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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461-470 订阅

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SFSC: Segment feature sampling classifier for time series classification 4th

SFSC: Segment feature sampling classifier for time series cl...

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4th international conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Meng, Fanshan Yue, Tianbai Wang, Hongzhi Gao, Hong Li, Yaping Massive Data Computing Research Center Harbin Institute of Technology Xidazhijie. 92 Harbin China

ISBN: (纸本)9789811322020

Time series classification research is important in data mining. However, the existing methods are not fast and accurate enough. Common classification algorithms cannot satisfy the requirements of time series classification. Since the dimensions of each time series sample may be different. Based on this character, we propose a new approach combining the feature sampling algorithm and the random forest classifier (SFSC). To test the efficiency and effectiveness of SFSC, UCR time series datasets are used for our experiments. We also discuss the performance of Dynamic Time Warping (DTW) compared with our approach. We conclude the suitable situations in which our method behaves better. Besides, We discuss the method to determine the best parameters for feature sampling method. The experiment results shows that in most cases, the SFSC algorithm behaves better than DTW and can be used for real-time query and large dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Time series

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A Random Forest Model for Mental Disorders Diagnostic Systems 2nd

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2nd international conference on advanced Intelligent Systems and Informatics (AISI)

作者： Abou-Warda, Horeya Belal, Nahla A. El-Sonbaty, Yasser Darwish, Sherif Arab Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol POB 1029 Alexandria Egypt El Mamoura Hosp Psychiat & Addict Treatment Dept Dual Diag Alexandria Egypt

ISBN: (纸本)9783319483085;9783319483078

data mining has established new applications in medicine over the last few years. Using mental disorders diagnostic systems, data possession, and data analysis has been of enormous succor for clinicians to recognize diseases more precisely, especially when dealing with overlapping mental symptoms. In this study, random forests used to predict a number of mental disorders and drug abuse. Two datasets were used, the first dataset consists of substances abuse patients derived from United States, and the second dataset consists of psychotic patients collected from Egypt. Random Forest classification technique was used to increase the accuracy rate of mental disorders prediction systems. The exploratory data mining analysis produced two models of the random forest algorithm with two groups of patients at different risks for substances abuse and psychotic diseases. This study presents a new data mining approach to enhance the diagnosis of mental disorders. This approach also serves therapeutic interventions.

关键词： data mining Mental disorders Psychotic diseases Random forest Substance abuse

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Perlustration on Techno Level Classification of Deduplication Techniques in Cloud for Big data Storage 8

Perlustration on Techno Level Classification of Deduplicatio...

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8th international conference on advanced computing (ICoAC)

作者： Karthika, R. N. Valliyammai, C. Abisha, D. Anna Univ MIT Dept Comp Technol Madras Tamil Nadu India

ISBN: (纸本)9781509058884

In the bygone, the exploration in computing focused on cloud computing, big data, and the internet of things (IOT). Irrespective of what computing is used, there is always a requirement for storage. The challenge faced in this area is storing the massive amount of data and their integrity. In the cloud, computing resources and services are made available on demand the most significant among them is their data storage. In this article, the focus is on the literature by providing an edifice and comprehensive overview of different de-duplication techniques that are used in distributed storage. Deduplication (de-dupe) technique is progressively accepted as an effective technique to reduce the cost of storage, where the de-dupe has become a vital part of the storage infrastructure. This paper also reviews the context and key features of data de-dupe and systematize the technetronic research in data de-duplication rendering the dynamic process flow. The summary and cataloguing of the high-tech on deduplication help to improvise the furthermost design considerations for data deduplication techniques. Finally, we summarize the open problems and future exploration area in a deduplication based on storage systems.

关键词： cloud computing big data internet of things de-duplication

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Knowledge inference from a small water quality dataset with multivariate statistics and data-mining

Knowledge inference from a small water quality dataset with ...

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international conference of ICT for Adapting Agriculture to Climate Change, AACC 2017

作者： Simmonds, Jose Gómez, Juan A. Ledezma, Agapito Departamento de Informática Universidad Carlos III de Madrid Avenida de la Universidad 30 Leganés28911 Spain Departamento de Biología Marina Universidad de Panamá Estafeta Universitaria Apartado 3366 Panamá 4 Panama

ISBN: (纸本)9783319701868

Multivariate analysis (MV) and data mining (DM) techniques were applied to a small water quality dataset obtained from the surface waters at three water quality monitoring stations in the Petaquilla River Basin, Panama, during the hydrological period of 2008 through 2011 for the assessment and understanding of the ongoing environmental stress within the river basin. From Factor Analysis (PCA/FA), results indicated that the factors which changed the quality of the water for the two seasons differed. During the dry (low flows) season, water quality showed to be strongly influenced by turbidity (NTU) and total suspended solids (TSS) concentrations. In contrast, during the wet (high flows) season the main changes on water quality sources were characterized by an inverse relation of NTU and TSS with the electrical conductivity (EC) and chlorides (CL), followed by significant sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied to the data. Cluster analysis was used to separate the stations based on their levels of pollution and the classification of stations was implemented by C5.0 algorithm to classify stations of unknown origin into one of the several known groups of water quality constituents. The study demonstrated that the major water pollution threats to the Petaquilla River Basin are industrial and urban development in character and uses of agricultural and grazing land which are defined as non-point sources. The use of DM techniques was to complement the MV analysis. Taking into account the limited data, the usage of these methodologies is regarded useful in aiding water managers for implementing water monitoring campaigns and in setting priorities for improving and protecting water quality sources that are impaired due to land disturbances from anthropogenic activities. © 2018, Springer international Publishing AG.

关键词： Water quality

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Performance Evaluation of Lazy and Decision Tree Classifier: A data mining Approach for Global Celebrity's Death Analysis

Performance Evaluation of Lazy and Decision Tree Classifier:...

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international conference on Research in Intelligent and computing in Engineering (RICE)

作者： Vivek Kumar Roman Zinovyev Abhishek Verma Prayag Tiwari Dept. of Automated Control System National University of Science & Technology-MiSiS Moscow Russian Federation Dept.of Semiconductor Electronic National University of Science & Technology-MiSiS Moscow Russian Federation Dept.of CSE Malaviya National Institute of Technology Jaipur India Dept.of Information Engineering University of PaPadova Italy

In present world data is a valuable asset. The best utilization of this asset by means of technology gives an upper hand to an organization. Technologies like machine learning, data mining, and artificial intelligence are no exception to this either. Celebrities influence common people behaviors through biological, psychological and social processes to a great extent. Simultaneously there is vast disparity amongst their demise over the cause, region, and age. Therefore, it is a challenging and interesting endorsement to work upon. The objective of this work is to come up with the comprehensive result to understand the celebrity deaths by investigating the incidence happened over the decade. The database for training is created from the public and open access databases for years 2006-2016 comprising of 11, 200 reported deaths over the globe. Findings of the work are year by year extraction of death, the cause behind it, age, gender, and place. Lazy and decision tree classifier model of data mining is being used for the analysis based on the profession as evaluation class.

关键词： Decision trees Entropy Support vector machines Text mining Information retrieval Machine learning

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Network Traffic Anomaly Detection Based on Wavelet Analysis 16

Network Traffic Anomaly Detection Based on Wavelet Analysis

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16th IEEE/ACIS international conference on Software Engineering Research, Management and Applications (SERA)

作者： Du, Zhen Ma, Lipeng Li, Huakang Li, Qun Sun, Guozi Liu, Zichang Jiangsu Key Lab Big Data & Secur & Intelligent Pr Nanjing 210023 Jiangsu Peoples R China NUPT Sch Comp Sci Nanjing 210023 Jiangsu Peoples R China Collaborat Innovat Ctr Econ Crime Invest & Preven Nanchang 330103 Jiangxi Peoples R China State Key Lab Math Engn & Adv Comp Wuxi 214125 Peoples R China

ISBN: (纸本)9781538658864

Network traffic anomaly detection is an important research content in the field of network and security management. By analyzing network traffic, the health of the network environment can be intuitively evaluated. In particular, analyzing network traffic provides practical and effective guidance for identification and classification of anomaly. This paper proposes a network traffic anomaly detection method based on wavelet analysis for pcap files contain two different delay injections. The wavelet analysis can effectively extract information from the signal and is suitable for the detection of anomaly. Firstly, wavelet analysis is used to extract the waveform features, and then the support vector machine is used for classification. In particular, packet lengths in the pcap files is parsed out to form a sequence of packet lengths in chronological order. Then followed by the wavelet analysis based packet length sequence feature extraction and feature selection methods, the resulting eigenvectors are used as input features to support vector machine for training the classifier. Thus to differentiate the two types of anomaly in the mixed traffic with both normal and abnormal traffic. The qualitative and quantitative experimental results show that our approach achieves good classification results.

关键词： Network Traffic Anomaly Detection Feature Extraction Wavelet Analysis

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2nd international Congress on Information and Communication Technology, ICICT 2016

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2nd international Congress on Information and Communication Technology, ICICT 2016

ISBN: (纸本)9789811055072

The proceedings contain 32 papers. The special focus in this conference is on Information and Communication Technology. The topics include: Preface;automation of railway engine pilot security system using multimodal biometric identification;restricted turn model fault tolerant routing techniques for 3D mesh network-on-chip: An evaluation;power issues of MANET;social media for enhanced e-education at Namibian schools;fifth-level second-generation wavelet-based image fusion algorithm for visual quality enhancement of digital image data;advanced teaching materials of inverted pendulum system by the PLC sequence method;intelligent locker system;precision agriculture system design using wireless sensor network;optimal tree search by a swarm of mobile robots;achieving guaranteed service with fault-tolerant resources in grid;a smart air pollution analytics framework;extracting hidden patterns within road accident data using machine learning techniques;an investigation of the classifiers to detect android malicious apps;a case-based reasoning framework for prediction of stroke;cognitive radio: A network structure perspective;comparative study of N-tier and cloud-based web application using automated load testing tool;the design of ultra-high frequency (UHF) radio frequency identification (RFID) reader antenna;provisioning of healthcare service in cloud;academic analytics implemented for students performance in terms of canonical correlation analysis and chi-square analysis;a pairwise alignment algorithm for long sequences of high similarity;information extraction approaches: A survey;the role of IoT-based devices for the better world;implementation of smart job first dynamic round robin (SJFDRR) scheduling algorithm with smart time quantum in multi-core processing system;feature extraction of protein contact maps from protein 3D-coordinates.

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FP-Growth Implementation Using Tries for Association Rule mining 1

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6th international conference on Soft computing for Problem Solving (SocProS)

作者： Goel, Manu Goel, Kanu Thapar Univ CSED Patiala Punjab India

ISBN: (数字)9789811033254

ISBN: (纸本)9789811033254;9789811033247

With the advent of technology the past few years have seen the rise in the field of data mining. data mining generally considered as the process of extracting the useful information by finding the hidden and the non-trivial information out of large chunks of dataset. Now here comes the role of association rule mining which forms a crucial component of data mining. Many recent applications like market basket analysis, text mining etc. are done using this approach. In this paper we have discussed the novel approach to implement the FP Growth method using the trie data structure. Tries provide a special feature of merging the shared sets of data with the number of occurrences that were already registered as count. So this paper widely gives an idea about how the interesting patterns are generated from the large databases using association rule mining methodologies.

关键词： FP Growth Trie Association rule mining data mining

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The design and implementation of building energy saving potential online diagnosis method Based on data mining techniques

The design and implementation of building energy saving pote...

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international conference on Intelligent Transportation, Big data & Smart City (ICITBS)

作者： Liu Jianwei Tianjin Light Ind Vocat Tech Coll Tianjin 300232 Peoples R China

ISBN: (纸本)9781509060610

In modern architecture, advanced building automation system is widely used for automatic monitoring and control of all kinds of building systems, but the actual operation data of the building which is stored in the system is not fully utilized. Firstly, this paper analyzed the characteristics of data mining technology and its application in the field of architecture, and then according to the selection of 82 buildings each functional area and the typical day energy consumption data as a sample, and by using multiple regression analysis data mining method, a new method of building energy saving potential was designed, finally by fitting the sample data, the results show that the proposed method can provide a powerful tool and method for the construction of energy saving potential.

关键词： data mining Building Energy Saving Potential Online Diagnosis Method

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Efficient Anonymous Password-Authenticated Key Exchange Scheme Using Smart Cards 4th

Efficient Anonymous Password-Authenticated Key Exchange Sche...

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4th Euro-China conference on Intelligent data Analysis and Applications, ECC 2017

作者： Wu, Tsu-Yang Fang, Weicheng Chen, Chien-Ming Wang, Eric Ke Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Harbin Institute of Technology Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9783319685267

Anonymous authentication is designed to hide the user’s identity from any verifiers during an authentication session. Since passwords prevail in many authentication systems, anonymous password-authenticated key exchange (APAKE) has become a candidate technique for privacy-enhancing applications. Recently, Shin and Kobara proposed an improved APAKE protocol using general devices such as public directories. However, we find that their scheme is vulnerable to a credential forgery attack. Then, we propose an efficient protocol using tamper resistant smart cards. The security and efficiency analysis shows that our protocol obtains high security and efficiency. © 2018, Springer international Publishing AG.

关键词： Smart cards

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