Palm oil trend analysis via logic mining with Discrete Hopfield Neural Network

Mohd. Asyraf Mansor, . and Nur Ezlin Zamri, . and Saratha Sathasivam, . and Alyaa Alway, . and Mohd Shareduwan Mohd Kasihmuddin, . Palm oil trend analysis via logic mining with Discrete Hopfield Neural Network. pp. 967-981. ISSN 2231-8526

Analyzing commodity prices contributes greatly to traders economists and analysts in ascertaining the most feasible investment strategies. Limited knowledge about the price trend of the commodities indeed will affect the economy because commodities like palm oil and gold contribute a huge source of income to Malaysia. Therefore it is important to know the optimal price trend of the commodities before making any investments. Hence this paper presents a logic mining technique to study the price trend of palm oil with other commodities. This technique employs 2-Satisfiability based Reverse Analysis Method (2-SATRA) consolidated with 2-Satisfiability logic in Discrete Hopfield Neural Network (DHNN2-SAT). All attributes in the data set are represented as a neuron in DHNN which will be programmed based on a 2-SAT logical rule. By utilizing 2-SATRA in DHNN2-SAT the induced logic is generated from the commodity price data set that explains the trend of commodities price. Following that the performance evaluation metric; error analysis and accuracy will be calculated based on the induced logic. In this case the experimental result has shown that the best-induced logic identifies which trend will lead to an increase in the palm oil price with the highest accuracy rate.

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Abstract

Analyzing commodity prices contributes greatly to traders economists and analysts in ascertaining the most feasible investment strategies. Limited knowledge about the price trend of the commodities indeed will affect the economy because commodities like palm oil and gold contribute a huge source of income to Malaysia. Therefore it is important to know the optimal price trend of the commodities before making any investments. Hence this paper presents a logic mining technique to study the price trend of palm oil with other commodities. This technique employs 2-Satisfiability based Reverse Analysis Method (2-SATRA) consolidated with 2-Satisfiability logic in Discrete Hopfield Neural Network (DHNN2-SAT). All attributes in the data set are represented as a neuron in DHNN which will be programmed based on a 2-SAT logical rule. By utilizing 2-SATRA in DHNN2-SAT the induced logic is generated from the commodity price data set that explains the trend of commodities price. Following that the performance evaluation metric; error analysis and accuracy will be calculated based on the induced logic. In this case the experimental result has shown that the best-induced logic identifies which trend will lead to an increase in the palm oil price with the highest accuracy rate.

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Item Type:	Article
AGROVOC Term:	Palm oils
AGROVOC Term:	Agricultural commodities
AGROVOC Term:	Analytical techniques
AGROVOC Term:	Neural networks
AGROVOC Term:	Data analysis
AGROVOC Term:	Agricultural prices
AGROVOC Term:	Market prices
AGROVOC Term:	Economic trends
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/9366

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