A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

Nain, Fatini Nadhirah Mohd; Malim, Nurul Hashimah Ahamed Hassain; Abdullah, Rosni; Rahim, Muhamad Farid Abdul; Mokhtar, Mohd Azinuddin Ahmad; Fauzi, Nurul Syafika Mohamad

doi:10.3390/a15060218

Cited by 3 publications

(1 citation statement)

References 157 publications

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“…The results showed that LSTM demonstrates significant improvement with the least RMSE, indicating it is better in determining the movement or behaviour of CPO price. Mohd Nain et al (2022) reviewed studies that used an artificial intelligence framework in CPO price prediction stated that SVR was the among the most promising machine learning methods for CPO price prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

Chai Wen Goh,

Jack Chai,

Amirah Rahman

et al. 2024

aamjaf

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Palm oil is one of the major export products of Malaysia. Predicting the price of crude palm oil futures (FCPO) traded on BURSA Malaysia Derivative is essential as agricultural markets have an inherent tendency towards instability, and thus are more vulnerable to price shocks than other industrial sectors. Hence, if the price of the futures contract on crude palm oil can be forecasted accurately, many parties such as farmers, refiners and distributors can manage the risk of price fluctuations through FCPO. This study proposes the metaheuristic and machine learning hybridised model of simulated annealing-based support vector regression (SA-SVR). The SVR in this model produces close price predictions of the FCPO with minimum deviation from the actual value with the help of SA, which first determines the best hyperparameter set to be utilised in the SVR. Although the proposed Radial Basis Function (RBF) kernelised SA-SVR model inputs only 10% of training data due to memory overload issues, it has produced a satisfying prediction result with an average execution time of 2 minutes and 34 seconds. The model performance was analysed further by using different ratios in data splitting, varying temperature combinations for the SA algorithm and initiating the parameter search based on the previous best hyperparameter set. Results show that keeping the test size constant and extracting more historical data on FCPO price for model training is better than varying train-test split ratios. The temperature schedule strategy showed that different initial and minimum SA temperature combinations affects the overall optimisation results. The best combination was the initial temperature of 100 and minimum of 40. In addition, the number of temperature reductions and average execution time to reach the best state decreases when the starting point of the parameter search space is close to the best values.

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Section: Literature Reviewmentioning

confidence: 99%

Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

Chai Wen Goh,

Jack Chai,

Amirah Rahman

et al. 2024

aamjaf

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Prediction of Oil Palm Bunch Production Using Artificial Neural Network

Magalhães,

Júnior,

Leite

et al. 2024

Preprint

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Artificial Intelligence in Biological Sciences

Bhardwaj

Kishore

Pandey

2022

Life

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Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.

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A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

Cited by 3 publications

References 157 publications

Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

Prediction of Oil Palm Bunch Production Using Artificial Neural Network

Artificial Intelligence in Biological Sciences

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