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

101 篇 会议
1 册 图书

馆藏范围

102 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

65 篇 工学
- 62 篇 计算机科学与技术...
- 18 篇 信息与通信工程
- 17 篇 电气工程
- 16 篇 控制科学与工程
- 16 篇 交通运输工程
- 15 篇 网络空间安全
- 13 篇 仪器科学与技术
- 13 篇 动力工程及工程热...
- 13 篇 安全科学与工程
- 6 篇 软件工程
- 3 篇 生物工程
- 2 篇 电子科学与技术（可...
- 2 篇 生物医学工程（可授...
- 1 篇 机械工程
- 1 篇 光学工程
- 1 篇 材料科学与工程（可...
20 篇 管理学
- 18 篇 管理科学与工程(可...
- 15 篇 图书情报与档案管...
- 2 篇 工商管理
18 篇 理学
- 13 篇 化学
- 4 篇 数学
- 3 篇 生物学
- 2 篇 统计学（可授理学、...
- 1 篇 系统科学
5 篇 教育学
- 5 篇 教育学
1 篇 法学
- 1 篇 社会学

主题

22 篇 deep learning
14 篇 machine learning
12 篇 data models
10 篇 training data
8 篇 smart cities
7 篇 data mining
7 篇 feature extracti...
6 篇 training
5 篇 object detection
5 篇 roads
5 篇 neural networks
5 篇 predictive model...
5 篇 federated learni...
5 篇 data augmentatio...
4 篇 task analysis
4 篇 image segmentati...
4 篇 computational mo...
4 篇 semantics
3 篇 natural language...
3 篇 energy consumpti...

机构

2 篇 graduate school ...
2 篇 univ monastir la...
2 篇 univ minho algor...
2 篇 univ moncton dep...
2 篇 ccg zgdv inst ep...
1 篇 key laboratory o...
1 篇 univ quebec dept...
1 篇 national institu...
1 篇 univ waterloo wa...
1 篇 graduate school ...
1 篇 college of infor...
1 篇 college of big d...
1 篇 faculty of trans...
1 篇 graduate school ...
1 篇 handong global u...
1 篇 univ monastir hi...
1 篇 school of inform...
1 篇 norwegian univer...
1 篇 school of big da...
1 篇 univ oxford dept...

作者

3 篇 novais paulo
3 篇 hammer barbara
2 篇 ben abdallah asm...
2 篇 cortez paulo
2 篇 hiroharu kawanak...
2 篇 tadanori fukami
2 篇 chen nan
2 篇 horvath tomas
2 篇 launet laetitia
2 篇 akhloufi moulay ...
2 篇 yang ruiqi
2 篇 colomer adrian
2 篇 pilastri andre
2 篇 bedoui mohamed h...
2 篇 zhang yue
2 篇 naranjo valery
2 篇 matos luis migue...
2 篇 dai qinling
2 篇 faria hugo
1 篇 karras aristeidi...

语言

101 篇 英文
1 篇 其他
1 篇 中文

检索条件"任意字段=23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022"

共 102 条记录，以下是11-20 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：

On Autonomous Drone Navigation Using Deep learning and an intelligent Rainbow DQN Agent 23rd

On Autonomous Drone Navigation Using Deep Learning and an In...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Karatzas, Andreas Karras, Aristeidis Karras, Christos Giotopoulos, Konstantinos C. Oikonomou, Konstantinos Sioutas, Spyros Univ Patras Comp Engn & Informat Dept Patras Greece Univ Patras Dept Management Sci & Technol Patras Greece Ionian Univ Dept Informat Corfu Greece

ISBN: (纸本)9783031217524;9783031217531

Drones are intelligent devices that offer solutions for a continuously expanding variety of applications. Therefore, there would be a significant improvement if these systems could explore space automatically and without human-supervision. This work integrates cutting-edge artificial intelligence techniques that allow drones to travel independently. Following an overview of reinforcement learning methods built for discrete action space settings, a multilayer Perceptron model is constructed for feature extraction along with Hybrid neural networks. The agent employed in the experiments is a Rainbow DQN agent trained on the AirSim simulator. The experimental results are encouraging as the agent was tested for 16 missions and the accuracy was higher than 93%. In particular, the success for action selection was 97% and 93 for mission success. Finally, future work related to the navigation of autonomous drones is discussed including current concepts and methods of integration with more sophisticated algorithmic approaches.

关键词： Drones Autonomous drone navigation Deep learning Reinforcement learning intelligent agents Swarm intelligence UAV

来源：评论

学校读者我要写书评

暂无评论

An intelligent Decision Support System for Road Freight Transport 23rd

An Intelligent Decision Support System for Road Freight Tran...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Carvalho, Hugo Silva Pilastri, Andre Matta, Arthur Matos, Luis Miguel Novais, Rui Cortez, Paulo CCG ZGDV Inst EPMQ Guimaraes Portugal ABMN Business Solut Azurem Guimaraes Portugal Univ Minho Dept Informat Syst ALGORITMI Res Ctr LASI Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

This paper presents an intelligent Decision Support System (IDSS) to optimize transport and logistics activities in a set of Portuguese companies currently operating in the freight transport sector. This IDSS comprises three main modules that can be used individually or chained together, dedicated to: a geographic clustering detection of transport services;a transport driver suggestion;and a route and truck-load optimization. The IDSS was entirely designed and developed to support real-time data and it consists of an end-to-end solution (E2ES), given that it covers all the main transport and logistics processes since the registration in the database to the optimized transport plan. The entire set of functionalities inserted in the IDSS was designed and validated by freight transport sector experts from the different companies that will use the proposed system.

关键词： intelligent decision support system Transport and logistics Geospatial clustering Resource optimization Route optimization TAM 3

来源：评论

学校读者我要写书评

暂无评论

Benchmarking data Augmentation Techniques for Tabular data 23rd

Benchmarking Data Augmentation Techniques for Tabular Data

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Machado, Pedro Fernandes, Bruno Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal

ISBN: (纸本)9783031217524;9783031217531

Imbalanced learning and small-sized datasets are usual in machine learning problems, even with the increased data availability provided by recent developments. The performance of learning algorithms in the presence of unbalanced data and significant class distribution skews is known as the "imbalanced learning problem". The models' performance on such problems can drastically decrease for certain classes with an uneven distribution because the models do not learn the distributive features of the data and present accuracy too favorable for a specific set of classes of data. As an example, this can have negative consequences when talking about cancer detection since the model may poorly identify unhealthy patients. Hence, data augmentation techniques are usually conceived to evaluate how models would behave in non-data-scarce environments, generating synthetic data that mimics the characteristics of real data. By applying those techniques, the amount of available data can be increased, balancing the class distributions. However, there are no standardized data augmentation processes that can be applied to every domain of tabular data. Therefore, this study aims to identify which characteristics of a dataset provide a better performance when synthesizing samples by a data augmentation technique in a tabular data environment.

关键词： data augmentation Imbalanced data Machine learning

来源：评论

学校读者我要写书评

暂无评论

Solving Multi-class Imbalance Problems Using Improved Tabular GANs 23rd

Solving Multi-class Imbalance Problems Using Improved Tabula...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Farou, Zakarya Kopeikina, Liudmila Horvath, Tomas Eotvos Lorand Univ Inst Ind Acad Innovat Dept Data Sci & Engn Pazmany Peter Setany 1-C H-1117 Budapest Hungary Pavol Jozef Safarik Univ Inst Comp Sci Jesenna 6 Kosice 04001 Slovakia

ISBN: (纸本)9783031217524;9783031217531

Multi-class imbalance problems are non-standard derivative data science problems. These problems are associated with the skewness in the data underlying distribution, which, in turn, raises numerous issues for conventional machine learning techniques. To address the lack of data in imbalance problems, we can either collect new data or oversample the underrepresented classes by synthesizing artificial data from original instances. This paper focuses on the latter and introduces two novel tabular GAN variants to handle multi-class imbalance problems. Empirical results on three datasets from the UCI repository demonstrated that the suggested approaches that use our proposed filtering algorithm based on neighboring rules improved the ability of the decision tree classification model to recognize underrepresented class instances, decreased the bias toward the majority class, and enhanced its generalization ability.

关键词： Imbalanced learning Generative adversarial networks data augmentation data filtering Multi-class classification

来源：评论

学校读者我要写书评

暂无评论

intelligent data engineering and automated learning – ideal 2022 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Hujun Yin David Camacho Peter Tino

ISBN: (数字)9783031217531

ISBN: (纸本)9783031217524

This book constitutes the refereed proceedings of the 23rd international conference on intelligent data engineering and automated learning, ideal 2022, which took place in Manchester, UK, during November 24-26, 2022.;The 52 full papers included in this book were carefully reviewed and selected from 79 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.

关键词： data Mining and Knowledge Discovery database Management Information Systems Applications (incl. Internet) Computer Communication Networks Artificial Intelligence

来源：评论

学校读者我要写书评

暂无评论

Detection of False Information in Spanish Using Machine learning Techniques 23rd

Detection of False Information in Spanish Using Machine Lear...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Tretiakov, Arsenii Martin, Alejandro Camacho, David Univ Politecn Madrid Dept Comp Syst Engn Calle Alan Turing Madrid 28031 Spain

ISBN: (纸本)9783031217524;9783031217531

This research focuses on the detection of false claims in Spanish through the use of machine learning techniques. Most of the current work related to automated, or semi-automated, fake news detections are carried out for the English language, however, there is still a large room for improvement in other languages such as Spanish. The detection of fake news content and its dissemination (spread) in online platforms is an open and hard problem, this work is focused on the detection of false and misleading information spreading during the election campaign in the Spanish Parliament and Catalonia crisis in 2019, migration crisis, COVID-19 pandemic, and hate speech against minorities. We propose the use of a machine learning model adapted for dealing with human language understanding tasks, called BERT, which has been trained and experimentally tested. We have collected a corpus of different types of false information and claims such as articles, posts on Facebook, WhatsApp's messages, tweets, and others. The results evidence how usage of machine learning techniques can help in the identification of false statements with more than 88% accuracy, and in collecting samples of false information. The experiments, with a comparison between different machine learning methods, have also been carried out using previous datasets, providing a comparison between different approaches.

关键词： Fake news False information Mass media Social media Machine learning Media literacy

来源：评论

学校读者我要写书评

暂无评论

Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual learning 23rd

Gradient Regularization with Multivariate Distribution of Pr...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Kim, Tae-Heon Moon, Hyung-Jun Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea Yonsei Univ Dept Artificial Intelligence Seoul 03722 South Korea

ISBN: (纸本)9783031217524;9783031217531

Continual learning is a novel learning setup for an environment where data are introduced sequentially, and a model continually learns new tasks. However, the model forgets the learned knowledge as it learns new classes. There is an approach that keeps a few previous data, but this causes other problems such as overfitting and class imbalance. In this paper, we propose a method that retrains a network with generated representations from an estimated multivariate Gaussian distribution. The representations are the vectors coming from CNN that is trained using a gradient regularization to prevent a distribution shift, allowing the stored means and covariances to create realistic representations. The generated vectors contain every class seen so far, which helps preventing the forgetting. Our 6-fold cross-validation experiment shows that the proposed method outperforms the existing continual learning methods by 1.14%p and 4.60%p in CIFAR10 and CIFAR100, respectively. Moreover, we visualize the generated vectors using t-SNE to confirm the validity of multivariate Gaussian mixture to estimate the distribution of the data representations.

关键词： Continual learning Memory replay Sample generation Multivariate gaussian distribution Expectation-maximization

来源：评论

学校读者我要写书评

暂无评论

EduBot: A Proof-of-Concept for a High School Motivational Agent 23rd

EduBot: A Proof-of-Concept for a High School Motivational Ag...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Faria, Hugo Barbosa, Maria Araujo Veloso, Bruno Marcondes, Francisco S. Lima, Celso Duraes, Dalila Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal High Sch Caldas Taipas Caldas Das Taipas Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

Motivation appears to plays a role in student dropout. This paper uses a dataset with high school information (756 entries) for creating a model that relates year failures with the other features on the dataset. The resulting model, based on XGBoost, shows that the top-ranked features are related with motivation issues corroborating with the initial observation. In this sense, a digital assistant embedded with a motivation module may aid on improving motivation and on avoiding dropout. In other words, if the predictor detects year failure possibility it start to act on motivating the student. Considering this paper being direct to underage students, this paper stops on the proof-of-concept level considering the actual dialogues and live tests are expected to performed with the support of educational psychologists.

关键词： Educational data mining Motivational assistant Predict at-risk students Student's performance

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network Approach for Multiple Sclerosis Lesion Segmentation 23rd

Convolutional Neural Network Approach for Multiple Sclerosis...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Messaoud, Nada Haj Mansour, Asma Ayari, Rim Ben Abdallah, Asma Aissi, Mouna Frih, Mahbouba Bedoui, Mohamed Hedi Univ Monastir Lab Technol & Med Imaging Fac Med Monastir Tunisia Fatouma Bouguiba Hosp Dept Neurol Monastir Tunisia Univ Monastir Fac Sci Monastir FSM Monastir Tunisia

ISBN: (纸本)9783031217524;9783031217531

Nowadays Deep learning (DL) based automatic segmentation has outperformed traditionalmethods. In the present paper, we are interested in automatic MS lesion segmentation of 2D images based on DL techniques. The main challenge consists in proposing a new model that takes advantage of referenced CNN models: U-Net, ResNet, and DenseNet with a reduced number of parameters and a shorter execution time. To evaluate the proposed approach named "Concat-U-Net", we compared its performance to those of three implemented models, namely U-Net, U-ResNet, and Dense-U-Net. Furthermore, we employed just one modality (FLAIR) from the public ISBI dataset to segment MS lesions accurately. The best Dice value obtained was 0.73, which outperformed those reported in the literature. Our approach reduced the elapsed execution time from 48 s to 7 s. By reducing the number of parameters, an 85.42% time gain was achieved.

关键词： Deep learning Multiple sclerosis segmentation MRI data augmentation

来源：评论

学校读者我要写书评

暂无评论

Association Rules Mining for Reducing Items from Emotion Regulation Questionnaires 23rd

Association Rules Mining for Reducing Items from Emotion Reg...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Khadimallah, Rihab Kallel, Ilhem Drira, Fadoua REGIM Lab Res Grp Intelligent Machines Sfax Tunisia FSEGS Fac Econ & Management Sfax Sfax Tunisia ISIMS Higher Inst Comp Sci & Multimedia Sfax Sfax Tunisia ENIS Natl Sch Engn Sfax Sfax Tunisia

ISBN: (纸本)9783031217524;9783031217531

Regulating learners' negative emotions during a learning session is an important factor in educational settings that aims at enhancing learner's cognitive performance and achievement outcomes. In this context, many researchers in psychology have proposed emotion regulation questionnaires that help to assess the use of emotion regulation strategies by learners in order to regulate their emotions while learning. However, the number of items in each questionnaire is large which may annoy the learner and prevent him/her from completing all of the items;this may lead to inappropriate emotional regulation. Thus, we propose in this paper a machine learning method for mining items in order to reduce the fully associated ones to one item. First of all, the paper presents a critical overview on statistical methods applied for reducing the large number of items in questionnaires. Then, after the introduction of the selected data set about the emotion regulation questionnaires, we detail the association rules mining method and discuss the obtained results about the significant association rules between the items. This can lead to uphold the items' reduction without loss of reliability.

关键词： Emotion regulation questionnaire (ERQ) Reducing items Association rules mining

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11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：