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  Integration of Multi Bank Account in Single Card with User Behavior Monitoring Using Hmm and Verification  
  Authors : Suresh R; Somasundaram M; Sethukarasi T
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In the EXISTING methodology, big data is an opportunity based environment. Big data analytics can lead to valuable knowledge for many organizations. In this paper, Integration of Big Data, Business analytical and RFID technology are recent trends in IT, which is a challenge oriented activity. We have MODIFIED AND IMPLEMENTED this application for developing Banking sector particularly for Debit / ATM \card section. We can use RFID smart card as ATM Card for transaction. User can create account and get the ATM card from the bank. The user can integrate all his bank accounts which can be integrated in this single card with unique PIN numbers accordingly. User behaviour is monitored through HMM Model and he can set up a formula based authentication. The user can include all his family members’ accounts details to this same card. The user can withdraw cash from their accounts after successful authentication of the corresponding PIN numbers.


Published In : IJCSN Journal Volume 6, Issue 3

Date of Publication : June 2017

Pages : 417-420

Figures :01

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Suresh R : Student, Department of Computer Science & Engineering Rmk Engineering College, Kavaraipettai- 601206, Tamilnadu, India.

Somasundaram M : Assistant Professor of CSE Department Rmk Engineering College, Kavaraipettai- 601206, Tamilnadu, India.

Sethukarasi T : Head of CSE Department Rmk Engineering College, Kavaraipettai- 601206, Tamilnadu, India.


RFID Card, Formulae Authentication, Hidden Markov Model, Email Alert

There is sufficient supporting evidence to conclude that data-driven approaches would be a growing research methodology/ philosophy in business operations. Countless application domains can be influenced by this big data fad. BI systems are definitely on the list as such systems highly rely on the input data to generate valuable outputs. That being said, the scope of BI systems is so wide and related research involved the multidisciplinary knowledge. Hence it is not surprising that the research focal points have been scattered around different disciplines. Consequently, it is not easy to generalize the results from previous studies. In this connection, emerging big-data-oriented research may need some adjustments. Synergizing multiple research methodologies could be one direction. Data mining is still the core engine of BI systems but previous data mining algorithms are very application-oriented. This is not a criticism but an observation. The main reason is due to the nature of the data involved.


[1] N. Manwani and P. S. Sastry, “Noise tolerance under risk minimization,”IEEE Trans. Cybern., vol. 43, no. p1146- 1151, Jun. 2013. [2] H. K. Chan and F. T. S. Chan, “Early order completion contract approach to minimize the impact of demand uncertainty on supply chains,” IEEE Trans. Ind. In format, vol. 2, no. 1, pp. 48–58, Feb. 2006. [3] G. M. Gaukler, “Item-level RFID in a retail supply chain with stock out-based substitution,” IEEE Trans. Ind. In format, vol. 7, no. 2,pp. 362–370, May 2011. [4] K. Govindan, A. Jafarian, M. E. Azbari, and T.-M. Choi, “Optimal bi-objective redundancy allocation for systems reliability and risk management,” IEEE Trans. Cybern., to be published. [5] B. Shen, T.-M. Choi, Y. Wang, and C. K. Y. Lo, “The coordination offashion supply chains with a risk-averse supplier under the markdown money policy,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 43, no. 2,pp. 266–276, Mar. 2013. [6] H. M. Markowitz, Portfolio Selection: Efficient Diversification of Investment. New York, NY, USA: Wiley, 1959. [7] D. D. Wu and D. Olson, “Enterprise risk management: A DEA VaRapproach in vendor selection,” Int. J. Prod. Res., vol. 48, no. 16,pp. 4919–4932, 2010.