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Towards Utilization of Neurofuzzy Systems for Taxonomic Identification Using Psittacines as a Case Study


Affiliations
1 Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States
2 Department of Computer Science, University of Illinois Springfield, Springfield, IL 62703, United States
 

Demonstration of the neurofuzzy application to the task of psittacine (parrot) taxonomic identification is presented in this paper. In this work, NEFCLASS-J neurofuzzy system is utilized for classification of parrot data for 141 and 183 groupings, using 68 feature points or qualities. The reported results display classification accuracies of above 95%, which is strongly tied to the setting of certain parameters of the neurofuzzy system. Rule base sizes were in the range of 1,750 to 1,950 rules.
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  • Towards Utilization of Neurofuzzy Systems for Taxonomic Identification Using Psittacines as a Case Study

Abstract Views: 89  |  PDF Views: 2

Authors

Shahram Rahimi
Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States
Cynthia R. Spiess
Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States
Bidyut Gupta
Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States
Elham Sahebkar
Department of Computer Science, University of Illinois Springfield, Springfield, IL 62703, United States

Abstract


Demonstration of the neurofuzzy application to the task of psittacine (parrot) taxonomic identification is presented in this paper. In this work, NEFCLASS-J neurofuzzy system is utilized for classification of parrot data for 141 and 183 groupings, using 68 feature points or qualities. The reported results display classification accuracies of above 95%, which is strongly tied to the setting of certain parameters of the neurofuzzy system. Rule base sizes were in the range of 1,750 to 1,950 rules.