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

6,674 篇 会议
329 册 图书
119 篇 期刊文献

馆藏范围

7,121 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,757 篇 工学
- 3,945 篇 计算机科学与技术...
- 2,010 篇 软件工程
- 805 篇 电气工程
- 764 篇 信息与通信工程
- 588 篇 控制科学与工程
- 394 篇 生物工程
- 380 篇 生物医学工程（可授...
- 293 篇 机械工程
- 293 篇 光学工程
- 212 篇 仪器科学与技术
- 180 篇 电子科学与技术（可...
- 138 篇 化学工程与技术
- 127 篇 材料科学与工程（可...
- 119 篇 网络空间安全
- 107 篇 安全科学与工程
- 91 篇 动力工程及工程热...
1,614 篇 理学
- 803 篇 数学
- 487 篇 物理学
- 486 篇 生物学
- 272 篇 统计学（可授理学、...
- 189 篇 化学
- 141 篇 系统科学
834 篇 管理学
- 455 篇 管理科学与工程(可...
- 422 篇 图书情报与档案管...
- 173 篇 工商管理
678 篇 医学
- 603 篇 临床医学
- 254 篇 基础医学(可授医学...
- 145 篇 药学(可授医学、理...
- 100 篇 公共卫生与预防医...
121 篇 法学
- 114 篇 社会学
90 篇 教育学
56 篇 农学
51 篇 经济学
43 篇 艺术学
12 篇 文学
3 篇 军事学
1 篇 哲学

主题

927 篇 pattern recognit...
801 篇 artificial intel...
446 篇 machine learning
291 篇 feature extracti...
237 篇 deep learning
204 篇 accuracy
185 篇 support vector m...
134 篇 data mining
128 篇 training
126 篇 neural networks
126 篇 face recognition
103 篇 local binary pat...
103 篇 computer imaging...
102 篇 computer vision
88 篇 real-time system...
88 篇 image processing...
86 篇 convolutional ne...
85 篇 information syst...
83 篇 pattern matching
83 篇 computational mo...

机构

39 篇 chitkara univers...
33 篇 maharishi school...
27 篇 school of comput...
27 篇 indian stat inst...
25 篇 centre of interd...
25 篇 the islamic univ...
23 篇 department of ar...
23 篇 concordia univ c...
22 篇 national univers...
20 篇 school of comput...
19 篇 univ chinese aca...
18 篇 univ waterloo de...
17 篇 vishwakarma inst...
17 篇 school of comput...
16 篇 national laborat...
15 篇 school of artifi...
15 篇 institute of aut...
14 篇 lovely professio...
14 篇 uttaranchal inst...
14 篇 college of pharm...

作者

54 篇 suen ching y.
24 篇 suen cy
22 篇 kamel mohamed s.
18 篇 lu bao-liang
18 篇 karray fakhri
17 篇 de-shuang huang
17 篇 abraham ajith
14 篇 maji pradipta
13 篇 dash yajnaseni
12 篇 c.y. suen
10 篇 liu cheng-lin
10 篇 venkatesh svetha
10 篇 wong alexander
10 篇 chauhan rahul
10 篇 natarajan s.
10 篇 zheng wei-long
10 篇 liu wei
10 篇 vinay a.
9 篇 zhang lei
9 篇 gill kanwarparta...

语言

6,808 篇 英文
286 篇 其他
58 篇 中文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

共 7122 条记录，以下是291-300 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：

An Analysis of Face based eCrime Identification using First Information Report 3

An Analysis of Face based eCrime Identification using First ...

引用

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

作者： Pandiaraja, P. Vanarasan, S. Dharani, S. Dharshini, R. Harani, S. Selvarathi, C. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Sri Sairam College of Engineering Department of Artificial Intelligence and Machine Learning Banglore562 106 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639 113 India

ISBN: (纸本)9798331509675

Face based e-crime identification initiative combines cutting-edge technologies to detect criminals in video footage, allowing for quick and effective online FIR (First Information Report) reporting. Furthermore, it uses blockchain technology to securely store and manage sensitive data, as well as to ensure the integrity of evidence and to facilitate robust facial recognition using the Inception V3 algorithm. The proliferation of electronic crimes (eCrimes) in today's digital age poses a huge challenge to law enforcement organizations worldwide. In the face of continuously shifting criminal strategies, traditional means of identifying and reporting offenders frequently fall short. The fundamental functionality of the technology is around identifying offenders using video footage. It makes use of the potent Inception V3 algorithm, a cutting-edge deep learning model noted for its efficient image recognition skills. This algorithm analyses video frames, detects and matches facial traits, and identifies suspects. This not only improves the accuracy of criminal identification but also speeds up the investigation. In addition to identifying criminals, the streamlines the FIR reporting process. Victims and witnesses can now conveniently file FIRs online, eliminating the need for physical visits to police stations. The integration of digital reporting platforms enhances the efficiency of law enforcement agencies, making the process more accessible to the public. Blockchain technology is used to provide data security and immutability. A blockchain-based database stores all data relevant to criminal identification and FIR reporting. The combination of improved facial recognition, online reporting, and secure blockchain data management promises to make identifying and reporting criminals more efficient and secure. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

SSVEP EEG signal classification model based on EEMD-FastICA-TRCA 24

SSVEP EEG signal classification model based on EEMD-FastICA-...

引用

3rd international conference on Artificial intelligence and Intelligent Information Processing, AIIIP 2024

作者： Liu, Yunfeng Zhang, Xueru Wan, Sijia Zhan, Weijian Liu, Yongming Zhao, Zhuanzhe School of Mechanical Engineering Anhui Polytechnic University Anhui Wuhu China School of Artificial Intelligence Anhui Polytechnic University Anhui Wuhu China

ISBN: (纸本)9798400707308

In order to retain as many effective signals in EEG as possible and improve the accuracy of a signal classification and the performance of the brain-computer interface system, a steady-state visual evoked potential EEG classification model combining ensemble empirical mode decomposition, fast, independent component analysis and task-related component analysis was proposed to solve the problem that noise would reduce the accuracy of signal classification and recognition. Firstly, it is proved that the proposed EEMD-FastICA noise reduction model has certain advantages over the standard noise reduction methods, and the performance of the model is verified by using the Benchmark dataset of the brain-computer interface group of Tsinghua University. On this basis, the EEMD-FastICA noise reduction model is combined with the TRCA algorithm to classify EEG signals, and the classification results are compared with other classification models. The comparison results show that under different time Windows, the classification accuracy and information transmission rate of the proposed model are higher than those of other models. Within 1.5 S, the highest classification recognition accuracy was 85.83%, and the highest ITR was 238.34 bits/min. © 2024 Copyright held by the owner/author(s).

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Application and Optimization of Artificial intelligence Algorithms in Cost Management in Civil Engineering 3

Application and Optimization of Artificial Intelligence Algo...

引用

3rd international conference on Data Analytics, Computing and Artificial intelligence, ICDACAI 2024

作者： Wang, Zhaogang Zhang, Jianqiao China Tobacco Corporation Guizhou Province Company Audit Chu Guizhou Guiyang550003 China City University of Macau 999078 China

ISBN: (纸本)9798331519032

Artificial intelligence (AI), as a key force driving industrial transformation in the new era, is profoundly changing various industries, especially in the field of civil engineering, where its potential is particularly significant. This system not only accelerates the comprehensive digital transformation of the civil engineering industry, but also greatly improves the accuracy and efficiency of engineering costs, becoming an important path to achieve automation, informatization, and even intelligent management. The AI based civil engineering cost management system discussed in this article innovatively integrates big data processing, machine learning (ML) algorithms, and deep learning (DL) technology, which can automatically analyze massive engineering data and achieve fast and accurate estimation of engineering costs. This system not only reduces the workload of cost engineers and minimizes human errors, but also significantly improves the timeliness and accuracy of cost forecasting, providing strong data support for project decision-making. The experimental results show that this system not only successfully reduces the time cost of engineering cost calculation, but also helps project managers make more scientific and reasonable decisions in cost control and resource allocation through intelligent optimization algorithms. © 2024 IEEE.

关键词： Cost engineering

来源：评论

学校读者我要写书评

暂无评论

Modeling and Performance Analysis of PEM Fuel Cells Using machine Learning Techniques 3

Modeling and Performance Analysis of PEM Fuel Cells Using Ma...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Gaddam, Shamitha Reddy, P. Srihith Joshi, Shruti V. Bnm Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Bnm Institute of Technology Department of Electrical Engineering Bengaluru India

ISBN: (纸本)9798331515911

Proton Exchange Membrane Fuel Cells (PEMFCs) play a vital role in the transition to clean energy technologies, requiring accurate and efficient models for their control, optimization, and real-time monitoring. This research focuses on creating machine learning models as Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, and Random Forest (RFR) and MLPRegressor to predict PEMFC key performance attributes. Using a dataset from Hamidi et al [1]., which focuses on Nafion 112 membranes and MEA activation tests. Input features include current density, voltage, power density, gas pressure, relative humidity, and impedance components (real and imaginary). The primary output is cell voltage, a key performance indicator. Among the models, Random Forest Regressor achieved the best results with an accuracy of 94.715, R2 score of 0.8049, MSE of 0.001036, and RMSE of 0.03219. Feature importance analysis revealed relative humidity, current density, and voltage as the most influential factors. This research illustrates the use of machine learning techniques in modeling the functioning of attributes of PEMFCs, providing insights for optimizing fuel cell performance and accelerating the inculcation of clean energy technologies. © 2025 IEEE.

关键词： Support vector regression

来源：评论

学校读者我要写书评

暂无评论

Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

machine Learning based Screening for Diabetes Risk Prediction 3

Machine Learning based Screening for Diabetes Risk Predictio...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kandula, Ashok Reddy Samhitha, Velevela Veda Harshitha, Vilasagarapu Sindhuri, Ragam Naga Nagavalli, Sangireddy Aarathi Department of Artificial Intelligence and Data Science Seshadri Rao Gudlavalleru Engineering College AndhraPradesh India

ISBN: (纸本)9798331532420

Diabetes Mellitus (DM) poses a major global health issue,impacting millions and significantly affecting quality of life. Early detection and risk prediction are crucial, but conventional diagnostic methods are often costly and limited by the availability of clinical markers. machine Learning (ML) approaches, however, have shown higher predictive accuracy, reaching 77.9%, compared to traditional methods. This study examines ML techniques, such as Support Vector machines (SVM) and Logistic Regression (LR), applied to datasets encompassing lifestyle, clinical, and demographic factors. Additionally, it explores the potential of integrating ML with wearables and electronic health records (EHR) to enable personalized, real-time risk assessments. These ML models consistently demonstrate improved accuracy, sensitivity, and specificity in predicting diabetes risk, with an accuracy rate of 77.9%, exceeding that of conventional diagnostic tools. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

A Prediction Model of Cervical Lymph Node Metastasis in Thyroid Cancer Was Constructed Based on Xgboost Artificial intelligence Combined with Ct 3

A Prediction Model of Cervical Lymph Node Metastasis in Thyr...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Yu, Bo Xu, Ning Fan, Weiye Yao, Jiaxing Zheng, Shijia Sun, Xiyuan The First Hospital of QIqihaer Heilongjiang Qiqihaer China

ISBN: (纸本)9798350354171

The construction of a prediction model for cervical lymph node metastasis of thyroid cancer plays an important role in modern scientific and technological medical treatment, but there is a problem of inaccurate prediction and positioning. The traditional machine learning algorithm cannot solve the problem of transfer and positioning in the power fault detection of smart grid, and the detection effect is not ideal. Therefore, this paper proposes to construct a prediction model for cervical lymph node metastasis of thyroid cancer based on CT based on Boost artificial intelligence combined with CT and analyze the localization of metastatic factors. Firstly, the influencing factors is located by using the improved decision tree theory, and the indicators is divided according to the requirements of constructing the prediction model of cervical lymph node metastasis of thyroid cancer, to reduce the interference factors in the construction of the prediction model of cervical lymph node metastasis of thyroid cancer. Then, the XGBoost artificial intelligence combined with CT was used to construct a prediction model for cervical lymph node metastasis for thyroid cancer, and the results of the prediction model for cervical lymph node metastasis for thyroid cancer were comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, XGBoost artificial intelligence combined with CT is superior to the traditional machine learning algorithm in constructing a prediction model for cervical lymph node metastasis in thyroid cancer and the time of influencing factors in constructing a prediction model for cervical lymph node metastasis for thyroid cancer. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

House Price Prediction Using Linear and Lasso Regression 3

House Price Prediction Using Linear and Lasso Regression

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Sharma, Mayank Chauhan, Rahul Devliyal, Swati Chythanya, Kanegonda Ravi Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350381931

This research paper aims to predict house prices with more efficiency and accuracy. The research paper involves three machine learning algorithms, "linear regression, lasso regression, and ridge regression,"that are applied to the data set of Bangalore and have various information about different places in Bangalore, like BHK, number of bathrooms, area, etc. The house price prediction is quite a difficult task, as predicting the price of the house involves many factors like area, number of bathrooms, climate condition, age of the property, and many other factors. To make predictions according to all the factors, the author has applied three algorithms, one by one. From these machine learning algorithms, we have developed a model that helps predict the prices of houses with more accuracy and a lower error percentage. This model can be very useful for both buyers and sellers, as it can justify the price of the house for both of them. © 2024 IEEE.

关键词： Houses

来源：评论

学校读者我要写书评

暂无评论

De-Styling Bias in #Climatical Tweets: A Neural Language Style Transfer Approach 3

De-Styling Bias in #Climatical Tweets: A Neural Language Sty...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： George, Elizabeth Leah Parthasarathy, Subashini Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India Centre for Machine Learning and Intelligence Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India

ISBN: (纸本)9798331515911

The spread of biased and misleading opinions on social media regarding climate change necessitates robust solutions to counteract misinformation and promote balanced discourse. In this study, we introduce the Semantic Neural Style Transfer Bias (SNLT) Framework, an AI-driven solution designed to identify and mitigate bias in climate-related tweets. Leveraging a curated dataset of climate change tweets collected from Twitter, we employ the all-MiniLM-L6-v2 sentence transformer for feature extraction and BERT-based embeddings to capture semantic nuances. Our methodology integrates semantic clustering models to evaluate clustering performance using silhouette scores, including KMeans, Gaussian KMeans, keyNMF clustering, and an Autoencoder-based model. DistilBERT is further employed to classify tweets as biased or unbiased, while a Named Entity recognition (NER) model trained on RobERTa transformers identifies word-level bias indicators. The fundamental basis of our framework is a transformer-based style transfer model, such as GPT-2, which adjusts the stylistic tone of biased text while preserving its semantic meaning. This approach employs a style-specific decoder to ensure grammatical accuracy and contextual relevance, producing text with a neutral tone through refined phrasing and word choice. Evaluation results demonstrate a substantial reduction in bias without compromising fluency or naturalness, validated through statistical metrics and human assessments. Additionally, Explainable AI techniques are integrated to enhance the transparency of model decisions, offering insights into bias mitigation processes. Our findings highlight the framework's efficacy in addressing climate misinformation and fostering constructive conversations on social media, underscoring its potential as a responsible AI tool for digital discourse. © 2025 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Research on English Online Classroom Learning System Based on Computer Intelligent Assistive Technology 3

Research on English Online Classroom Learning System Based o...

引用

3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Guangjie, Shen Qi, Wang Tianli, Zhang Qingling, Wang Weifang Engineering Vocational College Weifang China

ISBN: (纸本)9798350380989

The English learning system structure designed in this article is mainly based on the B/S three-level architecture. A relational database uses SQLite as the data layer. The system uses the Bootstrap system to automatically recognize English words, and uses custom tags to enable teachers to quickly review test papers and provide feedback. An automatic image recognition technology that combines similarity comparison based on field context and CNN+BLSTM is proposed. This paper analyzes the user registration model and query process, uses multiple information technologies to ensure the stability of online teaching and further expand teaching methods. Experiments have proven that the proposed English intelligent dictionary recommendation system has the same characteristics as the age similarity weight, and both show an increasing trend as the parameters increase. The system can clearly display the records of words that the learning object has learned after logging into the system, including the number of times the word has been used, achieving intelligent effects. © 2024 IEEE.

关键词： Computer English teaching image recognition intelligence intelligent teaching teaching system

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 << < 26 27 28 29 30 31 32 33 34 35 > >>

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：