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When heard about agarwood oil, it is very familiar with world community because of its beneficial. Unfortunately, there is no any standard grading model of agarwood oil was implemented. As a solution forms, it is very important to come out with a standard of quality classification model for agarwood oil grading's. By continuing of the research for the development of this standard, specific algorithm function has been used to make sure the ability of this model is totally not in doubt. Support vector machine (SVM) has been chosen as a main model and for the specific algorithm function that has been chosen was multiclass function. Then, in the function, the one versus one (OVO) strategy has been used to make multiclass work and can be applied on SVM. The analysis work has involving the data taken from the previous researcher that consists of four classes of agarwood oil quality's samples which are low, medium low, medium high and high quality. So, the output was the classification of quality between low, medium low, medium high or high quality while the input was the abundances (%) of compounds. The desk research has been conducted by using MATLAB software version r2020a for the simulation platform. The result showed that the model by using multiclass function has pass the performance criteria standard. The verdict in this research for sure will be valuable for the future research works of agarwood oil areas, especially quality classification part.

The advancement of computer algorithm and speed has brought about a revolution in modelling and simulation of especially complex systems. Various intelligent methods are available in an easy to use manner for examining and analyzing highly nonlinear systems. This paper demonstrates the elegant application of System Identification technique in processing complex sensor signals and turning them into easily understood and useable measurements. As example, System Identification technique was applied solve a unique issue of grading Agarwood oil which has been based on a manual technique for a long time. For this ground breaking research, data from a chemical analysis of the Agarwood oil, acquired via GCMS measurements, is employed for identification. A single sample of the oil can be represented by hundreds of chemical compounds; these were reduced via a data reduction method. After proper processing of the data thru the System Identification procedure, a valid model was obtained and used to produce a highly accurate grading for the Agarwood oil quality.

These days, agarwood oil becoming a high demand throughout the world and Malaysia is not excluded. It happens due to the variety of usages such as incense, traditional medicine, and perfumes. However, there has been a lack of research on the development of agarwood oil because there is no any standard grading method of agarwood oil was implemented. As a solution forms, it is very important to come out with a standard method of quality classification for agarwood oil grading’s. By continuing of the research for the development of this standard, the comparison of different type of kernel parameter on nonlinear data based on performance measure has been the main objective of this paper. Support Vector Machine (SVM) has been selected as intelligent technique to comparing the output of different type of kernel parameter used. The analysis work has involving the data taken from the previous researcher that consists of two classes of agarwood oil quality’s samples which is high and low quality. For the output of this research was the classification of two different quality while the input was the different percentage of the compounds added. The desk research has been conducted by using a software application named MATLAB with version R2016a. The research indicates that each of different kernel parameter used have pass the performance measures standard. The verdict in this research for sure will be valuable for the future research works of agarwood oil areas, especially quality classification part.

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 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 benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.

Multilayer perceptron (MLP) optimization is carried out to investigate the classifier's performance in discriminating the uniformity of reduced Graphene Oxide(rGO) thin-film sheet resistance. This study used three learning algorithms: resilient back propagation (RP), scaled conjugate gradient (SCG) and levenberg-marquardt (LM). The dataset used in this study is the sheet resistance of rGO thin films obtained from MIMOS Bhd. This work involved samples selection from a uniform and non-uniform rGO thin-film sheet resistance. The input and output data were under going data pre-processing: data normalization, data randomization and data splitting. The data were dividedin to three groups; training, validation and testing with a ratio of 70%: 15%: 15%, respectively. A varying number of hidden neurons optimized the learning algorithms in MLP from 1 to 10. Their behavior helped establish the best learning algorithms in discriminating MLP for rGO sheet resistance uniformity. The performances measured were the accuracy of training, validation and testing dataset, mean squared errors (MSE) andepochs. All the analytical work in this study was achieved automatically via MATLAB software version R2018a. It was found that the LM is dominant inthe optimization of a learning algorithm in MLP forrGO sheet resistance.The MSE for LM is the most reduced amid SCG and RP.

This paper presents an attemption to empirically assess statistical learning model Boolean Support Vector Machines (BSVM) to the problem of agarwood oil quality categorization. The modelling starts with data pre-processing of seven significant chemical compounds of agarwood oil, from high and low qualities. During this stage, the data was randomized, normalized and divided into training and testing parts. 80% of the training part was induced as examples and create the maximum margin hyperplane to separates high and low groups in a binary setting and build the model. Another 20% of testing part was used to validate the developed model. MATLAB software version R2016a was used to perform all the analysis. The result obtained a good model utilizing SVM in classifying agarwood oil significant volatile compound quality. The model achieved minimum of 80 % for precision, confusing matrix, accuracy, sensitivity and specificity. The finding in this study will benefit further work and application for agarwood oil research area especially its classification in quality of agarwood oil and many others.

This paper presents the analysis of agarwood oil compounds quality classification by tuning quadratic kernel parameter in Support Vector Machine (SVM). The experimental work involved of agarwood oil samples from low and high qualities. The input is abundances (%) of the agarwood oil compounds and the output is the quality of the oil either high or low. The input and output data were processed by following tasks; i) data processing which covers normalization, randomization and data splitting into two parts in which training and testing database (ratio of 80%:20%), and ii) data analysis which covers SVM development by tuning quadratic kernel parameter. The training dataset was used to be train the SVM model and the testing dataset was used to test the developed SVM model. All the analytical works are performed via MATLAB software version R2013a. The result showed that, quadratic tuned kernel parameter in SVM model was successful since it passed all the performance criteria’s in which accuracy, precision, confusion matrix, sensitivity and specificity. The finding obtained in this paper is vital to the agarwood oil and its research area especially to the agarwood oil compounds classification system.

Agarwood oil, famously known as costly oil, extracted from the resinous of fragrant heartwood. The oil is getting high demand in the market especially from China, Vietnam, India, Middle East countries, and Japan because of its unique odour. As one of the researches in grading the quality of agarwood oil, the evaluation of kernel tuned parameter using Radial Basics Function (RBF) and Multilayer Perceptron (MLP) are presented in this paper to classify the quality of agarwood oil by using support vector machine (SVM). The work involved of selected agarwood oil sample from high to low quality. The output was agarwood oil quality either low or high and the input was the abundances (%) of agarwood oil compoundS. The input and output data were pre-processed by following works; data processing (normalisation, randomisation and data splitting into two parts in which training and testing dataset (ratio of 80%:20%) and data analysis using SVM modelling. The training dataset was used to train in developing the SVM model and the testing dataset was used to test/validate the developed SVM model. All the analytical works were performed automatically via MATLAB software version R2013a. The result showed that SVM model with RBF tuning is better than SVM model with MLP tuning and passed all the performance measures; accuracy, precision, confusion matrix, sensitivity and specificity. The finding in this study is significant and benefits further work and application for agarwood oil research area especially its classification.

Agarwood oil is known as one of the most expensive and precious oils being traded. It is widely used in traditional ceremonies and religious prayers. Its quality plays an important role on the market price that it can be traded. This paper proposes on a proper classification method of the agarwood oil quality using machine learning model k-nearest neighbour (k-NN). The chemical compounds of the agarwood oil from high and low quality are used to train and build the k-NN classifier model. Correlation reduce the dimension of the data before it is being fed into the model. The results show a very high accuracy (100%) model trained and can be used to classify the agarwood oil quality accurately. Keywords: agarwood oil; k-nearest neighbours; quality; machine learning.

This paper presents the analysis of distance metric variations in KNN for agarwood oil compounds differentiation. The work involved of the development of k-Nearest Neighbor (KNN) by varying the distance metrics. The input is abundances (%) of agarwood oil compounds and the output is agarwood oil quality either high or low. The data is divided into two parts; training and testing dataset with ratio of 80% and 20% respectively. The training dataset is used to develop the KNN model from K equal to 1 until K equal to 5, and the testing dataset is used to test the developed model. During the training, distance metric parameters were varied using Euclidean, City-block, Cosine, and Correlation. The performance of each parameter was recorded and observed. All the analytical works are performed automatically via MATLAB software version R2014b. The results showed that, among four distance metric variations, Euclidean and City-block yield 100% accuracy for both training and testing dataset. After that, 89.5% of accuracy was obtained by Cosine and Correlation. In general, the accuracy yielded by all distance metrics is above 80.00% and indicating a good KNN model. This finding proved the capability of KNN in differentiating the agarwood oil compounds to high or low qualities. The results in this study are important and contributed to further research work in agarwood oil grading system.

This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating the qualities of agarwood oil significant compounds by different qualities based on three training algorithms namely Scaled Conjugate Gradient (SCG), Levernbergh-Marquardt (LM) and Resilient Backpropagation (RP) Neural Network by using Matlab version 2013a. The dataset used in this study were obtained at Forest Research Institute Malaysia (FRIM) and University Malaysia Pahang (UMP). Further, the areas (abundances, %) of chemical compounds is set as an input and the quality represented (high or low) as an output. The MLP performance was examined with different number of hidden neurons which is in the ranged of 1 to 10. Their performances were observed to accurately found the best technique of optimization to apply to the model. It was found that the LM is effective in reducing the error by enhancing the number of hidden neurons during the network development. The MSE of LM is the smallest among SCG and RP. Besides that, the accuracy of training, validation and testing of LM performed the best accuracy (100%).

This paper presents the modelling of agarwood oil (AO) significant compounds by different qualities using Scaled Conjugate Gradient (SCG) algorithm. This technique involved of data collection from Gas Chromatography-Mass Spectrometry (GC-MS) for compound extraction. The development of Multilayer perceptron (MLP) is used to discriminate the qualities of AO chemical compounds to the high and low quality. The input and output data was transferred to the MATLAB version R2013a for extended analysis. The input is the abundances of significant compounds (%) and the output is the oil quality either high or low. This involved of identification, selection and optimization of a MLP as classifiers to identify and classify the agarwood oil quality. The result showed that MLP as pattern classifier is successful classify agarwood oil quality using SCG algorithm with 100% accuracy. This finding is important in agarwood oil area especially in grading system.

In Malaysia, east coast of peninsular is experiencing the rainy season between mid - October until March every year. Heavy seasonal rains cause the Kelantan River to overflow and flood the surroundings area. In this paper, the application of feed forward multi-layer perceptron (FFMLP) in neural networks for flood water level prediction is presented. The method focused on the neuron variation in hidden layer. By using measured data of three stations; Tualang, Kuala Krai and Kusial, FFMLP neural networks was developed. The inputs are water level river at three stations and output is water level river at Kusial station. The numbers of neuron in hidden layer were varied from one to ten and Levenberg Marquadt algorithm is used to train the network. The performance of network was evaluated using Mean Square Error (MSE). It is shown that three neurons in hidden layer afforded the lowest MSE, 0.043. The Regression, R for training network is closed to 1 (0.991), supports that the model is acceptable and able in predicting water level at Kusial station.

This paper presents the comparison between ARX model and ARMAX model using the data from steam distillation essential oil extraction (SDEOE). The work is implementing system identification approach. The aim of this research is to identify the performance of ARX and ARMAX model toward the system. The input for every model is either Pseudo Random Binary Sequence (PRBS) or Pseudo Random Sequence (PRS) with temperature as output. Each dataset consists of 3000 sample, and being separated into estimation and validation for model estimation and validation with the ratio of 2000:1000, respectively. All the analysis work is done via Matlab R2013a. The result generally showed that ARX is slightly better than ARMAX model especially for model best fit. However, generally both models ARX and ARMAX can be implemented for PRBS and PRS perturbation since it passes all the validation's criteria in this study; model fits, correlation test and distribution of the residual. This finding is important and it will benefits for further work in steam distillation extraction especially for controller design and real time application.

This paper discusses the potential of applying VGG16 model architecture for plant classification. Flower images are used instead of leaves as in other plant recognition model because the structure of shape and color of leaves are similar in nature. This might be disadvantageous when we want to use only leaves images as a sole feature of plants to classify the species. Previous work has demonstrated the effectiveness of using transfer learning, dropout and data augmentation as a method to reduce overfitting problem of convolutional neural network model when applied in limited amount of images data. We have successfully build and train the VGG16 model with 2800 flower images. The model able to achieve a classification accuracy score of 96.25% for training set, 93.93% for validation set and 89.96% for testing set.

This paper presents the application of k-Nearest Neighbor (k-NN) in grading the quality agarwood oil. Six agarwood oil samples obtained at Forest Research Institute Malaysia (FRIM) were extracted and their chemical compounds were examined by GC-MS. The work is followed by the grading system using the proposed k-NN. The study shows that there are 10 significant chemical compounds of agarwood oils. They are β-agarofuran, α-agarofuran, 10-epi-□-eudesmol, □-eudesmol, longifolol, oxo-agarospirol, hexadecanol and eudesmol. These compounds are used as inputs to the k-NN algorithm for grading them. The performance of the k-NN is measured and the highest accuracy obtained by k-NN which is above 83.3% shows that k-NN is a reliable classifier in grading the agarwood oil quality.

This paper studied the robustness of ARX model by varying the noise sequence adapted to the output of a heating process. The ARX model is fitted from output data which is obtained from a pilot plant of essential oil extraction by applying steam distillation technique. The plant is supplied by 1.5kW power delivered to the heating element which then is converted into PRBS signal as input and produced output which is steam temperature from ambient (23°C) to steady state condition (93°C). The noises with different variations have been used to corrupt the validation data, which ranged from ±0.2°C to ±2.0°C. Noises and PRBS signal both are generated using Matlab software version R2008a. The study has shown that as noise is increased, there are small decrement of ARX model fit. Therefore the fitted ARX model is highly suitable for any extended contribution in controller design, prediction and simulation.