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New Dropout Prediction for Intelligent System

by Md.sarwar Kamal, Linkon Chowdhury, Sonia Farhana Nimmy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 16
Year of Publication: 2012
Authors: Md.sarwar Kamal, Linkon Chowdhury, Sonia Farhana Nimmy
10.5120/5778-8093

Md.sarwar Kamal, Linkon Chowdhury, Sonia Farhana Nimmy . New Dropout Prediction for Intelligent System. International Journal of Computer Applications. 42, 16 ( March 2012), 26-31. DOI=10.5120/5778-8093

@article{ 10.5120/5778-8093,
author = { Md.sarwar Kamal, Linkon Chowdhury, Sonia Farhana Nimmy },
title = { New Dropout Prediction for Intelligent System },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 16 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number16/5778-8093/ },
doi = { 10.5120/5778-8093 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:28.929270+05:30
%A Md.sarwar Kamal
%A Linkon Chowdhury
%A Sonia Farhana Nimmy
%T New Dropout Prediction for Intelligent System
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 16
%P 26-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main purpose of this research is to develop a dynamic dropout prediction model for universities, institutes and colleges. In this work, we first identify dependent and independent variables and dropping year to classify the successful from unsuccessful students. Then we have classify the data using Support Vector Machines(SVM). SVM helped the data set to be properly design and manipulated . The main purpose of applying this identification is to design a Knowledge Base which is sometimes known as joint probability distribution . The concepts of propositional logic helped to build the knowledge Base. Bayes theorem will perform the prediction by collecting the information from knowledge Base. Here we have considered most important factors to classify the successful students over unsuccessful students are gender, financial condition and dropping year. We also consider the socio-demographic variables such as age, gender, ethnicity, education, work status, and disability and study environment that may in-flounce persistence or dropout of students at university level

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Index Terms

Computer Science
Information Sciences

Keywords

Intelligent System Dynamic Dropout Prediction Joint Probability Distribution Bayes Theorem Dependent Ad Independent Variables Propositional Logic Knowledge Base mathlab svm

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