Abstract
This paper presents Case-Based Reasoning (CBR) system to asses water pollution based on fish liver histopathology as biomarker. The proposed approach utilizes fish liver microscopic images in order to asses water pollution based on knowledge stored in the case-based database and stores likelihood description of the previous solutions in order to make the knowledge stored more flexible. The proposed case-based reasoning system consists of 5 phases; namely case representation (pre-processing and feature extraction), retrieve, reuse/adapt, revise, and retain phases. After applying pre-processing and feature extraction algorithms on the input images, similarity between the input and case base database is being calculated in order to retrieve similarity. Experimental results show that the performance of CBR systems increases according to the number of retrieved cases in each scenario against each strategy. The proposed system achieved 95.9 \(\%\) accuracy for all water quality degrees.
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Sweidan, A.H., El-Bendary, N., Hegazy, O.M., Hassanien, A.E. (2015). Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning. In: Abraham, A., Jiang, X., Snášel, V., Pan, JS. (eds) Intelligent Data Analysis and Applications. Advances in Intelligent Systems and Computing, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-21206-7_47
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DOI: https://doi.org/10.1007/978-3-319-21206-7_47
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