Fish disease diagnosis is a complicated process and requires high level of expertise. However, there’s no accepted general knowledge in fish disease diagnosis. This paper describes a CBR (case-based reasoning) system for fish disease diagnosis. A two-step case retrieve model is proposed in this paper.
Chapter PDF
Similar content being viewed by others
References
Abdus Salam Khan, Achim Hoffmann. Building a case-based diet recommendation system without a knowledge engineer. Artificial Intelligence in Medicine, 2003(27): 155-179
Anil Varma, Nicholas Roddy. ICARUS: design and deployment of a case-based reasoning system for locomotive diagnostics. Engineering Applications of Artificial Intelligence, 1999 (12): 681-690
Daniel Zeldis, Shawn Prescott. Fish disease diagnosis program-problems and some solutions. Aquacultural Engineering, 2000(23): 3-1
Daoliang Li, Zetian Fu, Yanqing Duan. Fish-Expert: a web-based expert system for fish disease diagnosis. Expert Systems with Applications, 2002(23): 311-320
Florian Hartge, Thomas Wetter, Walter E. Haefeli. A similarity measure for case based reasoning modeling with temporal abstraction based on cross-correlation. Computer Methods and Programs in Biomedicine, 2006(8): 41-48
I. Watson. Case-based reasoning is a methodology not a technology. Knowledge-Based Systems, 1999(12): 303-308
Isabelle Bichindaritz, Cindy Marling. Case-based reasoning in the health sciences: What’s next? Artificial Intelligence in Medicine, 2006(36): 127-135
Isabelle Bichindaritz. Memoire: A framework for semantic interoperability of case-based reasoning systems in biology and medicine. Artificial Intelligence in Medicine, 2006(36): 177-192
Kyung-Sup Kim, Ingoo Han. The cluster-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases. Expert Systems with Applications, 2001(21): 147-156
Nirmalie Wiratunga, Susan Craw, Bruce Taylor, Genevieve Davis. Case-based reasoning for matching Smarthouse technology to people’s needs. Knowledge-Based Systems, 2004(17): 139-146
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 IFIP International Federation for Information Processing
About this paper
Cite this paper
Zhu, W., Li, D. (2008). A CBR System for Fish Disease Diagnosis. In: Li, D. (eds) Computer And Computing Technologies In Agriculture, Volume II. CCTA 2007. The International Federation for Information Processing, vol 259. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77253-0_97
Download citation
DOI: https://doi.org/10.1007/978-0-387-77253-0_97
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-77252-3
Online ISBN: 978-0-387-77253-0
eBook Packages: Computer ScienceComputer Science (R0)