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共 3354 条记录，以下是371-380 订阅

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Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture 23

Maintenance 4.0: Intelligent and Predictive Maintenance Syst...

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23rd IEEE international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Cachada, Ana Barbosa, Jose Leitao, Paulo Geraldes, Carla A. S. Deusdado, Leonel Costa, Jacinta Teixeira, Carlos Teixeira, Joao Moreira, Antonio H. J. Moreira, Pedro Miguel Romero, Luis Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Polytech Inst Braganca Campus Sta Apolonia P-5300253 Braganca Portugal Univ Minho Ctr ALGORITMI Campus Azurem P-4800058 Guimaraes Portugal Catraport Lda Zona Ind Mos Lote 1 P-5300692 Braganca Portugal 2Ai Polytech Inst Cavado & Ave Barcelos Portugal Inst Politecn Viana do Castelo ARC4DigiT Appl Res Ctr Digital Transformat Av Atlantico P-4900348 Viana Do Castelo Portugal

ISBN: (纸本)9781538671085

In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.

关键词： Industry 4.0 industrial maintenance predictive maintenance data analysis augmented reality

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Attacks and Solutions of a Mutual Authentication with Anonymity for Roaming Service with Smart Cards in Wireless Communications 11th

Attacks and Solutions of a Mutual Authentication with Anonym...

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11th international conference on Genetic and Evolutionary computing (ICGEC)

作者： Wu, Tsu-Yang Xiang, Bin Wang, Guangjie Chen, Chien-Ming Wang, Eric Ke Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Peoples R China Fujian Univ Technol Natl Demonstrat Ctr Expt Elect Informat & Elect T Fuzhou 350118 Peoples R China Harbin Inst Technol Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9789811064876;9789811064869

Recently, Liu et al. proposed a mutual authentication protocol with user anonymity for wireless communication. In their paper, the authors claimed that the protocol can resist several kinds of attacks even the secret information stored in the smart card is disclosed. However, we still find two vulnerabilities in this paper. First, this protocol still fails to protect user anonymity. Second, this protocol is vulnerable to an off-line password guessing attack if an adversary can derive the secret information stored in a smart card. To solve the problems, we propose a simple but effective patch to their protocol.

关键词： Anonymity Roaming service Wireless communication Cryptanalysis

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4th international conference on advanced computing, Networking and Informatics, ICACNI 2016

4th International Conference on Advanced Computing, Networki...

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4th international conference on advanced computing, Networking and Informatics, ICACNI 2016

ISBN: (纸本)9789811033728

The proceedings contain 114 papers. The special focus in this conference is on advanced computing, Networking and Informatics. The topics include: How meta-heuristic algorithms contribute to deep learning in the hype of big data analytics;using games to solve challenging multimedia problems;effective image encryption technique through 2D cellular automata;a dynamic model to recognize changes in mangrove species in sunderban delta using hyperspectral image analysis;a content-based visual information retrieval approach for automated image annotation;hand gesture-based stable powerpoint presentation using kinect;wavelet statistical feature selection using genetic algorithm with fuzzy classifier for breast cancer diagnosis;least square based fast denoising approach to hyperspectral imagery;image forgery detection using co occurrence-based texture operator in frequency domain;color components clustering and power law transform for the effective implementation of character recognition in color images;grayscale image enhancement using improved cuckoo search algorithm;segmentation of tomato plant leaf;a novel image steganography methodology based on adaptive PMS technique;defect identification for simple fleshy fruits by statistical image feature detection;extraction of FECG signal based on blind source separation using principal component analysis;an improved method for text segmentation and skew normalization of handwriting image;enhanced mutual information-based multimodal brain MR image registration using phase congruency;enhanced super-resolution image reconstruction using MRF model;signature classification using image moments and a Bengali handwritten vowels recognition scheme based on the detection of structural anatomy of the characters.

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Market Production Assessment Using Optimized MapReduce Programming

Market Production Assessment Using Optimized MapReduce Progr...

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international conference on Advances in Soft computing, Intelligent Systems and Applications (ASISA)

作者： Dayal, Mohit Singh, Nanhay Ambedkar Inst Adv Commun Technol & Res New Delhi India

ISBN: (纸本)9789811052729;9789811052712

Big data, an easily catching phrase is given to huge and complex amount of data where data size exceeds by terabytes and zetabytes and more. Twitter, Facebook, and other sites are generating enormous amount of data on regular basis and performing analysis on it using various tools and technologies. There is a surge in development of analyzing tools as this much amount of data cannot be processed by the traditional tools. Big data analytics refer to the techniques that can be used for converting raw data into meaningful information which helps in business analysis and forms a decision support system for the executives in the organization. In this paper, we analyzed and evaluated the market of 14 countries through optimized MapReduce program to find out the total percentage of products produced by different market of different countries. Based on the probability, the products are categorized on the basis of high, medium, and low and then for each country we found out the total count of the market that is producing widely. The objective of this analysis was to evaluate the market of each country on the basis of various goods produced by different market. Production of some goods is highly concentrated while others are widely produced. Market experts have to come up with new and innovative idea and technologies in order to increase the production of the concentrated products. So that even distribution of production can be seen in the country.

关键词： Big data Unstructured data Hadoop distributed file system MapReduce

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Optimizing the efficiency of data transfer in dataflow architectures 20

Optimizing the efficiency of data transfer in dataflow archi...

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20th IEEE international conference on High Performance computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on data Science and Systems (DSS)

作者： Feng, Yujing Xiang, Taoran Ye, Xiaochun Fan, Dongrui Wang, Da Wu, Dongdong Tang, Zhimin Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781538666142

dataflow architectures have been proposed in response to several emerging problems in processor design, such as computational efficiency, design complexity and power efficiency. A dataflow architecture is composed of multiple processing elements (PEs) that are organized into the form of grid. The instructions are compiled by compiler and are explicitly mapped to the PE grid using the instruction placement algorithm. The instruction placement algorithms generally take load balancing, low communication delay, and resource contention as input conditions, but most of them ignore the router congestion of the dataflow network. By applying three kinds of typical instruction placement algorithms on the dataflow model, we found that the dynamic packets that are transferred through the dataflow network is not evenly distributed. Due to the reason that dataflow network usually adopts the uniform structure for each router, congestion is susceptible to occur in the directions dealing with larger load. Partial congestion of the network will degrade the transfer efficiency of the dataflow network, which directly affects the execution efficiency of the dataflow processor. In order to optimize the transfer efficiency of routers and execution efficiency of dataflow processor under unevenly distributed network load, we proposed a cost-efficient hardware mechanism to dynamically detect the imbalances in different directions in each router and adaptively reallocate resources in the bottleneck router. The proposed mechanism is transparent to compiler and instruction placement algorithms. Besides, it can be easily applied as a supplementary hardware optimization to any instruction placement algorithm causing such kind of partial congestion problem. We evaluated the proposed hardware mechanism on a dataflow model, and the results show that our mechanism increases the average computational performance by 15.9%, with an increase in the average utilization of functional units by 15.6%. Crucia

关键词： dataflow architecture instruction placement algorithm router congestion

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Truth discovery on categorical multi-valued attributes with source relations 4

Truth discovery on categorical multi-valued attributes with ...

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4th IEEE international conference on Computer and Communications, ICCC 2018

作者： Zheng, Yan Luo, Junyong Yin, Meijuan Yang, Wen China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781538683392

In the era of big data, a mass of data fusion applications need to obtain accurate and complete attribute information about various objects. Most existing truth discovery methods are for single-valued attributes, which will be invalid for multi-valued attributes. They are usually based on the assumption that the data sources are independent of each other. It is not in line with the reality that related relationship occurs between them. Since multi-valued attributes are at large discrete, this paper proposes a truth discovery method incorporating the qulity and the correlations of data sources, namely Categorical Multi-Truths Finder algorithm (CMTF), which iteratively derives the accuracy and the recall rate of data source along with the truth set for each attribute. The experimental results show that CMTF outperforms the state-of-the-art. © 2018 IEEE.

关键词： data fusion

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A systematic map of data analytics in breast cancer 18

A systematic map of data analytics in breast cancer

引用

2018 Australasian Computer Science Week Multiconference, ACSW 2018

作者： Idri, Ali Chlioui, Imane El Ouassif, Bouchra Software Project Management Research Team ENSIAS Mohammed v University Rabat Morocco

ISBN: (纸本)9781450354363

data mining or data Analytics is a set of techniques that allows to analyzing data from different perspectives and summarizing it into useful information. It is the process of finding correlations or patterns in large historical datasets. It can be applied in almost any field ranging from business to education, then to medicine. data mining has been increasingly used in medicine, especially in oncology. Breast cancer (BC) becomes the most common cancer among females worldwide and the leading cause of death in developed countries. Many studies have attempted to apply data mining techniques to detect survivability of cancers in human beings. This paper aims to perform a systematic mapping study to analyze and synthesize studies on the application of data mining techniques in breast cancer. 403 articles published between 2000 and 2016 were therefore selected and analyzed according to five criteria: year and channel of publication, research type, medical task, empirical type and DM techniques. Results show that conferences and journals are the most publication venues, researchers were more interested in applying DM techniques for diagnosis of BC, historical based evaluation was the empirical type of studies most used in the evaluation of DM techniques in BC, and classification was the most investigated task of DM in BC. © 2018 ACM.

关键词： data Analytics

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A novel random forest approach using specific under sampling strategy 3rd

A novel random forest approach using specific under sampling...

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3rd international conference on Computer and Communication Technologies, IC3T 2016

作者： Surya Prasanthi, L. Kiran Kumar, R. Srinivas, Kudipudi Department of Computer Science Krishna University Machilipatnam India Department of Computer Science & Engineering V.R. Siddartha Engineering College Vijayawada India

ISBN: (纸本)9789811032226

In data mining the knowledge is discovered from the existing real world data sets. In real time scenario, the category of datasets varies dynamically. One of the emerging categories of dataset is class imbalance data. In Class Imbalance data, the percentages of instances in one class are far greater than the other class. The traditional data mining algorithms are well applicable for knowledge discovery from balance datasets. Efficient knowledge discovery is hampered in the case of class imbalance datasets. In this paper, we propose a novel approach dubbed as Under Sampling using Random Forest (USRF) for efficient knowledge discovery from imbalance datasets. The proposed USRF approach is verified on the 11 benchmark datasets from UCI repository. The experimental observations show that an improved accuracy and AUC is achieved with the proposed USRF approach with a good reduction in RMS error. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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DILOF: Effective and Memory Efficient Local Outlier Detection in data Streams 18

DILOF: Effective and Memory Efficient Local Outlier Detectio...

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24th ACM SIGKDD conference on Knowledge Discovery and data mining (KDD)

作者： Na, Gyoung S. Kim, Donghyun Yu, Hwanjo POSTECH Pohang Univ Sci & Technol Pohang South Korea

ISBN: (纸本)9781450355520

With precipitously growing demand to detect outliers in data streams, many studies have been conducted aiming to develop extensions of well-known outlier detection algorithm called Local Outlier Factor (LOF), for data streams. However, existing LOF-based algorithms for data streams still suffer from two inherent limitations: 1) Large amount of memory space is required. 2) A long sequence of outliers is not detected. In this paper, we propose a new outlier detection algorithm for data streams, called DILOF that effectively overcomes the limitations. To this end, we first develop a novel density-based sampling algorithm to summarize past data and then propose a new strategy for detecting a sequence of outliers. It is worth noting that our proposing algorithms do not require any prior knowledge or assumptions on data distribution. Moreover, we accelerate the execution time of DILOF about 15 times by developing a powerful distance approximation technique. Our comprehensive experiments on real-world datasets demonstrate that DILOF significantly outperforms the state-of-the-art competitors in terms of accuracy and execution time. The source code for the proposed algorithm is pavailable at our website: http://***/DILOF.

关键词： Outlier detection data streams Density-based sampling

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Experiences in the development of a data management system for genomics 6th

Experiences in the development of a data management system f...

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6th international conference on data Science, Technology and Applications, data 2017

作者： Ceri, Stefano Canakoglu, Arif Kaitoua, Abdulrahman Masseroli, Marco Pinoli, Pietro Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (纸本)9783319948089

GMQL is a high-level query language for genomics, which operates on datasets described through GDM, a unifying data model for processed data formats. They are ingredients for the integration of processed genomic datasets, i.e. of signals produced by the genome after sequencing and long data extraction pipelines. While most of the processing load of today’s genomic platforms is due to data extraction pipelines, we anticipate soon a shift of attention towards processed datasets, as such data are being collected by large consortia and are becoming increasingly available. In our view, biology and personalized medicine will increasingly rely on data extraction and analysis methods for inferring new knowledge from existing heterogeneous repositories of processed datasets, typically augmented with the results of experimental data targeting individuals or small populations. While today’s big data are raw reads of the sequencing machines, tomorrow’s big data will also include billions or trillions of genomic regions, each featuring specific values depending on the processing conditions. Coherently, GMQL is a high-level, declarative language inspired by big data management, and its execution engines include classic cloud-based systems, from Pig to Flink to SciDB to Spark. In this paper, we discuss how the GMQL execution environment has been developed, by going through a major version change that marked a complete system redesign;we also discuss our experiences in comparatively evaluating the four platforms. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Genome

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