A parallel soft computing model for identifying lost student in an incomplete and imprecise environment
Автор: Mahendra Kumar Gourisaria, Susil Rayaguru, Satya Ranjan Dash, Sudhansu Shekhar Patra
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 4 vol.10, 2018 года.
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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.
Soft computing, parallel soft computing model, symbolic similarity measure, fuzzy theory and lost student tracking
Короткий адрес: https://sciup.org/15016481
IDR: 15016481 | DOI: 10.5815/ijisa.2018.04.07
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