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1341-1350 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：

Framework for Alzheimer Diseases Classification using machine learning algorithms

Framework for Alzheimer Diseases Classification using Machin...

引用

Emerging Trends in Engineering and Medical Sciences (ICETEMS), International Conference on

作者： Pushkar Wankhede Nandini Sakhare Yogita Dubey Manu Varghese Shrutika Gawande Manoj Patil Department of Electronics and Telecommunication Yeshwantrao Chavan College of Engineering Nagpur India Department of Research and Development Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences (DU) Wardha India

At Least 50 million people in the world are assumed to have Alzheimer Disease (AD). Hence early diagnosis as well as early dose of memantine is required to inhibit further increase of this disease. In this paper a framework is proposed for classification of Alzheimer using five different machine learning models. Dataset is taken from Kaggle website after that Data preprocessing takes place. Five different models viz. Logistic Regression, Support Vector machine, Random Forest, Decision Tree and Gradient Boosting Classifier are compared using various evaluation metrics such as Accuracy, Precision, Recall, Log loss and AUC-ROC.

关键词： Support vector machines Measurement machine learning algorithms Data preprocessing Predictive models Market research Decision trees

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Behavioral Artifacts for Malicious Software Detection Using machine learning algorithms

Analyzing the Behavioral Artifacts for Malicious Software De...

引用

Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Nikkam Chandini C. Kiran Mai Department of Computer Science and Engineering VNRVJIET Hyderabad India

Malicious software intentionally affects your computer system. Malware is analyzed using static or dynamic analysis techniques. These techniques are used to classify and predict unique patterns for correctly identifying malware. Over the last decade, many malware detection techniques have been proposed using a variety of techniques. In most cases, network-based attacks such as DDoS attacks are known network security risks. These causes usually cause network devices to overflow with more requests than they can handle, preventing the server from responding to legitimate requests. The biggest security risk is related to software. Software attacks can exploit the entire system, steal information, modify data, deny services, and endanger or damage devices. In particular, I am interested in dynamic analysis for developing malware detection systems that utilize machine learning techniques. This document proposes behavior-based malware detection techniques. To develop this model, we arrange a dynamic analysis approach and run malware samples using classifiers. To extracted for the malware detection system. To develop this technique, we create a dynamic analysis approach and run malware using classification algorithms. Computer programs connected to Internet are increasingly needed to analyze and prevent malware. The software exploits system vulnerabilities to unknowingly steal sensitive information and secretly send it to an attacker-controlled remote server. This approach uses the ML classifier to detect malware and identify the type of malware attacked in the dataset.

关键词： machine learning algorithms Computer hacking Heuristic algorithms Software algorithms Prediction algorithms Malware Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Classifying and Prediction for Patient Disease Using machine learning algorithms

Classifying and Prediction for Patient Disease Using Machine...

引用

Information Technology To Enhance e-learning and Other Application (IT-ELA)

作者： Sura Salah Rasheed Ismaael Hadi Glob Department of Information Technology Middle Technical University/Administrative Technical college/ Baghdad Baghdad Iraq

With the technological advancements occurring in the world, particularly in the medical field, it was important to utilize machine learning algorithms due to their significance in health reality and illness prediction. These techniques were used in many hospitals to improve the early detection of diseases via the use of medical databases for patients. This study seeks to give a review of machine learning techniques used throughout disease detection and prediction. Several papers over the last three years that used machine learning techniques have been examined by Naive Bayes. To evaluate algorithms, evaluation procedures, and outcomes to detect various diseases in the medical profession, was superior to other algorithms by (84%) in terms of accuracy.

关键词： Support vector machines machine learning algorithms Liver diseases machine learning Prediction algorithms Lipidomics Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning algorithms in Project Economics Review

Application of Machine Learning Algorithms in Project Econom...

引用

Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Chenhong Zheng Mengzhe Liu Ying Wang Cong Zeng State Grid Fujian Economic Research Institute Fuzhou China

In order to solve the problem that the traditional query based on secondary retrieval is too rigid so as to automatically filter out valuable target documents, and repeated queries consume a lot of time, an intelligent interactive information retrieval process and processing flow for fund project document queries is proposed. Based on the feedback information of users evaluating project documents, ID3 algorithm, CLCC algorithm and SVM classification function are used to learn the potential intention and target of users' query respectively, and the learned rule knowledge or classification function is applied to support the project document query. The experimental computations and analysis are conducted for the query of project documents in a fund review management as an example. The results show that the number of project documents read and evaluated by the user in each interactive query loop is no more than 5% of the total number of documents returned from the previous query or 20 items, and together with the project documents already read and evaluated, they constitute the set of machine learning samples. The maximum number of interactive query cycle is set to 5. Among the three machine learning methods, ID3 also shows good prediction performance when post-processing algorithm is used. ID3 generates a decision tree with large width and small height. CLCC algorithm is better than ID3 algorithm, mainly because the rule post-processing of CLCC is more flexible, and the generated concept rules contain more merge rules and each merge rule is shorter. The SVM method has the best prediction performance, mainly because the project document keyword vectors are all continuous real values. It is concluded that the fund project intelligent interactive information retrieval process and processing flow accurately describes the potential query intention and target of user evaluation project documents, and establishes a user query project document classification learning knowledge

关键词： Support vector machines machine learning algorithms Query processing Knowledge based systems machine learning Prediction algorithms Libraries

来源：评论

学校读者我要写书评

暂无评论

Congestive heart failure prediction based on feature selection and machine learning algorithms

Congestive heart failure prediction based on feature selecti...

引用

Iberian Conference on Information Systems and Technologies (CISTI)

作者： Diego Morillo-Velepucha Ruth Ré ategui Priscila Valdiviezo-Dí az Luis Barba-Guamá n Universidad T&#x00E9 cnica Particular de Loja Loja Ecuador

Obesity is one of the main public health problems worldwide that present an association with many medical conditions. Therefore, obesity and its comorbidities such as congestive heart failure are a subject of investigation in many areas. One of these areas is the application of computer science for solving problems in the medical field. This paper shows the evaluation of machine learning algorithms for the prediction of congestive heart failure disease. A dataset of 412 medical summaries was used. It has 342 features with information of diseases, medications, treatments among others. Due to the extensive number of features, feature selection techniques such as Chi Square and Mutual Information were applied. The results showed that the highest precision metric of 0.94 was obtained with the Random Forest algorithm with 23 best features selected by the Mutual Information technique. Regarding AUC, the algorithm the highest value of 0.89 was obtained with the Naïve Bayes algorithm with 24 best features selected by the Chi Square technique.

关键词： Obesity machine learning algorithms Computational modeling Prediction algorithms Feature extraction Data models Congestive heart failure

来源：评论

学校读者我要写书评

暂无评论

Heart Anomalies Prediction Utilizing a Variety of machine learning algorithms

Heart Anomalies Prediction Utilizing a Variety of Machine Le...

引用

International Conference on Contemporary Computing and Informatics (IC3I)

作者： Neha Shukla Anand Pandey A P Shukla CSE Department SRM Institute of Science and Technology Delhi-NCR Meerut Road Modi Nagar India KIET Group of Institutions Delhi-NCR Ghaziabad Technical Education Department Government of Uttar Pradesh India

We are aware that cardiovascular diseases are very lethal, patients do not get enough time for treatment and the treatment is also expensive for most people. The goal of this study is to predict the likelihood of an acute heart attack using a variety of machine learning approaches, including K closest neighbour, logistic regression, random forest classifier, support vector machine, and XGB classifier. The accuracy score obtained by all the machine learning algorithms has been demonstrated with the help of a table.

关键词： Heart Support vector machines machine learning algorithms Computational modeling Cardiac arrest Predictive models Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

DNA Sequencing Using machine learning algorithms

DNA Sequencing Using Machine Learning Algorithms

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M S Antony Vigil M Mirutuhula Sure Sarvagna R Supraja Gadusunari Priyanka Reddy Department of Computer Science Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamil Nadu India

ISBN: (纸本)9798350333855

A genome consists of the genetic information of the organism. Sequencing thousands and millions of DNA molecules in a human individual gives the doctor the information about the genetic makeup that is the genetic information carried out in the human body. This method plays a vital role in determining the variations and mutations within a specific genetic disease. Researchers and doctors use this piece of information to more fully understand the patient’s current health status and help them to make a future plan for treatment and operations. DNA sequencing is done to track epidemics, learn about the past portfolio of the individual and find better treatments for life threatening diseases like Cancer and arthritis. Understanding a patient’s unique genetic profile can allow drug makers to target specific subgroups of individuals grouped together by similar genetic makeup. This can result in more exact and tailored information such as in the medication type itself down to the dosage level. Hence DNA sequencing plays an integral role in the biomedical research process. In this project we will be using different machine learning techniques like SVM, CNN, LSTM, Random forest classifier, Adaboost, naive bayes, SVM, KNN to sequence the DNA on a human dataset, this study analysis can aid us understand and conclude which algorithm works better and can give a satisfying accuracy.

关键词： Support vector machines Sequential analysis machine learning algorithms DNA Genomics Medical services Libraries

来源：评论

学校读者我要写书评

暂无评论

Feasibility of Tree-based machine learning algorithms fed with surface electromyographic features to discriminate risk classes according to NIOSH

Feasibility of Tree-based Machine Learning algorithms fed wi...

引用

IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Leandro Donisi Edda Capodaglio Gaetano Pagano Federica Amitrano Mario Cesarelli Monica Panigazzi Giovanni D' Addio dept. of DICMaPI University of Naples Federico II Naples Italy dept. of Ergonomics ICS Maugeri Pavia Italy dept. of Bioengineering ICS Maugeri Bari Italy dept. of DIETI University of Naples Federico II Naples Italy dept. of Physiatry ICS Maugeri Montescano Italy dept. of Bioengineering ICS Maugeri Telese Terme Italy

ISBN: (数字)9781665482998

ISBN: (纸本)9781665483001

Many work activities can imply a biomechanical overload. Among these activities, lifting loads may determine work-related musculoskeletal disorders. In order to limit injuries, the National Institute for Occupational Safety and Health (NIOSH) proposed a methodology for assessing biomechanical risk in lifting tasks by means of a math formula based on intensity, duration, frequency and other geometrical characteristic of the lifting. In this work, we explored the feasibility of tree-based machine learning algorithms to classify biomechanical risk according to the Revised NIOSH lifting equation. Electromyography signals acquired from the biceps during lifting loads were collected using a wearable sensors for surface electromyography on a study population composed of 5 healthy young subjects. The EMG signals were segmented in order to extract the region of interest related to the lifting actions and for each region of interest several features in time and frequency domains were extracted. High results - greater than 95% - were obtained in terms of evaluation metrics for a binary risk/no-risk classification. In conclusion, this work indicates the proposed combination of features and machine learning algorithms represents a valid approach to automatically classify risk activities according to the Revised NISOH lifting equation. Future investigation on enriched study population could confirm the potentiality of this methodology to automatically classify potential risky activities.

关键词： Biomechanics Musculoskeletal system machine learning algorithms Occupational safety Sociology Feature extraction Electromyography

来源：评论

学校读者我要写书评

暂无评论

A Review on Radiomics and machine learning algorithms in Prediction and Progression of Parkinson’s Disease

A Review on Radiomics and Machine Learning Algorithms in Pre...

引用

Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Redhya M K. Sathesh Kumar Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

Parkinson's disease (PD) is an irreversible neurodegenerative condition that impairs dopamine production in a specific area of the brain. Diagnosis of PD in the early stage is a tedious task as symptoms are not very prominently shown in the first stage. machine learning techniques had been used for the analysis and progression of PD. Radiomics analysis has helped in extracting large amount of quantifiable features from images which can be used as biomarkers for predictive models. This paper gives an idea on different machine learning algorithms and the application of radiomics which has proven its efficacy in the classification and progression of PD. Finally, the limitations and accuracy of the different techniques are also discussed.

关键词： Support vector machines machine learning algorithms Artificial neural networks Production Feature extraction Task analysis Random forests

来源：评论

学校读者我要写书评

暂无评论

Enhanced machine learning algorithms for Validating the Biometrics in VANET

Enhanced Machine Learning Algorithms for Validating the Biom...

引用

machine learning, Computer Systems and Security (MLCSS), International Conference on

作者： Premkumar Chithaluru Lambodar Jena Bichitrananda Patra Niranjan Panda Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation (KLEF) Vaddeswaram Andhra Pradesh India Department of Computer Applications Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar Odisha India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar Odisha India

In this rapidly sprouting technological era, trans-portation is also becoming intelligent and driverless. Ad hoc networking plays an essential role in making the vehicle to vehicle and vehicle to infrastructure communication environment compatible with all types of users. As technology grows, the risk of security and privacy also grows side by side. However, the intruders can easily find out holes in the security network and gather information from them. The scope of the paper is to propose a methodology that inflates the security of stored data in the hardware of vehicular networks and identity verification of vehicles in communication easier. The importance of the present research is that these proposed simulation techniques help in maintaining the security of temper proof devices used in VANETs for the storage of private data. Authors communicate all sensitive data like bank details, vehicle details and personal details over wireless networks during communication. Biometric data and many other cryptographic techniques are used to secure the communication. Proposed methodologies are very efficient, less time consuming and less cost effective too which can be used in any type of intelligent vehicle with no extra hardware requirement.

关键词： Privacy machine learning algorithms Biometrics (access control) Intelligent vehicles Wireless networks Vehicular ad hoc networks 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500页 << < 131 132 133 134 135 136 137 138 139 140 > >>

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：