Pattern Recognition Neural Network and Class of Grades

OJUAWO, Olutayo and OLAIJU, O.A and AFOLAYAN, O (2015) Pattern Recognition Neural Network and Class of Grades. The International Institute for Science, Technology and Education (IISTE)., 5 (2). ISSN 2225-0603

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Abstract

We investigated the ability of neural network to recognize pattern by using recognition neural network tool box to predict the class of Science Laboratory Technology students in Federal Polytechnic, Ilaro. A total final result of 431 students of the department between 2005 and 2010 was used. The data contained results of ten courses offered by the students together with their GPA and the grade of each student. The marks scored by 420 students in each course together with the grade obtained in each course were used for training, testing and validation of the neural network. 11 students’ data were used for prediction purpose. We used a two-layer feed forward network, with sigmoid hidden and output neurons (newpr), which can classify vectors arbitrarily well, given enough neurons in its hidden layer. The network was trained with scaled conjugate gradient back propagation (trainscg). The marks scored by 294 (70%) students in each course together with the grade obtained in each course were used for training, 63 (15%) for testing and 63(15%) for validation of the neural network. The grades were classified into five categories: probation, Pass, Lower credit, upper credit and distinction. We predict the type of grade based on scores of the students in each course. The training, validation and test were performed with different neuron numbers in the hidden layer i.e 20, 15, 10 and 5 neurons. The results showed that among the ANN models, ANN 20 performed best with MSE = 1.511623803293947e-07 and Confusion = 0.06122489795918 followed by ANN 5 with MSE = 1.804838395630207e-07 and Confusion = 0.052721088435374. Our research showed clearly that neural network pattern recognition tools can predict student grade perfectly well if given enough data to train.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mr Taiwo Egbeyemi
Date Deposited:	11 Jun 2020 15:42
Last Modified:	11 Jun 2020 15:42
URI:	http://eprints.federalpolyilaro.edu.ng/id/eprint/519

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