Wednesday 20th of September 2017
 

Predictive Analysis for Journal Abstracts using Polynomial Neural Networks Algorithm


Adebola K. Ojo

Academic journals are an important outlet for dissemination of academic research. In this study, Neural Networks model was used in the prediction of abstracts from The Institute of Electrical and Electronics Engineers (IEEE) Transactions on Computers. Simulation of results was done using the Polynomial Neural Networks algorithm. This algorithm, which is based on Group Method of Data Handling (GMDH) method, utilizes a class of polynomials such as linear, quadratic and modified quadratic. The prediction was done for a period of twenty-four months using a predictive model of three layers and two coefficients. The performance measures used in this study were mean square errors, mean absolute error and root mean square error.

Keywords: Polynomial Neural Networks, IEEE, GMDH, mean square errors, mean absolute error, Root mean square error

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ABOUT THE AUTHOR

Adebola K. Ojo
Adebola K. OJO is a lecturer and a researcher in the Department of Computer Science, University of Ibadan, Nigeria. She is a registered member of the Computer Professional of Nigeria (CPN). She had her Master of Science and PhD Degrees in Computer Science from University of Ibadan, Nigeria. She had her Bachelor of Science (BSc) in Computer Engineering from Obafemi Awolowo University (OAU), Ile-Ife, Nigeria. Her research interests are in Digital Computer Networks, Data and Text Mining, and Computer Simulation. She is also into data warehouse architecture, design and data quality via data mining approach.


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