检索结果-内蒙古大学图书馆

2022 International Conference on cloud computing, Internet of Things, and Computer Applications, CICA 2022

作者： Moshayedi, Ata Jahangir Kolahdooz, Amin Roy, Atanu Shuvam Rostami, Seyyed Ali Latifi Xie, Xiaoyun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Faculty of Technology School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou China

ISBN: (纸本)9781510656673

The term telemedicine was first used in the 1920s, although used many years ago and has continued to evolve today. Medical diagnoses usually require visual information, but remote display systems have recently become a special place due to the constant unavailability of the treating physician or the remoteness of medical centers and the constant need of some patients for round-the-clock care. In this article, an Arduino-based heart rate information system is designed and implemented. Due to the reasonable price and easy accessibility of the created system in the fraction, it has many applications. The results of the designed system showed the system's capabilities to track and know the person's heart rate. © 2022 SPIE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

First-Order Meta-Learning in Node Classification with Graph Convolutional Network 4

First-Order Meta-Learning in Node Classification with Graph ...

引用

4th International Conference on Artificial Intelligence and big data, ICAIBD 2021

作者： Cao, Jing Xu, Yi Song, Xuening School of Information and Software Engineering University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9780738131702

In recent years, research on graph representation learning within graph neural networks has made great progress. Among them, graph convolutional neural networks (GCN), which is based on spectral domain, have drawn great attention and become one of the mainstream graph embedding approaches. However, few-shot learning on graphs has little related work, as we know. Toward this problem, we propose a meta-learning method on graphs, FOML-GCN (First order meta-learning on GCN), aimed to be applied in various kinds of models of graph convolutional networks. As many common approaches in meta-learning, FOML-GCN involves two phases, meta-training and meta-testing. By learning the potential Knowledge from a small number of samples in existing classes during meta-training, our method enhances the ability in node classification of the base model by training within new few data that only appear in the meta-testing step. The experiment on two benchmark datasets shows that our FOML-GCN performs better than base GCN and other models of graph network in few-shot node classification tasks. © 2021 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Truncated Nuclear Norm Based Low Rank Embedding 12th

Truncated Nuclear Norm Based Low Rank Embedding

引用

12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Zhang, Fanlong Chang, Heyou Yang, Guowei Yang, Zhangjing Wan, Minghua School of Technology Nanjing Audit University Nanjing211815 China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing211171 China

ISBN: (纸本)9783319699226

Dimensionality reduction, also called feature extraction, is an important issue in pattern recognition. However, many existing dimensionality reduction methods, such as principal component analysis, fail when there exist noises in data, especially for noise caused by outliers or corruption. Recently, a robust method, named low-rank embedding (LRE) is proposed, which uses the nuclear norm for characterizing the low rank structure hided in the data. However, one major limitation of the nuclear norm is that each singular value is treated equally, since the nuclear norm is defined as the sum of all singular values. Thus the rank function may not be well approximated in practice. To overcome this drawback, this paper presents a truncated nuclear norm based low rank embedding (Truncated-LRE). The truncated nuclear norm can approximate the rank function more accurately than nuclear norm. Experimental results show encouraging results of the proposed methods in comparison with the state-of-the-art matrix dimensionality reduction methods. © 2017, Springer International Publishing AG.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

引用

19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Some Thoughts on the Classification Standard of Intelligent Autonomous Level of Measurement and Control System 9

Some Thoughts on the Classification Standard of Intelligent ...

引用

9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Yuan, Guangfu Yang, Liu Han, Haoran Li, Yong Shao, Cong Zhan, Siyu Gansu Photoelectric Measurement Technology Research Laboratory Gansu735018 China University of Electronic Science and Technology of China Laboratory of Intelligent Collaborative Computing Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350330076

Intelligence is an important trend in the development of measurement and control systems. However, there is still a lack of unified understanding of the intelligent autonomous classification of the measurement and control system. The establishment of intelligent evaluation standards in this field has become an urgent research topic. This paper first gives an overview of the aviation measurement and control system, and then takes the classification standards of autonomy in the fields of automobile driving automation in China and the United states, the autonomy and autonomy level of typical ships in China, and the automation of civil unmanned aircraft system in China as a reference. A 5-level intelligent grading standard based on task adaptability, environmental complexity and human-computer interaction is proposed. The classification standard is practical and easy to understand. It can provide reference for the establishment of intelligent standards for measurement and control systems, improve the application speed of intelligent autonomous technology in the field of measurement and control, and broaden the application prospect of intelligent measurement and control systems. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

HGeoHashBase: an optimized storage model of spatial objects for location-based services

引用

Frontiers of Computer Science 2020年第1期14卷 208-218页

作者： Jingwei ZHANG Chao YANG Qing YANG Yuming LIN Yanchun ZHANG Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Automatic Measurement Technology and Instrument Guilin University of Electronic TechnologyGuilin 541004China Centre for Applied Informatics College of Engineering and ScienceVictoria UniversityMelbourne 8001Australia

Many location-based services need to query objects existing in a specific space,such as location-based tourism resource *** a large number of spatial objects and the real-time object access requirements of location-based services pose a big challenge for spatial object storage and query *** this paper,we propose HGeoHashBase,an improved storage model by integrating GeoHash with key-value structure,to organize spatial objects for efficient range *** is responsible for spatial encoding and key-value structure as underlying data *** the similarity of the encodings for objects in the close geographical locations and the multi-version data mechanism are blended into the proposed model *** the tradeoff between encoding precision and query performance,a theoretical proof is *** experiments are designed and conducted,whose results show that the proposed model can gain significant performance improvement.

关键词： location-based services distributed storage model storage and access optimization

来源：评论

学校读者我要写书评

暂无评论

Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Publication recommendations of manuscripts using improved C4.5 decision tree algorithm 2nd

Publication recommendations of manuscripts using improved C4...

引用

2nd International Conference on Intelligent and Interactive Systems and Applications, IISA 2017

作者： Jia, Didi Xie, Wu Chen, Zhiyong Qiang, Baohua School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin541004 China

ISBN: (纸本)9783319690957

In view of the problem that the result accuracy of the research publication recommendations of manuscripts is often low. It is very difficult for many contributors to find the suitable journals from many recommendation results. To solve this problem, a novel method of publication recommendations for authors’ manuscripts is proposed using our improved C4.5 decision tree algorithm. For the shortcomings of too many values of data samples during dealing with the traditional C4.5 methods, the five-value logic ideas are adopted to improve this C4.5 algorithm to apply in the research fields of publication recommendations. The experimental results show that the related publications are easier to obtain for authors via the decision trees with the improved C4.5 method than before, and this method has higher accuracy than ever. It is of great significance to help the researchers, especially for those who have no enough experience to choose the most suitable journals from a lot of publications. © 2018, Springer International Publishing AG.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

引用

2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Qian, Jin Chang, Yu-Hui Lv, Ping Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou213015 China Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781509003891

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets. © 2016 IEEE.

关键词： Decision tables

来源：评论

学校读者我要写书评

暂无评论

A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：