检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

18,505 篇 会议
162 篇 期刊文献
10 册 图书

馆藏范围

18,676 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

10,642 篇 工学
- 8,420 篇 计算机科学与技术...
- 4,925 篇 信息与通信工程
- 4,618 篇 软件工程
- 2,338 篇 电气工程
- 1,075 篇 控制科学与工程
- 681 篇 电子科学与技术（可...
- 472 篇 机械工程
- 462 篇 生物工程
- 388 篇 光学工程
- 352 篇 仪器科学与技术
- 305 篇 动力工程及工程热...
- 294 篇 生物医学工程（可授...
- 238 篇 网络空间安全
- 227 篇 交通运输工程
- 200 篇 建筑学
- 184 篇 安全科学与工程
- 179 篇 土木工程
2,681 篇 理学
- 1,616 篇 数学
- 584 篇 物理学
- 513 篇 统计学（可授理学、...
- 485 篇 生物学
- 299 篇 系统科学
- 197 篇 化学
1,852 篇 管理学
- 1,420 篇 管理科学与工程(可...
- 512 篇 图书情报与档案管...
- 446 篇 工商管理
475 篇 医学
- 404 篇 临床医学
- 178 篇 基础医学(可授医学...
185 篇 法学
- 148 篇 社会学
136 篇 经济学
- 136 篇 应用经济学
99 篇 农学
52 篇 教育学
50 篇 文学
47 篇 军事学
15 篇 艺术学

主题

2,398 篇 computer network...
1,649 篇 local area netwo...
1,178 篇 wireless sensor ...
899 篇 neural networks
868 篇 protocols
843 篇 computer science
840 篇 bandwidth
800 篇 throughput
751 篇 computer archite...
680 篇 routing
665 篇 delay
641 篇 wireless lan
611 篇 telecommunicatio...
593 篇 wireless network...
548 篇 network topology
488 篇 computational mo...
466 篇 ip networks
455 篇 quality of servi...
438 篇 ad hoc networks
436 篇 feature extracti...

机构

85 篇 chitkara univers...
37 篇 school of comput...
34 篇 school of comput...
30 篇 graphic era deem...
29 篇 computer science...
28 篇 department of co...
27 篇 ieee
26 篇 department of co...
23 篇 school of comput...
22 篇 shandong provinc...
22 篇 institute of com...
22 篇 department of el...
22 篇 department of el...
20 篇 university of ch...
19 篇 school of comput...
19 篇 department of co...
19 篇 computer science...
18 篇 department of co...
17 篇 school of electr...
17 篇 department of co...

作者

54 篇 kukreja vinay
35 篇 jayasumana anura...
30 篇 shiva mehta
30 篇 boukerche azzedi...
30 篇 anon
27 篇 akkaya kemal
26 篇 hossam s. hassan...
22 篇 hollick matthias
22 篇 matthias hollick
20 篇 damla turgut
19 篇 p. martini
18 篇 hassanein hossam...
18 篇 mohamed younis
16 篇 vats satvik
15 篇 turgut damla
14 篇 m. gerla
14 篇 younis mohamed
14 篇 wu jie
14 篇 aschenbruck nils
14 篇 peter martini

语言

17,452 篇 英文
1,167 篇 其他
53 篇 中文
3 篇 西班牙文
2 篇 法文
2 篇 土耳其文

检索条件"任意字段=IEEE Conference on Local Computer Networks"

共 18677 条记录，以下是611-620 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing networks 24

Fake Node-Based Perception Poisoning Attacks against Federat...

引用

61st ACM/ieee Design Automation conference, DAC 2024

作者： Xiao, Xiong Duan, Mingxing Song, Yingjie Tang, Zhuo Yang, Wenjing College of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9798400706011

Federated learning (FL) supports massive edge devices to collaboratively train object detection models in mobile computing scenarios. However, the distributed nature of FL exposes significant security vulnerabilities. Existing attack methods either require considerable costs to compromise the majority of participants, or suffer from poor attack success rates. Inspired by this, we devise an efficient fake node-based perception poisoning attacks strategy (FNPPA) to target such weaknesses. In particular, FNPPA poisons local data and injects multiple fake nodes to participate in aggregation, aiming to make the local poisoning model more likely to overwrite clean updates. Moreover, it can achieve greater malicious influence on target objects at a lower cost without affecting the normal detection of other objects. We demonstrate through exhaustive experiments that FNPPA exhibits superior attack impact than the state-of-the-art in terms of average precision and aggregation effect. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Interpretable Deep Learning for Facial Feature Detection: A Comprehensive Study on Face and Eyes Recognition with LIME Explanations 9

Interpretable Deep Learning for Facial Feature Detection: A ...

引用

9th ieee International conference for Convergence in Technology, I2CT 2024

作者： Jain, Nikunj Hawari, Jawed Jha, Priyanshu Nair, Priyanka C Sampath, Nalini Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Facial recognition has been nowadays a key role in computer field. The rapid advancement of Machine Learning techniques has been revolutionizing the field of computer vision. This paper presents a comprehensive investigation into the application of Machine Learning techniques. The study comprehensively reviews the various state-of-the-art facial and ocular algorithms. These modern learning techniques help to concatenate the various model which exist. A detailed examination of dataset and evaluation metrics are used for assessing detection performance is also presented to provide a foundation for comparative analysis. This article is used for designing and implementing of novel models for face and eye detection. Inception v3 was used to assist in removing overfitting using efficient convolution and parallel layers. This paper contributes to the ongoing discourse on Machine learning approaches to face and eye recognition by providing new examples, performance reviews, and ethical considerations. This article focuses on using the capabilities Multi-task Cascaded Convolutional networks (MTCNN) for precise and efficient face and eye detection. Additionally, the study incorporates interpretability methodologies such as LIME(local Interpretable Model-agnostic Explanations). The results of this study have important implications for the development of more accurate, more efficient, and more responsible facial and visual recognition systems, widely used in various industries. © 2024 ieee.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural networks that Employs local Phase Quantization Features 15

Pattern Recognition in Lung Disease Classification: Leveragi...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sree, M. Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction. © 2024 ieee.

关键词： CNNs Deep learning Early detection Feature extraction GLCM local Phase Quantization Lung disease diagnosis Medical imaging Texture analysis

来源：评论

学校读者我要写书评

暂无评论

Preserving edge with Transformer for deep image inpainting 3

Preserving edge with Transformer for deep image inpainting

引用

3rd ieee International conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Yan, Liang Li, Chengyang Wang, Chengduan Qufu Normal University School of Computer Rizhao China Peking University School of Computer Science Beijing China Weifang University School of Computer Engineering Weifang China

ISBN: (纸本)9781665490788

Most inpainting methods adopts convolutional neural networks (CNNs) due to its remarkable modeling capabilities. However, CNNs have certain limitations in understanding the global edge structure due to their inherent properties, such as local inductive priors. This has led to poor performance in reconstructing reasonable edge structures. Recently, deep generative methods have made considerable progress in image restoration by introducing edge priors. In contrast to CNNs, Transformers have demonstrated powerful capabilities in edge reconstruction. To this end, we propose a two-stage model PE-Net that combines a Transformer edge generator and a CNN image generator network. The edge generator is responsible for reconstructing the edges of the missing regions of the image, while the image generator uses the edges from the previous stage as auxiliary information to fill the missing regions. We conduct qualitative and quantitative experiments on the publicly available datasets CelebA and Places2, and the results demonstrate the superiority of the proposed method. © 2023 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis Between Feedforward Neural Network and CNN-LSTM Neural Network To Predict Household Electrical Energy Consumption

Comparative Analysis Between Feedforward Neural Network and ...

引用

2023 International conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Tufail, Shahid Tariq, Mohd Batool, Shanzeh Sarwat, Arif Florida International University Electrical and Computer Engineering Miami United States Vellore Institute of Technology School of Computing Science Bhopal India

ISBN: (纸本)9798350322972

This paper compares the accuracy of energy prediction using Feedforward Neural networks (FNN) with a hybrid Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) approach. The research builds two models, a FNN and a CNN-LSTM, and tests them on a large dataset of energy usage records. To extract local data and preserve long-range temporal relationships, the CNN-LSTM model draws on the best of convolutional and recurrent neural networks. Metrics like mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used to compare the two models' efficacy. The CNN-LSTM model shows superior accuracy and generalization capabilities over the FNN model when predicting energy usage. These results have the potential to improve energy management system accuracy and reliability, which in turn can increase building energy efficiency and sustainability. © 2023 ieee.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Efficient CNN-Transformer Aggregation Network for Remote Sensing Image Change Detection

Efficient CNN-Transformer Aggregation Network for Remote Sen...

引用

2024 ieee International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kong, Bingjie Meng, Qinglong Hao, Fengqi Bai, Jinqiang Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Change detection is a crucial task in the field of remote sensing, focusing on identifying areas where significant changes occur between two remote sensing images captured at different time points. However, existing change detection methods based on CNNs or Transformers face two main challenges. Firstly, CNNs often lack the ability to effectively model long-range dependencies, resulting in diminished recognition performance for targets sharing the same semantics but having different features. Secondly, Transformers leverage self-attention mechanisms to capture long-range dependencies adeptly but struggle to capture local details and multi-scale features effectively. In response to these challenges, we propose a CNN and Transformer Aggregation Network (CTA-Net) for change detection in remote sensing images. Specifically, we devise two encoders based on Transformers and CNNs, respectively, facilitating the generation of complementary global and local features during the encoding phase. In the decoding phase, we design a pyramid-structured complementary decoder to aggregate multi-level complementary features from the CNN and Transformer branches. Furthermore, we propose a novel skip connection strategy to exploit the relationship between decoded features and multi-level complementary features, thereby enhancing the model's multi-scale invariance. Extensive experiments conducted on multiple benchmark datasets validate the effectiveness of our proposed CTA-Net model. © 2024 ieee.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

GATBind: Accurate protein-RNA binding sites prediction via graph attention networks with pre-trained language model

GATBind: Accurate protein-RNA binding sites prediction via g...

引用

2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shen, Siyuan Wu, Shangqian Chen, Jie Huang, Zhijian Deng, Lei Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Urumqi China

ISBN: (纸本)9798350386226

Protein-RNA interactions play a pivotal role in various biological processes, making them essential for discovering novel therapeutic targets. Understanding these interactions is crucial for identifying potential drug targets and designing effective therapeutics. However, traditional experimental approaches are costly and time-consuming, highlighting the need for accurate and efficient computational methods to predict RNA-binding sites. In this study, we propose a novel method called GATBind, designed to predict RNA-binding sites on proteins. GATBind introduces a local environment-aware module that captures the local structural information surrounding target residues. Additionally, we incorporate sequence and structural features, including PSSM, HMM, DSSP, and sequence embeddings generated by the pre-trained protein language model ESMFold-2. These features are integrated and processed through a Graph Attention Network (GAT) module to predict RNA-binding sites accurately. Experimental results demonstrate that GATBind outperforms existing state-of-the-art methods, and case studies further validate its effectiveness in predicting RNA-binding sites. © 2024 ieee.

关键词： Binding sites

来源：评论

学校读者我要写书评

暂无评论

Multimodal Brain Tumor Segmentation Using a Dual-Branch Vision Transformer Integrated with Region-Attention Fusion Network 3

Multimodal Brain Tumor Segmentation Using a Dual-Branch Visi...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Yadwad, Sunita Rajeswari, C. Raj, D. Nirmal Dange, Pravin Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Gujarat India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Pune India Pune India

ISBN: (纸本)9798331508456

Accurate segmentation of brain tumors is a critical task in medical imaging, aiding diagnosis, treatment planning, and prognosis. This paper introduces a novel framework for brain tumor segmentation that leverages a Dual-Branch Vision Transformer (DB-ViT) integrated with a Region-Attention Fusion Network (RAFN) to address the limitations of conventional convolutional neural networks. The DB-ViT processes multimodal MRI sequences, including T1, T1c, T2, and FLAIR, to extract global contextual features while preserving modality-specific information. Simultaneously, the RAFN enhances segmentation precision by refining local features and focusing on critical regions of interest through an attention mechanism. The proposed method effectively combines global and local feature representations, ensuring accurate delineation of tumor boundaries. Extensive experiments were conducted on the BraTS dataset, demonstrating the superiority of our approach over existing methods such as U-Net, Attention U-Net, and TransUNet. Quantitative evaluation metrics, including Dice Score and Intersection over Union (IoU), indicate significant improvements in segmentation accuracy. The framework also achieves computational efficiency and robustness across diverse tumor morphologies. Visual results further validate its ability to enhance boundary delineation, particularly in heterogeneous tumor regions. The proposed methodology represents a significant advancement in automated medical image analysis, offering potential for real-time clinical applications. Future work aims to explore broader clinical validation and real-time deployment of this framework. © 2025 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Feature Adaptive Fusion Algorithm for Breast Cancer Histopathology Image Classification

Deep Feature Adaptive Fusion Algorithm for Breast Cancer His...

引用

2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Wei Yu, Wenming Wuxi University School of Internet of Things Engineering Wuxi China Nanjing University of Information Science & Technology School of Automation Nanjing China

ISBN: (纸本)9798350386226

computer-aided automatic classification of tissue pathological images is crucial for the early diagnosis of breast cancer. When dealing with multiple features, a Convolutional Neural Network (CNN) typically prioritizes obtaining global semantic features, while overlooking local details, which results in insufficient feature information. In this paper, we propose a dual-channel deep feature adaptive fusion algorithm called DEADF-Net. DEADF-Net first uses the EfficientNetV2 architecture network with an efficient channel-attention ECA to obtain global semantic features, and then utilizes the MobileNetV2 architecture network to obtain local detail features. Then, combine the two normalized depth features into fused features. Finally, the fused features are inputted into the fully connected layers for classification of benign and malignant tumors. In addition, the method uses transfer learning to optimize the depth features, thus improving the performance and robustness of the model. The performance of DEADF-Net was evaluated using two commonly used benchmark datasets for breast cancer histopathological image classification, namely BreakHis and BHI. The results showed that the accuracy on BreakHis is 40 ×: 95.8%, 100 ×: 96.1%, 200 ×: 97.4%, 400 ×: 96.6%, and BHI is 96.3%, which is superior to most mainstream single feature classification models. © 2024 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Node centrality algorithm based on Fluid Bernoulli Static Pressure 9

Node centrality algorithm based on Fluid Bernoulli Static Pr...

引用

9th International conference on computer and Communications, ICCC 2023

作者： Borong, Zhang Wu, Muqing Zhao, Min Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (纸本)9798350317251

Node importance measurement is of great significance for studying the robustness and vulnerability of power communication network. The structure of large-scale real power communication networks tends to change over time and has limitations in obtaining global network information for assessing the importance of nodes. A node importance assessment algorithm Similar Fluid Bernoulli Static Pressure (SBSP) is proposed by quantifying the current intensity generated by the external sources of the local network topology. Aiming at the neighborhood connection information in two hops of the central node and the characteristics of the neighboring nodes, the algorithm combines the generalized resistance distance and Fluid Bernoulli equation to characterize the structure importance of the central node in the network. Based on six classical ieee power grid topologies, the network is gradually attacked in order of node importance. By comparing the experimental results of the maximum connectivity subgraph, network efficiency and node importance differentiation, it is proved that the proposed algorithm is superior to the four traditional algorithms and three abstract algorithms based on physical model proposed in recent years. © 2023 ieee.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 58 59 60 61 62 63 64 65 66 67 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：