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

2,973 篇 会议
176 册 图书
24 篇 期刊文献

馆藏范围

3,172 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,043 篇 工学
- 1,825 篇 计算机科学与技术...
- 960 篇 软件工程
- 329 篇 电气工程
- 313 篇 信息与通信工程
- 214 篇 控制科学与工程
- 207 篇 生物工程
- 149 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
- 51 篇 电子科学与技术（可...
- 47 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
648 篇 理学
- 352 篇 数学
- 244 篇 生物学
- 140 篇 物理学
- 135 篇 统计学（可授理学、...
- 95 篇 系统科学
- 62 篇 化学
408 篇 管理学
- 244 篇 图书情报与档案管...
- 182 篇 管理科学与工程(可...
- 83 篇 工商管理
262 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...
- 40 篇 公共卫生与预防医...
46 篇 法学
- 42 篇 社会学
38 篇 教育学
- 32 篇 教育学
33 篇 经济学
- 31 篇 应用经济学
23 篇 农学
6 篇 艺术学
4 篇 文学

主题

575 篇 machine learning
564 篇 data mining
323 篇 pattern recognit...
212 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...
66 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 centre for patte...
4 篇 chongqing univer...
4 篇 national laborat...
4 篇 sun yat sen univ...

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

2,869 篇 英文
290 篇 其他
34 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 3173 条记录，以下是961-970 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 Lightweight Network to Learn Optical Flow from Event data 25

A Lightweight Network to Learn Optical Flow from Event Data

引用

25th international conference on pattern recognition (ICPR)

作者： Li, Zhuoyan Shen, Jiawei Liu, Ruitao Wuhan Univ Sch Elect & Informat Wuhan Peoples R China George Washington Univ Dept Stat Washington DC 20052 USA

ISBN: (纸本)9781728188089

Existing deep neural networks have found success in estimation of event-based optical flow, but are at the expense of complicated architectures. Moreover, few prior works discuss how to tackle with the noise problem of event camera, which would severely contaminate the data quality and make estimation an ill-posed problem. In this work, we present a lightweight pyramid network with attention mechanism to learn optical flow from events data. Specially, the network is designed according to two-well established principles: Laplacian pyramidal decomposition and channel attention mechanism. By integrating Laplacian pyramidal processing into CNN, the learning problem is simplified into several subproblems at each pyramid level, which can be handled by a relatively shallow network with few parameters. the channel attention block, embedded in each pyramid level, treats channels of feature map unequally and provides extra flexibility in suppressing background noises. the size of the proposed network is about only 5% of previous methods while our method still achieves state-of-the-art performance on the benchmark dataset. the experimental video samples of continuous flow estimation is presented at :https://***/xfleezy/blob.

关键词： Bio-inspired device Optical flow estimation CNN

来源：评论

学校读者我要写书评

暂无评论

Human Activity recognition Using machine learning Algorithms Based on IMU data

Human Activity Recognition Using Machine Learning Algorithms...

引用

international conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Nafiseh Ghaffar Nia Erkan Kaplanoglu Ahad Nasab Hong Qin College of Engineering and Computer Science University of Tennessee at Chattanooga Chattanooga USA

Enhancing Human Activity recognition (HAR) using Inertial Measurement Unit (IMU) sensors has garnered significant attention in healthcare, particularly wearable devices. HAR plays a crucial role in prosthetic and wearable robotics by accurately identifying and responding to user movements, enabling personalized, adaptive, and intuitive control of these devices. IMU sensors capture both acceleration and angular velocity, delivering valuable data for HAR. these sensors find widespread use in prosthetic and wearable robotics, allowing for the detection and monitoring of user limb or body activities. Various machine learning algorithms can be leveraged further to advance HAR systems. these techniques hold promise in enhancing the accuracy and robustness of HAR systems, especially when dealing with complex and diverse movements. In this study, we implement four machine learning models, including Artificial Neural Networks (ANN), Decision Tree Classifiers (DTC), Random Forest Classifiers (RFC), and K-Nearest Neighbors (KNN). Our objective is to accurately recognize human activities such as walking upstairs, walking downstairs, walking normally, walking on the toe, walking on the heel, and performing sit-up tasks. Among these models, the RFC model exhibits superior performance compared to the others, achieving an impressive accuracy score of 97.67% across all evaluation metrics for activity recognition. this work is a foundational step in augmenting wearable robotics’ functionality, usability, and personalization, ultimately improving the quality of life for individuals with disabilities or injuries.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GCNs-Based Context-Aware Short Text Similarity Model 25

GCNs-Based Context-Aware Short Text Similarity Model

引用

25th international conference on pattern recognition (ICPR)

作者： Sun, Xiaoqi Wu, Shaochun Liu, Yue Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai Inst Adv Commun & Data Sci Shanghai Engn Res Ctr Intelligent Comp Syst Shanghai 200444 Peoples R China

ISBN: (纸本)9781728188089

Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. the essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which can not capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extends the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Research on Enhanced Graph Neural Network for Automated Knowledge Graph Construction

Research on Enhanced Graph Neural Network for Automated Know...

引用

machine learning and Computer Application (ICMLCA), international conference on

作者： Qianyu Chen School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

the construction of automated knowledge graphs has become a significant undertaking in the realms of artificial intelligence and natural language processing. It offers a structured representation of knowledge, which can be leveraged for a multitude of downstream applications. However, conventional methodologies frequently prove inadequate when attempting to discern intricate relationships and to process data that is sparse or of poor quality. To address these challenges, we present a novel framework based on enhanced Graph Neural Networks (GNNs) to enhance the precision and efficiency of knowledge graph construction. this method introduces a multi-layer attention mechanism to dynamically measure the relative importance of nodes and edges, and a hybrid loss function is designed to achieve a balance between entity recognition and relationship extraction. Furthermore, a data augmentation strategy is employed to address the issue of data sparsity, thereby enhancing the model's resilience. Extensive experimental analysis was conducted on multiple benchmark datasets, and the results demonstrate that the framework outperforms current state-of-the-art models in terms of accuracy and recall. this verifies the effectiveness and potential of the framework in practical applications.

关键词： Attention mechanisms Computational modeling Knowledge graphs Spread spectrum communication Reinforcement learning data augmentation Loss measurement Graph neural networks data models Resilience

来源：评论

学校读者我要写书评

暂无评论

Text-to-CQL Based on Large Language Model and Graph pattern Enhancement

Text-to-CQL Based on Large Language Model and Graph Pattern ...

引用

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

作者： Xinke Zhao Hankiz Yilahun Askar Hamdulla School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the wide application of knowledge graphs in various scenarios and Neo4j graph databases becoming excellent knowledge graph carriers, Cypher (CQL for short) has become the most popular graph database query language. However, when performing graph database retrieval, the complex pattern and syntax make constructing CQL statements a complicated and time-consuming task. therefore, similar to Text-to-SQL, it is necessary and urgent to study an effective method for end-to-end transformation of natural languages into CQL. there have been many advances in Text-to-SQL for traditional relational databases, but these methods cannot be well applied to Text-to-CQL, so there is still a lack of effective work on Text-to-CQL for graph databases. In recent years, as large language models have been widely applied to different tasks with good results, and considering that the fundamental difference between CQL and SQL is the representation of graph patterns. We propose in this paper PA-LLM, a Text-to-CQL method that utilizes large language models combined with graph patterns enhancement, which combines large language models and subdivides the graph patterns into three categories according to their respective characteristics. they are simple query patterns, multi-hop query patterns, and function query patterns. For different graph patterns, the method optimizes the process of generating CQL for the model, which can be subdivided into four sub-methods, simple pattern enhancement method, multi-hop pattern enhancement method, function pattern enhancement method, and entity-relationship enhancement method. the experimental results show that the method improves the quality of the CQL statements generated by the model, including the logical accuracy ACC LX and the execution result accuracy ACC EX , and achieves better results on the SpCQL dataset proposed by the National Defense University of Science and Technology (NDUST). It provides an effective solution for the task of converti

关键词： Structured Query Language Accuracy Quantization (signal) Large language models Natural languages Knowledge graphs Relational databases Syntactics Real-time systems pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Change Detection of Open Pit mining Areas in Remote Sensing Images Based on Coupled Local-Global Features

Change Detection of Open Pit Mining Areas in Remote Sensing ...

引用

machine learning and Computer Application (ICMLCA), international conference on

作者： Xiaolei Wang Jingguo Lv Xiaojuan Zhang Aiyuan Zhang School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

In this paper, a coupled local-global feature-based change detection method Swin-LGCNet is proposed for remote sensing image open pit mining areas the model interactively fuses local and global features extracted by ResNet and Swin Transformer through the local-global feature coupling module, which effectively improves the feature expression capability. the Mish activation function is used instead of ReLU, which reduces the information loss and enhances the model's ability to capture complex changing features. Meanwhile, the differential operation is introduced to further enhance the difference between the dual-time phase features and significantly reduce the leakage detection rate. the experimental results show that Swin-LGCNet outperforms the traditional network in both global information extraction and local detail performance, and the detection accuracy is significantly improved compared with the comparison networks, such as PSPNet, Deeplabv3+, UNet and UNet++, especially in boundary identification and noise suppression. the method makes full use of the temporal sequence of remote sensing images, and provides efficient technical support for dynamic change monitoring and ecological protection in open pit mining areas.

关键词： Couplings Biological system modeling Noise reduction Feature extraction Transformers Sensors data mining Protection Remote sensing Monitoring

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Prediction Using Different Classification Algorithms with Various Feature Selection Strategies 5

Breast Cancer Prediction Using Different Classification Algo...

引用

5th international conference on Informatics and Computational Sciences (ICICoS)

作者： Sabha, Mohamad Tugrul, Bulent Ankara Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781665438070

Breast cancer has always been one of the most dangerous diseases that threaten women's lives. If the disease is not detected in the early stages, it can result in the death of the patient. the term breast cancer is referring to a malignant tumor that happened due to the unexpected development of breast's cells which can probably have the ability to spread through other different parts of the patient's body. the occurrence of cancer is often a result of the abnormal growth of cells in our bodies. Cancers generally are classified into two types, Benign (non-cancerous cell) and Malignant (cancerous cell). the earlier the cancer is diagnosed, the better the patient's chance of recovery. Being able to accurately predict breast cancer present in patients has always been an important issue for cancer researchers. machine learning (ML) and data mining (DM) have always been a point of interest in the scientific community in the hope that they can yield accurate results. We aimed in this study to predict the tumor at early stages using some classification algorithms. After the dataset was collected and the outliers and skewness in the data set were removed, different classification algorithms were applied, focusing on the effect of the feature selection step in the model building phase. After conducting multiple experiments, we got the best overall accuracy by Support Vector machine (SVM) classifier based on feature selection using Recursive Feature Elimination (RFE) with Random Forest (RF) technique with an accuracy of 98.25%.

关键词： Breast Cancer Feature Selection Classification data mining

来源：评论

学校读者我要写书评

暂无评论

A Deep learning Approach for Face Detection using Max Pooling 5

A Deep Learning Approach for Face Detection using Max Poolin...

引用

5th international conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Mehedi Shamrat, F.M. Javed Jubair, Md. Al Billah, Md. Masum Chakraborty, Sovon Alauddin, Md. Ranjan, Rumesh Daffodil International University Department of Software Engineering Dhaka Bangladesh European University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Department of Plant Breeding and Genetics Punjab Agriculture University Punjab India

ISBN: (纸本)9781665415712

Deep learning is a trendy term these days, and it refers to a modern age in machine learning in which algorithms are taught to identify patterns in vast amounts of data. It mostly refers to studying various layers of representation, which assists in the understanding of data that includes text, sound, and pictures. To interact with the objects in a video series, many researchers use a form of deep learning called a CNN. Face detection involves several face-related technologies, such as face authentication, facial recognition, and face clustering. For identification and understanding, effective preparation must be carried out. the standard technique did not produce a positive outcome in terms of face recognition precision. the objectives of this research are by using a deep learning model to enhance the accuracy of face detection. For recognizing faces from datasets, the proposed model utilizes a deep learning technique named convolutional neural networks. the proposed work is applied using Max Pooling, a well-known deep learning process. Our model is trained and validated using the LFW dataset, which includes 13000 photos collected from Kaggle. the training accuracy of the model was 95.72% percent, and the validation accuracy was 96.27%. © 2021 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Corn Seed Classification Using Deep learning as an Effort to Increase Corn Productivity 5

Corn Seed Classification Using Deep Learning as an Effort to...

引用

5th international conference on Informatics and Computational Sciences (ICICoS)

作者： Satoto, Budi Dwi Wahyuningrum, Rima Tri Univ Trunojoyo Madura Dept Informat Syst Bangkalan Indonesia Univ Trunojoyo Madura Dept Informat Engn Bangkalan Indonesia

ISBN: (纸本)9781665438070

Corn has an important meaning in the development of the agricultural industry because it is one of the raw materials for the food industry and animal feed industry. With the development of the food processing industry, the demand for corn has increased. there is one important obstacle, namely the selection of the quality of corn seeds so that the quality of corn produced is maximized. To overcome these obstacles, one of them is machine-based learning. the aim is to perform automatic classification of the seed quality of corn seeds. One method that can be used is the convolutional neural network because this algorithm has very good classification accuracy results, but the weakness is that the training time is quite high. the architecture chosen is the residual network and custom layer. Improvements made to the method are the use of a custom layer to reduce training time, the addition of a proposal region to focus more on corn seeds, and augmentation to increase the variation in the amount of data. the results showed that the results of the accuracy of the pattern recognition of corn seeds produced on average were 99,3% with the computation time of the training process with a custom layer of 2 minutes 32 seconds. the Results of average classification error for both architectures are Mean Square Error 0,0080, Root Mean Square Error 0,0935, and Mean Absolute Error 0,0063.

关键词： Corn seed Resnet Region proposal Custom Convolutional Neural Network

来源：评论

学校读者我要写书评

暂无评论

Detailed analysis of machine learning and Deep learning technologies for Web Security 5

Detailed analysis of Machine Learning and Deep Learning tech...

引用

5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Nayak, Chhaya Raman, Ramakrishnan Dileep, P. Gehlot, Anita Priyanka, Gogula Laxmi Kapila, Dhiraj G. H. Raisoni College of Engineering and Management Maharashtra Pune India Maharashtra Pune India Malla Reddy College of Engineering and Technology Kompally Department of Computer Science and Engineering Telangana Hyderabad India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Electronics and Communication Engineering St Martin's Engineering College Secunderabad India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350398267

As the Internet has expanded, web attacks have become more sophisticated, and web security is currently in a poor position. Websecurity technology is used to protect networks, programmes, and systems from webattacks. Webattacks are capable of getting access to delete or alter sensitive data, demand payment from users, and hinder company activities as usual. Currently, many technologies are becoming smarter than hackers and people, which make it challenging to put websecurity precautions into practise. Notwithstanding the primary writing surveys on ML and DL methods for network investigation and interruption recognition, this review report contains a concise instructional exercise portrayal of every ML/DL strategy. In light of the sequential or warm connections between's the works that addressed every strategy, they were listed, checked out, and summed up. We feature probably the most generally utilized network datasets;make sense of the troubles utilizing ML/DL for websecurity, and make ideas for future review since information are so essential to ML/DL strategies. © 2022 IEEE.

关键词： Intrusion detection

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318页 << < 93 94 95 96 97 98 99 100 101 102 > >>

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：