Optimising the parameters of a RBFN network for a teaching learning paradigm

Academic performance of students has been a concern worldwide. Despite efforts made by educational institutions there has been a rise in poor academic performance. In our research study we have proposed a model to pre-determine the academic performance of students using a Radial Basis Function netwo...

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Main Authors: Chaudhury, Pamela (Author), Tripathy, Hrudaya Kumar (Author)
Other Authors: Optimising the parameters of a RBFN network for a teaching learning paradigm (Contributor)
Format: EJournal Article
Published: Institute of Advanced Engineering and Science, 2019-07-01.
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042 |a dc 
100 1 0 |a Chaudhury, Pamela  |e author 
100 1 0 |a Optimising the parameters of a RBFN network for a teaching learning paradigm  |e contributor 
700 1 0 |a Tripathy, Hrudaya Kumar  |e author 
245 0 0 |a Optimising the parameters of a RBFN network for a teaching learning paradigm 
260 |b Institute of Advanced Engineering and Science,   |c 2019-07-01. 
500 |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/15535 
520 |a Academic performance of students has been a concern worldwide. Despite efforts made by educational institutions there has been a rise in poor academic performance. In our research study we have proposed a model to pre-determine the academic performance of students using a Radial Basis Function network (RBFN) using primary data. The proposed model has been developed by using algorithms like differential evolution (DE) and teaching learning based optimization (TLBO). This model can be used by academic institutions to identify the academically weaker students and take preventive steps to reduce the number of academic failures. 
540 |a Copyright (c) 2019 Institute of Advanced Engineering and Science 
540 |a http://creativecommons.org/licenses/by-nc/4.0 
546 |a eng 
690
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655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
655 7 |2 local 
786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 15, No 1: July 2019; 435-442 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v15.i1 
787 0 |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/15535/12801 
856 4 1 |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/15535/12801  |z Get fulltext