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

88,692 篇 会议
234 篇 期刊文献
118 册 图书

馆藏范围

89,044 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

51,555 篇 工学
- 38,420 篇 计算机科学与技术...
- 31,320 篇 软件工程
- 13,050 篇 信息与通信工程
- 10,746 篇 控制科学与工程
- 8,463 篇 电气工程
- 5,600 篇 电子科学与技术（可...
- 5,037 篇 生物工程
- 4,273 篇 机械工程
- 4,063 篇 生物医学工程（可授...
- 3,094 篇 动力工程及工程热...
- 3,059 篇 仪器科学与技术
- 2,784 篇 光学工程
- 2,665 篇 安全科学与工程
- 2,480 篇 交通运输工程
- 1,679 篇 化学工程与技术
- 1,628 篇 网络空间安全
21,721 篇 理学
- 11,656 篇 数学
- 5,335 篇 生物学
- 5,010 篇 物理学
- 3,655 篇 统计学（可授理学、...
- 3,646 篇 系统科学
- 1,882 篇 化学
13,909 篇 管理学
- 9,385 篇 管理科学与工程(可...
- 5,791 篇 图书情报与档案管...
- 4,610 篇 工商管理
3,450 篇 医学
- 2,782 篇 临床医学
- 2,655 篇 基础医学(可授医学...
- 1,566 篇 药学(可授医学、理...
2,119 篇 法学
- 1,686 篇 社会学
1,692 篇 经济学
- 1,684 篇 应用经济学
1,088 篇 农学
1,069 篇 教育学
338 篇 文学
207 篇 军事学
132 篇 艺术学
1 篇 哲学

主题

3,165 篇 computer archite...
2,773 篇 computer science
2,492 篇 computational mo...
2,298 篇 feature extracti...
2,252 篇 control systems
1,968 篇 real-time system...
1,663 篇 training
1,618 篇 application soft...
1,603 篇 accuracy
1,537 篇 conferences
1,491 篇 hardware
1,250 篇 neural networks
1,227 篇 data mining
1,186 篇 costs
1,158 篇 robot sensing sy...
1,132 篇 monitoring
1,129 篇 testing
1,101 篇 protocols
1,099 篇 computer vision
1,099 篇 robustness

机构

148 篇 school of comput...
140 篇 school of comput...
120 篇 department of el...
118 篇 school of comput...
82 篇 school of comput...
67 篇 institute of inf...
64 篇 college of compu...
61 篇 school of automa...
60 篇 chitkara univers...
55 篇 department of sy...
54 篇 department of co...
52 篇 department of el...
52 篇 school of electr...
49 篇 ieee
48 篇 department of el...
46 篇 college of compu...
46 篇 department of el...
46 篇 department of el...
45 篇 department of el...
44 篇 school of comput...

作者

35 篇 lei wang
34 篇 zhang tao
34 篇 wang jing
33 篇 zhang lei
31 篇 li yang
28 篇 jun wang
26 篇 wang lei
25 篇 yu wang
24 篇 li xin
24 篇 ning xi
23 篇 rui wang
22 篇 sudeep tanwar
21 篇 weiming shen
21 篇 li peng
20 篇 dhabaleswar k. p...
20 篇 chengkuo lee
19 篇 hisao ishibuchi
19 篇 xiao liu
19 篇 hui wang
19 篇 yang bo

语言

86,495 篇 英文
2,111 篇 其他
461 篇 中文
14 篇 土耳其文
6 篇 西班牙文
5 篇 俄文
2 篇 法文
1 篇 葡萄牙文

检索条件"任意字段=IEEE International Conference and Workshops on Engineering of Computer Based Systems"

共 89044 条记录，以下是241-250 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：

Novel Real-Time EEG Signal Decoding Algorithm based on Deep Learning 3

Novel Real-Time EEG Signal Decoding Algorithm based on Deep ...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Jin, Hulin Jin, Zhiran Kim, Yong-Guk Li, Kun Anhui University School of Computer Science and Technology Hefei230031 China Foothill Preparatory School Temple CityCA91780 United States Sejong University Department of Computer Engineering Seoul05006 Korea Republic of Anhui University School of Electronic and Information Engineering Hefei230031 China

ISBN: (纸本)9798331509675

This study proposes the novel real-time electroencephalogram (EEG) signal decoding algorithm based on deep learning from the theoretical modeling level. The original intention of the design of this algorithm is to optimize the decoding accuracy and timeliness of EEG signals in the brain-computer interface (BCI) system. Considering the low efficiency of feature extraction and data processing in traditional EEG signal decoding methods, this study adopted a multi-core collaborative signal transmission architecture and deep learning model for overall optimization. First, the study used multi-core parallel computing mode to increase data processing speed. This operation can reduce system latency and achieve efficient decoding of real-time EEG signals. In terms of core architecture, convolutional neural network (CNN) is used to extract and classify EEG signals. This operation can further improve the accuracy of signal decoding. In addition, in order to solve the problem of artifacts in EEG signals, independent component analysis (ICA) is used to pre-process the original signal to remove the interference of noise actions such as eye movement, electromyography and the other noise on data. The experimental results show that the proposed deep learning algorithm can accurately analyze the characteristics of EEG signals in the different frequency bands, thereby significantly improving the decoding accuracy. © 2025 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Image-based DeepFake Detection Using Artificial Intelligence 14

Image-based DeepFake Detection Using Artificial Intelligence

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Singh, Shailendra Kumar Kumar, Gautam Dubey, Aditya Kumar Yadav, Ritik Rai, Vishal Sharma, Anil Department of Computer Science and Engineering Amity University Noida India

ISBN: (纸本)9798331531935

The increasing sophistication of deepfake technology poses significant risks to privacy, security, and public trust in digital media. As a result, deepfake detection has become a critical research area, with artificial intelligence -driven approaches playing a central role. This paper presents a comprehensive analysis of convolutional neural network -based deepfake detection methods, highlighting key challenges such as dataset limitations, social media compression effects, real-time detection constraints, and the rapid evolution of manipulation techniques. While existing detection models demonstrate promising accuracy, challenges remain in terms of generalizability, computational efficiency, and robustness against complex manipulations, including audio and full-body deepfakes. This study explores strategies to enhance detection performance through data augmentation and optimized architectural design. Furthermore, it discusses potential future directions, such as leveraging multimodal analysis, federated learning, and explainable AI, to improve the reliability and adaptability of deepfake detection systems. © 2025 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Improving Robotic Arms Through Natural Language Processing, computer Vision, and Edge Computing 3

Improving Robotic Arms Through Natural Language Processing, ...

引用

3rd international conference on Mechatronics, Control and Robotics, ICMCR 2025

作者： Sikorski, Pascal Yu, Kaleb Billadeau, Lucy Esposito, Flavio Aliakbarpour, Hadi Babaias, Madi Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Aerospace & Mechanical Engineering Department Saint Louis United States Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Aerospace & Mechanical Engineering Department Saint Louis United States

ISBN: (纸本)9798331544546

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. © 2025 ieee.

关键词： Robotic arms

来源：评论

学校读者我要写书评

暂无评论

Food Hazines and Tracking System for Farmers using Blockchain Technologies 1

Food Hazines and Tracking System for Farmers using Blockchai...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Jananipriya, R. Ramya, P. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

It is quite challenging to monitor goods quality or security issues due to the intricacy of a tracking system, particularly for the fundamental farming food supply chains that contribute to making up everyday feeds of individuals. There are a number of significant issues with the current agricultural food tracking systems, including a large number of subjects, challenging interactions brought on by lengthy logistics phases, and user and centrally managed distrust of data. The traceability issue in agricultural food tracking systems is successfully resolved by the development of blockchain technology. In order to reduce the requirement for central organizations and departments and enhance the dependability, safety, and authenticity of records of transactions, this work suggests a structure based on smart contracts and consortiums to monitor and document the process of agricultural food tracking system, implement transparency and accessibility, and dismantle data centers between businesses. Farmers simultaneously store file hashes in smart contracts and record environmental and agricultural development information in the InterPlanetary File System (IPFS), which improves the safety of information and mitigates the issue of blockchain storage overflow. The proposed system demonstrated superior performance with reduced delay in response (0.035) and increased transmission speed (265), reception speed (305) and processing power (17.65) compared to Ethereum and Hyperledger Fabric. © 2025 ieee.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Development of EEG-based Prosthetic Limb for Paraplegic and Quadriplegic Patients 2

Development of EEG-Based Prosthetic Limb for Paraplegic and ...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Devi, Chelli N. Krishnamoorthi, Raja Nayak, Sumit Kalasalingam Academy of Research and Education Department of Biomedical Engineering Krishnankoil India International Relations Kalasalingam Academy of Research and Education Krishnankoil India Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9798331505745

Paraplegic and quadriplegic patients and amputees face severe difficulties in performing their daily activities. A viable and effective solution for this is the development of prosthetic limbs that enable them to carry out their everyday chores. In this work, a model is proposed for electroencephalograph (EEG)-based control of these prosthetic limbs. The study compares a multi-layer perceptron and support vector machine based models for movement classification based on EEG waveforms. The raw EEG signals were obtained from the open source Kaggle website. The signals were then preprocessed, followed by data splitting, model training and evaluation, visualization and final support vector machine (SVM)-based classification. The performance of the model was evaluated based on the following metrics: precision, recall, F1-score and accuracy. The proposed SVM-based model achieved an overall accuracy of 86%. The proposed model shows great promise in developing a complete brain computer interface for controlling the prosthetic limbs. The future scope of the study entails improving the accuracy of the developed model by incorporating techniques like data augmentation and ensemble learning. © 2025 ieee.

关键词： Artificial limbs

来源：评论

学校读者我要写书评

暂无评论

Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 ieee.

关键词： Peer to peer networks

来源：评论

学校读者我要写书评

暂无评论

Building Secure and Decentralized Survey Methodology: A Comprehensive Solution with FIDO2 Authentication and Fluree 14

Building Secure and Decentralized Survey Methodology: A Comp...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Balachandher, Jawahar Dongre, Chandana Bhattacharyya, Somanshu Hunashikatti, Rohit Sarasvathi, V. Department of Computer Science and Engineering PES University Karnataka Bangalore India

ISBN: (纸本)9798331531935

Traditional survey systems struggle with data immutability, privacy protection, and reliable storage. These limitations compromise transparency, security, and data integrity. This paper proposes a decentralized and secure survey platform to address these challenges. Fast Identity Online 2 (FIDO2) passwordless authentication strengthens user verification, backed by public key cryptography. Fluree, a permissioned blockchain based graph database, ensures decentralized storage and minimizes data tampering. Smart codes are designed based on user requirements, while integrated statistical analyses enhance data insights. This approach of integrating these technologies prioritizes transparency, security, and integrity in the data collection process while maintaining a seamless user experience. By fostering trust among stakeholders, it enables reliable, data driven decision-making. © 2025 ieee.

关键词： Public key cryptography

来源：评论

学校读者我要写书评

暂无评论

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent Intrusion Detection System 2

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent...

引用

2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Shadeja, Mohit Singh, Shashwat Prakash, Ayushi Dept. of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexible and susceptible to evasion tactics, making them ineffective against novel and sophisticated attacks. In contrast, machine learning-powered anomaly-based intrusion detection systems demonstrate improved detection capabilities, but their advantages are often outweighed by substantial drawbacks, including significant computational resource requirements when the dataset and its features for behavior analysis increase. Also, there are high power consumption and latency issues in communication that compromise network performance. This study proposes a hybrid ANN-LSTM approach for performing deep packet inspection and efficiently detecting malicious network packets. By analyzing binary container data, our model reduces time and space complexity, thereby mitigating the computational resources and power consumption challenges but also increasing the efficiency of intrusion detection by 3%, making it more intelligent than various established IDS. © 2025 ieee.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Recognition of Bacterial Brain Abscesses: A Comprehensive Approach Towards Brain Disease Detection 2

Deep Learning-based Recognition of Bacterial Brain Abscesses...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Venkatarao, Kommuri Rama Surya Kumar, Manimala Vyasa Prasanna, Gade Lakshmi Krishna Reddy, Sandadi Vamsi Lakireddy Balireddy College of Engineering Mylavaram NTR Dt. Department of Computer Science and Engineering Andhra Pradesh 521230 India

ISBN: (纸本)9798331505745

This research paper focuses on developing an advanced deep learning-based framework for the automated detection of brain abscesses disease, a severe condition characterized by the accumulation of pus in the brain tissue due to bacterial infections. This study analyzes MRI images with exceptional precision using convolutional neural networks (CNNs), specifically the DenseNet-121 architecture. By training the model on a diverse and well-curated dataset, the proposed system results in a significant improvement in accuracy, achieving an average accuracy of 95% detection rate, surpassing the performance of models such as ResNet-50 and InceptionV3, which achieved an average of 80% accuracy. This model can accurately identify and delineate abscess regions, eliminating the need for manual intervention. Rigorous evaluations confirm its reliability in differentiating between healthy and infected brain tissues. The findings underscore the potential of this automated approach to expedite diagnosis, enhance clinical decision-making, and improve patient outcomes by facilitating timely intervention in the management of brain abscesses. © 2025 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Toxic Comment Detection based on Improved Bi Directional Long Short Term Memory 1

Toxic Comment Detection based on Improved Bi Directional Lon...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Kamma, Vidya Wbaid, Saef Sudhakar, K. Nisha, A. Saravanan, T. Neil Gogte Institute of Technology Department of Computer Science and Engineering Rangareddy Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, toxic comment detection evolved as important in detecting hatred and harmful content present in user-generated content. However, a few challenges are raised in detecting toxic comments like context sensitivity and missing toxic content. To overcome these issues, improved Bi-directional Long Short-Term Memory (Bi-LSTM) is proposed for toxic comment detection. Initially, data is preprocessed with lemmatization and stop words removal methods for converting the texts to its base form and eliminates the common words respectively. Then, the Word to Vector (Word2Vec) technique converts the words into a dense vector representation by capturing semantic relationships and contextual meanings. Finally, improved Bi-LSTM is employed for detecting the toxic comments by capturing both past as well as future context with its bidirectional architecture and focuses on important words within the context dynamically. From the results, proposed Improved Bi-LSTM model attained better outcomes in terms of accuracy (acc) of 94.32%, precision (pr) of 98.07%, and recall of 95.98% compared to existing Convolutional Neural Networks (CNN)-BiLSTMs. © 2025 ieee.

关键词： Semantics

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 << < 21 22 23 24 25 26 27 28 29 30 > >>

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：