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  • Open Access

    ARTICLE

    Adaptive Multi-Scale HyperNet with Bi-Direction Residual Attention Module for Scene Text Detection

    Junjie Qu, Jin Liu*, Chao Yu

    Journal of Information Hiding and Privacy Protection, Vol.3, No.2, pp. 83-89, 2021, DOI:10.32604/jihpp.2021.017181

    Abstract Scene text detection is an important step in the scene text reading system. There are still two problems during the existing text detection methods: (1) The small receptive of the convolutional layer in text detection is not sufficiently sensitive to the target area in the image; (2) The deep receptive of the convolutional layer in text detection lose a lot of spatial feature information. Therefore, detecting scene text remains a challenging issue. In this work, we design an effective text detector named Adaptive Multi-Scale HyperNet (AMSHN) to improve texts detection performance. Specifically, AMSHN enhances the sensitivity of target semantics in… More >

  • Open Access

    ARTICLE

    A Knowledge-Enhanced Dialogue Model Based on Multi-Hop Information with Graph Attention

    Zhongqin Bi1, Shiyang Wang1, Yan Chen2,*, Yongbin Li1, Jung Yoon Kim3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 403-426, 2021, DOI:10.32604/cmes.2021.016729

    Abstract With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to respond to consumer questions, but the current customer service robots tend to respond to specific questions. For many questions that lack background knowledge, they can generate only responses that are biased towards generality and repetitiveness. To better promote the understanding of dialogue and generate more meaningful responses, this paper… More >

  • Open Access

    ARTICLE

    AF-Net: A Medical Image Segmentation Network Based on Attention Mechanism and Feature Fusion

    Guimin Hou1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1877-1891, 2021, DOI:10.32604/cmc.2021.017481

    Abstract Medical image segmentation is an important application field of computer vision in medical image processing. Due to the close location and high similarity of different organs in medical images, the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation. To address these challenges, we propose a medical image segmentation network (AF-Net) based on attention mechanism and feature fusion, which can effectively capture global information while focusing the network on the object area. In this approach, we add dual attention blocks (DA-block) to the backbone network, which comprises parallel channels and spatial attention branches, to adaptively calibrate and weigh… More >

  • Open Access

    ARTICLE

    An Attention Based Neural Architecture for Arrhythmia Detection and Classification from ECG Signals

    Nimmala Mangathayaru1,*, Padmaja Rani2, Vinjamuri Janaki3, Kalyanapu Srinivas4, B. Mathura Bai1, G. Sai Mohan1, B. Lalith Bharadwaj1

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2425-2443, 2021, DOI:10.32604/cmc.2021.016534

    Abstract Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine. Detecting arrhythmia from ECG signals is considered a standard approach and hence, automating this process would aid the diagnosis by providing fast, cost-efficient, and accurate solutions at scale. This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography (ECG) signals causing arrhythmia. In this era of applied intelligence, automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions. In this research, our contributions are two-fold. Firstly, the Dual-Tree Complex Wavelet Transform (DT-CWT) method is implied… More >

  • Open Access

    ARTICLE

    Short-term Wind Speed Prediction with a Two-layer Attention-based LSTM

    Jingcheng Qian1, Mingfang Zhu1, Yingnan Zhao2,*, Xiangjian He3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 197-209, 2021, DOI:10.32604/csse.2021.016911

    Abstract Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power. Temporal-spatial wind speed features contain rich information; however, their use to predict wind speed remains one of the most challenging and less studied areas. This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attention-based long short-term memory (LSTM), termed 2Attn-LSTM, a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data. To eliminate the unevenness of the original wind speed,… More >

  • Open Access

    ARTICLE

    Mixed Attention Densely Residual Network for Single Image Super-Resolution

    Jingjun Zhou1,2, Jing Liu3, Jingbing Li1,2,*, Mengxing Huang1,2, Jieren Cheng4, Yen-Wei Chen5, Yingying Xu3,6, Saqib Ali Nawaz1

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 133-146, 2021, DOI:10.32604/csse.2021.016633

    Abstract Recent applications of convolutional neural networks (CNNs) in single image super-resolution (SISR) have achieved unprecedented performance. However, existing CNN-based SISR network structure design consider mostly only channel or spatial information, and cannot make full use of both channel and spatial information to improve SISR performance further. The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information. Specifically, we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network. This structure… More >

  • Open Access

    ARTICLE

    Algorithm of Helmet Wearing Detection Based on AT-YOLO Deep Mode

    Qingyang Zhou1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 159-174, 2021, DOI:10.32604/cmc.2021.017480

    Abstract The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements, but they can’t accurately detect small objects and objects with obstructions. Therefore, we propose a helmet detection algorithm based on the attention mechanism (AT-YOLO). First of all, a channel attention module is added to the YOLOv3 backbone network, which can adaptively calibrate the channel features of the direction to improve the feature utilization, and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the… More >

  • Open Access

    ARTICLE

    ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module

    Yudong Zhang1,3,*, Xin Zhang2,*, Weiguo Zhu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1037-1058, 2021, DOI:10.32604/cmes.2021.015807

    Abstract Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network for COVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed to avoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structure of which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracy of our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: This proposed ANC method is superior to 9 state-of-the-art approaches. More >

  • Open Access

    ARTICLE

    Chinese Q&A Community Medical Entity Recognition with Character-Level Features and Self-Attention Mechanism

    Pu Han1,2, Mingtao Zhang1, Jin Shi3, Jinming Yang4, Xiaoyan Li5,*

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 55-72, 2021, DOI:10.32604/iasc.2021.017021

    Abstract With the rapid development of Internet, the medical Q&A community has become an important channel for people to obtain and share medical and health knowledge. Online medical entity recognition (OMER), as the foundation of medical and health information extraction, has attracted extensive attention of researchers in recent years. In order to further improve the research progress of Chinese OMER, LSTM-Att-Med model is proposed in this paper to capture more external semantic features and important information. First, Word2vec is used to generate the character-level vectors with semantic features on the basis of the unlabeled corpus in the medical domain and open… More >

  • Open Access

    ARTICLE

    Joint Event Extraction Based on Global Event-Type Guidance and Attention Enhancement

    Daojian Zeng1, Jian Tian2, Ruoyao Peng1, Jianhua Dai1,*, Hui Gao3, Peng Peng4

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4161-4173, 2021, DOI:10.32604/cmc.2021.017028

    Abstract Event extraction is one of the most challenging tasks in information extraction. It is a common phenomenon where multiple events exist in the same sentence. However, extracting multiple events is more difficult than extracting a single event. Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long. In addition, the current argument extraction relies on the results of syntactic dependency analysis, which is complicated and prone to error transmission. In order to solve the above problems, a joint event extraction method based on global event-type guidance and attention enhancement was… More >

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