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RETRACTED ARTICLE: Support vector regression methodology for prediction of output energy in rice production

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This article was retracted on 29 May 2019

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Abstract

The increase in world population has led to a significant increase in food demand throughout the world, so agricultural policy makers in all countries try to estimate their annual food requirements in advance in order to provide food security for their people. In order to achieve this goal, this study developed a novel predictive model based on the energy inputs employed during the production season. Rice caters more than 30 % of the calorie requirement for the Asian countries. In Iran too rice is one of the most important agricultural products. Therefore, objective of this study was to develop a model based on artificial intelligence for predicting the output energy in rice production. Such a model could help farmers and policy makers. This model employed the polynomial and radial basis function (RBF) as the kernel function for support vector regression (SVR). The input energies from different sources used during rice production were given as the inputs to the model, and the output energy was chosen as the output of the model. In order to achieve generalized performance, SVRpoly and SVRrbf tried to minimize the generalization error bound, instead of minimizing the training error. The results show that the proposed model improves the predictive accuracy and capability of generalization. Results show that SVRs can serve as a promising alternative for existing prediction models.

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Change history

  • 29 May 2019

    The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of peer review and authorship manipulation. The authors do not agree to this retraction.

  • 29 May 2019

    The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of peer review and authorship manipulation. The authors do not agree to this retraction.

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Acknowledgments

The first two authors express their thanks for support provided by Tehran University. Also, this research was supported financially by University of Malaya Research Grant (no. RG316-14AFR). We thank the reviewers and associate editor for their comments which improved this manuscript.

Author information

Authors and Affiliations

  1. Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

    Marziye Yousefi & Benyamin Khoshnevisan

  2. Department of Computer System and Information Technology, Faculty of Computer System and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband & Muhammad Arif

  3. Institute of Ocean and Earth Sciences (IOES), University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shervin Motamedi

  4. Department of Software Engineering, Faculty of Computer System and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Mohd Hairul Nizam Md. Nasir & Rodina Ahmad

Authors
  1. Marziye Yousefi
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  2. Benyamin Khoshnevisan
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  3. Shahaboddin Shamshirband
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  4. Shervin Motamedi
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  5. Mohd Hairul Nizam Md. Nasir
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  6. Muhammad Arif
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  7. Rodina Ahmad
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Corresponding author

Correspondence to Shahaboddin Shamshirband.

Additional information

The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of peer review and authorship manipulation. The authors do not agree to this retraction.

References

1. Yousefi, M., Khoshnevisan, B., Shamshirband, S. et al. Stoch Environ Res Risk Assess (2015) 29: 2115. https://doi.org/10.1007/s00477-015-1055-z

Appendix

Appendix

Theorem 1

A subset of canonical hyperplane defined on X* ∊ Rn

$$ \left| x \right| \le D,\quad x \in X^{*} $$

satisfying the constraint

$$ \left| \psi \right| \; \le A, $$

has the dimension h bounded as follows:

$$ h\, \le\, \hbox{min} \left( {\left[ {D^{2} A^{2} } \right],n} \right) + 1, $$

where [a] denotes the integer part of a.

Theorem 2

For optimal hyperplanes passing through the origin the following inequality

$$ ER(\alpha_{l} ) \le \frac{{E\left( {\frac{{D_{l + 1} }}{{\rho_{l + 1} }}} \right)^{2} }}{l + 1} $$

holds true, where Dl+1 and ρl+1 are (random) values that for a given training set of size l+1 define the maximal norm of support vectors x and the margin.

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Yousefi, M., Khoshnevisan, B., Shamshirband, S. et al. RETRACTED ARTICLE: Support vector regression methodology for prediction of output energy in rice production. Stoch Environ Res Risk Assess 29, 2115–2126 (2015). https://doi.org/10.1007/s00477-015-1055-z

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