Examining the Forecasting Movement of Palm Oil Price Using RBFNN-2SATRA Metaheuristic Algorithms for Logic Mining

Alzaeemi, Shehab Abdulhabib; Sathasivam, Saratha

doi:10.1109/access.2021.3054816

Cited by 20 publications

(4 citation statements)

References 32 publications

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“…However, the prediction accuracy of this method is low, and there is a big gap between it and the ideal application effect. Reference [6] uses data mining technology based on energy minimization technology to complete the logical mining task. ese empirical results provide useful insights for decision-making and policy implementation, including the development of strategies to help the industry cope with ongoing price volatility, thus enabling the Malaysian palm oil industry to continue to dominate the international market.…”

Section: Introductionmentioning

confidence: 99%

Economic Growth Prediction Algorithm of Coastal Area Based on Impulse Response Function

Qiu

Ding

Han

2021

Security and Communication Networks

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In order to solve the problems of low accuracy and long prediction time of traditional economic growth prediction algorithms in coastal areas, an algorithm based on impulse response function was designed to analyze economic growth prediction in coastal areas. Crawler technology is used to capture the economic data of coastal areas and normalize the captured data. Based on the processed data, the impulse response function is used to analyze the relationship between different economic variables, so as to build the PSO-LSTM model, which is used to predict the economic growth trend of coastal areas. The experimental results show that, compared with the experimental comparison algorithm, the prediction accuracy of the algorithm designed in this paper is always above 97%, and the prediction time is always below 1 s, which has certain practical significance.

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Section: Introductionmentioning

confidence: 99%

Economic Growth Prediction Algorithm of Coastal Area Based on Impulse Response Function

Qiu

Ding

Han

2021

Security and Communication Networks

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“…Note that the authors of [48] have proposed logic mining that utilizes a log-linear model, but the order of the logic is lower than what we propose in this paper. In addition, our proposed G3SATRAµ model is incomparable with the work in [49] due to the structure of the radial basis function neural network (RBFNN), which only produced a single L induced i . Thus, our proposed G3SATRAµ model is compared with the following state-of-the-art logic mining methods:…”

Section: Baseline Methodsmentioning

confidence: 95%

Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network

et al. 2023

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Choosing the best attribute from a dataset is a crucial step in effective logic mining since it has the greatest impact on improving the performance of the induced logic. This can be achieved by removing any irrelevant attributes that could become a logical rule. Numerous strategies are available in the literature to address this issue. However, these approaches only consider low-order logical rules, which limit the logical connection in the clause. Even though some methods produce excellent performance metrics, incorporating optimal higher-order logical rules into logic mining is challenging due to the large number of attributes involved. Furthermore, suboptimal logical rules are trained on an ineffective discrete Hopfield neural network, which leads to suboptimal induced logic. In this paper, we propose higher-order logic mining incorporating a log-linear analysis during the pre-processing phase, the multi-unit 3-satisfiability-based reverse analysis with a log-linear approach. The proposed logic mining also integrates a multi-unit discrete Hopfield neural network to ensure that each 3-satisfiability logic is learned separately. In this context, our proposed logic mining employs three unique optimization layers to improve the final induced logic. Extensive experiments are conducted on 15 real-life datasets from various fields of study. The experimental results demonstrated that our proposed logic mining method outperforms state-of-the-art methods in terms of widely used performance metrics.

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“…The proposed 2SATRA extracts the best induced logic for league of legends. The proposed logic mining was extended to various application such as Palm oil pricing [12,13] and football [14]. After the introduction of 2SATRA in the field of logic mining, [15] proposed the energy-based logic mining namely E2SATRA by considering only global neuron state during retrieval phase of DHNN.…”

Section: Introductionmentioning

confidence: 99%

Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

Jamaludin¹,

Mansor²,

Baharum³

et al. 2023

Computers, Materials &Amp; Continua

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The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data. Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions. The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy. To address the issues, the logical permutation serves as an alternative mechanism that can enhance the probability of the 2 Satisfiability logical rule becoming true by utilizing the definitive finite arrangement of attributes. This work aims to examine and analyze the significant effect of logical permutation on the performance of data extraction ability of the logic mining approach incorporated with the recurrent discrete Hopfield Neural Network. Based on the theory, the effect of permutation and associate memories in recurrent Hopfield Neural Network will potentially improve the accuracy of the existing logic mining approach. To validate the impact of the logical permutation on the retrieval phase of the logic mining model, the proposed work is experimentally tested on a different class of the benchmark real datasets ranging from the multivariate and timeseries datasets. The experimental results show the significant improvement in the proposed logical permutation-based logic mining according to the domains such as compatibility, accuracy, and competitiveness as opposed to the plethora of standard 2 Satisfiability Reverse Analysis methods.

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Examining the Forecasting Movement of Palm Oil Price Using RBFNN-2SATRA Metaheuristic Algorithms for Logic Mining

Cited by 20 publications

References 32 publications

Economic Growth Prediction Algorithm of Coastal Area Based on Impulse Response Function

Economic Growth Prediction Algorithm of Coastal Area Based on Impulse Response Function

Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network

Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

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