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

3,470 篇 会议
1,278 篇 期刊文献
30 册 图书

馆藏范围

4,778 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,721 篇 工学
- 2,109 篇 计算机科学与技术...
- 1,731 篇 软件工程
- 764 篇 信息与通信工程
- 515 篇 控制科学与工程
- 392 篇 电气工程
- 329 篇 生物工程
- 297 篇 生物医学工程（可授...
- 286 篇 电子科学与技术（可...
- 241 篇 网络空间安全
- 197 篇 光学工程
- 189 篇 动力工程及工程热...
- 186 篇 安全科学与工程
- 167 篇 机械工程
- 154 篇 化学工程与技术
- 139 篇 交通运输工程
- 126 篇 材料科学与工程（可...
1,526 篇 理学
- 766 篇 数学
- 404 篇 物理学
- 384 篇 生物学
- 265 篇 统计学（可授理学、...
- 181 篇 化学
- 170 篇 系统科学
871 篇 管理学
- 525 篇 管理科学与工程(可...
- 389 篇 图书情报与档案管...
- 275 篇 工商管理
359 篇 医学
- 281 篇 临床医学
- 267 篇 基础医学(可授医学...
- 202 篇 公共卫生与预防医...
- 158 篇 药学(可授医学、理...
175 篇 法学
- 145 篇 社会学
102 篇 经济学
90 篇 农学
47 篇 教育学
22 篇 文学
10 篇 军事学
3 篇 艺术学

主题

287 篇 accuracy
205 篇 deep learning
172 篇 machine learning
157 篇 real-time system...
153 篇 internet of thin...
144 篇 feature extracti...
141 篇 computational mo...
138 篇 convolutional ne...
120 篇 predictive model...
112 篇 support vector m...
112 篇 training
97 篇 optimization
91 篇 security
87 篇 data models
85 篇 artificial intel...
80 篇 cloud computing
80 篇 technological in...
75 篇 machine learning...
72 篇 data mining
69 篇 neural networks

机构

114 篇 lovely professio...
112 篇 department of co...
99 篇 department of co...
64 篇 department of co...
61 篇 school of comput...
55 篇 gla university d...
51 篇 maharishi school...
49 篇 department of co...
47 篇 centre of resear...
46 篇 department of co...
45 篇 chitkara centre ...
45 篇 lloyd law colleg...
41 篇 lloyd institute ...
40 篇 gla university d...
38 篇 chitkara univers...
37 篇 graphic era deem...
37 篇 the islamic univ...
34 篇 department of co...
34 篇 department of co...
34 篇 department of co...

作者

52 篇 neeraj varshney
44 篇 varshney neeraj
42 篇 singh kamred udh...
40 篇 singh teekam
39 篇 rakesh kumar
35 篇 kumar ankit
34 篇 singh ashutosh k...
33 篇 kumar rakesh
28 篇 tina babu
28 篇 kumar gaurav
27 篇 asha v.
27 篇 saloni bansal
26 篇 teekam singh
25 篇 kamred udham sin...
24 篇 n. naga saranya
23 篇 babu tina
23 篇 ankit kumar
23 篇 pan jeng-shyang
23 篇 patra sudhansu s...
20 篇 bibhuti bhusan d...

语言

4,299 篇 英文
462 篇 其他
25 篇 中文
1 篇 西班牙文
1 篇 法文
1 篇 俄文
1 篇 斯洛文尼亚文

检索条件"机构=Computer Applications in Science and Engineering Department"

共 4778 条记录，以下是551-560 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 Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning 3

A Hybrid Edge-Cloud Computing Approach for Energy-Efficient ...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Jagendra Dabas, Partibha Bhati, Sunita Kumar, Santosh Upreti, Kamal Shaik, Nazeer School of Computer Science Engineering and Technology Bennett University Greater Noida India Dr Akhilesh das Gupta Institute of Technology and Management Dept. of Computer Science & Engineering Delhi India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India Department of Computer Science Ghaziabad India SRIT-Autonomous Anantapur Department of Computer Science Engineering India

ISBN: (纸本)9798350342338

This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. © 2023 IEEE.

关键词： Cloud Data Centers Deep Reinforcement Learning Edge Computing Energy Efficiency Smart Surveillance

来源：评论

学校读者我要写书评

暂无评论

AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Data science - A Compendious Study on Statistical Methods and Visualization Techniques

Data Science - A Compendious Study on Statistical Methods an...

引用

2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Singh, Kamred Udham Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Teekam Kumar, Gaurav Kumar, Ankit School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Knowledge and insights may be gleaned from both organized and unstructured data through the use of Data science, which is itself a multidisciplinary discipline. Finding patterns and trends in data requires a wide range of methods, including but not limited to cleaning, transformation, analysis, and visualization. A data scientist's primary responsibility is to transform unstructured data into a form suitable for analysis through the processes of processing and cleaning. This approach utilizes several statistical procedures, such as calculating the mean, mode, median, standard deviation, correlation, regression, and hypothesis testing. These techniques are useful for discovering abnormalities, missing values, and outliers in data, as well as for eliminating them. When the data has been cleansed and organized, it will be analyzed using statistical and machine-learning techniques to draw conclusions and make forecasts. To find patterns, trends, and correlations in the data, this study may make use of methods including clustering, classification, regression, and time series analysis. Data visualization is crucial to data science because it facilitates the dissemination of ideas and conclusions to stakeholders who may not have technical expertise. Scatter plots, bar charts, heat maps, and network diagrams are just a few of the data visualization methods that may be utilized to convey information interestingly. A wide variety of fields, from economics and finance to medicine and social media, can benefit from data science. Data science is useful in business for several purposes, including cost reduction, client retention, and discovery of untapped markets. As a field of study, data science has the potential to improve healthcare by aiding in areas such as disease detection, treatment, and the creation of new medications. Data science may be applied to social media to study user behavior and sentiment and to spot and stop criminality. It is a dynamic and promising new area

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Apple Fruit Flower Detection Using Auto-Encoder Convolutional Neural Network

Apple Fruit Flower Detection Using Auto-Encoder Convolutiona...

引用

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Yashi Rawat, Ruchira Shrivastava, Anurag Rashmi Bansal, Saloni Gupta, Priyanka Al-Fatlawy, Ramy Riad Lloyd Institute of Engineering & Technology Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Computer Science and IT Noida International University U.P. India Department of Computer Engineering and Applications GLA University Mathura India Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided by bloom intensity, i.e., the quantity of flowers presents in a plantation. The intensity of Bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend close by designed procedures that work just under explicit conditions and with restricted *** work comprises of four significant advances, (I) System preparing for Fully Convolutional Network (FCN), (ii) Preprocessing, (iii) Component Extraction, (iv) Division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics Processing Unit (GPU). Then, secondly a non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. Thirdly, highlight extraction is valuable to diminish the quantity of assets required for handling without losing significant or applicable data. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate a image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired scoremaps to figure final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision(P) and Recall(R). © 2024 IEEE.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

来源：评论

学校读者我要写书评

暂无评论

Classification and Discovery of Brain Tumor Using Ensemble Deep Learning 2

Classification and Discovery of Brain Tumor Using Ensemble D...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Srilatha, Kasanagottu Sravani, Chekka Vasudevarao, Mikkilineni Kiran, Ajmeera Chitra, D. Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Department of Data Science Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India Dayananda Sagar Business Academy Department Of Computer Applications Karnataka India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350372892

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Content Moderation System using Adversarial Machine Learning 3

A Multimodal Content Moderation System using Adversarial Mac...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Barveen, A. Mohamed Faizal, M.K. Geetha, S. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTM and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Map Reduce Techniques for Machine Learning in Big Data Analytics 7

Map Reduce Techniques for Machine Learning in Big Data Analy...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kukreti, Sanjeev Al-Jawahry, Hassan M. Shrivastava, Anurag Bansal, Saloni Raju, Hemanth Begum, Rowsonara Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

One of the most groundbreaking approaches to the processing and analysis of large datasets has been created as a result of the combination of MapReduce techniques and machine learning algorithms within the framework of big data analytics. The problems of traditional data processing in terms of handling the volume, velocity, and variety of big data are addressed by the distributed computing architecture introduced by MapReduce. This research investigates the ways in which MapReduce may be used to simplify the process of machine learning, with a particular focus on the capabilities of the framework to manage enormous volumes of data and to parallelize operations. By contrasting the performance of MapReduce with that of more traditional methods, the study solves the problems of scalability and computing efficiency that are brought about by massive amounts of data. Through an in-depth analysis, the paper highlights the optimization potential of MapReduce as well as its implications for next-generation machine learning applications across a variety of industries. © 2024 IEEE.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

Deep Learning: The Future of Medical Image Processing

Deep Learning: The Future of Medical Image Processing

引用

2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

ISBN: (纸本)9798350323917

Health care is a vital service that is constantly in high demand since everyone needs it. Individuals have higher hopes for advancement in this profession than for receiving the status quo since they would rather be treated better. Sometimes, or maybe more accurately, in many situations, the findings are not clear and the sickness cannot be understood at the first stage from a manual reading of the report. When it comes to viewing medical images, the fact that they must be interpreted by hand inevitably leads to delays and errors. Many deep learning and machine learning approaches can be used to address this issue. Therefore, although machine learning may be thought of as a subset of deep learning, the reverse is not true. Let's talk about how deep-learning models will be used to process medical images. Medical image processing is one area where deep learning models are having a profound effect. Convolutional neural networks (CNNs) and other deep learning techniques have made it feasible to automate the processing of medical pictures and improve diagnostic and treatment accuracy. It is widely used in radiology to examine medical pictures such as X-rays, CT scans, and MRIs. In order to aid radiologists in their diagnostic work, deep learning models may be taught to identify patterns and characteristics in medical pictures that are diagnostic of certain diseases or ailments. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence-Enabled regression model for used car price prediction

Artificial Intelligence-Enabled regression model for used ca...

引用

2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Gaurav Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Kamred Udham Singh, Teekam Kumar, Ankit Gla University Department of Computer Engineering & Applications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Deciding if the recorded cost of a pre-owned vehicle is a difficult errand, because of the numerous variables that drive a pre-owned car's cost. The focal point of this venture is creating AI models that can precisely anticipate the cost of a trade-in vehicle dependent on its highlights, to make educated buys. We'll be going to look at changed models and calculations which can anticipate the cost of pre-owned vehicles proficiently. More than 70 million passenger vehicles were shipped in 2016. This represents a significant increase in vehicle production over the past decade. As a result, the secondary market for used cars has grown rapidly in recent years. We'll be noticing various strategies and afterward propose the best-fit model to foresee the cost of pre-owned vehicles. Choosing whether a pre-owned vehicle merits the posted cost when you see postings online can be troublesome. A few components, including mileage, make, model, year, and so forth can impact the real worth of a vehicle. From the point of view of a dealer, it is likewise a quandary to value a pre-owned vehicle properly. In light of existing information, the point is to utilize AI calculations to foster models at anticipating utilized vehicle costs. © 2023 IEEE.

关键词： Decision trees

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478页 << < 52 53 54 55 56 57 58 59 60 61 > >>

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：