Abstract
This paper presents a bio-inspired optimized classification model for assessing water quality. As fish gills histopathology is a good biomarker for indicating water pollution, the proposed classification model uses fish gills microscopic images in order to asses water pollution and determine water quality. The proposed model comprises five phases; namely, case representation for defining case attributes via pre-processing and feature extraction steps, retrieve, reuse/adapt, revise, and retain phases. Wavelet transform and edge detection algorithms have been utilized for feature extraction stage. Case-based reasoning (CBR) has been employed, along with the bio-inspired Gray Wolf Optimization (GWO) algorithm, for optimizing feature selection and the k case retrieval parameters in order to asses water pollution. The datasets used for conducted experiments in this research contain real sample microscopic images for fish gills exposed to copper and water \(\textit{pH}\) in different histopathlogical stages. Experimental results showed that the average accuracy achieved by the proposed GWO-CBR classification model exceeded 97.2 % considering variety of water pollutants.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Reddy, P., Rawat, S.S.: Assessment of aquatic pollution using histopathology in fish as a protocol. Int. Res. J. Environ. Sci. 2(8), 79–82 (2013)
Hauser-Davis, R.A., de Campos, R.C., Ziolli, R.L.: Fish Metalloproteins as Biomarkers of Environmental Contamination, vol. 218, pp. 101–123. Springer, US (2012)
Omar, W.A., Zaghloul, K.H., Abdel-Khalek, A.A., Abo-Hegab, S.: Risk assessment and toxic effects of metal pollution in two cultured and wild fish species from highly degraded aquatic habitats. Arch. Environ. Contam. Toxicol. 65(4), 753–764 (2013)
Jahanbakhshi, A., Hedayati, A.: Gill histopathological changes in great sturgeon after exposure to crude and water soluble fraction of diesel oil. Comp. Clin. Pathol. 22(6), 1083–1086 (2013)
Zheng, H., Liu, R., Zhang, R., Hu, Y.: A method for real-time measurement of respiratory rhythms in medaka (Oryzias latipes) using computer vision for water quality monitoring. J. Ecotoxicol. Environ. Saf. 100(4), 76–86 (2014)
Liao, Y., Jian-yu, X., Wang, Z.: Application of biomonitoring and support vector machine in water quality assessment. J. Zhejiang Univ. 13(4), 327–334 (2012)
Su, J., Wang, X., Zhao, S., Chen, B., Li, C., Yang, Z.: A structurally simplified hybrid model of genetic algorithm and support vector machine for prediction of chlorophyll a in reservoirs. J. Water 7(4), 1610–1627 (2015). Multidisciplinary Digital Publishing Institute
Sweidan, A.H., El-Bendary, N., Hassanien, A.E., Hegazy, O.M., Mohamed, A.B.D.E.-K.: Machine learning based approach for water pollution detection via fish liver microscopic images analysis. In: The 9th IEEE International Conference on Computer Engineering and Systems (ICCES 2014), Cairo, Egypt, pp. 253–258, 21–23 Dec 2014
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. J. Adv. Eng. Softw. 69, 46–61 (2014). Elsevier
El-Gaafary, A.A.M., Mohamed, Y.S., Hemeida, A.M., Mohamed, A.A.A.: Grey wolf optimization for multi input multi output system. Univers. J. Commun. Netw. 1(1), 1–6 (2015)
Emary, E., Zawbaa, H.M., Grosan, C., Hassenian, A.E.: Feature subset selection approach by gray-wolf optimization. In: Abraham, A., Krömer, P., Snasel, V. (eds.) Afro-European Conf. for Ind. Advancement. AISC, vol. 334, pp. 1–14. Springer, Heidelberg (2015)
Vanitha, L., Venmathi, A.R.: Classification of medical images using support vector machine. In: International Conference on Information and Network Technology, IACSIT, vol. 4 (2011)
Ashok Kumar, K., Bhaskar Reddy, Y.V.: Content based image retrieval using SVM algorithm. Int. J. Electr. Electron. Eng. (IJEEE) 1(3), 2231–5284 (2012)
Kumar, E., Khan, Z., Jain, A.: A review of content based image classification using machine learning approach. Int. J. Adv. Comput. Res. 2(5), 55–60 (2012)
Suralkar, S.R., Karode, A.H., Pawade, P.W.: Texture image classification using support vector machine. Int. J. Comput. Appl. Technol. 3(1), 71–75 (2012)
Adnan, A., Gul, S., Ali, M., Dar, A.H.: Content based image retrieval using geometrical-shape of objects in image. Int. Conf. Emerg. Technol. ICET 8(9), 222–225 (2007)
Akansu, A.N., Serdijn, W.A., Selesnick, I.W.: Wavelet transforms in signal processing: a review of emerging applications. Phys. Commun. 3(1), 1–18 (2010)
Suganthy, M., Ramamoorthy, P.: Principal component analysis based feature extraction, morphological edge detection and localization for fast iris recognition. J. Comput. Sci. 8(9), 1428–1433 (2012)
Xue, L.-Y., Pan, J.-J.: Edge detection combining wavelet transform and canny operator based on fusion rules. In: International Conference Wavelet Analysis and Pattern Recognition, pp. 324–228 (2009)
Mohamed, A.B.D.E.-K., El-sayed, N., El-Shershaby, A.-F., Zaghloul, K.H.H.: Taxicological and histopathological studies on Nile Tilapia Oreochromis niloticus exposed to copper individually and in mixture with zinc at different pH value, Ph.D. thesis, Cairo univeristy (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sweidan, A.H., El-Bendary, N., Hassanien, A.E., Hegazy, O.M., Mohamed, A.EK. (2016). Grey Wolf Optimizer and Case-Based Reasoning Model for Water Quality Assessment. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-26690-9_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26688-6
Online ISBN: 978-3-319-26690-9
eBook Packages: Computer ScienceComputer Science (R0)