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检索条件"任意字段=2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016"

共 3354 条记录，以下是281-290 订阅

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An exploratory study and application of data mining: Railway alarm data 5th

An exploratory study and application of data mining: Railway...

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5th international conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Yang, Yichuan Yuan, Hanning Li, Dapeng Shi, Tianyun Cheng, Wen International School of Software Beijing Institute of Technology Beijing100081 China Institute of Computing Technologies China Academy of Railway Sciences Beijing100081 China School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811308956

The railway industry generates large data but there are few researches on railway data analysis. The paper presented an exploratory study and application of data mining from railway alarm data. The railway alarm data is analyzed to find the correlation between alarm items and between railway bureaus when alarm occurred and predict the alarm occurring. The paper proposed an alternative measurement mode with three values: support, Kulc and balance to mine the correlation from alarm data analysis, and the results finally indicated the very possibility of associated railway bureaus. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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IoT Service Based on JointCloud Blockchain: The Case Study of Smart Traveling 12

IoT Service Based on JointCloud Blockchain: The Case Study o...

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12th IEEE international Symposium on Service-Oriented System Engineering (SOSE) / 9th international Workshop on Joint Cloud computing (JCC)

作者： Chen, Weili Ma, Mingjie Ye, Yongjian Zheng, Zibin Zhou, Yuren Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Natl Engn Res Ctr Digital Life Guangzhou Peoples R China

ISBN: (纸本)9781538652060

With the advancements in Internet technologies and Wireless Sensor Networks (WSN), a new era of the Internet of Things (IoT) is being realized. IoT produces a lot of information which can be used to improve the efficiency of our daily lives and provides advanced services in a wide range of application domains. However, the privacy and the data fusing problems remain major challenges, mainly due to the massive scale and distributed nature of IoT networks and the amount of data collected from IoT increasing at an exponential rate. Thus, a privacy-protected and inter-cloud data fusing platform is needed to the demand for data mining and analytic activities in IoT. In this paper, we propose such a platform based on JointCloud Blockchain and study a novel case of smart traveling based on the proposed platform.

关键词： Service-oriented architecture Semantics Cloud computing data privacy

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Spatiotemporal Analysis and Prediction of Crime Events in Atlanta Using Deep Learning

Spatiotemporal Analysis and Prediction of Crime Events in At...

引用

international conference on Image, Vision and computing (ICIVC)

作者： Shihuai Wang Kunxiaojia Yuan School of Computer Science and Technology Heilongjiang University Harbin China Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley USA

ISBN: (纸本)9781728123264

As a major social problem in Atlanta, one of the cities with high criminal rates in United States, crime has been threatening public security for a long time. Many researches proposed that crime events have characteristics of spatial aggregation and temporal dependence. Therefore, criminal events are predictable, and criminal analysis and prediction is very helpful for law enforcement agencies to more efficiently allocate the limited police forces. In this paper, we use the real crime data set in Atlanta area from 2009 to 2016 to obtain the criminal spatiotemporal distribution features, and make spatiotemporal statistics and visualizations. To predict the daily occurrences of Atlanta crime events, we choose LSTM (long-short term memory) to capture the dependence in time lag and spatial distribution of criminal events. In addition, we discuss the effects of different spatiotemporal scales on the accuracy of crime prediction. When the input time series length is 50 days and the spatial cell size is 0.05 degree, the correlation coefficient R value between the predicted value and the observed records reaches over 0.87. The results provide references for citizens or travelers to avoid hazardous locations, and help law enforcement agencies to allocate resources appropriately.

关键词： Spatiotemporal analysis Crime prediction data mining Atlanta crime analysis LSTM Spatio-Temporal Analysis vandalism crime data mining Criminals Law Enforcement Social Problems predicted value

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Analysis and Exploitation of Twitter data Using Machine Learning Techniques

Analysis and Exploitation of Twitter Data Using Machine Lear...

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

作者： Shidaganti, Ganeshayya Hulkund, Rameshwari Gopal Prakash, S. MS Ramaiah Inst Technol Dept Comp Sci & Engn Bangalore 560054 Karnataka India Dr Ambedkar Inst Technol Dept Comp Sci & Engn Bangalore 560056 Karnataka India

ISBN: (纸本)9789811052729;9789811052712

In the present era, Internet is a well-developed technology that supports most of the social media analysis for various businesses such as marketing of a product, analysis of opinions of different customers, and advertising most of the brands. This gathered huge popularity among different users with a fresh way of interaction and sharing the thoughts about the things and materials. Hence, social media comprises of huge data that categorizes the attributes of Big data, namely volume, velocity, and variety. This leads to the research work of this huge data related to different organizations and enterprise firms. To analyze the demands, customer's efficient data mining techniques are required. Nowadays, twitter is the one among the social networks which is dealing with millions of people posting millions of tweets. This paper exemplifies the data mining with machine learning techniques such as TF-TDF and clustering algorithms such as hierarchical clustering, k-means clustering, k-medoid clustering, and consensus clustering along with their efficiencies.

关键词： Twitter data Machine learning technique Consensus clustering Big data Social media TF-IDF K-medoid clustering

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SSS: An Accurate and Fast Algorithm for Finding Top-k Hot Items in data Streams

SSS: An Accurate and Fast Algorithm for Finding Top-<i>k</i>...

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IEEE international conference on Big data and Smart computing (BigComp)

作者： Gong, Junzhi Tian, Deyu Yang, Dongsheng Yang, Tong Dai, Tuo Cui, Bin Li, Xiaoming Peking Univ Dept Comp Sci Beijing Peoples R China NUDT Collaborat Innovat Ctr High Performance Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781538636497

Finding top-k hot items in a data stream is a critical problem in big data management. It can benefit various kinds of applications, such as data mining, databases, network traffic measurement, security, etc. However, as the speed of data streams become increasingly large, it becomes more and more challenging to design an accurate and fast algorithm for this problem. There are several existing algorithms, including Space-Saving, Frequent, Lossy counting, with Space-Saving being the most widely used among them. Unfortunately, all these existing algorithms cannot achieve high memory efficiency and high accuracy at the same time. In this paper, we propose an enhanced algorithm based on Space-Saving, named Scoreboard Space-Saving (SSS), which not only achieves much higher accuracy, but also works at fast and constant speed. The key idea of SSS is to predict whether each incoming item is a hot item or not by scoring. Experimental results show that SSS algorithm achieves up to 62.4 times higher accuracy than Space-Saving. The source code of SSS is available at GitHub [1].

关键词： data Structures Finding Top-k Items Space-Saving

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Access Control Models for Cloud computing: A Review 2

Access Control Models for Cloud Computing: A Review

引用

2nd international conference on Telematics and Future Generation Networks, TAFGEN 2018

作者： Shan, Ting Li Ismail, Saiful Adli Azizan, Azizul Advanced Informatics School Univesiti Teknologi Malaysia Malaysia

ISBN: (纸本)9781538612750

Cloud computing has emerged as a de-facto standard that aims at delivering computing as a service. Due to significant ease of access advantages using cloud computing, organizations stored their sensitive data into cloud storage, which brings data security and privacy challenges. Access Control methods implemented in cloud need to balance the ease of user access while safeguarding against unauthorized access. In this paper, we will explain cloud computing infrastructure, the corresponding data security threats in clouds and review Role-based Access Control model from existing literature which only support for cross domain authentication. © 2018 IEEE.

关键词： Sensitive data

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A resource usage prediction-based energy-aware scheduling algorithm for instance-intensive cloud workflows 14th

A resource usage prediction-based energy-aware scheduling al...

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14th EAI international conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Wang, Zhibin Wen, Yiping Zhang, Yu Chen, Jinjun Cao, Buqing School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan China Swinburne Data Science Research Institute Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783030129804

The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage Prediction-based Energy-Aware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Scheduling algorithms

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DeepDiagnosis: DNN-based Diagnosis Prediction from Pediatric Big Healthcare data 6

DeepDiagnosis: DNN-based Diagnosis Prediction from Pediatric...

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6th international conference on advanced Cloud and Bigdata (CBD)

作者： Shi, Jia Fan, Xiaoliang Wu, Jinzhun Chen, Jian Chen, Wenbo Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China Xiamen Univ Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Xiamen Univ Affiliated Hosp 1 Xiamen Peoples R China ZOE Soft Co Ltd Xiamen Peoples R China Xiamen Univ Digital Fujian Inst Healthcare & Biomed Big Data Xiamen Peoples R China

ISBN: (纸本)9781538680346

mining electronic health records (EHRs) has been considered as a major decision-making tool for clinical diagnosis. In fact, it is difficult to extract the valuable information from EHRs due to free-text writing, incomplete description, and high variabilities of diseases. Especially for pediatric EHRs, the shortage of experienced pediatricians as well as complex environmental factors such as seasonal variations, cross infections from kindergartens, make it extremely challenging to conduct a precise diagnosis. To address those challenges, we proposed DeepDiagnosis, a novel deep neural network-based diagnosis prediction algorithm by mining massive pediatric EHRs. First, we pre-process the unstructured EHRs dataset in Chinese and transfer them into sentence vectors by natural language processing technologies. Second, we construct the bidirectional recurrent neural networks (BiRNN) model to catch the patients' clinical symptoms as well as their interaction. Finally, we train and evaluate our model using a real-world dataset containing 81,476 pediatric EHRs. Experimental results show that the proposed method outperforms many baseline methods.

关键词： electronic health records diagnosis prediction deep neural networks

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7th EAI international conference on Cloud computing, CloudComp 2016 and 1st EAI international conference on Security and Privacy in New computing Environments, SPNCE 2016

7th EAI International Conference on Cloud Computing, CloudCo...

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7th EAI international conference on Cloud computing, CloudComp 2016 and 1st EAI international conference on Security and Privacy in New computing Environments, SPNCE 2016

ISBN: (纸本)9783319696041

The proceedings contain 22 papers. The special focus in this conference is on Cloud computing. The topics include: Generalized Format-Preserving Encryption for Character data;data Sharing with Fine-Grained Access Control for Multi-tenancy Cloud Storage System;ring Signature Scheme from Multilinear Maps in the Standard Model;a Revocable Outsourcing Attribute-Based Encryption Scheme;operational-Behavior Auditing in Cloud Storage;efficient Verifiable Multi-user Searchable Symmetric Encryption for Encrypted data in the Cloud;secure Searchable Public-Key Encryption for Cloud Storage;Adaptive Algorithm Based on Reversible data Hiding Method for JPEG Images;efficient Authenticated Key Exchange Protocols for Large-Scale Mobile Communication Networks;efficient Graph mining on Heterogeneous Platforms in the Cloud;DMSD-FPE: data Masking System for database Based on Format-Preserving Encryption;delay-Tolerant Network Based Secure Transmission System Design;An Internal Waves Detection Method Based on PCANet for Images Captured from UAV;correlation-Aware Virtual Machine Placement in data Center Networks;Connectivity-Aware Virtual Machine Placement in 60 GHz Wireless Cloud Centers;ethical Trust in Cloud computing Using Fuzzy Logic;answer Ranking by Analyzing Characteristic of Tags and Behaviors of Users;mobile Cloud Platform: Architecture, Deployment and Big data Applications;Research on Algorithm and Model of Hand Gestures Recognition Based on HMM;Question Recommendation Based on User Model in CQA.

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Minimizing Ciphertext in Homomorphic Encryption Scheme for Cloud data

Minimizing Ciphertext in Homomorphic Encryption Scheme for C...

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international conference on advanced computing and Intelligent Engineering (ICACIE)

作者： Potey, Manish M. Dhote, C. A. Sharma, Deepak H. KJ Somaiya Coll Engn Dept Comp Engn Bombay Maharashtra India PRMIT&R Dept Informat Technol Amravati India

ISBN: (纸本)9789811068720;9789811068713

data security is a major concern in cloud computing. It must satisfy the three goals of security in computing-integrity, confidentiality, and availability. Homomorphic encryption is a technique in which user or cloud service provider (CSP) can perform operations on cloud data without performing decryption. Many algorithms are available for homomorphic encryption. But these algorithms generate large size ciphertext. This paper focuses on homomorphic encryption which generates small size ciphertext. It is a variant of scheme proposed by Dijk et al. In an experimentation of this scheme, encrypted data are stored in DynamoDB of Amazon Web service (AWS) public cloud. When user requires data, it can be downloaded on users machine and then decrypted.

关键词： data security Cloud computing Homomorphic encryption AWS DynamoDB CSP Security goals

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