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

1,133 篇 会议
11 篇 期刊文献
7 册 图书

馆藏范围

1,151 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

752 篇 工学
- 711 篇 计算机科学与技术...
- 444 篇 软件工程
- 137 篇 信息与通信工程
- 108 篇 电气工程
- 73 篇 生物医学工程（可授...
- 68 篇 生物工程
- 65 篇 控制科学与工程
- 32 篇 安全科学与工程
- 29 篇 化学工程与技术
- 21 篇 网络空间安全
- 20 篇 动力工程及工程热...
- 19 篇 机械工程
- 19 篇 电子科学与技术（可...
- 17 篇 仪器科学与技术
253 篇 理学
- 185 篇 数学
- 78 篇 生物学
- 78 篇 系统科学
- 64 篇 统计学（可授理学、...
- 24 篇 化学
- 15 篇 物理学
216 篇 管理学
- 122 篇 图书情报与档案管...
- 104 篇 管理科学与工程(可...
- 52 篇 工商管理
77 篇 医学
- 68 篇 临床医学
- 61 篇 基础医学(可授医学...
- 47 篇 药学(可授医学、理...
- 27 篇 公共卫生与预防医...
34 篇 法学
- 33 篇 社会学
31 篇 教育学
- 27 篇 教育学
22 篇 经济学
- 22 篇 应用经济学
15 篇 农学
1 篇 文学
1 篇 军事学

主题

374 篇 data mining
237 篇 machine learning
57 篇 support vector m...
56 篇 machine learning...
48 篇 feature extracti...
45 篇 data models
44 篇 accuracy
42 篇 sentiment analys...
40 篇 big data
39 篇 classification a...
35 篇 predictive model...
34 篇 classification
31 篇 deep learning
30 篇 training
29 篇 prediction algor...
26 篇 forecasting
23 篇 neural networks
23 篇 decision trees
20 篇 artificial intel...
19 篇 learning systems

机构

4 篇 college of techn...
3 篇 department of sc...
3 篇 galgotias univer...
3 篇 shanghai univers...
3 篇 uttaranchal univ...
3 篇 uttaranchal inst...
2 篇 new prince shri ...
2 篇 china transport ...
2 篇 miet haldwani
2 篇 school of it viv...
2 篇 state grid custo...
2 篇 world college of...
2 篇 school of econom...
2 篇 china electric p...
2 篇 college of compu...
2 篇 shandong institu...
2 篇 chitkara univers...
2 篇 school of comput...
2 篇 college of infor...
2 篇 state key labora...

作者

3 篇 guidotti riccard...
3 篇 raman ramakrishn...
3 篇 vysotska victori...
3 篇 chen feng
2 篇 chen haifeng
2 篇 yakovyna vitaliy
2 篇 kulvinder singh
2 篇 monreale anna
2 篇 buddhima attanay...
2 篇 jacob shomona gr...
2 篇 madhurya j a
2 篇 wang hui
2 篇 gama j
2 篇 kalinina irina
2 篇 suwimon kooptiwo...
2 篇 nitin n. sakhare
2 篇 gozhyj aleksandr
2 篇 das rama krushna
2 篇 xu weiqiang
2 篇 k.k. senthilkuma...

语言

1,062 篇 英文
87 篇 其他
18 篇 中文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining, MLDM 2003"

共 1151 条记录，以下是141-150 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：

Oracle Bone Inscriptions Recognition Based on Transfer learning and Improved ResNet50 3rd

Oracle Bone Inscriptions Recognition Based on Transfer Learn...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Li, Zengming He, Xuansen Lu, Haihui Mo, Jianming Wen, Guanjin Huang, Ziyun Zhang, Wenjian Guangzhou College of Commerce Guangzhou511363 China College of Information Science and Engineering Hunan University Changsha410082 China

ISBN: (纸本)9789819624676

Oracle Bone Inscriptions (OBI) are the earliest Chinese characters found so far, they are the treasures of Chinese traditional culture and have high research value. However, the machine recognition of OBI is a difficult problem. To address this challenge, this paper presents a new method of OBI recognition based on transfer learning and improved ResNet50 network. Firstly, the deep convolutional neural network structure of ResNet50 is used to obtain rich feature representations by learning on large general dataset, and then it is transferred to the recognition of OBI, so as to effectively extract the key features of OBI. On this basis, the parameters of ResNet50 model are fine-tuned on a small data set of a specific OBI topology to improve the recognition rate of OBI. The experiment utilized rubbed images of the original OBI as the dataset. The findings demonstrated that the proposed method achieved superior recognition accuracy, reaching 95.641% compared to alternative methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Staying Ahead of the Curve: Early Prediction of Academic Probation among First-Year CS Students 3

Staying Ahead of the Curve: Early Prediction of Academic Pro...

引用

3rd international conference on Applied Artificial Intelligence (ICAPAI) - Human-Centric AI for the Digital Society

作者： Neda, Barbara Martinez Wang, Max Singh, Amanjeet Gago-Masague, Sergio Wong-Ma, Jennifer Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9798350328929

Application processes in higher education, particularly in fields like Computer Science (CS), have become more competitive in recent years. Additionally, at the University of California, Irvine (UCI), 57% of students who were admitted and enrolled in the CS program between Fall 2011 and Fall 2015 failed to maintain satisfactory academic standing and entered academic probation. Of these probation students, 41% entered probation during their first year. The objective of this study is to explore the feasibility of using machine learning tools to identify the academic probation status of first-year CS students at an early stage, with the ultimate aim of providing timely support to those who are at the greatest risk of entering academic probation. We trained various classifiers on a dataset containing demographic and academic performance variables for CS students at UCI, and performed feature analysis to identify the most influential variables. Since it is our priority to identify students who enter academic probation, our focus was on maximizing recall. The best-performing classifiers were Linear Support Vector machines (recall of 0.838) and Logistic Regression (recall of 0.812). Our feature analysis revealed that ethnicity, student region, and having taken the Advanced Placement CS exam had a greater impact on the classification task than GPA and summer course enrollment. Future work could focus on expanding the dataset to include academic data available after enrollment to improve our current results. Our findings could also be applied to assess their impact in strategies for student retention in CS. Overall, this study demonstrates the potential for machine learning tools to assist in identifying at-risk students in CS, and highlights the importance of considering a range of demographic and academic variables in this process.

关键词： academic performance prediction computer science education educational data mining academic probation academic probation detection machine learning first-year probation

来源：评论

学校读者我要写书评

暂无评论

2023 3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

2023 3rd International Conference on Smart Generation Comput...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

ISBN: (纸本)9798350319125

The proceedings contain 441 papers. The topics discussed include: evaluating the impact of image restoration on digital image processing using compression technique;blockchain's transcendent evolution: uncovering its impact across industries and cutting-edge applications;analysis of the construction of digital education resource sharing in universities from the perspective of blockchain;examining the impact of data mining on decision-making processes;the synchronous dislocation scheduling algorithm: cloud-based performance improvement;the multicarrier NOMA- assisted full duplex IoT networks for robust resource allocation using lightweight secure transmission;research on English teaching quality evaluation based on fuzzy comprehensive evaluation based on k-means clustering algorithm;and predicting life expectancy using machine learning approach through linear regression and decision tree classification techniques.

关键词：

来源：评论

学校读者我要写书评

暂无评论

machine learning Based Cardiovascular Disease Prediction 3

Machine Learning Based Cardiovascular Disease Prediction

引用

3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Saraswathi, P. Prathiksaa, J.G. Prabha, M. Jhoshitha, N.A. It Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350330724

Heart disease is an illness that affects the heart and manifests itself in a variety of ways, including blood vessel issues, irregular heartbeats, and diseases of the heart muscle or valves. In recent years, researchers have found that machine learning can help to detect heart problems by analyzing digital electrocardiogram (ECG) data. By using this technology, doctors can detect heart issues earlier and save lives. The process involves analyzing large sets of medical data, which includes information about patients' medical history. Logistic Regression, Support Vector machine (SVM), and Random Forest are most widely utilized machine learning techniques to perform classification. Also, it predicts whether a patient has heart ailments or not. In this research, Decision Tree Classifiers are modeled along with the mentioned techniques which predict the heart related issues. A performance measure like accuracy of each technique is analyzed and the best one is identified to guess the heart disease. © 2023 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

3rd EAI international conference on IoT and Big data Technologies for Health Care, IotCARE 2022

3rd EAI International Conference on IoT and Big Data Technol...

引用

3rd EAI international conference on IoT and Big data Technologies for Health Care, IotCARE 2022

ISBN: (纸本)9783031335440

The proceedings contain 23 papers. The special focus in this conference is on IoT and Big data Technologies for Health Care. The topics include: Personalized Recommendation Method of Maternal and Child Health Education Resources Based on Association Rule mining Algorithm;methods of Integrating Ideological and Political Education into Health Management in Colleges and Universities Based on Internet of Things Technology;data mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses;intelligent Imaging Method of Nuclear Magnetic Resonance Medical Devices Based on Compression Sensing;evaluation of Post Fitness of Employees in Health Care Enterprises Based on Big data;design of Telemedicine and Health Care System Based on Embedded Technology;anomaly Detection Method of Healthcare Internet of Things Gateway Supporting Edge Computing;research on Fast Encryption of Electronic Health Record data Based on Privacy Protection;research on Secure Storage of Healthcare data in the Environment of Internet of Things;research on Cloud Health Privacy Information Protection Algorithm Based on data mining;architecture of Wide Area Health Monitoring System;internet of Things Technologies in Healthcare for People with Hearing Impairments;artificial Intelligence-Based Early Warning Method for Abnormal Operation and Maintenance data of Medical and Health Equipment;abnormal Signal Recognition Method of Wearable Sensor Based on machine learning;data Security mining Method for Social Media Users’ Mental Health Status Test Based on machine learning Algorithm;early Warning Method of College Students Mental Subhealth Based on Internet of Things.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of a model for predicting failures of high-Tech production equipment based on machine learning algorithms 3

Development of a model for predicting failures of high-Tech ...

引用

3rd international conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Chernukhin, A.V. Bogdanova, E.A. Savitskaya, T.V. Sverchkov, A.M. Dementienko, A.V. Mendeleev University of Chemical Technology of Russian Moscow Russia Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9781510674486

This article proposes a new approach to predicting failures of high-Tech manufacturing equipment using machine learning methods. As part of the study, various modern approaches for solving similar problems were analyzed, their advantages and disadvantages relative to the proposed method were described. Model training data includes sensor readings measuring temperatures, pressures, currents, and vibrations for six exhausters over three years. In the process of data preprocessing, standardization, noise removal and extraction of new features were carried out. Since blower failure is a rare and short-Term occurrence, the use of supervised learning methods is difficult. In this regard, it is necessary to introduce an additional stage of screening out periods of normal operation. Thus, the process of early diagnosis of failures consisted of two successive stages. Firstly, anomalous operating modes of equipment were identified using the isolation forest method. Next, intervals were classified using a recurrent neural network, defined as anomalous, according to the technical locations that caused the equipment failure. As a result, on the test sample it was possible to achieve an average value of the F1 metric equal to 0.74 for all analyzed technical locations. © 2024 SPIE. All rights reserved.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning Algorithms in Predicting the Heart Disease in Patients 3

Application of Machine Learning Algorithms in Predicting the...

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Razia Sulthana, A. Jaithunbi, A.K. Sunraja, P. University of Greenwich Department of Computing and Mathematical Sciences Old Royal Naval College London United Kingdom RMD Engineering College Department of Computer Science and Engineering Kavarapettai India SRM Institute of Science and Technology Department of Computer Science Kattankulathur India

ISBN: (纸本)9781665494007

Healthcare services save the life of human beings by making timely effective decisions. The use of data mining tools is crucial for decision making, forecasting, and disease prediction. In this study, data mining algorithms are applied to predict heart disease. The dataset contains 14 attributes such as age, gender, blood pressure, blood fat, etc. These parameters are analyzed to predict the probability of patients prone to heart disease in future. Initially, the relationship between the parameters is analyzed. Following which Naïve Bayes, decision trees and Naïve Bayes with k-means clustering are applied over it for classification and prediction. These algorithms were employed to train the dataset and create a binary classification. The proposed system shows a better prediction of heart disease. The performance measures of the system are measured, and the obtained results illustrate the system can forecast the probability of developing the heart diseases. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

The 3rd Workshop on Artificial Intelligence-Enabled Cybersecurity Analytics 23

The 3rd Workshop on Artificial Intelligence-Enabled Cybersec...

引用

29th ACM SIGKDD conference on Knowledge Discovery and data mining (KDD)

作者： Samtani, Sagar Yang, Shanchieh Chen, Hsinchun Indiana Univ Bloomington IN 47401 USA Rochester Inst Technol Henrietta NY USA Univ Arizona Tucson AZ USA

ISBN: (纸本)9798400701030

Artificial Intelligence (AI) has gripped modern society as a viable approach to revolutionize operational capabilities across multiple industries. One critical application area that could stand to benefit from the capabilities of AI is cybersecurity. Increasingly, federal funding agencies such as the National Science Foundation are calling for enhanced AI-enabled analytics capabilities to improve cyber threat intelligence, cyber defense generation, and more. To this end, this half-day workshop, not in its third year at ACM KDD, sought to attain significant contributions related to various aspects of AI-enabled cybersecurity analytics. This workshop received a record number of submissions. Submissions were reviewed by a highly-qualified, interdisciplinary group of AI for cybersecurity researchers and practitioners spanning academia and private industry firms.

关键词： Cybersecurity artificial intelligence analytics large language models machine learning

来源：评论

学校读者我要写书评

暂无评论

Automatic Assessment System for English Pronunciation machine Quality Based on GloVe-CNN Algorithm 3

Automatic Assessment System for English Pronunciation Machin...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Tian, Tian School of International Business Communication Dongbei University of Finance and Economics Liaoning Dalian China

ISBN: (纸本)9798350317022

In recent years, people have paid more and more attention to machine quality assessment of pronunciation, and some progress has been made. For a long time in the past, people only paid attention to the symbols and phonetic information in mechanical language, but did not pay attention to whether the relationship between multiple corpus materials was accurate and reliable. Moreover, with the rapid development of computer technology, network communication technology, and the continuous maturity and improvement of digital coding research methods and theories, the English pronunciation machine evaluation system is also constantly being updated, and the quality evaluation level of pronunciation machines is getting higher and higher. This article aims to explore this method's assessment of users' English proficiency from three aspects: speech quality, grammatical structure and semantic features. First, this article introduces the problem of automatic assessment of speech quality and its related influencing factors;secondly, elaborates on the common measurement standards for judging whether the speaker is 'Chinese-English bilingual';finally, introduces the data mining result model based on machine learning algorithms, and explains the function used to construct sentence fuzzy sets. The overall accuracy of the system in this article is over 94%. Therefore, the system has an effective mechanism to detect and correct learners' errors in pronunciation of phonetic symbols. The system uses convolutional neural network to learn the complex relationship between pronunciation quality and features. CNN can effectively learn different levels of features and extract meaningful information for pronunciation quality assessment. © 2023 IEEE.

关键词： Assessment System Automatic Quality Assessment English Pronunciation GloVe-CNN Algorithm

来源：评论

学校读者我要写书评

暂无评论

Anomaly detection based on improved isolated forest 3

Anomaly detection based on improved isolated forest

引用

3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Cheng, Xinyun Liu, Zhe Tang, Ming Du, Yuanhan Xu, Chenwei Information and Communication Branch of State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (纸本)9781665490788

Anomaly detection is one of the hot research fields of machine learning and data mining, which is mainly used in fault diagnosis, intrusion detection, fraud detection and other fields. At present, there have been a lot of effective research work, especially the anomaly detection method based on isolated forest, but there are still many difficulties in processing high-dimensional data. A new anomaly detection algorithm k-nearest neighbor based isolation forest (KNIF) is proposed. In this method, the hypersphere is used as the isolation tool, the k-nearest neighbor method is used to construct the isolation forest, and the outlier calculation method based on distance is constructed. Sufficient experiments show that KNIF method can effectively detect anomalies in complex distribution environment and adapt to different distribution scenarios. © 2023 IEEE.

关键词： 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116页 << < 11 12 13 14 15 16 17 18 19 20 > >>

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：