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http://dx.doi.org/10.6109/jicce.2021.19.4.234

Hybrid Fuzzy Association Structure for Robust Pet Dog Disease Information System  

Kim, Kwang Baek (Department of Artificial Intelligence, Silla University)
Song, Doo Heon (Department of Computer Games, Yong-in Art and Science University)
Jun Park, Hyun (Division of Software Convergence, Cheongju University)
Publication Information
Journal of information and communication convergence engineering / v.19, no.4, 2021 , pp. 234-240 More about this Journal
Abstract
As the number of pet dog-related businesses is rising rapidly, there is an increasing need for reliable pet dog health information systems for casual pet owners, especially those caring for older dogs. Our goal is to implement a mobile pre-diagnosis system that can provide a first-hand pre-diagnosis and an appropriate coping strategy when the pet owner observes abnormal symptoms. Our previous attempt, which is based on the fuzzy C-means family in inference, performs well when only relevant symptoms are provided for the query, but this assumption is not realistic. Thus, in this paper, we propose a hybrid inference structure that combines fuzzy association memory and a double-layered fuzzy C-means algorithm to infer the probable disease with robustness, even when noisy symptoms are present in the query provided by the user. In the experiment, it is verified that our proposed system is more robust when noisy (irrelevant) input symptoms are provided and the inferred results (probable diseases) are more cohesive than those generated by the single-phase fuzzy C-means inference engine.
Keywords
Fuzzy associative memory; Hybrid inference; Pet dog; Pre-diagnosis; Robust inference;
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