检索结果-内蒙古大学图书馆

China Communications 2023年第2期 3-6页

作者： Yuan Wu Xu Chen Feng Lyu Xianfu Chen Xumin Huang Jie Gao Yueyue Dai Tony Q.S.Quek Yan Zhang the State Key Laboratory of Internet of Things for Smart City University of Macau the Department of Computer and Information Science University of Macau Sun Yat-sen University Sun Yat-sen University Institute of Advanced Networking and Computing Systems Sun Yat-sen University National and Local Joint Engineering Laboratory the School of Computer Science and Engineering Central South University the VTT Technical Research Centre of Finland the School of Automation Guangdong University of Technology the School of Information Technology Carleton University Huazhong University of Science and Technology Singapore University of Technology and Design(SUTD) the IEEE Communications Society Technical Committee on Green Communications and Computing(TCGCC)

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...

关键词：

来源：评论

学校读者我要写书评

暂无评论

ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

引用

computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Cross-Guided Attention and Combined Loss for Optimizing Gastrointestinal Visual Question Answering

Cross-Guided Attention and Combined Loss for Optimizing Gast...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qian Yang Lijun Liu Xiaobing Yang Li Liu Wei Peng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming P. R. China Yunnan Key Laboratory of Computer Technology Application Kunming China P. R. China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical Visual Question Answering (Med-VQA) aims at answering clinical questions related to medical imaging, providing important reference for medical imaging diagnosis. Gastrointestinal visual question answering can deeply understand and analyze gastrointestinal medical images, provides supporting information to answer questions about the diagnostic process, and improves diagnostic accuracy and efficiency. However, most existing Med-VQA can only handle simple and general questions, struggling to tackle visual question answering related to complex diseases. Therefore, this paper proposes a Med-VQA model called CACL, which uses cross-guidance and improved combined loss. The model focuses on gastrointestinal image analysis and achieves efficient and accurate feature extraction for gastrointestinal images. A cross-guided attention module is also designed to enhance the model’s reasoning ability when dealing with complex cross-modal tasks. In addition, a multi-task composite loss function is designed to balance the loss of segmentation task and classification task, and improve the overall performance. Experimental results show that the proposed method can effectively improve the accuracy of gastrointestinal visual question answering.

关键词： Visualization Image segmentation Accuracy Image analysis Feature extraction Multitasking Gastrointestinal tract Question answering (information retrieval) Medical diagnostic imaging Diseases

来源：评论

学校读者我要写书评

暂无评论

An Automated and Interpretable computer-Aided Approach for Skin Cancer Diagnosis Using Genetic Programming

引用

IEEE Transactions on Evolutionary Computation 2024年

作者： Yu, Kunjie Lian, Jintao Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie Zhengzhou University School of Electrical and Information Engineering Zhengzhou China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang China Longmen Laboratory Luoyang China Zhongyuan University of Technology School of Electronic and Information Zhengzhou China Henan Institute of Technology School of Electrical Engineering and Automation Xinxiang China Victoria University of Wellington School of Engineering and Computer Science Centre for Data Science and Artificial Intelligence Wellington New Zealand

Malignant melanoma is a very deadly form of skin cancer and early diagnosis can significantly reduce the mortality rate. Many computer-aided diagnosis (CAD) systems have been developed as second opinion diagnostic aids to assist dermatologists in diagnosing malignant melanoma. However, traditional CAD systems often require domain knowledge for feature extraction, while neural network-based CAD systems require specialized knowledge for designing network structures and often have poor interpretability. In this paper, we propose a new skin cancer CAD system based on genetic programming (GP) to automatically learn effective features for classification with strong interpretability. The approach can automatically evolve variable-length models to extract informative features for describing skin cancer images based on a relatively simple program structure, a new function set, and a terminal set. In addition, compared with other GP methods, this approach employs a newly proposed duplicate subtree removing mechanism, which can effectively prevent the duplication of features, thereby simplifying the model and enhancing its interpretability. The proposed approach has been examined on five real-world skin cancer classification tasks. The results suggest that the proposed approach achieves better performance than GP-based, neural network-based feature learning comparison methods and traditional comparison methods in most cases. Further analysis shows that the proposed approach has employed a smaller tree structure and can automatically evolve/learn models with potentially high interpretability. © 1997-2012 IEEE.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Classification-Detection of Medical Images by Visualizing Hidden-Layer Features of a Deep Learning Approach 12th

Classification-Detection of Medical Images by Visualizing Hi...

引用

12th International Conference on computer engineering and Networks, CENet 2022

作者： Tong, Ziyu Deng, Xing Shao, Haijian Wang, Xia School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China School of Automation Key Laboratory of Measurement and Control for CSE Ministry of Education Southeast University Nanjing Jiangsu210096 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Deep learning algorithms, such as CNN, have been widely used on a variety of computer vision problems due to their powerful capacity to extract nonlinear features for medical picture analysis. However, due to the decreased identification rate for medical images, CNNs continue to fail to offer an interpretable depiction of the hidden-layers’ features. Furthermore, the enormous amount of redundant parameters from the convolutional to fully connected layers will surely add to the computational complexity. This work provides a set of fast deep learning architectures for visualizing and analyzing hidden-layer properties in medical images. The clustered feature map is used to simplify the model parameters and minimize redundancy parameters in order to meet the goal of a streamlined trade-off between model performance and architecture. The testing results on the medial images reveal that 41% of redundant channels may be removed while 100% of performance can be preserved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

BedIDS: An Effective Network Anomaly Detection Method by Fusing Behavior Evolution characteristics

BedIDS: An Effective Network Anomaly Detection Method by Fus...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhen Liu Changzhen Hu Chun Shan Junkai Yi Computer School Beijing Information Science & Technology University Beijing China Beijing Key Laboratory of Software Security Engineering Technology School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Automation Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Leveraging artificial intelligence models to enhance the performance of intrusion detection systems has become an important component in the field. However, as the scale of networks continues to expand, the structure of networks becomes more complex, and the amount of data in the networks grows larger. Existing methods are facing numerous challenges, including difficulties in constructing training datasets for models, challenges in transferring and reusing models, and high costs associated with model training. This paper introduces a novel approach named BedIDS. This method involves constructing the evolutionary process of network behavior and calculating the evolutionary characteristics of network behavior. Using only the most fundamental five network traffic features, including IP addresses, BedIDS achieves rapid and accurate detection performance on a device equipped with a 3060ti graphics card. We conducted tests using the CICIDS2017 and UNSW-NB15 datasets to evaluate its performance. Experimental results demonstrate that BedIDS maintains high detection accuracy and improves detection speed while requiring a relatively low AI computing force.

关键词： Training Performance evaluation Radio frequency Privacy Costs Accuracy Intrusion detection Telecommunication traffic Feature extraction Security

来源：评论

学校读者我要写书评

暂无评论

Improved mayfly optimization algorithm by adaptive random opposition-based learning 6

Improved mayfly optimization algorithm by adaptive random op...

引用

6th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2022

作者： Zhang, Hengqi Lin, Yong Qian, Qian Pan, Jiawen Feng, Yong Fu, Yunfa Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming Yunnan650500 China Library and Archives Yunnan Communications Vocational and Technical College Kunming Yunnan650500 China

ISBN: (纸本)9781510656635

An adaptive random reverse learning mayfly algorithm is proposed to overcome the shortcomings of mayfly optimization. Specifically, hyperbolic tangent function is used as adaptive curve to flexibly optimize the personal experience and social experience coefficients of the algorithm, so that the algorithm can better balance the global and local search capabilities through the evolutionary iterations. In addition, on the basis of elite mayfly in each generation, random reverse learning mechanism is used to expand population diversity and speed up algorithm convergency. The calculation results of several test functions show that the algorithm presented has good convergence accuracy and improved algorithm performance. © 2022 SPIE.

关键词： Hyperbolic functions

来源：评论

学校读者我要写书评

暂无评论

Cooperative Global K-exponential Tracking Control of Multiple Mobile Robots-Extended Version

arXiv

引用

arXiv 2024年

作者： Xu, Liang Su, Youfeng Cai, He College of Computer and Data Science Fuzhou University Fuzhou350116 China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen518055 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China

This paper studies the cooperative tracking control problem for multiple mobile robots over a directed communication network. First, it is shown that the closed-loop system is uniformly globally asymptotically stable under the proposed distributed continuous feedback control law, where an explicit strict Lyapunov function is constructed. Then, by investigating the convergence rate, it is further proven that the closed-loop system is globally K-exponentially stable. Moreover, to make the proposed control law more practical, the distributed continuous feedback control law is generalized to a distributed sampled-data feedback control law using the emulation approach, based on the strong integral input-to-state stable Lyapunov function. Numerical simulations are presented to validate the effectiveness of the proposed control methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions

来源：评论

学校读者我要写书评

暂无评论

Tackling Noisy Labels with Network Parameter Additive Decomposition

arXiv

引用

arXiv 2024年

作者： Wang, Jingyi Xia, Xiaobo Lan, Long Wu, Xinghao Yu, Jun Yang, Wenjing Han, Bo Liu, Tongliang The Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Automation University of Science and Technology of China Hefei230026 China The Department of Computer Science Hong Kong Baptist University Hong Kong

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage. In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e. , parameters w are decomposed as w = σ + γ. Afterward, the parameters σ are considered to memorize clean data, while the parameters γ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters σ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters γ are the opposite. In testing, only the parameters σ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method. The implementation is available at: https://***/wangjingyi9924/TNLPAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Fusing Vehicle Trajectories and GNSS Measurements to improve GNSS Positioning Correction based on Actor-Critic Learning

Fusing Vehicle Trajectories and GNSS Measurements to improve...

引用

2023 International Technical Meeting of The Institute of Navigation, ITM 2023

作者： Zhao, Haoli Li, Zhenni Chen, Ci Wang, Lujia Xie, Kan Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China

ISBN: (纸本)9781713871378

Obtaining high-accuracy vehicle localization is essential for safe navigation in automated vehicles, however, complex urban environments result in inaccurate positioning because of multipath and non-line-of-sight errors, making precise positioning in complex environments an important and unsolved problem. Different from conventional model-based approaches which require rigid assumptions on sensors and noise models, data-driven artificial intelligent approaches can provide new solutions for Global Navigation Satellite System (GNSS) positioning problems in multipath environments. Recent learning-based works employ the generalization ability of the deep neural network to model these complex errors in urban environments, and provide effective positioning corrections for rough GNSS localizations by learning from a vehicle trajectory-based Reinforcement Learning (RL) environment. However, existing RL-based approaches employ insufficient features as input, and cannot take advantage of the vehicle trajectory *** tackle these issues, we propose a novel Deep Reinforcement Learning-based (DRL) positioning correction framework with fused features considering the vehicle trajectory information and GNSS features in the multi-input *** help the agent learn the correction policy efficiently with comprehensive inputs, we develop a new positioning correction RL environment with multi-input observations, which augments GNSS device readings of different satellites as complementary to the vehicle trajectory, and moreover employ a correction advantage reward to help the agent learn an efficient policy in positioning correction. To fully utilize comprehensive multi-input observations in the proposed environment, we employ the LSTM module to separately extract features to fuse brief states from input time series data. Finally, to address the long-term positioning accuracy, we construct the learning model based on actor-critic DRL structure with the cumulative reward

关键词： Statistical tests

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：