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Research on feature classification method of network text data based on association rules

International Journal of Computers and Applications 2020年第2期42卷 157-163页

作者： Huang, Hongben Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China

Due to the large number of features, sparse data, low precision of feature extraction, and long time-consuming. Using the current method to classify the text data features, it is difficult to achieve better results. A method of feature classification of network text data based on association rules is proposed. A single word is used as a classification feature to extract the association feature item of text data. The feature item after dimension reduction is to construct classifier. Through the support vector machine algorithm, the network text data feature classification is realized. The highest precision ratio of Hangxia et al.’s study [Hangxia Z, Jiajun Y, Huan R. Text categorization based on deep belief network. Comput Eng Sci. 2016;38(5):871–876] is 69%, the highest precision ratio of Wenjuan et al.’s study [Wenjuan S, Shun L, Fei Y. Iterative text classification framework based on background learning. Comput Eng Applic. 2015;51(9):129–134] is 85%, and the highest precision ratio of the proposed method is 93%. The precision of this method is higher, which shows that the method can accurately reflect the feature class information of text data and reduce the error rate of text classification. Experimental results show that the proposed method can improve the accuracy of classification results and has high robustness. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Association rules

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Focusing on diacritics to improve Vietnamese scene text detection

Focusing on diacritics to improve Vietnamese scene text dete...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenhui Huang Shaoliang Shi Yimin Wen Guilin University of Electronic Technology Guilin China China Academy of Science and Technology Development GuangXi Branch Nanning China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Scene text detection is usually recognized as one of the fundamental challenges in computer vision and robotics. While the detection of scene text for English and Chinese has achieved great progress, Vietnamese scene text detection is still a relatively new field. In Vietnamese scene text detection, the diacritics of Vietnamese characters are often too small to be detected, making it difficult to accurately extract their features. The bounding boxes are usually placed close to Latin letters, resulting in incomplete covering for diacritics. To overcome these challenges, we propose a method of focusing on diacritics to detect Vietnamese scene text. Firstly, we propose detailed information embedding to enhance the features of diacritics. Secondly, we introduce IoU-v to allow the proposals to match the boundary of Vietnamese text instances more accurately. Lastly, in mask branch, we incorporate a diacritics feature fusion module to further enhance the features of diacritics. The experimental results illustrate the proposed method outperforms the existing approaches. The code of the proposed algorithm is available at https://***/mlmmwym/FD2VSTD.

关键词： Computer vision Codes Text recognition Neural networks Text detection Focusing Feature extraction

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A Novel Dual-supervised Convolutional Network for Retinal Vessel Segmentation

A Novel Dual-supervised Convolutional Network for Retinal Ve...

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image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Zizheng Li Junlin Guan Huadeng Wang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781665464697

Retinal vascular images play an essential role in the early diagnosis of ophthalmic diseases. Automatic segmentation of retinal blood vessels is a challenging task due to the complex vascular interlacing of fundus images and the noise problems during acquisition. Because of the above problems, we propose a new network structure, which is effective for vascular tree segmentation and can segment more fine blood vessels. Firstly, to improve the segmentation ability of the network to blood vessels, we design a novel convolutional structure of FMCD from the primary level. Then, to focus on the learning of vascular characteristics from the perspectives of deep segmentation and shallow segmentation, we design a deep-shallow dual-level network from the local level. Finally, to assist in supervising the deep and shallow networks to improve the segmentation accuracy of blood vessels and complement the segmentation output of the deep and shallow networks, to segment more microvessels, we use the multi-level semantic fusion module (MSFM) from the overall level. Our Acc, Sen, and F1 on the DRIVE dataset achieve high accuracy of 0.9706, 0.8404, and 0.8332, respectively, outperforming mainstream methods. The proposed method has important clinical implications.

关键词： image segmentation Computer vision Semantics Machine learning Blood vessels Retinal vessels Task analysis

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Clglf: Confidence Learning Guides Label Fusion for Multimodal Named Entity Recognition Method

SSRN

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SSRN 2024年

作者： Wang, Hairong Wang, Tong Wang, Yiyan Chen, Fangping School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

A B S T R A C TTo solve the visual semantic understanding bias and multimodal semantic bias in multimodal named entity recognition, the Confidence Learning Guides Label Fusion for Multimodal Named Entity Recognition (CLGLF) method is proposed. This method invokes the BLIP-2 pre-trained model to generate image captions, concatenates them with the input texts, and performs joint coding to achieve multimodal feature fusion. The candidate labels and text labels are obtained after decoding the multimodal representations and text representations. Based on using the KL divergence loss function to align the two groups of labels, the confidence score is calculated to evaluate the quality of the multimodal representation, and a confidence threshold is set to help screen out the biased candidate labels, the text labels in the corresponding positions are used to replace the biased candidate labels, to achieve the label fusion, and finally complete the multimodal named entity recognition. In order to verify the proposed method, experiments are carried out on the Twitter-2015 and Twitter-2017 multimodal datasets, and the experimental results are compared with 7 mainstream methods, such as MSB and UMT. The experimental results show the effectiveness of the CLGLF method. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Visual Semantic Enhancement for Multi-Modal Named Entity Recognition

SSRN

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SSRN 2024年

作者： Wang, Tong Wang, Hairong Xu, Xi Chen, Fangping School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

In recent years, a series of methods have been proposed to use image semantics to assist in extracting named entities. However, in these multi-modal named entity recognition methods, there are problems of visual semantic missing and weak semantic constraints in multi-modal representation. Therefore, a visual semantic enhancement for multi-modal named entity recognition (EVS-MNER) method is proposed. In this method, multiple visual features are used for collaborative representation to obtain more complete visual semantics. The semantic information in multiple visual features is integrated through the multi-modal features fusion module to generate visual semantic features, and the semantics are passed to the text features to obtain semantically enhanced multi-modal text representation. The visual entity classifier is used to decode visual semantic features, and the semantic consistency constraint of visual features is realized. The multi-task label decoder is called to mine the fine-grained semantics of the multi-modal text representation and text features, and the problem of semantic bias is solved by joint decoding, to further improve the accuracy of named entity recognition. On two multimodal entity recognition datasets of Twitter-2015 and Twitter-2017, the proposed method is compared with 10 methods such as PCEN and M3S. The experimental results show that the proposed method is effective. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Teeth YOLACT: An Instance Segmentation Model Based on Impacted Tooth Panoramic X-Ray images

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Computers, Materials and Continua 2025年第3期83卷 4815-4834页

作者： Tao Zhou Yaxing Wang Huiling Lu Wenwen Chai Yunfeng Pan Zhe Zhang School of Computer Science and Engineering North Minzu University Yinchuan 750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan 750021 China School of Medical Information & Engineering Ningxia Medical University Yinchuan 750004 China

The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly researched. However, due to the complex structure, low contrast, and complex background of teeth in panoramic X-ray images, the task of instance segmentation is technically tricky. In this study, the contrast between impacted Teeth and periodontal tissues such as gingiva, periodontal membrane, and alveolar bone is low, resulting in fuzzy boundaries of impacted teeth. A model based on Teeth YOLACT is proposed to provide a more efficient and accurate solution for the segmentation of impacted teeth in oral panoramic X-ray films. Firstly, a Multi-scale Res-Transformer Module (MRTM) is designed. In the module, depthwise separable convolutions with different receptive fields are used to enhance the sensitivity of the model to lesion size. Additionally, the Vision Transformer is integrated to improve the model’s ability to perceive global features. Secondly, the Context Interaction-awareness Module (CIaM) is designed to fuse deep and shallow features. The deep semantic features guide the shallow spatial features. Then, the shallow spatial features are embedded into the deep semantic features, and the cross-weighted attention mechanism is used to aggregate the deep and shallow features efficiently, and richer context information is obtained. Thirdly, the Edge-preserving perception Module (E2PM) is designed to enhance the teeth edge features. The first-order differential operator is used to get the tooth edge weight, and the perception ability of tooth edge features is improved. The shallow spatial feature is fused by linear mapping, weight concatenation, and matrix multiplication operations to preserve the tooth edge information. Finally, comparison experiments and ablation experiments are conducted on the oral panoramic X-ray image datasets. The results show that the APdet, APseg, ARdet, ARseg, mAPdet, and mAPseg indicators of the proposed model are 89.9%, 91.9%, 77.4%, 77.6%, 72.8%, an

关键词： The oral panoramic X-ray instance segmentation impacted teeth vision transformer the edge-preserving

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Cross-patch feature interactive net with edge refinement for retinal vessel segmentation

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Computers in Biology and Medicine 2024年 174卷 108443-108443页

作者： Kang, Ning Wang, Maofa Pang, Cheng Lan, Rushi Li, Bingbing Guan, Junlin Wang, Huadeng School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Department of Pathology Ganzhou Municipal Hospital Ganzhou341000 China

Retinal vessel segmentation based on deep learning is an important auxiliary method for assisting clinical doctors in diagnosing retinal diseases. However, existing methods often produce mis-segmentation when dealing with low contrast images and thin blood vessels, which affects the continuity and integrity of the vessel skeleton. In addition, existing deep learning methods tend to lose a lot of detailed information during training, which affects the accuracy of segmentation. To address these issues, we propose a novel dual-decoder based Cross-patch Feature Interactive Net with Edge Refinement (CFI-Net) for end-to-end retinal vessel segmentation. In the encoder part, a joint refinement down-sampling method (JRDM) is proposed to compress feature information in the process of reducing image size, so as to reduce the loss of thin vessels and vessel edge information during the encoding process. In the decoder part, we adopt a dual-path model based on edge detection, and propose a Cross-patch Interactive Attention Mechanism (CIAM) in the main path to enhancing multi-scale spatial channel features and transferring cross-spatial information. Consequently, it improve the network's ability to segment complete and continuous vessel skeletons, reducing vessel segmentation fractures. Finally, the Adaptive Spatial Context Guide Method (ASCGM) is proposed to fuse the prediction results of the two decoder paths, which enhances segmentation details while removing part of the background noise. We evaluated our model on two retinal image datasets and one coronary angiography dataset, achieving outstanding performance in segmentation comprehensive assessment metrics such as AUC and CAL. The experimental results showed that the proposed CFI-Net has superior segmentation performance compared with other existing methods, especially for thin vessels and vessel edges. The code is available at https://***/kita0420/CFI-Net. © 2024 Elsevier Ltd

关键词： Blood vessels

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SpliceCannon: A novel framework for the prediction of canonical and non-canonical splice sites based on deep learning

SpliceCannon: A novel framework for the prediction of canoni...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanju Zhang Wangjing Qi Ruohan Lin Jinli Fan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China College of Computer Science and Technology Huaqiao University Xiamen China

Accurately predicting splice sites in DNA sequences is not only of utmost importance in the study of gene expression and protein synthesis, but also crucial to precisely understanding the pathogenesis of various diseases related to splicing. In the past few decades, several tools for identifying splice sites have been developed, however, many of them ignore the contextual influence in DNA sequences in the process of encoding, consequently leading to considerable false positives in prediction. Additionally, most methods solely investigate canonical splicing whereas pay little attention to non-canonical ones. To address the issues mentioned above, we propose a comprehensive method, SpliceCannon, for accurately predicting both canonical and non-canonical splice sites based on neural networks. This method uses the combination of k-nucleotide frequencies and Doc2vec to encode the sequence, then employs a series of comprehensive neural networks such as bidirectional long short-term memory networks and residual networks to effectively extract features from the original sequence while avoiding the problems such as gradient vanishing. The results demonstrate that our method consistently achieves high accuracy when tested on multiple species. Compared to other recent methods, our model exhibits superior performance, stronger generalization ability, and wider applicability. In particular, The recognition accuracy is also remarkably high on the non-canonical dataset. We envision that this proposed method will serve as a promising tool for splice sites prediction to facilitate genomic research.

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Tourismner:A Tourism Named Entity Recognition Method Based on Entity Boundary Jointprediction

SSRN

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SSRN 2024年

作者： Zhou, Jiahao Chi, Yunxian Wen, Yimin Gao, Kai School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China Guangxi Key Laboratory of Culture and Tourism Smart Technology Guilin Tourism University Guilin541006 China Guangxi Key Laboratory of Image and Graphic intelligent processing Guilin University of Electronic Technology Guilin541004 China

Tourism named entity recognition is indispensable in tourism information extraction, and plays a crucial role in constructing tourismknowledge map and enhancing tourism knowledge quiz system. The difficulty of tourism named entity recognition lies in itscomplex nested structure, and the lengthy entity naming length. To address these existing problems, we propose a tourism namedentity recognition model that jointly predicts entity boundaries, adopting a training strategy of data preprocessing to enhance themodel’s ability for tourism named entity boundary recognition, while our model introduces a pre-trained Bert model as well asBiLSTM coding to enhance the representation of the model’s contexts, and uses a combined predictor of Biaffine and MLP toenhance the model’s recognition performance for boundaries, as well as introducing label smoothing cross entropy to smooth thetarget labels during the training process. Experiments are conducted on three datasets with different granularities. From the analysisof the experimental results, it can be seen that the named entity recognition method achieves higher accuracy and F1 value comparedwith the optimal baseline model, and also proves the effectiveness and generality of the modeling method proposed in this paper. © 2024, The Authors. All rights reserved.

关键词： Tourism

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GLAND SEGMENTATION VIA DUAL ENCODERS AND BOUNDARY-ENHANCED ATTENTION

arXiv

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arXiv 2024年

作者： Wang, Huadeng Yu, Jiejiang Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Pathology Ganzhou Municipal Hospital Ganzhou China

Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts advanced Semantic features, uses the proposed feature decoder to restore feature space information, and then enhances the boundary features of the gland through boundary enhancement attention. The local semantic extraction network uses the pre-trained DeepLabv3+ as a Local semantic-guided encoder to realize the extraction of edge features. Experimental results on two public datasets, GlaS and CRAG, confirm that the performance of our method is better than other gland segmentation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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