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  • Open Access

    ARTICLE

    Pyramid Separable Channel Attention Network for Single Image Super-Resolution

    Congcong Ma1,3, Jiaqi Mi2, Wanlin Gao1,3, Sha Tao1,3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4687-4701, 2024, DOI:10.32604/cmc.2024.055803 - 12 September 2024

    Abstract Single Image Super-Resolution (SISR) technology aims to reconstruct a clear, high-resolution image with more information from an input low-resolution image that is blurry and contains less information. This technology has significant research value and is widely used in fields such as medical imaging, satellite image processing, and security surveillance. Despite significant progress in existing research, challenges remain in reconstructing clear and complex texture details, with issues such as edge blurring and artifacts still present. The visual perception effect still needs further enhancement. Therefore, this study proposes a Pyramid Separable Channel Attention Network (PSCAN) for the… More >

  • Open Access

    ARTICLE

    CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

    Fatma S. Alrayes1, Mohammed Zakariah2, Syed Umar Amin3,*, Zafar Iqbal Khan3, Jehad Saad Alqurni4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586 - 20 June 2024

    Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… More >

  • Open Access

    ARTICLE

    Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

    Feng Zhao, Guangdi Liu*, Xiaoqiang Chen, Ying Wang

    Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209 - 11 June 2024

    Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

  • Open Access

    ARTICLE

    An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

    Yanmei Li1,*, Jinghong Tang1, Weiwu Ding1, Jian Luo2, Naveed Ahmad3, Rajesh Kumar4

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2023.046872 - 30 January 2024

    Abstract Alzheimer’s disease (AD) is a complex, progressive neurodegenerative disorder. The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice. In this study, we introduce an advanced diagnostic methodology rooted in the Med-3D transfer model and enhanced with an attention mechanism. We aim to improve the precision of AD diagnosis and facilitate its early identification. Initially, we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation, which are commonly observed in imaging datasets. Subsequently, an attention mechanism is incorporated to More >

  • Open Access

    ARTICLE

    A New Vehicle Detection Framework Based on Feature-Guided in the Road Scene

    Tianmin Deng*, Xiyue Zhang, Xinxin Cheng

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 533-549, 2024, DOI:10.32604/cmc.2023.044639 - 30 January 2024

    Abstract Vehicle detection plays a crucial role in the field of autonomous driving technology. However, directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection. Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes. This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network (FBPN). Firstly, to tackle challenges like vehicle occlusion and significant background interference, the efficient feature filtering module (EFFM) is introduced into the deep network,… More >

  • Open Access

    ARTICLE

    Improved Blending Attention Mechanism in Visual Question Answering

    Siyu Lu1, Yueming Ding1, Zhengtong Yin2, Mingzhe Liu3,*, Xuan Liu4, Wenfeng Zheng1,*, Lirong Yin5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1149-1161, 2023, DOI:10.32604/csse.2023.038598 - 26 May 2023

    Abstract Visual question answering (VQA) has attracted more and more attention in computer vision and natural language processing. Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks. Analysis of all features may cause information redundancy and heavy computational burden. Attention mechanism is a wise way to solve this problem. However, using single attention mechanism may cause incomplete concern of features. This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention More >

  • Open Access

    ARTICLE

    Crack Segmentation Based on Fusing Multi-Scale Wavelet and Spatial-Channel Attention

    Peng Geng*, Ji Lu, Hongtao Ma, Guiyi Yang

    Structural Durability & Health Monitoring, Vol.17, No.1, pp. 1-22, 2023, DOI:10.32604/sdhm.2023.018632 - 02 March 2023

    Abstract Accurate and reliable crack segmentation is a challenge and meaningful task. In this article, aiming at the characteristics of cracks on the concrete images, the intensity frequency information of source images which is obtained by Discrete Wavelet Transform (DWT) is fed into deep learning-based networks to enhance the ability of network on crack segmentation. To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed. The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module. And the… More >

  • Open Access

    ARTICLE

    Facial Expression Recognition Based on Multi-Channel Attention Residual Network

    Tongping Shen1,2,*, Huanqing Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 539-560, 2023, DOI:10.32604/cmes.2022.022312 - 29 September 2022

    Abstract For the problems of complex model structure and too many training parameters in facial expression recognition algorithms, we proposed a residual network structure with a multi-headed channel attention (MCA) module. The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples. The designed MCA module is integrated into the ResNet18 backbone network. The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights, and the multi-head structure focuses more on the local features of the pictures, which More >

  • Open Access

    ARTICLE

    Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet

    Helong Yu, Xianhe Cheng, Ziqing Li, Qi Cai, Chunguang Bi*

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 711-738, 2022, DOI:10.32604/cmes.2022.020263 - 27 June 2022

    Abstract To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks, a lightweight ResNet (LW-ResNet) model for apple disease recognition is proposed. Based on the deep residual network (ResNet18), the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features. By improving the identity mapping structure to reduce information loss. By introducing the efficient channel attention module (ECANet) to suppress noise from a complex background. The experimental… More >

  • Open Access

    ARTICLE

    Image Inpainting Detection Based on High-Pass Filter Attention Network

    Can Xiao1,2, Feng Li1,2,*, Dengyong Zhang1,2, Pu Huang1,2, Xiangling Ding3, Victor S. Sheng4

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1145-1154, 2022, DOI:10.32604/csse.2022.027249 - 09 May 2022

    Abstract Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks More >

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