Zhihong Lin¹, Zeng Zeng², Yituan Yu², Yinlin Ren¹, Xuesong Qiu^1,*, Jinqian Chen¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1641-1665, 2024, DOI:10.32604/cmc.2024.055802 - 15 October 2024

Abstract For permanent faults (PF) in the power communication network (PCN), such as link interruptions, the time-sensitive networking (TSN) relied on by PCN, typically employs spatial redundancy fault-tolerance methods to keep service stability and reliability, which often limits TSN scheduling performance in fault-free ideal states. So this paper proposes a graph attention residual network-based routing and fault-tolerant scheduling mechanism (GRFS) for data flow in PCN, which specifically includes a communication system architecture for integrated terminals based on a cyclic queuing and forwarding (CQF) model and fault recovery method, which reduces the impact of faults by simplified… More >

Yu Song, Pengcheng Wu, Dongming Dai, Mingyu Gui, Kunli Zhang^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4767-4790, 2024, DOI:10.32604/cmc.2024.053506 - 12 September 2024

Abstract The growing prevalence of knowledge reasoning using knowledge graphs (KGs) has substantially improved the accuracy and efficiency of intelligent medical diagnosis. However, current models primarily integrate electronic medical records (EMRs) and KGs into the knowledge reasoning process, ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text. To better integrate EMR text information, we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning (GATiT), which comprises text representation, subgraph construction, knowledge reasoning, and diagnostic classification. In the… More >

Ayesha Khaliq¹, Salman Afsar Awan¹, Fahad Ahmad^2,*, Muhammad Azam Zia¹, Muhammad Zafar Iqbal³

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3221-3242, 2024, DOI:10.32604/cmc.2024.053488 - 15 August 2024

Abstract The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity. Current approaches in Extractive Text Summarization (ETS) leverage the modeling of inter-sentence relationships, a task of paramount importance in producing coherent summaries. This study introduces an innovative model that integrates Graph Attention Networks (GATs) with Transformer-based Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA), further enhanced by Term Frequency-Inverse Document Frequency (TF-IDF) values, to improve sentence selection by capturing comprehensive topical information. Our… More >

Xin Shen¹, Jiahao Li¹, Yujun Yin¹, Jianlin Tang^2,3,*, Weibin Lin^2,3, Mi Zhou^2,3

Energy Engineering, Vol.121, No.7, pp. 1945-1961, 2024, DOI:10.32604/ee.2024.048388 - 11 June 2024

Abstract Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice, which is of immense importance in mobilizing the entire society to reduce carbon emissions. The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid. Therefore, it cannot provide carbon factor information beforehand. To address this issue, a prediction model based on the graph attention network is proposed. The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised More >

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 >

Fan Xiao¹, Xiong Ping¹, Yeyang Li^2,*, 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 >

Yan Li¹, Xiaoguang Zhang^1,*, Tianyu Gong¹, Qi Dong¹, Hailong Zhu¹, Tianqiang Zhang¹, Yanji Jiang^2,3

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 >

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

Shun Wang¹, Lin Qiao², Wei Fang³, Guodong Jing⁴, Victor S. Sheng⁵, Yong Zhang^1,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 673-687, 2022, DOI:10.32604/cmc.2022.028411 - 18 May 2022

Abstract PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the… More >

Xin Zihao¹, Wang Hongyuan^1,*, Qi Pengyu¹, Du Weidong², Zhang Ji¹, Chen Fuhua³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5925-5938, 2022, DOI:10.32604/cmc.2022.026943 - 21 April 2022

Abstract For a long time, the detection and extraction of printed surface defects has been a hot issue in the print industry. Nowadays, defect detection of a large number of products still relies on traditional image processing algorithms such as scale invariant feature transform (SIFT) and oriented fast and rotated brief (ORB), and researchers need to design algorithms for specific products. At present, a large number of defect detection algorithms based on object detection have been applied but need lots of labeling samples with defects. Besides, there are many kinds of defects in printed surface, so… More >