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

33 篇 会议
9 篇 期刊文献

馆藏范围

42 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

21 篇 工学
- 15 篇 计算机科学与技术...
- 14 篇 软件工程
- 7 篇 生物医学工程（可授...
- 6 篇 电气工程
- 6 篇 信息与通信工程
- 4 篇 生物工程
- 2 篇 仪器科学与技术
- 2 篇 控制科学与工程
- 2 篇 安全科学与工程
- 2 篇 网络空间安全
- 1 篇 机械工程
- 1 篇 电子科学与技术（可...
18 篇 理学
- 13 篇 数学
- 6 篇 统计学（可授理学、...
- 5 篇 生物学
- 1 篇 物理学
- 1 篇 系统科学
8 篇 医学
- 7 篇 基础医学(可授医学...
- 5 篇 临床医学
- 3 篇 公共卫生与预防医...
- 1 篇 中西医结合
- 1 篇 药学(可授医学、理...
- 1 篇 医学技术（可授医学...
5 篇 管理学
- 5 篇 图书情报与档案管...
3 篇 法学
- 2 篇 社会学
- 1 篇 法学

主题

11 篇 feature extracti...
5 篇 accuracy
5 篇 convolutional ne...
4 篇 internet of thin...
3 篇 deep learning
3 篇 sentiment analys...
3 篇 circuits
2 篇 intrusion detect...
2 篇 power grids
2 篇 generative adver...
2 篇 frequency estima...
2 篇 computer archite...
2 篇 convolution
2 篇 optimization
2 篇 risk management
2 篇 real-time system...
2 篇 particle swarm o...
2 篇 social networkin...
2 篇 security
2 篇 transforms

机构

11 篇 department of ai...
7 篇 nitte meenakshi ...
7 篇 school of electr...
5 篇 department of ai...
5 篇 nitte meenakshi ...
5 篇 school of electr...
4 篇 department of co...
3 篇 new prince shri ...
3 篇 department of el...
3 篇 department of co...
3 篇 school of comput...
3 篇 college of techn...
3 篇 sr university de...
3 篇 sapiens internat...
3 篇 dept. of informa...
3 篇 adobe inc. dept....
3 篇 school of comput...
3 篇 colorado technic...
3 篇 school of comput...
3 篇 sapiens internat...

作者

18 篇 piyush kumar par...
14 篇 pareek piyush ku...
5 篇 sanjaikanth e va...
5 篇 pillai sanjaikan...
4 篇 kiran polimetla
4 篇 polimetla kiran
3 篇 veeramachaneni v...
3 篇 vinod veeramacha...
3 篇 kumar pareek piy...
3 篇 reddy r. archana
3 篇 zanke pankaj
3 篇 r. archana reddy
3 篇 pankaj zanke
2 篇 tiannan guo
2 篇 pillai sanjaikan...
2 篇 prakash chidural...
2 篇 liu qi
2 篇 mahantesh mathap...
2 篇 wilson keith t.
2 篇 huo yuankai

语言

38 篇 英文
4 篇 其他

检索条件"机构=Department of Computer Science and Engineering and Head of IPR Cell"

共 42 条记录，以下是11-20 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：

Detection of Electrical Transmission Line Overload with Blockchain using Isolation Forest with Recurrent Neural Network 4

Detection of Electrical Transmission Line Overload with Bloc...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Veeramachaneni, Vinod Fallah, Mohammed H. Reddy, Gurram Vijendar Pareek, Piyush Kumar Monisha Jothi, R. Colorado Technical University Department of Information Technology Colorado Springs United States College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Nitte Meenakshi Institute of Technology Department of Aiml & Ipr Cell Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, energy has emerged as a dynamic support of modern society, with electricity playing a crucial role in powering our daily lives. As the world's dependence on electricity continues to grow, the significance of a consistent and reliable supply cannot be exaggerated. Power companies face the daunting task of meeting this escalating demand, while also ensuring the delivery of high-quality energy services to consumers, where even a momentary disruption can have severe consequences for businesses, communities, and individuals alike. To overcome these limitations, an Isolation Forest with Recurrent Neural Network (IF-RNN) for detecting overload in electrical transmission lines using Power Demand Benchmarking Electric Power Load (PDB-EPL) dataset. For preprocessing, a Singular Value Decomposition (SVD) is employed for reducing noise and irrelevant features, for improving generalization, and dimensionality. Then, Blockchain is considered for feature extraction that performs decentralization, fault tolerance, immutability, auditability, and transparency. Eventually, IF-RNN is utilized as classifier for anomaly detection, high-dimensional data handling, and scalability. The results conclusively demonstrate the efficacy of the proposed IF-RNN interms of balanced accuracy, and accuracy when compared to the existing method like Feed-forward Neural Network (FNN). © 2024 IEEE.

关键词： Power transmission lines

来源：评论

学校读者我要写书评

暂无评论

Fault Detection and Classification in Power Grids Using Shannon Entropy with Convolution Distributed Autoencoder 4

Fault Detection and Classification in Power Grids Using Shan...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Veeramachaneni, Vinod Husain, Saef Obad Khanna, Virat Pareek, Piyush Kumar Bhuvaneshwari, P. Colorado Technical University Department of Information Technology Colorado Springs United States College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq University Centre for Research & Development Chandigarh University Mohali India Nitte Meenakshi Institute of Technology Department of Aiml & Ipr Cell Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, Power grids have undergone significant transformations as the demand on electricity increased tremendously. Power grids are the primary sources for electricity generation and power distribution to the consumers need, but it faces numerous faults during this process. The traditional approaches for detection of faults in power grids had faced challenges, that includes accurately detecting and classifying which type of faults in the power grid. Therefore, this research proposes Shannon Entropy-Convolution Distributed Autoencoder (SE-CDAE) for fault detection and classification in power grids. The proposed method employed on Power Grid dataset which provides the current signals data that includes normal current signals and fault current signals. Then, the gathered data is pre-processed by fast wavelet transform which converts the time-domain signals into wavelet coefficient by removing the noise and dimensionality. These pre-processed data optimal features are extracted by using SE that effectively eliminates the irregular patterns in data. Finally, CDAE is employed for detecting and classifying the faults from extracted optimal features. The proposed SE-CDAE effectively detected and classified the faults in power grids and succeeded with high results including accuracy (97.98%), precision (94.83%), recall (96.28%), F1-score (97.35%) when compared with existing Semi-ProtoPNet Visual Geometry Group-19 (SPPN-VGG-19). © 2024 IEEE.

关键词： Power grids

来源：评论

学校读者我要写书评

暂无评论

Improved Whale Optimization Algorithm and Optimized Long Short-Term Memory for DDoS Cyber Security Threat 3

Improved Whale Optimization Algorithm and Optimized Long Sho...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Polimetla, Kiran Velpula, Srikanth Kumar Pareek, Piyush Sontakke, Dipti School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc Dept. Of Information Technology AustinTX United States Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India Capgemini Inc United States

ISBN: (纸本)9798350318609

The Distributed Denial of Service (DDoS) attacks is a critical cyber security threat that blocks the services for users and leads to important damage for reputation and effective customers. The existing methods provides low classification accuracy due to irrelevant features in classification performance. The Improved Whale Optimization Algorithm (IWOA) is proposed for the selecting of relevant features which improved the classification performance. The selected features are detected and classified by Optimized Long Short-Term Memory (OLSTM) that provided high detection rate of DDoS attacks. The one-hot encoding and min-max normalization techniques are used in the data pre-processing stage to improve the performance of classification. The CIC-DDoS 2019 dataset are used for evaluating the proposed method. The IWOA-OLSTM method attained highest accuracy 97.12%, precision 96.74%, recall 96.27%. f1-score 96.54% and DR 93.26% on CIC-DDoS 2019 dataset which is effective than Convolutional Neural Network (CNN) - Bidirectional Long Short-Term Memory (BiLSTM) and CNN-LSTM. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Risk Prediction and Management for Effective Cyber Security Using Weighted Fuzzy C Means Clustering 3

Risk Prediction and Management for Effective Cyber Security ...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Polimetla, Kiran Prakash, Chidurala Sai Pareek, Piyush Kumar Zanke, Pankaj School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350318609

The necessity of the continuous risk assessment as well as management is attracting the people due to the requirement of protecting the risk. The management of the risk plays a significant part in solving the cyber threats within Cyber-Physical System (CPS). However, because of the enhanced complexity of CPS, the cyber-attacks are the most urbane as well as low predictable, which made the risk management tasks more challenging. This research proposes the efficient Cyber Security Risk Management (CSRM) repetition by the utilization of the Weighted Fuzzy C Means (WFCM) clustering for the prediction and management of the cyber risk. Initially, VCDB dataset is collected to estimate the effectiveness of the model and Term Frequency-Inverse Document Frequency (TF-IDF) approach is utilized for the extraction of feature from the collected data. Then, feature selection is performed by using Principal Component Analysis (PCA) and prediction is performed by the utilization of the WFCM. The effectiveness of the WFCM is estimated by using various performance metrices and it achieves the accuracy of 84.2% and precision of 0.746 when compared to the existing methods such as fuzzy and DeepSpamPhisNet (DSPN). © 2024 IEEE.

关键词： Risk management

来源：评论

学校读者我要写书评

暂无评论

Mayfly Optimization Algorithm with Bidirectional Long-Short Term Memory for Intrusion Detection System in Internet of Things 3

Mayfly Optimization Algorithm with Bidirectional Long-Short ...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Polimetla, Kiran Alsalami, Zaid Pareek, Piyush Kumar Kumar, Deepak School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India University Of The Cumberlands Department Of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350318609

The rapid development of number of connected devices exchange sensitive and personal significant data through Internet of Things (IoT)-assisted global network, attacks which are targeting security services are also enhancing every day. This paper proposed a Mayfly Optimization Algorithm (MOA) with Bidirectional Long-Short Term Memory (BiLSTM) for Intrusion Detection System (IDS) in IoT. The MOA is used for feature selection which select relevant features through balancing exploration and exploitation. The BiLSTM is used for classifying network into attack and normal which process the data in both forward and backward directions, allowing them to capture dependencies between events that might be far apart in sequence. The min-max normalization is used to normalize the continuous features from NSL-KDD, ToN-IoT and UNSW-NB15 datasets. The recall, accuracy, f1-score and precision are considered to evaluate MOA-BiLSTM performance. The MOA-BiLSTM realizes accuracy of 99.25%, 89.61% and 99.35% accuracy for NSL-KDD, ToN-IoT and UNSW-NB15 datasets when compared to existing techniques such as LSTM and Deep Neural Network (DNN). © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

IoT Security Detection and Evaluation for Smart Cyber Infrastructures Using LSTMs with Attention Mechanism 3

IoT Security Detection and Evaluation for Smart Cyber Infras...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E Vadakkethil Somanathan Polimetla, Kiran Prakash, Chidurala Sai Pareek, Piyush Kumar Pawar, Priyanka Pramod School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India University Of The Cumberlands Department Of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350318609

A computer network may be impacted by malware, computer viruses, and other hostile attacks. One of the most important elements of network security as an active defence technology is Intrusion Detection System (IDS). Issues with traditional intrusion detection systems include low detection effectiveness, inaccurate results, and an inability to handle new types of intrusions. This paper implemented a deep LSTMs with attention mechanism approach for IDS. The proposed technique is determined on Bot-IoT dataset, and z-score normalization is performed to normalize data. Information Gain (IG) is utilized to choose the features. A deep LSTMs with attention mechanism is used to effectively identify and categorize IDS effectively. The IG-deep LSTMs with attention mechanism achieves better accuracy using of 99.70% and 99.60% in NSL-KDD and CIC-IDS2017 datasets respectively. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Optimized Convolution Neural Network Based Fake News Detection Using Sentiment Analysis

Optimized Convolution Neural Network Based Fake News Detecti...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Karthik Rajashekaran R. Archana Reddy Piyush Kumar Pareek Dipti Sontakke School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Department of Computer Science New York University New York NY USA School of Computer Science and Engineering SR University Warangal Telangana India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Capgemini Inc USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In the era of social media, the social media and smartphone popularity has enhanced exponentially. By the electronic media, fake news has rising quick with new information which are hugely untrustworthy. The search engine like google are incapable for fraudulent of news because its limitation with restricted keywords. The Optimized Convolution Neural Network (OPCNN) is proposed for classifying fake news into actual and fraudulent news based on sentiment analysis. which accommodate varying complexity levels by adjusting its architecture in Fake News Detection (FND). The Principal Component Analysis (PCA) is used to extract features from preprocessed images which reduces the data dimension comprising numerous related variables and recalls the high change in real data. The ISOT dataset is preprocessed through four various techniques. The recall, accuracy, f1 score and precision with ISOT dataset are considered to evaluate OPCNN performance. The OPCNN realizes 99.58% recall, 99.67% accuracy, 99.61% f1score and 99.64% precision for ISOT dataset when matched to previous techniques like Random Forest (RF) and Sea Turtle Foraging Optimization-based Fake News Detection and Classification (STODL-FNDC).

关键词： Sentiment analysis Convolution Social networking (online) Neural networks Data preprocessing computer architecture Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Human Activity Classification using G-XGB

Human Activity Classification using G-XGB

引用

2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Kataria, Aman Puri, Vikram Pareek, Piyush Kumar Rani, Sita Amity Institute of Defence Technology Amity University Noida Campus Sector-125 Noida India Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam Cell Nitte Meenakshi Institute of Technology Dept. of Artificial Intelligence and Machine Learning and Ipr Bengaluru India Guru Nanak Dev Engineering College Department of Computer Science and Engineering Ludhiana India

ISBN: (纸本)9798350301595

Wearable body sensors have recently gained popularity because they monitor user behaviors and vital indicators. These sensors are designed to monitor various physiological factors, such as motion, temperature, and heart rate, among others. Making sense of the data gathered by these sensors requires separating the dependent and independent features. The dependent parameters are those that body-worn sensors can read in this scenario. Examples of such elements include blood oxygen levels, heart rate, temperature, mobility, and airflow. These sensors capture data about a person's activity and health. The independent parameters in wearable body sensors are related to the dependent parameters, whereas the dependent parameters are those that the sensors can measure directly. Such characteristics include age, sex, weight, and environmental influences. For example, humidity and airflow might impair a temperature sensor's accuracy. The user's age or level of physical fitness may also influence the accuracy of a heart rate monitor. Activity monitors are used in this study to capture the actions of various people. Static and dynamic properties are derived from the activity monitor recording. The performance evaluations of the eight distinct machine-learning models that were created using the retrieved features are discussed in this paper. The Grid Search method is used to enhance the performance of the XGB model. © 2023 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Robust Fingerprint Minutiae Extraction and Matching Using Fully Connected Deep Convolutional Neural Network and Improved SIFT

Robust Fingerprint Minutiae Extraction and Matching Using Fu...

引用

Data science and Network Security (ICDSNS), International Conference on

作者： Saif O. Husain R.Archana Reddy Piyush Kumar Pareek Srinivasan Jagannathan M. Jyothi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science and Engineering SR University Warangal India Department of AIML & IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Applications Madanapalle institute of technology & Science Madanapalle India Department of Information Technology G.Narayanamma Institute of Technology and Science Hyderabad India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Fingerprint is a most well-known biometric based authentication system that gives a unique identity for each person. In this paper, an authentication framework to enhance the Fingerprint Minutiae Extraction and Matching (FMEM) technique is proposed. A Fully Connected Deep Convolutional Neural Network with Improved Scale-Invariant Feature Transform (FCDCNN-ISIFT) is proposed for feature extraction and feature matching. Initially, the fingerprint images are collected from Fingerprint Verification Competition 2004 (FVC2004) which are first pre-processed using Histogram Equalization (HE) and Normalization. The images are segmented and enhanced by estimating ridge orientation and ridge frequency, and performing Gabor filtering, binarization and thinning. The fingerprint feature extraction and matching test is done using FCDCNN-ISIFT. The FCDCNN is built for detecting minutiae and the ISIFT is applied to the high contrast images to enhance the quality of latent fingerprints. Finally, the loss function is measured to determine the minutiae detection loss. The experimental results show better accuracy of 99.41%, f1-score of 97.73%, and Equal Error Rate (EER) of 1.75% when compared to results of existing approaches like Contrast Limited Adaptive Histogram Equalization - Random Sample Consensus (CLAHE - RANSAC) and Deep Convolutional Neural Network (DCNN).

关键词： Image segmentation Histograms Image matching Authentication Transforms Fingerprint recognition Feature extraction Loss measurement Frequency estimation Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Quantum Machine Learning in Healthcare: Developments and Challenges

Quantum Machine Learning in Healthcare: Developments and Cha...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sita Rani Piyush Kumar Pareek Jaskiran Kaur Meetali Chauhan Pankaj Bhambri Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Department of Artificial Intelligence and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology Guru Nanak Dev Engineering College Ludhiana India

Machine learning is playing a very significant role to process voluminous data and its classification in a variety of domains. Due to better performance and rapid development in the last decade, quantum computing is also benefiting many areas. With the amalgamation of these two technologies, a new domain for processing big data more efficiently and accurately has evolved, known as quantum machine learning. Healthcare is one of the prominent domains where a huge volume of data is produced from several processes. Efficient processing of healthcare data and records is very important to facilitate many biological and medical processes to provide better treatments to patients. The fundamental aim of this paper is to present a state-of-the-art review of quantum computing concepts, quantum machine learning framework, and the various applications of quantum machine learning in the domain of healthcare. The comparison of QML healthcare models with ML-based healthcare applications is discussed in this work. The authors also present the various challenges faced in the deployment of quantum machine learning algorithms in the domain of healthcare and possible future research directions.

关键词： Integrated circuits Quantum computing Machine learning algorithms Quantum algorithm Communication systems Computational modeling Quantum mechanics

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5页 << < 1 2 3 4 5 > >>

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：