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

2,644 篇 会议
61 篇 期刊文献
38 册 图书
3 篇 学位论文

馆藏范围

2,746 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,237 篇 工学
- 1,991 篇 计算机科学与技术...
- 779 篇 软件工程
- 488 篇 电气工程
- 437 篇 信息与通信工程
- 174 篇 控制科学与工程
- 167 篇 生物工程
- 119 篇 生物医学工程（可授...
- 56 篇 仪器科学与技术
- 55 篇 化学工程与技术
- 50 篇 安全科学与工程
- 46 篇 机械工程
- 42 篇 电子科学与技术（可...
- 38 篇 光学工程
- 26 篇 动力工程及工程热...
537 篇 理学
- 279 篇 数学
- 187 篇 生物学
- 114 篇 物理学
- 108 篇 系统科学
- 108 篇 统计学（可授理学、...
- 54 篇 化学
266 篇 管理学
- 141 篇 图书情报与档案管...
- 136 篇 管理科学与工程(可...
- 64 篇 工商管理
146 篇 医学
- 122 篇 临床医学
- 56 篇 基础医学(可授医学...
- 36 篇 药学(可授医学、理...
- 27 篇 特种医学
- 25 篇 医学技术（可授医学...
41 篇 法学
- 33 篇 社会学
35 篇 教育学
- 28 篇 教育学
22 篇 经济学
22 篇 农学
8 篇 文学
5 篇 艺术学
1 篇 军事学

主题

897 篇 machine learning
218 篇 deep learning
123 篇 feature extracti...
106 篇 training
103 篇 machine learning...
85 篇 classification
72 篇 cloud computing
70 篇 support vector m...
70 篇 neural networks
62 篇 big data
60 篇 data mining
60 篇 computational mo...
53 篇 artificial intel...
49 篇 sentiment analys...
45 篇 predictive model...
43 篇 intrusion detect...
40 篇 reinforcement le...
38 篇 data models
35 篇 neural network
34 篇 internet of thin...

机构

12 篇 univ chinese aca...
11 篇 institute of mac...
7 篇 school of comput...
7 篇 fudan university
7 篇 school of electr...
7 篇 guangxi academy ...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 in2p3 lpc campus...
6 篇 institut univers...
6 篇 national centre ...
6 篇 tno technical sc...
6 篇 université de st...
6 篇 nikhef national ...
6 篇 university würzb...
6 篇 université de ha...
6 篇 university of jo...
6 篇 university moham...
6 篇 astrocent nicola...

作者

14 篇 qin xiao
8 篇 yuan chang-an
8 篇 huang de-shuang
7 篇 chen hao
7 篇 yuan changan
7 篇 wu hongjie
7 篇 zhao xingming
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.

语言

2,639 篇 英文
69 篇 中文
39 篇 其他

检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

共 2746 条记录，以下是341-350 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：

Hierarchical Classification of Android Malware Traffic

Hierarchical Classification of Android Malware Traffic

引用

IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Giampaolo Bovenzi Valerio Persico Antonio Pescapé Anna Piscitelli Vincenzo Spadari University of Napoli "Federico II" Italy

In the last few years, Android mobile devices have encountered a large spread and nowadays a huge part of the traffic traversing the Internet is related to them. In parallel, the number of possible threats and attacks has also increased, thus emphasizing the need for accurate automatic malware detection systems. In this paper, we design and evaluate a system to detect whether a traffic object (biflow) is benign or malicious, possibly understanding its specific nature in the latter case. The proposal leverages machine learning in a hierarchical fashion, in order to capitalize on the structure of the traffic data and reap both design and performance benefits. The comparative evaluation—performed considering the public CICAndMal2017 dataset—assesses the performance of several machine-learning algorithms and witnesses that the hierarchical approach leads to improved performance w.r.t. the flat approach (up to +0.18 F1-score, depending on the granularity of the analysis and the machine learning algorithm considered). In addition, we evaluate the impact of a reject-option mechanism, showing the trade-off between classification accuracy and ratio of classified biflows.

关键词： Deep learning Privacy machine learning algorithms Feature extraction Malware Classification algorithms Internet

来源：评论

学校读者我要写书评

暂无评论

A Secure and Verifiable Outsourcing Scheme for machine learning Data 3rd

A Secure and Verifiable Outsourcing Scheme for Machine Learn...

引用

3rd international conference on Security and Privacy in New computing Environments, SPNCE 2020

作者： Li, Cheng Yang, Li Ma, Jianfeng Xidian University Xi’an Shaanxi710071 China

ISBN: (纸本)9783030669218

In smart applications, such as smart medical devices, in order to prevent privacy leaks, more data needs to be processed and trained locally or near the local end. However, the storage and computing capabilities of smart devices are limited, so some computing tasks need to be outsourced;concurrently, the prevention of malicious nodes from accessing user data during outsourcing computing is required. Therefore, this paper proposes EVPP (efficient, verifiable, and privacy-preserving), a machine learning method based on a collaboration of edge computing devices. In this solution, the computationally intensive part of the model training process is outsourced. Meanwhile, a random encryption perturbation is performed on the outsourced training matrix, and verification factors are introduced to ensure the verifiability of the results. In addition, when a malicious service node is found, verifiable evidence can be generated to build a trust mechanism. Through the analysis of theoretical and experimental data, it can be shown that the scheme proposed in this paper can effectively use the computing power of the equipment. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Security Intelligence for Real-Time Security Monitoring software 1

引用

22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

作者： Poniszewska-Marańda, Aneta Grela, Radoslaw Kryvinska, Natalia Institute of Information Technology Lodz University of Technology Lodz Poland Information Systems Department Comenius University in Bratislava Bratislava Slovakia

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Security Intelligence (SI) describes the practice of collecting, standardizing and analysing data generated by networks, applications and other IT infrastructure in real time and using this information to assess and improve the security status of an organization. Security Intelligence involves deploying software and personnel resources to discover practical and useful insights that impact risk mitigation and risk reduction for the organization. Security Intelligence may also be referred to as intelligent security analysis. The paper presents the analysis of current state-of-the-art in the use of Security Intelligence and its impact on the development of current software engineering methods and approaches together with the built solution monitoring the software security with the use of SI and ML. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Predicting Academic Performance of University Students Using machine learning: A Case Study in the UK

Predicting Academic Performance of University Students Using...

引用

IEEE WIC ACM international conference on Web Intelligence (WI)

作者： Titilayo Olabisi Soyoye Tianhua Chen Richard Hill Keith Mccabe School of Computing and Engineering University of Huddersfield Huddersfield UK Planning and Business Intelligence University of Huddersfield Huddersfield UK

Students' performance is one of the key success factors in educational institutions. Understanding the performance of students can help identify issues, enabling real-time action where and when necessary. Further, early identification and improvement of students' academic performance at all levels has been a major challenge in these institutions. Students may experience some difficulties which can impair their study and can negatively impact their academic performance. These issues can be efficiently addressed if students' data is pre-analysed, and students' performance predicted early to allow immediate decisions on support. Early prediction by educators and policy makers can assist in improving student and class performance. This work applied machine learning algorithms to analyse significant factors that influence students' academic performance, which could be used to inform decisions on support, or to identify and notify the students who require assistance; thus, taking effective steps to improving their performance. We used the academic records of computer-science students at the University of Huddersfield from 2017–2022, which provided several features that useful in predicting the students' performance. Evaluation of the results showed that decision tree and Ada Boost regression have higher accuracy score.

关键词：

来源：评论

学校读者我要写书评

暂无评论

machine learning Approaches for Predicting Tree Growth Trends Based on Basal Area Increment 18th

Machine Learning Approaches for Predicting Tree Growth Tren...

引用

Proceedings of the 18th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Casas-Gómez, Pablo Martínez-Álvarez, Francisco Troncoso, Alicia Linares, Juan Carlos Physical Chemical and Natural Systems Department Faculty of Experimental Sciences Universidad Pablo de Olavide Seville41013 Spain Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783031425288

This paper explores the potential of machine learning in predicting Basal Area Increment (BAI) for the species Abies spectabilis, a commonly used metric for measuring tree growth. machine learning algorithms are used to analyze environmental factors, biotic responses, growth, and their interactions to obtain accurate predictions of BAI under different climatic scenarios. The study aims to identify vulnerable tree at risk of dieback due to changes in climate or other environmental factors. The methodology includes data acquisition, preprocessing, feature selection, model development, and evaluation. The results reported in the study show M5’ performs better in predicting BAI than other machine learning models. The study’s findings can help in forest management and conservation decisions, such as selective harvesting, reforestation, carbon sequestration, and biodiversity conservation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Dynamic Model for the pH in a Raceway Reactor Using Deep learning Techniques 14th

Dynamic Model for the pH in a Raceway Reactor Using Deep Lea...

引用

14th APCA international conference on Automatic Control and soft computing (CONTROLO)

作者： Otalora, Pablo Guzman, Jose Luis Berenguel, Manuel Acien, Francisco Gabriel Univ Almeria Dept Informat CIESOL Sacramento S-N Almeria 04120 Spain Univ Almeria Dept Chem Engn CIESOL Sacramento S-N Almeria 04120 Spain

ISBN: (纸本)9783030586553;9783030586539;9783030586522

This paper presents a black-box dynamic model for microalgae production in raceway reactors. The black-box model, developed using Deep learning techniques, allows the estimation of the pH in a 100m(2) raceway reactor. The model has been created using only and exclusively data, what gives a high ease of use. The results obtained verify the effectiveness of this type of techniques for the modelling of complex dynamic processes. The model was validated for different weather conditions obtaining satisfactory results. Thus, the obtained model is fairly useful for simulation purposes or for the implementation of model-based control techniques.

关键词： Deep learning Neural network Microalgae production Raceway reactor

来源：评论

学校读者我要写书评

暂无评论

Dual Principal Component Pursuit for learning a Union of Hyperplanes: Theory and Algorithms 24

Dual Principal Component Pursuit for Learning a Union of Hyp...

引用

24th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Ding, Tianyu Zhu, Zhihui Tsakiris, Manolis Vidal, Rene Robinson, Daniel Johns Hopkins Univ Baltimore MD 21218 USA Univ Denver Denver CO 80208 USA ShanghaiTech Univ Shanghai Peoples R China Lehigh Univ Bethlehem PA 18015 USA

State-of-the-art subspace clustering methods are based on convex formulations whose theoretical guarantees require the subspaces to be low-dimensional. Dual Principal Component Pursuit (DPCP) is a non-convex method that is specifically designed for learning high-dimensional subspaces, such as hyperplanes. However, existing analyses of DPCP in the multi-hyperplane case lack a precise characterization of the distribution of the data and involve quantities that are difficult to interpret. Moreover, the provable algorithm based on recursive linear programming is not efficient. In this paper, we introduce a new notion of geometric dominance, which explicitly captures the distribution of the data, and derive both geometric and probabilistic conditions under which a global solution to DPCP is a normal vector to a geometrically dominant hyperplane. We then prove that the DPCP problem for a union of hyperplanes satisfies a Riemannian regularity condition, and use this result to show that a scalable Riemannian subgradient method exhibits (local) linear convergence to the normal vector of the geometrically dominant hyperplane. Finally, we show that integrating DPCP into popular subspace clustering schemes, such as K-ensembles, leads to superior or competitive performance over the state-of-the-art in clustering hyperplanes.

关键词： Linear programming

来源：评论

学校读者我要写书评

暂无评论

An Analysis Employing Various machine learning Algorithms for Detection of Malicious URLs 14th

An Analysis Employing Various Machine Learning Algorithms fo...

引用

12th international Advanced computing conference, IACC 2022

作者： Rizvi, Fizza Mohi ud din, Saika Sharma, Nonita Sharma, Deepak Kumar Department of Information Technology Indira Gandhi Delhi Technical University for Women Kashmere Gate Delhi India

ISBN: (纸本)9783031356438

Currently, there are millions and trillions of webpages online. Therefore, in order to protect their data, users must be able to distinguish between trustworthy and dangerous websites. Since they are on the rise, malicious URLs (Uniform Resource Locators) are one of the most common cybersecurity concerns nowadays. They host free content (like malware, ransomware, trojans, password sniffers, etc.), and by clicking on this free content, especially the links found in emails, messages, and other formats, unwary users become the targets of these scams, have their personal information compromised, or have their system infected with malware, resulting in the loss of billions of rupees. There are more threats to cybersecurity than ever before, so a quick fix is required. In this study, we investigate how well different machine learning methods work in spotting potentially dangerous URLs. To identify both dangerous and benign URLs, this study employs a variety of machine learning methods. Decision Tree classifier, Random Forest classifier, AdaBoost classifier, K-Nearest Neighbors classifier, Gaussian Nave Bayes classifier, and XGB classifier are just a few of the machine learning methods that are used. Finding the most effective algorithm for accurately detecting fraudulent URLs is the main objective of this project. The accuracy metric was utilised as the evaluation tool to determine the accuracy of the various algorithms, with Random Forest having the highest accuracy (92.6%) of all of them. © 2023, Springer Nature Switzerland AG.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

One-Dimensional Dilated Hypothesized learning Method for Intrusion Detection System Under Constraint Resource Environment

One-Dimensional Dilated Hypothesized Learning Method for Int...

引用

Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： B. Shuriya Umamaheswari S A. Rajendran P. Sivaprakash Dept of Computer Science and Engineering PPG Institute of Technology Coimbatore Dept of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore Dept of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore Dept of Computer Science and Engineering Rathinam College of Arts and Science Coimbatore

When a breach is discovered, intrusion detection systems can alert server administrators and researchers and channel packets for cyber security incidents. In complex systems, these reports are becoming uncontrollable. To create a flexible and effective system for detecting intrusions for unforeseen threats, a number of challenges must be overcome. Systems that detect intrusions have frequently used advanced machine learning approaches, but they frequently require large amounts of computational power and processing time. In this article, the classification model is an IDS based on a one-dimensional-dilated hypothesized learning algorithm (1-DDHL). In order to account for the greatest possible pooling layer's complexity and eliminate the data redundancies brought on by pooling as well as down-sampling, dilated hypothesized inversion with a dilatation rate that includes two is initiated. In order to gather morecontexts, the dilated hypothesized convolution can widen its convolution layers. To analyze the effectiveness of the suggested solution, experiments were conducted using the CIC-IDS2017( Dataset 1)& CSE-CIC-IDS2018 (Dataset 2) sets of data, 2 well-known datasets that are publicly accessible. The 1-DDHL derived method is more accurate than some other deep learning methods currently in use, according to simulation results. With a problematic occurrence and avoidance rate of approximately 99.8% over dataset 1 and 99.97% for dataset 2, the suggested method was remarkably accurate.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The performance comparison of some methods based on machine learning and deep learning for the web attack detection

The performance comparison of some methods based on machine ...

引用

2021 international conference on Electronic Information Engineering and Computer Technology, EIECT 2021

作者： Li, Bodong Lin, Xuanxuan Lu, Xi Computer Information System Post University Waterbury06723 United States Information and Computing Science Ningbo Tech University Ningbo315100 China Electronic Commerce South China University of Technology Guangzhou510006 China

ISBN: (数字)9781510650510

ISBN: (纸本)9781510650503

Over the past few decades, 'as all kinds of network attacks have bad effects on people's daily life, cybersecurity has become an issue that is commonly concerned by society. In traditional ways, it belongs to human's responsibility that estimates whether the traffic requests received by the network are attacks. This method is not only inefficient but also has bad timeliness. Later, with the popularization of machine learning, people attempt to train some models by constructing features to detect network attacks. However, the labor cost of feature construction is very high. In recent years, with the development of deep learning, this method has become a more popular choice because of its relatively low cost and outstanding effect on prediction. According to the above analysis, this paper firstly uses machine learning-based methods including Random Forest, Gaussian NB, XGBOOST, Decision-Tree, Logistic Regression, KNN to train the effective model. Besides, we also trained a Multi-Layer Perceptron (MLP) model to compare the differences between machine learning and deep learning in predicting network traffic attacks on the CIC-ID2017 dataset. The results show that the XGBOOST, Random Forests, KNN, Decision Tree, Logistic Regression, and Gaussian Naive Bayesian respectively achieve accuracy with 0.999, 0.998, 0.991, 0.979, 0.568, and 0.509, while MLP obtains accuracy 0.643 with loss NaN. This fact suggests that the MLP method is unsuitable for the web attack detection task compared with the machine learning based approaches. © 2021 SPIE.

关键词： Decision trees

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275页 << < 31 32 33 34 35 36 37 38 39 40 > >>

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：