Kang Xiaofeng¹, Hu Kun^2,*, Ran Li³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2963-2974, 2023, DOI:10.32604/csse.2023.025908

Abstract Acoustic emission (AE) is a nondestructive real-time monitoring technology, which has been proven to be a valid way of monitoring dynamic damage to materials. The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning. Considering that the huge success of deep learning technologies, where the Recurrent Neural Network (RNN) has been widely applied to sequential classification tasks and Convolutional Neural Network (CNN) has been widely applied to image recognition tasks. A novel three-streams neural network (TSANN) model is proposed in this paper to deal with fault detection More >

Xiaorui Zhang^1,2,3,*, Chunlin Yuan¹, Wei Sun^3,4, Sunil Kumar Jha⁵

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4273-4291, 2023, DOI:10.32604/cmc.2023.036975

Abstract The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image. Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image. However, existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset. Therefore, this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation. To perform more accurate emotional region prediction, this… More >

Zibo Wang^1,3, Chaobin Huo², Yaofang Zhang^1,3, Shengtao Cheng^1,3, Yilu Chen^1,3, Xiaojie Wei⁵, Chao Li⁴, Bailing Wang^1,3,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2957-2979, 2023, DOI:10.32604/cmc.2023.035694

Abstract With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific… More >

Xiaorui Zhang^1,2,3,4,*, Yingying Wang¹, Wei Sun^4,5, Qi Cui⁶, Xindong Wei⁷

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3057-3071, 2023, DOI:10.32604/iasc.2023.033578

Abstract Signature verification, which is a method to distinguish the authenticity of signature images, is a biometric verification technique that can effectively reduce the risk of forged signatures in financial, legal, and other business environments. However, compared with ordinary images, signature images have the following characteristics: First, the strokes are slim, i.e., there is less effective information. Second, the signature changes slightly with the time, place, and mood of the signer, i.e., it has high intraclass differences. These challenges lead to the low accuracy of the existing methods based on convolutional neural networks (CNN). This study… More >

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

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 >

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… More >

Mi Zhou¹, Rui Liu^1,*, Pengfei Yi¹, Dongsheng Zhou^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2093-2109, 2023, DOI:10.32604/cmes.2023.024189

Abstract Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios. With the introduction of end-to-end direct regression methods, the field has entered a new stage of development. However, the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method. In this paper, we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy, which is applied to the multi-view multi-person 3D More > Graphic Abstract

Pan Zhang, Hongwei Deng^*, Zhong Chen

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

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 >

Longjuan Wang^1,2, Chunjie Cao^1,2, Binghui Zou^1,2, Jun Ye^1,2,*, Jin Zhang³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1801-1814, 2023, DOI:10.32604/cmc.2023.032785

Abstract License plate recognition technology use widely in intelligent traffic management and control. Researchers have been committed to improving the speed and accuracy of license plate recognition for nearly 30 years. This paper is the first to propose combining the attention mechanism with YOLO-v5 and LPRnet to construct a new license plate recognition model (LPR-CBAM-Net). Through the attention mechanism CBAM (Convolutional Block Attention Module), the importance of different feature channels in license plate recognition can be re-calibrated to obtain proper attention to features. Force information to achieve the purpose of improving recognition speed and accuracy. Experimental More >

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),… More >