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  • Open Access


    Image Captioning Using Detectors and Swarm Based Learning Approach for Word Embedding Vectors

    B. Lalitha1,*, V. Gomathi2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 173-189, 2023, DOI:10.32604/csse.2023.024118

    Abstract IC (Image Captioning) is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements. However, in existing works, because of the complexity in images, neglecting major relation between the object in an image, poor quality image, labelling it remains a big problem for researchers. Hence, the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC. So in this research work the main contribution deals with the framework consists of three phases that is image understanding, textual understanding and… More >

  • Open Access


    Insider Threat Detection Based on NLP Word Embedding and Machine Learning

    Mohd Anul Haq1, Mohd Abdul Rahim Khan1,*, Mohammed Alshehri2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 619-635, 2022, DOI:10.32604/iasc.2022.021430

    Abstract The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires state-of-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which… More >

  • Open Access


    Enhancing Embedding-Based Chinese Word Similarity Evaluation with Concepts and Synonyms Knowledge

    Fulian Yin, Yanyan Wang, Jianbo Liu*, Meiqi Ji

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 747-764, 2020, DOI:10.32604/cmes.2020.010579

    Abstract Word similarity (WS) is a fundamental and critical task in natural language processing. Existing approaches to WS are mainly to calculate the similarity or relatedness of word pairs based on word embedding obtained by massive and high-quality corpus. However, it may suffer from poor performance for insuf- ficient corpus in some specific fields, and cannot capture rich semantic and sentimental information. To address these above problems, we propose an enhancing embedding-based word similarity evaluation with character-word concepts and synonyms knowledge, namely EWS-CS model, which can provide extra semantic information to enhance word similarity evaluation. The More >

  • Open Access


    Improving Chinese Word Representation with Conceptual Semantics

    Tingxin Wei1, 2, Weiguang Qu2, 3, *, Junsheng Zhou3, Yunfei Long4, Yanhui Gu3, Zhentao Xia3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1897-1913, 2020, DOI:10.32604/cmc.2020.010813

    Abstract The meaning of a word includes a conceptual meaning and a distributive meaning. Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity, especially for low-frequency words. In knowledge bases, manually annotated semantic knowledge is stable and the essential attributes of words are accurately denoted. In this paper, we propose a Conceptual Semantics Enhanced Word Representation (CEWR) model, computing the synset embedding and hypernym embedding of Chinese words based on the Tongyici Cilin thesaurus, and aggregating it with distributed word representation to have both distributed information and the conceptual meaning More >

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