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1281-1290 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：

A Comparative study for sentiment analysis of raw and translated text

A Comparative study for sentiment analysis of raw and transl...

引用

The 2nd International conference on distributed Sensing and Intelligent Systems (ICDSIS 2021)

作者： S. N. Monjoor O. Faruk K. M. Mahmudul Haque F. B. Iqbal M. A. Mitu M. A. Islam M. S. H. Mukta United International University Dhaka Bangladesh Military Institute of Science and Technology Dhaka Bangladesh

Sentiment analysis is a type of opinion mining study analyzing people's thoughts, feelings, and assessments evaluations of society things such as products, services, and institutions individuals, organizations, events and etc. This paper focuses on an effective machine learning model, Recurrent neural network (RNN), for sentiment analysis of both Bengali and English text. For sentiment analysis of Bengali text, we construct a novel dataset accumulated from different open source sites and social media. The dataset comprises over 17,000 texts, which are labelled as neutral, positive, and negative. Our study provides a rigorous comparative analysis on the performance of RNN in terms of sentiment analysis of both raw text and corresponding translated text (for both English and Bengali language). In summary, we achieve 82.9% and 84.5% accuracies for sentiment analysis of Bengali and English text respectively using RNN.

关键词： data mining learning (artificial intelligence) natural language processing recurrent neural nets text analysis

来源：评论

学校读者我要写书评

暂无评论

Grid text classification method based on DNN neural network

Grid text classification method based on DNN neural network

引用

2019 International conference on Computer Science Communication and network Security, CSCNS 2019

作者： Huang, Jutao Zheng, Jiesheng Gao, Shang Liu, Wenbin Lin, Jiaxin Guangdong Power Information Technology Co. Ltd Yuedian Buliding Guangzhou Guangdong China

With the rapid development of network technology, the electric power Internet of Things needs to face a large number of electronic texts and a large number of distributed data access and analysis requirements. If the system wants to complete accurate and efficient data analysis and build an existing data and service standard system covering the entire chain of energy and power business on the existing basis, it must implement massive electronic text retrieval, information extraction and classification in the power grid system. In order to achieve this purpose, a DNN neural network classification model is constructed to classify the text information of the power grid, and the effectiveness of the method is verified by experiments based on data from the substation information system. © The Authors, published by EDP Sciences, 2020.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

network Classifiers Based on Social Learning

Network Classifiers Based on Social Learning

引用

IEEE International conference on Acoustics, Speech and Signal processing

作者： Virginia Bordignon Stefan Vlaski Vincenzo Matta Ali H. Sayed School of Engineering Ecole Polytechnique Fédérale de Lausanne (EPFL) DIEM University of Salerno

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase. By doing so, the proposed architecture is able to improve prediction performance over time with unlabeled data. Inspired by social learning algorithms, which require prior knowledge of the observations distribution, we propose a Social Machine Learning (SML) paradigm that is able to exploit the imperfect models generated during the learning phase. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers. Simulations with an ensemble of feedforward neural networks are provided to illustrate the theoretical results.

关键词： Machine learning algorithms conferences Signal processing algorithms Machine learning Signal processing Prediction algorithms Feedforward neural networks

来源：评论

学校读者我要写书评

暂无评论

Parallelization Techniques for Verifying neural networks 20

Parallelization Techniques for Verifying Neural Networks

引用

20th International conference on Formal Methods in Computer-Aided Design, FMCAD 2020

作者： Wu, Haoze Ozdemir, Alex Zeljic, Aleksandar Julian, Kyle Irfan, Ahmed Gopinath, Divya Fouladi, Sadjad Katz, Guy Pasareanu, Corina Barrett, Clark Stanford University United States Nasa Ames Kbr Inc. United States The Hebrew University of Jerusalem Israel Carnegie Mellon University United States

ISBN: (纸本)9783854480426

Inspired by recent successes of parallel techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics to leverage parallelism and improve the scalability of neural network verification. We present a general description of the Split-and-Conquer partitioning algorithm, implemented within the Marabou framework, and discuss its parameters and heuristic choices. In particular, we explore two novel partitioning strategies, that partition the input space or the phases of the neuron activations, respectively. We introduce a branching heuristic and a direction heuristic that are based on the notion of polarity. We also introduce a highly parallelizable pre-processing algorithm for simplifying neural network verification problems. An extensive experimental evaluation shows the benefit of these techniques on both existing and new benchmarks. A preliminary experiment ultra-scaling our algorithm using a large distributed cloud-based platform also shows promising results. © 2020 FMCAD Association.

关键词： Computer aided design

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Parallel and Scalable GPU Based Convolutional neural network

Performance Analysis of Parallel and Scalable GPU Based Conv...

引用

International conference on Computing in Engineering and Technology, ICCET 2019

作者： Chavan, Umesh Kulkarni, Dinesh Walchand College of Engineering Sangli India

ISBN: (纸本)9789813295148

Convolutional neural networks (CNN) have succeeded great impact in various tasks of machine learning. Training CNN model is a computationally intensive task. Scalability and performance of CNN with GPU is demonstrated in this study using CUDA (Compute Unified Device Architecture) framework. We evaluated performance characteristics using our own designed CNN model. The model is configured for Facial expression Recognition classification task. The novelty of this effort is to demonstrate performance acceleration in scalable CNN. The parallel tasks using the hardware feature of general-purpose- computing on graphics processing unit (GPGPU) has been shown to be appropriate to be applied to CNN. We implemented at multi-node distributed training;which allows us to efficiently parallelize deep networks across multiple servers, in order to minimize the time to train. The experiment result shows that the proposed experiment in this study gained over 7 times speedup. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Region-level arabic dialect identification using deep learning models

Region-level arabic dialect identification using deep learni...

引用

The 2nd International conference on distributed Sensing and Intelligent Systems (ICDSIS 2021)

作者： A. A. Sayadi A. Abulohoom S. A. Uddin M. Lataifeh A. Elnagar Dept. of Computer Science University of Sharjah Sharjah UAE

The automatic classification of the Arabic language dialects is the preliminary step towards establishing many dialectal-sensitive Arabic natural language processing tasks. Arabic dialect identification entails predicting the dialects associated with specific textual inputs and classifying them under their respected labels. In this paper, we propose a novel approach by merging several distinct datasets to obtain a large, diverse, and bias-free dialectal corpus. Our dataset is a collection of parallel sentences translated into multiple dialects (MADAR), as well as tweets gathered from Twitter users (NADI-2020, NADI-2021, QADI, & ARAP-Tweet 2.0). The collected dataset is classified into seven labels: Gulf, Levant, Iraq, Maghreb, Nile Basin, Yemen, and Modern Standard Arabic. The merged dataset was cleansed to produce Arabic sentences free of punctuation, non-Arabic characters, numbers, repeating characters, empty lines, and elongation characters. We obtained a high dialectal classification accuracy using a new Word2vec embedding model on the merged dialectal dataset. Seven deep learning systems were trained on a balanced subset of the dataset. These models are Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional-LSTM (BILSTM), Bidirectional-GRU (BIGRU), Convolutional-LSTM (CLSTM), and Convolutional-GRU (CGRU). Single-label classification tests ran on these trained models acquired a minimum accuracy of 77.90% (CNN) and a maximum accuracy of 81.52% (BILSTM). We further evaluated the accuracy of tests ran for short and long sentences with 87.52% attained accuracy for classifying short sentences with CGRU and 94.06% for long sentences using BIGRU, both of which are indications of proposed approach efficacy to move forward.

关键词： convolutional neural nets deep learning (artificial intelligence) natural language processing pattern classification recurrent neural nets social networking (online) text analysis

来源：评论

学校读者我要写书评

暂无评论

4th International conference on Computational Intelligence and networks, CINE 2020

4th International Conference on Computational Intelligence a...

引用

4th International conference on Computational Intelligence and networks, CINE 2020

ISBN: (纸本)9781728156880

The proceedings contain 21 papers. The topics discussed include: complete neighborhood centrality and its application;breaking CAPTCHA characters using multi-task learning CNN and SVM;an unsupervised approach for gait phase detection;prediction of missing EEG channel waveform using LSTM;localizing oscillatory sources in a brain by MEG data during cognitive activity;interactions within the spatially-distributed cortical neural network during visual attentional tasks;handwritten Gujarati word image matching using autoencoder;and an efficient algorithm to minimize makespan on parallel batch processing machines.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A survey on approaches and applications of knowledge representation learning 5

A survey on approaches and applications of knowledge represe...

引用

5th IEEE International conference on Data Science in Cyberspace, DSC 2020

作者： Li, Chenchen Li, Aiping Wang, Ye Tu, Hongkui Song, Yichen National University of Defense Technology College of Computer ChangSha China National University of Defense Technology College of Computer Science and Technology on Parallel and Distributed Processing Laboratory ChangSha China

ISBN: (纸本)9781728195582

Knowledge representation learning (KRL) is one of the important research topics in artificial intelligence and Natural language processing. It can efficiently calculate the semantics of entities and relations in a low-dimensional space, and effectively solve the problem of data sparsity, which significantly improve the performance of knowledge acquisition, fusion and reasoning and so on. Starting from the five perspectives of distance-based, semantic matching, bilinear-based, neural network model and additional information model, this paper first introduces the overall framework and specific model design, and then correspondingly introduces the experimental evaluation tasks, metrics and benchmark datasets of each model. On this basis, how to apply KRL to various downstream tasks is summarized. © 2020 IEEE.

关键词： Knowledge representation

来源：评论

学校读者我要写书评

暂无评论

Capture Uncertainties in Deep neural networks for Safe Operation of Autonomous Driving Vehicles

Capture Uncertainties in Deep Neural Networks for Safe Opera...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Liuhui Ding Dachuan Li Bowen Liu Wenxing Lan Bing Bai Qi Hao Weipeng Cao Ke Pei Southern University of Science and Technology Shenzhen China Sifakis Research Institute for Trustworthy Autonomous Systems Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Huawei Technologies Co. Ltd. Shenzhen China

Uncertainties in Deep neural network (DNN)-based perception and vehicle’s motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep neural network model which detects 3D objects and quantitatively capture the associated aleatoric and epistemic uncertainties of DNNs; (2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle’s motion. The proposed approaches are validated via simulated complex scenarios built in CARLA. Experimental results show that the proposed motion planning scheme can cope with uncertainties of DNN-based perception and vehicle motion, and improve the operational safety of autonomous vehicles while still achieving desirable efficiency.

关键词： Deep learning Solid modeling Uncertainty Three-dimensional displays Object detection Planning Trajectory

来源：评论

学校读者我要写书评

暂无评论

Edge-Detect: Edge-Centric network Intrusion Detection using Deep neural network

Edge-Detect: Edge-Centric Network Intrusion Detection using ...

引用

Consumer Communications and networking conference, CCNC IEEE

作者： Praneet Singh Jishnu Jaykumar P Akhil Pankaj Reshmi Mitra Indian Institute of Science Bengaluru India Southeast Missouri State University Cape Girardeau USA

ISBN: (数字)9781728197944

ISBN: (纸本)9781728197951

Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints and is set to become part of a multi-billion industry. The resource constraints in this novel network infrastructure tier constricts the deployment of existing network Intrusion Detection System with Deep Learning models (DLM). We address this issue by developing a novel light, fast and accurate `Edge-Detect' model, which detects distributed Denial of Service attack on edge nodes using DLM techniques. Our model can work within resource restrictions i.e. low power, memory and processing capabilities, to produce accurate results at a meaningful pace. It is built by creating layers of Long Short-Term Memory or Gated Recurrent Unit based cells, which are known for their excellent representation of sequential data. We designed a practical data science pipeline with Recurring neural network to learn from the network packet behavior in order to identify whether it is normal or attack-oriented. The model evaluation is from deployment on actual edge node represented by Raspberry Pi using current cybersecurity dataset (UNSW2015). Our results demonstrate that in comparison to conventional DLM techniques, our model maintains a high testing accuracy of ~99% even with lower resource utilization in terms of cpu and memory. In addition, it is nearly 3 times smaller in size than the state-of-art model and yet requires a much lower testing time.

关键词： Training Industries Image edge detection neural networks Pipelines network intrusion detection Testing

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 << < 125 126 127 128 129 130 131 132 133 134 > >>

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：