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

2,984 篇 会议
176 册 图书
25 篇 期刊文献

馆藏范围

3,184 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,060 篇 工学
- 1,841 篇 计算机科学与技术...
- 960 篇 软件工程
- 343 篇 电气工程
- 329 篇 信息与通信工程
- 227 篇 控制科学与工程
- 208 篇 生物工程
- 150 篇 生物医学工程（可授...
- 71 篇 光学工程
- 64 篇 机械工程
- 62 篇 化学工程与技术
- 53 篇 电子科学与技术（可...
- 50 篇 仪器科学与技术
- 46 篇 安全科学与工程
- 36 篇 交通运输工程
- 34 篇 动力工程及工程热...
653 篇 理学
- 353 篇 数学
- 245 篇 生物学
- 141 篇 物理学
- 135 篇 统计学（可授理学、...
- 95 篇 系统科学
- 65 篇 化学
419 篇 管理学
- 247 篇 图书情报与档案管...
- 192 篇 管理科学与工程(可...
- 83 篇 工商管理
263 篇 医学
- 241 篇 临床医学
- 123 篇 基础医学(可授医学...
- 74 篇 药学(可授医学、理...
- 41 篇 公共卫生与预防医...
54 篇 法学
- 50 篇 社会学
41 篇 经济学
- 39 篇 应用经济学
38 篇 教育学
23 篇 农学
6 篇 艺术学
4 篇 文学

主题

579 篇 machine learning
567 篇 data mining
323 篇 pattern recognit...
216 篇 deep learning
201 篇 feature extracti...
154 篇 support vector m...
145 篇 artificial intel...
129 篇 machine learning...
120 篇 accuracy
120 篇 training
91 篇 data mining and ...
90 篇 data models
72 篇 neural networks
71 篇 classification
69 篇 predictive model...
67 篇 face recognition
61 篇 learning systems
52 篇 computer imaging...
50 篇 convolutional ne...
48 篇 classification a...

机构

9 篇 school of comput...
9 篇 chinese academy ...
8 篇 zhejiang univers...
8 篇 chitkara univers...
7 篇 guangdong univer...
7 篇 school of inform...
6 篇 xi'an university...
6 篇 indian statistic...
6 篇 central south un...
6 篇 department of co...
6 篇 unsw sydney
6 篇 xi'an university...
6 篇 southern univers...
5 篇 indian institute...
5 篇 study group aust...
5 篇 peking universit...
4 篇 shanghai jiao to...
4 篇 centre for patte...
4 篇 chongqing univer...
4 篇 national laborat...

作者

11 篇 de-shuang huang
8 篇 petra perner
7 篇 krasotkina olga
7 篇 mottl vadim
7 篇 ying tan
6 篇 zheng-guang wu
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 phung dinh
6 篇 venkatesh svetha
6 篇 hongyi li
6 篇 jacek m. zurada
6 篇 chaojie li
6 篇 biao luo
6 篇 ilias maglogiann...
6 篇 lotfi a. zadeh
6 篇 leszek rutkowski
6 篇 lazaros iliadis
6 篇 long cheng
5 篇 vitoantonio bevi...

语言

2,758 篇 英文
413 篇 其他
42 篇 中文
1 篇 土耳其文

检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3185 条记录，以下是511-520 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：

On Divergence-Free Neural ODE for Classification 27th

On Divergence-Free Neural ODE for Classification

引用

27th international conference on pattern recognition, ICPR 2024

作者： Jarraya, Zakaria Drumetz, Lucas Benaïchouche, Simon Salem, Douraied Ben Rousseau, François IMT Atlantique LaTIM U1101 INSERM Brest France IMT Atlantique Lab-STICC UMR CNRS 6285 Brest France Univ Brest CHU Brest UMR 1101 Brest29200 France

ISBN: (纸本)9783031781858

Inspired by the principles of fluid dynamics, divergence-free neural ODEs represent a promising class of neural networks that can improve classification performance and robustness. In this work, we propose methods for constructing divergence-free vector fields in high-dimensional latent spaces and thus extending their application to real-world scenarios. Our experimental results show that divergence-free neural ODEs can outperform their conventional counterparts in three classification tasks. In particular, divergence-free models show remarkable resilience to adversarial perturbations. Furthermore, divergence-free models are more effective at preserving the underlying structure of the data. this research provides a physics-based perspective on machine learning applications, in particular classification tasks, by interpreting neural networks as dynamical systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

2022 IEEE 5th international conference on Artificial Intelligence and Big data, ICAIBD 2022

2022 IEEE 5th International Conference on Artificial Intelli...

引用

5th IEEE international conference on Artificial Intelligence and Big data, ICAIBD 2022

ISBN: (纸本)9781665499132

the proceedings contain 119 papers. the topics discussed include: thoughts on whether unmanned driving can be safe on the road;a review of research on feature extraction for emotion recognition based on EEG signals;boost speech recognition without training;research on the shared supply mode of urban public transportation under the Internet of things - a case study based on hello travel platform;deep reinforcement learning scheduling of container cloud workflow considering invalid time-consuming and reliability;review of panel data clustering methods and applications;fungi recognition based on coordinate attention and EfficientNetV2;prediction of protein post-translational modifications in rice based on multi-head self-attention;and research on the application of data mining in teaching system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Parkinson's Disease Classification with Self-supervised learning and Attention Mechanism 26

Parkinson's Disease Classification with Self-supervised Lear...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zhang, Yuchen Lei, Haijun Huang, Zhongwei Zhao, Menglu Li, Zhen Liu, Chuan-Ming Lei, Baiying Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Natl Taipei Univ Technol Comp Sci & Informat Engn Taipei Taiwan Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Parkinson's disease (PD) is a neurodegenerative geriatric disease commonly occurring in middle-aged and elderly adults. Since PD is irreversible and its treatment only slows down its rate of development, the early diagnosis by accurate prediction is of great significance to retard its deterioration. However, the existing computer-assisted diagnosis methods for PD have limitations in exploring the implicit and spatial information in the brain. In view of this limitation, a 3D network based on self-supervised learning strategy and attention mechanism is proposed for PD classification in this paper. the proposed method put the input of successive frames from the preprocessed magnetic resonance imaging (MRI) data into 3D ResNet18 with the classifier module for PD classification. Specifically, the attention mechanism is used to explore the discriminative features. Meanwhile, a self-supervised learning pretext task and a regression task are designed to assist in training and improve the robustness of the proposed model. We use a 5-fold cross-validation strategy to corroborate our method's effectiveness on the Parkinson's Progression Markers Initiative (PPMI) dataset. the experimental results indicate that our proposed method has achieved an accuracy of 87.50% for PD classification, which outperforms the most state-of-the-art deep learning methods.

关键词： Parkinson's disease self-supervised learning attention mechanism magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Image recognition of Wild Birds in Yunnan Based on Improved Mobile Deep learning 5

Image Recognition of Wild Birds in Yunnan Based on Improved ...

引用

5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Li, Yucheng Zhou, Hua Zhao, Yili Yang, Huaiyuan Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

the bird resources in Yunnan Province have great ornamental value and economic value. these variable and colorful features bring many challenges to their intelligent recognition and efficient management. the primary requirement of bird image intelligent recognition is convenience, portability and rapidity. this paper proposes MobileNet to recognize the bird images because MobileNet has smaller scale, fewer parameters and faster speed. the experiment results show MobileNet also have higher accuracy and can be applied in the bird watching and wild bird recognition. therefore, this method has good practicability and can be generalized to the identification of other wild animals. © 2022 IEEE.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Online Election Management System Using machine learning 5

Online Election Management System Using Machine Learning

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Selvaganesh, M. Logesh, P. Manishkumar, K. Ece Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9798350321425

the Online Election Management System is a web-based application. the system has a centralized database to keep the details or records of all the voters, candidates and final Results. the System was implemented based on image classification technique and sending the passwords to voters through email, for login. the System uses HTML and CSS for front-end designing and for backend python programming language and python's web framework called Django for authentication and authorization, database management. the major work in the system is image recognition, for image recognition Convolutional Neural Network (CNN) is used. CNN needs less data to train the model and efficiency is more compared with other classification algorithm YOLO. On using this application, the voter need not go to the booth. So, it is user convenient and also it is easy for the admin to manage the voters and candidate information. It is secure because it uses a two-step verification method. First the user has to login using credentials then using image classification, it recognizes the exact voter. the system prepares the dataset at the time of user registration the user credentials by generating the password randomly for the voter and sending the voter credentials to the voter registered mail id. this research work gives a complete solution for an online voting system with higher accuracy and lesser time. © 2023 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Inevitable Exposure: Analysing the Privacy Paradox in the Age of Digital Connectivity with machine learning Paradigms

Inevitable Exposure: Analysing the Privacy Paradox in the Ag...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Ming Cheung Stephanie Ng James Zhang Chee Peng Lim School of Art Nanjing University of Information Science and Technology Nanjing China Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

this study investigates the privacy paradox, a phenomenon whereby individuals express concerns about privacy while actively engaging in activities that compromise their personal data. through a topic analysis of video transcripts from Douyin, China's most popular short video-sharing platform, we investigate the application of text analytics to examine the privacy paradox phenomenon. We leverage BERTopic, a state-of-the-art topic modelling technique, and the Meta Llama 3 Large Language Model to conduct a topic analysis of discourses related to privacy issues in Douyin videos. Our analysis generates useful insights into where and how the privacy paradox manifests in various aspects of daily life. the findings underscore the need to put in place proactive data protection measures to address public concerns about privacy.

关键词： Privacy Analytical models Soft sensors Large language models Keyword search data protection machine learning Media data models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Multi-Sensor and Deep learning based Ankle Joint Motion Intention Prediction under Diverse Gait Speed Conditions 20

Multi-Sensor and Deep Learning based Ankle Joint Motion Inte...

引用

IEEE 20th international conference on Automation Science and Engineering (CASE)

作者： Gong, Jumin Wei, Dunwen Gao, Tao Liu, Zekun Univ Elect Sci & Technol China Dept Mech & Elect Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

Motion intention prediction is an essential challenge in improving the efficacy of exoskeleton human-machine interactions. Traditional gait phase detection and motion pattern recognition methods can only identify the current gait state, lacking the ability to adjust motion parameters based on step speed dynamically, and real-time performance is difficult to guarantee. In this paper, we proposed a deep learning model TCN-LSTM, which integrated Time Convolutional Network (TCN) and Long Short-Term Memory Network (LSTM), to predict human ankle joint angles under different walking speeds based on data from Inertial Measurement Unit (IMU) and Goniometer (GON). We selected 5 subjects in a public dataset for experimental validation. Experimental results demonstrated that the proposed model could accurately and rapidly predict ankle joint angles. the MAE of the individual-specific model was 1.191 degrees, outperforming the 3 baseline models: LSTM (1.750 degrees), Gated Recurrent Unit (GRU) (1.653 degrees), and TCN (1.340 degrees). the Mean Absolute Error (MAE) of the general model is reduced by 14.3% compared to the 3 baseline models. the prediction error of TCN-LSTM with GON is reduced by 13.69%. Furthermore, the average prediction time of our model is only 3.7ms. therefore, the accuracy and real-time performance of the model can meet the continuous prediction of human ankle joint angle.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Contextual Analysis Using Deep learning for Sensitive Information Detection 5

Contextual Analysis Using Deep Learning for Sensitive Inform...

引用

5th international conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Liang, Yaxin Gao, Erdi Ma, Yuhan Zhan, Qishi Sun, Dan Gu, Xingxin University of Southern California Los Angeles United States New York University New York United States Johns Hopkins University Baltimore United States Marquette University Milwaukee United States Washington University in St. Louis St. Louis United States Northeastern University Boston United States

ISBN: (纸本)9798331527662

this project intends to study A semantic feature extraction algorithm that combines semantic feature-oriented lexical vector representation with attention mechanism (A-ELMO). Firstly, the fast comparison of the dictionary tree reduces the comparison of invalid features as much as possible and greatly improves the speed of retrieval. A language pattern lexical vector (ELMo) model for context analysis is established. the method uses a dynamic word vector to describe the context, which improves the extensibility of the system. the attention mechanism is introduced to enhance the recognition degree of the sensitive features in the image. We improved detection rate of sensitive data. Compared to the traditional word-level emotion parsing algorithm, the proposed algorithm shows significant improvements in accuracy. Additionally, the accuracy of this algorithm surpasses that of traditional keyword comparison algorithms. the experimental results demonstrate that the proposed algorithm has clear advantages in enhancing the sensitivity and detection ability of the sensor. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

End-to-End Tibetan U-Tsang Dialect Based Speech recognition Research

End-to-End Tibetan U-Tsang Dialect Based Speech Recognition ...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Jingbo Wang Jie Zhu School of Information Science and Technology Tibet University Lhasa Tibet China Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region Lhasa Tibet China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

To address the challenge of how to improve the accuracy and efficiency of low-resource speech recognition, we chose the Tibetan U-Tsang dialect as the object of study and increased the diversity of training data through data augmentation techniques thereby achieving the goal of improving the efficiency of speech recognition. In order to better save the information in the feature map, we use the stepwise convolution combined with the activation function approach for feature extraction; the attention layer of the Conformer model is enhanced by introducing the rotational position encoding and the enhanced model can be better adapted to different lengths. the experimental results show that our end-to-end model has a significant improvement in recognition effect, with a word error rate of only 18.73%, which effectively improves the accuracy of speech recognition in Tibetan U - Tsang dialect, and provides a new solution for speech recognition in low-resource languages.

关键词： Adaptation models Accuracy Error analysis Convolution Training data Speech recognition machine learning Feature extraction data augmentation Encoding

来源：评论

学校读者我要写书评

暂无评论

machine learning Based Cardiovascular Disease Prediction 3

Machine Learning Based Cardiovascular Disease Prediction

引用

3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Saraswathi, P. Prathiksaa, J.G. Prabha, M. Jhoshitha, N.A. It Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350330724

Heart disease is an illness that affects the heart and manifests itself in a variety of ways, including blood vessel issues, irregular heartbeats, and diseases of the heart muscle or valves. In recent years, researchers have found that machine learning can help to detect heart problems by analyzing digital electrocardiogram (ECG) data. By using this technology, doctors can detect heart issues earlier and save lives. the process involves analyzing large sets of medical data, which includes information about patients' medical history. Logistic Regression, Support Vector machine (SVM), and Random Forest are most widely utilized machine learning techniques to perform classification. Also, it predicts whether a patient has heart ailments or not. In this research, Decision Tree Classifiers are modeled along with the mentioned techniques which predict the heart related issues. A performance measure like accuracy of each technique is analyzed and the best one is identified to guess the heart disease. © 2023 IEEE.

关键词： Electrocardiograms

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319页 << < 48 49 50 51 52 53 54 55 56 57 > >>

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：