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

724 篇 会议
3 篇 期刊文献
2 册 图书

馆藏范围

729 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

474 篇 工学
- 379 篇 计算机科学与技术...
- 273 篇 软件工程
- 175 篇 信息与通信工程
- 106 篇 电气工程
- 81 篇 生物工程
- 76 篇 生物医学工程（可授...
- 47 篇 控制科学与工程
- 33 篇 电子科学与技术（可...
- 29 篇 机械工程
- 27 篇 光学工程
- 26 篇 化学工程与技术
- 18 篇 仪器科学与技术
- 14 篇 安全科学与工程
- 13 篇 交通运输工程
- 13 篇 农业工程
214 篇 理学
- 114 篇 数学
- 89 篇 生物学
- 67 篇 物理学
- 41 篇 系统科学
- 38 篇 统计学（可授理学、...
- 24 篇 化学
95 篇 医学
- 91 篇 临床医学
- 56 篇 基础医学(可授医学...
- 46 篇 药学(可授医学、理...
- 13 篇 公共卫生与预防医...
95 篇 管理学
- 57 篇 图书情报与档案管...
- 41 篇 管理科学与工程(可...
- 16 篇 工商管理
17 篇 农学
- 17 篇 作物学
11 篇 法学
- 11 篇 社会学
7 篇 教育学
6 篇 经济学
5 篇 军事学

主题

168 篇 machine learning
50 篇 deep learning
46 篇 support vector m...
46 篇 feature extracti...
41 篇 signal processin...
24 篇 signal processin...
23 篇 machine learning...
18 篇 image processing
18 篇 training
16 篇 artificial intel...
16 篇 accuracy
15 篇 electroencephalo...
14 篇 decision trees
14 篇 computer vision
13 篇 natural language...
13 篇 neural networks
13 篇 classification a...
13 篇 convolutional ne...
12 篇 data mining
12 篇 classification

机构

6 篇 xinjiang key lab...
6 篇 school of inform...
6 篇 fujian provincia...
6 篇 xinjiang key lab...
5 篇 beijing universi...
3 篇 fujian provincia...
2 篇 college of engin...
2 篇 university of jo...
2 篇 school of electr...
2 篇 china electric p...
2 篇 department of ar...
2 篇 department of it...
2 篇 valencian resear...
2 篇 ugam solutions c...
2 篇 department of co...
2 篇 chongqing univ s...
2 篇 guangzhou xinhua...
2 篇 college of elect...
2 篇 school of comput...
2 篇 guangzhou power ...

作者

10 篇 hamdulla askar
6 篇 ablimit mijit
6 篇 li zuoyong
3 篇 vysotska victori...
3 篇 teng shenghua
2 篇 veeresh
2 篇 xue jingfeng
2 篇 mohanty mihir na...
2 篇 agarwal harsh
2 篇 jin lingmin
2 篇 kalinina irina
2 篇 jia cairang
2 篇 renjith p.n.
2 篇 gozhyj aleksandr
2 篇 wen zhang
2 篇 li zheng
2 篇 selvi g. thamara...
2 篇 pandey trilok na...
2 篇 islam md. adnanu...
2 篇 kama mohd nazri

语言

700 篇 英文
28 篇 其他
4 篇 中文

检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 729 条记录，以下是541-550 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 machine learning Distracted Driving Prediction Model 3

A Machine Learning Distracted Driving Prediction Model

引用

3rd international conference on Vision, Image and signal processing (ICVISP)

作者： Ahangari, Samira Jeihani, Mansoureh Dehzangi, Abdollah Morgan State Univ Dept Transportat & Infrastruct Studies Baltimore MD 21239 USA Morgan State Univ Dept Comp Sci Baltimore MD 21239 USA

ISBN: (纸本)9781450376259

Distracted driving is known to be one of the core contributors to crashes in the U.S., accounting for about 40% of all crashes. Drivers' situational awareness, decision-making, and driving performance are impaired due to temporarily diverting their attention from the primary task of driving to other tasks not related to driving. Detecting driver distraction would help in adapting the most effective countermeasures. To find the best strategies to overcome this problem, we developed a Bayesian Network (BN) distracted driving prediction model using a driving simulator. In this study, we use a Bayesian Network classifier as a powerful machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of hand-held calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performances such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Here we investigated different optimization models to build the best BN in which a Genetic Search Algorithm obtained the best performance. As a result, we achieved a 67.8% prediction accuracy using our model to predict driver distraction. We also achieved 62.6% true positive rate, which demonstrates the ability of our model to correctly predict distractions.

关键词： Distracted Driving machine learning Bayesian Network Driving Simulator Data Mining

来源：评论

学校读者我要写书评

暂无评论

Training Encrypted Models with Privacy-preserved Data on Blockchain 3

Training Encrypted Models with Privacy-preserved Data on Blo...

引用

3rd international conference on Vision, Image and signal processing (ICVISP)

作者： Liu, Lifeng Hu, Yifan Yu, Jiawei Zhang, Fengda Huang, Gang Xiao, Jun Wu, Chao Zhejiang Univ Hangzhou Zhejiang Peoples R China Cybervein Fdn Shanghai Peoples R China Zhejiang Lab Hangzhou Zhejiang Peoples R China Zhejiang Univ Data Sci Inst Imperial Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450376259

Currently, training neural networks often requires a large corpus of data from multiple parties. However, data owners are reluctant to share their sensitive data to third parties for modelling in many cases. Therefore, Federated learning (FL) has arisen as an alternative to enable collaborative training of models without sharing raw data, by distributing modelling tasks to multiple data owners. Based on FL, we premodel sent a novel and decentralized approach to training encrypted models with privacy-preserved data on Blockchain. In our approach, Blockchain is adopted as the machine learning environment where different actors (i.e., the model provider, the data provider) collaborate on the training task. During the training process, an encryption algorithm is used to protect the privacy of data and the trained model. Our experiments demonstrate that our approach is practical in real-world applications.

关键词： Blockchain machine learning Distributed learning Privacy

来源：评论

学校读者我要写书评

暂无评论

Effect of Familiarity on Recognition of Pleasant and Unpleasant Emotional States Induced by Hindi Music Videos 3rd

Effect of Familiarity on Recognition of Pleasant and Unpleas...

引用

3rd international conference on Advanced Computing and Intelligent Engineering (ICACIE)

作者： Daimi, Syed Naser Jain, Soumil Saha, Goutam Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9789811510816;9789811510809

Valence is an important dimension representing the hedonic value of emotion labeled as positive or negative. Inducing these emotions and making an understanding from brain responses have huge practical significance. Music is a powerful tool to induce emotions, and the induced emotions are generally getting influenced by factors external to music such as familiarity. This work presents a novel study on the effect of familiarity on recognition of pleasant (positive) and unpleasant (negative) emotional states induced by Hindi music videos. For this, we recorded 32-channel EEG from six healthy subjects while they watched Hindi music videos and self-reported ratings of felt emotions on valence and familiarity scale. We used a machine learning framework for emotion classification from power spectral and functional connectivity features. The framework consists of SVD-QRcp and F-ratio based feature selection and an SVM classifier. The classification was performed under three cases of familiarity, namely, familiar, unfamiliar, and regardless of familiarity of the music videos. We found that for the familiar case, the classification performance was higher than unfamiliar and regardless of familiarity cases for all considered features. The best performing features were from the individual electrodes and these features were from the frontal and left parietal regions which indicate the lateralized processing of valence. In addition to classification, we analyzed the feature and electrode usage for all the cases of familiarity. It was found that the features from theta, alpha, and gamma band covering the frontal and parietal brain regions were dominantly involved.

关键词： Familiarity Valence Power spectral density Functional connectivity Support vector machine

来源：评论

学校读者我要写书评

暂无评论

A Survey of the Development of Artificial Intelligence Technology

A Survey of the Development of Artificial Intelligence Techn...

引用

IEEE international conference on Unmanned Systems (ICUS)

作者： Zhi Li Yilin Wang Yunfang Ji Wenyu Yang Beijing HIWING Scientific and Technological Information Institute Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Artificial intelligence technology is currently a hot spot for research and application, and the new cross-technology formed by artificial intelligence and multiple technologies has been widely used in Internet, finance, security and other industries. This paper first introduces the development of artificial intelligence, and then analyzes the key technologies of artificial intelligence, including computer vision, machine learning, natural language processing, human-computer exchange, virtual reality, biometric recognition, etc. Finally, the development trend of artificial intelligence technology is described.

关键词： Handheld computers Virtual reality Natural language processing Computer vision Smart homes Manufacturing machine learning

来源：评论

学校读者我要写书评

暂无评论

Cerebral Microbleeds Detection Based on 3D Convolutional Neural Network 1

引用

3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Zhao, Meng Jin, Chen Jin, Lingmin Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Cerebral microbleeds (CMBs) are important imaging and diagnostic biomarkers for cerebrovascular diseases and cognitive dysfunctions. Reliable detection of the location and amount of CMBs in brain tissue is crucial for the diagnosis, prevention and treatment of related diseases, where traditional Convolutional Neural Network (CNN) has been applied but may fail to achieve high enough detection accuracy. To alleviate this issue, we utilize 3D Fully Convolutional Networks (FCN) and 3D AlexNet to establish a cascade coarse-to-fine detection manner. Specifically, CMBs candidates are first screened out using 3D FCN, followed by 3D AlexNet which extracts the spatial features of CMBs and distinguishes the false positive samples from candidate regions. Experimental results show that the proposed method can realize precise detection of CMBs in magnetic resonance images (MRI) by improving detection sensitivity and reducing false positive samples. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

Research on TSV Void Defects Based on machine learning 19

Research on TSV Void Defects Based on Machine Learning

引用

3rd international conference on Computer Science and Application Engineering (CSAE)

作者： Chen, Shouhong Kang, Huaiqiang Ma, Jun Guo, Ling Hou, Xingna Guilin Univ Elect Technol Guangxi Key Lab Automat Detecting Technol & Instr Sch Elect Engn & Automat Guilin Peoples R China

ISBN: (纸本)9781450362948

With the rapid development of 3D TSV (through silicon via) technology, it is particularly important to improve the yield for TSV fault detection. Aiming at TSV void defects, the paper adopts supervised machine learning method to train S parameters in TSV model with void faults, and carries out classification processing, then predicts the size of void faults through stimulus signal and S parameters. The results show that for spherical void defects detection, the classification accuracy of ELM (Extreme learning machine) algorithm and KNN (K-Nearest Neighbor) algorithm is above 85%, while for TSV cylindrical void defects detection, the classification accuracy of ELM algorithm is 96%.

关键词： machine learning TSV fault detection Classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Mini-DDSM: Mammography-based Automatic Age Estimation 20

Mini-DDSM: Mammography-based Automatic Age Estimation

引用

2020 3rd international conference on Digital Medicine and Image processing

作者： Charitha Dissanayake Lekamlage Fabia Afzal Erik Westerberg Abbas Cheddad Blekinge Institute of Technology Sweden

ISBN: (纸本)9781450389044

Age estimation has attracted attention for its various medical applications. There are many studies on human age estimation from biomedical images. However, there is no research done on mammograms for age estimation, as far as we know. The purpose of this study is to devise an AI-based model for estimating age from mammogram images. Due to lack of public mammography data sets that have the age attribute, we resort to using a web crawler to download thumbnail mammographic images and their age fields from the public data set; the Digital Database for Screening Mammography. The original images in this data set unfortunately can only be retrieved by a software which is broken. Subsequently, we extracted deep learning features from the collected data set, by which we built a model using Random Forests regressor to estimate the age automatically. The performance assessment was measured using the mean absolute error values. The average error value out of 10 tests on random selection of samples was around 8 years. In this paper, we show the merits of this approach to fill up missing age values. We ran logistic and linear regression models on another independent data set to further validate the advantage of our proposed work. This paper also introduces the free-access Mini-DDSM data set.

关键词： Age estimation Applied machine learning Deep learning Feature extraction Image segmentation Mammograms Regression analysis

来源：评论

学校读者我要写书评

暂无评论

Text Segregation on Asynchronous Group Chat 3

Text Segregation on Asynchronous Group Chat

引用

3rd international conference on Computing and Network Communications, CoCoNet 2019

作者： Sinha, Abhishek Midhush Manohar, T.K. Subramanian, Srikumar Das, Bhaskarjyoti PES University Banashankari Bengaluru Karnataka560085 India

Ability to successfully segregate texts around different topics can lead to successful text summarization. Though text summarization has been well researched, summarization of multi-party asynchronous chat has not been attempted. Popular systems such as Whatsapp, Telegram etc. have such kind of chat scenarios. Such chats are asynchronous as participants can respond to a thread after a long delay and not as an immediate reply to ongoing conversation. Though there are few chat data sets available, annotated data sets of such multi-party asynchronous chats are not yet available even though summarization in this particular domain presents a good use case for chat participants. With such a summarization feature, a user can quickly review the summary of past conversations during his period of inactivity. However, this is challenging as any summarization attempt on a data set like this must address three aspects i.e. threads of discussion, time window and topic/sub-topics in an inter-woven sequence of messages that does not have any usual sense of sequence. In the absence of annotated data sets and challenge of addressing these three aspects in seemingly non-sequential messages, machine learning based approaches will not work well. In this work, an innovative pipeline based on heuristics is designed to address text segregation for such a scenario. Once text segregation is achieved, text summarization may become a lesser challenge. It is observed that this approach is promising enough to warrant further research and enhancements. © 2020 The Authors. Published by Elsevier B.V.

关键词： Text processing

来源：评论

学校读者我要写书评

暂无评论

Training an Artificial Neural Network Using Qubits as Artificial Neurons: A Quantum Computing Approach 3

Training an Artificial Neural Network Using Qubits as Artifi...

引用

3rd international conference on Computing and Network Communications, CoCoNet 2019

作者： Chalumuri, Avinash Kune, Raghavendra Manoj, B.S. Indian Institute of Space Science and Technology Thiruvananthapuram695547 India Advanced Data Processing Research Institute Hyderabad500009 India

Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer. © 2020 The Authors. Published by Elsevier B.V.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Research on Text Detection and Recognition Based on OCR Recognition Technology

Research on Text Detection and Recognition Based on OCR Reco...

引用

international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Yuming He Software Engineering Shanghai Starriver Bilingual School Shanghai China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

Optical character recognition (OCR) is an important branch in the field of machine vision. It involves pattern recognition, image processing, digital signal processing, artificial intelligence and other disciplines. It is a comprehensive. It has important use value and theoretical significance in high-tech fields such as word information processing, office automation, machine translation and real-time monitoring system. In the 21st century, with the popularity of smart phones with high-definition cameras, OCR has a new pursuit in its development: more and more people pick up their mobile phones to photograph the things and scenes they see and obtain the text information in the pictures. Therefore, the recognition of characters in natural scenes has become a brand-new topic. In the past, text detection and text recognition algorithms were basically based on artificially designed features and traditional image processing methods. These features and algorithms were difficult to design and needed a lot of professional knowledge and experience support, so the accuracy was not high and they were not generalized. In recent years, with the rapid development of deep learning technology, breakthroughs have been made in the fields of computer vision such as image classification, object detection and semantic segmentation. Deep learning algorithm is a data-driven algorithm. The algorithm based on deep learning can automatically discover and learn the hidden feature rules in a large number of data through iterative training, without too much human intervention, so it has better generalization than traditional image processing related algorithms.

关键词： Deep learning Image recognition Text recognition signal processing algorithms Classification algorithms Optical character recognition software Character recognition

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73页 << < 51 52 53 54 55 56 57 58 59 60 > >>

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：