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Classification Performance Enhancement for Students Realisation Model


Tarik A. Rashid

Software Engineering, College of Engineering, Salahaddin University





Abstract 
    This research work aims at enhancing a classification task for student’s realisation model
at Salahadin University, Hawler, Kurdistan. 1000 records of data from different colleges
and departments at Salahadin University are collected to conduct this research work. The
collected data has been pre-processed, cleaned, filtered, normalized, then after, feature
selection techniques are applied to reduce the dimensionally of the data, finally a
classification task is carried out to find the realization of students. The results show that a
model of Support Vector Machine +Genetic Algorithm + Artificial Neural Network
produces promising results than other models.

Key Words: Forecasting Student Performance, Feature Reduction, Neural Networks, Support Vector Machines, K- Nearest Neighbors,
Genetic Algorithms.



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