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  • Open Access


    EDU-GAN: Edge Enhancement Generative Adversarial Networks with Dual-Domain Discriminators for Inscription Images Denoising

    Yunjing Liu1,, Erhu Zhang1,2,,*, Jingjing Wang3, Guangfeng Lin2, Jinghong Duan4

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1633-1653, 2024, DOI:10.32604/cmc.2024.052611

    Abstract Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue. Different from natural images, character images pay more attention to stroke information. However, existing models mainly consider pixel-level information while ignoring structural information of the character, such as its edge and glyph, resulting in reconstructed images with mottled local structure and character damage. To solve these problems, we propose a novel generative adversarial network (GAN) framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework, i.e., EDU-GAN. Unlike existing frameworks, the generator introduces the… More >

  • Open Access


    Detecting XSS with Random Forest and Multi-Channel Feature Extraction

    Qiurong Qin, Yueqin Li*, Yajie Mi, Jinhui Shen, Kexin Wu, Zhenzhao Wang

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 843-874, 2024, DOI:10.32604/cmc.2024.051769

    Abstract In the era of the Internet, widely used web applications have become the target of hacker attacks because they contain a large amount of personal information. Among these vulnerabilities, stealing private data through cross-site scripting (XSS) attacks is one of the most commonly used attacks by hackers. Currently, deep learning-based XSS attack detection methods have good application prospects; however, they suffer from problems such as being prone to overfitting, a high false alarm rate, and low accuracy. To address these issues, we propose a multi-stage feature extraction and fusion model for XSS detection based on… More >

  • Open Access


    Heat Transfer Enhancement of the Absorber Tube in a Parabolic Trough Solar Collector through the Insertion of Novel Cylindrical Turbulators

    Yasser Jebbar1,2,*, Fadhil Fluiful2, Wisam Khudhayer3

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1279-1297, 2024, DOI:10.32604/fdmp.2024.050753

    Abstract This study includes an experimental and numerical analysis of the performances of a parabolic trough collector (PTC) with and without cylindrical turbulators. The PTC is designed with dimensions of 2.00 m in length and 1.00 m in width. The related reflector is made of lined sheets of aluminum, and the tubes are made of stainless steel used for the absorption of heat. They have an outer diameter of 0.051 m and a wall thickness of 0.002 m. Water, used as a heat transfer fluid (HTF), flows through the absorber tube at a mass flow rate… More >

  • Open Access


    BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

    Xuejie Wang1, Jianxun Zhang1,*, Ye Tao2, Xiaoli Yuan1, Yifan Guo1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4621-4639, 2024, DOI:10.32604/cmc.2024.051556

    Abstract While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity… More >

  • Open Access


    MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

    Tao Zhou1,3, Yujie Guo1,3,*, Caiyue Peng1,3, Yuxia Niu1,3, Yunfeng Pan1,3, Huiling Lu2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4863-4882, 2024, DOI:10.32604/cmc.2024.050767

    Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the… More >

  • Open Access


    Research on Enhanced Contraband Dataset ACXray Based on ETL

    Xueping Song1,*, Jianming Yang1, Shuyu Zhang1, Jicun Zhang1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4551-4572, 2024, DOI:10.32604/cmc.2024.049446

    Abstract To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications, a method has been proposed that employs the Extract-Transform-Load (ETL) approach to create an X-ray dataset of contraband items. Initially, X-ray scatter image data is collected and cleaned. Using Kafka message queues and the Elasticsearch (ES) distributed search engine, the data is transmitted in real-time to cloud servers. Subsequently, contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for… More >

  • Open Access


    Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

    Ahmed H. Alhadethi1,*, Ikram Smaoui2, Ahmed Fakhfakh3, Saad M. Darwish4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4825-4844, 2024, DOI:10.32604/cmc.2024.047852

    Abstract The act of transmitting photos via the Internet has become a routine and significant activity. Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced. This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images. The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression. This paper introduces… More >

  • Open Access


    Pairwise Reversible Data Hiding for Medical Images with Contrast Enhancement

    Isaac Asare Boateng1,2,*, Lord Amoah2, Isogun Toluwalase Adewale3

    Journal of Information Hiding and Privacy Protection, Vol.6, pp. 1-19, 2024, DOI:10.32604/jihpp.2024.051354

    Abstract Contrast enhancement in medical images has been vital since the prevalence of image representations in healthcare. In this research, the PRDHMCE (pairwise reversible data hiding for medical images with contrast enhancement) algorithm is proposed as an automatic contrast enhancement (CE) method for medical images based on region of interest (ROI) and non-region of interest (NROI). The PRDHMCE algorithm strategically enhances the ROI after segmentation using histogram stretching and data embedding. An initial histogram evaluation compares histogram bins with their neighbours to select the bin with the maximum pixel count. The selected bin is set as More >

  • Open Access


    Enhancing Relational Triple Extraction in Specific Domains: Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

    Jiakai Li, Jianpeng Hu*, Geng Zhang

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2481-2503, 2024, DOI:10.32604/cmc.2024.050005

    Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first… More >

  • Open Access


    A Study on the Role of Tourism in Enhancing Personal Mental Health in the Post-Epidemic Era

    Ruiqin Tian*, Yue Feng, Lingqi Zhan

    International Journal of Mental Health Promotion, Vol.26, No.4, pp. 325-334, 2024, DOI:10.32604/ijmhp.2023.042827

    Abstract With the advent of the post-epidemic era, a great wave of tourism has been ushered in everywhere. The relationship between tourism and mental health has become a hot topic in society. This paper investigates the enhancement of people’s mental health after tourism through social survey. Using Hangzhou as the sample collection site, this paper conducted a study on the role of tourism in enhancing personal mental health through descriptive analysis, factor analysis and structural equation modeling, and further specifically analyzed the role of mediating variables. The results showed that: (1) The purpose of tourism is… More >

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