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

22,750 篇 会议
3,246 篇 期刊文献
19 篇 学位论文
7 册 图书

馆藏范围

26,021 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,179 篇 工学
- 3,111 篇 计算机科学与技术...
- 2,554 篇 电气工程
- 1,395 篇 信息与通信工程
- 691 篇 控制科学与工程
- 600 篇 软件工程
- 288 篇 仪器科学与技术
- 222 篇 环境科学与工程（可...
- 189 篇 机械工程
- 183 篇 生物医学工程（可授...
- 164 篇 交通运输工程
- 150 篇 测绘科学与技术
- 131 篇 生物工程
- 124 篇 电子科学与技术（可...
- 124 篇 石油与天然气工程
- 120 篇 材料科学与工程（可...
- 115 篇 网络空间安全
- 96 篇 土木工程
- 95 篇 动力工程及工程热...
- 65 篇 水利工程
951 篇 理学
- 290 篇 生物学
- 190 篇 物理学
- 159 篇 化学
- 139 篇 地球物理学
- 135 篇 数学
- 84 篇 系统科学
616 篇 管理学
- 529 篇 管理科学与工程(可...
- 69 篇 图书情报与档案管...
437 篇 医学
- 284 篇 临床医学
- 95 篇 基础医学(可授医学...
227 篇 农学
- 148 篇 作物学
53 篇 经济学
52 篇 法学
49 篇 文学
47 篇 教育学
17 篇 艺术学
3 篇 军事学
2 篇 哲学

主题

26,022 篇 machine learning...
8,400 篇 machine learning
4,276 篇 support vector m...
4,237 篇 accuracy
4,197 篇 prediction algor...
3,618 篇 predictive model...
3,490 篇 training
3,224 篇 classification a...
3,187 篇 feature extracti...
1,955 篇 data models
1,814 篇 computational mo...
1,787 篇 deep learning
1,684 篇 random forests
1,469 篇 neural networks
1,400 篇 real-time system...
1,399 篇 clustering algor...
1,276 篇 data mining
1,147 篇 optimization
1,004 篇 decision trees
977 篇 measurement

机构

120 篇 chitkara univers...
39 篇 lovely professio...
36 篇 chitkara centre ...
30 篇 computer science...
30 篇 centre of resear...
28 篇 department of co...
26 篇 school of comput...
21 篇 univ chinese aca...
21 篇 computer science...
21 篇 school of comput...
20 篇 computer science...
18 篇 school of comput...
17 篇 school of comput...
17 篇 department of co...
16 篇 brac university ...
16 篇 khulna universit...
15 篇 symbiosis instit...
14 篇 department of co...
13 篇 school of comput...
13 篇 computer science...

作者

25 篇 ashwini kodipall...
24 篇 mahendra kumar g...
15 篇 rakesh kumar
13 篇 wei liu
13 篇 atul kumar
13 篇 surendran r
12 篇 qiangfu zhao
12 篇 amit kumar
12 篇 satvik vats
11 篇 jun zhang
11 篇 li li
11 篇 liu yang
11 篇 wei li
10 篇 sudeep tanwar
10 篇 himanshu sharma
10 篇 amiya ranjan pan...
9 篇 lei wang
9 篇 tohari ahmad
9 篇 shanmugasundaram...
9 篇 rishabh sharma

语言

25,809 篇 英文
155 篇 其他
33 篇 中文
4 篇 法文
2 篇 西班牙文
2 篇 荷兰文
2 篇 土耳其文

检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是1701-1710 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：

Remote Sensing Based Vegetation Classification Using machine learning algorithms

Remote Sensing Based Vegetation Classification Using Machine...

引用

Advances in the Emerging Computing Technologies (AECT), International Conference on

作者： Arbab Mansoor Ahmad Nasru Minallah Nasir Ahmed Arbab Masood Ahmad Nouman Fazal Department of Computer Systems Engineering University of Engineering & Technology Peshawar Pakistan

ISBN: (数字)9781728144528

ISBN: (纸本)9781728144535

Vegetation is one of the most important part of an ecosystem. It is responsible for providing oxygen and gets in carbon dioxide, hence providing a suitable place for the human beings to live. The information about this vegetation is very critical. Using remote sensing, this information can be taken and gathered and later on used for different purposes. This paper aims to classify vegetation into different types and categories. Three machine learning algorithms i.e. K-means, Support Vector machine (SVM) and Artificial Neural Networks (ANN) have been used because of their being the most popular and well known algorithms of the current time to classify vegetation. K-means being unsupervised classifier is used to compare it to two supervised classifiers i.e. SVM and ANN. Non-vegetation including buildings, roads, rivers etc. are also classified into their respective categories. This classification can be useful in many ways. They can be used by government agencies and authorities to get information about the yield of a specific crop e.g. tobacco, maize etc. This information could be very useful for gathering statistics of the crop and its location on map. These locations can be used for extracting the crops and for future planning regarding it. The information about buildings and roads can help in town planning for future.

关键词： Classification algorithms Vegetation mapping Support vector machines Neural networks machine learning algorithms Remote sensing Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

A Comparison of machine learning algorithms for Fall Detection using Wearable Sensors

A Comparison of Machine Learning Algorithms for Fall Detecti...

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Nicolas Zurbuchen Pascal Bruegger Adriana Wilde Institute of Complex Systems (iCoSys) School of Engineering and Architecture of Fribourg Switzerland HES-SO University of Applied Sciences and Arts Western Switzerland Fribourg Switzerland Centre for Health Technologies (CHT) School of Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (数字)9781728149851

ISBN: (纸本)9781728149868

The proportion of people 60 years old and above is expected to double globally to reach 22 % by 2050. This creates societal challenges such as the increase of age-related illnesses and the need for caregivers. Falls are a major threat for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper presents the development of a Fall Detection System (FDS) using an accelerometer combined with a gyroscope worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We compared five machine learning algorithms. We first applied preprocessing and a feature extraction stage before using five machine learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%.

关键词： Feature extraction Sensors Legged locomotion Senior citizens machine learning algorithms Accelerometers Biomedical monitoring

来源：评论

学校读者我要写书评

暂无评论

Development of Sanitary Landfill's Carbon Dioxide Concentration Models Using machine learning algorithms

Development of Sanitary Landfill's Carbon Dioxide Concentrat...

引用

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

作者： Zoren Mabunga Jennifer Dela Cruz Glenn Magwili Angelica Samortin School of Graduate Studies College of Engineering Mapua University Southern Luzon State University Manila Philippines School of EECE Mapua University Manila Philippines School of Graduate Studies Mapua University Manila Philippines

ISBN: (纸本)9781665429979

Carbon dioxide is one of the major component of landfill gas being emitted by sanitary landfills. High concentration of this gas may cause several health condition. It is also one of the greenhouse gas that consistently contributes to climate change. Monitoring and assessing the carbon dioxide concentration in landfills is vital to ensure better living conditions. This study presents the development of carbon dioxide concentration model based on machine learning algorithms. A prototype was developed using Arduino Uno, Wi-Fi module, DHT11 temperature and humidity sensor, MQ4 and MQ135 gas sensors. This prototype was used to gather CO 2 and CH 4 concentrations, humidity and air temperature of the sanitary landfill. Five machine learning model based on linear regression, support vector machine, regression trees, boosted regression trees and neural network was trained and evaluated. Matlab software was used in this study for the development of each model. The R-square and MSE of each model was calculated and compared which results to an almost identical r-square value of 0.75 and 0.76. An MSE of 6.90857e-05 for the neural network model followed by SVM, Boosted Regression Trees, Regression Trees and Linear Regression with an MSE of 8.8168e-05, 9.0085e-05, 9.4227e-05 and 9.4652e-05 respectively was also obtained. Based on these results, it was concluded that the machine learning model based on neural network is the best algorithm for the carbon dioxide concentration modelling in sanitary landfills since it obtained the lowest MSE among the five models.

关键词： Temperature sensors machine learning algorithms Atmospheric modeling Neural networks Carbon dioxide Mathematical model Regression tree analysis

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of Polycystic Ovary Syndrome Using machine learning algorithms

Diagnosis of Polycystic Ovary Syndrome Using Machine Learnin...

引用

IEEE Region 10 Symposium

作者： Subrato Bharati Prajoy Podder M. Rubaiyat Hossain Mondal Institute of Information and Communication Technology Bangladesh University of Engineering and TechnologyDhakaBangladesh

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

This paper focuses on the data-driven diagnosis of polycystic ovary syndrome (PCOS) in women. For this, machine learning algorithms are applied to a dataset freely available in Kaggle repository. This dataset has 43 attributes of 541 women, among which 177 are patients of PCOS disease. Firstly, univariate feature selection algorithm is applied to find the best features that can predict PCOS. The ranking of the attributes is computed and it is found that the most important attribute is the ratio of Follicle-stimulating hormone (FSH) and Luteinizing hormone (LH). Next, holdout and cross validation methods are applied to the dataset to separate the training and testing data. A number of classifiers such as gradient boosting, random forest, logistic regression, and hybrid random forest and logistic regression (RFLR) are applied to the dataset. Results show that the first 10 highest ranked attributed are good enough to predict the PCOS disease. Results also demonstrate that RFLR exhibits the best testing accuracy of 91.01% and recall value of 90% using 40-fold cross validation applied to the 10 most important features. Hence, RFLR is suitable for reliably classifying PCOS patients.

关键词： Feature extraction Random forests Logistics Boosting machine learning algorithms Testing Biochemistry

来源：评论

学校读者我要写书评

暂无评论

Using Attribute-based Feature Selection Approaches and machine learning algorithms for Detecting Fraudulent Website URLs

Using Attribute-based Feature Selection Approaches and Machi...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Mustafa Aydin Ismail Butun Kemal Bicakci Nazife Baykal Banking Regulation and Supervision Agency Istanbul Turkey Chalmers University of Technology Goteborg Sweden TOBB University of Economics and Technology Ankara Turkey Northern Cyprus Campus of METU Guzelyurt Cyprus

ISBN: (数字)9781728137834

ISBN: (纸本)9781728137841

Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naïve Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm; Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly; SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naïve Bayes performed poor and is the least recommended method among all.

关键词： Phishing Feature extraction Uniform resource locators Classification algorithms machine learning algorithms machine learning

来源：评论

学校读者我要写书评

暂无评论

Assessing the democratic legitimacy of public decisions based on machine learning algorithms

Assessing the democratic legitimacy of public decisions base...

引用

Swiss Conference on Data Science (SDS)

作者： María Carolina Jiménez Doctoral candidate Dept. of Political Science University of Geneva Geneva Switzerland

ISBN: (数字)9781728171777

ISBN: (纸本)9781728171784

This paper explores how issues around the opacity and fairness of machine learning algorithms may affect the legitimacy of their use in the context of public decision-making. It identifies shortcomings in current research attempting to ground the justification of the decisions of machine learning algorithms on the general design of such models, as well as limitations of current research on algorithmic fairness. The paper concludes that even if challenges related to opacity and fairness cannot be completely overcome through technical means, the legitimacy of machine learning algorithms and their decisions can be enhanced through political processes based on the deliberative democratic paradigm.

关键词： machine learning algorithms Decision making Computational modeling Measurement Data science Public policy

来源：评论

学校读者我要写书评

暂无评论

Supervised machine learning algorithms for Credit Card Fraud Detection: A Comparison

Supervised Machine Learning Algorithms for Credit Card Fraud...

引用

International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Samidha Khatri Aishwarya Arora Arun Prakash Agrawal Department of Computer Science & Engineering Amity University Uttar Pradesh Department of Computer Science & Engineering Sharda University Greater Noida

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

In today's economic scenario, credit card use has become extremely commonplace. These cards allow the user to make payments of large sums of money without the need to carry large sums of cash. They have revolutionized the way of making cashless payments and made making any sort of payments convenient for the buyer. This electronic form of payment is extremely useful but comes with its own set of risks. With the increasing number of users, credit card frauds are also increasing at a similar pace. The credit card information of a particular individual can be collected illegally and can be used for fraudulent transactions. Some machine learning algorithms can be applied to collect data to tackle this problem. This paper presents a comparison of some established supervised learning algorithms to differentiate between genuine and fraudulent transactions.

关键词： machine learning Credit cards Data models Sensitivity machine learning algorithms Predictive models Mathematical model

来源：评论

学校读者我要写书评

暂无评论

Twitter Sentiment Analysis Using machine learning algorithms: A Case Study

Twitter Sentiment Analysis Using Machine Learning Algorithms...

引用

Advances in Computing, Communication & Materials (ICACCM), 2020 International Conference on

作者： Sheresh Zahoor Rajesh Rohilla Electronics and Communication Delhi Technological University Delhi India

ISBN: (数字)9781728197852

ISBN: (纸本)9781728197869

Sentiment analysis, also referred to as opinion mining or emotion extraction is the classification of emotions within a textual data. This technique has been widely used over the years in order to determine the sentiments, emotions within a particular textual data. Twitter is a social media platform that has been mostly used by people to express emotions for particular events. In this paper, we have collected tweets for a number of events, analyzed them using a number of machine learning algorithms like Naïve Bayes, SVM, Random Forest classifier and LSTM and compared the results.

关键词： machine learning algorithms Support vector machines Sentiment analysis Forestry machine learning Logic gates Twitter

来源：评论

学校读者我要写书评

暂无评论

Evaluating machine learning algorithms For Bengali Fake News Detection

Evaluating Machine Learning Algorithms For Bengali Fake News...

引用

International Conference on Computer and Information Technology (ICCIT)

作者： Shafayat Bin Shabbir Mugdha Sayeda Muntaha Ferdous Ahmed Fahmin United International University Dhaka Bangladesh

ISBN: (数字)9781665422444

ISBN: (纸本)9781665446686

In this world of modern technologies and media, online news publications and portals are increasing at a high speed. That is why, nowadays, it has become almost impossible to check out the traditional fact of news headlines and examine them due to the increase in the number of content writers, online media portals, and news portals. Mostly, fake headlines are filled with bogus or misleading content. They attract the commoners by putting phony words or misleading fraudulent content in the headlines to increase their views and share. But, these fake and misleading headlines create havoc in the commoner's life and misguide them in many ways. That is why we took a step so that the commoners can differentiate between fake and real news. We proposed a model that can successfully detect whether the story is fake or accurate based on the news headlines. We created a novel data set of Bengali language and achieved our aim and reached the target using the Gaussian Naive Bayes algorithm. We have used other algorithms, but the Gaussian Naive Algorithm has performed well in our model. This algorithm used a text feature dependent on TF-IDF and an Extra Tree Classifier to choose the attribute. In our model, using Gaussian Naive Bayes we got 87% accuracy which is comparatively best than any other algorithm we used in this model.

关键词： machine learning algorithms Media Feature extraction Classification algorithms Naive Bayes methods Information technology Portals

来源：评论

学校读者我要写书评

暂无评论

Speaker Accent Recognition Using machine learning algorithms

Speaker Accent Recognition Using Machine Learning Algorithms

引用

Innovations in Intelligent Systems and Applications Conference

作者： Ahmet Aytuğ Ayrancı Sergen Atay Tülay Yıldırım Yıldız Technical University İstanbul Turkey

ISBN: (数字)9781728191362

ISBN: (纸本)9781728191379

Speaker recognition is a system that recognizes the speaker from the recorded voice signal. Speech and speaker recognition are important for many areas like online banking, telephone shopping, and security applications. In order to analyze and verify speech and speaker, machine learning (ML) algorithms can be used. With enough data it is possible to train a program to identify speech and speaker identity. In this paper, several ML algorithms used to identify speaker accents. Data set includes 329 speakers with 6 different accents and English and these words have been converted to metric representation using Mel-Frequency Cepstral Coefficients (MFCCs). MFCC is the most used technique in speech recognition because of the high performance of feature extraction performance and this data set utilizes MFCC to convert speech to data. In this study, 7 classification type ML algorithms used, including Multilayer Perceptron (MLP), Random Forest (RF), Decision Tree (DT), Radial Basis Function (RBF), k-Nearest Neighbor (k-NN), Naive Bayes (NB) and Logistic Model Tree (LMT) methods used for Speaker Accent Recognition data set on UCI ML Repository. Performance metrics, compared using accuracy, Kappa Statistics, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), have been acquired and compared for each algorithm.

关键词： Mel frequency cepstral coefficient Radio frequency machine learning algorithms Kernel Feature extraction Measurement Filter banks

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 << < 167 168 169 170 171 172 173 174 175 176 > >>

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：