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

2,959 篇 会议
39 篇 期刊文献
7 册 图书

馆藏范围

3,005 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,760 篇 工学
- 1,407 篇 计算机科学与技术...
- 684 篇 软件工程
- 490 篇 电气工程
- 397 篇 信息与通信工程
- 203 篇 控制科学与工程
- 130 篇 生物工程
- 79 篇 电子科学与技术（可...
- 65 篇 生物医学工程（可授...
- 59 篇 机械工程
- 54 篇 仪器科学与技术
- 49 篇 光学工程
- 34 篇 网络空间安全
- 30 篇 动力工程及工程热...
- 24 篇 材料科学与工程（可...
- 24 篇 安全科学与工程
- 22 篇 化学工程与技术
- 20 篇 测绘科学与技术
- 19 篇 石油与天然气工程
517 篇 理学
- 226 篇 物理学
- 190 篇 数学
- 143 篇 生物学
- 58 篇 统计学（可授理学、...
- 40 篇 系统科学
- 27 篇 化学
203 篇 管理学
- 135 篇 管理科学与工程(可...
- 81 篇 图书情报与档案管...
- 31 篇 工商管理
135 篇 医学
- 112 篇 临床医学
- 38 篇 基础医学(可授医学...
37 篇 法学
- 20 篇 社会学
18 篇 文学
14 篇 农学
10 篇 教育学
9 篇 经济学
2 篇 军事学

主题

590 篇 neural networks
173 篇 training
168 篇 artificial neura...
165 篇 deep learning
135 篇 computational mo...
124 篇 feature extracti...
115 篇 neural network
114 篇 convolutional ne...
88 篇 machine learning
87 篇 neurons
86 篇 parallel process...
79 篇 deep neural netw...
74 篇 computer archite...
73 篇 distributed comp...
65 篇 signal processin...
62 篇 distributed proc...
62 篇 accuracy
60 篇 recurrent neural...
55 篇 data models
50 篇 convolutional ne...

机构

11 篇 institute of inf...
9 篇 school of cyber ...
8 篇 univ chinese aca...
8 篇 natl univ def te...
7 篇 college of compu...
7 篇 carnegie mellon ...
7 篇 national laborat...
7 篇 beijing universi...
7 篇 natl univ def te...
6 篇 shanghai jiao to...
6 篇 the islamic univ...
6 篇 department of co...
5 篇 science and tech...
5 篇 ibm tj watson re...
5 篇 school of inform...
5 篇 king saud univer...
5 篇 shandong normal ...
5 篇 kaust thuwal
5 篇 university of ch...
5 篇 natl univ def te...

作者

8 篇 li dongsheng
8 篇 dou yong
8 篇 jie liu
6 篇 richtarik peter
6 篇 ribeiro alejandr...
6 篇 liu jie
6 篇 niu xin
6 篇 yong dou
6 篇 shulian yang
6 篇 zhang wei
5 篇 ryabinin max
5 篇 segarra santiago
5 篇 lai zhiquan
5 篇 ding bo
5 篇 qiao peng
5 篇 xu jiren
5 篇 kokkinos yiannis
5 篇 peng yuxing
5 篇 wang huaimin
5 篇 li aiping

语言

2,925 篇 英文
72 篇 其他
8 篇 中文
2 篇 土耳其文

检索条件"任意字段=Conference on Neural Network and Distributed Processing"

共 3005 条记录，以下是1001-1010 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：

Wireless Traffic Prediction with Series Fluctuation Pattern Clustering

Wireless Traffic Prediction with Series Fluctuation Pattern ...

引用

IEEE International conference on Communications (ICC)

作者： Xing, Xudong Lin, Yuanjie Gao, Hui Lu, Yueming Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ Beijing 100876 Peoples R China

ISBN: (纸本)9781728194417

Accurate traffic prediction is vital in intelligent wireless network, which enables intelligent operation to improve the quality of experience of users. In this paper, we propose a wireless traffic prediction architecture with series fluctuation pattern clustering. We first extract fluctuation pattern, i.e., the baseline feature, from the original series, while retaining the noise component, i.e., the residual feature. Then a hierarchical density-based strategy is proposed to cluster the baseline feature and extract the unique fluctuation pattern. Furthermore, we propose a LSTM-based neural network model, called TPBLN, to fit the baseline feature of each cluster set with a parameterized residual feature processing. As compared to conventional schemes without series fluctuation pattern extracting and clustering, or boosting tree-based schemes, our proposed architecture reduces the mutual influence among series with different fluctuation patterns before the predicting. Moreover, we use real data to experiment and therefore have the potential to better adapt to the real environment. The experiment results show that the proposed scheme effectively improves the prediction performance compared to the reference schemes.

关键词： time series analysis density-based clustering deep learning LSTM wireless traffic prediction

来源：评论

学校读者我要写书评

暂无评论

Malicious URL Detection with distributed Representation and Deep Learning 21

Malicious URL Detection with Distributed Representation and ...

引用

21st International conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Do, Nguyet Quang Selamat, Ali Lim, Kok Cheng Krejcar, Ondrej Universiti Teknologi Malaysia Kuala Lumpur Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Universiti Teknologi Malaysia Johor Bahru Malaysia Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Kajang Malaysia

ISBN: (纸本)9781643683164

There exist numerous solutions to detect malicious URLs based on Natural Language processing and machine learning technologies. However, there is a lack of comparative analysis among approaches using distributed representation and deep learning. To solve this problem, this paper performs a comparative study on phishing URL detection based on text embedding and deep learning algorithms. Specifically, character-level and word-level embedding were combined to learn the feature representations from the webpage URLs. In addition, three deep learning models, including Convolutional neural network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM), were constructed for effective classification of phishing websites. Several experiments were conducted and various evaluation metrics were used to assess the performance of these deep learning models. The findings obtained from the experiments indicated that the combination of the character-level and word-level embedding approach produced better results than the individual text representation methods. Also, the CNN-based model outperformed the other two deep learning algorithms in terms of both detection accuracy and execution time. © 2022 The authors and IOS Press. All rights reserved.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Data compression methods based on neural networks 5

Data compression methods based on Neural Networks

引用

5th International conference on Future networks and distributed Systems: The Premier conference on Smart Next Generation networking Technologies, ICFNDS 2021

作者： Berezkin, Aleksandr Slepnev, Alexey Kirichek, Ruslan Kukunin, Dmitry Matveev, Daniil Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia

ISBN: (纸本)9781450387347

The modern stage of the development of telecommunication systems is characterized by the widespread development of wired and wireless data transmission networks. The growth in the number of users of such networks, the emergence of new multimedia services impose high demands on speed, reliability, and delay time in information processing. One of the factors in increasing the data transfer rate is the on-the-fly compression of information at the link level. The paper describes a method for compressing data in a communication channel using error-correcting BCH codes and a feed-forward neural network autoencoder. This method converts a binary vector of user data into BCH codewords, which are used to train the autoencoder. This ultimately makes it possible to reduce the number of transmitted bits in the communication channel. © 2021 ACM.

关键词： Data transfer

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Framework by Leveraging CNN and LSTM Hybridization for Effective DDoS Attack Detection in IoT Environments

A Deep Learning Framework by Leveraging CNN and LSTM Hybridi...

引用

Next Generation Communication & Information processing (INCIP), International conference on

作者： S. Sathiya Devi K. Lalitha Department of Computer Science and Engineering University College of Engineering BIT Campus Anna University Tiruchirappalli India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Tiruchirappalli India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

distributed Denial of Service (DDoS) attacks represent the biggest threat towards the security and stability of IoT networks, and they can even cause a server crash, severely affecting services for users. Increased growth in IoT networks has accelerated a greater demand for efficient and accurate detection mechanisms for DDoS. Machine learning and deep learning have been powerful techniques that advance the state of detecting such attacks. We propose, in this paper, the novel concept of a hybrid model between CNN and LSTM to detect DDoS attacks effectively in the IoT environment. To extract spatial features, the architecture comprises of three convolutional layers with kernel sizes of 3, neuron count of 64, neuron count of 128, and neuron count of 256. These layers were followed by LSTM layers that process sequential dependencies in the data for better detection as they learn temporal patterns. The evaluation was done on the CICIOT 2023 dataset. These experiments have shown that the proposed hybrid CNN-LSTM model can be achieved to get an accuracy of 96.78% in training and 96.08% during testing, outperforming all the traditional methods considered above. The above results establish the model as effective in the detection of DDoS attacks on IoT environments, providing a robust real-time intrusion detection solution. This work proves that DNN models can provide highly accurate and robust network security from DDoS attacks while enhancing the reliability and stability of IoT networks.

关键词： Deep learning Accuracy Neurons Intrusion detection Denial-of-service attack Feature extraction Stability analysis Convolutional neural networks Computer crime Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time distributed Deep CNNs, RNNs and Attention-Based Mechanisms 5

Analysis of Real-Time Hostile Activitiy Detection from Spati...

引用

5th IEEE International conference on Image processing Applications and Systems (IPAS)

作者： Siddique, Labib Ahmed Junhai, Rabita Reza, Tanzim Khan, Salman Sayeed Rahman, Tanvir BRAC Univ Dept Comp Sci & Engn 66 Mohakhali Dhaka 1212 Bangladesh Univ Delaware Coll Engn Dept Comp & Informat Sci Newark DE 19716 USA

ISBN: (纸本)9781665462198

Real-time video surveillance, through CCTV camera systems has become essential for ensuring public safety which is a priority today. Although CCTV cameras help a lot in increasing security, these systems require constant human interaction and monitoring. To eradicate this issue, intelligent surveillance systems can be built using deep learning video classification techniques that can help us automate surveillance systems to detect violence as it happens. In this research, we explore deep learning video classification techniques to detect violence as they are happening. Traditional image classification techniques fall short when it comes to classifying videos as they attempt to classify each frame separately for which the predictions start to flicker. Therefore, many researchers are coming up with video classification techniques that consider spatiotemporal features while classifying. However, deploying these deep learning models with methods such as skeleton points obtained through pose estimation and optical flow obtained through depth sensors, are not always practical in an IoT environment. Although these techniques ensure a higher accuracy score, they are computationally heavier. Keeping these constraints in mind, we experimented with various video classification and action recognition techniques such as ConvLSTM, LRCN (with both custom CNN layers and VGG-16 as feature extractor) CNNTransformer and C3D. We achieved a test accuracy of 80% on ConvLSTM, 83.33% on CNN-BiLSTM, 70% on VGG16-BiLstm,76.76% on CNN-Transformer and 80% on C3D.

关键词： Deep learning Video classification neural network Attention based encoder Violence detection LRCN ConvLSTM Transformer C3D

来源：评论

学校读者我要写书评

暂无评论

Architecture of a Multistage Anomaly Detection System in Computer networks

Architecture of a Multistage Anomaly Detection System in Com...

引用

Siberian conference on Control and Communications (SIBCON)

作者： Mikhail Grekov Department of Information Security Tula State University Tula Russia

ISBN: (纸本)9781665476294

The operation of anomaly detection systems in modern computer networks, as a rule, is associated with the processing of large amounts of traffic. With the increase in the scale of computer networks and the growing complexity of network attacks, it becomes necessary to detect multi-stage attacks in real time. This paper presents the architecture of a multi-stage anomaly detection system. The features of the system are the use of generative adversarial neural networks and the minimization of processed traffic using an attacker’s behavior model. The described architecture has a multilevel structure and allows monitoring in distributed computer networks.

关键词： Recurrent neural networks Training data Computer architecture network security Minimization Computer networks Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Text Classification In The Wild: A Large-Scale Long-Tailed Name Normalization Dataset

Text Classification In The Wild: A Large-Scale Long-Tailed N...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jiexing Qi Shuhao Li Zhixin Guo Yusheng Huang Chenghu Zhou Weinan Zhang Xinbing Wang Zhouhan Lin Shanghai Jiao Tong University Shanghai China IGSNRR Chinese Academy of Sciences Beijing China

Real-world data usually exhibits a long-tailed distribution, with a few frequent labels and a lot of few-shot labels. The study of institution name normalization is a perfect application case showing this phenomenon: there are many institutions worldwide, with enormous variations of their names in the publicly available literature. In this work, we first collect a large-scale institution name normalization dataset containing over 25k classes whose frequencies are naturally long-tail distributed. We construct our test set from four different subsets: many-, medium-, and few-shot sets, as well as a zero-shot open set, which are meant to isolate the few-shot and zero-shot learning scenarios from the massive many-shot classes. We also replicate several important benchmarks on our data, covering a wide range from search-based methods to neural network methods. Further, we propose our specially pretrained, BERT-based model that shows better out-of-distribution generalization on few-shot and zero-shot test sets. Compared to other datasets focusing on the long-tailed phenomenon, our dataset has one order of magnitude more training data than the largest existing long-tailed datasets and is naturally long-tailed rather than manually synthesized. We believe it provides an important and different scenario to study this problem. To our best knowledge, this is the first natural language dataset that focuses on this long-tailed and open-set classification problem. 1

关键词： Text categorization neural networks Natural languages Training data Focusing Signal processing Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Enhancing Small Object Detection in Aerial Imagery Based on Strong Feature Extraction and Batch Dimension

Enhancing Small Object Detection in Aerial Imagery Based on ...

引用

International Joint conference on neural networks (IJCNN)

作者： Bingnan Cao Di Wang Ying Guo Hu Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The Institute of Data Science City University of Macau Macau China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection is a fundamental component of computer vision, playing a pivotal role in various applications. However, the accurate detection of small and densely distributed objects remains a challenging problem in this field. This challenge is particularly exacerbated in the context of aerial imagery, characterized by its distinctive bird’s-eye view, intricate backgrounds, and the variability in object appearances. This paper addresses these persistent challenges in object detection, with a focus on the specific difficulties posed by aerial images. We propose a deformable end-to-end object detection with transformers (DETR)-based framework to enhance small object detection accuracy, ultimately contributing to improved computer vision capabilities in domains like remote sensing, surveillance, and autonomous aerial systems. Firstly, in order to aggregate the entire input sequence information in the backbone network and improve the detection accuracy of small objects, we propose DMCA based on deformable features and attention mechanisms. Secondly, in order to capture and model the relationships between samples for dense pixel-level representations in small objects and improve the detection accuracy of small objects, we try to introduce BatchEncoder by implementing an encoder in the batch dimension. Experimental results show that, compared to the baseline, our method significantly improves the accuracy of small object detection in aerial images. The processing and analysis of a large amount of remote sensing data require powerful computing power. The computing power Internet can integrate the scattered computing resources to provide flexible and scalable computing power to meet the computing needs of different sizes and types.

关键词： Computer vision Accuracy Surveillance Object detection Detectors Transformers Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Delayed algorithms for distributed stochastic weakly convex optimization 23

Delayed algorithms for distributed stochastic weakly convex ...

引用

Proceedings of the 37th International conference on neural Information processing Systems

作者： Wenzhi Gao Qi Dengt Stanford University Shanghai University of Finance and Economics

This paper studies delayed stochastic algorithms for weakly convex optimization in a distributed network with workers connected to a master node. Recently, Xu et al. 2022 showed that an inertial stochastic subgradient method converges at a rate of O(τmax/√K) which depends on the maximum information delay τmax. In this work, we show that the delayed stochastic subgradient method (DSGD) obtains a tighter convergence rate which depends on the expected delay $\bar{\tau}$. Furthermore, for an important class of composition weakly convex problems, we develop a new delayed stochastic prox-linear (DSPL) method in which the delays only affect the high-order term in the complexity rate and hence, are negligible after a certain number of DSPL iterations. In addition, we demonstrate the robustness of our proposed algorithms against arbitrary delays. By incorporating a simple safeguarding step in both methods, we achieve convergence rates that depend solely on the number of workers, eliminating the effect of the delay. Our numerical experiments further confirm the empirical superiority of our proposed methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

50th International conference on Parallel processing Workshop, ICPP 2021 - Proceedings

50th International Conference on Parallel Processing Worksho...

引用

50th International conference on Parallel processing Workshop, ICPP 2021

ISBN: (纸本)9781450384414

The proceedings contain 38 papers. The topics discussed include: automated arrhythmia detection using Hilbert-Huang transform based convolutional neural network;self-stabilization with selfish agents;new evacuation guidance using augmented reality for emergency rescue evacuation support system (ERESS);analysis on nursing care activity related stress level for reduction of caregiving workload;warp-centric k-nearest neighbor graphs construction on GPU;explaining the classification performance of supervised and semi-supervised methods for automated sparse matrix format selection;an intelligent parallel distributed streaming framework for near real-time science sensors and high resolution medical images;and intra- and inter- layer transformation to reduce memory traffic for CNN computation.

关键词：

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301页 << < 97 98 99 100 101 102 103 104 105 106 > >>

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：