An intelligent decision support system for credit card assessment based on a machine learning technique

2002 ◽  
Vol 2 (2) ◽  
pp. 243-260 ◽  
Author(s):  
Nikolaos F Matsatsinis
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Credit Card ◽  
Machine Learning Technique ◽  
Intelligent Decision Support System ◽  
Intelligent Decision ◽  
Learning Technique ◽  
Intelligent Decision Support

scholarly journals Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique

2021 ◽  
Vol 30 (1) ◽  
pp. 739-749
Author(s):  
Sohaib Latif ◽  
Fang XianWen ◽  
Li-li Wang
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Research Work ◽  
Individual Student ◽  
Intelligent Decision Support System ◽  
Intelligent Decision ◽  
The Individual ◽  
Intelligent Decision Support

Abstract In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are used. The ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared ( χ ) \left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems.

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