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Frequent Subgraph Mining Algorithms – A Survey

Procedia computer science 2015年 47卷 197-204页

作者： T. Ramraj R. Prabhakar Assistant Professor Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore - 641014 India Emeritus Professor Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore - 641014 India

Graphs are common data structures used to represent / model real-world systems. Graph Mining is one of the arms of Data mining in which voluminous complex data are represented in the form of graphs and mining is done to infer knowledge from them. Frequent sub graph mining is a sub section of graph mining domain which is extensively used for graph classification, building indices and graph clustering purposes. The frequent sub graph mining is addressed from various perspectives and viewed in different directions based upon the domain expectations. In this paper, a survey is done on the approaches in targeting frequent sub graphs and various scalable techniques to find them.

关键词： Candidate Graphs Support Apriori-based Pattern-growth.

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Symptoms Based Treatment Based on Personal Health Record Using Cloud Computing

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Procedia computer science 2015年 47卷 22-29页

作者： P. Dhivya S. Roobini A. Sindhuja Assistant Professor Department of Computer Science and Engineering Coimbatore India Student Department of Computer Science and Engineering SNS College of Technology Coimbatore India

Cloud architecture is used for maintaining the personal health record and provide symptoms based treatment to the patients. The details of a patient need to be stored in a secured manner. It is to create a cloud storage server for long term storage over the internet. The storage server will act as a database server. Uploaded data stored in the cloud server through proxy re-encryption method. A secured threshold proxy re-encryption server and integrates it with a decentralized erasure code such that a secure distributed storage system. To generate proxy re-encryption key for one-time data access. A proxy server will be created virtually for one time data access. We can achieve the symptoms based treatment by secure Personal Health Record in cloud storage when applying the proposed encryption algorithm.

关键词： PersonalHealth Record(PHR) Encryption proxy treatments cloud computing

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Suspicious face detection based on key frame recognition under surveillance video

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9712 LNCS卷 645-652页

作者： Zheng, Xiaohui Ning, Yi Chen, Xianjun Zhan, Yongsong Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9783319409993

Surveillance video is characterized by large amount of data and redundancy, which makes the suspicious face detection to be a problem. To solve the problem above we proposed suspicious face detection based on key frame. Surveillance video has the type of fixed background, so this paper used the frame difference method with low computational complexity and small computation to extract the key frame. We proposed a new method combined DPM with skin color detection to detect the suspicious face in the key frames. To solve the speed bottleneck of the traditional DPM, we proposed to use the fast HOG LUT feature extraction and the near optimal cost sensitive decision making improving the traditional method. Meanwhile we used YCrCb + otsu skin color segmentation. Since the otsu is easily affected by the illumination, we proposed an improved skin color detection. Experiment results show that the proposed algorithms are robust and accurate for real-time. © Springer International Publishing Switzerland 2016.

关键词： Face recognition

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Single Sign on Mechanism Using Attribute Based Encryption in Distributed computer Networks

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Procedia computer science 2015年 47卷 441-451页

作者： M. Surya N. Anithadevi PG Student Computer Science and Engineering Coimbatore Institute of Technology Coimbatore- 641014 India Assistant Professor Computer Science and Engineering Coimbatore Institute of Technology Coimbatore- 641014

Single Sign On (SSO) is an authentication mechanism that enables legal user with a single credential to be authenticated by multiple service providers in a distributed computer networks. SSO obtains credential from trusted authorities i.e., Smart Card Producing Center (SCPC) and Trusted Credential Privacy (TCP) which is used for mutual authentication and authorization of legal users. Chang-Lee coined a new SSO scheme which makes use of SCPC for mutual authentication and session key establishment whereas Schnorr signature makes use of TCP which generates and verifies the signature for user's authentication. RSA algorithm and Attribute Based Encryption (ABE) is used for encryption and decryption of messages in which ABE tends to be more efficient than RSA based algorithm. ABE is a new public key based on one-to-many encryption that allows users to decrypt the message based on set of attributes and access policies. Decryption is an expensive process and this ABE system eliminates the decryption overhead using outsourced decryption. Integrity of data is maintained by verification of the cipher text which guarantees that the encrypted and decrypted files are same and original message is recovered using hash technique in case of any modifications in the file.

关键词： Encryption Decryption Ciphertext Trusted Authority Credential.

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Semantic Mining of Social Networks

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2015年

作者： Jie Tang Juanzi Li

ISBN: (数字)9781608458585

Online social networks have already become a bridge connecting our physical daily life with the (web-based) information space. This connection produces a huge volume of data, not only about the information itself, but also about user behavior. The ubiquity of the social Web and the wealth of social data offer us unprecedented opportunities for studying the interaction patterns among users so as to understand the dynamic mechanisms underlying different networks, something that was previously difficult to explore due to the lack of available data. In this book, we present the architecture of the research for social network mining, from a microscopic point of view. We focus on investigating several key issues in social networks. Specifically, we begin with analytics of social interactions between users. The first kinds of questions we try to answer are: What are the fundamental factors that form the different categories of social ties? How have reciprocal relationships been developed from parasocial relationships? How do connected users further form groups? Another theme addressed in this book is the study of social influence. Social influence occurs when one"s opinions, emotions, or behaviors are affected by others, intentionally or unintentionally. Considerable research has been conducted to verify the existence of social influence in various networks. However, few literature studies address how to quantify the strength of influence between users from different aspects. In Chapter 4 and in [138], we have studied how to model and predict user behaviors. One fundamental problem is distinguishing the effects of different social factors such as social influence, homophily, and individual"s characteristics. We introduce a probabilistic model to address this problem. Finally, we use an academic social network, ArnetMiner, as an example to demonstrate how we apply the introduced technologies for mining real social networks. In this system, we try to mine knowledge from both

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Health-aware multi-UAV planning using decentralized partially observable semi-Markov decision processes

Health-aware multi-UAV planning using decentralized partiall...

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AIAA Infotech @ Aerospace Conference, 2016

作者： Omidshafiei, Shayegan Agha-Mohammadi, Ali-Akbar Amato, Christopher Liu, Shih-Yuan How, Jonathan P. Vian, John Department of Aeronautics and Astronautics MIT CambridgeMA02139 United States Qualcomm Research Center San DiegoCA92121 United States Department of Computer Science University of New Hampshire DurhamNH03824 United States Department of Aeronautics and Astronautics MIT CambridgeMA02139 United States Richard C. Maclaurin Professor of Aeronautics and Astronautics MIT CambridgeMA02139 United States Boeing Research & Technology The Boeing Company SeattleWA98124 United States

ISBN: (数字)9781624103889

ISBN: (纸本)9781624103889

Decentralized planning for large teams of robots is challenging, requiring control and coordination of the multi-robot team in settings with noisy sensors and stochastic transition dynamics. Although the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) provides a general representation of this problem, solving it is often computationally infeasible for real-world domains. Recent work has focused on an extended representation of this framework, relying on usage of macro-actions (high-level, temporally- extended actions). This paper uses a macro-action based, Semi-Markovian decentralized framework for multi-UAV coordination in a health-aware setting. We also present hardware experiments for a constrained package delivery domain with asynchronous decision-making, demonstrating viable usage of this framework in real-world settings with continuous state spaces. © 2016, American Institute of Aeronautics and Astronautics. All rights reserved.

关键词： Stochastic systems

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An Improved SRL Based Plagiarism Detection Technique Using Sentence Ranking

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Procedia computer science 2015年 46卷 223-230页

作者： Merin Paul Sangeetha Jamal Department of Computer Science Rajagiri School of Engineering and Technology Kochi-39 India Assistant Professor Department of Computer Science Rajagiri School of Engineering and Technology Kochi-39 India

Plagiarism means intellectual theft which consists of turning someone else's work as your own. Plagiarism has become widespread in many fields like institutions, companies etc. This paper proposes a new technique which uses Semantic Role Labelling and Sentence Ranking for plagiarism detection. Sentence ranking gives suspicious and original sentence pairs through vectorising the document. Then proposed method analyses and compares the ranked suspected and original documents based on the semantic allocation of each term in the sentence using SRL. It was found out that the application of sentence ranking in plagiarism detection method decreases the time of checking.

关键词： Plagiarism Plagiarism detection Semantic Role Labeling Sentence Ranking Semantic Similarity

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RANKING SOFTWARE QUALITY FACTORS USING INTUITIONISTIC PREFERENCE RELATIONS AND GROUP DECISION MAKING

RANKING SOFTWARE QUALITY FACTORS USING INTUITIONISTIC PREFER...

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International Conference on Green Computing and Internet of Things

作者： Ritu Shrivastava Professor Computer Science & Engineering Sagar Inst of Research Technology & Science

ISBN: (纸本)9781467379113

It is well known that developers and professionals do not agree on common factors that derive quality of software. Therefore it is appropriate to apply group decision taking to rank quality factors of software. The present research considers three decision takers, viz., users, developers and professionals to decide ranking of quality factors. In this process, each decision maker provides his/her intuitionistic fuzzy preference values rather than exact values for each factor. These preference values are aggregated using averaging operator, which is further aggregated using weighted arithmetic averaging operator. Finally eight considered factors are ranked.

关键词： Quality factors Intuitionistic fuzyy set Intuitionistic preference Group decision taking

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Analysis for Maximal Optimized Penalty for the Scheduling of Jobs with Specific Due Date on a Single Machine with Idle Time

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Procedia computer science 2015年 47卷 247-254页

作者： D. Jagan A.N. Senthilvel R. Prabhakar S. Uma Maheswari PG Scholar Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore - 641014 India Assistant Professor(SG) Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore - 641014 India Emeritus Professor Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore - 641014 India Associate Professor Department of Electronics and Communication Engineering Coimbatore Institute of Technology Coimbatore641014 India

In the real world the Scheduling of Jobs in industries is provided without any idle time which is very tedious. Practically it becomes difficult when any of the spare part has started to malfunction and has to be changed in the machine then some idle time is needed in order to undergo the change. In this proposed work some amount of idle time is allotted to schedule the jobs in a single machine which includes three stages namely scheduling strategy, inserting idle time and optimizing the net penalty value of all the jobs.

关键词： Single machine scheduling Early/Tardy scheduling optimization techniques

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Refining of image using self-organizing map with clustering

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AIP Conference Proceedings 2016年第1期1715卷

作者： Neeraj Dahiya Surjeet Dalal Gundeep Tanwar 1)Assistant Professor Department of Computer Science & EngineeringSRM University Haryana India 2)Associate Professor Department of Computer Science & Engineering SRM University Haryana India 3)Associate Professor Department of Computer Science & Engineering BRCM College of Engineering & Technology BahalBhiwani Haryana India

Self Organization Map(SOM) is an automatic tool in data analysis in data mining,it is used to explore the multi-dimentional data which simplifies complexity and produce meaningful relation with each other or high dimentional into low dimentional .the powerful method of SOM i.e learning method results excellent performance .the SOM algorithum have various steps from starting stage to the final neuron and their weight updation and modification, these procedure resultant a lot of compplexity accoording to the parameters on the basis of experiments .this paper will compare and discuss various papameters and their result or factors that can improve and refine the image through varius process of SOM.

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