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

23,359 篇 会议
2,934 篇 期刊文献
16 篇 学位论文

馆藏范围

26,309 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

5,648 篇 工学
- 3,392 篇 计算机科学与技术...
- 3,142 篇 电气工程
- 1,808 篇 信息与通信工程
- 640 篇 控制科学与工程
- 538 篇 软件工程
- 481 篇 仪器科学与技术
- 300 篇 机械工程
- 300 篇 测绘科学与技术
- 284 篇 生物医学工程（可授...
- 148 篇 电子科学与技术（可...
- 114 篇 生物工程
- 108 篇 交通运输工程
- 107 篇 环境科学与工程（可...
- 75 篇 网络空间安全
- 56 篇 航空宇航科学与技...
- 43 篇 材料科学与工程（可...
841 篇 理学
- 194 篇 物理学
- 158 篇 生物学
- 137 篇 地球物理学
- 124 篇 数学
- 122 篇 系统科学
- 96 篇 地理学
- 50 篇 化学
- 42 篇 统计学（可授理学、...
526 篇 管理学
- 481 篇 管理科学与工程(可...
493 篇 医学
- 369 篇 临床医学
- 100 篇 基础医学(可授医学...
195 篇 农学
- 99 篇 作物学
- 80 篇 农业资源与环境
75 篇 文学
- 70 篇 新闻传播学
23 篇 教育学
21 篇 经济学
19 篇 艺术学
13 篇 法学
7 篇 军事学
2 篇 历史学

主题

26,309 篇 classification a...
6,463 篇 feature extracti...
5,177 篇 training
4,677 篇 accuracy
4,372 篇 support vector m...
3,194 篇 machine learning...
2,671 篇 prediction algor...
2,638 篇 clustering algor...
2,490 篇 algorithm design...
2,350 篇 data mining
2,253 篇 machine learning
1,593 篇 data models
1,402 篇 deep learning
1,358 篇 optimization
1,338 篇 decision trees
1,172 篇 predictive model...
1,164 篇 testing
1,138 篇 computational mo...
1,094 篇 neural networks
1,020 篇 databases

机构

63 篇 chitkara univers...
23 篇 centre of resear...
23 篇 college of infor...
19 篇 school of comput...
18 篇 tsinghua univers...
18 篇 university of ch...
17 篇 chitkara centre ...
17 篇 school of inform...
17 篇 department of co...
16 篇 school of comput...
16 篇 school of comput...
16 篇 school of electr...
14 篇 computer science...
14 篇 univ chinese aca...
14 篇 microsoft resear...
14 篇 school of comput...
13 篇 school of electr...
13 篇 school of electr...
13 篇 school of comput...
12 篇 department of in...

作者

29 篇 wei li
22 篇 hui wang
22 篇 jun wang
21 篇 lei wang
17 篇 xia sun
17 篇 yang yang
16 篇 jing zhang
15 篇 jun zhang
15 篇 liu yang
14 篇 xi chen
14 篇 li zhang
14 篇 jie li
13 篇 jian wang
13 篇 rakesh kumar
13 篇 jie yang
13 篇 jun shi
13 篇 chen chen
12 篇 bing xue
12 篇 ahmad jalal
12 篇 xuegang hu

语言

26,172 篇 英文
91 篇 其他
33 篇 中文
4 篇 土耳其文
2 篇 法文
1 篇 西班牙文
1 篇 俄文

检索条件"主题词=Classification Algorithms"

共 26309 条记录，以下是191-200 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：

Examining the Efficiency of Various classification algorithms in Spam Detection Datasets

Examining the Efficiency of Various Classification Algorithm...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Mohd. Umar Mahima Gupta Rishabh Didwania Rajat Verma Namrata Dhanda Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India Department of Computer Science and Engineering Amity University Lucknow Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study investigates the relationship between the accuracy of Machine Learning (ML) models used to differentiate between Spam and Normal Messages for different dataset sizes. Efficiency evaluation of four classification models (Decision Tree, Passive Aggressive, Random Forest, and Regression) using 1500, 3000, and 5500 samples from three distinct dataset sizes is performed in this paper. The primary objective is to determine how variations in dataset size impact these models’ accuracy. This work not only quantifies performance shifts but also offers insights into how responsive these models are to changes in data amount through careful experimentation and analysis. These findings enhance understanding of ML model behaviour in identifying unwanted messages and provide practical guidance for selecting and configuring models based on available dataset sizes. They also help to improve the design of trustworthy and accurate categorization systems.

关键词： Analytical models Accuracy Computational modeling Data models classification algorithms Regression tree analysis Random forests

来源：评论

学校读者我要写书评

暂无评论

Development of a web-based blind test to score and rank hyperspectral classification algorithms

Development of a web-based blind test to score and rank hype...

引用

Western New York Image Processing Workshop (WNYIPW)

作者： K. King J. Kerekes Chester F. Carlson Center of Imaging Science Rochester Institute of Technology USA

Remotely sensed hyperspectral imagery plays an important role in land cover classification by supplying the user with additional spectral data as compared to high-resolution color imagery. The web application described in this paper enables users to test their classification algorithms without the risk of bias by withholding the majority of the true classification data and only providing a small section of the truth data to be used for training user algorithms. After downloading the dataset, users run their classification algorithms and upload their results back to the web application. The blind test site automatically scores and ranks the uploaded result. The classification Blind Test web application can be found at: http://***/classtest/.

关键词： classification algorithms Pixel Hyperspectral imaging Reflectivity Training Buildings

来源：评论

学校读者我要写书评

暂无评论

Performance Comparison of classification algorithms for Diagnosing Chronic Kidney Disease

Performance Comparison of Classification Algorithms for Diag...

引用

International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Justin D. de Guia Ronnie S. Concepcion Argel A. Bandala Elmer P. Dadios Electronics and Communications Engineering Department De La Salle University Manila Philippines Manufacturing Engineering and Management Department De La Salle University Manila Philippines

ISBN: (数字)9781728130446

ISBN: (纸本)9781728130453

Chronic kidney disease (CKD) is one of the diseases with high mortality rate. It is a disease resulted from kidney function loss over a long period of time. The disease shows no symptoms during initial stage. When left not medicated, a person may suffer from other complications such as high blood pressure, anemia, malnutrition, increased risk of cardiovascular disease, cognitive impairment and impaired physical function. Automated diagnosis by using classification algorithms has been an interest of researchers. In this study, six machine learning algorithms were used for classification and its prediction performance was compared based on training time and F1 score, with and without hypertuning the parameters. Of all the six algorithms, KNN has the best F1 score of 0.992248 and minimal training time of 46.999ms. The performance of decision trees was improved with hypertuning, having a F1 score from 0.96 to 0.99. Overall, machine learning algorithms are significant tool to assess chronic kidney disease.

关键词： classification algorithms Kidney Diseases Prediction algorithms Decision trees Multilayer perceptrons Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Comparison of Data Mining classification algorithms for Student Performance

Comparison of Data Mining Classification Algorithms for Stud...

引用

IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Emny Harna Yossy Yaya Heryadi Lukas Computer Science Department Bina Nusantara University Indonesia

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

Student performance has an important role to measure student quality. Student quality can be measured through predictions of student performance. Prediction can be done using data mining techniques. One technique that can be used is the classification method. The research aims to find out which classification model has the best performance related to student performance data. The data used is taken from UCI Machine Learning, namely student performance. The study used seven methods, namely K-nearest neighbor, classification and regression trees, naïve bayes, adaboost, extratree, bernaoulli naïve bayes, random forest. The technology used to compare the seven methods uses Python programming. Testing the performance of methods using cross validation. The results of this study are the comparison of student performance classification algorithms on student math, namely K-Nearest Neighboring of 86.52%, classification and regression tests of 86.08%, naïve bayes of 84.78%, adaboost of 88.04%, extratree of 81.30%, bernaoulli naïve bayes of 79.34%, random forest E of 87.82%, random forest G of 89.78%. Based on these results we know that the best classification method is the random forest G of 89.78%.

关键词： Data mining classification algorithms Prediction algorithms Decision trees Computer science Random forests Data models

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis of Customer Reviews Based Texts Using classification algorithms

Sentiment Analysis of Customer Reviews Based Texts Using Cla...

引用

International Conference on Network and Information Systems for Computers (ICNISC)

作者： T. Velmurugan M. Archana U. Latha Department of Computer Science Dwaraka Doss Goverdhan Doss Vaishnav College Chennai India Department of Computer Applications Dwaraka Doss Goverdhan Doss Vaishnav College Chennai India

ISBN: (纸本)9781665453523

Nowadays, a large number of text-based sentiments posted by customers about their reviews on products. They provide their sentiments via their reviews. Sentiment analysis is also known as an opinion mining. Businesses frequently do sentiment analysis on textual data to track the perception of their brands and products in customer reviews and to better understand their market. In this research work, it is implemented that the Naive Bayes, Random Forest, Decision Tree and Support Vector Machines classifiers and the results are compared and examined. This work has a performance-based analysis of different classifiers by assessing the accuracy of classification depending on the size of the product data sets of mobile products. The dataset which has been collected from the shopping websites of Amazon, Flipkart, Snapdeal and which is used to find the classification accuracy by its methods used for the analysis. Finally, it compares the performance of four classification methods like Naive Bayes, Random Forest, Decision Tree and Support Vector Machines to identify the best method.

关键词： Support vector machines Sentiment analysis Linear regression Forestry Prediction algorithms Feature extraction classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Predicting Prior Engine Failure with classification algorithms and web-based IoT Sensors

Predicting Prior Engine Failure with Classification Algorith...

引用

Computing, Communication and Electronics (ETCCE), Emerging Technology in

作者： Ali Fattah Dakhil Wafaa Mohammed Ali Ali Atshan Abdulredah Collage of CS and Information Tech University of Sumer Collage of Pure Science University of Thi-Qar Thi-Qar Iraq

ISBN: (数字)9781665419628

ISBN: (纸本)9781665419635

Machine learning classification techniques play a significant role in engine failure issues and machinery maintenance. With the help of Internet of Things, IoT industry, connected sensors have a considerable impact on data collection and remote engine monitoring. Mechanical engineers and professionals have difficulties determining when an engine is going to have a malfunction. So, engine maintenance requires an adequate strategy to predict the closest time in which an incident would likely to occur. This research investigates a perfect solution so that engineers will have an earlier alert about the potential incident which might exist. This study gives a visualized time left for how long an engine lifetime is present, accordingly, the system notifies the engineers of the best time to implement the maintenance. The methodology that we follow is setting up an appropriate mechanism by collecting data with IoT, and analyzing such data with classification algorithms. These algorithms categorize the status of an engine into particular conditions, so they indicate how far an engine going to work in an optimal state. Experiments have proved that K-Near Neighbor is the best algorithm for this kind of work in between others like; decision tree and linear discriminant with accuracy 82.9%, 51.0%, and 64.9% respectively. Consequently, classification techniques confidently distinguish the engine condition and warning for necessity of maintenance at the right time and right status.

关键词： Temperature sensors Machine learning Maintenance engineering classification algorithms Internet of Things Engines Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Automatic Digital Modulation classification algorithms

Automatic Digital Modulation Classification Algorithms

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Wei Zhang Hu Yang Coellge of Electron. Sci. & Eng. Nat. Univ. of Defense Technol. Changsha

In this paper, we develop a new modulation classification of MPSK signal, gain approximately classify statistics via received signal sampled and main parameters approximated. Simulation results indicate that the algorithms can gain high probability of correct classification and low compute quantity, when an appropriate sampling plan is used by SNR condition

关键词： Digital modulation classification algorithms Gaussian noise Statistics Sampling methods Additive white noise AWGN Modulation coding Educational institutions Electronic mail

来源：评论

学校读者我要写书评

暂无评论

The analysis of feature selection methods and classification algorithms in permission based Android malware detection

The analysis of feature selection methods and classification...

引用

IEEE Symposium on Computational Intelligence in Cyber Security, CICS

作者： Uğur Pehlivan Nuray Baltaci Cengiz Acartürk Nazife Baykal CyDeS Cyber Defense and Security Laboratory of METU-COMODO Informatics Institute Middle East Technical University (METU) Ankara Turkey

Android mobile devices have reached a widespread use since the past decade, thus leading to an increase in the number and variety of applications on the market. However, from the perspective of information security, the user control of sensitive information has been shadowed by the fast development and rich variety of the applications. In the recent state of the art, users are subject to responding numerous requests for permission about using their private data to be able run an application. The awareness of the user about data protection and its relationship to permission requests is crucial for protecting the user against malicious software. Nevertheless, the slow adaptation of users to novel technologies suggests the need for developing automatic tools for detecting malicious software. In the present study, we analyze two major aspects of permission-based malware detection in Android applications: Feature selection methods and classification algorithms. Within the framework of the assumptions specified for the analysis and the data used for the analysis, our findings reveal a higher performance for the Random Forest and J48 decision tree classification algorithms for most of the selected feature selection methods.

关键词： classification algorithms Malware Feature extraction Smart phones Androids Humanoid robots

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Kidney Disease Prediction Using Machine Learning classification algorithms and Ensemble Techniques

Performance Analysis of Kidney Disease Prediction Using Mach...

引用

Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Deepika Yadav N. P. Singh School of Engineering & Technology MVN University Palwal Haryana India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Chronic kidney disease (CKD) is very dangerous disease and also represented by chronic kidney failure. In CKD, continuous loss of kidneys with time and stops functioning after some time. According to various survey report of World Health Organization, growth rate of CKD patients is increasing rapidly worldwide every year. Kidneys are the preliminary source of our body for blood cleaning by removing waste and it is also highly responsible for regulating salt, potassium and $\mathrm{p H}$ levels in the body. It manages production of red blood cells and also regulates blood pressure in the body by producing hormones. The main aim of this research paper to design predictive model for detection of early CKD that helps patients get medical treatment at the right time and helps to reduce the loss of lives. Artificial Intelligence playing a very demanded role in field of health sector f or prediction of disease and uncountable research studies have been conducted for categorizing CKD using various machine learning classification algorithms. The effectiveness of any algorithm depends upon selecting the specific key features, applying the best feature extraction techniques, and productive classifier models. Proposed model, uses the Pearson correlation coefficient (PCC) method for feature selections and various classification and ensembles algorithms. The accuracy of ensemble models is better than as compared to machine learning models. The accuracy of KNN model is lowest among all model with score ${0 . 6 9 \%}$ and Random Forest model achieved highest accuracy score with value $0,91 \%$. The accuracy score of ensemble boosting model is found to ${0 . 9 8 \%}$. Various accuracy metrics are applied to measure the performance of proposed models like confusion metrics, F1- score, Precision and Recall etc. Among all applied models, the RF classification algorithm and AdaB ensemble methods have achieved high accuracy and linear regression achieved the lowest accuracy.

关键词： Measurement Accuracy Machine learning algorithms Computational modeling Predictive models Feature extraction Boosting Chronic kidney disease classification algorithms Diseases

来源：评论

学校读者我要写书评

暂无评论

Predicting Academic Grades of Students in Computer Programming Using classification algorithms

Predicting Academic Grades of Students in Computer Programmi...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： C P Pavan Kumar Hota V. Asanambigai D. Lakshmi Department of CSE Annamalai University Tamilnadu India School of Computing Science and Engineering (SCSE) VIT Bhopal University Madhya Pradesh India

In computer science education, teaching and learning programming is difficult. Understanding and coding programmes are regarded as extremely difficult in computer science education. This is because practical ability is valued more than theoretical knowledge. According to research, students with metacognitive management skills outperform lower-performing students in programming. The more difficult the programming task, the more important it is for the programmer to have metacognitive control skills. The cognitive processes involved in learning computer programming necessitate the development of metacognitive skills in the novice programmer. This study’s main objective is to predict computer programming students’ academic grades using classification algorithms. The predictive analysis used 151 records that were gathered and used. Three methods are considered in order to find the best classifier: Rule Based classification, Decision Tree classification, and Nave Bayesian classification. The confusion matrix, common metrics like precision, recall, ROC curve, kappa statistics, mean absolute error, root mean squared error, relative absolute error, and root relative squared error are all used to assess how well each classifier classified the dataset. Conclusion: With 95% accuracy, the ID3 classifier outperformed the other six predictive models.

关键词： Measurement Decision making Predictive models Prediction algorithms classification algorithms Computer science education Numerical models

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500页 << < 16 17 18 19 20 21 22 23 24 25 > >>

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：