10.5120/ijais12-450654
- M Narayana Swamy and M. Hanumanthappa 2012. Predicting Academic Success from Student Enrolment Data using Decision Tree Technique. International Journal of Applied Information Systems. 4, 3 (September 2012), 1-6. DOI=http://dx.doi.org/10.5120/ijais450654
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@article{10.5120/ijais2017451568, author = {M Narayana Swamy and M. Hanumanthappa}, title = {Predicting Academic Success from Student Enrolment Data using Decision Tree Technique}, journal = {International Journal of Applied Information Systems}, issue_date = {September 2012}, volume = {4}, number = {}, month = {September}, year = {2012}, issn = {}, pages = {1-6}, numpages = {}, url = {/archives/volume4/number3/278-0654}, doi = { 10.5120/ijais12-450654}, publisher = { xA9 2010 by IJAIS Journal}, address = {} } -
%1 450654 %A M Narayana Swamy %A M. Hanumanthappa %T Predicting Academic Success from Student Enrolment Data using Decision Tree Technique %J International Journal of Applied Information Systems %@ %V 4 %N %P 1-6 %D 2012 %I xA9 2010 by IJAIS Journal
Abstract
The recently introduced cashless economy with the cash-lite banking by the Central Bank of Nigeria (CBN) has engineered most Nigeria banks to introduce e-payment and e-transact solutions to support the policy. However, one of the major problems limiting the growth of this new move in Nigeria is the absence of secure and reliable e-payment systems. The problems associated with the implementation of a secure e-payment systems in the country stem from card thefts, internet fraud and identity theft e. t. c, which runs into millions of US dollars annually. This has adversely affected the integrity, development of e-commerce and the country's active participation in the international market. Hence, the need for a secured and reliable mechanism for a proficient implementation of e-payment system in the country. This paper is focused on securing reliable authentication scheme for e-payment system in Nigeria through effective biometric authentication technology.
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Keywords
Educational Data mining, Classification, Decision Tree, Higher Education
