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  • Open Access

    ARTICLE

    Social Robot Detection Method with Improved Graph Neural Networks

    Zhenhua Yu, Liangxue Bai, Ou Ye*, Xuya Cong

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1773-1795, 2024, DOI:10.32604/cmc.2023.047130 - 27 February 2024

    Abstract Social robot accounts controlled by artificial intelligence or humans are active in social networks, bringing negative impacts to network security and social life. Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships, which makes it difficult to accurately describe the difference between the topological relations of nodes, resulting in low detection accuracy of social robots. This paper proposes a social robot detection method with the use of an improved neural network. First, social relationship subgraphs are constructed by leveraging the user’s social… More >

  • Open Access

    ARTICLE

    The Short-Term Prediction of Wind Power Based on the Convolutional Graph Attention Deep Neural Network

    Fan Xiao1, Xiong Ping1, Yeyang Li2,*, Yusen Xu2, Yiqun Kang1, Dan Liu1, Nianming Zhang1

    Energy Engineering, Vol.121, No.2, pp. 359-376, 2024, DOI:10.32604/ee.2023.040887 - 25 January 2024

    Abstract The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale. Therefore, wind power forecasting plays a key role in improving the safety and economic benefits of the power grid. This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data. Based on the graph attention network and attention mechanism, the method extracts spatial-temporal characteristics from the data of multiple wind farms. Then, combined with a deep… More >

  • Open Access

    ARTICLE

    Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports

    Yan Li1, Xiaoguang Zhang1,*, Tianyu Gong1, Qi Dong1, Hailong Zhu1, Tianqiang Zhang1, Yanji Jiang2,3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3691-3705, 2023, DOI:10.32604/cmc.2023.040492 - 08 October 2023

    Abstract Automatic text summarization (ATS) plays a significant role in Natural Language Processing (NLP). Abstractive summarization produces summaries by identifying and compressing the most important information in a document. However, there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics. In particular, Chinese complaint reports, generated by urban complainers and collected by government employees, describe existing resident problems in daily life. Meanwhile, the reflected problems are required to respond speedily. Therefore, automatic summarization tasks for these reports have been More >

  • Open Access

    ARTICLE

    MVCE-Net: Multi-View Region Feature and Caption Enhancement Co-Attention Network for Visual Question Answering

    Feng Yan1, Wushouer Silamu2, Yanbing Li1,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 65-80, 2023, DOI:10.32604/cmc.2023.038177 - 08 June 2023

    Abstract Visual question answering (VQA) requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately. However, existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images, which limits the understanding depth of the image content. The images contain more than just visual objects, some images contain textual information about the scene, and slightly more complex images contain relationships between individual visual objects. Firstly, this paper proposes a model using image description for feature enhancement. This model encodes… More >

  • Open Access

    ARTICLE

    FSA-Net: A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization

    Zhipeng Bin1, Huihuang Zhao1,2,*, Xiaoman Liang1,2, Wenli Chen1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 971-983, 2023, DOI:10.32604/iasc.2023.037270 - 29 April 2023

    Abstract The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images. In this study, the Face Swapping Attention Network (FSA-Net) is proposed to generate photorealistic face swapping. The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint (cheek, mouth, eye, nose, etc.), which causes artifacts and makes the generated face silhouette non-realistic. To address this problem, a novel reinforced multi-aware attention module, referred to as RMAA, is proposed for handling facial fusion and expression occlusion flaws. The framework includes two stages. In the first stage, a More >

  • Open Access

    ARTICLE

    Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

    Changhui You1,2, Hong Zheng1,2,*, Zhongyuan Guo2, Tianyu Wang2, Jianping Ju3, Xi Li3

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3431-3452, 2023, DOI:10.32604/cmc.2023.035897 - 31 March 2023

    Abstract The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anti-counterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed… More >

  • Open Access

    ARTICLE

    RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

    Pan Zhang, Hongwei Deng*, Zhong Chen

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1411-1430, 2023, DOI:10.32604/cmc.2023.034876 - 06 February 2023

    Abstract In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection… More >

  • Open Access

    ARTICLE

    CLGA Net: Cross Layer Gated Attention Network for Image Dehazing

    Shengchun Wang1, Baoxuan Huang1, Tsz Ho Wong2, Jingui Huang1,*, Hong Deng1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4667-4684, 2023, DOI:10.32604/cmc.2023.031444 - 28 December 2022

    Abstract In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images. Compared with the previous dehazing network, the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extractor, combined with the channel attention mechanism, to better extract the restored features. A large amount of experimental data proves that the defogging model proposed in this paper is superior to previous defogging technologies in terms of structure similarity index (SSIM), peak signal to noise ratio (PSNR) and subjective visual quality. In order… More >

  • Open Access

    ARTICLE

    Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

    Sukhendra Singh1, Sur Singh Rawat2, Manoj Gupta3, B. K. Tripathi4, Faisal Alanzi5, Arnab Majumdar6, Pattaraporn Khuwuthyakorn7, Orawit Thinnukool7,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1673-1691, 2023, DOI:10.32604/cmc.2023.032364 - 22 September 2022

    Abstract In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and… More >

  • Open Access

    ARTICLE

    Continuous Sign Language Recognition Based on Spatial-Temporal Graph Attention Network

    Qi Guo, Shujun Zhang*, Hui Li

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1653-1670, 2023, DOI:10.32604/cmes.2022.021784 - 20 September 2022

    Abstract Continuous sign language recognition (CSLR) is challenging due to the complexity of video background, hand gesture variability, and temporal modeling difficulties. This work proposes a CSLR method based on a spatial-temporal graph attention network to focus on essential features of video series. The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatial-temporal graph to reflect inter-frame relevance and physical connections between nodes. The graph-based multi-head attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration, and short-term motion correlation modeling More > Graphic Abstract

    Continuous Sign Language Recognition Based on Spatial-Temporal Graph Attention Network

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