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Search Results (106)
  • Open Access

    ARTICLE

    Model Agnostic Meta-Learning (MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

    Yasir Maqsood1, Syed Muhammad Usman1,*, Musaed Alhussein2, Khursheed Aurangzeb2,*, Shehzad Khalid3, Muhammad Zubair4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2795-2811, 2024, DOI:10.32604/cmc.2024.049410

    Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing public datasets, and the difficulty… More >

  • Open Access

    ARTICLE

    Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid (MHAVH) Model

    Hina Naz1, Zuping Zhang1,*, Mohammed Al-Habib1, Fuad A. Awwad2, Emad A. A. Ismail2, Zaid Ali Khan3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2673-2696, 2024, DOI:10.32604/cmc.2024.049186

    Abstract Cardiovascular disease is the leading cause of death globally. This disease causes loss of heart muscles and is also responsible for the death of heart cells, sometimes damaging their functionality. A person’s life may depend on receiving timely assistance as soon as possible. Thus, minimizing the death ratio can be achieved by early detection of heart attack (HA) symptoms. In the United States alone, an estimated 610,000 people die from heart attacks each year, accounting for one in every four fatalities. However, by identifying and reporting heart attack symptoms early on, it is possible to reduce damage and save many… More >

  • Open Access

    ARTICLE

    Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

    Jianfeng He1,2, Haowei Ye1, Jie Ning1, Hui Zhou1,2,*, Bo She3,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3355-3372, 2024, DOI:10.32604/cmc.2024.048706

    Abstract In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our study introduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learning-based framework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques, which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency and overemphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective feature extraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Lie group domains to highlight fundamental motion patterns, coupled with employing competitive weighting for precise target deformation field generation.… More >

  • Open Access

    ARTICLE

    Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention

    Yifan Gao*, Jieming Zhang, Zhanchen Chen, Xianchao Chen

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 493-507, 2024, DOI:10.32604/cmc.2024.048615

    Abstract In this paper, we propose a novel anomaly detection method for data centers based on a combination of graph structure and abnormal attention mechanism. The method leverages the sensor monitoring data from target power substations to construct multidimensional time series. These time series are subsequently transformed into graph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matrices and additional weights associated with the graph structure, an aggregation matrix is derived. The aggregation matrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features. Moreover, both the multidimensional time series segments and… More >

  • Open Access

    ARTICLE

    HCSP-Net: A Novel Model of Age-Related Macular Degeneration Classification Based on Color Fundus Photography

    Cheng Wan1, Jiani Zhao1, Xiangqian Hong2, Weihua Yang2,*, Shaochong Zhang2,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 391-407, 2024, DOI:10.32604/cmc.2024.048307

    Abstract Age-related macular degeneration (AMD) ranks third among the most common causes of blindness. As the most conventional and direct method for identifying AMD, color fundus photography has become prominent owing to its consistency, ease of use, and good quality in extensive clinical practice. In this study, a convolutional neural network (CSPDarknet53) was combined with a transformer to construct a new hybrid model, HCSP-Net. This hybrid model was employed to tri-classify color fundus photography into the normal macula (NM), dry macular degeneration (DMD), and wet macular degeneration (WMD) based on clinical classification manifestations, thus identifying and resolving AMD as early as… More >

  • Open Access

    ARTICLE

    Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

    Chunming Wu1, Wukai Liu2,*, Xin Ma3

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1441-1461, 2024, DOI:10.32604/cmc.2024.048136

    Abstract A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase the visual impression of fused images by improving the quality of infrared and visible light picture fusion. The network comprises an encoder module, fusion layer, decoder module, and edge improvement module. The encoder module utilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformer to achieve deep-level co-extraction of local and global features from the original picture. An edge enhancement module (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy is introduced to enhance the… More >

  • Open Access

    ARTICLE

    Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

    Ankita Gangwar1, Vijaypal Singh Dhaka1, Geeta Rani2,*, Shrey Khandelwal1, Ester Zumpano3,4, Eugenio Vocaturo3,4

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 117-142, 2024, DOI:10.32604/cmc.2024.048119

    Abstract A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are one of the major causes… More >

  • Open Access

    ARTICLE

    An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

    Yu Zhou1, Bosong Lin1, Siqi Hu2, Dandan Yu3,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 161-182, 2024, DOI:10.32604/cmc.2024.047836

    Abstract This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view enhancement model grounded on the… More >

  • Open Access

    ARTICLE

    DCFNet: An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

    Chengzhang Zhu1,2, Renmao Zhang1, Yalong Xiao1,2,*, Beiji Zou1, Xian Chai1, Zhangzheng Yang1, Rong Hu3, Xuanchu Duan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1103-1128, 2024, DOI:10.32604/cmes.2024.048453

    Abstract Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multi-scale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer. To address… More >

  • Open Access

    ARTICLE

    Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

    Changfeng Feng1, Chunping Wang2, Dongdong Zhang1, Renke Kou1, Qiang Fu1,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3993-4013, 2024, DOI:10.32604/cmc.2024.048351

    Abstract Transformer-based models have facilitated significant advances in object detection. However, their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle (UAV) imagery. Addressing these limitations, we propose a hybrid transformer-based detector, H-DETR, and enhance it for dense small objects, leading to an accurate and efficient model. Firstly, we introduce a hybrid transformer encoder, which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently. Furthermore, we propose two novel strategies to enhance detection performance without incurring additional inference computation. Query filter is designed… More >

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