Abstract
Ammonia is defined as the hidden assassin of a variety of aquatic organisms including fish. Moreover, The ammonia concentrations affect fish hatcheries and growth rates. Relatively, this affects a main source of protein for human consumption, plus the toxicity that may be a cause of convulsions, coma, and human death. Therefore, monitoring ammonia levels and their effects are important for human safety. This paper introduces a classification of growth performance levels those produced by Nile Tilapia fish, which are related to the concentration of ammonia in water. The proposed approach is a hybrid approach that uses Water Wave optimization (WWO) technique and Support Vector Machine (SVM) classifier for optimally classify fish growth level. Through breeding 160 Tilapia fish, a number of physical measurements, such as length, weight and protein level of a fish, were gathered through time duration of 60 days. The experimental results show an improved classification accuracy of the proposed hybrid approach over the traditional SVM that reached to 90.48% detection accuracy.
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References
Handy, R., Poxton, M.: Nitrogen pollution in mariculture toxicity and excretion of nitrogenous compounds by marine fish. Rev. Fish Biol. Fish. 3(3), 205–241 (1993)
De Croux, P., Julieta, M., Loteste, A.: Lethal effects of elevated pH and ammonia on juveniles of neotropical fish Colosoma macropomum (Pisces, Caracidae). J. Environ. Biol. Acad. Environ. Biol. India 25(1), 7–10 (2004)
Canadian Council of Fisheries, Oceans, Haywood, G., Pacific Biological Station.: Ammonia toxicity in teleost fishes: a review. Fisheries and Oceans, Nanaimo (1983)
Poleksic, V., Mitrovic-Tutundzic, V.: Fish gills as a monitor of sublethal and chronic effects of pollution (1994)
Fouad, M.M.M., Zawbaa, H.M., El-Bendary, N., Hassanien, A.E.: Automatic nile tilapia fish classification approach using machine learning techniques. In: 2013 13th International Conference on Hybrid Intelligent Systems (HIS), pp. 173–178. IEEE (2013)
Hu, J., Li, D., Duan, Q., Han, Y., Chen, G., Si, X.: Fish species classification by color, texture and multi-class support vector machine using computer vision. Comput. Electron. Agric. 88, 133–140 (2012)
Gu, J., Deng, C., Lin, X., Yu, D.: Expert system for fish disease diagnosis based on fuzzy neural network. In: 2012 Third International Conference on Intelligent Control and Information Processing (ICICIP), pp. 146–149. IEEE (2012)
Tharwat, A., Gaber, T., Fouad, M.M., Snasel, V., Hassanien, A.E.: Towards an automated zebrafish-based toxicity test model using machine learning. Procedia Comput. Sci. 65, 643–651 (2015)
Cococcioni, M., Ducange, P., Lazzerini, B., Marcelloni, F.: A new multi-objective evolutionary algorithm based on convex hull for binary classifier optimization. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 3150–3156. IEEE (2007)
Peterson, M.R., Doom, T.E., Raymer, M.L.: GA-facilitated KNN classifier optimization with varying similarity measures. In: The 2005 IEEE Congress on Evolutionary Computation, 2005, vol. 3, pp. 2514–2521. IEEE (2005)
Nguyen, H.-N., Ohn, S.-Y., Chae, S.-H., Song, D.H., Lee, I.: Optimizing weighted kernel function for support vector machine by genetic algorithm. In: MICAI 2006: Advances in Artificial Intelligence, pp. 583–592. Springer (2006)
Gao, S., Sun, Q.: Classifier optimization for multimedia semantic concept detection. In: 2006 IEEE International Conference on Multimedia and Expo, pp. 1489–1492. IEEE (2006)
Subasi, A.: Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput. Biol. Med. 43(5), 576–586 (2013)
Gaspar, P., Carbonell, J., Oliveira, J.L.: On the parameter optimization of support vector machines for binary classification. J. Integr. Bioinform. 9(3), 201 (2012)
Nieto, P.G., García-Gonzalo, E., Fernández, J.A., Muñiz, C.D.: A hybrid PSO optimized SVM-based model for predicting a successful growth cycle of the Spirulina platensis from raceway experiments data. J. Comput. Appl. Math. 291, 293–303 (2015)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
Fouad, M.M., Zawbaa, H.M., Gaber, T., Snasel, V., Hassanien, A.E.: A fish detection approach based on BAT algorithm. In: The 1st International Conference on Advanced Intelligent System and Informatics (AISI 2015), 28–30 November 2015, Beni Suef, Egypt, pp. 273–283. Springer (2016)
Zheng, Y.-J.: Water wave optimization: a new nature-inspired metaheuristic. Comput. Oper. Res. 55, 1–11 (2015)
Zhang, B., Zhang, M.-X., Zhang, J.-E., Zheng, Y.-J.: A water wave optimization algorithm with variable population size and comprehensive learning. In: Intelligent Computing Theories and Methodologies, pp. 124–136. Springer (2015)
El-Shebly, A., Gad, H.: Effect of chronic ammonia exposure on growth performance, serum growth hormone (GH) levels and gill histology of nile tilapia (Oreochromis niloticus). J. Microbiol. Biotechnol. Res. 1(4), 183–197 (2011)
Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62(1), 77–89 (1997)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Tao, Y., Skubic, M., Han, T., Xia, C.X., Chi, X.: Performance evaluation of SIFT-based descriptors for object recognition. In: Proceedings of the International Multiconference of Engineers and Computer Scientisits, IMECS. Citeseer (2010)
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Kilany, M., Fouad, M.M., Hemdan, A.M., Hassanien, A.E. (2018). Fish Growth Performance Classification Based on Ammonia Concentrations. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_33
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