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  • Open Access


    Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

    Fangfang Shan1,2,*, Mengyao Liu1,2, Menghan Zhang1,2, Zhenyu Wang1,2

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1521-1542, 2024, DOI:10.32604/cmc.2024.053937

    Abstract Social media has become increasingly significant in modern society, but it has also turned into a breeding ground for the propagation of misleading information, potentially causing a detrimental impact on public opinion and daily life. Compared to pure text content, multmodal content significantly increases the visibility and share ability of posts. This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection. To effectively address the critical challenge of accurately detecting fake news on social media, this paper proposes a fake… More >

  • Open Access


    A Comprehensive Survey on Deep Learning Multi-Modal Fusion: Methods, Technologies and Applications

    Tianzhe Jiao, Chaopeng Guo, Xiaoyue Feng, Yuming Chen, Jie Song*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1-35, 2024, DOI:10.32604/cmc.2024.053204

    Abstract Multi-modal fusion technology gradually become a fundamental task in many fields, such as autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction. It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities. Under complex scenes, multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions. However, achieving outstanding performance is challenging because of equipment performance limitations, missing information, and data noise. This paper comprehensively reviews existing methods based on multi-modal fusion techniques and completes a detailed and in-depth analysis.… More >

  • Open Access


    A Hybrid Feature Fusion Traffic Sign Detection Algorithm Based on YOLOv7

    Bingyi Ren1,4, Juwei Zhang2,3,4,*, Tong Wang2,4

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1425-1440, 2024, DOI:10.32604/cmc.2024.052667

    Abstract Autonomous driving technology has entered a period of rapid development, and traffic sign detection is one of the important tasks. Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced, and traffic sign targets are small and have unclear features, which makes detection more difficult. Therefore, we propose a Hybrid Feature Fusion Traffic Sign detection algorithm based on YOLOv7 (HFFT-YOLO). First, a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales; Secondly, the cross-scale fusion part of the neck introduces a… More >

  • Open Access


    Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

    Yongguo Li, Yuanrong Wang, Jia Xie*, Caiyin Xu, Kun Zhang

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 467-486, 2024, DOI:10.32604/cmc.2024.051426

    Abstract To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle (USV) perception, this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection. Firstly, the visual recognition component employs an improved YOLOv7 algorithm based on a self-built dataset for the detection of water surface targets. This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure, addressing the problem of excessive redundant information during feature extraction in the original YOLOv7 network model. Simultaneously, this modification simplifies… More >

  • Open Access


    Pulmonary Edema and Pleural Effusion Detection Using EfficientNet-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

    Anas AbuKaraki1, Tawfi Alrawashdeh1, Sumaya Abusaleh1, Malek Zakarya Alksasbeh1,*, Bilal Alqudah1, Khalid Alemerien2, Hamzah Alshamaseen3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1055-1073, 2024, DOI:10.32604/cmc.2024.051420

    Abstract This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data… More >

  • Open Access


    A Comprehensive Survey of Recent Transformers in Image, Video and Diffusion Models

    Dinh Phu Cuong Le1,2, Dong Wang1, Viet-Tuan Le3,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 37-60, 2024, DOI:10.32604/cmc.2024.050790

    Abstract Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks (CNNs). The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism. This study aims to provide a comprehensive survey of recent transformer-based approaches in image and video applications, as well as diffusion models. We begin by discussing existing surveys of vision transformers and comparing them to this work. Then, we review the main components of a vanilla transformer network, including the self-attention mechanism, feed-forward network, position encoding, etc. In the main part of More >

  • Open Access


    Automatic Rule Discovery for Data Transformation Using Fusion of Diversified Feature Formats

    G. Sunil Santhosh Kumar1,2,*, M. Rudra Kumar3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 695-713, 2024, DOI:10.32604/cmc.2024.050143

    Abstract This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost, a machine learning algorithm renowned for its efficiency and performance. The framework proposed herein utilizes the fusion of diversified feature formats, specifically, metadata, textual, and pattern features. The goal is to enhance the system’s ability to discern and generalize transformation rules from source to destination formats in varied contexts. Firstly, the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model. Subsequent sections expound… More >

  • Open Access


    Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things: A Hybrid Approach

    Amal Al-Rasheed1, Rahim Khan2,3,*, Tahani Alsaed4, Mahwish Kundi2,5, Mohamad Hanif Md. Saad6, Mahidur R. Sarker7,8

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1305-1323, 2024, DOI:10.32604/cmc.2024.049215

    Abstract Due to the overwhelming characteristics of the Internet of Things (IoT) and its adoption in approximately every aspect of our lives, the concept of individual devices’ privacy has gained prominent attention from both customers, i.e., people, and industries as wearable devices collect sensitive information about patients (both admitted and outdoor) in smart healthcare infrastructures. In addition to privacy, outliers or noise are among the crucial issues, which are directly correlated with IoT infrastructures, as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing, i.e.,… More >

  • Open Access


    CFD Investigation of Diffusion Law and Harmful Boundary of Buried Natural Gas Pipeline in the Mountainous Environment

    Liqiong Chen1, Kui Zhao1, Kai Zhang1,*, Duo Xv1, Hongxvan Hu2, Guoguang Ma1, Wenwen Zhan3

    Energy Engineering, Vol.121, No.8, pp. 2143-2165, 2024, DOI:10.32604/ee.2024.049362

    Abstract The leakage gas from a buried natural gas pipelines has the great potential to cause economic losses and environmental pollution owing to the complexity of the mountainous environment. In this study, computational fluid dynamics (CFD) method was applied to investigate the diffusion law and hazard range of buried natural gas pipeline leakage in mountainous environment. Based on cloud chart, concentration at the monitoring site and hazard range of lower explosion limit (LEL) and upper explosion limit (UEL), the influences of leakage hole direction and shape, soil property, burial depth, obstacle type on the diffusion law… More >

  • Open Access


    Improving VQA via Dual-Level Feature Embedding Network

    Yaru Song*, Huahu Xu, Dikai Fang

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 397-416, 2024, DOI:10.32604/iasc.2023.040521

    Abstract Visual Question Answering (VQA) has sparked widespread interest as a crucial task in integrating vision and language. VQA primarily uses attention mechanisms to effectively answer questions to associate relevant visual regions with input questions. The detection-based features extracted by the object detection network aim to acquire the visual attention distribution on a predetermined detection frame and provide object-level insights to answer questions about foreground objects more effectively. However, it cannot answer the question about the background forms without detection boxes due to the lack of fine-grained details, which is the advantage of grid-based features. In… More >

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