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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 694 条记录，以下是181-190 订阅

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Unsupervised feature selection via adaptive embedding and sparse learning for Parkinson’s disease diagnosis 1

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3rd international Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2019

作者： Huang, Zhongwei Lei, Haijun Chen, Guoliang Li, Shiqi Li, Hancong Elazab, Ahmed Lei, Baiying Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China

ISBN: (数字)9783030323912

ISBN: (纸本)9783030323905

Parkinson’s disease (PD) is known as a progressive neurodegenerative disease in elderly people. Apart from decelerating the disease exacerbation, early and accurate diagnosis also alleviates mental and physical sufferings and provides timely and appropriate medication. In this paper, we propose an unsupervised feature selection method via adaptive manifold embedding and sparse learning exploiting longitudinal multimodal neuroimaging data for classification and regression prediction. Specifically, the proposed method simultaneously carries out feature selection and dynamic local structure learning to obtain the structural information inherent in the neuroimaging data. We conduct extensive experiments on the publicly available Parkinson’s progression markers initiative (PPMI) dataset to validate the proposed method. Our proposed method outperforms other state-of-the-art methods in terms of classification and regression prediction performance. © Springer Nature Switzerland AG 2019.

关键词： Neuroimaging

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A Multi-location Defence Scheme Against SSDP Reflection Attacks in the Internet of Things 3rd

A Multi-location Defence Scheme Against SSDP Reflection Atta...

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3rd international conference on Cyberspace data and Intelligence, Cyber DI 2019, and the international conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Liu, Xin Zheng, Liang Cao, Shuai Helal, Sumi Zhou, Jiehan Jia, Hunfu Zhang, Weishan College of Computer Science and Technology China University of Petroleum Qingdao266580 China School of Computing Lancaster University Bailrigg United Kingdom Information Technology and Electrical Engineering University of Oulu Oulu Finland Electrical and Computer Engineering University of Toronto Toronto Canada Nankai University Tianjin China

ISBN: (纸本)9789811519215

The proliferation of the Internet of Things (IoT) has led to a rapid increase in SSDP (Simple Service Discovery Protocol) reflection attacks. However, there is very scarce work on defending these attacks, with only some engineering advices on shutting down attacked services. This paper proposes a comprehensive approach to defend SSDP reflection attacks, which is called multi-location defence scheme (MLDS). MLDS operates at multiple places, working throughout the attacking link, starting from attack sources to victims, without prior detecting attacks. Attackers usually utilized bots in a botnet to launch attacks, but bots can act as defenders to carry out defence strategies in our MLDS, which is an unconventional approach to make the defence effective. Finally, we analyzed thoroughly packet traffic situations when deploying MLDS to different defence locations. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Location

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Performance Analysis of User Influence Algorithm under Big data Processing Framework in Social Networks 3

Performance Analysis of User Influence Algorithm under Big D...

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3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Quan, Yong Zhang, Liang Jia, Yan Zhou, Bin Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781538643013

Social influence plays an essential role in spreading information within online social networks, and can be modeled or measured by analyzing various social networking data, such as published content, users' attributes or interactions among them. Because of the massive social data, researchers often fail to quantify user influence in an accurate and high efficient way. Big data technique can be adopted to alleviate this problem. In this paper, we introduce a kind of classical individual influence algorithm, and implement two parallel versions of this algorithm based on different big data processing framework Experiment results on a large-scale real dataset demonstrate that the computational efficiency of influence algorithm can be improved significantly in massive data sets by virtue of big data processing framework

关键词： influence measurement performance analysis big data technique distributed computing social networks

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Application of data Mining Technology in Evaluating Real Estate Investment Plan Based on GRA-AHP 3

Application of Data Mining Technology in Evaluating Real Est...

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2019 3rd international conference on data Mining, Communications and Information Technology, DMCIT 2019

作者： Meier, Zhuang Pan, Wen-Tsao Zhuohong, Shi Yingying, Zhou Zuchang, Zhong Guangdong University of Foreign Studies Guangzhou Guangdong China

data mining and information technology is increasingly important in the era of big data. Many researches have been discussing the application of data mining and information technology in all walks of life. To further explore the practical value of data mining technology in economic and business field, this paper studies its application in evaluating real estate investment plans. R software is applied as the statistical computing console and relative program code is created to implement grey relational analysis-analytical hierarchy process(GRA-AHP), which establishes an improved evaluation model of decision-making. Firstly, the related data of the research is divided into several layers, namely the target layer, criteria layer and plan layer. In the criteria layer, the processing method is different from previous research because in this paper the indicator value of the plan layer to the criteria layer is calculated based on grey relational analysis rather than the pairwise comparison judgment matrix proposed by previous scholars, which makes this analysis more scientific and accurate. Then the plan layer single sort weight and the total sort weight of each plan can be calculated to provide reference for the final investment decision. This paper aims to propose a better evaluation method in the real estate investment and promote the application of data mining technology in economic fields. © 2019 Published under licence by IOP Publishing Ltd.

关键词： data mining

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Mobile data Mining System based-on Cloud computing 3

Mobile Data Mining System based-on Cloud Computing

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IEEE 3rd international conference on Signal and Image Processing (ICSIP)

作者： Huang, Zhirui He, Xiaxu Liu, Pengfei Chen, Yanhua Zhang, Weifeng South China Agr Univ Coll Math & Informat Guangzhou Guangdong Peoples R China South China Univ Technol Res Ctr Social Work Guangzhou Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538663967

With the emergence of big data and the development of mobile devices, mobile data mining has received more and more attention. It shows its unique advantages, but it also exposes its inability to handle large datasets efficiently. Based on the traditional mobile data mining project, we combined cloud computing and proposed and implemented the MobileWeka2 model based on cloud computing. In order to prove the feasibility of the model, we conducted different data mining experiments on multiple data sets. Experimental results show that this model can efficiently process large data sets and solve the problems of traditional mobile data mining.

关键词： cloud computing data mining weka mobile computing

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Construction of Deep Resolution and Retrieval Platform for Large Scale Scientific and Technical Literature 3

Construction of Deep Resolution and Retrieval Platform for L...

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3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Wu Suyan Li Wenbo Wu Jiangrui Beijing Municipal Inst Sci & Technol Informat Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Henan Inst Technol Xinxiang Henan Peoples R China

ISBN: (纸本)9781538643013

In the three aspects of information retrieval, scientific paper evaluation, revealing the knowledge structure evolution, citation analysis plays a vital role. With the appearance of full-text literature repositories, Citation analysis entered the 4 Era-full-text citation analysis age. However There is no Chinese full-text literature database. Secondly, in the two key points of citation content analysis, the citation topic analysis and the citation sentiment analysis are still lack of effective methods especially for Chinese literature. These have greatly restricted the research and application of full text citation analysis in Chinese Literature. In this project, our basic idea is: Through the study of establish a set of effective research for Chinese large-scale literature citation content analysis two key research points and efficient method for the analysis Chinese full citation analysis based on data, to enhance the effectiveness of citation analysis in our country in the field of science and technology. The main research topics include: (1) Studying the methods of real-time construction of standardized data Set for citation content analysis based on spark. (2) A scientific and technical literature retrieval platform based on Citation content is proposed. In order to improve the research efficiency of science and technology in China and to improve the accuracy of sci-tech literature searching, the full text citation analysis is promoted

关键词： full text citation analysis information extraction information retrieval

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Concept Alignment of Product Taxonomies Based on Semantic Similarity 3

Concept Alignment of Product Taxonomies Based on Semantic Si...

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3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Jiang, Weimin Lin, Yumin Li, You Guilin Univ Elect Technol Sch Comp Informat & Secur Guilin Guangxi Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin Guangxi Peoples R China

ISBN: (纸本)9781538643013

The product taxonomy can provide knowledge support for many knowledge-based Web services. Merging different taxonomies is an effective way to build a complete product taxonomy. To achieve this goal, a core task is to align the concepts of different taxonomies. The traditional methods of conceptual alignment are insufficient in accuracy and efficiency. In this paper, we propose a new concept alignment by integrating three factors simultaneously: the concept's path semantic, topic similarity and dictionary-based semantic similarity. A series of experiments on real datasets show that the proposed method has a great improvement on the accuracy and efficiency of concept alignment.

关键词： product taxonomy alignment semantic computing similarity evaluation

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Towards Better soft-Tissue Segmentation Based on Gestalt Psychology 3

Towards Better Soft-Tissue Segmentation Based on Gestalt Psy...

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3rd IEEE international conference on Image, Vision and computing (ICIVC)

作者： Bo, Qirong Feng, Jun Li, Pan Lv, Zhaohui Zhang, Jing Northwest Univ Inst Informat Sci & Technol Xian Shaanxi Peoples R China Lamar Univ Comp Sci Dept Beaumont TX 77710 USA

ISBN: (纸本)9781538649916

According to gestalt psychology theory, the human brain merges and simplifies unrelated units by some relations through eyes for subsequent cognition. We introduce a new segmentation framework based on gestalt psychology in this paper. An input image is first divided into visual patches using two gestalt principles, similarity and proximity, by a clustering method, and then the visual patches are grouped to form soft tissues by a classification step using the spatial relationship and texture features. We evaluated the proposed method using TCIA database at both sectional level and volumetric level. The experimental results demonstrated the efficiency and robustness of the presented method and indicated its promising applications in the field of medical image processing.

关键词： abdominal CT image soft tiusse segmentation visual patch pattern recognition

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An Unsupervised Grey Incidence Clustering Method Based on Multi-Dimensional Dynamic Time Warping Distance 3

An Unsupervised Grey Incidence Clustering Method Based on Mu...

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3rd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Dai, Jin He, Yuhong Wang, Zu Wang, Mei Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing 400065 Peoples R China

ISBN: (纸本)9781538643013

Grey incidence clustering is an effective tool for uncertainty information system processing. Existing grey incidence clustering methods can only deal with one-dimensional and complete sequences. This results in a lot of extra works to be done, such as alignment sequences and assignment cluster threshold artificially. It will greatly affect the clustering performance. To solve these problems, an unsupervised grey incidence clustering method based on multi-dimensional dynamic time warping distance is proposed. It measures the incidence degree between multi-dimensional sequences by calculating the minimum distance of a dynamic warping path. On the foundation, an improved k-means clustering method is constructed to realize grey incidence clustering by the strategy of gradually increasing the number of categories. The experiment result shows that the proposed method has a perfect performance on clustering especially on dealing with multi-dimensional or incomplete sequences.

关键词： dynamic time warping distance grey incidence degree k-means dynamic clustering grey incidence clustering

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Effects of Lecture Video Types on Student Learning: An Analysis of Eye-Tracking and Electroencephalography data 3rd

Effects of Lecture Video Types on Student Learning: An Analy...

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3rd international conference on Intelligent, Interactive Systems and Applications, IISA2018

作者： Cao, Xiaoming Cheng, Miaoting Xue, Xiya Zhu, Shan Normal College Shenzhen University Shenzhen China Division of Information and Technology Studies Faculty of Education The University of Hong Kong Hong Kong

ISBN: (纸本)9783030028039

Previous research studies have demonstrated the influence of different user interface designs on student learning. This paper aims to examine the effect of different lecture video types on student learning using the eye-tracking and electroencephalography (EEG) techniques jointly. A two-factor experimental study was conducted on 62 undergraduate students who were randomly assigned to two groups, one of which used lecture video without teacher presence (Group 1, N = 31) and the other used lecture video with teacher presence (Group 2, N = 31). A survey was also conducted on both groups of students to collect data on their cognitive load and perceived satisfaction towards the lecture videos. The results of eye-tracking and EEG data indicate that teacher presence influences learners’ concentration and attention towards lecture video learning. Moreover, the learners’ perceived satisfaction are also related to students’ learning. Results of this study could provide future directions for studies on massively open online courses (MOOCs) and insights for MOOCs designers and educators to improve student learning with online courses. © Springer Nature Switzerland AG 2019.

关键词： Electroencephalography

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