the behavioral features with the text features of each review and train a Long-Short-Term Memory (LSTM)… More >

  • Open Access


    Rank-Order Correlation-Based Feature Vector Context Transformation for Learning to Rank for Information Retrieval

    Jen-Yuan Yeh

    Computer Systems Science and Engineering, Vol.33, No.1, pp. 41-52, 2018, DOI:10.32604/csse.2018.33.041

    Abstract As a crucial task in information retrieval, ranking defines the preferential order among the retrieved documents for a given query. Supervised learning has recently been dedicated to automatically learning ranking models by incorporating various models into one effective model. This paper proposes a novel supervised learning method, in which instances are represented as bags of contexts of features, instead of bags of features. The method applies rank-order correlations to measure the correlation relationships between features. The feature vectors of instances, i.e., the 1st-order raw feature vectors, are then mapped into the feature correlation space via projection to derive the context-level… More >

  • Open Access


    A Method of Text Extremum Region Extraction Based on JointChannels

    Xueming Qiao1, Yingxue Xia1, Weiyi Zhu2, Dongjie Zhu3, *, Liang Kong1, Chunxu Lin3, Zhenhao Guo3, Yiheng Sun3

    Journal on Artificial Intelligence, Vol.2, No.1, pp. 29-37, 2020, DOI:10.32604/jai.2020.09955

    Abstract Natural scene recognition has important significance and value in the fields of image retrieval, autonomous navigation, human-computer interaction and industrial automation. Firstly, the natural scene image non-text content takes up relatively high proportion; secondly, the natural scene images have a cluttered background and complex lighting conditions, angle, font and color. Therefore, how to extract text extreme regions efficiently from complex and varied natural scene images plays an important role in natural scene image text recognition. In this paper, a Text extremum region Extraction algorithm based on Joint-Channels (TEJC) is proposed. On the one hand, it can solve the problem that… More >

Displaying 1-10 on page 1 of 5. Per Page  

Share Link