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

2,984 篇 会议
176 册 图书
25 篇 期刊文献

馆藏范围

3,184 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,060 篇 工学
- 1,841 篇 计算机科学与技术...
- 960 篇 软件工程
- 343 篇 电气工程
- 329 篇 信息与通信工程
- 227 篇 控制科学与工程
- 208 篇 生物工程
- 150 篇 生物医学工程（可授...
- 71 篇 光学工程
- 64 篇 机械工程
- 62 篇 化学工程与技术
- 53 篇 电子科学与技术（可...
- 50 篇 仪器科学与技术
- 46 篇 安全科学与工程
- 36 篇 交通运输工程
- 34 篇 动力工程及工程热...
653 篇 理学
- 353 篇 数学
- 245 篇 生物学
- 141 篇 物理学
- 135 篇 统计学（可授理学、...
- 95 篇 系统科学
- 65 篇 化学
419 篇 管理学
- 247 篇 图书情报与档案管...
- 192 篇 管理科学与工程(可...
- 83 篇 工商管理
263 篇 医学
- 241 篇 临床医学
- 123 篇 基础医学(可授医学...
- 74 篇 药学(可授医学、理...
- 41 篇 公共卫生与预防医...
54 篇 法学
- 50 篇 社会学
41 篇 经济学
- 39 篇 应用经济学
38 篇 教育学
23 篇 农学
6 篇 艺术学
4 篇 文学

主题

579 篇 machine learning
567 篇 data mining
323 篇 pattern recognit...
216 篇 deep learning
201 篇 feature extracti...
154 篇 support vector m...
145 篇 artificial intel...
129 篇 machine learning...
120 篇 accuracy
120 篇 training
91 篇 data mining and ...
90 篇 data models
72 篇 neural networks
71 篇 classification
69 篇 predictive model...
67 篇 face recognition
61 篇 learning systems
52 篇 computer imaging...
50 篇 convolutional ne...
48 篇 classification a...

机构

9 篇 school of comput...
9 篇 chinese academy ...
8 篇 zhejiang univers...
8 篇 chitkara univers...
7 篇 guangdong univer...
7 篇 school of inform...
6 篇 xi'an university...
6 篇 indian statistic...
6 篇 central south un...
6 篇 department of co...
6 篇 unsw sydney
6 篇 xi'an university...
6 篇 southern univers...
5 篇 indian institute...
5 篇 study group aust...
5 篇 peking universit...
4 篇 shanghai jiao to...
4 篇 centre for patte...
4 篇 chongqing univer...
4 篇 national laborat...

作者

11 篇 de-shuang huang
8 篇 petra perner
7 篇 krasotkina olga
7 篇 mottl vadim
7 篇 ying tan
6 篇 zheng-guang wu
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 phung dinh
6 篇 venkatesh svetha
6 篇 hongyi li
6 篇 jacek m. zurada
6 篇 chaojie li
6 篇 biao luo
6 篇 ilias maglogiann...
6 篇 lotfi a. zadeh
6 篇 leszek rutkowski
6 篇 lazaros iliadis
6 篇 long cheng
5 篇 vitoantonio bevi...

语言

2,758 篇 英文
413 篇 其他
42 篇 中文
1 篇 土耳其文

检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3185 条记录，以下是421-430 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：

A Review on Diabetes Prediction Using machine learning Techniques 5th

A Review on Diabetes Prediction Using Machine Learning Techn...

引用

5th international conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Tanwar, Alka Bhatia, Pradeep Kumar Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Hisar India

ISBN: (纸本)9789819716814

Diabetes is a chronic disease that affects a large number of people worldwide. If you have this illness even once, you will have it for the rest of your life. According to the World Health Organization (WHO), it is one of the top ten deadliest illnesses in the world. Having an excess of glucose in the blood can harm health and cause heart disease, kidney disease, nerve problems, and many other high-risk problems. However, diabetes can be a serious condition if not handled properly. Many of these issues can be prevented with effective management and regular checkups. machine learning and data mining techniques have emerged as powerful tools for diabetes prediction, leveraging the analysis of large-scale datasets to identify patterns and risk factors. the goal of this study is to analyze different existing research and find a more efficient model in this field. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Efficient Dense-Graph Convolutional Network with Inductive Prior Augmentations for Unsupervised Micro-Gesture recognition 26

Efficient Dense-Graph Convolutional Network with Inductive P...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Shah, Atif Chen, Haoyu Shi, Henglin Zhao, Guoying Univ Oulu Ctr Machine Vis & Signal Anal CMVS Oulu Finland

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Skeleton-based action/gesture recognition has already witnessed excellent progress on processing large-scale, laboratory-based datasets with pre-defined skeleton joint topology. However, it's still an unsolved task when it comes to real-world scenarios with practical limitations such as small-scaled dataset sizes, few-labeled samples, and various skeleton topologies. In this paper, we work on the recognition of micro-gestures, which are subtle body gestures collected in real-world scenarios. Specifically, we utilize contrastive learning to heritage the knowledge from known large-scale datasets for enhancing the learning on fewer samples of micro-gestures. To overcome the gap caused by various domain distributions and structure topologies between the datasets, we compute skeleton representations from augmented sequences via momentum-based efficient and scalable encoders as additional inductive priors. Importantly, we propose an effective dense-graph based unsupervised architecture that resorts to a queue-based dictionary to store positive and negative keys for better contrast with queries to learn substantially efficient and discriminant patterns in the feature space. Together with cross-dataset experimental results show that our model significantly improves the accuracies on two micro-gesture datasets, SMG by 7.4% and iMiGUE by 18.41% advocating its superiority.

关键词： Dictionaries Computer architecture Skeleton Robustness data models Topology pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Enhancing Predictive Modeling: Comparative Analysis of Decision Tree and Neural Network Approaches with Feature Selection 7

Enhancing Predictive Modeling: Comparative Analysis of Decis...

引用

7th international conference on New Media Studies, CONMEDIA 2023

作者： Adhli, Yuma Luqman Oetama, Raymond Sunardi Universitas Multimedia Nusantara Information System Department Tangerang Indonesia

ISBN: (纸本)9798350307504

this research is focused on enhancing the efficacy of direct marketing for bank deposit products through data mining analysis. the primary challenge lies in accurately predicting and targeting prospective customers. the study conducts a comparative analysis between two machine learning techniques: Decision Trees and Neural Networks. the research findings reveal that the original Neural Network achieves an impressive accuracy of 96.20%, substantiating its proficiency in effectually classifying data instances. the precision score of 0.6344 contributes to precise and accurate positive predictions, and the F-Score of 0.5086 reflects a well-balanced performance across various evaluation metrics. the forward approach adopted in this study maintains a high accuracy rate of 96.23% and markedly improves precision to 0.6733 while upholding a harmonized F-Score of 0.4720. Both methods underscore the effectiveness of the Neural Network in meticulous data classification and the facilitation of accurate positive predictions. the original and forward approaches possess distinct merits, heightening predictive accuracy and fostering pattern recognition within direct marketing strategies for bank deposit products. © 2023 IEEE.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Support Vector machine and Boosted Regression Tree as Effective data mining Tools for Predicting Students' Academic Performance 5

Support Vector Machine and Boosted Regression Tree as Effect...

引用

5th IEEE international conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Oparauwah, Nnaemeka M. Njoku, Donatus O. Onyemauche, Uchenna C. Okpalla, Chidimma L. Odii, Juliet N. Jibiri, Janefrances E. Okolie, Stanley A. Federal University of Technology Department of Computer Science Imo State Owerri Nigeria Federal University of Technology Department of Information Technology Imo State Owerri Nigeria

ISBN: (纸本)9798331542559

this paper reports the application of data mining techniques;Support Vector Regression (SVR) and Boosted Regression Tree (BRT) in prediction of students' academic performance in schools. the experimental approach in this study compared the performances of the two data mining techniques to ascertain the best performing model on an academic dataset. Publicly available dataset was used for this study and was retrieved from UCI machine learning Repository, Center for machine learning and Intelligent Systems. the data was collated from school reports and constructed questionnaires from two public secondary schools in Portugal regarding students' performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). data analysis was conducted in two schemes, first with the complete feature sets and then with the reduced feature set (containing 5 selected best performing features). Evaluation results showed that the BRT model outperformed the SVR model with 0.97% and 0.96% accuracy on the complete and reduced feature sets respectively. © 2024 IEEE.

关键词： Academic performance Boosted regression tree data mining Prediction Support vector machine

来源：评论

学校读者我要写书评

暂无评论

An Integrated Model for Time Series Price Prediction Based on MODWT and BP Neural Network 5

An Integrated Model for Time Series Price Prediction Based o...

引用

5th international conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Wang, Jin Sinounited Investment Group Corporation Limited Postdoctoral Programme Beijing China

ISBN: (纸本)9798350309034

Time series exist widely in the real world and are often used to study price forecasting. By modeling the time series, it is possible to predict the future values of specific research objectives. With the continuous improvement of people's income and financial literacy, they are increasingly emphasizing the mining and analysis of data in the financial market. therefore, the stock price time series is often used for quantitative investment modeling to identify investment opportunities. However, the time series in the financial market is relatively complex, and the relevant data has characteristics such as nonlinearity and non-stationary. these characteristics make it difficult to achieve research objectives using general statistical econometric methods. While machine learning methods can effectively process complex data. In fact, with the continuous development of machine learning technology, it has also been used for related research in the financial market. Considering this, this paper uses BP neural network to model the time series. Furthermore, considering that the original time series and the new subsequences obtained by decomposing the original time series based on specific techniques have different characteristics. By studying new subsequences and modeling them, better research results can be achieved. therefore, this paper further constructs an integrated model based on MODWT and BP neural network to forecast the time series price. It is found that the integrated model based on MODWT and BP neural network shows better prediction effect, with smaller root mean square error and mean absolute error, and the fluctuation range of error is smaller and more concentrated. © 2023 IEEE.

关键词： BP neural network machine learning MODWT price prediction time Series wavelet transform

来源：评论

学校读者我要写书评

暂无评论

IProceedings of the 4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

IProceedings of the 4th International Conference on Artifici...

引用

4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

ISBN: (纸本)9798400708831

the proceedings contain 200 papers. the topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machine learning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.

关键词：

来源：评论

学校读者我要写书评

暂无评论

learning a Latent Space with Triplet Network for Few-Shot Image Classification 26

Learning a Latent Space with Triplet Network for Few-Shot Im...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Wu, Jiaying Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Fukuoka 8080135 Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Few-shot image classification has attracted much attention due to its requirement of limited training data for target classes. Existing methods usually pretrain a network with images from the base set as feature extractor to obtain features of images from novel set. However, the pretrained feature extractor cannot extract accurate representation for categories have never seen, making images from novel set difficult to distinguish. To be specific, in the pretrained feature space, there exist a large number of overlapped areas between novel categories. To address this issue, it is crucial to acquire a space, where features from same class are gathering together and features from different classes are far away from each other. Since lots of experiments have proved that the triplet network is effective to achieve this goal, in this paper, we base our network on the Maximum a posteriori (MAP), learning a latent space with triplet network to project features from pretrained feature space into a more discriminative one. Experimental results on four few-shot benchmarks show that it significantly outperforms the baseline methods, improves around 1.09% similar to 13.09% than the best results in each dataset on both 1- and 5-shot tasks.

关键词： Training data Benchmark testing Feature extraction pattern recognition Task analysis Image classification

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Framework for Automated Teaching Quality Assessment of One-to-many Online Instruction Videos 26

A Multimodal Framework for Automated Teaching Quality Assess...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Pan, Yueran Wu, Jiaxin Ju, Ran Zhou, Ziang Gu, Jiayue Zeng, Songtian Yuan, Lynn Li, Ming Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Duke Kunshan Univ Ctr Teaching & Learning Kunshan Jiangsu Peoples R China Duke Kunshan Univ Data Sci Res Ctr Kunshan Jiangsu Peoples R China Fumi Hlth & Technol LLC Hunan Fujian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machine learning framework1 for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.

关键词： Teaching Quality Assessment Multimodal Behavior Coding Interptretable Feature Extraction Speaker Diarization Emotion recognition

来源：评论

学校读者我要写书评

暂无评论

Grid Investment Performance Portfolio Forecasting Model Based on PLS-VIP-GA-ELM 5th

Grid Investment Performance Portfolio Forecasting Model Base...

引用

Proceedings of the 5th international conference on Clean Energy and Electrical Systems - Proceedings of CEES 2023

作者： Li, Yizheng Peng, Dong Zhao, Lang Liu, Cong Xue, Yawei State Grid Economic and Technological Research Institute Co. Ltd. Beijing102209 China Economic and Technological Research Institute of State Grid Hunan Electric Power Co. Ltd. Changsha410004 China

ISBN: (纸本)9789819938872

this paper conducts data mining on the basic data of Hunan power grid investment performance indicators and 170 influencing factors from 2010 to 2020, compares and determines the ga-elm algorithm through a variety of machine learning prediction methods, processes the data of various influencing factors by using PLS-VIP and GA-ELM combined machine learning algorithm, and obtains the prediction results of power grid investment performance in 2020. By analyzing the final predicted values of all performance indicators, the overall prediction effect is good and the prediction accuracy is high. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Federated learning for Lung Sound Analysis 5th

Federated Learning for Lung Sound Analysis

引用

5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Farjana, Afia Makkar, Aaisha Univ South Dakota Dept Comp Sci Appl Res Lab 414 E Clark St Vermillion SD 57069 USA

ISBN: (纸本)9783031235986;9783031235993

Despite the general success of employing artificial intelligence (AI) to help radiologists perform computer-aided patient diagnosis, creating good models with tiny datasets at different sites is still tough. Medical image analysis is crucial for the quick and precise detection of lung disease and helps clinicians treat patients effectively while averting more fatalities. this is why real-time medical data management is becoming essential in the healthcare industry, especially for systems that monitor patients from a distance. To overcome this challenge, we propose a different approach by utilizing a relatively new learning framework. Individual sites may jointly train a global model using this approach, referred to as federated learning. Without explicitly sharing datasets, federated learning combines training results from various sites to produce a global model. this makes sure that patient confidentiality is upheld across all sites. Additionally, the additional supervision gained from partner sites' results enhances the global model's overall detection capabilities. this study's primary goal is to determine how the federated learning (FL) approachmay offer amachine learning average model that is robust, accurate, and unbiased in detecting lung disorders. For this aim, we analyze 325 Lung Sound audio recordings collected from https://***/and, transform this audio signal into Melspectrograms. Once the labeling and preprocessing steps were carried out, a Convolutional Neural Network (CNN)(FederatedNet) model was used to classify the respiratory sounds into healthy and unhealthy. We achieved the result of almost 88% validation accuracy. Furthermore, this paper discusses the application of FL and its overview. Lastly, we discuss the main challenges to federated learning adoption and potential future benefits.

关键词： Federated learning Lung sound machine 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319页 << < 39 40 41 42 43 44 45 46 47 48 > >>

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：