检索结果-内蒙古大学图书馆

49th IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wang, Huadeng Yu, Jiejiang Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China Ganzhou Municipal Hosp Dept Pathol Ganzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

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.

关键词： Gland Segmentation Dual Encoder Local Semantic Guided Encoder Boundary Enhance Attention

来源：评论

学校读者我要写书评

暂无评论

Locating the propagation source in complex networks with observers-based similarity measures and direction-induced search

引用

SOFT COMPUTING 2023年第21期27卷 16059-16085页

作者： Yang, Fan Li, Chungui Peng, Yong Liu, Jingxian Yao, Yabing Wen, Jiayan Yang, Shuhong Guangxi Univ Sci & Technol Sch Comp Sci & Technol Liuzhou 545006 Peoples R China Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Guangxi Univ Sci & Technol Key Lab Intelligent Informat Proc & Graph Proc Liuzhou 545006 Peoples R China

Locating the propagation source is one of the most important strategies to control the harmful diffusion process on complex networks. Most existing methods only consider the infection time information of the observers, but the diffusion direction information of the observers is ignored, which is helpful to locate the source. In this paper, we consider both of the diffusion direction information and the infection time information to locate the source. We introduce a relaxed direction-induced search (DIS) to utilize the diffusion direction information of the observers to approximate the actual diffusion tree on a network. Based on the relaxed DIS, we further utilize the infection time information of the observers to define two kinds of observers-based similarity measures, including the Infection Time Similarity and the Infection Time Order Similarity. With the two kinds of similarity measures and the relaxed DIS, a novel source locating method is proposed. We validate the performance of the proposed method on a series of synthetic and real networks. The experimental results show that the proposed method is feasible and effective in accurately locating the propagation source.

关键词： Complex networks Propagation source locating Direction-induced search (DIS) Observers-based similarity measures Diffusion direction information Infection time information

来源：评论

学校读者我要写书评

暂无评论

Convergence analysis of a survey propagation algorithm

引用

JOURNAL OF intelligent & FUZZY SYSTEMS 2023年第6期45卷 9239-9252页

作者： Xie, Zhixin Wang, Xiaofeng Yang, Lan Pang, Lichao Zhao, Xingyu Yang, Yi Northern Univ Natl Sch Comp Sci & Engn Yinchuan Ningxia Peoples R China Northern Univ Natl Key Lab Image & Graph Intelligent Proc State Ethn Affairs Commiss Yinchuan Ningxia Peoples R China

The survey propagation algorithm is the most effective information propagation algorithm for solving the 3-SAT problem. It can effectively solve the satisfiability problem when it converges. However, when the factor graph structure is complex, the algorithm often does not converge and the solution fails. In order to give a theoretical explanation to this phenomenon and to analyze the convergence of the survey propagation algorithm effectively, a connected treewidth model of the propositional formula was constructed by using the connected tree decomposition method, and the connected treewidth of the factor graph was calculated. The relationship between the connected treewidth and the convergence of the survey propagation algorithm is established, and the convergence judgment condition of the survey propagation algorithm based on the connected tree width is given. Through experimental analysis, the results show that the method is effective, which is of great significance for analyzing the convergence analysis of other information propagation algorithms.

关键词： Survey propagation algorithm convergence connected treewidth propositional formula satisfiability problem

来源：评论

学校读者我要写书评

暂无评论

Concept-Based label Distribution Learning for Text Classification

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-17页

作者： Li, Hui Huang, Guimin Li, Yiqun Zhang, Xiaowei Wang, Yabing Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

Text classification is a crucial task in data mining and artificial intelligence. In recent years, deep learning-based text classification methods have made great development. The deep learning methods supervise model training by representing a label as a one-hot vector. However, the one-hot label representation cannot adequately reflect the relation between an instance and the labels, as labels are often not completely independent, and the instance may be associated with multiple labels in practice. Simply representing the labels as one-hot vectors leads to overconfidence in the model, making it difficult to distinguish some label confusions. In this paper, we propose a simulated label distribution method based on concepts (SLDC) to tackle this problem. This method captures the overlap between the labels by computing the similarity between an instance and the labels and generates a new simulated label distribution for assisting model training. In particular, we incorporate conceptual information from the knowledge base into the representation of instances and labels to address the surface mismatching problem when instances and labels are compared for similarity. Moreover, to fully use the simulated label distribution and the original label vector, we set up a multi-loss function to supervise the training process. Expensive experiments demonstrate the effectiveness of SLDC on five complex text classification datasets. Further experiments also verify that SLDC is especially helpful for confused datasets.

关键词： Text classification label distribution learning Concept knowledge base graph attention network

来源：评论

学校读者我要写书评

暂无评论

A novel dataset and a two-stage deep learning method for breast cancer mitosis nuclei identification

引用

DIGITAL SIGNAL procESSING 2025年 158卷

作者： Wang, Huadeng Liu, Zhipeng Pan, Xipeng Yu, Kang Lan, Rushi Guan, Junlin Li, Bingbing Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Guangxi Key Lab Image & G Intelligent Proc Guilin 541004 Peoples R China Ganzhou Municipal Hosp Dept Pathol Ganzhou 341000 Peoples R China

Mitosis nuclei counting is one of the important indicators for the pathological diagnosis and histological grade of breast cancer. With the development of deep learning methods, there have been some models for the automatic recognition of mitosis nuclei with good performance. However, due to the complex and diverse evolution stages of mitosis nuclei, automatic recognition of mitosis nuclei is very challenging, and the performance and generalization ability of the currently proposed models need to be greatly enhanced. Meanwhile, the manual annotation of images in deep learning-based model training requires experienced pathologists, which is very timeconsuming and inefficient. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation network achieves high recall performance by incorporating the proposed depthwise separable convolution residual block and a channel-spatial attention gate, where the latter innovatively combines both channel and spatial attention mechanisms and utilizes a simple GRU unit for effective feature fusion. Then, a classification network is cascaded to improve the detection performance of mitosis nuclei further. The proposed model is verified on the pixel-level annotated ICPR 2012 dataset, which was annotated by professional pathologists, achieving the highest F1-score of 0.8687 compared with the current state-of-the-art algorithms. Additionally, the model demonstrates superior performance on the Ganzhou Municipal Hospital (GZMH) dataset, also annotated by professional pathologists, which was first released with this paper by the authors.

关键词： Mitosis nuclei dataset Nuclei segmentation and classification Depthwise separable convolution residual block Channel-spatial attention gate

来源：评论

学校读者我要写书评

暂无评论

SpliceSCANNER: An Accurate and Interpretable Deep Learning-Based Method for Splice Site Prediction 1

引用

19th International Conference on Advanced intelligent Computing Technology and Applications (ICIC)

作者： Wang, Rongxing Xu, Junwei Huang, Xiaodi Qi, Wangjing Zhang, Yanju Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Guangxi Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Huaqiao Univ Xiamen Key Lab CVPR Xiamen 361021 Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

The identification of splice sites is significant to the delineation of gene structure and the understanding of complicated alternative mechanisms underlying gene transcriptional regulation. Currently, most of the existing approaches predict splice sites utilizing deep learning-based strategies. However, they may fail to assign high weights to important segments of sequences to capture distinctive features. Moreover, they often only apply neural network as a 'black box', arising criticism for scarce reasoning behind their decision-making. To address these issues, we present a novel method, SpliceSCANNER, to predict canonical splice sites via integration of attention mechanism with convolutional neural network (CNN). Furthermore, we adopted gradient-weighted class activation mapping (Grad-CAM) to interpret the results derived from models. We trained ten models for donor and acceptor on five species. Experiments demonstrate that SpliceSCANNER outperforms state-of-the-art methods on most of the datasets. Taking human data for instance, it achieves accuracy of 96.36% and 95.77% for donor and acceptor respectively. Finally, the cross-organism validation results illustrate that it has outstanding generalizability, indicating its powerful ability to annotate canonical splice sites for poorly studied species. We anticipate that it can mine potential splicing patterns and bring new advancements to the bioinformatics community. SpliceSCANNER is freely available as a web server at http://***/SpliceSCANNER/.

关键词： Splice site prediction CNN Attention mechanism Interpretation

来源：评论

学校读者我要写书评

暂无评论

Zeno-free event-triggered fixed-time control of two-time-scale systems by Chang transformation and backstepping design

引用

NONLINEAR DYNAMICS 2023年第7期111卷 6379-6393页

作者： Lei, Yan Wang, Yan-Wu Liu, Xiao-Kang Yang, Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

This paper investigates fixed-time stabilization of linear two-time-scale systems. The existing fixed-time control techniques are no longer applicable due to the inherent ill-conditioned numerical issues, which is induced by the time-scale separation of two-time-scale systems when carrying out stability analysis. Alternative challenge is that the considered systems are not fully actuated, such that some full rank conditions regarded to the control matrix do not hold anymore. All of these pose difficulties to the fixed-time controller design and stability analysis. To handle these issues, appropriate state transformations are proposed to decouple the system into slow and fast subsystems with approximate strict feedback structure. Then, by the backstepping method, a continuous-time controller is designed to achieve fixed-time stabilization. Furthermore, a Zeno-free event-triggered strategy is proposed to reduce the control updates and then a practical stability result of two-time-scale systems is obtained. A numerical simulation is presented to illustrate the effectiveness of the results.

关键词： Two-time-scale systems Event-triggered fixed-time stability Zeno-free dynamics Non-full actuated systems Backstepping control design

来源：评论

学校读者我要写书评

暂无评论

Identity-Mapping ResFormer: A Computer-Aided Diagnosis Model for Pneumonia X-Ray images

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhou, Tao Peng, Caiyue Guo, Yujie Wang, Hongxia Niu, Yuxia Lu, Huiling North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China North Minzu Univ Key Lab Image & Graph Intelligent Proc State Ethn Yinchuan 750021 Peoples R China Ningxia Med Univ Sch Med Informat & Engn Yinchuan 750004 Peoples R China

Pneumonia is a serious threat to human health and causes great harm to the human respiratory system. Pneumonia recognition based on lung X-ray images is an effective auxiliary diagnosis method. In the lung X-ray images, the lesion areas are complex and diverse, and the boundary is not clear. There is a gradient inconsistency problem in the feature interaction process of deep learning. They prevent the model from focusing well on diseased areas in the image. To solve these problems, this article proposes a computer-aided diagnosis model for pneumonia X-ray images-identity-mapping ResFormer. The main innovations are as follows: First, the multiconvolution cascade residual module (MCCRM) is designed to extract local image features of different sizes and shapes. The MCCRM is a "parallel-cascade" structure, which enhances the feature extraction capability of the backbone network by nesting four convolution operations. Second, the enhanced multipatch transformer (EMPT) is designed in the auxiliary network to extract the multiperceptive field's global attention features. It enables the network to focus on prominent area features of the image. Third, the identity-mapping transformer module (IMTM) is designed to solve the gradient inconsistency problem in different stage features. Transformer operations are used in this module to fuse gradient features in different stages. Finally, the model is validated on a lung X-ray dataset. The accuracy, F1 , recall, precision, and specificity are 97.6679%, 95.3378%, 95.3368%, 95.4602%, and 98.4452%, respectively. Identity-mapping ResFormer can assist doctors to make efficient and accurate pneumonia diagnoses.

关键词： Feature extraction Convolution Lungs Pneumonia Transformers X-ray imaging Computational modeling Shape Lesions Residual neural networks Cross-scale fusion identity-mapping pneumonia computer-aided diagnosis residual neural network (ResNet) transformer

来源：评论

学校读者我要写书评

暂无评论

All Roads Lead to Rome: Achieving 3D Object Encryption Through 2D image Encryption Methods

引用

IEEE TRANSACTIONS ON image procESSING 2025年 34卷 1075-1089页

作者： Zhao, Ruoyu Zhang, Yushu Lan, Rushi Yi, Shuang Hua, Zhongyun Weng, Jian Jiangxi Univ Finance & Econ Sch Comp & Artificial Intelligence Nanchang 330013 Peoples R China Jiangxi Prov Key Lab Multimedia Intelligent Proc Nanchang 330013 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Southwest Univ Polit Sci & Law Criminal Invest Sch Chongqing 401120 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

In this paper, we explore a new road for format-compatible 3D object encryption by proposing a novel mechanism of leveraging 2D image encryption methods. It alleviates the difficulty of designing 3D object encryption schemes coming from the intrinsic intricacy of the data structure, and implements the flexible and diverse 3D object encryption designs. First, turning complexity into simplicity, the vertex values, real numbers with continuous values, are converted into integers ranging from 0 to 255. The simplification result for a 3D object is a 2D numerical matrix. Second, six prototypes for three encryption patterns (permutation, diffusion, and permutation-diffusion) are designed as exemplifications to encrypt the 2D matrix. Third, the integer-valued elements in the encrypted numeric matrix are converted into real numbers complying with the syntax of the 3D object. In addition, some experiments are conducted to verify the effectiveness of the proposed mechanism.

关键词： Encryption Three-dimensional displays Matrix converters Roads Prototypes Standards Distance measurement Data structures Two-dimensional displays Electronic mail 3D object encryption image encryption multimedia security

来源：评论

学校读者我要写书评

暂无评论

intelligent Speech Enhancement Algorithm Model Based on Recorded Classroom Audio

Intelligent Speech Enhancement Algorithm Model Based on Reco...

引用

2024 IEEE International Conference on Cognitive Computing and Complex Data

作者： Zhang, Xinyun Shen, Li Zhou, Li Luo, Dawei Dejiangmo Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China Gannan Normal Univ Sch Phys & Elect Informat Ganzhou Peoples R China Northwest Minzu Univ Sch Math & Comp Sci Lanzhou Peoples R China

ISBN: (纸本)9798350352900;9798350352894

With advancements in communication technology, there is increasing demand for high-quality voice communication and accurate speech *** the complex acoustic environment of a recording classroom, noise, reverberation and other interferences can seriously affect the quality and intelligibility of the picked-up target speech signal, which in turn affects the effectiveness of the subsequent speech communication and recognition, so it is crucial to enhance the target speech signal in order to satisfy people's demand for clear and recognizable speech information. In this paper, based on the network model of Deep Complex Convolutional Recurrent Network (DCCRN), we introduce the F-T-LSTM structure to more accurately describe the correlation between the time and frequency domains of speech, and at the same time, we integrate the channel attention mechanism and the spatial attention mechanism to carry out a more comprehensive feature extraction of the speech features in the complex domain, and we skillfully concentrate the computational power in the area with the most feature-rich information, so that the quality and intelligibility of speech signals under the complex domain are significantly improved. significantly improve the overall performance of speech enhancement networks in the complex domain.

关键词： intelligent Speech Enhancement F-T-LSTM DCCRN channel attention spatial attention

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：