Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (6)
  • Open Access

    ARTICLE

    Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

    Zhizhong Liu*, Bin Zhou, Lingqiang Meng, Guangyu Huang

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1963-1981, 2023, DOI:10.32604/iasc.2023.038835

    Abstract Recently, multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams, which has great potential to surpass unimodal sentiment analysis. One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy. Unfortunately, existing work always considers feature-level fusion or decision-level fusion, and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion. To improve the performance of multimodal sentiment analysis, we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy (BAHFS). Firstly, we apply BiGRU to learn the unimodal features of… More >

  • Open Access

    ARTICLE

    Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

    Junfeng Sun1, Chenghai Li1, Yafei Song1,*, Peng Ni2, Jian Wang1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 993-1021, 2023, DOI:10.32604/csse.2023.039215

    Abstract The accuracy of historical situation values is required for traditional network security situation prediction (NSSP). There are discrepancies in the correlation and weighting of the various network security elements. To solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network is proposed, which is optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO). This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data. Furthermore, a prediction model of TCAN-BiGRU… More >

  • Open Access

    ARTICLE

    Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model

    Guangxia Xu1,*, Lei Liu2, Jingnan Dong3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 903-922, 2023, DOI:10.32604/cmes.2023.026627

    Abstract In recent years, with the great success of pre-trained language models, the pre-trained BERT model has been gradually applied to the field of source code understanding. However, the time cost of training a language model from zero is very high, and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present. In this paper, we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained language model BERT and connected to a bidirectional gate recurrent unit model. The downstream neural network adopts… More >

  • Open Access

    ARTICLE

    Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

    Junjie Zhou, Hongkui Xu*, Zifeng Zhang, Jiangkun Lu, Wentao Guo, Zhenye Li

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2277-2297, 2023, DOI:10.32604/csse.2023.036419

    Abstract Fraud cases have been a risk in society and people’s property security has been greatly threatened. In recent studies, many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis. These algorithms are also suitable for fraudulent phone text recognition. Compared to these tasks, the semantics of fraudulent words are more complex and more difficult to distinguish. Recurrent Neural Networks (RNN), the variants of RNN, Convolutional Neural Networks (CNN), and hybrid neural networks to extract text features are used by most text classification research. However, a single network or a simple network combination cannot… More >

  • Open Access

    ARTICLE

    Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

    Samer Abdulateef Waheeb*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 981-998, 2023, DOI:10.32604/csse.2023.035753

    Abstract Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging. Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field. Using a sentiment dictionary and feature engineering, the researchers primarily mine semantic text features. However, choosing and designing features requires a lot of manpower. The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space. A composite model including Active Learning (AL), Convolutional Neural Network (CNN), BiGRU,… More >

  • Open Access

    ARTICLE

    Chinese Relation Extraction on Forestry Knowledge Graph Construction

    Qi Yue, Xiang Li, Dan Li*

    Computer Systems Science and Engineering, Vol.37, No.3, pp. 423-442, 2021, DOI:10.32604/csse.2021.014448

    Abstract Forestry work has long been weak in data integration; its initial state will inevitably affect the forestry project development and decision-quality. Knowledge Graph (KG) can provide better abilities to organize, manage, and understand forestry knowledge. Relation Extraction (RE) is a crucial task of KG construction and information retrieval. Previous researches on relation extraction have proved the performance of using the attention mechanism. However, these methods focused on the representation of the entire sentence and ignored the loss of information. The lack of analysis of words and syntactic features contributes to sentences, especially in Chinese relation extraction, resulting in poor performance.… More >

Displaying 1-10 on page 1 of 6. Per Page