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

22,562 篇 会议
3,220 篇 期刊文献
19 篇 学位论文
7 册 图书

馆藏范围

25,807 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,149 篇 工学
- 3,093 篇 计算机科学与技术...
- 2,542 篇 电气工程
- 1,383 篇 信息与通信工程
- 686 篇 控制科学与工程
- 593 篇 软件工程
- 285 篇 仪器科学与技术
- 217 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...
- 163 篇 交通运输工程
- 149 篇 测绘科学与技术
- 130 篇 生物工程
- 122 篇 石油与天然气工程
- 121 篇 电子科学与技术（可...
- 117 篇 材料科学与工程（可...
- 111 篇 网络空间安全
- 98 篇 土木工程
- 92 篇 动力工程及工程热...
- 66 篇 水利工程
943 篇 理学
- 289 篇 生物学
- 188 篇 物理学
- 155 篇 化学
- 139 篇 地球物理学
- 132 篇 数学
- 84 篇 系统科学
611 篇 管理学
- 524 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

25,808 篇 machine learning...
8,337 篇 machine learning
4,247 篇 support vector m...
4,161 篇 prediction algor...
4,141 篇 accuracy
3,577 篇 predictive model...
3,462 篇 training
3,199 篇 classification a...
3,156 篇 feature extracti...
1,930 篇 data models
1,793 篇 computational mo...
1,774 篇 deep learning
1,639 篇 random forests
1,463 篇 neural networks
1,386 篇 clustering algor...
1,368 篇 real-time system...
1,271 篇 data mining
1,121 篇 optimization
995 篇 decision trees
971 篇 measurement

机构

120 篇 chitkara univers...
37 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...
15 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
12 篇 qiangfu zhao
12 篇 amit kumar
12 篇 surendran r
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

25,604 篇 英文
147 篇 其他
32 篇 中文
5 篇 法文
4 篇 土耳其文
2 篇 西班牙文
2 篇 荷兰文
1 篇 德文

检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是541-550 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：

The Use of machine learning algorithms in Optimising SGS for Synchronising 4

The Use of Machine Learning Algorithms in Optimising SGS for...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Ramesh, P.S. Naik, Pramod Kumar Afreen Banu, E. Praveenkumar, C. Owaied, Huda Qasim Hassan, Enas Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Computer Science and Engineering India Dayananda Sagar University Department of Cse Bengaluru India Cse Saveetha School of Engineering Chennai India Sri Ramakrishna Engineering College Department of Eee Coimbatore India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

An examination of the relative efficacy of machine learning (ML) algorithms for power supply and demand prediction in smart grid systems is warranted by their growing application. Through meticulous hyperparameter tweaking, we assessed six well-known machine learning algorithms: support vector machine (SVM), random forest, k-nearest neighbor, linear regression, artificial neural network, and Gaussian regression (GR). When the ML algorithms were appropriately calibrated, we found little to no significant difference in performance among them when we used Eskom database data for day-ahead forecasting, including hourly demand and renewable energy generation. On the other hand, wind power forecast proved more difficult because of its unpredictable nature than photovoltaic (PV) electricity. With the exception of GR, statistical analysis showed no discernible changes in timing performance amongst the algorithms, despite SVM's somewhat quicker processing time. The implication is that, with appropriate hyperparameter fine-tuning, using different machine learning algorithms for power prediction - particularly for sources with regular patterns, such as solar PV or daily consumption rates-may not always result in statistically significant variations in prediction quality. © 2024 IEEE.

关键词： Eskom database hyperparameter tuning machine learning algorithms power demand prediction smart grid systems supply prediction

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study on machine learning algorithms for Intrusion Detection System 10

A Comprehensive Study on Machine Learning Algorithms for Int...

引用

10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Mirlekar, Swati Kanojia, Komal Prasad Electronics and Telecommunication Engineering St. Vincent Pallotti College of Engineering & Technology MS Nagpur India Electronics and Communication Engg RKDF INSTITUTE of SCIENCE and TECHNOLOGY BHOPAL SRK University MP Bhopal India

ISBN: (纸本)9781665467414

Rapid advancements in areas of communication and the internet ensued a significant boost in data and capacity of the network. As a consequence, a plethora of novel threats are being built, creating it harder for network security to efficiently identify breaches. Furthermore, intruders also initiate multiple attacks inside this network must be taken into account. An intrusion Detection System (IDS) is a method that checks network activity for availability, consistency, and secrecy to defend the network from potential invasions. Although the greatest efforts of professionals, IDSs continue to struggle with enhancing detecting accurateness whilst lowering false alarm rates and detecting newer intrusions. IDS systems based on ML (machine learning), as well as DL (Deep learning), have recently been deployed as feasible procedures for swiftly identifying network intrusions. The taxonomy presented in this article is based on well-known ML as well as DL approaches for building network-based IDS (NIDS) systems, as well as it first defines the concept of IDS. In this comprehensive review of existing NIDS-based research, the merits and limitations of the suggested solutions are thoroughly explored. This evaluation may act as a standard for researchers and industry in terms of the advancement and development of Network Intrusion Detection Systems in the future. © 2022 IEEE.

关键词： Deep learning Industries machine learning algorithms Taxonomy Network intrusion detection Information processing Network security

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Educational Content Recommendation System for UPSC as prints Based on their Past Performance Using machine learning algorithms

An Adaptive Educational Content Recommendation System for UP...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Kundra, Danish Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319200

Within the space of competitive examinations, such as the Union Open Benefit Commission (UPSC) exams, the requirement for personalized and versatile instructive substance proposal frameworks has gotten to be progressively imperative. This inquiry about presents an inventive approach to building a Versatile Instructive Substance Proposal Framework (AECRS) custom-made particularly for UPSC hopefuls, leveraging their past execution information. This framework points to optimizing the learning involvement and upgrading the general victory rate of hopefuls by giving them with personalized ponder materials and assets. The proposed framework utilizes machine learning calculations, particularly the Network Factorization strategy, to extricate important bits of knowledge from the verifiable execution information of UPSC competitors. By analyzing the perplexing connections between the aspirants' execution measurements and the differing instructive substance accessible, the framework can precisely foresee the inclinations and learning designs of personal clients. Through the successful utilization of this prescient examination, the framework encourages the creation of personalized learning ways, empowering hopefuls to center on regions of shortcomings and strengthen ranges of quality. Besides, the AECRS joins a versatile learning system that ceaselessly upgrades and refines its proposals based on real-time input and client intuition. This versatile instrument guarantees that the framework remains responsive to the advancing needs and learning advance of each hopeful. By powerfully altering the prescribed substance based on the aspirants' continuous execution and inclinations, the framework maximizes engagement and maintenance, subsequently cultivating a more productive and viable learning preparation. The exploratory comes about to illustrate the viability of the proposed framework in improving the learning results of UPSC hopefuls. By giving custom-made instructive substance

关键词： Adaptive Educational Content Recommendation System etc machine learning algorithms Matrix Factorization Method Personalized learning UPSC Aspirants

来源：评论

学校读者我要写书评

暂无评论

machine learning algorithms for Predicting Chronic Diseases

Machine Learning Algorithms for Predicting Chronic Diseases

引用

Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Furkan Buluş Bilal Saoud Ibraheem Shayea Zuleikha Syzdykova Dept. Electronics & Communications Engineering Faculty of Electrical and Electronics Engineering Istanbul Technical University (ITU) Istanbul Turkey Dept. Electrical Engineering Faculty of Sciences and Applied Sciences LISEA Laboratory University of Bouira Bouira Algeria Dept. of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Chronic diseases are the leading cause of death and disability worldwide, necessitating early detection and management to mitigate their adverse effects on health and improve quality of life. Leveraging machine learning algorithms has become a prominent approach in predicting the risk of various chronic diseases. These algorithms excel in analyzing complex datasets to identify patterns and risk factors associated with chronic conditions. This study explores the application of two different machine learning algorithms on an open-source dataset to predict the risk of chronic diseases. The outcomes of these implementations are analyzed and discussed in the final section, providing insights into their effectiveness and potential for enhancing chronic disease management.

关键词： machine learning algorithms Data analysis Prevention and mitigation Medical services Predictive models Prediction algorithms Robustness Bayes methods Decision trees Diseases

来源：评论

学校读者我要写书评

暂无评论

Twitter Sentiment Analysis using machine learning algorithms for COVID-19 Outbreak in New Zealand

Twitter Sentiment Analysis using Machine Learning Algorithms...

引用

11th IEEE International Conference on System Engineering and Technology, ICSET 2021

作者： Baker, Oras Liu, Jay Gosai, Mayur Sitoula, Suyog Southern Institute of Technology School of Computing Invercargill New Zealand

ISBN: (纸本)9781665437660

The use of data obtained from social media for data mining has benefited the process of analysing public opinions. Sentiment analysis, also referred to as opinion mining, helps public health officials and other governmental agencies to understand the public's concerns, panics, emotions and interactions to provide effective services and information. This research focuses on the sentiment analysis of the COVID-19 pandemic outbreak in New Zealand using Twitter data. The analyses derived from several machine learning classification techniques, in particular Naive Bayes, K-Nearest Neighbour (KNN), Convolutional Neural Network (CNN), and Support Vector machine (SVM). In addition, the researchers implemented these algorithms in two different data mining platforms, namely Python and RapidMiner, then compared the metrics obtained from these techniques and platforms to identify the best algorithm suited for sentiment analysis. Finally, the researchers illustrate the experimental results that show the performance of Naïve Bayes and SVM, which indicates a longer computational time and led to an improved Twitter sentiment analysis result that outperforms the other models. After that, validate these models' effectiveness by comparing the obtained results for the same models in Python and RapidMiner. © 2021 IEEE.

关键词： Support vector machines COVID-19 Sentiment analysis machine learning algorithms Social networking (online) Pandemics Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Predicting countermovement jump heights by time domain, frequency domain, and machine learning algorithms

Predicting countermovement jump heights by time domain, freq...

引用

作者： Zhou, Zhaoxian Shakya, Sarbagya Sha, Zhanxin School of Computing University of Southern Mississippi HattiesburgMS United States School of Kinesiology University of Southern Mississippi HattiesburgMS United States

ISBN: (纸本)9781538636749

In this paper, we introduce an experiment evaluating performance of football players in countermovement jumps (CMJs). Three methods including time domain, frequency domain, and machine learning algorithms are proposed for performance evaluation. Correlation coefficients and p-values are given for time domain and frequency domain methods, and prediction errors are given for different machine learning algorithms. © 2017 IEEE.

关键词： Countermovement jump Frequency domain method machine learning algorithms Sports performance evaluation Time domain method Vertical ground reaction force

来源：评论

学校读者我要写书评

暂无评论

Analyzing and Contrasting machine learning algorithms for Predicting the Risk of Cardiovascular Disease

Analyzing and Contrasting Machine Learning Algorithms for Pr...

引用

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Purohit, Harshita Purohit, Sarvesh Mirajkar, Riddhi Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350372229

Cardiovascular conditions (CVDs) remain a major global health concern, challenging early threat assessment and forestallment. In this study, we employ three distinct machine learning algorithms - K Nearest Neighbors(KNN), Random Forest, and Decision Tree - to prognosticate the threat of cardiovascular conditions. The dataset used in this exploration consists of 14 essential features and has been sourced from Kaggle, a prominent platform for data wisdom competitions. Our primary idea is to estimate the prophetic performance of these algorithms and determine which algorithm gives us maximum accuracy. Among the algorithms examined, KNN classifier emerges as the top pantomime, achieving an accuracy of 84.48. This result showcases the efficacity of the KNN algorithm in handling the complexity of CVD threat vaticination. These findings give critical guidance for healthcare professionals and experimenters in enforcing effective prophetic models for early cardiovascular complaint threat identification and intervention. © 2024 IEEE.

关键词： Cardiovascular conditions (CVDs) Dataset Decision Tree K Nearest Neighbors (KNN) Kaggle machine learning algorithms Random Forest

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning algorithms in Network Intrusion Detection 7

Application of Machine Learning Algorithms in Network Intrus...

引用

7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Zhang, Shuchang Li, Yanling Shi, Yu Hua, Man School of Computer Science Civil Aviation Flight University of China Guanghan China School of Science Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9781665487115

With the rapid development of computer network technology, cyberspace security has received more and more attention. As an important means of ensuring cyberspace security, intrusion detection technology has also developed rapidly in recent years. machine learning is improving the efficiency of the intrusion detection and reducing the false positive. This paper is mainly divided into three parts. The first part briefly introduces the application process and common data sets of machine learning methods in the field of intrusion detection, and conducts an in-depth analysis of the application of traditional machine learning algorithms in intrusion detection. The second part expounds the specific application of deep learning as a subfield of machine learning in intrusion detection. In this part, according to the classification of generative model, recognition models and hybrid models, it is found that the intrusion detection system based on deep learning generally has a higher detection rate, which can make up for the defects of machine learning algorithm. The third part summarizes the full text and makes an outlook on the future development direction of intrusion detection technology. © 2022 IEEE.

关键词： Deep learning Cloud computing machine learning algorithms Intrusion detection Cyberspace Network intrusion detection Big Data

来源：评论

学校读者我要写书评

暂无评论

Predicting Employee Voluntary Departure: Leveraging machine learning algorithms for Improved Retention Strategies 2

Predicting Employee Voluntary Departure: Leveraging Machine ...

引用

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sarmento, Gonçalo Mota, Mickael Abbasi, Maryam Martins, Pedro Department of Informatics Polytechnic of Viseu Viseu Portugal Polytechnic Institute of Coimbra Applied Research Institute Coimbra Portugal Research Centre in Digital Services Polytechnic of Viseu Viseu Portugal

ISBN: (纸本)9798350305418

Employee voluntary departure presents significant challenges for organizations, affecting productivity and incurring substantial costs related to recruitment and training. This study aims to tackle this crucial issue by utilizing a comprehensive human resources dataset, which includes 14,999 records across ten columns. Its objective is to predict whether an employee will remain with the company or leave. To achieve this, seven distinct machine learning algorithms were employed: Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector machines, Logistic Regression, and Multi-Layer Perceptron. The performance of each algorithm was assessed through a series of experiments involving different percentages of test data and variations in dataset processing. Notably, the Random Forest classifier emerged as the most accurate model, achieving an impressive accuracy rate of 98.47%. © 2023 IEEE.

关键词： Algorithm selection Comparative analysis Human resources machine learning algorithms Performance evaluation Predictive modeling

来源：评论

学校读者我要写书评

暂无评论

Identification of Nitrogen in Soil from Remote Sensing Data Using machine learning algorithms 1

Identification of Nitrogen in Soil from Remote Sensing Data ...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Anuradha, G. Yendluri, Pravalika Vasana, Medhini Bala Department of CSE Vijayawada India

ISBN: (纸本)9798350356816

Nitrogen is essential for the health of the soil and the development of crops for various reasons including photosynthesis, leaf and stem development, and also synthesis of proteins in crops. In order to increase agriculture by decreasing environmental effects and for maintaining efficient supervision of nitrogen levels present in soil. Great amount of Nitrogen results in plant stress and lower fruit quality where as insufficient nitrogen amount in soil causes stunted growth, chlorosis, and delayed maturity. Typically nitrogen comprises a percentage of to 0.5 of the soil's total weight. This project utilizes Remote Sensing Technology and machine learning algorithms with a particular focus on Landsat-8 Imagery. Traditional soil testing techniques include time-consuming processes and a restricted geographic reach. The Normalized Vegetation Index ( NDVI) is derived from Landsat-8 Imagery and correlated with ground data values of soil to determine the nitrogen level in the soil. NDVI indirectly reflects the nitrogen level with the help of many other factors. The project workflow includes gathering ground data using conventional soil testing techniques, pre-processing to extract NDVI values, and acquisition of Landsat-8 data. These datasets are used to develop and test an effective machine-learning model that can handle intricate correlations between nitrogen levels and NDVI. One of the solutions is to create a user interface that incorporates the machine learning model. The goal is to reflect the levels of nitrogen in soil in a particular field. © 2024 IEEE.

关键词： Ground-Truth Data Landsat-8 machine learning algorithms Normalized Difference Vegetation Index(NDVI) Remote Sensing User Interface

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 << < 51 52 53 54 55 56 57 58 59 60 > >>

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：