iosr-JCE   Volume-8 ~ Issue-4

Abstract:In many face recognition systems the important part is face detection. The task of detecting face is complex due to its variability present across human faces including colour, pose, expression, position and orientation. So using various modeling techniques it is convenient to recognize various facial expressions. In the field of image processing it is very interesting to recognize the human gesture by observing the different movement of eyes, mouth, nose, etc. Classification of face detection and token matching can be carried out any neural network for recognizing the facial expression. Facial expression provides vital cues about the emotional status of a person. Thus an automatic face expression system (FER) that can track the human expressions and correlate the mood of the person can be used to detect the deception among humans. Other applications of automatic facial expression recognition system include human behavior interpretation and human computer interaction. This paper proposes a method using artificial neural networks to find the facial expression among the three basic expressions given using MATLAB (neural network) toolbox.
Keywords: Artificial Neural Networks, Image processing, Discrete Fourier Transform (DCT)

[1] "Facial Expression Recognition: A Brief Tutorial overview", C.C. Chibelushi and F. Bourel 2002.
[2] F. Bourel, C.C. Chibelushi, A.A. Low, "Robust Facial Expression Recognition Using a State-Based Model of Spatially-Localized
Facial Dynamics", Proc. Fifth IEEE Int. Conf. Automatic Face and Gesture Recognition, pp.106-111, 2002
[3] Facial expression recognition technique using 2D DCT and k means algorithm, Liying Ma and K.Khorasani, IEEE Transactions on
Systems, Man and Cybernetics, Vol.34, No.3 June 2004
[4] V. Bruce, "What the Human Face Tells the Human Mind: Some Challenges for the Robot-Human Interface", Proc. IEEE Int.
Workshop Robot and Human Communication, pp. 44-51, 1992
[5] "Facial Expression Recognition using Back propagation", A. Sulistijono, Z. Darojah, A. Dwijotomo, D.Pramdihanto 2002
[6] Zhang Z. Feature-Based Facial Expression Recognition Experiments With a Multi-Layer Perceptron. International Journal of
Pattern Recognition & Artificial Intelligence. 1999; 13:893–912.
[7] Ekman P, Friesen WV. The Facial Action Coding System A Technique for the Measurement of Facial Movement. San Francisco:
Consulting Psychologists Press; 1978.
[8] Sebe N, Lew MS, Sun Y, Cohen L, Gevers T, Huang TS. Authentic Facial Expression Analysis. Image and Vision Computing. 2007; 25:1856–1863.

Abstract:This paper presented study on convex drawing of planar graph. In graph theory, a planar graph is a graph that can be embedded in the plane. A planar graph is one that can be drawn on a plane in such a way that there are no "edge crossings," i.e. edges intersect only at their common vertices. Convex polygon has all interior angles less than or equal to 180°. A graph is called a convex drawing if every facial cycle (face) is drawn as a convex polygon. In a convex drawing of a planar graph, all edges are drawn by straight line segments in such a way that every face boundary in a convex polygon. This paper describes some of the recent works on convex drawing on planar graph.
Keywords: Biconnected, Convex Drawing, Internally Triconnected, Planar Graph.

[1] W. T. Tutte, Convex representations of graphs, Proc. of London Math, Soc. 10, no. 3, 1960, 304-320.
[2] W. T. Tuttle, How to draw a graph, Proc. London math, Soc. 13, 1963, 743-768.
[3] C. Thomassen, Plane representations of graphs, Progress in Graph Theory, J. A. Bondy and U. S. R. Murty (Eds.), Academic Press,
1984, 43-69.
[4] N. Chiba, T. Yamanouchi and T. Nishizeki, Linear algorithms for convex drawings of planar Graphs, Progress in Graph Theory,
Academic Press, 1984, 153-173.
[5] N. Bonichon, S. Felsner and M. Mosbah, Convex drawings of 3-connected plane graphs, Proc. of Graph Drawing 2004, 2005, 60- 70.
[6] M. Chrobak, M. T. Goodrich and R. Tamassia, Convex drawings of graphs in two and three Dimensions, Proc. of SoCG 1996,
1996, 319-328.
[7] M. Chrobak and G. Kant, Convex grid drawings of 3-connected planar graphs, International Journal of Computational Geometry
and Applications, 7, 1997, 211-223.
[8] K. Miura, S. Nakano and T. Nishizeki, Convex grid drawings of four-connected plane Graphs, International Journal of Foundations
of Computer Science, 17(5), 2006, 1031-1060.
[9] K. Miura, M. Azuma and T. Nishizeki, Convex drawings of plane graphs of minimum outer apices, Proc. of Graph Drawing 2005,
2006, 297-308.
[10] G. Rote, Strictly convex drawings of planar graphs, Proc. of SODA 2005, 2005, 728-734.

Abstract: The Association Rule Mining methods are used to mine attribute relationships. The Support and Confidence values are estimated for all item-sets. Minimum Support and Confidence values are used to select frequent patterns. Classification technique is applied to assign labels for the transactions. Learning phase is carried out for transaction pattern identification. Testing process handles the pattern matching and label assignment task.
Keywords: Associative Classifiers, ARM, Pruning Classification Association Rule (PCAR), Weighted Association Rule Mining (WARM).

[1] Andreeva P., M. Dimitrova and A. Gegov, "Information Representation in Cardiological Knowledge Based System", SAER'06,
pp: 23-25 Sept, 2006.
[2] Heon Gyu Lee, Ki Yong Noh, Keun Ho Ryu, "Mining Biosignal Data: Coronary Artery Disease Diagnosis using Linear and
Nonlinear Features of HRV," LNAI 4819: Emerging Technologies in Knowledge Discovery and Data Mining, pp. 56 -66, May 2007.
[3] Hsinchun Chen, Sherrilynne S. Fuller, Carol Friedman, and William Hersh, "Knowledge Management, Data Mining, and Text
Mining In Medical Informatics", Chapter 1, eds. Medical Informatics: Knowledge Management And Data Mining In Biomedicine,
New York, Springer, pp. 3-34, 2005.
[4] Newman.D, Hettich.J.S,Blake.C.L.S, and C.J.Merz, "UCI Repository of machine learning databases" Irvine, CA: University of
California, Department of Information and Computer Science.1998, last accessed: 1/10/2009.
[5] Niti Guru, Anil Dahiya, Navin Rajpal, "Decision Support System for Heart Disease Diagnosis Using Neural Network", Delhi
Business Review, Vol. 8, No. 1 (January - June 2007.
[6] Sellappan Palaniappan, Rafiah Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques", IJCSNS
International Journal of Computer Science and Network Security, Vol.8 No.8, August 2008.
[7] Tzung-I Tang, Gang Zheng, Yalou Huang, Guangfu Shu, Pengtao Wang, "A Comparative Study of Medical Data Classification
Methods Based on Decision Tree and System Reconstruction Analysis", IEMS, Vol. 4, No. 1, pp. 102-108, June 2005.

Abstract: Fuzzy set theory has been extensively applied to extend various database models(conceptual and logical) and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. So this paper reviews fuzzy logical database models, in which fuzzy relational databases are discussed.
Keywords: Fuzzy databases, fuzzy set, possibility distribution, database models.

[1] Z.M. Ma, A conceptual design methodology for fuzzy relational databases .Journal of Database Management , Vol. 16, No. 2 ,
2005 ,66-83.
[2] Zadeh, L. A., Fuzzy sets. Information and Control, 8, 1965, 338-353.
[3] Zadeh, L. A., A computational approach to fuzzy quantifiers in natural languages. Computer Mathematics with Applications,
9,1983, 149-183.
[4] Galindo, J., Urrutia, A., Piattini, M.: Fuzzy Databases: Modeling Design and Implementation. IDEA Group Publishing, Hershey,
USA. (2006)
[5] Zadeh, L. A. , Similarity relations and fuzzy orderings. Information Sciences, 3,1971, 177-200
[6] Buckles, B. P., & Petry, F. E. , Uncertainty models in information and database systems. Information Sciences, 11,1985, 77-87.
[7] Prade, H., & Testemale, Fuzzy relational databases: Representational issues and reduction using similarity measures. J. Am. Soc.
Information Sciences, 38(2),1987, 118-126.
[8] Codd, E. F. , The relational model for database management, Version 2,1990, Reading, MA: Addison-Wesley.
[9] Umano, M., & Fukami, S. , Fuzzy relational algebra for possibilitydistribution- fuzzy-relation model of fuzzy data. Journal of
Intelligent Information Systems,1994, 3, 7-28.
[10] Zemankova-Leech, M., & Kandel, A., Implementing imprecision in information systems. Information Sciences, 37, 1985,107-141.

Abstract:One of the critical tasks in Computer Vision is Detection and Tracking of objects. But still now, the issues related to this are developing. For the automatic detection of moving objects, some of the monitoring systems cannot able to find the difference, when the difference of brightness between the background and the moving objects is small. The costs are very high in these systems. Most of the previous methods, only concentrated on detecting rough area of targets. The accurate moving target detection cannot be achieved. It makes the result to be shown with noise and heals and the computation time is also increased. Many systems are unable to solve critical solutions such as Partial Occlusions and Cross Targets. In my proposed system the Soft Computing techniques can handle objectives and arbitrary constraints with a high degree of simplicity, we use one of the Evolutionary Approach of Soft Computing Technique in this paper. We demonstrate the object detection and tracking methods separately. Initially, a visual object detection approach is presented for minimizing the computation time, in order to achieve high detection accuracy. In this approach, three key contributions are described.
Keywords: Video surveillance, Soft computing, object detection, tracking.

[1] Sherin M. Youssef, Meer A. Hamza, and Arige F. Fayed, "Detection and Tracking of Multiple Moving Objects with Occlusion in
Smart Video Surveillance Systems", In Proceedings of IEEE Conference of Intelligent Systems, pp. 120-125, 2010.
[2] Yuping Lin, Qian Yu, and Gérard Medioni, "Efficient detection and tracking of moving objects in geo-coordinates", Machine
Vision and Applications, Vol. 22, pp. 505–520, 2011.
[3] Codrut Ianasi, Vasile Gui, Corneliu I. Toma, and Dan Pescaru, "A Fast Algorithm for Background Tracking in Video Surveillance,
Using Nonparametric Kernel Density Estimation", Electronics and Energetics, Vol. 18, No. 1, pp. 127-144, 2005.
[4] Masayuki Yokoyama, and , "A Contour-Based Moving Object Detection and Tracking", In proceedings of Visual Surveillance and
Performance Evaluation of Tracking and Surveillance, 2005.
[5] Rita Cucchiara, Costantino Grana, Massimo Piccardi, and Andrea Prati, "Detecting Moving Objects, Ghosts and Shadows in Video
Streams", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, pp. 1337-1342, October 2003.
[6] Vasant Manohar, Padmanabhan Soundararajan, HarishRaju, Dmitry Goldgof, Rangachar Kasturi, and John Garofolo, "Performance
Evaluation of Object Detection and Tracking in Video", In Proceedings of 7th Asian Conference on Computer Vision, Hyderabad,
pp. 151–161, 2006.
[7] Kaiqi Huanga, Liangsheng Wanga, Tieniu Tana, and Steve Maybank, "A real-time object detecting and tracking system for outdoor
night surveillance", ELSEVIER Pattern Recognition, Vol. 41, pp. 432-444, 2008.
[8] Thi Thi Zin, Pyke Tin, Takashi Toriu and Hiromitsu Hama, "A Probability-based Model for Detecting Abandoned Objects in
Video Surveillance Systems", In Proceedings of the World Congress on Engineering 2012, London, U.K., Vol 2, 2012.
[9] Isaac Cohen, and Gerard Medioni, "Detecting and Tracking Moving Objects for Video Surveillance", In IEEE Proceedings of
Computer Vision and Pattern Recognition, pp. 2319-2325, 1999.
[10] C. Kamath, A. Gezahegne, S. Newsam, and G. M.Roberts ,"Salient Points for Tracking Moving Objects in Video", Image and
Video Communications and Processing, 2005.

Abstract: Electronic voting has attracted much interest recently and a variety of schemes have been proposed. Generally speaking, all these schemes can be divided into three main approaches: based on blind signature, based on mix networks and based on homomorphism encryption. Schemes based on blind signature are thought to be simple, efficient, and suitable for large scale elections. With the help of networking and internet we can easily replace the traditional election process with the electronic voting system. In the proposed cryptographic technique we are implementing new blind signature based on linear matrix function and modulation. The new scheme fully conforms to the requirement of large scale election such as privacy, fairness and security. The voter's private key for digital signature is protected by using linear square matrix based on block cipher algorithm and it can be make many combination so that only valid voter can use it. In the cryptography history, block cipher used in symmetric key algorithm, this is first time we are introducing block cipher as a public key algorithm.
Keywords: E-voting system (EVS), Blinding, Signing, Unblinding, Block cipher, Ballot, Authenticator, Registrar etc.,

Journal Papers:
[1] Patil V.M. "Secure EVS by using blind signature and cryptography for voter‟s privacy & Authentication", Journal of Signal and
Image Processing, Vol. 1, Issue 1, 2010, PP-01-06.
[2] Subariah Ibrahim, Maznah Kamat, Mazleena Salleh, Shah Rizan Abdul Aziz, "Secure E-Voting With Blind Signature",
3262/1/ieee02 in 2008.
[3] Prakash Kuppuswamy, Dr.C. Chandrasekar, "Enrichment of Security through Cryptographic Public key Algorithm Based on Block
cipher", IJCSE, ISSN : 0976-5166 Vol. 2 No. 3 Jun-Jul 2011 PP 347-355.
Books:
[4] John C. Bowman, Math 422 Coding Theory & Cryptography, University of Alberta, Edmonton, Canada.
[5] Denning, D.E. "Cryptography and Data Security. Reading (MA)": Addison-Wesley, 1982.
[6] Diffie, W. & Landau, S. "Privacy on the Line. Boston", MIT Press, 1998.
Proceedings Papers:
[7] Nidhi Gupta, Praveen Kumar and Satish Chhokar, "A Secure Blind Signature Application in E Voting", Proceedings of the 5th
National Conference; Computing For Nation Development, New Delhi, March,2011.
[8] R. Cramer, R. Gennaro, and B. Schoenmakers, and M. Yung, "Multi-Authority Secret-Ballot Elections with Linear Works",
Eurocrypt ‟96, LNCS 1070, pp 72 – 83, 1996.
[9] L.R. Cranor, and R.K. Cytron, "Design and Implementation of a Practical Security-Conscious Electronic Pollind System,"
Washington University: Computer Science Technical Report, 1996.
[10] Zhe Xia, Steve Schneider, "A New Receipt-Free E-Voting Scheme Based on Blind Signature", may 25, 2006.

Abstract: This research work demonstrates the Extraction, compression and query processing of XML documents for Adaptive Compression Techniques and Efficient Query Evaluation. Proposed here are the algorithms for xml compression and Efficient Query Evaluation as - Feasible XML compression using data compression algorithm. Qurey Processor using Sax parsing and Interfaces. It is shown that using the proposed techniques for xml data compression will pave a way for better compression and improve the compression ratio and performance of compressor system.
Keywords: XML compression, partial decompression, SAX parser, compressor Systems, Query processor, Ziv- Lempel algorithm.

[1] James Cheng and Wilfred Ng ―XQzip: Querying Compressed XML Using Structural Indexing‖ E. Bertino et al. (Eds.): EDBT
2004, LNCS 2992, pp. 219–236, 2004. _c Springer-Verlag Berlin Heidelberg 2004
[2] Andrei Arion, Angela Bonifati, Ioana Manolescu, Andrea Pugliese ―XQueC: A Query-Conscious Compressed XML Database‖
ACM Journal Name, Vol. , No. , 20, Pages 1–31.
[3] JunKi Min MyungJae Park ChinWan Chung ―XPRESS: A Queriable Compression for XML Data‖ SIGMOD 2003, June 912, 2003, San Diego, CA. Copyright 2003 ACM 158113634X/ 03/06
[4] Mustafa Atay, Yezhou Sun, Dapeng Liu, Shiyong Lu, Farshad Fotouhi ―MAPPING XML DATA TO RELATIONAL DATA: A DOMBASED APPROACH‖ Department of Computer Science Wayne State University, Detroit, MI 48202
[5] Sherif Sakr ―XML compression techniques: A survey and comparison ‖ National ICT Australia (NICTA), 223 Anzac Parade, NSW 2052, Sydney, Australia Journal of Computer and System Sciences 75 (2009) 303–322
[6] Pankaj M. Tolani Jayant R. Haritsa XGRIND: A Query-friendly XML Compressor‖ Proceedings of the 18th International
Conference on Data Engineering (ICDE.02) 1063-6382/02 $17.00 © 2002 IEEE
[7] Wilfred Ng · Wai-Yeung Lam Peter T. Wood · Mark Levene ―XCQ: A queriable XML compression system‖ Knowl Inf Syst (2006) DOI 10.1007/s10115-006-0012-z
[8] Wilfred Ng Lam Wai Yeung James Cheng ―Comparative Analysis of XML Compression Technologies‖ Department of Computer
Science The Hong Kong University of Science and Technology Hong Kong
[9] Weimin Li ―XCOMP: AN XML COMPRESSION TOOL‖ A thesis presented to the University of Waterloo. Waterloo, Ontario,
Canada, 2003
[10] Michael Ley ―DBLP — Some Lessons Learned‖ VLDB ‗09, August 2428, 2009, Lyon, France Copyright 2009 VLDB Endowment, ACM 0000000000000/ 00/00.

Abstract: Wireless Sensor Networks (WSN), an element of pervasive computing, are presently being used on a large scale to monitor real-time environmental status. WSN has the potential of significantly enhancing our ability to monitor and interact with our physical environment. Fault tolerance is one of the main issues in Wireless Sensor Networks (WSNs) since it becomes critical in real deployment environment where reliability and reduced inaccessibility times are important. So, we propose a fault–tolerance technique for coverage area of the sensor network that enhances the energy efficiency by reducing the communication, with the help of Constrained Delaunay Triangulation. Further by applying the above approach, we reduce the energy consumption and congestion in the network. In last, we compared our approach (CDT) with previous approach Delaunay Triangulation (DT) and concluded that our approach is better in fault tolerance and energy saving.
Keywords: Wireless sensor networks, Fault-tolerance, Coverage Approach, Energy-efficiency, Event reporting, Delaunay Triangulation, Constrained Delaunay Triangulation.

[1] M. Cardei, D. MacCallum, X. Cheng, M. Min, X. Jia, D. Li, and D.Z. Du, Wireless sensor networks with energy efficient
organization, J. Interconnection Networks, 3, pp. 213–229, 2002
[2] S. Slijepcevic and M. Potkonjak, Power efficient organization of wireless sensor networks, IEEE Int. Conf. Commun., 2, pp. 472–
476, 2001
[3] Z. Zhou, S. Das, and H. Gupta, Connected k-coverage problem in sensor networks, in Proc. Int. Conf. Computer Communications
and Networks (ICCCN), pp. 373-378, 2004.
[4] M. Kallmann, H. Bieri, and D. Thalmann. Fully dynamic constrained delaunay triangulations. Geometric Modelling for Scientific
Visualization, 2003.
[5] R.Dubey, S.K.Swain, R.Bera, C.P.Kashayap, Fault Tolerance in Wireless Sensor Networks using Constrained Delaunay
Tringulation, IRnet, ICEECS,.pp.172-178, 2012
[6] M. Cardei, J. Wu, Hand book of Sensor Networks: compact wireless and wired sensing systems, M.Ilyas, I.Mahgoub, (CRC Press ,
New York, London, Boca Raton, 2005), pp. 361-372.
[7] P. Kumari, Y.Singh, Delaunay Triangulation Coverage Strategy for Wireless Sensor Netowrks, IEEE, ICCCI, pp.1-5, 2012
[8] D. Tian and N.D. Georganas, A coverage-preserving node scheduling scheme for large wireless sensor networks, 1st ACM
Workshop Wireless Sensor Networks and Applications, pp. 32–41, 2002
[9] F. Ye, G. Zhong, S. Lu, and L. Zhang, Energy efficient robust sensing coverage in large sensor Networks, Technical report, UCLA,
2002.

Abstract: We describe the design and implementation of a high performance cloud that we have used to archive, analyze and mine large distributed data sets. By a cloud, we mean an infrastructure that provides resources and/or services over the Internet. A storage cloud provides storage services, while a compute cloud provides compute services. High-performance can be reasonably intended as a intermediate step of high-performance data mining activities over large-scale amounts of data, while still keeping unaltered the primary and self-contained focus of achieving effectiveness and efficiency in these task themselves. In this paper we propose an algorithm to mine the data from the cloud using sector/sphere framework and association rules. We also describe the programming paradigm supported by the Sphere compute cloud and Association rules. Sector and Sphere are discussed for analyzing large data sets using computer clusters connected with wide area high performance networks. Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Mining association rules is one of the most important aspects in data mining.
Keywords: Sphere, Sector, Data mining, Cloud computing, Classification, Genetic algorithm, Cloud Model, High performance cloud.

[1] Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. ―The Google File System‖. In SOSP, 2003.

[2] Dhruba Borthaku. ―The hadoop distributed file system: Architecture and design‖. retrieved fromlucene.apache.org/hadoop, 2007. [3] Y unhong Gu and Robert L. Grossman. ―UDT: UDPbased data transfer for high-speed wide area networks‖. Computer Networks, 51(7):1777—1799, 2007.

[4] Han J , Kamber M. ―Data Mining: Concepts and Techniques‖. 2/e San Francisco: CA Morgan Kaufmann Publishers, an imprint of Elsevier. pp- 259-261, 628-640 (2006).

[5] D. Chappell, ―Introducing windows azure,‖ Microsoft, Inc, Tech. Rep., 2009.

[6] K. Keahey, R. Figueiredo, J. Fortes, T. Freeman, and M. Tsugawa, ―Science clouds: Early experiences in cloud computing for scientific applications,‖ Cloud Computing and Applications, vol. 2008, 2008.

[7] I. Foster, T. Freeman, K. Keahy, D. Scheftner, B. Sotomayer, and X. Zhang, ―Virtual clusters for grid communities,‖ Cluster Computing and the Grid, IEEE International Symposium on, vol. 0, pp. 513–520, 2006.

[8] R. Creasy, ―The origin of the VM/370 time-sharing system,‖ IBM Journal of Research and Development, vol. 25, no. 5, pp. 483–490, 1981.

[9] Amazon elastic compute cloud,‖ [Online], http://aws.amazon.com/ec2/.

[10] K. Keahey, I. Foster, T. Freeman, and X. Zhang, ―Virtual workspaces: achieving quality of service and quality of life in the Grid,‖ Scientific Programming, vol. 13, no. 4, pp. 265–275, 2005

Abstract: The paper titled "HUMAN RESOURCE MANAGEMENT SYSTEM" is basically concerned with managing the Administrator of HUMAN RESOURCE Department in a company. A Human Resource Management System (HRMS), refers to the systems and processes at the intersection between human resource management (HRM) and information technology. It merges HRM as a discipline and in particular its basic HR activities and processes with the information technology field, whereas the programming of data processing systems evolved into standardized routines and packages of enterprise resource planning (ERP) software[1]. The main objective of this paper is to reduce the effort of Administrator to keep the daily events such as attendance, projects, works, appointments, etc.
Keywords: Human Resource, Administrator, Employee

Websites :
[1] http://en.wikipedia.org/wiki/Human_resource_ management_system
Books:
[2] James Goodwill, PURE Java Server Pages 3rd Edition
[3] Larne Pekowsky, Java Server Pages 2nd Edition
[4] Simon Brown, Sam Dalton, Daniel Jepp, Dave Johnson, Pro JSP 3rd Edition
[5] Thearon Willis, SQL Server 2000 2nd Edition.

Abstract: The paper examines the spatial distribution of police station in Kano Metropolis. Global Positioning System (GPS) was used to measure the coordinates (latitude and longitude) of the stations in the area, while the data pertaining to the number of police personnel in each station were sourced from interview and documented data sourced. The data were anlysed using simple and inferential statistics. Also ArcGIS 9.3(Version) Software was used to draw the map of the distribution. A nearest neighborhood analysis has shown that the distribution of stations is random in the area. One and two kilometer buffer zones were generated and the result shows that the old city of Kano and the eastern part of the metropolis were fully served while the west and southern part were underserved. The ratio of police officer to population in the area is 1: 539 in the area which is far below the United Nation recommended figure of 450. It was also discovered that there is neither significant relationships between the numbers of the station nor between the number personnel in the station and population in the area. The research recommends the needs for population consideration in citing station in the areas as one of the means for achieving better security situations.
Keywords: Police, Kano Metropolis, Manpower, GIS

[1] Ackerman W.V. & Murray A.T. (2004) Assessing Spatial Patterns of Crime. Lima, OhioSA Cities, Vol. 21, No. 5, p. 423–437, 2004.
[2] Allan T. Murray, Ingrid McGufog, John S. Wester and Patric Mulins (2001) "Exploratory Spatial Data Analysis Techniques for
Examining Urban Crime. Brit. J. Criminol Vol. 4, pg. 309-329.
[3] Anne Chen (2004) GIS Fights Crime in Chicago. Article in eWEEK.com Enterprise
[4] Aygün Erdoğan, Ayşe Gedik, H. Şebnem Düzgün (2000) Integrated Analysis of Crime Incidents Within a Loose-Coupled Gis-Based
SystemCase of Etlik Police Station Zone.
[5] Canter, P.R.(1998) Geographic Information Systems and Crime Analysis. Baltimore Country, Maryland
[6] Chaffin, J. W. (2004) Criminal Intelligence Analyst Traffic Crash Management System, Traffic Analysis Program. Tampa, FL,
Interview on 4th March 2004
[7] Babajide Maiyegun (2006) GIS Approach to Crime Mapping and Management in Nigeria: A CaseStudy of Victoria Island. Lagos-
Nigeria.
[8] Bonham-Carter, G.F. (1994) Geographic Information Systems for Geoscientists.
[9] Fattah E. (1997) Criminology: past, present and future. Basingsoke, Mcmillan England.
[10] Francis Fajemirokun, O. Adewale, Timothy Idowu, Abimbola Oyewusi and Babajide Maiyegun, (2006) A GIS Approach to Crime
Mapping and Management in Nigeria: A Case Study of Victoria Island Lagos- Nigeria.

Abstract: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data is leaked and found in an unauthorized place (e.g., on the web or somebody's laptop). The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases we can also inject "realistic but fake" data records to further improve our chances of detecting leakage and identifying the guilty party.
Keywords: Allocation strategies, data leakage, data privacy, fake records, leakage model.

[1] Technical Report TR-BGU-2409-2010 24 Sept. 2010 1 A Survey of Data Leakage Detection and Prevention Solutions P.P (1 -5, 24-
25) A. Shabtai, a. Gershman, M. Kopeetsky, y. Elovici Deutsche Telekom Laboratories at Ben-Gurion University, Israel.
[2] IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 22, NO. 3, MARCH 2011 Data Leakage
Detection Panagiotis Papadimitriou, Member, IEEE, Hector Garcia-Molina, Member, IEEE P.P (2,4-5)
[3] Data Leakage: What You Need to Know by Faith M. Heikkila, Pivot Group Information Security Consultant. P.P (1 -3)
[4] International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE II, JUNE 2011] [ISSN: 2231-4946] P.P (1,
4) Development of Data leakage Detection Using Data Allocation Strategies Rudragouda G Patil Dept of CSE, The Oxford College
of Engg, Bangalore.
[5] Mr.V.Malsoru, Naresh Bollam/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 1, Issue 3, pp.1088-1091 1088 | P a g e REVIEW ON DATA LEAKAGE DETECTION.
[6] Mr.V.Malsoru, Naresh Bollam/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 1, Issue 3, pp.1088-1091 1088 | P a g e REVIEW ON DATA LEAKAGE DETECTION.
[7] International Journal of Computer Applications in Engineering Sciences[VOL I, ISSUE II, JUNE 2011] [ISSN: 2231-4946] P.P (1,
4)Development of Data leakage Detection Using Data Allocation StrategiesRudragouda G Patil Dept of CSE, The Oxford College
of Engg, Bangalore.patilrudrag@gmail.com
[8] A Model for Data Leakage Detection Panagiotis Papadimitriou 1, Hector Garcia-Molina 2 Stanford University 353 Serra Street,
Stanford, CA 94305, USA P.P (1, 4-5) 1papadimitriou@stanford.edu
[9] Web-based Data Leakage Prevention Sachiko Yoshihama1, Takuya Mishina1, and Tsutomu Matsumoto2 1 IBM Research - Tokyo,
Yamato, Kanagawa, Japan fsachikoy, tmishinag@jp.ibm.com, P.P (2,14) 2 Graduate School of Environment and Information
Sciences, Yokohama National University, Yokohama, Kanagawa, Japan tsutomu@ynu.ac.jp
[10] Data Leakage: Affordable Data Leakage Risk Management by Joseph A. Rivela Senior Security Consultant P.P (4-6)