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

1,719 篇 会议
45 册 图书
12 篇 期刊文献

馆藏范围

1,775 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,224 篇 工学
- 1,093 篇 计算机科学与技术...
- 629 篇 软件工程
- 253 篇 信息与通信工程
- 200 篇 电气工程
- 177 篇 生物工程
- 99 篇 控制科学与工程
- 98 篇 生物医学工程（可授...
- 60 篇 光学工程
- 46 篇 化学工程与技术
- 45 篇 电子科学与技术（可...
- 41 篇 机械工程
- 41 篇 仪器科学与技术
- 40 篇 安全科学与工程
- 30 篇 交通运输工程
- 28 篇 动力工程及工程热...
- 27 篇 材料科学与工程（可...
456 篇 理学
- 199 篇 数学
- 197 篇 生物学
- 144 篇 物理学
- 83 篇 统计学（可授理学、...
- 51 篇 系统科学
- 46 篇 化学
233 篇 管理学
- 167 篇 图书情报与档案管...
- 86 篇 管理科学与工程(可...
- 43 篇 工商管理
182 篇 医学
- 160 篇 临床医学
- 72 篇 基础医学(可授医学...
- 39 篇 药学(可授医学、理...
- 32 篇 公共卫生与预防医...
34 篇 法学
- 30 篇 社会学
17 篇 经济学
14 篇 教育学
11 篇 农学
2 篇 艺术学
1 篇 军事学

主题

236 篇 machine learning
181 篇 deep learning
137 篇 pattern recognit...
107 篇 feature extracti...
89 篇 support vector m...
88 篇 data mining
74 篇 accuracy
72 篇 training
57 篇 convolutional ne...
50 篇 machine learning...
46 篇 artificial intel...
45 篇 data models
40 篇 neural networks
39 篇 face recognition
38 篇 computational mo...
36 篇 learning systems
36 篇 adversarial mach...
33 篇 predictive model...
32 篇 classification
32 篇 image recognitio...

机构

6 篇 college of compu...
5 篇 xi'an university...
4 篇 badji mokhtar un...
4 篇 shanghai jiao to...
4 篇 institute of big...
4 篇 chitkara centre ...
4 篇 chitkara univers...
4 篇 library shanghai...
3 篇 school of comput...
3 篇 univ chittagong ...
3 篇 chongqing univer...
3 篇 centre of resear...
3 篇 school of comput...
3 篇 school of comput...
3 篇 school of design...
3 篇 korea univ dept ...
3 篇 school of automa...
3 篇 university of mi...
3 篇 shenyang jianzhu...
3 篇 xi'an university...

作者

5 篇 venkatesh svetha
4 篇 ashwini kodipall...
4 篇 zhang yan
4 篇 srinivasa gowri
4 篇 kamel mohamed s.
4 篇 lall brejesh
4 篇 trupthi rao
3 篇 andersson karl
3 篇 peng yang
3 篇 malerba donato
3 篇 karl andersson
3 篇 chaudhury santan...
3 篇 de-shuang huang
3 篇 sekhar c. chandr...
3 篇 wang shuo
3 篇 kamata sei-ichir...
3 篇 mohammad shahada...
3 篇 azizi nabiha
3 篇 phung dinh
3 篇 krasotkina olga

语言

1,696 篇 英文
71 篇 其他
22 篇 中文

检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1776 条记录，以下是1-10 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：

2023 IEEE 4th international conference on pattern recognition and machine learning, PRML 2023

2023 IEEE 4th International Conference on Pattern Recognitio...

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

ISBN: (纸本)9798350324303

the proceedings contain 98 papers. the topics discussed include: self-supervised learning for point clouds through multi-crop mutual prediction;assessment and prediction of corrosion rate of marine railway bridges based on ridge regression model;Chunk-BERT: boosted keyword extraction for long scientific literature via BERT with chunking capabilities;chunking of Setswana noun and verb phrase;RarKGQA: multi-hop question and answering method based on knowledge graph embedding;diagnosis and identification of citrus canker growth rate using machine learning;joint spatial similarity-based attention for retina vessel segmentation with super resolution encoding;mixed attention interleaved execution cascade architecture for fittings instance segmentation in overhead transmission line images;and RFBiCF: a relation-first bidirectional cascade framework for relational triple extraction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Perspective of deep learning strategies for analysis of 1D biomedical signals 4th

Perspective of deep learning strategies for analysis of 1D b...

引用

4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Vishwanath, K.R. Rohith, V. Vinutha, D.C. Vijay, C.P. Rao, Soundarya Govinda Sriraam, N. VTU India VVCE India

ISBN: (纸本)9781032426853

Artificial Intelligence and machine learning encompass a broader range of technologies, of which Deep learning is a specific subset and it is expeditiously evolving in the medical field. Most medical devices or imaging techniques use deep learning as a critical tool for analysing the data. the proposed study is a literature review on the methodologies that are used for the sleep -wake cycle detection. the importance of deep learning in the field of ECG, EEG and analysis of infant cry signals is also demonstrated in the sections below. Further analytical studies explain about the facial pattern recognition of the new-born in NICU and also discuss the benefits of analysing the data especially in the case of infants. © 2024 the Author(s).

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

machine learning Techniques for pattern recognition in High-Dimensional data Mining 4

Machine Learning Techniques for Pattern Recognition in High-...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Li, Pochun Northeastern University Boston United States

ISBN: (纸本)9798331534622

this paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and sparse data environments. By converting the frequent pattern mining task into a classification problem, the SVM model is introduced to improve the accuracy and robustness of pattern extraction. In terms of method design, the kernel function is used to map the data to a high-dimensional feature space, so as to construct the optimal classification hyperplane, realize the nonlinear separation of patterns and the accurate mining of frequent items. In the experiment, two public datasets, Retail and Mushroom, were selected to compare and analyze the proposed algorithm with traditional FP-Growth, FP-Tree, decision tree and random forest models. the experimental results show that the algorithm in this paper is significantly better than the traditional model in terms of three key indicators: support, confidence and lift, showing strong pattern recognition ability and rule extraction effect. the study shows that the SVM model has excellent performance advantages in an environment with high data sparsity and a large number of transactions, and can effectively cope with complex pattern mining tasks. At the same time, this paper also points out the potential direction of future research, including the introduction of deep learning and ensemble learning frameworks to further improve the scalability and adaptability of the algorithm. this research not only provides a new idea for frequent pattern mining, but also provides important technical support for solving pattern discovery and association rule mining problems in practical applications. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Tool Wear State recognition Based on Multi-source Feature Fusion and Deep learning 4

Tool Wear State Recognition Based on Multi-source Feature Fu...

引用

4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Song, Ning Yu, Yuna Han, Tongtongn Xie, Guihua Mo, Desheng Li, Na Qilu Inst Technol Jinan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Aiming at the problems of single feature dimension and low accuracy in condition monitoring only from vibration characteristics and tool wear characteristics. In this paper, a tool wear state recognition method based on multi-source feature fusion and deep learning is proposed. the main innovation of our work lies in two aspects: firstly, the tool wear data is processed by multi-source feature fusion method, which effectively fuses multiple sensor signals and improves the efficiency and accuracy of data processing. Secondly, we optimize GRU (Gated Recursive Unit) model by combining whale algorithm for parameter optimization and add XGBoost to improve the prediction performance and robustness. Different milling wear experimental data sets are used to verify the recognition performance of the training model. the experimental results show that compared with the traditional methods, our proposed method has obvious advantages in tool wear state recognition.

关键词： tool wear multi-source feature fusion deep learning GRU whale algorithm

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Deep learning Models for Human Activity recognition in Edge Computing Environments 4

Privacy-Preserving Deep Learning Models for Human Activity R...

引用

4th international conference on Artificial Intelligence and Signal Processing

作者： Kumar, Kambala Vijaya Pranitha, Challari Tanuja, Annam PriyaDarsini, Pagolu Indira Patnala, Eswar Dasari, Vinay Kumar Koneru Lakshmaiah Educ Fdn Dept CSE Guntur Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

the increase of wearable devices and smart sensors led to increase of human activity recognition system;this plays a vital role in various domains like healthcare and mainly we need to take further modifications on education, sports and security. Human activity recognition system raises privacy concerns, it often involves the collection of sensitive data of users, there is an approach to modernizing human activity recognition system by using machine learning techniques and data identification and lattice based methods to ensure privacy while restructuring and managing unwanted data or activities in the recognition process. Human activity recognition system involves the continuous monitoring and analyzing the individual activities. To address this, we can use machine learning approaches like deep learning models which improve the accuracy of activity recognition while minimizing the intrusion on the user privacy. Here we use machine learning for accurate the activity recognition and deep-deep learning techniques for preserving privacy and restructuring of unwanted activities. Combining these techniques we can safeguard user privacy in an increasingly data centric world. In this paper we are seeing as the negligible of human exercises from the pictures or recordings taken from the different public where the high goal can be distinguished and getting the exceptionally low goal and protection can be stowed away from such pictures or recordings.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

A Review on the Applications of machine learning and Deep learning Algorithms for Image recognition 4

A Review on the Applications of Machine Learning and Deep Le...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of machine learning and Deep learning algorithms together. this study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. this paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. the latest image processing techniques include ViTs alongside GANs and Few-Shot learning but developers need to achieve better results in future improvements. the main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. this paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Securing Personal Identity in Facial recognition: the Shift to machine Unlearning 4

Securing Personal Identity in Facial Recognition: The Shift ...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Uday Kiran, G. Srilakshmi, V. Moulika, B. Mahitha, G.S. Laukya, G. Ruthick, M. Telangana Narsapur India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

machine Unlearning, which enables the successful removal of specific data from machine learning models, is a crucial approach for ensuring data privacy and compliance. Most existing unlearning techniques have been evaluated on conventional benchmark datasets, which often fail to replicate the complexity of real-world settings, especially when dealing with sensitive data such as private personal information. the majority of machine learning research has been conducted on traditional datasets, which may not fully capture the challenges encountered in real-world scenarios, particularly concerning sensitive and private data. To address this gap, the proposed method utilizes two datasets, MUFAC and MUCAC, specifically designed to evaluate the effectiveness of machine Unlearning in facial recognition contexts. Following the training of the model using facial data, a machine learning framework is developed. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A machine learning-Based Method for Assisted Analysis and Decision Making of Wafer Yield 4

A Machine Learning-Based Method for Assisted Analysis and De...

引用

4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Qin, Jieli Xiamen Inst Technol Higher Educ Key Lab Flexible Mfg Equipment Integr Xiamen 361021 Peoples R China Xiamen Inst Technol Sch Mech Elect & Informat Engn Xiamen 361021 Fujian Peoples R China

ISBN: (纸本)9798350375084;9798350375077

With the rapid development of the semiconductor industry, the process technology has become more complex, and it is increasingly important to maximize the control of defects in production and improve wafer yield. Defects shown in Wafer Bin Maps (WBMS) are often strongly correlated with local system failures in production. In view of this, this paper proposes the convolutional neural networks (CNNs) architecture based on machine learning, and realizes the classification of hybrid defects by designing a separate classification model of minimum defects and using the probability analysis of recognition results. the purpose is to classify and statistics the faults of the wafer diagram fault diagram, perform yield analysis and calculation, identify defect types according to the wafer (fault pattern recognition), and take corresponding decisions to adjust the process in view of the systematic fault chip. the effectiveness of the method is proved by experiments, and the identification accuracy of single defects is higher, and the detection performance of mixed defects is also improved, and the overall performance is better.

关键词： Wafer Bin Maps(WBMs) yield Convolutional neural networks defect identification

来源：评论

学校读者我要写书评

暂无评论

A Novel Model for Recognising Handwritten Devanagari Numerals using machine learning 4

A Novel Model for Recognising Handwritten Devanagari Numeral...

引用

4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Prathima, Ch Arava, Ramprakash Reddy Sevitha, K. Manikanth, G. Vinay, D. Surya, C. Mohan Babu Univ Sch Comp Tirupati Andhra Pradesh India KSRM Coll Engn Dept CSE Kadapa Andhra Pradesh India Sree Vidyanikethan Engn Coll Dept CSSE Tirupati Andhra Pradesh India

ISBN: (纸本)9798350386356;9798350386349

this research study performs a comprehensive comparative analysis aimed at developing effective machine learning models for classifying handwritten Devanagari numerals. the research focuses on evaluating the performance of various models to determine the most accurate classification approach. Initiating with data pre-processing, including feature extraction and normalization, the data is prepared for model training and assessment. A diverse range of machine learning models, from traditional methods like Support Vector machines (SVM) to advanced techniques such as Random Forests, K-Nearest Neighbors, and Convolutional Neural Networks, are considered for the comparative analysis, ensuring a thorough assessment of classification capabilities. Cross-validation techniques are employed during model training and testing to enhance reliability. Statistical tests are utilized to assess the performance variations among models, enhancing the robustness of the analysis. Visual representations of performance metrics and comparison results offer clear insights. this research study aims to identify the most suitable machine learning model for handwritten Devanagari numeral classification, potentially advancing character recognition systems and linguistic applications.

关键词： Handwritten character recognition machine learning Support Vector machine(SVM) data augmentation Transfer learning Digital document processing

来源：评论

学校读者我要写书评

暂无评论

Metagenomic Face: Applying machine learning to Facial Microbiome data for pattern recognition and Disease Classification 4

Metagenomic Face: Applying Machine Learning to Facial Microb...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Guru Prasad, M.S. Devadas, Raghavendra M Hiremani, Vani Jahan, Afsar Praveen Gujjar, J. Dahiya, Aryan Department of Computer Science and Engg Dehradun India Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India University Pune India Department of Computer Applications Dehradun India Faculty of Management Studies Bengaluru India

ISBN: (纸本)9798350349900

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework's efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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178页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：