Neural computing: theory and practice
Neural computing: theory and practice
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Determining the Significance of Input Parameters using Sensitivity Analysis
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Using integer programming to guide college admissions decisions: a preliminary report
Journal of Computing Sciences in Colleges
Building a Recommender Agent for e-Learning Systems
ICCE '02 Proceedings of the International Conference on Computers in Education
Case studies in admissions to and early performance in computer science degrees
Working group reports from ITiCSE on Innovation and technology in computer science education
Temperature prediction in electric arc furnace with neural network tree
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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Predicting university admission is a complex decision making process that is more than merely relying on test scores. It is known by researchers that students' backgrounds and other factors correlate to the performance of their tertiary education. This paper proposes a hybrid model of neural network and decision tree classifier that predicts the likelihood of which university a student may enter, by analysing his academic merits, background and the university admission criteria from that of historical records. Our prototype system was tested with live data from sources of Macau secondary school students. In addition to the high prediction accuracy rate, flexibility is an advantage as the system can predict suitable universities that match the students' profiles and the suitable channels through which the students are advised to enter. Our model can be generalized with other attributes and perform faster when compared to using a neural network alone.