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

6,718 篇 会议
329 册 图书
119 篇 期刊文献

馆藏范围

7,165 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,792 篇 工学
- 3,971 篇 计算机科学与技术...
- 2,033 篇 软件工程
- 809 篇 电气工程
- 773 篇 信息与通信工程
- 593 篇 控制科学与工程
- 395 篇 生物工程
- 381 篇 生物医学工程（可授...
- 297 篇 光学工程
- 296 篇 机械工程
- 214 篇 仪器科学与技术
- 182 篇 电子科学与技术（可...
- 138 篇 化学工程与技术
- 128 篇 材料科学与工程（可...
- 121 篇 网络空间安全
- 109 篇 安全科学与工程
- 91 篇 动力工程及工程热...
1,630 篇 理学
- 810 篇 数学
- 493 篇 物理学
- 490 篇 生物学
- 273 篇 统计学（可授理学、...
- 191 篇 化学
- 141 篇 系统科学
845 篇 管理学
- 464 篇 管理科学与工程(可...
- 425 篇 图书情报与档案管...
- 181 篇 工商管理
679 篇 医学
- 604 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...
122 篇 法学
- 115 篇 社会学
92 篇 教育学
58 篇 农学
52 篇 经济学
43 篇 艺术学
12 篇 文学
4 篇 军事学
1 篇 哲学

主题

927 篇 pattern recognit...
801 篇 artificial intel...
447 篇 machine learning
291 篇 feature extracti...
243 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...
88 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 school of comput...
18 篇 univ waterloo de...
17 篇 vishwakarma inst...
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
11 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

6,735 篇 英文
403 篇 其他
65 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 7166 条记录，以下是551-560 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Examining Internet-based Hate Crime Through Artificial intelligence 3

Examining Internet-based Hate Crime Through Artificial Intel...

引用

3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Saxena, Sagar Dixit, Anil Kumar Pandey, Shweta Pachouri, Vikrant Aluvala, Srinivas Juyal, Ashima Uttaranchal University Law College Dehradun Dehradun India Uttaranchal University LCD Division of Research and Innovation Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Uttaranchal University Division of Research and Innovation Dehradun India

ISBN: (纸本)9798350396157

Internet has emerged as a platform for propagating hatred. This study examines the relationship between the usage of social networking sites and hate crimes. The research findings imply that social media may serve as a channel for the spread of hated digital information and violent behaviors. As well as the usage of machine learning, which is a subfield of AI to the study of hate crimes and the elements that lead to them on the internet. Further in this study authors in this paper investigate the causes of hate crimes and the elements that contribute to their development. Because of their unique significance, hate crimes require consideration. Crimes motivated by prejudice intimidate the survivor and the victim's society. That is why it necessary to investigate the side of protection and prevention policies of international laws that are given to be implemented by the different international bodies. The article's methodology is based on an extensive review of the literature, study of websites through them the researchers examine importantly, fighting hate crimes requires first monitoring and analyzing their dynamics to properly comprehend their nature. Since the propagation of hateful communication can be an early indicator of violence, especially heinous crimes, curbing hate crime could help minimize its effects. Thus, this paper addresses the targets of hatred on the Internet, presents a framework through which issues may be detected and remedied by emphasizing moral and social responsibility, and outlines potential legislatives to counteract this growing scourge on the Internet. © 2024 IEEE.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

machine Learning based Intelligent Diagnosis of Orthodontics: A comprehensive review 3

Machine Learning based Intelligent Diagnosis of Orthodontics...

引用

3rd IEEE international conference on Computational intelligence and Knowledge Economy, ICCIKE 2023

作者： Iwin Thanakumar Joseph, S. Sarveshwaran, Velliangiri Vijayakumar, K. Raj, S Benson Edwin Elngar, Ahmed A Devadurai, Anand Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Guntur India SRM Institute of Science and Technology Kattankulathur Campus Department of Computer Science and Engineering Chennai India Higher Colleges of Technology Department of Computer Information Sciences Fujairah United Arab Emirates Beni-Suef University Faculty of Computers and Artificial Intelligence Beni-Suef City Egypt King Khalid University Department of Computer Science Gregar Abha Saudi Arabia

ISBN: (纸本)9798350338263

An effective prediction of orthodontics treatment outcome is highly essential for further scheme of clinical treatment for the patient with the simplified cost. An appropriate utilization of information technology simplifies the treatment for teeth irregularities and misaligned jaws in an effective manner. Lot of research works carried out in the area of dentistry to come out with an optimistic result. With the advent of artificial intelligence and machine learning methodologies in recent years, lot of clinical problems and difficulties can be efficiently managed and overcome by orthodontists. This research article provides the complete insight about the impact of intelligent technologies in the field of orthodontics. It provides a complete overview of futuristic applications in the field of orthodontic clinical diagnosis, planning of treatment for various cases and the correct prediction of outcome of clinical treatment which regularly faced by an orthodontist in the health care sector. © 2023 IEEE.

关键词： Dentistry

来源：评论

学校读者我要写书评

暂无评论

An Efficient Ensemble approach for Fake Reviews Detection 3

An Efficient Ensemble approach for Fake Reviews Detection

引用

3rd IEEE international conference on Artificial intelligence, ICAI 2023

作者： Iqbal, Asif Rauf, Muhammad Arslan Zubair, Muhammad Younis, Tanveer Riphah College of Computing Riphah International University Faisalabad Pakistan School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350322125

People prefer to buy items and services online to save time in the current age of growing e-commerce. these internet purchases are heavily impacted by the reviews or opinions of people who have already purchased them. customers provide comments to businesses on how to improve product quality, develop, and monitor business strategies in order to boost sales and profits. customers may also use these comments to choose the proper items with less effort and time spent. giving fake review is the practice of fraudulent people who wants to promote or degrade products or services for illegitimate monetary gain. in this research paper, we present an ensemble machine-learning model to identify whether a review is fraudulent or authentic. to achieve this objective, amazon reviews dataset is used. the proposed ensemble model outperformed as compared to other individual classifiers. random forest provides 99% accuracy which is better than other algorithms. © 2023 IEEE.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

Multiple Teaching Objectives-oriented Automatic Test Paper Generation System 24

Multiple Teaching Objectives-oriented Automatic Test Paper G...

引用

3rd international conference on Artificial intelligence and Education, ICAIE 2024

作者： Gao, Yanfang Ma, Shideng Tian, Jia Computer College Guangdong Neusoft University Guangdong Foshan China Foshan Intelligent Software Engineering Technology Research Center Guangdong Foshan China

ISBN: (纸本)9798400712692

The current methods used for generating test papers are mainly traditional manual methods and recently emerged AI based methods. The former has problems such as low efficiency, high error rate, and incomplete coverage of test questions;the latter has disadvantages such as high complexity and poor practicality. A multiple objective-oriented automatic test paper generation method is proposed to address the above issues. This method adopts a cyclic iterative algorithm to enable the test paper to match multiple teaching objectives. These teaching objectives include comprehensive coverage of teaching knowledge points, support for various types of test questions, controllable difficulty levels of various question types, and low reuse rates of test questions. Based on this method, a multiple objective-oriented automatic test paper generation system is designed and implemented using MySQL database and Python language. The system operation results have verified that the test paper generation method is practical and has achieved good results, meeting the design requirements. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Automatic test pattern generation

来源：评论

学校读者我要写书评

暂无评论

Enhancing Epilepsy Diagnosis through EEG-Based machine Learning: A Comparative Analysis of Classification Algorithms 3

Enhancing Epilepsy Diagnosis through EEG-Based Machine Learn...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Santhiya, S. Keerthika, S. Sharmila, C. Sruthi, K. Naveen, B. Suganth, V. Kongu Engineering College Artificial Intelligence Department Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India Kongu Engineering College Department of Artificial Intelligence Erode India

ISBN: (纸本)9798350343274

Epilepsy is a non-contagious condition of the brain which affects individualsof all age groups, with approximately 60% of people worldwide suffering from it. The unpredictable nature of epileptic seizures necessitates accurate diagnosis for timely intervention. Electroencephalography (EEG) is a pivotal tool in epilepsy diagnosis, as it captures transient changes in brain electrical activity. This paper presents a comparative analysis of various machine learning algorithms applied to EEG data for classifying different seizure types. Our approach focuses on improving performance through data augmentation and preprocessing techniques. We explore the efficacy of algorithms such as Decision Trees, Gaussian Naive Bayes, Multi-Layer Perceptrons. K-Nearest Neighbors, Random Forest, and Support Vector Classifier. The results highlight the superior performance of Multi-Layer Perceptron, achieving an accuracy of 96%, surpassing other algorithms. This research underscores the possibility of machine learning in enhancing epilepsy diagnosis and seizure prediction, contributing to improved patient care. © 2023 IEEE.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Malaria Detection in Microscopic Blood Smears using Deep Learning: A CNN-based Framework 3

Malaria Detection in Microscopic Blood Smears using Deep Lea...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Bura Vijay Chauhan, Shanvi School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

Still a life-threatening illness brought on by parasites spread by mosquito bites, malaria presents a major danger to world health. Effective therapy and disease control depend on timely and precise identification of malaria-infected cells in blood smears since delays can cause major health problems or death. In this work, we constructed a convolutional neural network (CNN) model to identify malaria in microscopic images of blood smears. The training set consisted of 220 photos of parasite-infected cells and 196 uninfected cells from a smaller dataset;the test set included 91 images of infected cells and 43 uninfected cells. Trained to identify cells as either "Parasite" or "Uninfected" depending on visual patterns, the model Our approach proved potential in helping malaria diagnosis since it achieved 97% validation accuracy and 87% testing accuracy. This study fits objectives of world health, poverty elimination, lower inequality, and creative solutions to solve health disparities. Although there is always room for development, the suggested CNN model shows promise as a means of enhancing diagnosis capacity, especially in environments with low resources where professional pathologists might not be easily accessible. This paper emphasizes the need of using machine learning to handle worldwide health issues. © 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

Facial Expression recognition Based on Deep Convolutional Neural Network 3

Facial Expression Recognition Based on Deep Convolutional Ne...

引用

3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Mao, Rui Meng, Runxin Sun, Ruijing Shien-ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou510000 China College of Artificial Intelligence Southwest University Chongqing400715 China College of Engineering Ocean University of China Shandong Qingdao266100 China

ISBN: (纸本)9781510661301

Facial expression is a part of body language, which can often convey physiological and psychological reactions. Facial expression has always been a hot topic in the area of image analysis and pattern recognition, whose basic task is to model facial images to recognize human emotions at a certain time. Due to CNN's strong ability on feature expression, the researches of facial expression recognition based on deep learning have developed rapidly. In this paper, in order to improve the accuracy of facial expression recognition which is low because of the complex environments and structure of the existing expression recognition network, we propose a facial expression recognition method based on region of interest extraction, which uses ROI pooling to extract the local features of different face regions to construct a fixed size shared feature layer. In addition, we also add L2 regularization and learning rate decay mechanisms to find the optimal solution. Quantitative and qualitative experimental outcomes show that it is an effective way to achieve the facial expression task. © 2023 SPIE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of the Use of machine Learning Techniques to Optimize the Information Retrieval Process 3

A Comprehensive Analysis of the Use of Machine Learning Tech...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Hemelatha, S. Kalanandhini, G. Chaudhari, Prasad B. Sharma, Rajeev Kumar, Jambi Ratna Raja Mondal, Dipannita Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Dr. D.Y.Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India

ISBN: (纸本)9798350319125

This explores using a system to gain knowledge of strategies that allow you to optimize the facts retrieval manner. Facts retrieval is a complicated technique;it is becoming even more complicated as advancements in generation have furnished tools to make the gathering, employing, and evaluating information less complicated and faster. Research techniques, including artificial intelligence, neural networks, and machine learning, can speed up records retrieval and improve outcomes' accuracy. This look examines the different machine learning techniques to optimize the facts retrieval process and the advantages and downsides of each technique. Furthermore, this study looks at how gadgetstudying algorithms improve a person's enjoyment of retrieval systems and how their use can be integrated into cutting-edge practices. Subsequently, methods for improving the performance of machine learning algorithms are discussed in addition to capacity destiny directions inside the use of gadget studying to optimize statistics retrieval. © 2023 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Hybrid CNN-GRU Model for Predicting Dysarthric Speech using Deep Learning Approaches 3

Hybrid CNN-GRU Model for Predicting Dysarthric Speech using ...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chelliah, Suresh Kumar Arivazhagan, N. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331527549

This research study presents the novel method for dysarthria speech detection and classification. In order to create systems that are capable of accurately categorizing speech patterns that are impacted by dysarthria, it is vital to have speech detection and classification capabilities for dysarthria. The condition known as dysarthria, which is characterized by difficulties in articulation, phonation, resonance, and prosody, is a disorder that leads to a reduction in the intelligibility of speech. This method of classification necessitates the development of machine learning or deep learning models, more specifically a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), in order to accomplish the objective of automatically classifying dysarthric speech into categories such as severity or presence/absence of dysarthria. The primary purpose of dysarthria speech categorization is to bring about the development of a reliable instrument that can be used for identifying, categorizing, and managing dysarthric speech. As an added benefit, the classification of dysarthric speech allows for the adaptation of treatment methods and the acquisition of information about the underlying causes of the condition. The individuals who are affected by this speech difficulty are the target audience for this strategy, which ultimately aims to enhance the quality of life of those individual. The study addresses the challenge of classifying dysarthric speech caused by neurological impairments using a Hybrid CNN-GRU model, achieving accuracies of 70.3% (high-severity), 38% (severe-moderate), 34.5% (moderate), and 10.3% (mild dysarthria). © 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

Enhanced Activity recognition Through Joint Utilization of Decimal Descriptors and Temporal Binary Motions 16th

Enhanced Activity Recognition Through Joint Utilization of D...

引用

16th international conference on Computational Collective intelligence (ICCCI)

作者： Gnouma, Mariem Yahia, Samah Ejbali, Ridha Zaied, Mourad Res Team Intelligent Machines Gabes Tunisia Modelisat Anal & Control Syst Gabes Tunisia Univ Gabes Natl Engn Sch Gabes St Omar Ibn El Khattab Gabes 6029 Zrig Tunisia

ISBN: (纸本)9783031708183;9783031708190

Human Action recognition (HAR) has been a prominent area of research within machine learning over the last few decades. Its applications span domains such as visual surveillance, robotics, and pedestrian detection. Despite the numerous techniques introduced by computer vision researchers to address HAR, persistent challenges include dealing with redundant features and computational cost. This paper specifically addresses the challenge of silhouette-based human activity recognition. While previous research on silhouette-based HAR has predominantly focused on recognition from a singular perspective, the aspect of view invariance has often been overlooked. This paper presents a novel framework that aims to achieve view-invariant Human Action recognition. The proposed approach integrates a pre-processing stage based on the extraction of multiple 2D Differential History Binary Motions (DHBMs) from spatio-temporal frames capturing human motion. These multi-batch DHBMs are then used to capture and analyse human behaviour using the Decimal Descriptor pattern (DDP) approach. This strategy enhances the extraction of intricate details from image data, contributing to a more robust HAR methodology. The selected features are processed by the Sparse Stacked Auto-encoder (SSAE), a representative of deep learning methods, to provide effective detection of human activity. The subsequent classification is performed using Softmax. The experiments are conducted on publicly available datasets, namely IXMAS and KTH. The results of the study demonstrate the superior performance of our methodology compared to previous approaches, achieving higher levels of accuracy.

关键词： Human activities recognition Differential History of Binary Motion Decimal Descriptor pattern Deep Learning

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

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：