Shehab Abdulhabib Alzaeemi1, Saratha Sathasivam2,*, Majid Khan bin Majahar Ali2, K. G. Tay1, Muraly Velavan3
Computer Systems Science and Engineering, Vol.47, No.2, pp. 1471-1491, 2023, DOI:10.32604/csse.2023.037366
Abstract Rubber producers, consumers, traders, and those who are involved in the rubber industry face major risks of rubber price fluctuations. As a result, decision-makers are required to make an accurate estimation of the price of rubber. This paper aims to propose hybrid intelligent models, which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data, spanning from January 2016 to March 2021. The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining (RBFNN-kSAT). These algorithms, including Grey Wolf Optimization Algorithm, Artificial Bee Colony… More >
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