Local Maxima Niching Genetic Algorithm Based Automated Water Quality Management System for Betta splendens

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Ferdin Joe John Joseph
Deepali Nayak
Salinla Chevakidagarn

Abstract

     Rearing Betta splendens is one of the most popular aquarium hobbies around the world. There are many IoT solutions done so far to monitor the water quality of the aquarium. Some machine learning and IoT based solutions are also available to do regression on the sensor data. In this paper, we propose a new framework and algorithm to predict the abnormalities in water quality which may affect the health of the fish. The algorithm proposed in this paper uses a local maxima niching genetic algorithm for optimization which effectively finds the local maxima on the new data streaming in and provides the approximate timestamp on the next possible water change or treatment to avoid the fish from getting infected. Many existing timestamp methods are seasonal but in terms of optimization in terms of unpredictable environment such as water, there needs a better technique for optimization.  The qualitative and quantitative results proved that the health of fish using the proposed framework had better living conditions and avoided the attack of parasitic infections than those in existing and normal captivity methods. The accuracy of the proposed methodology increased by 5% within the variations made.

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References

[1] J. B. Gratzek, “Parasites associated with ornamental fish,” Veterinary Clinics of North America: Small Animal Practice, vol. 18, no. 2, pp. 375–399, Mar. 1988.

[2] E. D. Clotfelter, E. P. O’Hare, M. M. McNitt, R. E. Carpenter, and C. H. Summers, “Serotonin decreases aggression via 5-HT1A receptors in the fighting fish Betta splendens,” Pharmacology Biochemistry and Behavior, vol. 87, no. 2, pp. 222–231, 2007.

[3] T. L. Dzieweczynski, R. L. Earley, T. M. Green, and W. J. Rowland, “Audience effect is context dependent in Siamese fighting fish, Betta splendens,” Behavioral Ecology, vol. 16, no. 6, pp. 1025–1030, Sep. 2005.

[4] F. Dore, L. Lefebvre, and R. Ducharme, “Threat display in Betta splendens: Effects of water condition and type of agonistic stimulation,” Animal Behaviour, vol. 26, pp. 738–745, Aug. 1978.

[5] R. Schneider and B. L. Nicholson, “Bacteria associated with fin rot disease in hatchery-reared Atlantic salmon (Salmo salar),” Canadian Journal of Fisheries and Aquatic Sciences, vol. 37, no. 10, pp. 1505–1513, Oct. 1980.

[6] A. Dolan, “The effects of aquarium size and temperature on color vibrancy size and physical activity in bettasplendens,” Maryville College, Maryville, TN, USA, Tech. Rep. 53309811, 2015.

[7] F. J. J. Joseph, “IoT based weather monitoring system for effective analytics,” International Journal of Engineering and Advanced Technology, vol. 8, no. 4, pp. 311–315, Apr. 2019.

[8] S. W. Mahfoud, “Niching Methods for Genetic Algorithms,” Ph.D. dissertation, University of Illinois at Urbana Champaigne, Urbana, IL, USA, 1995.

[9] C. M. Fonseca and P. J. Fleming, “Genetic algorithms for multiobjective optimization: formulation, discussion and generalization,” in Proc. 5th International Conference on Genetic Algorithms, Urbana-Champaign, IL, USA, Jun. 1993, pp. 416–423.

[10] K. Deb, “Multi-objective genetic algorithms: Problem difficulties and construction of test problems,” Evolutionary Computation, vol. 7, no. 3, pp. 205–230, Sep. 1999.

[11] S. Auwatanamongkol, “Pattern recognition using genetic algorithm,” in Proc. 2000 Congress on Evolutionary Computation, La Jolla, CA, USA, Jul. 2000, pp. 822–828.

[12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.

[13] C. Chang and C. Lin, “LIBSVM: A library for support vector machines,” ACM Transaction on Intelligent Systems and Technology, vol. 2, no. 3, pp. 1-27, Apr. 2011.

[14] R. G. Oldfield, “Gonad development in Midas cichlids and the evolution of sex change in fishes,” Evolution & Development, vol. 13, no. 4, pp. 352–360, Jul. 2011.

[15] S. P. Basquill and J. W. A. Grant, “An increase in habitat complexity reduces aggression and monopolization of food by zebra fish (Danio rerio),” Canadian Journal of Zoology, vol. 76, no. 4, pp. 770–772, Apr. 1998.

[16] Y. B. Lin and H. C. Tseng, “FishTalk: An IoT-based mini aquarium system,” IEEE Access, vol. 7, pp. 35457–35469, 2019.

[17] Y. H. Cheng, W. Q. Chen, K. H. Lin, and Z. Y. Zhou, “Smart cloud IoT aquarium,” in Proc. 13th International Conference on Advanced Information Technologies (AIT 2019), Taichung, Taiwan, Mar. 2019, pp. 274–278.

[18] S. Kori, S. Ayatti, V. Lalbeg, and A. Angadi, “Smart live monitoring of aquarium - An IoT application,” in Information and Communication Technology for Intelligent Systems, Singapore: Springer, 2019, pp. 1–9.

[19] Y. Kim, N. Lee, B. Kim, and K. Shin, “Realization of IoT based fish farm control using mobile app,” in Proc. 2018 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan, Dec. 2018, pp. 189–192.

[20] M. S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, and A. P. Sheth, “Machine learning for Internet of Things data analysis: A survey,” Digital Communications and Networks, vol. 4, no. 3, pp. 161–175, Aug. 2018.

[21] J. Siryani, B. Tanju, and T. J. Eveleigh, “A machine learning decision-support system improves the internet of things’ smart meter operations,” IEEE Internet Things Journal, vol. 4, no. 4, pp. 1056–1066, 2017.

[22] S. Trilles, J. T. Sospedra, Ó. Belmonte, F. J. Z. Soria, A. G. Pérez, and J. Huerta, “Development of an open sensorized platform in a smart agriculture context: A vineyard support system for monitoring mildew disease,” Sustainable Computing: Informatics and Systems, vol. 28, pp. 1-11, Dec. 2020, doi: 10.1016/j.suscom.2019.01.011.

[23] G. S. Sampaio, G. M. F. Correia, and L. A. D. Silva, “AcquaSmart: An environment big data analytics and Internet of Things to education and research,” International Journal for Innovation Education and Research, vol. 7, no. 1, pp. 93–104, Jan. 2019.

[24] Y. Xuesong, S. Jie, and H. Chengyu, “Research on contaminant sources identification of uncertainty water demand using genetic algorithm,” Cluster Computing, vol. 20, no. 2, pp. 1007–1016, Jun. 2017.

[25] S. Avila, L. Krahenbuhl, and B. Sareni, “A Multi-niching multi-objective genetic algorithm for solving complex multimodal problems,” in Proc. 9th Workshop on Optimization and Inverse Problems in Electromagnetics, Sorrento, Italy, Sep. 2006, pp. 115–116.

[26] C. Ding, L. Chen, and B. Zhong, “Exploration of intelligent computing based on improved hybrid genetic algorithm,” Cluster Computing, vol. 22, no. S4, pp. 9037–9045, Jul. 2019.

[27] X. Wang and R. Qing-dao-er-ji, “Application of optimized genetic algorithm based on big data in bus dynamic scheduling,” Cluster Computing, vol. 22, no. S6, pp. 15439–15446, Nov. 2019.

[28] M. Potegal, “The reinforcing value of several types of aggressive behavior: A review,” Aggressive Behavior, vol. 5, no. 4, pp. 353–373, Jan. 1979.

[29] T. Ichihashi, Y. Ichikawa, and T. Matsushima, “A non-social and isolate rearing condition induces an irreversible shift toward continued fights in the male fighting fish (Betta splendens),” Zoological Science, vol. 21, no. 7, pp. 723–730, Jul. 2004.

[30] S. W. Lee, R. Farhan, W. Z. M. Wendy Wee, and C. O. Ibrahim, “The effects of tropical almond, Terminalia catappa L., leaf extract on breeding activity of Siamese Gourami, Trichogaster pectoralis”, International Journal of Fisheries and Aquatic Studies, vol. 4, no. 4, pp. 431-433, 2016.