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International Conference on Soft Computing and Data Mining

SCDM 2020: Recent Advances on Soft Computing and Data Mining pp 15–24Cite as

Fuzzy Random Based Mean Variance Model for Agricultural Production Planning

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 978))

Abstract

Observation and measurement data are the basis of an analysis which usually contains uncertainties. The uncertainties in data need to be properly described as they may increase error in the prediction model. The collected data which contains uncertainty should be adequately treated before analysis. In the portfolio selection problem, uncertainty involves are characterized as fuzzy and random. Hence fuzzy random variables are accounted as input values in the portfolio selection analysis. It is important to preprocess the data sufficiently due to the uncertainties issue. However, only a few studies discuss the systematic procedure for data processing whereby the uncertainties exist. Hence, this study introduces a structure for fuzzy random data processing which deals with fuzziness and randomness in data for building a portfolio selection model. The fuzzy number is utilized to treat the fuzziness and the probability distribution used to treat randomness. The proposed model is applied for agricultural planning. Five types of industrial plants are assessed using the proposed method. The result of this study demonstrates that the proposed method of fuzzy random based data Pre-processing can treat the uncertainties. The systematic procedure of fuzzy random data Pre-processing in this study is important to enable data uncertainties treatment and to reduce error in the early stage of problem model building.

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Acknowledgments

The author would like to extend its appreciation to the Ministry of Higher Education (MOHE) and Universiti Tun Hussein Onn Malaysia (UTHM). This research is supported by Fundamental Research Grant Scheme (FRGS) Vote No FRGS/1/2019/ICT02/UTHM/02/7 and Geran Penyelidikan Pascasiswazah (GPPS) grant (Vote H332 & Vote U975). The author thanks the anonymous reviewers for the feedback.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mohammad Haris Haikal Othman, Nureize Arbaiy & Muhammad Shukri Che Lah

  2. Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Rd., Taichung, Taiwan

    Pei-Chun Lin

Authors
  1. Mohammad Haris Haikal Othman
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  2. Nureize Arbaiy
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  3. Muhammad Shukri Che Lah
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  4. Pei-Chun Lin
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Corresponding author

Correspondence to Nureize Arbaiy .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  4. School of Information Technology, Deakin University, Geelong Waurn Ponds Campus, VIC, Australia

    Jemal H. Abawajy

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Othman, M.H.H., Arbaiy, N., Lah, M.S.C., Lin, PC. (2020). Fuzzy Random Based Mean Variance Model for Agricultural Production Planning. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_2

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