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

1,129 篇 会议
11 篇 期刊文献
7 册 图书

馆藏范围

1,147 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

748 篇 工学
- 708 篇 计算机科学与技术...
- 441 篇 软件工程
- 136 篇 信息与通信工程
- 108 篇 电气工程
- 72 篇 生物医学工程（可授...
- 67 篇 生物工程
- 65 篇 控制科学与工程
- 32 篇 安全科学与工程
- 29 篇 化学工程与技术
- 21 篇 网络空间安全
- 20 篇 动力工程及工程热...
- 19 篇 机械工程
- 19 篇 电子科学与技术（可...
- 17 篇 仪器科学与技术
252 篇 理学
- 185 篇 数学
- 78 篇 系统科学
- 77 篇 生物学
- 64 篇 统计学（可授理学、...
- 24 篇 化学
- 15 篇 物理学
215 篇 管理学
- 121 篇 图书情报与档案管...
- 104 篇 管理科学与工程(可...
- 52 篇 工商管理
77 篇 医学
- 68 篇 临床医学
- 61 篇 基础医学(可授医学...
- 47 篇 药学(可授医学、理...
- 27 篇 公共卫生与预防医...
33 篇 法学
- 32 篇 社会学
31 篇 教育学
- 27 篇 教育学
22 篇 经济学
- 22 篇 应用经济学
15 篇 农学
1 篇 文学
1 篇 军事学

主题

374 篇 data mining
237 篇 machine learning
56 篇 support vector m...
56 篇 machine learning...
48 篇 feature extracti...
45 篇 data models
44 篇 accuracy
42 篇 sentiment analys...
40 篇 big data
39 篇 classification a...
35 篇 predictive model...
34 篇 classification
31 篇 deep learning
30 篇 training
29 篇 prediction algor...
26 篇 forecasting
23 篇 neural networks
23 篇 decision trees
20 篇 artificial intel...
19 篇 learning systems

机构

4 篇 college of techn...
3 篇 department of sc...
3 篇 galgotias univer...
3 篇 shanghai univers...
3 篇 uttaranchal univ...
3 篇 uttaranchal inst...
2 篇 new prince shri ...
2 篇 china transport ...
2 篇 miet haldwani
2 篇 school of it viv...
2 篇 state grid custo...
2 篇 world college of...
2 篇 school of econom...
2 篇 china electric p...
2 篇 college of compu...
2 篇 shandong institu...
2 篇 chitkara univers...
2 篇 school of comput...
2 篇 college of infor...
2 篇 state key labora...

作者

3 篇 guidotti riccard...
3 篇 raman ramakrishn...
3 篇 vysotska victori...
3 篇 chen feng
2 篇 chen haifeng
2 篇 yakovyna vitaliy
2 篇 kulvinder singh
2 篇 monreale anna
2 篇 buddhima attanay...
2 篇 jacob shomona gr...
2 篇 madhurya j a
2 篇 wang hui
2 篇 gama j
2 篇 kalinina irina
2 篇 suwimon kooptiwo...
2 篇 nitin n. sakhare
2 篇 gozhyj aleksandr
2 篇 das rama krushna
2 篇 xu weiqiang
2 篇 k.k. senthilkuma...

语言

1,081 篇 英文
64 篇 其他
17 篇 中文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining, MLDM 2003"

共 1147 条记录，以下是11-20 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：

3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

3rd International Conference on Machine Learning and Big Dat...

引用

3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

The proceedings contain 150 papers. The topics discussed include: analysis of communication strategy of Guzheng art based on artificial intelligence;research on the health detection and seismic performance evaluation of high-rise buildings;application of ecological energy saving design in building reconstruction project;computer network communication security encryption system based on ant colony optimization algorithm;an analysis of data mining techniques in software engineering database design;intelligent analysis algorithm for hidden danger identification of intelligent network monitoring system from the perspective of big data;human behavior recognition of video surveillance system based on neural network;application of AI intelligent learning system in multimedia demonstration;and application of artificial intelligence technology in power system stability assessment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Construction and Validation of Big data Analytics in Air Traffic Control Flow Prediction Models 24

Construction and Validation of Big Data Analytics in Air Tra...

引用

3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Wang, Minghua Qiao, Jiantong Air Force Engineering University ATC&GCL College Shaanxi Xi'an China

ISBN: (纸本)9798400718120

Air traffic control flow prediction is one of the key issues in aviation transportation management, crucial for optimizing resource allocation, enhancing control efficiency, and ensuring flight operation safety. This paper proposes a solution for air traffic control flow prediction based on big data analysis and machine learning techniques. Through extensive feature engineering and model construction using large-scale historical data, multiple prediction models including Support Vector Regression, Random Forest, Gradient Boosting Decision Tree, and an ensemble model were trained. Comprehensive performance evaluations using cross-validation and multiple metrics were conducted. Experimental results demonstrate that the proposed ensemble model effectively combines the strengths of multiple base models, significantly outperforming individual models in terms of prediction accuracy, stability, and generalization ability. This provides an efficient and reliable technical path for air traffic control flow prediction, with broad application prospects. © 2024 Copyright held by the owner/author(s).

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

A Tennis Momentum Analysis Method Based on Gaussian Dynamics and machine learning 24

A Tennis Momentum Analysis Method Based on Gaussian Dynamics...

引用

3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Long, Zheng Li, Na Sun, Mengqing Luo, Jiawen Pan, Ting Yin, Yujie Yang, Xinhua School of Computer and Communication Lanzhou University of Technology Gansu Lanzhou China School of Economics and Management Lanzhou University of Technology Gansu Lanzhou China College of Energy and Power Engineering Beihang University Beijing Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing Beijing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China College of Electrical and Information Engineering Lanzhou University of Technology Gansu Lanzhou China

ISBN: (纸本)9798400718120

Momentum analysis in tennis matches has predominantly focused on qualitative methods, lacking systematic quantitative approaches. This study proposes a novel method that integrates Gaussian dynamics models with machine learning techniques to quantitatively analyze and predict momentum changes in tennis matches. By selecting and defining key indicators such as serve success rate and scoring rate, we developed an algorithm to identify and predict momentum shifts. The proposed method employs ensemble learning techniques, specifically Stacking, to combine multiple machine learning models, enhancing prediction accuracy and execution efficiency. Experimental validation using data from major tennis tournaments, including the 2023 Wimbledon Championships, demonstrates the effectiveness of the proposed approach. The results show that the Stacking model outperforms individual models in accuracy and robustness, providing scientific decision support for coaches and players. This method has low computational requirements, is simple to implement, and considers a wide range of variables, making it highly efficient. Future research will focus on further optimizing the model and applying it to other sports and match types. © 2024 Copyright held by the owner/author(s).

关键词： Gaussian dynamics model

来源：评论

学校读者我要写书评

暂无评论

Enhanced data mining Algorithms for Sybil Attack Prevention in Truth Discovery 24

Enhanced Data Mining Algorithms for Sybil Attack Prevention ...

引用

3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Shao, Yanjing Zhao, Wei Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology China National Key Laboratory of Security Communication China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400718120

In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However, these systems are vulnerable to Sybil attacks, where attackers create multiple fake contributors to gain undeserved rewards. The presence of differential privacy noise complicates the detection of these fraudulent contributors, thereby reducing the accuracy of truth discovery. To address this challenge, we introduce an enhanced data mining algorithm, Truth Discovery against Sybil Attacks (TDSA). This algorithm is specifically designed to improve truth discovery by effectively mitigating Sybil attacks and reducing transmission errors in data mining contexts, utilizing machine learning clustering algorithms and lattice-based linear coding techniques. TDSA optimizes the mining process to better handle malicious data contributions and reduces errors associated with data aggregation. Experimental results on real-world datasets demonstrate that TDSA improves accuracy by approximately 20% compared to conventional methods lacking Sybil attack defenses, highlighting its efficacy in advanced data mining applications. © 2024 Copyright held by the owner/author(s).

关键词： data aggregation

来源：评论

学校读者我要写书评

暂无评论

Big data mining of Website Recruitment data Based on Multi-source data Fusion and machine learning Methods 3rd

Big Data Mining of Website Recruitment Data Based on Multi-s...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Dong, Xiaoyang Wang, Jing Strategic Assessments and Consultation Institute Academy of Military Sciences Beijing100091 China

ISBN: (纸本)9789819616978

With the development of information technology, a large amount of data is generated and correlated. Realizing the integration of data from different channels, comprehensive analysis and data mining of multi-source data have gradually become a significant research issue. As the needs of various types of talent recruitment increases, website recruitment has been universal, along with multi-source data environment. It is a typical scenario for data fusion and data mining. The research proposes data comprehensive mining framework based on data fusion and machine learning methods. After that, taking website data analysis as an example, the research integrates various open-source data such as website data, city developing data, time data and other relevant data as well as displays the distribution characteristics of website recruitment of talents. It systematically excavates the inherent law of website recruitment of talents, identifies the factors affecting position popularity, and puts forward policy suggestions. It provides the theoretical reference and case application for the comprehensive framework of data fusion and data mining. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

3rd international conference on Intelligent data Engineering and Automated learning, IDEAL 2002

引用

3rd international conference on Intelligent data Engineering and Automated learning, IDEAL 2002

ISBN: (纸本)9783540440253

The proceedings contain 89 papers. The special focus in this conference is on data mining and Knowledge Engineering. The topics include: mining frequent sequential patterns under a similarity constraint;pre-pruning classification trees to reduce overfitting in noisy domains;data mining for fuzzy decision tree structure with a genetic program;co-evolutionary data mining to discover rules for fuzzy resource management;discovering temporal rules from temporally ordered data;automated personalisation of internet users using self-organising maps;data abstractions for numerical attributes in data mining;calculating aggregates with range-encoded bit-sliced index;a classification algorithm for data mining;a hierarchical model to support Kansei mining process;indexing and mining of the local patterns in sequence database;a knowledge discovery by fuzzy rule based hopfield network;fusing partially inconsistent expert and learnt knowledge in uncertain hierarchies;organisational information management and knowledge discovery in email within mailing lists;design of multi-drilling gear machines by knowledge processing and machine simulation;approach based on hierarchically structured subject domain;a knowledge-based information extraction system for semi-structured labeled documents;measuring semantic similarity between words using lexical knowledge and neural networks;extraction of hidden semantics from web pages;self-organising maps for hierarchical tree view document clustering using contextual information;schema discovery of the semi-structured and hierarchical data;indexing and retrieving web document using computational and linguistic techniques and an intelligent mobile commerce system with dynamic contents builder and mobile products browser.

关键词：

来源：评论

学校读者我要写书评

暂无评论

3rd international conference on machine learning, Optimization, and Big data, MOD 2017

引用

3rd international conference on machine learning, Optimization, and Big data, MOD 2017

ISBN: (纸本)9783319729251

The proceedings contain 49 papers. The special focus in this conference is on machine learning, Optimization, and Big data. The topics include: Nonlinear methods for design-space dimensionality reduction in shape optimization;a differential evolution algorithm to develop strategies for the iterated prisoner’s dilemma;automatic creation of a large and polished training set for sentiment analysis on twitter;forecasting natural gas flows in large networks;a differential evolution algorithm to semivectorial bilevel problems;evolving training sets for improved transfer learning in brain computer interfaces;hybrid global/local derivative-free multi-objective optimization via deterministic particle swarm with local linesearch;artificial bee colony optimization to reallocate personnel to tasks improving workplace safety;multi-objective genetic algorithm for interior lighting design;improving support vector machines performance using local search;an elementary approach to the problem of column selection in a rectangular matrix;A simple and effective lagrangian-based combinatorial algorithm for S3VMs;A heuristic based on fuzzy inference systems for multiobjective IMRT treatment planning;data-driven machine learning approach for predicting missing values in large data sets: A comparison study;mineral: Multi-modal network representation learning;visual perception of mixed homogeneous textures in flying pigeons;estimating dynamics of honeybee population densities with machine learning algorithms;SQG-differential evolution for difficult optimization problems under a tight function evaluation budget;age and gender classification of tweets using convolutional neural networks;approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem;projective approximation based quasi-newton methods;a data mining tool for water uses classification based on multiple classifier systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Content Recommendations Using machine learning-Based Approaches 3rd

Content Recommendations Using Machine Learning-Based Approac...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Indian, Ajay Meena, Gaurav Mohbey, Krishna Kumar Yadav, Bharati Central University of Rajasthan Ajmer India

ISBN: (纸本)9789819624676

In today’s society, recommendation systems are critical. Because there is a huge amount of data around us, whether music, book, or movie data, numerous programs or businesses, including Netflix, Amazon, and Spotify, focus on recommendation systems such as music apps. Systems for recommending content are intended to assist viewers by reducing the need for them to choose from a large variety that may number in the thousands or even millions. With the help of the movie lens dataset, this work aims to conduct a study to analyze the effectiveness of machine learning techniques, like K-NN, XG-Boost algorithms with Pearson Correlation Coefficient, and Count Vectorizer model to develop the Con-tent Recommender System for suggesting the movie. The performance of the proposed systems is evaluated in this study using the RMSE of 1.1062 and 1.1089 for KNN and XG-Boost algorithms, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

PL-attack: Projected Light-based Adversarial Attacks against Deep Neural Networks in the Physical World 24

PL-attack: Projected Light-based Adversarial Attacks against...

引用

3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Huang, Haojie Chen, Jinyin Zheng, Haibin Chen, Yixin Huang, Haoben College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

The rapid development of artificial intelligence has led to the increasing application of image recognition in autonomous driving and surveillance. However, these systems are often vulnerable to adversarial attacks. In this paper, a multi-target optimized projection attack method based on image recognition is designed and implemented to evaluate and improve the security of image recognition system. By controlling the projection parameters, you can control the color, clarity, shape, and size of the projection interference, projecting it onto the object to test the recognition effect. We built a physical platform, implemented the existing attack, and then designed a multi-target optimized projection attack method to optimize the projection properties through particle swarm optimization. Tests on ResNet18 and custom fruit models have shown a 95% success rate for indoor and 90% outdoor dark attacks. This highlights the vulnerability of image recognition to physical adversarial attacks and highlights the need to enhance the system’s anti-interference capabilities, thus providing insights for protecting intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Predicting Course Grades for Students Using machine learning: Determine Early Prediction Time 3rd

Predicting Course Grades for Students Using Machine Learning...

引用

3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, Thi Hong Bui, Quang Vinh Nguyen, Viet Anh Do, Trung Kien Hanoi National University of Education Hanoi Viet Nam VNU University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9783031809422

Predicting learning outcomes (LO) is an important problem in educational data mining (EDM). Despite extensive research, there remains a gap in determining the optimal timing for predicting early LO with acceptable accuracy. This study proposes a methodology for developing a predictive model using machine learning techniques to identify the appropriate time to forecast students’ course scores in a blended learning environment. The scope of the research includes students participating in the subject ‘Introduction to Computer Science’ at Hanoi National University of Education (HNUE). The experimental data includes data from the learning management system (LMS) and the face-to-face classroom. The research model is built based on machine learning algorithms, including Linear Regression (LN), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The research results show that the attributes most strongly influencing course scores are average practice score, midterm score, and major. The ANN model provides the highest efficiency with R2 = 0.6033. Early prediction of final exam scores can be made from week 7. © 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

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115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：