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Lost Student Tracking in an Incomplete and Imprecise Information Environment Using Soft Computing Paradigm

Lost Student Tracking in an Incomplete and Imprecise Information Environment Using Soft Computing Paradigm

Satya Ranjan Dash, Susil Rayaguru, Satchidananda Dehuri, Sung-Bae Cho
Copyright: © 2012 |Volume: 3 |Issue: 4 |Pages: 17
ISSN: 1947-3087|EISSN: 1947-3079|EISBN13: 9781466610927|DOI: 10.4018/ijalr.2012100103
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MLA

Dash, Satya Ranjan, et al. "Lost Student Tracking in an Incomplete and Imprecise Information Environment Using Soft Computing Paradigm." IJALR vol.3, no.4 2012: pp.32-48. http://doi.org/10.4018/ijalr.2012100103

APA

Dash, S. R., Rayaguru, S., Dehuri, S., & Cho, S. (2012). Lost Student Tracking in an Incomplete and Imprecise Information Environment Using Soft Computing Paradigm. International Journal of Artificial Life Research (IJALR), 3(4), 32-48. http://doi.org/10.4018/ijalr.2012100103

Chicago

Dash, Satya Ranjan, et al. "Lost Student Tracking in an Incomplete and Imprecise Information Environment Using Soft Computing Paradigm," International Journal of Artificial Life Research (IJALR) 3, no.4: 32-48. http://doi.org/10.4018/ijalr.2012100103

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Abstract

In a country like India, the growth rate of the number of academic institutions is at par with the lost student rate. Hence when a lost student is found we need to identify the student on the basis of information such as name of the student, institution name where he studies, class or branch of the student, etc. But the fact is that in most of the cases one never gets complete and precise information to identify a lost student. Hence, in such environment a soft computing model can be an attractive alternative to identify a lost student on the basis of imprecise or partial information. This paper presents a soft computing model for identifying lost student on the basis of imprecise and partial information. In this model student information is represented as a symbolic student object. Symbolic student object is further processed using a fuzzy symbolic model for identifying the lost student. The authors have devised a symbolic knowledge base which acts as a repository of information pertaining to student of different institutions that assist in creating student object and identifying the lost student. A fuzzy technique “symbolic similarity measure” is devised for generating symbolic student object and mapping the symbolic student object with student information present in knowledge base. This system has been tested scrupulously and an efficiency of above 90% has been achieved in identifying the lost student.

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