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

2,967 篇 会议
174 册 图书
25 篇 期刊文献

馆藏范围

3,165 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,043 篇 工学
- 1,824 篇 计算机科学与技术...
- 959 篇 软件工程
- 330 篇 电气工程
- 313 篇 信息与通信工程
- 214 篇 控制科学与工程
- 207 篇 生物工程
- 149 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
- 51 篇 电子科学与技术（可...
- 48 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
649 篇 理学
- 352 篇 数学
- 245 篇 生物学
- 140 篇 物理学
- 135 篇 统计学（可授理学、...
- 95 篇 系统科学
- 63 篇 化学
408 篇 管理学
- 244 篇 图书情报与档案管...
- 182 篇 管理科学与工程(可...
- 83 篇 工商管理
262 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...
- 40 篇 公共卫生与预防医...
46 篇 法学
- 42 篇 社会学
38 篇 教育学
- 32 篇 教育学
33 篇 经济学
- 31 篇 应用经济学
23 篇 农学
6 篇 艺术学
4 篇 文学

主题

573 篇 machine learning
564 篇 data mining
320 篇 pattern recognit...
209 篇 deep learning
201 篇 feature extracti...
154 篇 support vector m...
145 篇 artificial intel...
129 篇 machine learning...
120 篇 training
117 篇 accuracy
90 篇 data models
89 篇 data mining and ...
71 篇 neural networks
71 篇 classification
67 篇 predictive model...
66 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 study group aust...
5 篇 peking universit...
4 篇 centre for patte...
4 篇 chongqing univer...
4 篇 national laborat...
4 篇 sun yat sen univ...
4 篇 school of inform...

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

2,998 篇 英文
154 篇 其他
25 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 3166 条记录，以下是661-670 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：

Developing and Deploying Open-Access Web-Based Educational Tools for Teaching data Preprocessing and Classification in Bioinformatics 5th

Developing and Deploying Open-Access Web-Based Educational T...

引用

5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Ottappurakkal, Bhavya Kumar, Dhanush Vijayan, Asha Radhamani, Rakhi Achuthan, Krishnashree Diwakar, Shyam Amrita Mind Brain Center Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India Department of Electronics and Communication Engineering School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India

ISBN: (纸本)9789819747108

machine learning and data mining techniques have become indispensable in the field of bioinformatics with the advancements in big data analysis such as omics, biomedical imaging, and signal processing. Toward developing a pedagogical solution for data preprocessing, this paper explores design architecture of easily accessible, free-cost novel open-source web-based virtual laboratory to simplify the data preprocessing and visualization steps for big data analysis. A user-friendly interface combines computational capabilities with HTML and JavaScript as the underlying technology. this virtual laboratory architecture was designed to be compatible for access using mobile phones, tablets, and desktop computers having an active internet connection, making the education resource usage independent of end user dependencies such as specific operating systems, installed apps or other not so easily available applications. the software implementation was created to allow student learners to preprocess and classify data, incorporating techniques like variable type conversion, normalization, standardization. With this web-based virtual laboratory, large datasets can also be preprocessed and classified that may help as education resources for students, data analysts and researchers from different domains. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Personal computers

来源：评论

学校读者我要写书评

暂无评论

A Scene-Aware Prompt Template of Chinese Text data Augmentation Method Based on Large Language Model

A Scene-Aware Prompt Template of Chinese Text Data Augmentat...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Lingfei Kong Gang Wang Jingheng He Ying Wang Yunpeng Gao School of Data Science and Applications Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In Natural Language Processing (NLP) tasks, the target domain data is usually limited and the quality is poor. Text data augmentation is one of the effective methods to solve the problem of insufficient sample size in many NLP tasks. At present, on the one hand, the text data augmentation method is difficult to achieve the diversity and authenticity of the generated data text structure, on the other hand, it is also a challenge for the screening and elimination of low-quality samples in the generated data. therefore, this paper proposes a scene-aware template of chinese text data augmentation method (Scene-Aware Prompt Template, SAPT) based on large language model. the SAPT method first uses prompt learning to construct a Scene-Aware Prompt (SAP)template. Secondly, combined with the text understanding and generation ability of the large language model, the SAP is improved by analyzing the semantic and contextual features of the original data to generate scene prompt words. then, the original data is re-expressed into multiple data samples with different text structures but similar semantics by using the perfect SAP and large language model. Further, the quality of generated data samples is evaluated by fusing Rouge score and cosine similarity score, and low-quality data samples are filtered. the final generated data will be used for training the target domain task model. the experimental results on the Chinese news dataset thUCNews show that the overall performance of the classification model trained by the data generated by the SAPTDA method proposed in this paper reaches 89.89 %, which is 2.79 % higher than the baseline model, and has superior performance compared with the existing text data augmentation methods.

关键词： Training Large language models Semantics machine learning data augmentation Natural language processing data models pattern recognition Context modeling

来源：评论

学校读者我要写书评

暂无评论

Research on Woodcut Engraving Image Classification Method Based on data Augmentation and Improved MobileViT

Research on Woodcut Engraving Image Classification Method Ba...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xin Yang Qijun Zhao Yukai Xian Dingguo Gao Xianmu Cairang School of Information Science and Technology Tibetan Information Technology Innovative Talent Cultivation Demonstration Base Tibet University Lhasa China College of Computer Science Sichuan University ChengDu China School of Information Science and Technology Tibet University Lhasa China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Tibetan woodcut engraving is a living fossil of Tibetan culture and embodies the wisdom of the Tibetan people. the study of Tibetan woodcut engravings not only helps protect and inherit Tibetan cultural heritage but also promotes the development of related industries and economic prosperity. In this paper, we address the problem of low classification accuracy in traditional algorithms when dealing with the complex textures of Tibetan woodcut engravings. We propose a classification algorithm based on data Augmentation and an improved MobileViT model. Firstly, the long-tail distribution problem of woodcut data is alleviated by data Augmentation techniques, and the classification effect is improved by expanding the dataset with various data Augmentation methods. Secondly, by introducing a coordinate attention mechanism after the MobileViT Block, the model can capture subtle line changes and complex texture details, thereby improving its ability to extract key features from the woodblocks. Given the lack of publicly available datasets, this paper constructs a new dataset of woodcut engraving images and tests the proposed algorithm on it. the experimental results show that compared to the initial MobileViT model, the Fl score of the improved model is increased by 14.74%, enabling quick and accurate classification of woodcut engraving images.

关键词： Deep learning Industries Accuracy Attention mechanisms Scalability data augmentation Feature extraction data models Classification algorithms pattern recognition

来源：评论

学校读者我要写书评

暂无评论

5th international conference on Advanced Informatics for Computing Research, ICAICR 2021

5th International Conference on Advanced Informatics for Com...

引用

5th international conference on Advanced Informatics for Computing Research, ICAICR 2021

ISBN: (纸本)9783031094682

the proceedings contain 23 papers. the special focus in this conference is on Advanced Informatics for Computing Research. the topics include: Efficient Entity Resolution for Bibliographic data Using MapReduce;securing of Identification System data Transmission Using Deep Autoencoders and data Hiding;a Hybrid Filter/Wrapper machine learning Model for Classification Cancer dataset;edge-Assisted IoT Architecture: A Case of Air Pollution Monitoring Frameworks;optimized Analysis Using Feature Selection Techniques for Drug Discovery Detection;crop Identification and Disease Detection by Using Convolutional Neural Networks;an Efficient Novel Approach for Detection of Handwritten Numericals Using machine learning Paradigms;multiclass Classification in machine learning Algorithms for Disease Prediction;CREA-Components Reusability Evaluation and Assessment: An Algorithmic Perspective;capsule Network Based Speech Emotion recognition for Efficient Capturing of Spatial Features;a Survey and Analysis on the Distraction patterns of the Students in E-learning and M-learning Environments;relationships Among Human Genome Graph Elements Using Clusters Detection;transfer learning Architecture Approach for Smart Transportation System;preface;a Blockchain-Based Solution for Electronic Medical Records System in Healthcare;analysing Sentiments of People Over Vaccines in Reddit Posts Using Natural Language Processing;segmentation of Tumor Region from Mammogram Images Using Deep learning Approach;review on IoT Based Real-Time Healthcare Monitoring System;application of IoT in 5G Wireless Communication: A Detailed Review;enhanced Service Point Approach for Microservices Based Applications Using machine learning Techniques;Worldwide Vaccination Report for COVID-19 Analysis and Visualization Using Deep learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Generative Design of Pocket Park Layout Based on machine learning

Research on Generative Design of Pocket Park Layout Based on...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Weishi Zhou Lu Gao Qian Shi Wanqin Zhang Yang Yao Qiao Ai Chongqing Jianzhu College Chongqing China Chongqing Jiaotong University Chongqing China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the rapid pace of urbanization, the demand for urban public green space is becoming increasingly evident, and pocket parks are emerging as an ideal solution in high-density urban environments due to their notable advantages, including small footprint, diverse forms, and flexible functions. However, traditional pocket park design processes face challenges such as short design cycles and time-consuming multi-program conceptualization. In response, this paper proposes an automatic generation method for pocket park layout plans based on generative adversarial networks (GANs). By constructing a training dataset consisting of 119 pairs of finely labeled images, a model capable of generating pocket park layout plans is successfully trained. the performance of the model is comprehensively and meticulously evaluated from both quantitative and qualitative perspectives to validate the feasibility and efficiency of the method. Experimental results show that the proposed method rapidly generates a large number of pocket park layouts with high design quality and innovation, significantly improving design efficiency.

关键词： Training Technological innovation Layout Urban areas machine learning Surfaces Generative adversarial networks Real-time systems pattern recognition Optimization

来源：评论

学校读者我要写书评

暂无评论

Research on the Application of Deep learning-Based Image recognition Algorithm in Signal Processing

Research on the Application of Deep Learning-Based Image Rec...

引用

Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Jing Lv Hunan Chemical Vocational and Technical College Zhuzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. the generator can produce real images or remove noise by learning the signal image. then, the image is compared with the real image. In the pre-processing phase, the standard, denotation and enhancement of the signal image are carried out to guarantee the data quality and the validity of the characteristic. Experiments show that the precision of GAN-based image recognition is up to 96.3%, which is more than 15% than traditional methods (e.g., SVM). In the signal denoising task, the model significantly improves the signal-to-noise ratio (SNR) by 20% and effectively removes image noise. In addition, GAN also performs extremely well in pattern recognition tasks, with a correct recognition rate of 94.7%. Compared with traditional deep learning algorithms, the GAN model shows stronger robustness when processing signals with more noise or incomplete data.

关键词： Deep learning Training Image recognition Signal processing algorithms Generative adversarial networks Generators Robustness pattern recognition Image denoising Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Research on the Construction of Enterprise Financial Risk Early Warning Model Based on data mining 24

Research on the Construction of Enterprise Financial Risk Ea...

引用

2024 5th international conference on Big data Economy and Information Management, BDEIM 2024

作者： Du, Shihui Weifang Engineering Vocational College Shandong Weifang China

ISBN: (纸本)9798400711862

In order to effectively identify and respond to enterprise financial risks, this study adopts data mining technology to construct an enterprise financial risk early warning model. through data preprocessing and feature selection, representative financial indicators are extracted, and the risk warning model is built by combining machine learning algorithms. Based on historical financial data and industry cases, the study selects indicators such as accuracy, recall and F1 value to evaluate the model performance, and proposes optimization strategies to enhance the model’s early warning effect. the results show that the optimized model can effectively improve the accuracy and stability of financial risk early warning, and provide decision support for enterprise managers. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： data mining Enterprise management Financial risk Model optimization Risk warning model

来源：评论

学校读者我要写书评

暂无评论

A Method for Chinese Sarcasm Detection Based on Enhanced Cross-Entropy and Regularization

A Method for Chinese Sarcasm Detection Based on Enhanced Cro...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Hang Ren Jing Zhang Yongqiang A Nuo Qun Shengqiao Ni Jin Yang Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region School of Information Science and Technology Tibet University Lhasa China School of Information Science and Technology Tibet University Lhasa China School of Mechanical Engineering Sichuan University Chengdu China School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In the context of social media, the automatic identification of sarcastic discourse presents a complex challenge within the field of natural language processing. Due to the significant disparity between the literal and implied meanings of sarcastic statements, traditional text analysis algorithms struggle to accurately detect them. this paper introduces a method for detecting Chinese sarcastic comments on social media, which integrates an improved cross-entropy loss function with regularization techniques. the method employs a dynamic adjustment factor to optimize weighted cross-entropy loss function, calibrating the model's penalty intensity for high-confidence predictions, and enhances the consistency of the model's output through batch-averaged Kullback-Leibler divergence. Experiments were conducted using the Chinese-RoBERTa model as a baseline, validated on full data and small sample sizes (50%, 10%). the results demonstrate that, compared to the baseline model, the proposed method achieved accuracy improvements of 3.7%, 2.5%, and 2.3% across different data volumes, confirming its effectiveness and applicability in the detection of Chinese sarcastic discourse.

关键词： Visualization Text analysis Social networking (online) machine learning Predictive models Prediction algorithms data models Natural language processing pattern recognition Optimization

来源：评论

学校读者我要写书评

暂无评论

Efficient Object Detection with Deformable Convolution for Optical Remote Sensing Imagery 5

Efficient Object Detection with Deformable Convolution for O...

引用

5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Kang, Haonan Liu, Yundong National University of Singapore Department of Statistics and Data Science Singapore Zhongyuan University of Technology School of Electronic and Information Engineering China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Deep learning-based object detection in remote sensing images is an important yet challenging task because of the complex background and large variations in size of the targets. Currently, many detectors have made significant progress in improving detection accuracy, but they have not shown good performance in terms of detection speed and model size. To address these issues, a lightweight and efficient object detector in remote sensing images is proposed. Specifically, we utilize MobileNetv3 with Shuffle Attention as the feature extraction backbone network to reduce the parameter of the model. Meanwhile, deformable convolution is introduced to adapt to the deformation of the target and obtain stronger geometric feature expression ability. Extensive experiments on two remote sensing public datasets (RSOD and DIOR) show good performance for the efficiency of our detector. In particular, our strategy achieves 71.3 mAP on the DIOR dataset with 3.05 parameters and a test speed of 12.1 ms, and it also has good performance on the RSOD dataset. © 2022 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

machine learning Application for Diabetes Prediction Using Ensemble Classification

Machine Learning Application for Diabetes Prediction Using E...

引用

international conference on Inventive Research in Computing Applications (ICIRCA)

作者： Tanu Dhiman Shefali Goyal Nongmeikapam thoiba Singh Rohit Kumar Singhal Department of Computer Science Engineering Chandigarh University Punjab India

data mining is a useful method for extracting meaningful information from massive volumes of records. Statistics and machine learning may be utilised to evaluate large-scale patterns in massive databases. Numerous researchers use image processing methods to predict disease, utilising data mining and machine learning approaches. Disease data can be collected from various sources. A bioinformatics technology will be used for keeping, retrieving, and interpreting information with biological molecules. A forecast can be explained as a description of upcoming events based on current conditions. the aim of this study is to create machine learning and data mining algorithms for diabetes prediction. there are various steps in predicting diabetes. In this study, a vote-based classifier is developed to predict diabetes. the proposed algorithm, is used to optimize the diabetes prediction performance up to 2%.

关键词：

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317页 << < 63 64 65 66 67 68 69 70 71 72 > >>

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：