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

203 篇 会议
4 篇 期刊文献
3 册 图书

馆藏范围

210 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

178 篇 工学
- 164 篇 计算机科学与技术...
- 75 篇 软件工程
- 22 篇 信息与通信工程
- 11 篇 控制科学与工程
- 10 篇 生物工程
- 9 篇 电气工程
- 7 篇 生物医学工程（可授...
- 6 篇 机械工程
- 6 篇 电子科学与技术（可...
- 6 篇 化学工程与技术
- 3 篇 安全科学与工程
- 2 篇 材料科学与工程（可...
- 2 篇 动力工程及工程热...
- 2 篇 交通运输工程
68 篇 理学
- 51 篇 数学
- 18 篇 生物学
- 18 篇 统计学（可授理学、...
- 6 篇 化学
- 5 篇 物理学
- 5 篇 系统科学
39 篇 管理学
- 26 篇 图书情报与档案管...
- 16 篇 管理科学与工程(可...
- 10 篇 工商管理
7 篇 法学
- 7 篇 社会学
5 篇 医学
- 5 篇 基础医学(可授医学...
- 5 篇 临床医学
- 3 篇 药学(可授医学、理...
4 篇 经济学
- 4 篇 应用经济学
4 篇 教育学
- 4 篇 教育学
4 篇 农学
- 4 篇 作物学

主题

14 篇 machine learning
9 篇 data mining
8 篇 neural networks
6 篇 algorithms
6 篇 genetic algorith...
5 篇 deep learning
5 篇 classification
5 篇 artificial intel...
4 篇 imbalanced data
4 篇 gene expression
4 篇 machine learning...
4 篇 forecasting
3 篇 learning systems
3 篇 document cluster...
3 篇 image segmentati...
3 篇 signal processin...
3 篇 reputation
3 篇 information mana...
3 篇 learning algorit...
3 篇 optimization

机构

4 篇 ucl dept comp sc...
4 篇 wroclaw univ sci...
4 篇 univ novi sad fa...
3 篇 peking univ lmam...
3 篇 lab excellence r...
2 篇 monash universit...
2 篇 univ novi sad fa...
2 篇 school of mechan...
2 篇 school of electr...
2 篇 department of ma...
2 篇 ajou univ div el...
2 篇 univ birmingham ...
2 篇 santander uk 2 t...
2 篇 the school of bi...
2 篇 wroclaw univ tec...
2 篇 univ london roya...
2 篇 peking univ sch ...
2 篇 school of mechan...
2 篇 univ politecn va...
2 篇 naval research l...

作者

5 篇 mitic peter
5 篇 simic dragan
4 篇 ksieniewicz pawe...
4 篇 freeman richard ...
4 篇 yin hujun
3 篇 lee yuh-jye
3 篇 zhang byoung-tak
3 篇 wozniak michal
3 篇 simic svetlana
3 篇 ma jw
3 篇 taniar david
3 篇 huang de-shuang
2 篇 chan tony f.
2 篇 stojic gordan
2 篇 ng michael k.
2 篇 ahmad k
2 篇 bellotti t
2 篇 luo zy
2 篇 wu edmond h.
2 篇 smith james f.

语言

201 篇 英文
6 篇 其他
3 篇 中文

检索条件"任意字段=6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005"

共 210 条记录，以下是1-10 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：

Quantitative Estimation of Reputation Risk 25th

Quantitative Estimation of Reputation Risk

引用

25th international conference on intelligent data engineering and automated learning

作者： Mitic, Peter UCL Dept Comp Sci Gower St London WC1E 6BT England

ISBN: (纸本)9783031777301;9783031777318

We present a quantitative definition of reputation risk, formulated in terms of a reputation time series comprising daily sentiment measurements. Self Supported learning is used to quantify reputation risk by progressively refining an initial proposal for a Minimum Acceptable Sentiment, calculated from descriptive statistics of the reputation data. the derived values are validated using a "sense test" based on a Loess quantile. the results show that the Minimum Acceptable Sentiment value is given approximately by a two standard deviation lower tail of the observed data.

关键词： Reputation Sentiment Time Series Descriptive Statistics Value-at-Risk Normal Mixture Distribution CNN Loess Self Supervised learning SSL NLP

来源：评论

学校读者我要写书评

暂无评论

Pipeline for Semantic Segmentation of Large Railway Point Clouds 25th

Pipeline for Semantic Segmentation of Large Railway Point Cl...

引用

25th international conference on intelligent data engineering and automated learning

作者： Gabrielidis, Hugo Gatti, Filippo Vialle, Stephane Univ Paris Saclay LMPS Lab Mecan Paris Saclay UMR 9026 CNRS Cent SupelecENS Paris Saclay F-91190 Gif Sur Yvette France Univ Paris Saclay LISN Lab Interdisciplinaire Sci Numer CNRS F-91405 Orsay France Cent Supelec F-91192 Gif Sur Yvette France

ISBN: (纸本)9783031777301;9783031777318

this paper presents a novel deep-learning pipeline to segment large railway datasets with minimal manual annotation, notoriously time consuming. the pipeline adapts DINOv2 [11] for labeling point clouds, with tailored self-distillation pre-training and fine-tuning. the adopted transformer architecture successfully generalizes to multiple railway datasets, with a lightweight pipeline that outperforms manual labeling speed by a factor of 6, despite requiring a final segmentation check and correction. this groundbreaking achievement bridges the gap between the need for annotated point clouds in railway industry and the lack of publicly available annotated datasets.

关键词： 3D Point Cloud Deep learning Semantic Segmentation Large Scale data

来源：评论

学校读者我要写书评

暂无评论

Automatic PDF Document Classification with Machine learning 25th

Automatic PDF Document Classification with Machine Learning

引用

25th international conference on intelligent data engineering and automated learning

作者： Llacer Luna, Socrates Garigliotti, Dario Martinez Plumed, Fernando Ferri Ramirez, Cesar Univ Politecn Valencia Valencia Spain Univ Bergen Bergen Norway

ISBN: (纸本)9783031777301;9783031777318

Universitat Polit`ecnica de Val`encia (UPV) faces challenges in managing its Alfresco document repository, which contains 600,000 PDF files, of which only 100,000 are correctly categorised. Manual classification is laborious and error-prone, hindering information retrieval and advanced search capabilities. this project presents an automated pipeline that integrates optical character recognition (OCR) and machine learning to efficiently classify documents. Our approach distinguishes between scanned and digital documents, accurately extracts text and categorises it into 51 predefined categories using models such as BERT and RF. By improving document organisation and accessibility, this work optimises UPV's document management and paves the way for advanced search technologies and real-time classification systems.

关键词： Document Classification OCR Machine learning Alfresco Repository

来源：评论

学校读者我要写书评

暂无评论

Noise Tolerance and Robustness Ranking in Machine learning Models 25th

Noise Tolerance and Robustness Ranking in Machine Learning M...

引用

25th international conference on intelligent data engineering and automated learning

作者： Padro-Ferragut, Cristina Ramirez-Quintana, Maria Jose Martinez-Plumed, Fernando Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9783031777370;9783031777387

Machine learning models often excel in controlled environments but may struggle with noisy, incomplete, or shifted real-world data. Ensuring that these models maintain high performance despite these imperfections is crucial for practical applications, such as medical diagnosis or autonomous driving. this paper introduces a novel framework to systematically analyse the robustness of Machine learning models against noisy data. We propose two empirical methods: (1) Noise Tolerance Estimation, which calculates the noise level a model can withstand without significant degradation in performance, and (2) Robustness Ranking, which ranks Machine learning models by their robustness at specific noise levels. Utilizing Cohen's kappa statistic, we measure the consistency between a model's predictions on original and perturbed datasets. Our methods are demonstrated using various datasets and Machine learning techniques, identifying models that maintain reliability under noisy conditions.

关键词： Model robustness Noise in data Machine learning Cohen's kappa Performanceevaluation

来源：评论

学校读者我要写书评

暂无评论

A Machine learning Approach for automated CV data Analysis and Job Profile Identification 6

A Machine Learning Approach for Automated CV Data Analysis a...

引用

6th international conference on intelligent Computing in data Sciences, ICDS 2024

作者： Azzou, Kaoutar Ben Talei, Hanaa School of Science and Engineering Al Akhawayn University Ifrane Morocco

ISBN: (纸本)9798350351200

Human resources is an important department in each organization as it manages the life cycle of employees from recruitment, training to retirement or termination of contracts. the recruitment process starts with a job opening, followed by a selection of the best fit candidates from all applicants. Matching the best profile for a job position requires manually reviewing numerous CVs, which is time-consuming and can sometimes result in suboptimal candidate selection. this paper aims to reduce the workload of HR personnel by automating the preliminary stages of candidate screening, thus enabling a more streamlined recruitment workflow. this tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide for the most fitting job profile for each candidate. Our approach utilizes natural language processing (NLP) techniques to identify and extract key features from unstructured text in CVs, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms. © 2024 IEEE.

关键词： Personnel training

来源：评论

学校读者我要写书评

暂无评论

Overview of UAV-Based Overhead Power Line Defect Detection Based on Deep learning 6

Overview of UAV-Based Overhead Power Line Defect Detection B...

引用

6th IEEE international conference on Power, intelligent Computing and Systems, ICPICS 2024

作者： Tian, Xiaoxi Wang, Xin Liu, Zhenpeng Peng, Baoquan Liu, Yuwei School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Shenyang Jianzhu University University Government Departments Shenyang China

ISBN: (纸本)9798350374315

Unmanned Aerial Vehicles (UAV) equipped with various sensors can efficiently and safely perform automated detection of overhead power lines. Deep learning-based image recognition technology plays a crucial role in this field, as it can automatically identify and classify power Line defects, enhancing the accuracy and efficiency of the detection process. this paper first outlines the main procedures of UAV-based overhead power line defect detection. It then provides an overview of the key technologies in deep learning-based UAV detection, focusing on algorithm analysis, dataset construction, evaluation metrics, and model lightweight. Finally, it discusses the challenges and prospects of deep learning-based UAV detection for overhead power line defects. © 2024 IEEE.

关键词： Algorithm optimization data set construction Deep learning Image defect detection Model lightweight UAV detection

来源：评论

学校读者我要写书评

暂无评论

Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

this research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. the system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. these data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. the machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. the system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. the solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. this integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

来源：评论

学校读者我要写书评

暂无评论

the Combination between Deep learning and Ensemble Stacking for a Fast and Accurate Detection of Diabetic Retinopathy Using Fundus Images 6

The Combination between Deep Learning and Ensemble Stacking ...

引用

6th international conference on intelligent Computing in data Sciences, ICDS 2024

作者： Tibari, Anass Ridouani, Mohammed CED Engineering Sciences Hassan II University RITM Laboratory Casablanca Morocco

ISBN: (纸本)9798350351200

Diabetic retinopathy (DR) is the leading cause of human vision loss in the world. To slow the progression of the disease we need early detection and diagnosis. Hence, rapid detection and accurate classification of DR is crucial in patient care. the use of Deep learning (DL) alone turns out to be slow and expensive, also the use of a single Machine learning (ML) algorithm for classification leaves a significant margin of error. the detection of the disease also proves to be more difficult, especially in its early stages, this is due to the characteristics of DR less clear and visible on images. therefore, the combination of Deep learning and ensemble stacking was a relevant solution that we chose to enhance the ability to detect the disease in its early stages. In our research, we introduce an innovative method utilizing an automated model to identify and classify the existence and stage of severity of DR directly from fundus images. We utilized both of Machine learning algorithms and a Deep learning model;to achieve an accurate and fast detection we used the transfer learning and ensemble stacking as techniques. the proposed model uses a dataset published on Kaggle;our solution achieves an accuracy of 99.50%. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Research on Street View Information Extraction Method Based on Convolutional Neural Network 6

Research on Street View Information Extraction Method Based ...

引用

6th IEEE international conference on Power, intelligent Computing and Systems, ICPICS 2024

作者： Chen, Zijing Yang, Hao College of Urban Construction and Design Urban Vocational College of Chengdu Chengdu China Guangdong Engineering Polytechnic Faculty of Architectural Engineering Qingyuan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374315

As the largest public space in the city, street is an important part of residents' life, and the study of street is conducive to improving the overall image of the city. However, the traditional street scene research relies on low throughput survey and limited data sources, and its efficiency is relatively low, which can no longer meet the growing needs of street research. With the combination of artificial intelligence learning technology and landscape architecture, it is possible to identify and evaluate streets intelligently. the results show that the semantic segmentation model has good performance in street view image processing, accuracy rate and mIOu value. the research of this paper lays a foundation and provides a thought for the evaluation of the elements after semantic segmentation of a large number of complex street view images at the same time, and provides technical support for automated street landscape evaluation. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

intelligent Sorting Method for Smartphones Based on Agents 6

Intelligent Sorting Method for Smartphones Based on Agents

引用

6th international conference on Robotics and Computer Vision, ICRCV 2024

作者： Hu, Xuejun Li, Jie Zheng, Hangbin Zhang, Gaohua School of Mechanical Engineering Donghua University Shanghai China School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331527426

automated disassembly of discarded smartphones is a critical step in electronic waste processing, yet existing automated systems often lack adaptability when handling various types of discarded smartphones. To address the issue of poor adaptability in sorting processes, this study proposes an agent-based sorting method for discarded smartphones. this method leverages large language model(LLM) for natural language understanding and generation, enhancing user-friendliness. It integrates information from knowledge graphs and historical sorting records to build a comprehensive knowledge base. By combining intelligent agents that track historical data and utilize external knowledge bases, the proposed method improves adaptability for sorting multiple phone types. this allows the system to dynamically formulate sorting strategies based on the specific characteristics of discarded smartphones, significantly enhancing the adaptability of disassembly lines. Furthermore, the mobile phone classification module draws inspiration from facial recognition model architectures. A deep learning-based classification method for discarded smartphones is proposed, achieving highly adaptable classification functionality for various phone types. Experimental results demonstrate that the proposed classification algorithm maintains high accuracy in recognizing phone models not included in the training set. © 2024 IEEE.

关键词： Smartphones

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21页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：