Prediction of Glucose Concentration Hydrolysed from Oil Palm Trunks Using a PLSR-Based Model

Yeo, Wan Sieng; Chan, Mieow Kee; Bukhari, Nurul Adela

doi:10.1007/978-3-030-93247-3_88

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Despite that, both PCR and PLSR models gave quite similar results in Tables 2 – 4 ; even PLSR includes the input and output variables in its model development while the PCR model involves the input variables only [ 25 , 39 ]. Their similar results are due to their assumption that the input variables have the same importance towards the prediction performance [ 23 ].…”

Section: Resultsmentioning

confidence: 99%

Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric

Pervez

Yeo

Shafiq

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric

Pervez

Yeo

Shafiq

et al. 2023

Heliyon

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“…As mentioned earlier, the outcomes of the predictive performances for LSSVR, PLSR, and PCR are R 2 , RMSE, MAE, and t. RMSE is a conventional method used in a model evaluation [21], [52] that obtains the differences between the real and predicted values using 11. When RMSE values are lower, they indicate a better predictive performance of the model [6], [53], [54], [55]. Aside from that, R 2 as shown in 12 is also used [56], [57].…”

Section: E the Predictive Performance Analysis And Computer Configura...mentioning

confidence: 99%

Introduction of LSSVR for the Prediction of the Yellowness Index

Yeo,

Saptoro

2024

IJCDS

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Data-driven models including principal component regression (PCR), partial least square regression (PLSR), and least square support vector regression (LSSVR) have been widely applied as predictive models in various applications. However, studies employing regression models to estimate the yellowness index (YI) are scarce in the literature. This study, therefore, focuses on developing non-destructive YI measurements using regression models. The collected RGB calculated XYZ and obtained CIE LAB values were set as the input variables. Meanwhile, the YI value was denoted as the output variable. Results indicated that the LSSVR model outperforms PCR and PLSR models in predicting YI in which the root means square errors of LSSVR for the training and testing datasets were found to be 261,406% to 294,218% and 725% to 772% lower than PLSR and PCR, respectively. LSSVR is also attributed to higher coefficients of determination (R 2 ) that are superior to PLSR and PCR, whose R 2 values are very close to 1. Nonetheless, the computational times of training and testing datasets for LSSVR are much longer than those of PLSR and PCR. Consequently, a highly accurate LSSVR model-based YI sensor shows promising applications particularly if the computational load can be further minimized.

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“…Tables 2, 3, 4 and 5 show that the findings from the PCR and PLSR models are quite comparable, although the PLSR model incorporates both the input and output variables. In contrast, the PCR model only uses the input variables 29,49 . The similarity in their findings might be attributed to the assumption that the input factors are equally crucial to the prediction accuracy 27 .…”

Section: Modelling Assessmentmentioning

confidence: 99%

Optimization and prediction of the cotton fabric dyeing process using Taguchi design-integrated machine learning approach

Pervez,

Yeo,

Lin

et al. 2023

Sci Rep

Self Cite

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The typical textile dyeing process calls for a wide range of operational parameters, and it has always been difficult to pinpoint which of these qualities is the most important in dyeing performance. Consequently, this research used a combined design of experiments and machine learning prediction models’ method to offer a sustainable and beneficial reactive cotton fabric dyeing process. To be more precise, we built a least square support vector regression (LSSVR) model based on Taguchi's statistical orthogonal design (L27) to predict exhaustion percentage (E%), fixation rate (F%), and total fixation efficiency (T%) and color strength (K/S) in the reactive cotton dyeing process. The model's prediction accuracy was assessed using many measures, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). Principal component regression (PCR), partial least square regression (PLSR), and fuzzy modelling were some of the other types of regression models used to compare results. Our findings reveal that the LSSVR model greatly outperformed competing models in predicting the E%, F%, T%, and K/S. This is shown by the LSSVR model's much smaller RMSE and MAE values. Overall, it provided the highest possible R2 values, which reached 0.9819.

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Prediction of Glucose Concentration Hydrolysed from Oil Palm Trunks Using a PLSR-Based Model

Cited by 6 publications

References 23 publications

Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric

Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric

Introduction of LSSVR for the Prediction of the Yellowness Index

Optimization and prediction of the cotton fabric dyeing process using Taguchi design-integrated machine learning approach

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