International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 10, No. 4, 8 Apr. 2018
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A Parallel Soft Computing Model for Identifying Lost Student in an Incomplete and Imprecise Environment
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Author(s)
Mahendra Kumar Gourisaria
1,*
Susil Rayaguru
2
Satya Ranjan Dash
3
Sudhansu Shekhar Patra
3
Index Terms
Soft computing, parallel soft computing model, symbolic similarity measure, fuzzy theory and lost student tracking
Abstract
The numbers of educational institutions are growing at par with the lost student rate in a country like India. When a missing student is found we need to identify the student on the strength of some common parameter like student name, his/her institution name, branch or class etc. But we never get accurate and complete information in most of the cases to identify or recognize a lost student. In such a situation, a soft computing model can be a striking choice to track a lost student on the basis of partial information. In the past we propose soft computing model for the same. This paper proposes a more optimized parallel soft computing model which takes half of the time taken by the earlier single thread model for identifying a lost student on the basis of imprecise and partial information. The system is tested meticulously on a database of 50000 records and an efficiency of 94% is obtained.
Cite This Paper
Mahendra Kumar Gourisaria, Susil Rayaguru, Satya Ranjan Dash, Sudhansu Shekhar Patra, "A Parallel Soft Computing Model for Identifying Lost Student in an Incomplete and Imprecise Environment", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.4, pp.58-67, 2018. DOI:10.5815/ijisa.2018.04.07
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