Pattern recognition using neural network time series

  • Authors

    • Nagarajan D
    • Kavitha G
    https://doi.org/10.14419/ijet.v7i4.21602
  • Abstract

    Pattern recognition mainly concentrate on identification of designs to identify the characterized by prefixed principle on a data. Classification of IRIS dataset has been taken to examine the petal and sepal size of the IRIS flower and to predict analyzing to which pattern the class of IRIS flower really belongs to. In this paper, the model has been trained with Neural Network time series analysis to recognize the pattern of IRIS flower. Pattern recognition is a field of cognate such as image processing and neural network .Pattern recognition mainly concentrate on identification of designs to identify the characterized by prefixed principle on a data. Classification of IRIS dataset has been taken to examine the petal and sepal size of the IRIS flower and to predict analyzing to which pattern the class of IRIS flower really belongs to. In this paper, the model has been trained with Neural Network time series analysis to recognize the pattern of IRIS flower. The paper applies neural networks for forecasting. The learning rule in neural network modifies the parameters for a given input to give a desired output. The proposed research work identifies patterns using supervised neural network training algorithm to accurately predict the behavioral pattern in IRIS flower species.

  • References

    1. [1] DiptamDutta, Argha Roy, KaustavChoudhury, “Training Aritificial Neural Network Using Particle Swarm Optimization Algorithmâ€, International Journal on Computer Science and Engineering (IJCSE), Volume 3, Issue 3, March 2013.

      [2] Poojitha V, Shilpi Jain, “A Collecation of IRIS Flower Using Neural Network Clusterimg tool in MATLABâ€, International Journal on Computer Science and Engineering (IJCSE).

      [3] Murphy P.M, Aha.D.W (1994), Repository of machine learning databases. [http.//www.ics.uci.edu/mlearn/ml-repository.html].

      [4] Poojitha V, MadhulikaBhadauria, Shilpi Jain, AnchalGarg, A collocation of IRIS flower using neural network clustering tool in MATLAB, Cloud System and Big Data Engineering(Confluence), 2016 6th International Conference.

      [5] Chong Wu, ChongluZhong and Yanlei Yin, “A Novel data classification method and its application in IRIS flower shapeâ€, International Journal of Hybrid Information Technology, Vol. 8, No. 11(2015), pp.161-170. https://doi.org/10.14257/ijhit.2015.8.11.13.

      [6] R. A. Abdulkadir., et al., “ Simulation of Back Propogation neural network for iris flower classification, Ameriacan Journal of Engineering Research, Vol. 6, Issue-1, pp. 200-205, 2017.

      [7] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang. Complex networks: Structure and dynamics. Physics Reports, 424(4– 5):175 – 308, 2006.

      [8] Aaron Clauset, M. E. J. Newman, and Cristo pher Moore. Finding community structure in very large networks. Phys. Rev. E, 70:066111, Dec 2004.

      [9] David Casado de Lucas. Classification Techniques for Time Series and Functional Data. PhD thesis, Universidad Carlos III de Madrid, 2003.

      [10] Janez Demˇsar. Statistical comparisons of classifiers over mul tiple data sets. J. Mach. Learn. Res., 7:1–30, December 2006.

      [11] PhilippeEsling and Carlos Agon. Time-series data mining. ACM Computing Surveys, 45(1):1–34, November 2012.

      [12] Leonardo N. Ferreira and Liang Zhao. Code and extra information for the paper: A time series clustering technique based on community detection in networks. http://lnferreira.github.io/ ts_clustering_via_community_detection/, Fev 2015. Accessed Feb- 2015.

      [13] Santo Fortunato. Community detection in graphs. Physics Reports, 486(3–5):75–174, 2010.

      [14] E. Frentzos, K. Gratsias, and Y. Theodoridis. Index-based most similar trajectory search. In Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on, pages 816–825, April 2007.

      [15] G. Gan, C. Ma, and J. Wu. Data Clustering: Theory, Algorithms, and Applications. Society for Industrial and Applied Mathematics, 2007.

      [16] M. Girvan and M. E. J. Newman. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12):7821–7826, 2002.

      [17] Xavier Golay, Spyros Kollias, Gautier Stoll, Dieter Meier, Anton Valavanis, and Peter Boesiger. A new correlation-based fuzzy logic clustering algorithm for fmri. Magnetic Resonance in Medicine,40(2):249–260, 1998.

      [18] Maria Halkidi, Yannis Batistakis, and Michalis Vazirgiannis. On clustering validation techniques. J. Intell. Inf. Syst., 17(2-3):107–145, December 2001.

      [19] Wei L Ratanamahatana C Keogh E, Xi X. The UCR time series dataset. http://www.cs.ucr. edu/~eamonn/time_series_data/, 2008.

      [20] CarlaS. M¨oller-Levet, Frank Klawonn, Kwang-Hyun Cho, and Olaf Wolkenhauer. Fuzzy clustering of short time-series and un evenly distributed sampling points. In Advances in Intelligent Data Analysis V, volume 2810 of Lecture Notes in Computer Science, pages 330–340. Springer Berlin Heidelberg, 2003.

      [21] Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, and Eamonn Keogh. Experimental comparison of representation methods and distance measures for time series data.Data Mining and Knowledge Discovery, 26(2):275-309, 2013.

      [22] T. Warren Liao. Clustering of time series data-a survey. Pattern Recogn., 38(11):1857–1874, November 2005.

      [23] Yimin Xiong and Dit-Yan Yeung. Time series clustering with arma mixtures. Pattern Recognition, 37(8):1675 – 1689, 2004.

      [24] Byoung-Kee Yi and Christos Faloutsos. Fast time sequence indeing for arbitrary lp norms. In Proceedings of the 26th International Conference on Very Large Data Bases, VLDB ’00, pages 385–394, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc.

      [25] Hui Zhang, Tu Bao Ho, Yang Zhang, and Mao Song Lin. Unsuper vised feature extraction for time series clustering using orthogonal wavelet transform. Informatica (Slovenia), 30(3):305–319, 2006.

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  • How to Cite

    D, N., & G, K. (2018). Pattern recognition using neural network time series. International Journal of Engineering & Technology, 7(4), 3357-3359. https://doi.org/10.14419/ijet.v7i4.21602

    Received date: 2018-11-25

    Accepted date: 2018-11-25