A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

Автор: Raju Dara, Ch.Satyanarayana, A. Govardhan

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 11 vol.6, 2014 года.

Бесплатный доступ

Many diversified applications do exist in science & technology, which make use of the primary theory of a recognition phenomenon as one of its solutions. Recognition scenario is incorporated with a set of decisions and the action according to the decision purely relies on the quality of extracted information on utmost applications. Thus, the quality decision making absolutely reckons on processing momentum and precision which are entirely coupled with recognition methodology. In this article, a latest rule is formulated based on the degree of correlation to characterize the generalized recognition constraint and the application is explored with respect to image based information extraction. Machine learning based perception called feed forward architecture of Artificial Neural Network has been applied to attain the expected eminence of elucidation. The proposed method furnishes extraordinary advantages such as less memory requirements, extremely high level security for storing data, exceptional speed and gentle implementation approach.

Еще

Image recognition, information retrieval, artificial neural network, degree of correlation, fault-tolerance

Короткий адрес: https://sciup.org/15013446

IDR: 15013446

Список литературы A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

  • “Pattern Classification”, Richard, Peter.E.Hart David G. Stork, Wiley Publications, Nov’ 9, 2000.
  • Om Preeti Chaurasia, “An Approach to Fingerprint Image Pre-Processing”,I.J. Image, Graphics and Signal Processing, Vol.4, No.6, July 2012, PP: 29-35 DOI:10.5815/ijigsp.2012.06.05.
  • Helena Galhardas, Antónia Lopes, Emanuel Santos, “ Support for User Involvement in Data Cleaning ”, LNCS, Springer, Data Warehousing and Knowledge Discovery,Vol. 6862,2011,pp:136-151.
  • Kalaivany Natarajan, Jiuyong Li, Andy Koronios, “Data mining techniques for data cleaning”, Springer, Engineering Asset Lifecycle Management, 2010, PP:796-804.
  • Tee Leong Kheng; Collin, C.; Ong Siong Lee, “ E-Clean: A Data Cleaning Framework for Patient Data ”, Informatics and Computer Intelligence (ICI), Page (s): 63 – 68.
  • Richard Y. Wang, Veda C. Storey, and Christopher P.Firth, “A Framework for Analysis of Data Quality Research”, IEEE Transactions on knowledge and Data engineering, Vol.7, NO, 4,1995, PP: 623-640.
  • Huang, K., Lee, Y.,and Wang,R. “Quality Information and Knowledge”,Prentice Hall, Upper Saddle River: N.J.1999
  • Kahn, B.K, Strong, D.M, and Wang, R. Y.“Information Quality Benchmarks: Product and Service Performance”. Commun. ACM, (2002).
  • Y. Won and P. Gader, “Morphological Shared -Weight Neural Network for Pattern and Target Detection”, University of Missouri-Columbia, 1995.
  • Shailendra Kumar Dewangan, “Real Time Recognition of Handwritten Devnagari Signatures without Segmentation Using Artificial Neural Network”, I.J.Image,Graphics,and Signal Processing, Vol.5, No.4, April 2013, PP.30- 37, DOI: 10.5815/ijigsp.2013.04.04.
  • “Face Recognition System Using Back Propagation Artificial Neural Networks”, N. Revathy, Guhan, International Journal of Advanced Engineering Technology, Vol. III, Issue I, Jan - Mar, 2012/321-324.
  • Ali Javed, “Face Recognition Based on Principal Component Analysis”,I.J.Image,Graphics,and Signal Processing, Vol.5, No.2, February 2013, PP.38-44, DOI: 10.5815/ijigsp.2013.02.06.
  • S. Marina, M. Gore, G. Soda.. And C. Society “Artificial neural networks for document Analysis and recognition”, IEEE transactions on pattern Analysis and machine intelligence, vol. 27, no.1, pp. 23-35, Jan. 2005.
  • Jose Luis Hidalgo, Salvador Espana, Marıa Jose Castro, and Jose Alberto Perez, “Enhancement and Cleaning of Handwritten Data by Using Neural Networks”, IbPRIA 2005, LNCS 3522, pp. 376–383, 2005.
  • Y. Won and P. Gader, “Morphological Shared-Weight Neural Network for Pattern and Automatic Target Detection”, University of Missouri-Columbia, 1995.
  • Ghada Kattmah,Gamil Abdel Azim, “Identification Based on Mutual Information and Neural Networks”, I.J. Image Graphics, and Signal Processing, Vol.5, No.9, July 2013, PP.50- 57, DOI: 0.5815/ijigsp.2013.09.08.
  • “Novelty Detection in image recognition using IRF Neural Networks properties”, Philippe Smagghe, Jean Luc Buessler, Jean - Philippe Urban, ESANN 2013proceedings, European Symposium on Artificia Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 24-26Apr 2013.
  • Qian Lin; Peng CAI; Feng Zhang;, “Image recognition via discrete Hopfield neural network”, IEEE, International conference on Advances in Energy Engineering(ICAEE),June2010,pp:339 – 342.
  • Mutter, K.N.; Kaream, I. I. A.; Moussa, H. A, “Gray Image Recognition Using Hopfield Neural Network With Multi-Bitplane and Multi-Connect Architecture”,IEEE International Conference on Computer Graphics, Imaging and Visualization, July 2006, PP: 236 -242.
  • Lisheng Xu, “Pulse image recognition using fuzzy neural network”, Expert Systems with Applications: An International Journal,Vol.36,Issue 2,March,2009.
  • Cheng, F.; Chen, F.N.; Ying, Y.B.; “Image Recognition of Unsound Wheat Using Artificial Neural Network”, IEEE, Second WRI Global Congress on Intelligent Systems (GCIS), Dec 2010, PP: 172 – 175.
  • Li Hanguang;Zhao Xiaoyu; Zheng Guansheng;,“Chemical Image Recognition Based on BP Neural Networks”, IEEE, International Conference on Information Science and Engineering (ICISE), Dec. 2009, pp: 1191 – 1195.
  • Aizenberg, I.; Alexander, S.; Jackson, J.; “Recognition of Blurred Images Using Multilayer Neural Network Based on Multivalued Neurons “IEEE International Symposium on Multiple-Valued Logic (ISMVL), May2011, pp: 282-287.
  • Hoshino, K.; Igarashi, H.;”An image recognition based on neural oscillator network “IEEE International Joint Conference on Neural Networks (IJCNN), July2010, PP: 1-6.
  • Huanglin Zeng and Yao Yi,”Image Recognition Using Adaptive Fuzzy Neural Network and Wavelet Transform “, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, LNCS, Volume 2639/2003, 590, springer.
  • Jian Wang; Jingfeng Yang; Shaofa Li;Qiufang, “Number Image Recognition Based on Neural Network Ensemble”, IEEE international conference on Natural Computation, 2007. ICNC 2007. Aug.2007pp:237-240.
  • Bock, S.; Newsome, S.; Wang, Q.; Zeng, W.; Lin, X.; Lu, J.; “image: An Image Based Information Retrieval Application for the iPhone”,IEEE,Consumer Communications and Networking Conference (CCNC), Jan. 2010, PP:1–2.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp