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

6,286 篇 会议
65 册 图书
32 篇 期刊文献

馆藏范围

6,383 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,474 篇 工学
- 2,814 篇 计算机科学与技术...
- 2,071 篇 软件工程
- 1,067 篇 信息与通信工程
- 406 篇 电气工程
- 349 篇 生物工程
- 315 篇 控制科学与工程
- 264 篇 生物医学工程（可授...
- 261 篇 电子科学与技术（可...
- 258 篇 光学工程
- 210 篇 机械工程
- 160 篇 化学工程与技术
- 141 篇 仪器科学与技术
- 115 篇 安全科学与工程
- 115 篇 网络空间安全
- 83 篇 交通运输工程
- 71 篇 动力工程及工程热...
1,734 篇 理学
- 1,177 篇 数学
- 441 篇 物理学
- 368 篇 生物学
- 232 篇 统计学（可授理学、...
- 174 篇 化学
- 141 篇 系统科学
888 篇 管理学
- 576 篇 图书情报与档案管...
- 383 篇 管理科学与工程(可...
- 145 篇 工商管理
309 篇 医学
- 289 篇 临床医学
- 170 篇 基础医学(可授医学...
- 129 篇 药学(可授医学、理...
213 篇 法学
- 160 篇 社会学
74 篇 文学
- 66 篇 新闻传播学
62 篇 教育学
51 篇 农学
37 篇 经济学
18 篇 军事学
11 篇 艺术学
2 篇 哲学

主题

454 篇 streaming media
413 篇 feature extracti...
327 篇 media
325 篇 discrete wavelet...
294 篇 wavelet transfor...
225 篇 social networkin...
223 篇 computational mo...
219 篇 sentiment analys...
213 篇 accuracy
201 篇 deep learning
175 篇 information proc...
172 篇 training
160 篇 image processing
158 篇 wavelet analysis
150 篇 visualization
149 篇 real-time system...
148 篇 natural language...
141 篇 signal processin...
138 篇 robustness
135 篇 data mining

机构

325 篇 school of comput...
111 篇 school of inform...
43 篇 school of inform...
42 篇 univ elect sci &...
40 篇 university of el...
38 篇 university of el...
29 篇 university of el...
27 篇 advanced cryptog...
26 篇 school of cybers...
25 篇 school of comput...
22 篇 school of comput...
20 篇 logist engn univ...
18 篇 department of co...
18 篇 institute of dig...
14 篇 chengdu universi...
14 篇 college of compu...
13 篇 sugon industrial...
13 篇 hubei province k...
12 篇 school of comput...
11 篇 school of comput...

作者

60 篇 li jianping
41 篇 jianping li
35 篇 jian ping li
34 篇 li jian-ping
32 篇 jian-ping li
29 篇 amin ul haq
29 篇 li jian ping
21 篇 huang yuanyuan
18 篇 wang zhou
18 篇 wen gao
15 篇 zhou wang
15 篇 di liu
15 篇 kuang ping
14 篇 bless lord y. ag...
14 篇 yujun yang
14 篇 yuanyuan huang
13 篇 ping kuang
13 篇 liu wei
13 篇 haq amin ul
13 篇 gao wen

语言

6,243 篇 英文
120 篇 其他
43 篇 中文
1 篇 土耳其文

检索条件"任意字段=International Computer Conference on Wavelet Active Media Technology and Information Processing"

共 6383 条记录，以下是41-50 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：

Bias Detection in media: Traditional Models vs. Transformers in Analyzing Social media Coverage of the Israeli-Gaza Conflict 1

Bias Detection in Media: Traditional Models vs. Transformers...

引用

1st international Workshop on Nakba Narratives as Language Resources, Nakba-NLP 2025

作者： Yahya, Marryam Ismail, Esraa Nabil, Mariam Ashraf, Yomna Radwan, Nada Elshaer, Ziad Mohamed, Ensaf School of Information Technology and Computer Science Nile University Giza Egypt

ISBN: (纸本)9798891762169

Bias in news reporting significantly influences public perception, particularly in sensitive and polarized contexts like the Israel-Gaza conflict. Detecting bias in such cases presents unique challenges due to political, cultural, and ideological complexities, often amplifying disparities in reporting. While prior research has addressed media bias and dataset fairness, these approaches inadequately capture the nuanced dynamics of the Israel-Gaza conflict. To address this gap, we propose an NLP-based framework that leverages Nakba narratives as linguistic resources for bias detection in news coverage. Using a multilingual corpus focusing on Arabic texts, we apply rigorous data cleaning, pre-processing, and methods to mitigate imbalanced class distributions that could skew classification outcomes. Our study explores various approaches, including Machine Learning (ML), Deep Learning (DL), Transformer-based architectures, and generative models. The findings demonstrate promising advancements in automating bias detection, and enhancing fairness and accuracy in politically sensitive reporting. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

引用

7th international conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

Approaches, Applications, and Challenges of Using Sentiment Analysis for False information Detection: A Systematic Literature Review 19

Approaches, Applications, and Challenges of Using Sentiment ...

引用

19th international conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Fanlau, Nathan Edmund Hiu, Michael Achmad, Said Sutoyo, Rhio Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331507817

In today’s digital age, the prevalence of false information on the internet, spanning from fake news to fake user accounts, poses a pressing concern that undermines the reliability of online information. Such misinformation can have harmful consequences, such as promoting biased ideologies, swaying public opinion, and inciting unrest. Implementing sentiment analysis technology in false information detection significantly impacts combating misinformation on digital platforms. It analyzes textual content in real time, which is crucial for effectively identifying and mitigating false information such as fake news, fake reviews, fake accounts, and fake websites on digital platforms. This paper explores sentiment analysis’s approaches, applications, and challenges for false information detection, providing insights for future research and application. A systematic literature review was conducted using the PRISMA methodologies to identify and evaluate relevant studies. The results of this paper contribute to understanding the current state of sentiment analysis in combating misinformation. This paper provided the common and available applications of sentiment analysis, such as fake news detection, fake review detection, fake social media account detection, and more. Approaches that can be taken to achieve these applications include the machine learning approach, neural network approach, hybrid approach, and more. Moreover, this work highlights the need for more advanced algorithms and ongoing research to address challenges such as sarcasm detection, bot account identification, and effectively interpreting informal language and slang in texts. © 2025 IEEE.

关键词： false information machine learning natural language processing sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Signup page using Face recognition authentication

Signup page using Face recognition authentication

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Modi, Akash Rayar, Prajwal V Kavya, A.G. Vaishnavi, R. Dravya, B.S. Tamilarisi, R. Alliance School of Advanced Computing Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529833

Improvements in artificial intelligence and machine learning have led to the growing integration of face recognition technology for a wide range of applications, from security systems to social media. Three related uses of facial processing technology are examined in this abstract: face detection, face recognition, and authentication through a face-based signup page. Finding and differentiating human faces in an image frame requires the first step, face detection. It also recognizes the facial features and separates them from other items in the frame using computer vision algorithms. Histogram of Oriented Gradients (HOG), and the Haar Cascade, are the methods used for face recognition. Still, due to failure in extracting facial features in harsh conditions, we use the more recent deep learning models like Convolutional Neural Networks (CNNs) to do this project. Building on detection and face recognition compares the observed faces to known faces in a database to identify or to confirm a person's identity. To assess distinctive facial traits (embeddings) or features and increase recognition accuracy across a variety of circumstances, like varying lighting and angles, modern face recognition systems employ deep learning models, such as Face Net and Deep Face. The Facial Recognition The signup page serves as an example of how face recognition can be used in authentication systems for secure apps. A user registers for this program by having their face captured, and this information is safely kept and acts as a unique identification. The solution gives a hands-free and password-less authentication mechanism during login by comparing the captured face with the stored data in the database. © 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

MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

来源：评论

学校读者我要写书评

暂无评论

2012 international conference on wavelet active media technology and information processing, ICWAMTIP 2012

2012 International Conference on Wavelet Active Media Techno...

引用

2012 international conference on wavelet active media technology and information processing, ICWAMTIP 2012

ISBN: (纸本)9781467346856

The proceedings contain 106 papers. The topics discussed include: research and implementation of data synchronization with SYNCML;association analysis on casualty and residential environment data of earthquake;grey GM (P) model based on differential equation;a BCD priority encoder designed by reversible logic;gait recognition using the magnitude and phase of quaternion wavelet transform;a research of spins based on identity authentication for wireless sensor networks;a framework of call center service management system based on PENTAHO;a survey of the research on multi-valued quantum circuits;an advanced spread-spectrum clock generator with triangular modulation for EMI suppressing;study and design of embedded trace debugging technology based on virtual technology;on the limiting distribution of the maximum with random sample size of regular variation function;and an authentication and key agreement protocol based on hummingbird-2 and its application in WSNs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Text-Based Sentiment Analysis: Experimentation with CNN and Basic Machine-Learning Approaches

Text-Based Sentiment Analysis: Experimentation with CNN and ...

引用

international conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Agrawal, Umang Kumar Das, Debashreet Ramana, B.V. Panda, Nibedan School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Centre for UG and PG Studies BPUT Odisha Rourkela India Department of Information Technology Aditya Institute of Technology and Management Andhra Pradesh Tekkali India

ISBN: (纸本)9789819613472

Sentiment analysis, a burgeoning sector within natural language processing (NLP) plays a crucial role in discerning and perceiving attitudes, emotions and opinions expressed in the form of textual data in the social media platform. With the growing proliferation of social networks, sentiment analysis has become indispensable for the government, firms, and researchers, providing beneficial insights into customer feedback, public perception and new actuality. Over the years, Convolutional Neural Networks (CNN) have emerged as the most influential approach in NLP to implement text classification. In this article, we have incorporated Deep Neural Network (DNN) namely CNN in contrast with various basic supervised machine-learning (ML) approaches evaluated on the IMDB Movie Review Dataset. We have analyzed the pros and cons of each approach, simplifying their efficiency in analyzing the sentiments to compel the manifest model into a prolific and reliable approach for better text classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Big data analytics in healthcare: Applications, examples and challenges 4th

Big data analytics in healthcare: Applications, examples and...

引用

4th international conference on Computational Methods in Science and technology, ICCMST 2024

作者： Kaur, A. Goyal, S. Batra, N. Gupta, N. Ambala Haryana Mullana India Computer Science and Engineering Department CGC-College of Engineering Landran Mohali India

ISBN: (纸本)9781032911571

The healthcare sector is evolving at an exponential rate due to the speed of technological disruption and digital innovation. The size of healthcare data is increasing exponentially, making it more challenging to locate any kind of relevant information. Big data is currently changing how data is delivered traditionally by employing big data analytics techniques to produce insightful knowledge. In the healthcare sector, big data analytics offers numerous advantages, including the ability to identify serious illnesses early on and provide more effective care to the appropriate patient at the appropriate moment, thereby enhancing the standard of care. The quick analysis and integration of massive amounts of different types of data generated by various clinical settings, hospitals, other social media platforms, and medical data lakes is made possible by big data analytics tools. Many challenges must be addressed by the current health data analytics systems, which offer technical means for gathering, combining, processing, analysing, visualising, and interpreting data. This article delves into the intriguing topic of healthcare big data analytics. Due to a dearth of comprehensive study, this topic has not gotten enough attention in the past. This article covers the unique characteristics of big data, big data analysis tools, and the steps the healthcare business takes from data collection to data distribution. © 2025 the Author(s).

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

EPI-Mamba: State Space Model for Semantic Segmentation from Light Fields

EPI-Mamba: State Space Model for Semantic Segmentation from ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Yan Chen, Jianan Wang, Qiong Xu, Jinshan College of Computer Science & Technology Zhejiang University of Technology Hangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Global contextual dependency is of significance for semantic segmentation from light fields. However, previous works mostly exploit attention mechanisms to model spatial context dependency and angular context dependency separately, since a light field capture is very data-intensive. Considering that self-attention is resource-consuming, existing methods only use it to capture spatial contextual dependency for the central view of light fields, and employ resource-efficient attention mechanisms to model angular view dependencies instead. Inspired by the recent success of State Space Sequence model (SSM), we present a novel SSM-based encoder for light field semantic segmentation, which models spatio-angular contextual dependency together. The proposed method is evaluated on the existing light field semantic segmentation datasets, and experimental results show that the proposed method significantly outperforms previous state-of-the-art methods. The code and models used in this work are available in https://***/YanWQ/EPI-Mamba. © 2025 IEEE.

关键词： Light field semantic segmentation Mamba State space model

来源：评论

学校读者我要写书评

暂无评论

A comprehensive analysis and sensitivity model for emotion detection in text 1st

A comprehensive analysis and sensitivity model for emotion d...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Singh, Pancham Asthana, Amita Solanki, Beerbal Tomar, Swati Kansal, Mrignainy Tyagi, Gagan Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad India Department of Electronics and Communication Engineering Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India Technical Architect Tech Mahindra India

ISBN: (纸本)9781032874036

People use a variety of communication channels, including text, voice, and gestures, to convey a wide range of emotions in their daily interactions. The problem of automatically identifying these emotions from textual information has important practical ramifications. This study focuses on natural language processing (NLP) methods for textual emotion identification, particularly in Twitter tweets. The study uses machine learning, sentiment analysis, tokenization, and other techniques with a focus on using APIs such as the Twitter API. With an emphasis on the use of unsupervised learning algorithms and data expansion methodologies, the focal issue examines relevant work in sentiment analysis on social media. The suggested approach tackles issues including tokenization, sentiment analysis, and noise reduction in order to categorize tweets into distinct moods. The front-end development using Flask provides a user-friendly interface to visualize real-time Twitter data based on user-defined keywords. The study achieved an overall accuracy rate of 92% in classifying human moods across a diverse dataset. © 2025 the Author(s).

关键词： Tweets

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 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：