Agarwood oil quality classification using cascade-forward neural network

Aziz, Maslina Abdul; Ismail, Nurlaila; Yassin, Ihsan Mohd; Zabidi, Azlee; Ali, Megat Syahirul Amin Megat

doi:10.1109/icsgrc.2015.7412475

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…In the Figure 3, the graph pattern shows that the MSE value at one hidden neuron for SCG, LM and RBP was the highest which the value are 0.0446, 0.0384 and 0.0468 respectively. The high value of MSE at the initial stage of training is due to the initial adjustment of weight, thus producing the significantly different outputs from the actual data [9]. However, the increasing number of hidden neurons made the MSE value decrease as the weights network training became more stable [9].…”

Section: Mlp Networkmentioning

confidence: 99%

“…The high value of MSE at the initial stage of training is due to the initial adjustment of weight, thus producing the significantly different outputs from the actual data [9]. However, the increasing number of hidden neurons made the MSE value decrease as the weights network training became more stable [9]. the best algorithm for agarwood oil classification.…”

Section: Mlp Networkmentioning

confidence: 99%

“…Essential oil quality classification can be performed based on chemical profiles, so that essential oil can be accurately classified according to their respective classes (high or low grade). Researchers now use a modern techniques for classifying agarwood oil using machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), multilayer perceptron (MLP) and also various analysis techniques [7], [9]- [13]. An existing study applied ANN technique for grading agarwood oil [10].…”

Section: Introductionmentioning

confidence: 99%

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Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

Mahabob

Yusoff

Amidon

et al. 2021

IJECE

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<span>Agarwood oil is in increasing demand in Malaysia throughout the world for use in incense, traditional medicine, and perfumes. However, there is still no standardized grading method for agarwood oil. It is vital to grade agarwood oil into high and low quality so that both qualities can be properly differentiated. In the present study, data were obtained from the Forest Research Institute Malaysia (FRIM), Selangor Malaysia and Bioaromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP). The work involves the data from a previous researcher. As a part of on-going research, the stepwise linear regression and multilayer perceptron have been proposed for grading agarwood oil. The output features of the stepwise regression were the input features for modeling agarwood oil in a multilayer perceptron (MLP) network. A three layer MLP with 10 hidden neurons was used with three different training algorithms, namely resilient backpropagation (RBP), levenberg marquardt (LM) and scaled-conjugate gradient (SCG). All analytical work was performed using MATLAB software version R2017a. It was found that one hidden neuron in LM algorithm performed the most <span>accurate result in the classification of agarwood oil with the lowest mean squared error (MSE) as compared to SCG and RBP algorithms. The findings in this research will be a</span> benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.</span>

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Section: Mlp Networkmentioning

confidence: 99%

Section: Mlp Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

Mahabob

Yusoff

Amidon

et al. 2021

IJECE

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“…This happens because human nose has sensory limitation as it will get fatigue when used to smell for a long period and high volume of sample [2][3]. In the past few years, few studies have been carried out to classify the agarwood oil using machine learning classifier model based on its chemical compound rather than using the colour and odour properties of the oil itself [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Agarwood Oil Quality Classification using Support Vector Classifier and Grid Search Cross Validation Hyperparameter Tuning

Abas¹

2020

IJETER

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This study investigates the performance of using grid search cross validation as a hyperparameter tuning method for support vector classifier in classifying the quality of agarwood oil. The data of agarwood oil sample were obtained from Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang, Malaysia. The chemical compound abundances of agarwood oil sample are used as its input whiles the quality of agarwood oil of high quality and low quality as the output. The parameter used to train the support vector machine classifier by using the grid search cross validation is the parameter C, gamma and kernel. Based on the results of the study, it shows that by using combination of C with value of 1, gamma value of 10 and radial basis function kernel gives the best classification accuracy of 100% and performance measure scores of 1.0.

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“…Cascade Forward Neural Network (CFNN) are similar in structure to MLP except that CFNN has a direct weighted connection from its input to output layer which enables it to learn highly complex patterns [10]. This allows the inputs to directly influence the output nodes by embedding additional information and features to it.…”

Section: Introductionmentioning

confidence: 99%

Comparison between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES)-Based Muscle Model

Yassin¹,

Jailani²,

Ali³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper presents the development and comparison of muscle models based on Functional Electrical Stimulation (FES) stimulation parameters using the Nonlinear Auto-Regressive model with Exogenous Inputs (NARX) using Multi-Layer Perceptron and Cascade Forward Neural Network (CFNN). FES stimulations with varying frequency, pulse width and pulse duration were used to estimate the muscle torque. About 722 data points were used to create muscle model. One Step Ahead (OSA) prediction, correlation tests and residual histogram analysis were performed to validate the model. The optimal Multi-Layer Perceptron (MLP) results were obtained from input lag space of 1, output lag space of 43 and hidden units 30. The MLP selected a total of three terms were selected to construct the final model, which producing a final Mean Square Error (MSE) of 1.1299. The optimal CFNN results were obtained from input lag space of 1, output lag space of 5 and hidden units 20 with similar terms selected. The final MSE produced was 1.0320. The proposed approach managed to approximate the behavior of the system well with unbiased residuals, which CFNN showing 8.66% MSE improvement over MLP with 33.33% less hidden units.

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Agarwood oil quality classification using cascade-forward neural network

Cited by 6 publications

References 3 publications

Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

Agarwood Oil Quality Classification using Support Vector Classifier and Grid Search Cross Validation Hyperparameter Tuning

Comparison between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES)-Based Muscle Model

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