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

995 篇 会议
23 册 图书
13 篇 期刊文献

馆藏范围

1,031 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

789 篇 工学
- 684 篇 计算机科学与技术...
- 364 篇 软件工程
- 198 篇 信息与通信工程
- 76 篇 电气工程
- 52 篇 生物工程
- 48 篇 控制科学与工程
- 48 篇 生物医学工程（可授...
- 34 篇 机械工程
- 29 篇 化学工程与技术
- 24 篇 安全科学与工程
- 23 篇 交通运输工程
- 22 篇 光学工程
- 22 篇 动力工程及工程热...
- 19 篇 仪器科学与技术
- 18 篇 土木工程
- 17 篇 电子科学与技术（可...
256 篇 理学
- 176 篇 数学
- 68 篇 统计学（可授理学、...
- 59 篇 生物学
- 43 篇 物理学
- 42 篇 系统科学
- 27 篇 化学
176 篇 管理学
- 100 篇 管理科学与工程(可...
- 89 篇 图书情报与档案管...
- 55 篇 工商管理
40 篇 医学
- 38 篇 临床医学
- 36 篇 基础医学(可授医学...
- 27 篇 药学(可授医学、理...
22 篇 法学
- 17 篇 社会学
19 篇 经济学
- 19 篇 应用经济学
16 篇 教育学
13 篇 农学
4 篇 军事学

主题

71 篇 big data
46 篇 cloud computing
44 篇 data mining
35 篇 machine learning
31 篇 security
29 篇 deep learning
29 篇 feature extracti...
27 篇 data models
22 篇 computational mo...
21 篇 accuracy
21 篇 training
20 篇 predictive model...
19 篇 servers
18 篇 internet of thin...
17 篇 blockchain
15 篇 protocols
15 篇 soft sensors
14 篇 support vector m...
14 篇 medical services
13 篇 real-time system...

机构

12 篇 jiangsu high tec...
11 篇 henan univ inst ...
9 篇 school of inform...
8 篇 nanjing univ pos...
6 篇 henan univ sch c...
6 篇 nanjing universi...
6 篇 department of co...
6 篇 department of co...
5 篇 school of comput...
5 篇 college of compu...
4 篇 beihang univ bei...
4 篇 univ exeter coll...
4 篇 school of comput...
3 篇 university of sc...
3 篇 school of mathem...
3 篇 soochow univ sch...
3 篇 chinese acad sci...
3 篇 national demonst...
3 篇 univ chinese aca...
3 篇 chinese acad sci...

作者

17 篇 han zhijie
10 篇 pan jeng-shyang
8 篇 elisa bertino
7 篇 liu pingzeng
7 篇 wenchao huang
6 篇 yan xiong
5 篇 li peng
5 篇 zhaoqing pan
5 篇 xingming sun
5 篇 wang ruchuan
5 篇 zhang jing
4 篇 hao zhou
4 篇 panlong yang
4 篇 zhang xinpeng
3 篇 zhang lei
3 篇 fuyou miao
3 篇 radha krishna ra...
3 篇 zhang chao
3 篇 jianmeng huang
3 篇 ge jingguo

语言

1,017 篇 英文
10 篇 其他
4 篇 中文

检索条件"任意字段=4th International Conference on Soft Computing in Data Science, SCDS 2018"

共 1031 条记录，以下是111-120 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：

A Method for Plant Diseases Detection Based on Transfer Learning and data Enhancement 4

A Method for Plant Diseases Detection Based on Transfer Lear...

引用

4th international conference on High Performance Big data and Intelligent Systems, HDIS 2022

作者： Liu, Jing Zhang, Guofeng Feng, Bin Hou, Yingkun Kang, Wendi Shen, Boyang Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China School of Information Science and Technology Taishan University Tai'an China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

With the wide application of deep learning in the field of agriculture, how to quickly and accurately identify and process structured and unstructured monitoring data to support intelligent decision-making has become an important research direction in the field of intelligent agriculture. In this paper, the multiple diseases of wild and introduced flowers such as canker, gray mold, brown spot and leaf blight were taken as the research objects, and the disease samples were collected according to the growth cycle of flowers, so as to construct a data set of flower diseases and insect pests under natural scenes. Based on Faster-RCNN framework, a three-stage pest and disease detection model PD-IFRCNN is proposed, which integrates transfer learning and data enhancement technology. In order to ensure the effectiveness of the model, this paper verifies the influence of the disequilibrium of the disease category of the self-built data set on the cost of wrong classification from two different perspectives: category balancing and label balancing, and makes a comparative analysis with the SSD model that takes VGG16 as the feature to extract the network. Experiments show that the proposed method can adapt to the detection of diseases with different scales and multiple leaves in natural scenes, and has high recognition accuracy. the proposed method not only breaks the traditional manual detection method of plant diseases, but also provides a new research idea for the recognition of plant diseases. It fundamentally solves the problems such as the lack of pertinence of traditional disease control and the lack of timely control caused by the inconveniences of manual detection due to bad weather and poor operating environment. © 2022 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Quantifying the Impact of Climatic Factors on Dengue Incidence Using Generalized Linear Mixed Model with Spatio-Temporal Bayesian Poisson Random Effects Approach 6th

Quantifying the Impact of Climatic Factors on Dengue Inciden...

引用

6th international conference on soft computing in data science (scds)

作者： Sapri, Nik Nur Fatin Fatihah Yaacob, Wan Fairos Wan Yap, Bee Wah Univ Teknol MARA Fac Comp & Math Sci Shah Alam 40450 Selangor Malaysia Univ Teknol MARA Cawangan Kelantan Fac Comp & Math Sci Kota Baharu 15050 Kelantan Malaysia Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IB Kompleks Al Khawarizmi Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9789811673344;9789811673337

Dengue fever is a global life-threatening vector-borne disease which is mainly distributed by the vector Aedes Aegypti mosquito. It is known that the development and survivorship of this vector depends on surrounding climate. the dengue outbreak in Kelantan, Malaysia is alarming. the aim of the study was to compare the fixed effects Negative Binomial GLM and Bayesian Poisson GLMM in prediction of dengue incidence in Kelantan. the data involved daily number of reported dengue cases (1st January 2013-31st December 2017) in ten districts of Kelantan which was collected from Ministry of Health Malaysia. the climate variables, average daily temperature, relative humidity and rainfall (climatic data) were obtained from NASA's Global Climate Change website, while the population data were from Department of Statistics Malaysia. Statistical modeling results revealed that the fixed effects Negative Binomial GLM failed to fit the daily dengue incidence when serious epidemic occurred. the spatio-temporal Bayesian Poisson GLMM model improved the prediction of dengue incidence. Relative humidity at lag 7 days and 21 days and average temperature at lag 21 days were found to be significant contributing factors of dengue incidence in Kelantan. the findings of the study are significant to respective local authorities in providing vital information for early dengue warning systems in a particular area. this is important for authorities to monitor and reduce dengue incidence in endemic areas and to safeguard the community from dengue outbreak.

关键词： Dengue Generalized Linear Model Generalized Linear Mixed Model Spatio-Temporal Bayesian Poisson Model Climate

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Algorithms used in Crop Yield Prediction for Precision Agriculture

Machine Learning Algorithms used in Crop Yield Prediction fo...

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： Umamaheswari.R S.Shanmugapriya P.Vimala R.Ganesan Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Chennai Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. these algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture.

关键词： Productivity Training Surveys Machine learning algorithms Rain Soil Predictive models Prediction algorithms Crop yield Meteorology

来源：评论

学校读者我要写书评

暂无评论

Kidney Disease Classification and Diagnosis: A Comprehensive Review of Current AI Techniques

Kidney Disease Classification and Diagnosis: A Comprehensive...

引用

Emerging Smart Technologies and Applications (eSmarTA), international conference on

作者： Ezzaldeen Esmail Ahmed A. A. Mohammed Rehab A. A. Mohammed Basheer Almuhaya Abdulrahman Fadhl Mahmood A. Bazel Software Engineering Dept Taiz University Taiz Yemen School of CSE Lovely Professional University Phagwara Punjab India Faculty of Engineering and Computing University of Science and Technology Taiz City Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Accurate and early diagnosis of kidney disease (KD) is crucial for effective treatment and improved patient outcomes. computer aided and AI techniques offers a promising solution in this domain, analyzing medical images to identify disease patterns. this research paper explores the potential of ML, particularly deep learning (DL) and transfer learning, for classifying kidney conditions from CT scans. We review existing research to identify the most effective ai algorithms for this task, evaluate performance metrics used to assess accuracy, and explore challenges like data availability and interpretability. By discussing future directions, including integration of additional data sources, this paper aims to provide a comprehensive analysis of AI techniques-based KD classification, paving the way for more accurate and efficient diagnostic tools.

关键词： Deep learning Accuracy Reviews soft sensors Computed tomography Transfer learning Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Enhanced Bone Cancer Detection in MRI Scans via a Hybrid Quantum-Classical Convolutional Neural Network and War Strategy Optimization

Enhanced Bone Cancer Detection in MRI Scans via a Hybrid Qua...

引用

Ubiquitous computing and Intelligent Information Systems (ICUIS), international conference on

作者： A. Harshavardhan S. Sudalaikani M. Saravanakumar S Saritha Pothamsetty Kasi V Rao Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Department of Mathematics MVJ college of Engineering Whitefield Karnataka India Department of Artificial Intelligence & Data Science Nehru Institute of Engineering and tech Coimbatore Tamil Nadu India Department of Computer Science and Engineering Immaculate College for Women Cuddalore Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Bone cancer is a relatively rare and aggressive form of cancer that originates in the bones and the adjacent soft tissues; the disease is characterized by marked morbidity and mortality. Frequently, early detection is possible, and such medical conditions should be treated to enhance the result of the therapy. MRI is very useful in diagnosing bone cancer since they give clear images of bones and soft tissues. In this work, a new method of bone cancer detection from MRI images is presented with unsharp structure guided filtering for preprocessing and a CSwin transformer to extract features from the preprocessed images. this method further improves visualization and selection of MRI images by improving the representation of features in the image. the main part of the designed method is the Hybrid Quantum-Classical Convolutional Neural Network with War Strategy Optimization (HQCCNN-WSO), which combines the fundamentals of quantum computing and traditional neural networks to enhance the classification performance. Based on the parameters of the War Strategy Optimization algorithm, the model distinguishes between cancerous lesions and healthy bone tissue, and the parameters are further refined for improved performance. the suggested approach demonstrates remarkable accuracy and dependability in the identification of bone cancer, achieving over 99% across all performance criteria.

关键词： Quantum computing Accuracy Magnetic resonance imaging Neural networks Bones Transformers Feature extraction Convolutional neural networks Optimization Cancer

来源：评论

学校读者我要写书评

暂无评论

A Trusted Mechanism for Participant Screening and Verification in Federated Learning

A Trusted Mechanism for Participant Screening and Verificati...

引用

Machine Learning and Intelligent Systems Engineering (MLISE), international conference on

作者： Yun Xie Yucheng Wu Hongyan Wang Ang Li Jiangsu Province Service Customization Network Application Engineering Research Center Nanjing Vocational College of Information Technology Nanjing China Nanjing Vocational College of Information Technology Nanjing China Nanjing University of Science and Technology Zijin College Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084

Federated learning(FL) solves the problem of “data Silos” achieving the dual-purpose of data retention and remote sharing, which is widely applied in fields such as healthcare, transportation, and manufacturing. Local participants (LPs) are the main entities in FL, contributing resources such as data, computing, communication, and energy. the actual contribution of LPs directly affects the performance of federated learning. Existing research has mostly focused on how to design efficient algorithms for LPs, while neglecting the credibility of them. Obviously, highly trusty LPs will provide high-quality data sources and model medium parameters, which are the core factors affecting the performance of FL. this paper designs a new mechanism based on the joining protocol to verify the legitimacy of LPs, and combines subjective logical models to evaluate the reputation of participants. It solves the credibility evaluation and screening problems of participants, as well as the fairness of rewards.

关键词： Protocols Federated learning soft sensors Transportation Medical services data models Manufacturing

来源：评论

学校读者我要写书评

暂无评论

Optimal Revenue Analysis of the Stubborn Mining Based on Markov Decision Process 4th

Optimal Revenue Analysis of the Stubborn Mining Based on Mar...

引用

4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhang, Yiting Liu, Ming Guo, Jianan Wang, Zhaojie Wang, Yilei Liang, Tiancai Singh, Sunil Kumar School of Computer Science Qufu Normal University Rizhao China China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China CCET Panjab University Chandigarh India

ISBN: (纸本)9783031200984

As one of the most popular cryptocurrencies, Bitcoin is essentially a decentralized ledger. Each node maintains the security of the blockchain through the workload proof mechanism, and the block that obtains the accounting right will receive a block reward in the form of Bitcoin. Because the Bitcoin system follows the "longest legal chain" principle, when a fork occurs, orphan blocks will inevitably be generated, and some miners’ computing power will be a waste. In recent years, researchers have discovered that miners can obtain profits disproportionate to their own computing power by deviating from Bitcoin’s honest mining. Selfish Mining (SM1) is a case in dishonest mining strategy, and dishonest miners (attackers) can obtain higher returns by retaining the blocks they create and selectively delaying their release. the stubborn mining strategy is a generalized form of selfish mining. It increases the revenue of the stubborn miner by adopting a wider range of parameters. Its three mining strategies are: Lead-Stubborn, Equal Fork stubborn and Trail stubborn. the mining problem can be formulated as a Markov Decision Process (MDP), which can be resolved to give the optimal mining strategy. this work describes the three mining strategies of stubborn mining as a Markov decision process, solves it and gives the lower bound of the highest return under the optimal stubborn mining strategy. Our experimental results demonstrate that the revenue of the optimal stubborn mining strategy is higher than SM1 under certain circumstances, and this strategy allows dishonest miners (stubborn miners) to obtain revenue that does not match the actual computing power paid. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Towards Early Stroke Prediction: Detecting Hidden Patterns with data Analytics

Towards Early Stroke Prediction: Detecting Hidden Patterns w...

引用

Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Priyanka P. Shinde Varsha P. Desai Gayatri S. Mirajkar Kavita S. Oza Department of MCA Government College of Engineering Karad Karad Maharashtra India Computer Science and Engineering D Y Patil Agriculture and Technical University Kolhapur Maharashtra India Arvind Gavali College of Engineering Satara Maharashtra India Computer Science Department Shivaji University Kolhapur Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

soft computing has found wide-ranging applications in diverse fields, including agriculture, finance, commerce, retail, education, healthcare, genetics, and genomics. Among these areas, healthcare is a critical domain that soft computing techniques can profoundly impact. the adoption of machine learning methods in the healthcare industry has steadily increased due to their potential to improve decision-making and uncover valuable patterns. the healthcare sector presents an ideal platform for implementing soft computing techniques, given the massive amounts of data generated daily in health records. Machine learning has demonstrated remarkable effectiveness in manually identifying hidden patterns that are difficult to discern. this capability of machine learning can lead to significant improvements in diagnosis, the development of innovative treatments, cost reduction, and enhanced patient care, making it a compelling choice for integration in the healthcare industry-diseases like heart disease, stroke, lung cancer, and diabetes demand intensive research and investigation. Brain stroke stands out as a severe and potentially fatal condition arising from interrupted blood supply to a portion of the brain. the lack of oxygen and glucose supply leads to the deterioration and eventual death of brain cells. As the second most common disease worldwide, early diagnosis and prediction become paramount for the survival of patients suffering from brain strokes. this research paper delves into utilizing machine learning techniques for early illness identification, primarily focusing on predicting the likelihood of strokes before they occur. the stroke probability dataset, encompassing input variables such as gender, the number of diseases, age, and smoking habits, was sourced from the kaggle database. Various machine learning algorithms were employed to construct prediction models, including Random Forest, KNN, Logistic Regression, SVM, and Decision Trees. through this work, the

关键词： Support vector machines Sentiment analysis Machine learning algorithms Medical services Machine learning Stroke (medical condition) Predictive models Nearest neighbor methods Brain modeling Stress

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Logic-based Energy Management for WSNs in Smart Grid Applications

Fuzzy Logic-based Energy Management for WSNs in Smart Grid A...

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： K. Vengatesan R. Vijayalakshmi Sayyad Samee Neha Chopade Shilpa Mahalle K. Bashkaran Department of AIDS Sanjivani University Kopargaon Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Dept.of Computer Science Al Sadara School Al Ain Abu Dhabi UAE SIES College of Management Studies Kongunadu College of Engineering and Technology Thottiam

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Fuzzy Logic-Based Energy Management (FLBEM) is an innovative approach for optimizing energy consumption in Wireless Sensor Networks (WSNs) utilized in Smart Grid applications. this technique employs fuzzy logic principles to dynamically adjust network parameters, such as transmission power and data sampling rates, to enhance energy efficiency while maintaining reliable communication. By intelligently adapting to network conditions and environmental factors, FLBEM extends the operational lifetime of sensor nodes and ensures the stability of the WSN, thus facilitating real-time monitoring and control of Smart Grid systems. the proposed FLBEM approach encompasses a comprehensive framework that includes the design of fuzzy logic controllers, the development of energy-aware routing protocols, and the integration of renewable energy sources for powering sensor nodes. Simulation results demonstrate significant improvements in energy efficiency, network lifetime, and overall performance, validating the effectiveness of the FLBEM strategy in the context of Smart Grid applications. As energy management remains a critical concern for WSNs, the implementation of FLBEM offers a promising solution to address the challenges of energy conservation and sustainability in modern power systems.

关键词： Fuzzy logic Wireless sensor networks Renewable energy sources Power system stability Energy efficiency Stability analysis Smart grids Power system reliability Reliability Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Brain Connectivity Analysis in Alzheimer's disease using Graph Convolutional Network

Brain Connectivity Analysis in Alzheimer's disease using Gra...

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： Rithika Ango Kishor Kumar Reddy C Shugufta Fatima Anindya Nag Department of Computer Science and Engineering Stanley College of Engineering & Technology for Women Hyderabad India Department of Computer Science and Engineering Khulna University Bangladesh Khulna Bangladesh

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Alzheimer’s disease (AD) can cause a lapse in cognitive capacity along with forgetfulness, and addressing this issue is vital for forthcoming treatment efforts. this study offers another approach to investigating brain links in Alzheimer’s disease with Graph Convolutional Networks (GCN). the GCN technology enables more appropriate utilization of the connections topology that is constructed by brain networks based on fMRI data. the purpose of this paper is to design brain connection networks utilizing modern computational technology. Furthermore, this research interprets the model's predictions using layer-wise relevance propagation, focusing on key brain regions and connections involved in Alzheimer's disease. this paper uses the ADNI dataset for early predictions of AD. the metrics in which the model suggested outperformed the available approaches are, the accuracy is 88.90%, error rate is 11.10% while its precision reached 91, sensitivity of 88 and specificity 89 under NC vs AD; the accuracy of 87.80%, error rate of 12.20% while its precision reached 89, sensitivity of 87 and specificity 88 under NC vs MCI; accuracy is 86.50%, error rate is 13.50% while its precision reached 87, sensitivity of 86 and specificity 87 under AD vs MCI. Hence this work gives a valuable tool for studying brain connections, assisting in the early detection and understanding of AD.

关键词： Accuracy Sensitivity Graph convolutional networks Error analysis Computational modeling Predictive models Brain modeling Topology Security Alzheimer's disease

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104页 << < 8 9 10 11 12 13 14 15 16 17 > >>

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：