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.