Evaluation of Neoergasilus japonicus (Ergasilidae) Occurrence on Rudd Scardinius erythrophthalmus Using Fuzzy Logic Approach

Öz

N. japonicus is a parasitic copepod from the family Ergasilidae (Copepoda, Poecilostomatoida) and is native to eastern Asia. N. japonicus has spread to many countries via different reasons. The fish host of this parasite in Lake Sapanca is S. erythrophthalmus. This study proposes a decision-support tool that uses a fuzzy-logic model of expert knowledge to assist in multi-criteria decision-making to forecast the existence of N. japonicus on fish according to the fish size and temperature of water by using MATLAB programming language to help the fishery industry to detect environments suitable for procreation of the parasite. The results produced by the software were compared with data obtained by parasitological examination of rudd, S. erythrophthalmus, from Lake Sapanca, Turkey. A total of 122 S. erythrophthalmus, including 64 males and 58 females with a mean length of 24.93 ± 3.65 cm (range: 15.2–34.0 cm), were examined. The findings indicated that although there are variations between examination results and those obtained from fuzzy software, the results are consistent with one another.  According to the results, 20.49% of the fish were infected according to the data obtained from the lake. The proposed FLS system predicted that 27.87% would be infected. Hence fuzzy logic based algorithm make it possible to evaluate N. japonicus infection

Anahtar Kelimeler

• Fuzzy Logic,Neoergasilus japonicus,Parasite,Scardinius erythrophthalmus

Evaluation of Neoergasilus japonicus (Ergasilidae) Occurrence on Rudd Scardinius erythrophthalmus Using Fuzzy Logic Approach

Öz

N. japonicus Ergasilidae (Copepoda, Poecilostomatoida) ailesinden parazit bir kopepoddur ve doğu Asya’da yaygın bir şekilde görülmektedir. N. japonicus farklı nedenlerden dolayı pek çok ülkeye yayılmıştır. Bu parazitin balık konağı Sapanca Gölün’de ki kızılkanat (S. erythrophtalmus) balığıdır. Bu çalışma, uzman bilgisi kullanarak bulanık mantık modeli oluşturup N. japonicus’un konak balıkta olma ihtimalini tahmin eden çoklu kriterler kullanan bir program sunmaktadır. Bunun için MATLAB programlama dili kullanılmış, balığın total boyu ve su sıcaklığı kullanılmıştır. Amaç, balıkçılık endüstrisinde bu parazitin üreme ihtimali olan ortamların tespit edilmesine yardımcı olmaktır. Program tarafından üretilmiş sonuçlar, Sapanca Gölü’nde kızılkanat balıkları üzerinde yapılan parazitolojik incelemeler ile karşılaştırılmıştır. Boy ortalamaları 24.93 ± 3.65 cm (aralık: 15.2–34.0 cm) toplam 122 kızılkanat (64 erkek, 58 dişi) incelenmiştir. Bulanık mantık ile üretilen sonuçlar parazitoloji incelemeleri ile ufak farklar gösterse de, içlerinde tutarlı olduğu gözlenmiştir. Sonuçların ışığında gölde incelenen balıkların %20.49’üne parazit bulaşmıştır. Sistem ise, verilere göre yaptığı tahminde bunun %27.87 olacağını öngörmüştür. Dolayısı ile bulanık mantık algoritması N. japonicus’un algılanmasını muhtemel kılabileceğini göstermiştir.

Anahtar Kelimeler

• Neoergasilus japonicus,Bulanık Mantık,Parazit,Scardinius erythrophthhalmus

Kaynakça

1. Allahverdi, N. 2009. Some applications of fuzzy logic in medical area. Paper presented at the Application of Information and Communication Technologies, AICT 2009. International Conference on.
2. Badwawi, R.A., Issa, W., Mallick, T., Abusara, M. 2016. DC microgrid power coordination based on fuzzy logic control. Paper presented at the 2016 18th European Conference on Power Electronics and Applications (EPE'16 ECCE Europe).
3. Bassford, M., Painter, B. 2016. Intelligent Bio-Environments: Exploring Fuzzy Logic Approaches to the Honeybee Crisis. Paper presented at the 2016 12th International Conference on Intelligent Environments (IE).
4. Berg, L.S. 1962. Freshwater fishes of the U.S.S.R. and adjacent countries. Jerusalem: Israel Program for Scientific Translations.
5. Bezdek, J.C. 1994. Fuzziness vs. probability. IEEE Transactions on Fuzzy Systems, 2, 1-3.
6. Boavida, I., Dias, V., Ferreira, M.T., Santos, J.M. 2014. Univariate functions versus fuzzy logic: Implications for fish habitat modeling. Ecological Engineering, 71, 533-538.
7. Buemi, A., Giacalone, D., Naccari, F., Spampinato, G. 2016. Efficient fire detection using fuzzy logic. Paper presented at the 2016 IEEE 6th International Conference on Consumer Electronics-Berlin (ICCE-Berlin).
8. Carbajal-Hernández, J.J., Sánchez-Fernández, L.P., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. 2012. Immediate water quality assessment in shrimp culture using fuzzy inference systems. Expert Systems with Applications, 39(12), 10571-10582.

9. Cheung, W.W.L., Pitcher, T.J., Pauly, D. 2005. A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing. Biological Conservation, 124(1), 97-111.
10. Cordón, O. 2011. A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems. International Journal of Approximate Reasoning, 52(6), 894-913.
11. Farghally, H.M., Atia, D.M., El-madany, H. T., Fahmy, F.H. 2014. Fuzzy Logic Controller based on geothermal recirculating aquaculture system. The Egyptian Journal of Aquatic Research, 40(2), 103-109.
12. Fat, R., Mic, L., Kilyen, A.O., Santa, M.M., Letia, T.S. 2016. Model and method for the stock market forecast. Paper presented at the 2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).
13. Gokmen, G., Akinci, T.Ç., Tektaş, M., Onat, N., Kocyigit, G., Tektaş, N. 2010. Evaluation of student performance in laboratory applications using fuzzy logic. Procedia-Social and Behavioral Sciences, 2(2), 902-909.
14. Hattab, T., Ben Rais Lasram, F., Albouy, C., Sammari, C., Romdhane, M.S., Cury, P., et al. 2013. The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning. PLoS ONE, 8(10), 76430.
15. Hudson, P.L., Bowen, C.A. 2002. First Record of Neoergasilus japonicus (Poecilostomatoida: Ergasilidae), A Parasitic Copepod New To The Laurentian Great Lakes. Journal of Parasitology, 88(4), 657-663.
16. Jarre, A., Paterson, B., Moloney, C.L., Miller, D.C.M., Field J.G., Starfield, A.M. 2008. Knowledge-based systems as decision support tools in an ecosystem approach to fisheries: Comparing a fuzzy-logic and a rule-based approach. Progress in Oceanography, 79(2–4), 390-400.
17. Katbab, A. 1995. Fuzzy logic and controller design-a review. Paper presented at the Southeastcon '95. Visualize the Future., Proceedings., IEEE.
18. Lea, R., Dohmann, E., Prebilsky, W., Lee, P., Turk, P., Hao, Y. 1998. A fuzzy logic application to aquaculture environment control. Paper presented at the Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American.
19. Margaliot, M. 2007. Mathematical Modeling of Natural Phenomena: A Fuzzy Logic Approach. In P. P. Wang, D. Ruan & E. E. Kerre (Eds.), Fuzzy Logic: A Spectrum of Theoretical & Practical Issues (113-134). Berlin, Heidelberg: Springer Berlin Heidelberg.
20. Mendel, J.M. 1995. Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83(3), 345-377.
21. Michalczuk, M., Ufnalski, B., Grzesiak, L. M. 2016. Particle swarm optimization of the fuzzy logic controller for a hybrid energy storage system in an electric car. Paper presented at the 2016 18th European Conference on Power Electronics and Applications (EPE'16 ECCE Europe).
22. Nguyen, H.T. 2002. A First Course in Fuzzy and Neural Control: CRC Press, Inc.
23. Ponyi, J., Molnar, K. 1969. Studies on the parasite fauna of fish in Hungary V. Parasitic copepods. Parasitologia Hungarica 2, 137-148.
24. Şen, C.G. 2009. An Integrateg Approach to Determination and Evaluation of Production Planning Performance Criteria. Journal of Engineering and Natural Sciences, 27, 4-5.
25. Setyaningrum, A.H., Swarinata, P.M. 2014. Weather prediction application based on ANFIS (Adaptive neural fuzzy inference system) method in West Jakarta region. Paper presented at the Cyber and IT Service Management (CITSM), International Conference on.
26. Sivanandam, S.N., Sumathi, S., Deepa, S.N. 2006. Introduction to Fuzzy Logic using MATLAB: Springer-Verlag New York, Inc.
27. Soylu, E., Soylu, M.P. 2012. First record of the non-indigenous parasitic copepod Neoergasilus japonicus (Harada, 1930) in Turkey. Turkish Journal of Zoology, 36(5), 662-667.
28. Turksen, I.B. 1997. Fuzzy logic: review of recent concerns. Paper presented at the Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., IEEE International Conference on.
29. Tuuha, H., Valtonen, E.T., Taskinen, J. 1992. Ergasilid copepods as parasites of perch Perca fluviatilis and roach Rutilus rutilus in central Finland: seasonality, maturity and environmental influence. Journal of Zoology, 228(3), 405-422.
30. Van Rijn, J., Tal, Y., Schreier, H.J. 2006. Denitrification in recirculating systems: theory and applications. Aquacultural engineering, 34(3), 364-376.
31. Zadeh, L.A. 1965. Fuzzy sets. Information and Control, 8(3), 338-353.
32. Zadeh, L.A. 1988. Fuzzy Logic. Computer, 21(4), 83-93.