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Prediction of Antioxidant Status in Fish Farmed on Selenium Nanoparticles using Neural Network Regression Algorithm

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

Abstract

Oxidative stress is the most common stress form which is responsible for the increased mortality as well as retardation of productivity in sheries. Selenium plays a vital role in combating oxidative stress. It appears as a potent antioxidant with reduced toxicity in its nanoscale form. In this paper, the effect of the different concentrations of Nano-selenium in the diet on the antioxidant status of common carp was investigated through the estimation of antioxidant enzymes activity and some biochemical blood prole. The adopted regression algorithm for prediction was Back-propagation Neural Network. The model compromised between fast analytical technologies and biological aspect through prediction the healthy status and expected hazards related to oxidative stress. The experiment was performed on four groups of common carp with measured rearing parameters and the same amount of diet at the rates of 0 (control), 0.5, 1 and 2 mg/k gm amount of Nano-selenium concentration in the ration, aiming to build preliminary prediction models to know the antioxidant status activity. The regression performance was tested by several mathematical validations including MSE (Mean squared error), RMSE (Root mean squared error), MSRE (mean squared relative error), MARE (mean absolute relative error), RMSRE (root mean squared relative error), MAE (Mean absolute error), MAPE (Mean absolute percentage error), MSPE (mean squared percentage error), RMSPE (root mean squared percentage error) which showed promising results of the regression model.

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Author information

Authors and Affiliations

  1. Faculty of Specific Education, Damietta University, Damietta, Egypt

    Ahmed Sahlol

  2. Faculty of Veterinary Medicine, Kafer-Elsheikh University, Kafr el-sheikh, Egypt

    Ahmed Monem Hemdan

  3. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  4. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Ahmed Sahlol, Ahmed Monem Hemdan & Aboul Ella Hassanien

Authors
  1. Ahmed Sahlol
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  2. Ahmed Monem Hemdan
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  3. Aboul Ella Hassanien
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Corresponding author

Correspondence to Ahmed Sahlol .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Sahlol, A., Hemdan, A.M., Hassanien, A.E. (2017). Prediction of Antioxidant Status in Fish Farmed on Selenium Nanoparticles using Neural Network Regression Algorithm . In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_34

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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