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  • Open Access

    ARTICLE

    Learning Discriminatory Information for Object Detection on Urine Sediment Image

    Sixian Chan1,2, Binghui Wu1, Guodao Zhang3, Yuan Yao4, Hongqiang Wang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 411-428, 2024, DOI:10.32604/cmes.2023.029485

    Abstract In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection methods have the potential… More > Graphic Abstract

    Learning Discriminatory Information for Object Detection on Urine Sediment Image

  • Open Access

    ARTICLE

    PanopticUAV: Panoptic Segmentation of UAV Images for Marine Environment Monitoring

    Yuling Dou1, Fengqin Yao1, Xiandong Wang1, Liang Qu2, Long Chen3, Zhiwei Xu4, Laihui Ding4, Leon Bevan Bullock1, Guoqiang Zhong1, Shengke Wang1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1001-1014, 2024, DOI:10.32604/cmes.2023.027764

    Abstract UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience, low cost and convenient maintenance. In marine environmental monitoring, the similarity between objects such as oil spill and sea surface, Spartina alterniflora and algae is high, and the effect of the general segmentation algorithm is poor, which brings new challenges to the segmentation of UAV marine images. Panoramic segmentation can do object detection and semantic segmentation at the same time, which can well solve the polymorphism problem of objects in UAV ocean images. Currently, there are few studies on UAV marine image recognition… More >

  • Open Access

    ARTICLE

    Contamination Identification of Lentinula Edodes Logs Based on Improved YOLOv5s

    Xuefei Chen1, Wenhui Tan2, Qiulan Wu1,*, Feng Zhang1, Xiumei Guo1, Zixin Zhu1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3143-3157, 2023, DOI:10.32604/iasc.2023.040903

    Abstract In order to improve the accuracy and efficiency of Lentinula edodes logs contamination identification, an improved YOLOv5s contamination identification model for Lentinula edodes logs (YOLOv5s-CGGS) is proposed in this paper. Firstly, a CA (coordinate attention) mechanism is introduced in the feature extraction network of YOLOv5s to improve the identifiability of Lentinula edodes logs contamination and the accuracy of target localization. Then, the CIoU (Complete-IOU) loss function is replaced by an SIoU (SCYLLA-IoU) loss function to improve the model’s convergence speed and inference accuracy. Finally, the GSConv and GhostConv modules are used to improve and optimize the feature fusion network to… More >

  • Open Access

    ARTICLE

    Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

    Guohui Song1,2, Yongbin Wang1,*, Jianfei Li1, Hongbin Hu1

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1621-1641, 2023, DOI:10.32604/cmc.2023.040095

    Abstract Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity. The… More >

  • Open Access

    ARTICLE

    Regional Economic Development Trend Prediction Method Based on Digital Twins and Time Series Network

    Runguo Xu*, Xuehan Yu, Xiaoxue Zhao

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1781-1796, 2023, DOI:10.32604/cmc.2023.037293

    Abstract At present, the interpretation of regional economic development (RED) has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure, the improvement of economic relations, and the change of institutional innovation. This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis. Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data. Finally, the regional economy is predicted according to the theoretical model. The specific research work mainly includes… More >

  • Open Access

    ARTICLE

    Faster RCNN Target Detection Algorithm Integrating CBAM and FPN

    Wenshun Sheng*, Xiongfeng Yu, Jiayan Lin, Xin Chen

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1549-1569, 2023, DOI:10.32604/csse.2023.039410

    Abstract Small targets and occluded targets will inevitably appear in the image during the shooting process due to the influence of angle, distance, complex scene, illumination intensity, and other factors. These targets have few effective pixels, few features, and no apparent features, which makes extracting their efficient features difficult and easily leads to false detection, missed detection, and repeated detection, affecting the performance of target detection models. An improved faster region convolutional neural network (RCNN) algorithm (CF-RCNN) integrating convolutional block attention module (CBAM) and feature pyramid networks (FPN) is proposed to improve the detection and recognition accuracy of small-size objects, occluded… More >

  • Open Access

    ARTICLE

    An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

    Athena George1, Bejoy Abraham2, Neetha George3, Linu Shine3, Sivakumar Ramachandran4,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2097-2118, 2023, DOI:10.32604/csse.2023.039262

    Abstract Neurodegeneration is the gradual deterioration and eventual death of brain cells, leading to progressive loss of structure and function of neurons in the brain and nervous system. Neurodegenerative disorders, such as Alzheimer’s, Huntington’s, Parkinson’s, amyotrophic lateral sclerosis, multiple system atrophy, and multiple sclerosis, are characterized by progressive deterioration of brain function, resulting in symptoms such as memory impairment, movement difficulties, and cognitive decline. Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases. Magnetic resonance imaging (MRI) is widely used by neurologists for diagnosing brain abnormalities. The majority of the research… More >

  • Open Access

    ARTICLE

    Multi-Target Tracking of Person Based on Deep Learning

    Xujun Li*, Guodong Fang, Liming Rao, Tengze Zhang

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2671-2688, 2023, DOI:10.32604/csse.2023.038154

    Abstract To improve the tracking accuracy of persons in the surveillance video, we proposed an algorithm for multi-target tracking persons based on deep learning. In this paper, we used You Only Look Once v5 (YOLOv5) to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to do cascade matching and Intersection Over Union (IOU) matching of person targets between different frames. To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification (ReID) network in the process of cascade matching, we introduced Spatial Relation-aware… More >

  • Open Access

    ARTICLE

    Quick and Accurate Counting of Rapeseed Seedling with Improved YOLOv5s and Deep-Sort Method

    Chen Su, Jie Hong, Jiang Wang, Yang Yang*

    Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2611-2632, 2023, DOI:10.32604/phyton.2023.029457

    Abstract The statistics of the number of rapeseed seedlings are very important for breeders and planters to conduct seed quality testing, field crop management and yield estimation. Calculating the number of seedlings is inefficient and cumbersome in the traditional method. In this study, a method was proposed for efficient detection and calculation of rapeseed seedling number based on improved you only look once version 5 (YOLOv5) to identify objects and deep-sort to perform object tracking for rapeseed seedling video. Coordinated attention (CA) mechanism was added to the trunk of the improved YOLOv5s, which made the model more effective in identifying shaded,… More >

  • Open Access

    ARTICLE

    Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

    Qingyue Zhao1, Qiaoyu Gu2, Zhijun Gao3,*, Shipian Shao1, Xinyuan Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500

    Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, an attention layer is designed… More >

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