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

22,626 篇 会议
3,225 篇 期刊文献
19 篇 学位论文
7 册 图书

馆藏范围

25,876 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,151 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
- 218 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
612 篇 管理学
- 525 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

25,877 篇 machine learning...
8,356 篇 machine learning
4,256 篇 support vector m...
4,174 篇 accuracy
4,172 篇 prediction algor...
3,590 篇 predictive model...
3,467 篇 training
3,208 篇 classification a...
3,170 篇 feature extracti...
1,941 篇 data models
1,801 篇 computational mo...
1,776 篇 deep learning
1,658 篇 random forests
1,466 篇 neural networks
1,387 篇 clustering algor...
1,384 篇 real-time system...
1,274 篇 data mining
1,133 篇 optimization
998 篇 decision trees
975 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...
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

25,672 篇 英文
147 篇 其他
33 篇 中文
5 篇 法文
2 篇 西班牙文
2 篇 荷兰文
2 篇 土耳其文
1 篇 德文

检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是631-640 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：

Enhanced AOD Retrieval Using machine learning algorithms and Driving Forces Analysis During COVID-19 Lockdown

Enhanced AOD Retrieval Using Machine Learning Algorithms and...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lulu Si Caiyun Deng Ran Kang Huiying Yin Li Zhang Hermann Josef Kaufmann School of Space Science and Physics Shandong University Weihai Shandong China Institute of Space Sciences Shandong University Shandong China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The measurement of aerosol concentration in the atmosphere, known as aerosol optical depth (AOD), stands as a crucial parameter in assessing air pollution comprehensively. Accurate estimation of surface reflectance is vital for precise determination of AOD, as inaccuracies can significantly impact retrieval results. We present optimized methodologies for extracting precise surface reflectance from Sentinel-2 imagery, tailored for both dark and bright areas, through seasonal two-group classifications. Comparative analysis with SONET site data shows that utilizing optimized surface reflectance yields the highest accuracy with the Back Propagation model (R 2 =0.822). Additionally, leveraging the MODIS AOD product to evaluate our new algorithm in regions without SONET sites reveals consistent spatial trends (R 2 =0.969). An examination of seasonal AOD values spanning 2018 to 2020 using the Geodetector method shows that meteorological factors primarily drive AOD dynamics despite reductions in human and social activities during the COVID-19 lockdown.

关键词： Reflectivity COVID-19 machine learning algorithms Atmospheric measurements Meteorological factors Surface contamination SONET

来源：评论

学校读者我要写书评

暂无评论

Utilization of Deep learning and machine learning algorithms in Detection, Segmentation, and Classification of Brain Metastasis: A Systematic Literature Review

Utilization of Deep Learning and Machine Learning Algorithms...

引用

IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Deepinder Kaur Jaspreet Singh Department of CSE Chandigarh University Mohali

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

One-tenth to around a quarter of cancer patients lose their lives to Brain Metastasis (BM) globally. Accurate neuroimaging of BM is essential to encourage early detection, decide treatment options, and increase survival rates. This systematic literature review (SLR) examines the utilization of machine/deep learning algorithms and imaging modalities in the detection, segmentation, and classification of BM. The study adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Statement) and meticulously evaluates 32 relevant articles spanning from 2018 to 2023. The investigation explores scholarly databases including Google Scholar, SCOPUS, Web of Science, PubMed, SCISPACE, IEEE, Science Direct, Hindawi, and EBSCOhost to pull out relevant studies. The four aspects viz. employment of various imaging modalities, utilization of machine/deep learning algorithms, evaluation of performance metrics, and investigation of software tools for the identification of BM considered in this study. The review identifies Convolutional Neural Networks (CNN) as the most frequently utilized algorithm with the highest AUC-ROC (0.98) and demonstrates its efficacy for BM detection. Furthermore, out of 32, 15 articles highlight the significance of contrast-enhanced T1weighted images (T1CE/ T1c). This SLR unearths the transformative impact of machine/deep learning algorithms and emphasizes the importance of imaging modalities for enhancing the accuracy of diagnostics.

关键词： Deep learning Image segmentation machine learning algorithms Accuracy Software algorithms Imaging Metastasis Classification algorithms Convolutional neural networks Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Analysis of Satellite Images for Historical Maps Classification using machine learning algorithms

Analysis of Satellite Images for Historical Maps Classificat...

引用

Expert Clouds and Applications (ICOECA), International Conference on

作者： Dheeraj Hebri S. Kirubakaran R. Rajitha Jasmine Anitha Poosamooper S. Rukmani Devi D Raja Joseph Department of Master of Computer Applications Srinivas Institute of Technology Valachil Mangalore Karnataka India Department of Computer Science Engineering CMR College of Engineering and Technology Hyderabad Telangana India Department of Information Technology R.M.K. Engineering College Kavaraipettai Thiruvallur Tamil Nadu India Department of Computer Applications Vel Tech Ranga Sanku Arts College Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India School of Aeronautical Sciences Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Remote sensing is concerned with high-resolution geographical data. Its applications include farming, water resources, ecology, forests, deserts, marine applications, soil types, disaster alerts, and management. To extract the necessary information from satellite images, considerable image processing is required. Image segmentation and categorization into land cover categories is an important stage in satellite image processing. This article describes a method for categorizing and detecting different forms of land cover from satellite data. In this study, we examine satellite images for historical map classification using machine learning algorithms. We acquire Landsat images to complete this tedious task. The images are carefully labeled using the ArcMap software. We categorize areas into five types: forest, building, water, barren, and agricultural land. The images are then sent to the Convolutional Neural Network (CNN) Model for feature extraction. The retrieved features were then exposed to three machine learning (ML) models: Support Vector machine (SVM), Naive Bayes (NB), and Decision Tree (DT). Standard classification measures are used to analyze the outcomes of all three models. When compared to the other two models in satellite image classification, CNN feature extraction with SVM classifier achieves the greatest accuracy of 95%.

关键词： Support vector machines Forests machine learning algorithms Biological system modeling Disasters Land surface Landsat

来源：评论

学校读者我要写书评

暂无评论

Construction of a Meteorological Data Support Vector machine Drought Prediction Model Based on machine learning algorithms

Construction of a Meteorological Data Support Vector Machine...

引用

Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Rui Zhang Jilin University of Agricultural Science and Technology Jilin China

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In recent years, the proliferation of machine learning algorithms in drought prediction has revolutionized the field, leveraging their robust data processing and pattern recognition abilities to offer fresh perspectives and methodologies. This article specifically employs machine learning to develop a support vector machine (SVM) drought prediction model grounded in meteorological data, aiming to enhance prediction accuracy. Following a comprehensive literature review outlining the fundamentals of machine learning and its applications in drought forecasting, the article delves into the construction process of the SVM-based model in detail. Experimental analysis validates the model’s strengths, revealing that despite Decision Tree and Linear Regression algorithms demonstrating improved accuracy trends (ranging from 0.7-0.86 and 0.65-0.87, respectively), the SVM algorithm consistently outperforms them, achieving superior prediction accuracy. This underscores the conclusion that the SVM algorithm is a highly effective tool for drought prediction, offering precision that surpasses traditional methods.

关键词： Support vector machines machine learning algorithms Accuracy Droughts Predictive models Prediction algorithms Feature extraction Data models Stability analysis Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling for Asthma Disease Detection: A Comparative Study of machine learning algorithms

Predictive Modeling for Asthma Disease Detection: A Comparat...

引用

International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Arka Mukherjee Junali Jasmine Jena Mahendra Kumar Gourisaria Sricheta Parui Sudhansu Shekhar Patra Parthasarathi Pattnayak School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India School of Computer Applications KIIT Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Artificial Intelligence (AI) and machine learning (ML) models have proven to be scalable approaches for handling several biomedical problems. Recent availability of high-quality datasets which captures various factors contributing to the respiratory disease, have enabled the development of robust models that deliver high accuracy and precision scores in early detection of respiratory diseases. Asthma is a common respiratory illness which causes shortness of breath, coughing, wheezing etc. This paper focuses on asthma disease detection. It makes two primary contributions: (1) an empirical evaluation of their performance on an asthma disease detection dataset with data mining and pre-processing techniques, and (2) the identification of the most effective approach for asthma disease detection based on rigorous evaluation using metrics such as precision, accuracy, recall, F-1, and F-beta scores. Cat Boost Classifier was found to be the best model which predicted asthma disease with a 96.04% accuracy.

关键词： Measurement Analytical models Accuracy machine learning algorithms Biological system modeling machine learning Predictive models Data mining Asthma

来源：评论

学校读者我要写书评

暂无评论

Optimizing Customer Retention in Urban Laundry Services: A Comparative Analysis of machine learning algorithms in Jakarta's MSME Sector

Optimizing Customer Retention in Urban Laundry Services: A C...

引用

International Conference on Information Management and Technology (ICIMTech)

作者： Rhisa Adika Putri Tuga Mauritsius Bryan Ananda Riyanto Jayadi Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

In the rapidly urbanizing context of major cities such as Jakarta, the demand for efficient and rapid laundry services poses a considerable challenge, particularly within residential settings like boarding houses or dormitories in Central Jakarta, where access to communal laundry facilities is markedly constrained. Against this backdrop, laundry enterprises categorized under Micro, Small, and Medium Enterprises (MSMEs) in the service sector have witnessed significant growth. These services typically encompass the collection, washing, drying, folding, and delivery of laundry to the customer's domicile. Nevertheless, the burgeoning proliferation of laundry businesses has precipitated intensified competition within the sector, compelling these enterprises to devise strategies for customer retention and revenue augmentation. This research endeavors to delineate effective strategies and solutions to navigate the competitive landscape of the laundry industry. The study undertakes a meticulous evaluation of various machine learning algorithms, including Decision Tree, Logistic Regression, Naïve Bayes, Support Vector machine, Random Forest, XGBoost, LightGBM, and CatBoost, based on their performance with unseen data. This evaluation employs a comprehensive set of metrics such as precision, recall, Fl-score, and ROC AUC to determine the efficacy of each model. The findings reveal that the CatBoost Classifier algorithm outperforms others, achieving a training accuracy of 95.9% and a testing accuracy of 85.7 % . The novelty of this study lies in its comprehensive comparison of multiple machine learning algorithms in the context of MSMEs in Jakarta's laundry sector, highlighting the superior performance of the CatBoost algorithm. Furthermore, the study contributes by demonstrating the practical application of these algorithms to improve customer retention strategies, enhance service quality, and optimize marketing tactics. Based on these results, the study recommends the impl

关键词： Support vector machines machine learning algorithms Accuracy Boosting Prediction algorithms Real-time systems Classification algorithms Random forests Business Testing

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning algorithms for Monitoring of Spoilage of Cow’s Milk Using the Cheap Gas Sensor

Application of Machine Learning Algorithms for Monitoring of...

引用

International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Kristian Dokic Bojan Radisic Hrvoje Kukina Faculty of Tourism and Rural Development University of Osijek Pozega Croatia Lipik development and tourism agency LIRA d.o.o Lipik Croatia

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Cow’s milk is very sensitive to spoilage processes that could negatively affect its quality. Several methods have been developed for the analysis of milk spoilage, and in recent times solutions using e-nose are more and more common. This paper analyzes the possibility of using the Bosch BME688 (BioMedical Engineering) sensor, which can detect different gases by analyzing their different electronic signatures. Samples of fresh, pasteurized and UHT (Ultra High Temperature) sterilized milk, which were at room temperature (22°C) during the 4-day experiment, were tested. The obtained data for every day were used to train nine models based on different classification algorithms (K Nearest Neighbor, Logistic Regression, Support Vector machine, Naive Bayes, Decision Tree, Random Forest, Gradient Boosted Trees, Probabilistic Neural Network) with the aim of distinguishing the type of milk with known time after opening, i.e. milking. The focus of the research was on the analysis of classification algorithms that can be used for the classification of milk with regard to the standing time at room temperature. The manufacturers of the sensor itself enabled the use of neural networks exclusively in the BME AI-Studio program, which is used for configuration and training of the model, and their decision is correct in most cases.

关键词： Temperature sensors Training Support vector machines Dairy products machine learning algorithms Neural networks Probabilistic logic

来源：评论

学校读者我要写书评

暂无评论

Enhancing Early Detection of Diabetes Using machine learning algorithms

Enhancing Early Detection of Diabetes Using Machine Learning...

引用

Innovative Engineering Sciences and Technological Research (ICIESTR), International Conference on

作者： Atiqa Saeed Hafiz Muhammad Salman Ajmal Muhammad Umair Ahmad Khan Waqas Tariq Toor Department of Biomedical Engineering School of Mechanical and Manufacturing Engineering NUST Islamabad Pakistan Department of Biomedical Engineering UET Lahore Narowal Pakistan Department of Electrical Engineering UET Lahore Narowal Pakistan

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

Diabetes mellitus, a chronic metabolic disorder, presents a significant global health problem with increasing prevalence. Early detection of disease is crucial for effective management and the prevention of complications. This research investigates the application of machine learning (ML) algorithms in diabetes prediction, to assess their effectiveness through analysis of a diverse dataset of 2000 individuals. Numerous ML techniques are used in this study, such as Naive Bayes, Logistic Regression, Support Vector machine, Random Forest, K-Nearest Neighbors, and Gradient Boosting. The dataset goes through appropriate preprocessing, data classification, and model building. Evaluation metrics include accuracy, receiver operating characteristic - area under the curve (ROC AUC), and interpretability of the models. The results demonstrate varied performance across algorithms. Random Forest emerged as the most successful, achieving 94.2% accuracy and a robust ROC-AUC of 92.1%. Logistic Regression and SVM exhibit moderate performance, while KNN and Gradient Boosting show notable improvements. The study contributes to understanding ML’s role in diabetes prediction and emphasizes the importance of model selection in healthcare analytics.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Medical services Predictive models Nearest neighbor methods Prediction algorithms Diabetes Random forests

来源：评论

学校读者我要写书评

暂无评论

Improving Accuracy in Network Intrusion Detection via machine learning algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

引用

Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Suriya Prakash J Trishlaa S Soniya R V Lakshmanan Kiran S Department of CSE JAIN (Deemed-to-be-University) Ramnagara India Department of Artificial Intelligence and Machine Learning Rajarajeshwari College of Engineering Bengaluru India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Mathematics Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31

关键词： Training machine learning algorithms Accuracy Nearest neighbor methods Boosting Data models Classification algorithms Decision trees Random forests Testing

来源：评论

学校读者我要写书评

暂无评论

Adaptive machine learning algorithms and Models for Real-Time Prediction of Traffic Flow in Urban Areas

Adaptive Machine Learning Algorithms and Models for Real-Tim...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： R. Hariharan T.S. Balaji S. Balaji S. Prasanna Bharathi Glaret Subin P Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai Tamilnadu India School of Electronics Engineering Vellore Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

This work models and analyzes city traffic patterns using adaptive machine learning techniques and statistical modeling. We pre-process historical traffic data, weather, network topology, and contextual information to derive dynamic traffic attributes. For role detection, the most significant algorithms—Random Forest, Gradient Boosting, LSTM networks, and ARIMA models—are used. We extensively experiment to ascertain algorithmic runtime performance. The average absolute error for Random Trees is 5.23, while Gradient Boosting has 4.91. The average root mean squared error is 7.81 and 7.25 for the algorithms, with R 2 values of 0.65 and 0.68, respectively. LSTM networks have 6.02 mean absolute error, 8.94 root mean squared error, and 0.59 correlation coefficient. ARIMA models have mean absolute error, root mean squared error, and correlation coefficients of 8.17, 10.72, and 0.45. Real-time case studies reveal that Random Forest and Gradient Boosting outperform LSTM networks and ARIMA models in forecasting. Random forest had 85% and 82% prediction accuracy over two 15-minute intervals. Gradient boosting had 87% and 88% prediction accuracy in two 15-minute intervals. At first, LSTM networks at 80% and 78% are slightly higher than ARIMA models at 75% and 72%. Random Forest and Gradient Boosting algorithms are efficient, taking 120 and 180 seconds for training and 10 and 15 milliseconds for inference. ARIMA models train in 5 milliseconds and infer in 50 milliseconds, while LSTM networks take 600 seconds. This strategy enhances model transparency, scaling, and computational efficiency for intelligent urban traffic management system implementation. Our method applies machine learning techniques to real concerns to improve the responsiveness and efficiency of the urban transportation system, improving mobility and quality of life.

关键词： Adaptation models machine learning algorithms Accuracy Computational modeling Urban areas Predictive models Boosting Real-time systems Long short term memory Random forests

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 << < 60 61 62 63 64 65 66 67 68 69 > >>

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：