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This study analyses the extraction process parameters of huracrepitan seed oil using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). The experiments were conducted at temperature (60–80 °C), time (4–6 h), and solute/solvent ratio (0.05–0.10) with output parameter as oil yield. Sensitivity analysis shows that temperature and time had the most significant effect on the oil yield. The oil yield estimation performance indicators are: ANN (R2 = 0.999, MSE = 5.63192E-13), ANFIS (R2 = 0.36945, MSE = 0.42331). The results show that ANN gave a better prediction than ANFIS.

This study analyses the extraction process parameters of huracrepitan seed oil using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). The experiments were conducted at temperature (60–80 °C), time (4–6 h), and solute/solvent ratio (0.05–0.10) with output parameter as oil yield. Sensitivity analysis shows that temperature and time had the most significant effect on the oil yield. The oil yield estimation performance indicators are: ANN (R2 = 0.999, MSE = 5.63192E-13), ANFIS (R2 = 0.36945, MSE = 0.42331). The results show that ANN gave a better prediction than ANFIS. [ABSTRACT FROM AUTHOR]

This study analyses the extraction process parameters of huracrepitan seed oil using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). The experiments were conducted at temperature (60–80 °C), time (4–6 h), and solute/solvent ratio (0.05–0.10) with output parameter as oil yield. Sensitivity analysis shows that temperature and time had the most significant effect on the oil yield. The oil yield estimation performance indicators are: ANN (R² = 0.999, MSE = 5.63192E-13), ANFIS (R² = 0.36945, MSE = 0.42331). The results show that ANN gave a better prediction than ANFIS. [ABSTRACT FROM AUTHOR]