Fast and Illumination Invariant Face Tracker Algorithm for Complex Video Environments
Research Paper | Journal Paper
Vol.3 , Issue.11 , pp.1-5, Nov-2015
Abstract
Video surveillance applications present a problem for the designer of computer vision algorithms. In most cases lighting condition is poor due to the environment and the distance of cameras affect the accuracy of detection. In this paper we develop first an algorithm that detects faces from a video file with a poor illumination, and then an efficient tracker is used to follow the continuity of the faces. Some image pre-processing algorithms are applied like (histogram equalization and manual-dynamic thresholding) to reduce the false faces rate. Hybrid face detector is applied (for both frontal and pose orientation faces) using haar-cascades frontal face and profile face classifiers. The proposed system will be tested on a complex video environment (fast objects in movement) to evaluate the performance in terms of accuracy detection and the efficiency. Test results show that the detection rate accuracy of the faces in the video with the complex environment is very high and reach about 99.05%.
Key-Words / Index Term
Face Detector; Face Tracker; Frontal Classifier; Profile Classifier; Complex Video Enviroment
References
[1] J. Suneetha, “A Survey on Video-based Face Recognition Approaches”, International Journal of Application or Innovation in Engineering & Management, Volume-3, Issue-2, Page No (208-215), 2014.
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Citation
Aree A. Mohammed and Yusra A. Salih, "Fast and Illumination Invariant Face Tracker Algorithm for Complex Video Environments," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.1-5, 2015.
Location Based Authentication of kNN Queries To Reduce The CPU Time
Research Paper | Journal Paper
Vol.3 , Issue.11 , pp.6-10, Nov-2015
Abstract
In outsourced spatial databases, the LBS provides query services to the clients on behalf of the data owner. However, the LBS provider is not always trustworthy and it may send incomplete or incorrect query results to the clients. Therefore ensuring spatial query integrity is critical. Efficient kNN query verification techniques which utilize the influence zone to check the integrity of query results. kNN query continuously reports the k results (restaurants) nearest to a moving query point. In order to minimize the communication cost between the service provider and the mobile client, a framework for authenticating both the query results and the safe regions of moving kNN queries is proposed. The proposed method can perform moving kNN query authentication with small communication costs and overhead.
Key-Words / Index Term
Query processing, security, integrity, nearest neighbor and protection
References
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[4] H. Hu, J. Xu, Q. Chen, and Z. Yang, “Authenticating locationbased services without compromising location privacy,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2012, pp. 301–312.
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[6] G. S. Iwerks, H. Samet, and K. P. Smith, “Maintenance of K-nn and spatial join queries on continuously moving points,” ACM Trans. Database Syst., vol. 31, no. 2, pp. 485–536, 2006.
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[11] X. Lin, J. Xu, and H. Hu, “Authentication of location-based skyline queries,” in Proc. 20th ACM Int. Conf. Inform. Knowl. Manage., 2011, pp. 1583–1588.
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[12] X. Luo, P. Zhou, E. W. W. Chan, W. Lee, R. K. C. Chang, and R. Perdisci, “Httpos: Sealing information leaks with browser-side obfuscation of encrypted flows,” in Proc. Netw. Distrib. Syst. Security Symp., 2011.
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[14] S. Nutanong, R. Zhang, E. Tanin, and L. Kulik, “The V*-Diagram:
A query-dependent approach to moving knn queries,” Proc.VLDB Endowment, vol. 1, no. 1, pp. 1095–1106, 2008.
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[16] H. Pang, A. Jain, K. Ramamritham, and K.-L. Tan, “Verifying completeness of relational query results in data publishing,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2005, pp. 407–418.
[17] H. Pang and K. Mouratidis, “Authenticating the query results of text search engines,” Proc. VLDB Endowment, vol. 1, no. 1, pp. 126–137, 2008.
[18] H. Pang, J. Zhang, and K. Mouratidis, “Scalable verification for outsourced dynamic databases,” Proc. VLDB Endowment, vol. 2,no. 1, pp. 802–813, 2009.
[19] S. Papadopoulos, Y. Yang, S. Bakiras, and D. Papadias, “Continuous spatial authentication,” in Proc. 11th Int. Symp. Adv. Spatial Temporal Databases, 2009, pp. 62–79.
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Citation
P.Sathish and S.Venkateswaran, "Location Based Authentication of kNN Queries To Reduce The CPU Time," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.6-10, 2015.
Parallel Indexing on Color and Texture Feature Extraction using R-Tree for Content Based Image Retrieval
Research Paper | Journal Paper
Vol.3 , Issue.11 , pp.11-15, Nov-2015
Abstract
Content Based Image Retrieval (CBIR) is a challenging method of capturing relevant image from a large storage space. This paper comprise of image features such as color and texture, which is intended to use in image retrieval. These features are extracted using fuzzy approaches. Numerous methods have been introduced in image retrieval systems. However, those methods have its drawbacks. In this paper novel system architecture for CBIR system which combines techniques includes CBIR and fuzzy based feature extraction, indexing procedure as well as genetic algorithm. This proposed approach is found to be very effective and efficient while comparing to previous methods and approaches in image retrieval in terms of retrieving most relevant images with less computational time.
Key-Words / Index Term
Image Retrieval, Parallel indexing, Content Based image Retrieval (CBIR), R Tree, FCTH, Fitness Score
References
[1] S. Theodoridis and K. Koutroumbas, - Pattern Recognition, 4th Edition, 2009.
[2] M. Lew, N. Sebe, C. Djeraba and R. Jain, “Content-based Multimedia Information Retrieval: State of the Art and Challenges”, ACM Transactions on Multimedia Computing, Communications, and Applications, Volume -02, Issue -01, Page No (1-19), February 2006.
[3] I. El-Naqa, Y. Yang, N. Galatsanos, R. Nishikawa and M. Wernick, “A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography”, IEEE Transactions on Medical Imaging, Volume -23, Issue -10, Page No (1233-1244), October 2004.
[4] F. Long, H. Zhang, H. Dagan, and D. Feng, “Fundamentals of Content Based Image Retrieval, Multimedia Signal Processing Book, Chapter 1, Springer-Verlag, Berlin Heidelberg New York, Page No (1-26), 2003.
[5] D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Schölkopf, “Ranking on data manifolds”, Proc. Adv. NIPS, Volume- 16, Page No(169–176), 2003.
[6] J. He, M. Li, H. Zhang, H. Tong, and C. Zhang, “Mani foldranking based image retrieval”, Proc. 12th Annu. ACM International Conference on Multimedia, Page No(9–16), 2004.
[7] B. Xu et al., “Efficient manifold ranking for image retrieval”, Proc. 34th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, Page No (525–534), 2011.
[8] M. Stonebraker, B. Rubenstein, and A. Guttman, ‘‘Application of Abstract Data Types and Abstract Indices to CAD Data Bases,’’ Tech. Report UCB/ERL M83/3, Electronics Research Laboratory, University of California, Berkeley, January 1983.
[9] Savvas A. Chatzichristofis and Yiannis S. Boutalis “FCTH: Fuzzy Color and Texture Histogram – Low level feature for accurate image retrieval”, IEEE DOI: 10.1109/WIAMIS(2008) Page No(191-196).
[10] S. Chatzichristofis and Y. Boutalis, “A Hybrid Scheme for fast and accurate image retrieval based on color descriptors”, IASTED International Conference on Artificial Intelligence and Soft Computing (ASC 2007), Page No(280-285),August 2007.
[11] Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison-Wesley, 1989.
[12] Hiroyasu T., “Diesel Engine Design using Multi-Objective Genetic Algorithm”, Technical Report, Workshop on Design Environment, 2004.
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Citation
L. Haldurai and V. Vinodhini, "Parallel Indexing on Color and Texture Feature Extraction using R-Tree for Content Based Image Retrieval," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.11-15, 2015.
Evaluating the Performance of MPLS Technology Using Voice and Data Type Traffic over Conventional Network
Research Paper | Journal Paper
Vol.3 , Issue.11 , pp.16-18, Nov-2015
Abstract
MPLS (Multiprotocol Label Switching) is a new technology for increasing the network speed, Quality of service, scalability and performance. This paper describes the advantage of MPLS over conventional network and also analyzes the impact of MPLS over conventional network using NS-2. MPLS used to combine the advantages of layer 3 and layer 2 of OSI model, it transmits the packet on the basis of label. MPLS supports the traffic engineering and fast re-routing. The paper shows that MPLS performs better on the basis of some essential parameter.
Key-Words / Index Term
MPLS, NS-2, LDP, IP, FTP, voice, data
References
[1] E. Rosen, A. Viswanathan, and R. Callon , Multiprotocol Label Switching Architecture, RFC
3031, January 2001.
[2] R.N.Pise et. al., “ Packet Forwarding with Multiprotocol Label Switching” World Academy of Science, Engineering and Technology 12 2007.
[3] Abinaiya N, Mrs J Jayageetha “ A survey on MPLS” IJTEEE 01, 2015.
[4] Ahn, Gaeil, and Woojik Chun. "Design and implementation of MPLS network simulator supporting LDP and CR-LDP." Proceedings. IEEE International Conference on. IEEE, 2000.
[5] Liwen He, SM IEEE, Paul. Botham, “Pure MPLS Technology”, © 2008 IEEE
Citation
Shubhi and Prashant Shukla, "Evaluating the Performance of MPLS Technology Using Voice and Data Type Traffic over Conventional Network," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.16-18, 2015.
Smart Grid: Advanced Metering Infrastructure (AMI) & Distribution Management Systems (DMS)
Review Paper | Journal Paper
Vol.3 , Issue.11 , pp.19-22, Nov-2015
Abstract
In this we have touched upon features and various implementation challenges of deploying of Electrical Smart Grid. Though the Electrical Smart Grid Architecture involves wide and exhaustive combination of Elements, we have limited the discussions to a simple Smart Architecture involving Distribution Management Systems (DMS) and Advanced Metering Infrastructure (AMI). There are various implementation challenges both technical and commercial such as interoperability, standardisation, cost implementations, process integrations etc. and the need of the hour is to explore the best solution to present power supply scenario.
Key-Words / Index Term
DMS, AMI,SCADA
References
[1] "Federal Energy Regulatory Commission Assessment of Demand Response & Advanced Metering" (PDF). United States Federal Energy Regulatory Commission. United States Federal Energy Regulatory Commission.
[2] Smart Grid Report, http://new.abb.com/smartgrids/why-smart-grids
[3] Department of Energy, “The Smart Grid: An Introduction”, at http://energy.gov/oe/downloads/smart‐grid‐introduction.
[4] Outage Management System http://www.elp.com/transmission-and-distribution/outage-management.html
[5] Science direct aricle on AMI http://www.sciencedirect.com/science/article/pii/S0142061514003743
[6] http://www.enernoc.com/our-resources/term-pages/what-is-demand-response
[7] C. W. Gellings, The Smart Grid: Enabling Energy Efficiency and Demand Response, CRC Press, Aug, 2009.
[8] A. Carvallo, The Advanced Smart Grid: Edge Power Driving Sustainability, Artech House, June, 2011.
[9] X. Fang, S. Misra, G. Xue, and D. Yang, "Smart Grid ‐ The New And Improved Power Grid: A Survey"; accepted for publication in IEEE Communications Surveys and Tutorials, 2012. Available at http://optimization.asu.edu/~xue/papers/SmartGridSurvey.pdf
[10] http://mycourses.ntua.gr/courses/ECE1220/document/Load_Flow_Analysis.pdf
[11] http://link.springer.com/chapter/10.1007/978-1-4613-1073-0_3#page-1
[12] http://www.smartgridnews.com/story/volt-var-optimization-it-energy-efficiency-or-demand-response/2011-08-22
Citation
Vinay Kumar K and Dr. Balakrishna R, "Smart Grid: Advanced Metering Infrastructure (AMI) & Distribution Management Systems (DMS)," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.19-22, 2015.
Analyzing Superiority of Tail Drop over Random Early Discard by evaluating throughput using varying queue length in 802.11
Research Paper | Journal Paper
Vol.3 , Issue.11 , pp.23-27, Nov-2015
Abstract
Wireless network is used because of cost effectiveness in network deployment, and its applicability to environment where wiring is not possible wireless is preferable solution compared to wired networks. Congestion is a problem that happens due to surpass in an aggregate demand as compare to the accessible ability of the resources. It has a greater impact on both the wired and wireless network. Ns-2 is used in simulating system models, the loss in the channel can be easily distinguished from the trace file. Even though RED sometimes has better performance than Tail Drop and vice versa, we cannot say that RED has the dominance, because the total number of nodes in a real wireless network changes every moment. The average queue or buffer size of the RED is smaller than the average queue length of Tail drop.
Key-Words / Index Term
Queue management, queue length, throughput, RED, Tail Drop
References
[1]. Jyoti Pandey and Aahish Hiradhar, “A Survey on AQM Control Mechanism for TCP/IP flow”, IJARCSSE, vol 4, april 2014
[2]. Sally Floyd and Von Jacobson “Random Early Detection Gateways for Congestion Avoidance” IEEE/ACM Transaction on networking vol. 1, aug, 1993
[3]. Kanojia Sindhuben Babulal, Rajiv Ranjan Tiwari,|” Cross layer Energy Efficient Routing .(XLE2R) for prolonging the lifetime of wireless sensor network,” IEEE 2nd International Conference on Computer and Communication Technology (ICCCT-2010) September 15-17, pp ,70-74, 2010
[4]. R. Guerin*, V. Peris “Quality-of-service in packet networks: basic mechanisms and directions” IBM T.J Watson research center, USA
[5]. https://www.cs.purdue.edu/homes/park/cs536-wireless-3.pdf
[6]. M.Allman, V.Paxson, W.R.Stevens, “TCP Congestion Control”, STD1, RFC 2581, April 1999.
[7]. http://www.labs.hpe.com/personal/Jean_Tourrilhes/Linux/Linux.Wireless.mac.html
[8]. Attiuttama and Akhilesh kumar singh, “An Approach to analyze Quality of Service”, International Journal Engineering and Innovative Technology, vol 4. May, 2015
[9]. .http://ietd.inflibnet.ac.in/bitstream/10603/4106/13/13_chapter%205.pdf
[10]. S.Schmid and R. Wattenhofer, “A TCP with guaranteed performance in network with dynamic congestion and wireless losses”, In proceeding of the 2nd Annual International wireless internet conference (WICON’06), Aug, 2006
[11]. .http://old.shahed.ac.ir/references/AnIntroductiontoNS,NamandOTclscripting.pdf
Citation
Attiuttama and Kanojia Sindhuben babulal, "Analyzing Superiority of Tail Drop over Random Early Discard by evaluating throughput using varying queue length in 802.11," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.23-27, 2015.
Harvesting the Resources of Invisible Web
Review Paper | Journal Paper
Vol.3 , Issue.11 , pp.28-32, Nov-2015
Abstract
The World Wide Web is constantly becoming an important part of social, cultural, political, educational, academic, and commercial life. Web contains a wide range of information and applications in areas that are of societal interest. A great number of World Wide Web users use search engines for information retrieval, but still hesitate before making a final decision, often because only rough and limited information about the products is made available. There are millions of high quality resources available on web that the general-purpose search engines can’t see. One of the supportive reasons for this could be use of irrelevant keyword(s) or choice of a wrong search engines for executing a particular request of the searcher. Many times search engine cannot find out what we exactly wanted from it. The major reason why sometimes we do not succeed to acquire efficient results, other than these reasons, is the technical inability of search engines to access and retrieve some of the contents present on the web. That is, some of the information is hidden from the eyes of even efficient search engines. Such information which remains inaccessible from web search engines is termed as “Invisible Web”. Invisible Web contains resources that are not indexed by general-purpose search engines, but this does not indicate that these resources are absolute leftovers and unimportant. The information that is not accessed by a search engine is as much significant as that which is accessed. Invisible web is a phenomenon to be reckoned with. This paper provides a view of Invisible Web and also delves into the reasons why search engines can’t see all of the web contents. Various resources present in invisible web are also discussed. Paper also provides a list of search engines that could mine and harvest Invisible Web.
Key-Words / Index Term
Search Engines; Invisible Web; Surface Web; Internet Portals.
References
[1] Jacsó, P. (2005), "Google Scholar: the pros and cons", Online Information Review, Vol. 29, No. 2, pp. 208-214.
[2] CompletePlanet. (2004). “Largest deep web sites”. BrightPlanet. Available: http://aip. completeplanet.com/aip-engines/help/largest_engines.jsp
[3] Devine, Jane, and Francine Egger-Sider. 2001. Beyond Google: The Invisible Web. Available: www.lagcc.cuny.edu/library/invisibleweb/definition.htm
[4] Bergman, Michael K. (2001). “The deep Web: Surfacing hidden value.” White paper. BrightPlanet. Available: www.brightplanet.com/images/stories/pdf/deepwebwhite paper. pdf
[5] Sullivan, Danny. (2008). “Google now fills out forms and crawls results.” Search Engine Land. Available: http://searchengineland.com/080411-140000.php
[6] Williams, M.E. (2005), "The state of databases today: 2005", in Gale Directory of Databases, Vol. 2, pp. XV-XXV, Gale Group, Detroit, MI.
[7] Ru, Y. and Horowitz, E. (2005), "Indexing the invisible web: a survey", Online Information Review, Vol. 29, No. 3, pp. 249-265.
[8] Calishain, Tara. 2005. “Has Google dropped their 101K cache limit?” ResearchBuzz! Available: www.researchbuzz.org/2005/01/has_google_dropped_their_101k.shtml
Citation
Hardeep Singh and Geet Bawa, "Harvesting the Resources of Invisible Web," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.28-32, 2015.
Automation Testing Frameworks for SharePoint application
Research Paper | Journal Paper
Vol.3 , Issue.11 , pp.33-38, Nov-2015
Abstract
This Automation Framework has been designed and developed for the SharePoint migration/up gradation for a large insurance company in UK. For any SharePoint migration Project to be automated in the different phases to detect the defects/bugs at early stages of the SDLC. The Current IT industry follows either waterfall model or an Agile methodology to execute a project. For the last two years, all the projects around the world are moving towards the Agile Methodology to execute it. Irrespective of the process or methodology to be followed, the following testing phases: Unit, Integration, System and User acceptance testing need to be followed before moving into production. Design and develop the automation framework which will be used as wrapper to test the SharePoint application based on the requirement from the client. Automation Framework will be using the Selenium/QTP Functional testing tool to develop the Automation Framework Engine or a Wrapper scripts to test the application. Based on the Architecture of the application, developed a framework which is very much similar to test the application. These frameworks were developed either Scenario based or Functionality based. In this Paper, designed and developed both the automation frameworks to achieve the goal of the testing in System Testing phase. At the same time, come-up with the steps to validate the Unit testing phase by simple automation framework. Even in this unit testing phase, can be identify the defects in early stages of the testing which will bring down cost of the defect. As well move the application faster to market because of the testing done by the automation framework in Unit and System testing. Main objective of this paper is to reduce the cost of the defect and delivering defect free quality to the customer.
Key-Words / Index Term
Middleware, Database, CAM model, Cloud networks, Migration Testing, Unit Testing, Integration Testing, System Testing, Automation Testing Framework
References
[1] "What is sharepoint?". Microsoft Office 2010 Answers. Microsoft.
[2] "What is an Enterprise Application?". Microsoft Office 2010 Answers. Microsoft.
[3] "SharePoint-2013". Microsoft Office 2010 Answers. Microsoft.
[4] "What is sharepoint?". Microsoft Office 2010 Answers. Microsoft.
[5] Gilbert, Mark R.; Shegda, Karen M.; Phifer, Gene; Mann, Jeffrey (19 October 2009)."SharePoint 2010 Is Poised for Broader Enterprise Adoption". Gartner. Retrieved13 August 2011
[6] www.microsoft.com
[7] Laurie Williams1 , Gunnar Kudrjavets “On the Effectiveness of Unit Test Automation at Microsoft”
Citation
Madhu Dande and Neelima Galla, "Automation Testing Frameworks for SharePoint application," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.33-38, 2015.
Comparative Analysis of Linked Unsupervised based Feature Selection Framework for Social Media Data
Research Paper | Journal Paper
Vol.3 , Issue.11 , pp.39-44, Nov-2015
Abstract
The explosive usage of social media produces massive amount of unlabeled and high- dimensional data. Feature selection has been proven to be effective in dealing with high-dimensional data for efficient learning and data mining. Unsupervised learning has been proven to be a powerful technique in unsupervised feature selection, which allows embedding feature selection into the classification (or regression) problem. In literature several numbers of feature selection methods such as supervised feature selection algorithms and unsupervised feature selection methods have been proposed to select dimensional feature in the social network. When compare to supervised methods ,unsupervised feature selection methods performs well since it perform operation without label information .But unsupervised feature selection is particularly difficult due to the definition of relevancy of features becomes unclear. To solve this problem , in this paper study a unsupervised feature selection algorithm the concept of pseudo-class labels to guide extracting constraints from link information and attribute- value information, resulting in a new Linked Unsupervised based feature selection framework (LUFS), for linked social media data. LUFS examine the differences between social media data and traditional attribute value data; investigate how the relations extracted from linked data can be exploited to help select relevant features for linked social media data. Furthermore, social theories are developed by sociologists to explain the formation of links in social media. Experimental results on various social media datasets demonstrate the effectiveness of the proposed framework LUFS is compared with existing schemas in terms of accuracy and Normalized Mutual Information (NMI). Design and conduct systemic experiments to evaluate the proposed framework on data sets from real-world social media websites.
Key-Words / Index Term
Unsupervised Feature Selection, Linked Data, Social Media, Pseudo Labels, Social Dimension Regularization.
References
[1]. Tang, J., & Liu, H. (2014). An unsupervised feature selection framework for social media data. Knowledge and Data Engineering, IEEE Transactions on, 26(12), 2914-2927.
[2]. H. Liu and H. Motoda,(2008) Computational Methods of Feature Selection. London, U.K. Chapman & Hall
[3]. H. Liu and L. Yu, (Apr. 2005) “Toward integrating feature selection algorithms for classification and clustering,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 491–502.
[4]. Huan Liu and Hiroshi Motoda. (2007) Computational methods of feature selection. CRC Press.
[5]. Zheng Zhao, Lei Wang, and Huan Liu. (July 11-15, 2010) Efficient spectral feature selection with minimum redundancy. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA.
[6]. Jiliang Tang, Salem Alelyani, and Huan Liu(2014) Feature selection for classification: A review. In Data Classification: Algorithms and Applications.
[7]. Jiliang Tang and Huan Liu.(2012) Unsupervised feature selection for linked social media data. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 904–912. ACM,
[8]. Salem Alelyani, Jiliang Tang, and Huan Liu.(2013) Feature selection for clustering: A review. In Data Clustering: Algorithms and Applications, pages 29–60. CRC Press.
[9]. Mingjie Qian and Chengxiang Zhai. Robust unsupervised feature selection. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, pages 1621–1627. AAAI Press.
[10]. Zheng Zhao and Huan Liu(2013) Spectral feature selection for supervised and unsupervised learning. In Proceedings of the 24th international conference on Machine learning, pages 1151–1157. ACM.
[11]. Deng Cai, Chiyuan Zhang, and Xiaofei(2010) The Unsupervised feature selection for multi-cluster data. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 333–342. ACM.
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Applications of Data Mining in Fraud Detection
Research Paper | Journal Paper
Vol.3 , Issue.11 , pp.45-53, Nov-2015
Abstract
It comes as no surprise to learn that from an economic standpoint, fraud continues to be a growing concern for organisations of all sizes, across all regions and in virtually every sector. A 2014 survey shows that 5% of the losses at an organization can be attributed to fraud, which applied to the Gross World Producttranslates to a projected global fraud revenue loss of nearly $3.7 trillion. [1] Due to ever increasing volume of data that needs to be analysed in order to detect these frauds, data mining methods and techniques are being used with increasing frequency in this domain. This paper is aimed at providing an expansive literature review of journal articles produced between 2008 and 2015 to demonstrate the extensive research that has been carried out in selected domains and also to highlight the gaps between industry need and research in the particular areas. We have classified the research papers based on the data mining technique used, the type of fraud targeted, year of publishing, etc. and analysed the results.
Key-Words / Index Term
Data Mining; Fraud; Fraud Detection; Classification; Support Vector Machine; Computer Intrusion
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Citation
Fuzail Misarwala, KausarMukadam, and Kiran Bhowmick, "Applications of Data Mining in Fraud Detection," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.45-53, 2015.