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  • 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


    Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

    Pengshou Xie1, Guoqiang Ma1, *, Tao Feng1, Yan Yan1, 2, Xueming Han1

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1127-1137, 2020, DOI:10.32604/cmc.2020.09695

    Abstract Undoubtedly, uncooperative or malicious nodes threaten the safety of Internet of Vehicles (IoV) by destroying routing or data. To this end, some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication, data, energy, etc., to detect and evaluate vehicle nodes. However, it is difficult to effectively assess the trust level of a vehicle node only by message forwarding, data consistency, and energy sufficiency. In order to resolve these problems, a novel mechanism and a new trust calculating model is proposed in this paper. First, the four tuple… More >

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