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

3,080 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,244 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,110 篇 工学
- 1,926 篇 计算机科学与技术...
- 1,072 篇 软件工程
- 412 篇 信息与通信工程
- 286 篇 电气工程
- 265 篇 生物工程
- 226 篇 控制科学与工程
- 169 篇 生物医学工程（可授...
- 81 篇 化学工程与技术
- 76 篇 机械工程
- 68 篇 光学工程
- 68 篇 仪器科学与技术
- 64 篇 网络空间安全
- 63 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 57 篇 动力工程及工程热...
767 篇 理学
- 435 篇 数学
- 290 篇 生物学
- 149 篇 统计学（可授理学、...
- 137 篇 物理学
- 128 篇 系统科学
- 90 篇 化学
502 篇 管理学
- 304 篇 图书情报与档案管...
- 228 篇 管理科学与工程(可...
- 93 篇 工商管理
222 篇 医学
- 191 篇 临床医学
- 123 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 59 篇 公共卫生与预防医...
69 篇 法学
- 67 篇 社会学
52 篇 教育学
- 47 篇 教育学
35 篇 经济学
28 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

571 篇 data mining
546 篇 machine learning
241 篇 pattern recognit...
183 篇 deep learning
173 篇 feature extracti...
156 篇 artificial intel...
151 篇 support vector m...
114 篇 accuracy
113 篇 machine learning...
111 篇 training
93 篇 data models
82 篇 neural networks
77 篇 data mining and ...
65 篇 classification
62 篇 sentiment analys...
61 篇 classification a...
59 篇 big data
57 篇 predictive model...
50 篇 face recognition
48 篇 prediction algor...

机构

7 篇 maharishi school...
6 篇 guangdong univer...
5 篇 centre of interd...
5 篇 chitkara univers...
5 篇 uttaranchal univ...
5 篇 uttaranchal inst...
5 篇 institute of aut...
4 篇 shanghai jiao to...
4 篇 chitkara univers...
4 篇 school of inform...
4 篇 indian statistic...
4 篇 south china univ...
4 篇 galgotias univer...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 pune
4 篇 university of ca...
4 篇 school of comput...
4 篇 department of co...

作者

9 篇 de-shuang huang
7 篇 coenen frans
7 篇 venkatesh svetha
7 篇 petra perner
7 篇 tianrui li
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 jacek m. zurada
6 篇 krasotkina olga
6 篇 lotfi a. zadeh
6 篇 mottl vadim
6 篇 leszek rutkowski
5 篇 vitoantonio bevi...
5 篇 sankar k. pal
4 篇 waiyamai kitsana
4 篇 sozo inoue
4 篇 sergei o. kuznet...
4 篇 duoqian miao
4 篇 shengli xie
4 篇 hepu deng

语言

3,100 篇 英文
135 篇 其他
32 篇 中文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是3141-3150 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：

Comparative Analysis of machine learning Algorithms for Predicting Undergraduate Academic Performance

Comparative Analysis of Machine Learning Algorithms for Pred...

引用

Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Euis Sitinur Aisyah Danny Manongga Ade Iriani Sugeng Santoso Faculty of Science and Technology University of Raharja Tangerang Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

Accurate prediction of student academic performance is crucial for educational institutions and parents to implement proactive intervention strategies. This study employs machine learning algorithms to predict the graduation outcomes of undergraduate students, focusing on the effectiveness of various algorithms. We used the Random Forest algorithm and compared its performance with Decision Tree, Nave Bayes, Artificial Neural Network, and Support Vector machine, utilizing the Orange data mining tool. Our evaluation metrics included Area Under Curve (AUC), accuracy, F1-score, precision, and recall, applied to a dataset of 387 students from the class of 2017 who graduated on time in 2021 and 2022. The features considered were ID, study program, age, sex, GPA of the first semester and GPA of the second semester. The Random Forest algorithm achieved the highest accuracy at 95%. This research contributes to the early identification of students at risk of academic failure and highlights the most effective machine learning algorithm for predicting academic outcomes in higher education.

关键词： Support vector machines machine learning algorithms Accuracy Education Predictive models Prediction algorithms Robustness Socioeconomics Random forests Testing

来源：评论

学校读者我要写书评

暂无评论

Multisensor image fusion & mining: from neural systems to COTS software

Multisensor image fusion & mining: from neural systems to CO...

引用

international conference on Integration of Knowledge Intensive Multi-Agent Systems (KIMAS)

作者： A.M. Waxman D.A. Fay R.T. Ivey N. Bomberger Cognitive Fusion Technology Directorate ALPHATECH Inc. Burlington MA U.S.A.

We summarize our methods for the fusion of multisensor imagery based on concepts derived from neural models of visual processing and pattern learning and recognition. These methods have been applied to real-time fusion of night vision sensors in the field, airborne multispectral and hyperspectral imaging systems, and space-based multiplatform multimodality sensors. The methods enable color fused 3D visualization, as well as interactive exploitation and data mining in the form of human-guided machine learning and search for targets and cultural features. Over the last year we have developed a user-friendly system integrated into a COTS exploitation environment known as ErdAS Imagine. We demonstrate fusion and interactive mining of low-light Visible/SWIR/MWIR/LWIR night imagery, and IKONOS multispectral imagery. We also demonstrate how target learning and search can be enabled over extended operating conditions by allowing training over multiple scenes. This is illustrated for detecting small boats in coastal waters using fused Visible/MWIR/LWIR imagery.

关键词： Image fusion Software systems Image sensors Hyperspectral sensors Sensor fusion Sensor systems Multimodal sensors pattern recognition Image recognition Real time systems

来源：评论

学校读者我要写书评

暂无评论

Visual Landmarks recognition of Urban Structures using Convolutional Neural Network

Visual Landmarks Recognition of Urban Structures using Convo...

引用

international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Syed Kazim Raza Tabarka Rajab Syed Ammar Ahmed Muhammad Khurram NED-UET NED-UET Karachi Pakistan NCAI Smart City Lab NCAI NED-UET Karachi Pakistan Habib University Habib University Karachi Pakistan RCAI PI - Smart City Lab NCAI Karachi Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

The landmark recognition of any urban structure is a frustrating task to accomplish. This paper presents a deep learning based approach for recognition of the urban structures. Urban structures are basically the usual offices, houses, apartments etc. A generalized word for all these buildings can be urban structures. This classification problem was identified by our team while making our way into the visual positioning system, we saw that the visual landmark recognition of urban structures was an unexplored domain by using deep learning approaches. We also Identified that unlike the landmarks such as the Eiffel Tower, Statue Of Liberty etc, urban structures were difficult to learn by a neural network, the data requirements were quite distinct compared to the ones mentioned for all these famous landmarks around the globe. Most of all the landmark database was created, augmented, labelled and preprocessed in lab. The transfer learning based approaches were not that useful since the overall size of the model would always be so enormous and integration of a transfer learning based model would be meaningless for any deployed web app or even for a local mobile based app. By collecting and engineering our own data using a data collection app, we have proposed a neural architecture which can be configured to learn multiple number of urban landmarks while retaining its overall size under 60MBs. This was achieved by a Convolutional Neural Network based binary classification model trained on landmarks existing in our university. Later this was deployed on a web based application and further on it is being made into an app.

关键词： machine learning Visualization Buildings Convolutional neural networks data models Mathematics Training

来源：评论

学校读者我要写书评

暂无评论

User Authentication Through Pen Tablet data Using Imputation and Flatten Function

User Authentication Through Pen Tablet Data Using Imputation...

引用

IEEE international conference on Knowledge Innovation and Invention (ICKII)

作者： Md. Azim Hossain Akash Nasima Begum Sayma Rahman Jungpil Shin Md Amiruzzaman Md Rashedul Islam University of Asia Pacific Dhaka Bangladesh School of Computer Science and Engineering University of Aizu Wakamatsu Fukushima Japan College of Aeronautics and Engineering Kent State University USA

ISBN: (数字)9781728193335

ISBN: (纸本)9781728193342

Identifying a user or a person through handwriting-data is a popular technique. Many researches have been done in this area most of which are image or pattern-based analysis. The accuracy level depends on the quality of the image or pattern. In this paper, we proposed user authentication system using an individual's pen tablet handwriting data. The proposed system is concerned with the numerical value of a person handwriting data getting from the digital pen and tablet device. Hence, user authentication through pen tablet data ensures more accuracy by working with user's real time handwritten data. In the proposed system, 24 persons writing samples(1262 .csv files and 23 class)are used for extracting features to identify a user based on their handwriting attributes. Six completely separated features are extracted after data analysis and pre-processing. The extracted features are mainly concerned with the vital attributes of a user's handwriting. The extracted features are used for classification. With this concern, we utilized different classification algorithms such as Support Vector machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA) and Random Forest (RF) classifier. From the implementation, different algorithms show different accuracy level. The testing accuracy rate of SVM, LR, LDA and RF is 87%, 85%, 76% and 77% respectively. The experimental analysis shows that we got more robust and satisfactory results which ensure the practicality of our system.

关键词： Feature extraction Writing Authentication Support vector machines Handwriting recognition data mining Radio frequency

来源：评论

学校读者我要写书评

暂无评论

Expression of Concern for: Applying Deep learning Strategies for Optimizing Real-Time data Analysis in machine learning

Expression of Concern for: Applying Deep Learning Strategies...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Varun Mishra Sudhanshu Dev Akhilendra Pratap Singh Gautham Krishna Geetha B Rutuja S. Kadam Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

Deep mastering is becoming increasingly essential for fact analysis in machine learning. It has been used to remedy various applications, from natural language processing to photograph recognition, and has made goodsized achievements in many fields. In this paper, we discover a way to use deep getting-to-know for actual-time data evaluation optimization in gadget learning. Mainly, we present a deep getting-to-know framework for real-time statistics analysis optimization, including an expansion of deep gaining knowledge of fashions and techniques. We speak about the capability of actual-time statistics evaluation optimization with deep getting-to-know and numerous improvement techniques for making the optimization method extra green. Eventually, we offer an empirical evaluation of the proposed framework in machine studying packages, demonstrating the usefulness of presenting a deep learning-based totally technique to real-time statistics analysis optimization. We finish the paper with a dialogue of destiny directions for deep learning research for actual-time information evaluation optimization.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Application of Deep learning in Anomaly Detection and Prediction in Intelligent Operation and Maintenance of data Assets

Application of Deep Learning in Anomaly Detection and Predic...

引用

Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Taipeng Zhu Min Zhong Ye Liu Yingwei Liang Guangdong Power Grid Company Information Center Guangzhou Guangdong China Meizhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Meizhou Guangdong China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In the intelligent operation and maintenance of data assets, the threshold triggering of network anomaly detection is mainly based on a single parameter, which has a high false positive rate and low efficiency. In order to detect network anomalies efficiently and accurately, an anomaly prediction model based on deep learning is proposed. Firstly, intelligent O&M application scenarios are designed to meet the O&M leaders' requirements for O&M support. Then, a long and short term memory autoencoder model is constructed to reduce the dimensionality of the data and lock the grid where the suspicious anomalies appear to facilitate the extraction of abnormal information. K-means clustering method is used to detect local anomalies, and finally realize anomaly detection and prediction in intelligent operation and maintenance of data asset operations. The results show that the root-mean-square error of anomaly detection is 2.2% and the average absolute error is 24.3% when the data set is 5000. The time of anomaly detection is 350ms, which has advantages and high efficiency.

关键词： Deep learning Training machine learning algorithms Predictive models data models Maintenance Object recognition Anomaly detection Monitoring Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Advanced machine learning Frameworks for Multimodal Image and Speech Signal Processing

Advanced Machine Learning Frameworks for Multimodal Image an...

引用

Advancement in Technology (ICONAT), international conference for

作者： P Ramesh Naidu Saurabh Shandilya Srividya C N Shantanu Sudhir Gujar Supriya Devi Dankan Gowda V Department OF Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Advance Computing Poornima College of Engineering Jaipur Rajasthan India Dept. of ECE Faculty of Engineering Management & Technology BGSIT Adichunchanagiri University B G Nagara India Department of CyberSecurity Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY Department of Mathematics Alliance University Bangalore Karnataka India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework; the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework’s efficiency in predicting the multimodal input and demonstrate superiority over the prior methods.

关键词： Accuracy Surveillance Taxonomy Speech recognition machine learning Speech enhancement Signal processing Feature extraction data processing Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Balancing Security and Privacy-Preserving Counterterrorism

Balancing Security and Privacy-Preserving Counterterrorism

引用

Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： R.Tamilkodi L.Venkateswara Kiran K.Sai Soudamini P.Pratibha P.Ramu M.Praneeth Dept of CSE(AIML&CS) Godavari Global University Rajahmundry AP Dept of MCA Godavari Global University Rajahmundry AP Dept of CSE(AIML) Godavari Institute of Engineering & Technology (A) Rajahmundry AP

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Through speeches, films, and controversial websites, the internet has emerged as a key channel for the spread of *** groups use social media to indoctrinate people and spread their message, recruiting those who are most likely to join them. An effective web data mining method is suggested in this project to automatically identify and highlight such web properties for human assessment. Text, photos, videos, and audio are all mixed together on web pages, which are made with HTML (Hypertext Markup Language). We can extract textual information from these online pages and find trends by using data mining and web mining tools. Additionally, our system integrates an email filtering mechanism to detect and redirect unwanted messages that may be linked to terrorism directly to the recipient's spam folder users are protected from potentially harmful content. Our approach leverages advanced data mining algorithms to automatically flag suspicious emails and web content for further examination, enhancing digital security and countering the spread of terrorist propaganda online.

关键词： machine learning algorithms Social networking (online) Law enforcement Terrorism Unsolicited e-mail Semantics Web pages Web mining Market research Videos

来源：评论

学校读者我要写书评

暂无评论

From data Pre-processing to Hate Speech Detection: An Interdisciplinary Study on Women-targeted Online Abuse

From Data Pre-processing to Hate Speech Detection: An Interd...

引用

Intelligent Technologies (CONIT), international conference on

作者： Rutuja G. Rathod Yashoda Barve Jatinderkumar R. Saini Sourav Rathod Surayadatta College of Management Information & Research Technology Pune India Symbiosis Institute of Computer Studies and Research Symbiosis International (Deemed University) Pune India AI&ML Deparment Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

The term “misogyny” conveys hatred and disrespect for women. It is a type of sexism that stems from the idea that women are less valuable than men and can take many forms, such as verbal abuse, sexual harassment, and physical violence. One area where misogyny is particularly prevalent is in online spaces. Women who speak out and share their experiences online often face vicious attacks, including violence, rape, and death threats. This phenomenon, often referred to as “online harassment,” can have devastating effects on women’s mental health and well-being. Despite the growing recognition of this issue, there is still a lack of understanding about the nature and extent of hate speech toward women on social media. In this paper, the authors aim to address this gap by analyzing a dataset of tweets on Twitter containing hate speech toward women. The authors introduced the Measuring Hate Speech corpus, a dataset used while studying hate speech towards women. From this dataset, the authors extract some of them to build the model. And authors proposed machine learning algorithms like Logistic Regression with 0.92% accuracy. And to look further to fill the gap, the authors developed Support Vector machine (SVM) algorithm, Topic Modeling technique to extract the topics from the corpus. This study contributes to a better understanding of hate speech on social media platforms as Twitter informs strategies for resisting this pervasive problem.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predictive Model Based on machine learning for the Detection of Physically Mistreated Women in the Peruvian Scope 19

Predictive Model Based on Machine Learning for the Detection...

引用

Proceedings of the 3rd international conference on Advances in Artificial Intelligence

作者： Nemias Saboya A. Angel Sullon Omar L. Loaiza Peruvian Union University Lurigancho Lima Peru Peruvian Union University Carretera Salida a Arequipa Juliaca Peru

ISBN: (纸本)9781450372534

Violence against women in recent years in Peru showed alarming results, where 65.0 % of them were victims of some type of violence at some time in their lives. The study consisted of the elaboration of a predictive model that allows the recognition of the future physically mistreated woman. The data used was from a public survey of national scope. The methodology specified the data obtained; three supervised learning models Random Forest Classifier, Decision Tree Classifier, and Extra Trees Classifier were developed with the intention of buying the results and selecting that of the best performance. Regarding the results, Random Forest Classifier was the best model to obtain a precision of 51.0 % and a recall of 40.0 %, above those obtained by the other algorithms used. Subsequently, the best model passed to a calibration process where the average score was 0.7182, the precision was 0.76, and the recall was 0.31; concluding that the model classifies 76.0% of cases as physically mistreated women of which 31.0 % are effectively mistreated.

关键词： Random Forest Physical violence Predictive model machine learning Decision Tree

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325页 << < 311 312 313 314 315 316 317 318 319 320 > >>

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：