检索结果-内蒙古大学图书馆

2018 3rd international conference on Computational Modeling, Simulation and Applied Mathematics (CMSAM 2018)

作者： Xing-ying LI Shan-zi LI Yi-xuan WU Ai-jia HE Xiao-ya HUANG Xin ZHAO Beijing Normal University

In the information age of the 21st century, a large amount of information is collected and applied. However, due to the heterogeneity of system environment for data storage and computing, how to mine these distributed data sources has become a valuable research topic that attracted more and more attention. In this paper, we firstly presented the problem scenario and main challenges confronting with the problem of distributed data mining on multiple sourced heterogeneous data sets. Then, we surveyed research works related to the problem and elicited their main features on different technology domains to show current distributed solutions for different data mining algorithm categories. Finally, we reviewed in detail the research works and discussed the challenges remained in the distributed data mining problem for multiple sourced heterogeneous data sets.

关键词： Multiple sourced heterogeneous data sets Distributed data mining Distributed algorithms

来源：评论

学校读者我要写书评

暂无评论

Classification for data stream clustering protocols in wireless sensor networks

Classification for data stream clustering protocols in wirel...

引用

international conference on Communication, Management and Information Technology, ICCMIT 2016

作者： Alghamdi, Yassmeen Abdullah, Manal Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9781315375083

The past few years have witnessed increased interest in the potential use of Wireless Sensor Networks (WSNs) in a wide range of applications and it has become a hot research area. Owing to the advances and growth in wireless communication technology, WSNs are becoming increasingly attractive for numerous application areas. Moreover, in some applications for sensor networks, the data usually arrives in an online fashion, is unlimited and there is no order in the arrival of data to be processed. data with these characteristics are called data streams. This paper sheds the light on the most important concepts of WSNs, data stream mining, and data streams and clustering algorithms. © 2017 Taylor & Francis Group, London.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Generation of the Association Rules Among Multidimensional data in DBMS Cache Environment

引用

13th international Scientific and Technical conference Computer Sciences and Information Technologies (CSIT)

作者： Fisun, Mykola Horban, Hlib Petro Mohyla Black Sea State Univ Mykolaiv Ukraine

ISBN: (纸本)9783319459912;9783319459905

This paper presents the information system of multidimensional data analysis and data mining by identification of associative dependences in multidimensional data, which was implemented in post-relational DBMS Cache Environment. Information system modules have been considered, which perform the next tasks: design of object database on the physical level and its provisioning, construction of multidimensional data structures for creation a database and association rules mining among multidimensional data. Methods of OLAP cubes construction have been considered as well as association rules mining in them which were implemented in the information system.

关键词： OLAP data mining Multidimensional data Inter-dimensional association rule Antecedent Consequent Template Dimension Set Combination

来源：评论

学校读者我要写书评

暂无评论

Image Texture-Based New Cryptography Scheme Using advanced Encryption Standard 3rd

引用

3rd international conference on Computational Intelligence in data mining (ICCIDM)

作者： Barik, Ram Chandra Changder, Suvamoy Sahu, Sitanshu Sekhar Natl Inst Technol Dept Comp Applicat Durgapur W Bengal India Birla Inst Technol Dept Elect & Commun Engn Ranchi Jharkhand India

ISBN: (纸本)9789811038747;9789811038730

Encapsulation of information using mathematical barrier for forbidding malicious access is a traditional approach from past to modern era of information technology. Recent advancement in security field is not restricted to the traditional symmetric and asymmetric cryptography;rather, immense security algorithms were proposed in the recent past, from which biometric-based security, steganography, visual cryptography, etc. gained prominent focus within research communities. In this paper, we have proposed a robust cryptographic scheme to original message. First, each message byte, the ASCII characters ranging from Space (ASCII-32) to Tilde (ASCII-126), is represented as object using flat texture in a binary image which is decorated as n by n geometrical-shaped object in images of size N x N. Create a chaotic arrangement pattern by using the prime number encrypted by advanced Encryption Standard (AES). The sub-images are shuffled and united as rows and columns to form a host covert or cipher image which looks like a grid-structured image where each sub-grid represents the coded information. The performance of the proposed method has been analyzed with empirical examples.

关键词： Grid structure Cryptography Texture Shuffling pattern AES (advanced Encryption Standard) Steganography Visual cryptography

来源：评论

学校读者我要写书评

暂无评论

An Ensemble Random Forest Algorithm for Insurance Big data Analysis 20

An Ensemble Random Forest Algorithm for Insurance Big Data A...

引用

20th IEEE international conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP international conference on Embedded and Ubiquitous computing (EUC)

作者： Wu, Ziming Lin, Weiwei Zhang, Zilong Wen, Angzhan Lin, Longxin SCUT Sch Comp Engn & Sci Guangzhou Guangdong Peoples R China Jinan Univ JNU Coll Informat Sci & Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538632215

Due to the imbalanced distribution of business data, missing of user features and many other reasons, directly using big data techniques on realistic business data tends to deviate from the business goals. It is difficult to model the insurance business data by classification algorithms like Logistic Regression and SVM etc. This paper exploits a heuristic bootstrap sampling approach combined with the ensemble learning algorithm on the large-scale insurance business data mining, and proposes an ensemble random forest algorithm which used the parallel computing capability and memory-cache mechanism optimized by Spark. We collected the insurance business data from China Life Insurance Company to analyze the potential customers using the proposed algorithm. Experiment result shows that the ensemble random forest algorithm outperformed SVM and other classification algorithms in both performance and accuracy within the imbalanced data.

关键词： Imbalance Classification Ensemble Learning Random Forest Big data Spark

来源：评论

学校读者我要写书评

暂无评论

Threshold Based Clustering Algorithm Analyzes Diabetic Mellitus 1

引用

5th international conference on Frontiers in Intelligent computing - Theory and Applications (FICTA)

作者： Mulay, Preeti Joshi, Rahul Raghvendra Anguria, Aditya Kumar Gonsalves, Alisha Deepankar, Dakshayaa Ghosh, Dipankar SIU Dept CS & IT SIT Pune Maharashtra India

ISBN: (数字)9789811031564

ISBN: (纸本)9789811031564;9789811031557

Diabetes Mellitus is caused due to disorders of metabolism and its one of the most common diseases in the world today, and growing. Threshold Based Clustering Algorithm (TBCA) is applied to medical data received from practitioners and presented in this paper. Medical data consist of various attributes. TBCA is formulated to effectually compute impactful attributes related to Mellitus, for further decisions. TBCAs primary focus is on computation of Threshold values, to enhance accuracy of clustering results.

关键词： Incremental clustering Knowledge augmentation Closeness factor based algorithm (CFBA) Threshold based clustering Diabetes mellitus data mining and TBCA

来源：评论

学校读者我要写书评

暂无评论

A Method for Mobility Management in Cellular Networks Using data mining 5th

A Method for Mobility Management in Cellular Networks Using ...

引用

5th international conference on Context-Aware Systems and Applications (ICCASA)

作者： Giang Minh Duc Le Manh Do Hong Tuan HCM City Univ Technol Ho Chi Minh City Vietnam Van Hien Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9783319563572;9783319563565

The Mobility prediction is one of the important issues in mobile computing systems. The moving logs of mobile users in mobile computing environment are stored in the Home Location Registry (HLR). The generated moving logs are used for mining mobility patterns. The discovered location patterns can be used to provide various location based services to the mobile user by the application server in mobile computing environment. Currently, some papers have written about mobility data mining methods of mobile users in cellular communications networks. In this paper, we propose a method which decrease time to compute the mobility patterns.

关键词： data mining Mobility rules Mobility prediction Cellular networks

来源：评论

学校读者我要写书评

暂无评论

Enriching Trajectories with Semantic data for a Deeper Analysis of Patterns Extracted 16th

Enriching Trajectories with Semantic Data for a Deeper Analy...

引用

16th international conference on Hybrid Intelligent Systems (HIS) / 8th World Congress on Nature and Biologically Inspired computing (NaBIC)

作者： Chakri, Sana Raghay, Said el Hadaj, Salah Cadi Ayyad Univ LAMAI Lab Dept Appl Math & Comp Sci Marrakech Morocco

ISBN: (纸本)9783319529417;9783319529400

Geographical Information System (GIS) stores several types of data collected from several sources in varied format. Thus geo-databases generate day by day a huge volume of data from satellite images and mobile sensors like GPS, among these data we find in one hand spatial features and geographical data, and in other hand trajectories browsed by several moving objects in some period of time. Merging these types of data leads to produce semantic trajectory data. Enriching trajectories with semantic geographical information lead to facilitate queries, analysis, and mining of moving object data. Therefore applying mining techniques on semantic trajectories continue to proof a success stories in discovering useful and non-trivial behavioral patterns of moving objects. The objective of this paper is to envisage an overview of semantic trajectory knowledge discovery, and spatial data mining approaches for geographic information system. Based on analysis of various literatures, this paper proposes a concept of multi-layer system architecture for raw trajectory construction, trajectory enrichment, and semantic trajectory mining.

关键词： Spatiotemporal data mining Semantic enrichment process Geographic information system Semantic trajectory knowledge discovery Extracting behavioral knowledge

来源：评论

学校读者我要写书评

暂无评论

A data Hiding Technique Based on Magic Squares

A Data Hiding Technique Based on Magic Squares

引用

international conference on data mining and advanced computing (sapience)

作者： Kurup, Sujitha Rodrigues, Anjana Bhise, Archana SVKMs NMIMS Univ MPSTME Dept Elect & Telecommun Mumbai Maharashtra India

ISBN: (纸本)9781467385947

Steganography, the study of invisible communication, deals with ways of hiding the existence of the communicated data in unsuspected digital media, such that it remains confidential. The objectives to be considered in the steganography methods are high capacity, imperceptibility and robustness. In this paper, a new data hiding scheme based on magic square blocks is proposed to obtain better image quality and higher embedding capacity while scrambling the secret image using magic square provides security. In the proposed method, a secret digit is embedded into each cover pixel pair with the help of a reference matrix consisting of connected magic square blocks. The experimental results evaluated on 6 cover images show that the new scheme can enhance the security significantly compared with other spatial domain based approaches preserving higher visual quality of stego images at the same time.

关键词： data hiding magic square pixel pair reference table steganography

来源：评论

学校读者我要写书评

暂无评论

Embedding automatic risk-level estimation and performance analysis in drilling plans

引用

JPT, Journal of Petroleum Technology 2017年第9期69卷 88-90页

作者： Cayeux, Eric Daireaux, Benoît Balov, Mohsen Karimi Haavardstein, Stein Stokland, Leif Magne Saasen, Arild IRIS Belgium Statoil Norway ConocoPhillips United States Wintershall Germany Det Norske Norway

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181024, “Recovery-Factor Prediction for Deepwater Gulf of Mexico Oil Fields by Integration of Dimensionless Numbers With data-mining Techniques,” by Priyank Srivastava and Xingru Wu, SPE, University of Oklahoma, and Amin Amirlatifi, SPE, Mississippi State University, prepared for the 2016 SPE Intelligent Energy international conference and Exhibition, Aberdeen, 6–8 September. The paper has not been peer reviewed. Using attributes from a database of 395 deepwater Gulf of Mexico oil fields, a set of dimensionless numbers is calculated that helps in scaling attributes for all the oil fields. On the basis of these dimensionless numbers, various data-mining techniques are used to classify the oil fields. Subsequently, partial-least-square (PLS) regression is used to relate the dimensionless numbers to the recovery factor. This study shows that dimensionless numbers, together with data-mining techniques, can predict field behavior in terms of recovery factor for sparse data *** The digitization of information and the rise of inexpensive sensor technologies have ushered in a new era of computing in which acquired data are used to show hidden patterns and trends. This method of computing is very efficient in solving inverse problems where parameters affecting system characteristics are not completely known. Hydrocarbon reservoirs provide a classic case of a natural system where engineers have limited control on the design of the system that they work with; thus, they have to rely on indirect measurements to determine properties of the reservoir and use these properties for prediction of future trends. Performance prediction is usually accomplished with either analytical material-balance equations or numerical reservoir simulation. However, both methods use a bottom-up work flow, which suffers from a drawback: the need for accurate representation of subsurface geo

关键词： Upstream Oil & Gas Artificial Intelligence information recovery factor data mining reservoir performance dimensionless number Predict Recovery Factor oil field characteristic data mining Information Management and Systems

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：