Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12540/450
Title: A system of serial computation for classified rules prediction in non-regular ontology trees
Authors: Ehimwenma, Kennedy E. 
Crowther, Paul 
Beer, Martin 
Issue Date: 2016
Publisher: AIRCC Publishing Corporation
Source: Ehimwenma, K. E., Crowther, P., & Beer, M. (2016). A system of serial computation for classified rules prediction in non-regular ontology trees. International Journal of Artificial Intelligence and Applications (IJAIA), 7(2), 21-33.
Journal: International journal of artificial intelligence and applications (IJAIA) 
Abstract: Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters.
URI: https://hdl.handle.net/20.500.12540/450
DOI: 10.5121/ijaia.2016.7202
Appears in Collections:Scholarly Publications

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