Search

Using essential oils derived from trees for pharmaceutical purposes, incense, aromatherapy, and other areas has expanded its popularity on the international market. However, since human sensory evaluation is still the primary technique used to grade essential oils in Malaysia, the classification technique for determining their grade is still below standard. Nonetheless, prior studies established new approaches for classifying the grade of essential oils by studying their chemical compounds. Therefore, agarwood essential oil was selected for the suggested model due to the increasing demand and the high cost of the world's natural raw materials. The support vector machine (SVM) using one versus all (OVA) approach was selected as the classifier for agarwood essential oil. This study provides an overview of essential oils and their prior research techniques. In addition, a review of SVM is conducted to demonstrate that the technique is appropriate for future studies.

Agarwood oil quality is often separated into two or three categories. This makes classifying agarwood oil quality using current methods difficult. Current approaches rely solely on human perception to determine the quality of agarwood, whether in raw material or oil. This technique has other undesirable implications. It can affect the human sensory system, particularly the eyes and nose. Categorization takes time, which is a considerable expense to succeed in this method. As a result, a new classification system should be devised. The chemical components in agarwood oil are used to classify it in this study. In this study, samples with preprocessing data from two to five quality levels were used. The purpose is to categorize this data based on its qualities and analyze whether this new quality group is acceptable. The K-nearest neighbours (KNN) approach was used to classify all samples and their properties for this dataset. All samples may be correctly classified by grade without any errors. This shows the chemical compound-based classification of agarwood oil can be retained. With these findings, future agarwood oil research may focus on building a new classification.

Essential oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.

Agarwood oil is well known for its unique scent and has many usages; as an incense, as ingredient in perfume, is burnt during religious ceremonies and is used in traditional medical preparation. Therefore, agarwood oil has high demand and is traded at different price based on its quality. Basically, the oil quality is classified by using physical properties (odor and color) and this technique has several problems: not consistent in term of accuracy. Thus, this study presented a new technique to classify the quality of agarwood oil based on chemical properties. The work focused on the k-Nearest Neighbor (k-NN) modelling by varying Mahalanobis and Correlation in distance metric for agarwood oil quality classification. It involved of 96 samples of agarwood oil, data pre-processing (data randomization, data normalization, and data division to testing and training datasets) and the development of k-NN model. The training dataset is used to train the k-NN model, and the testing dataset is used to test the developed model. During the model development, Mahalanobis and Correlation are varied in k-NN distance metric. The k-NN values are ranging from 1 to 10. Several performance criteria including resubstitution error (closs), cross-validation error (kloss) and accuracy were applied to measure the performance of the built k-NN model. All the analytical work was performed via MATLAB software version R2020a. The result showed that the accuracy of Mahalanobis distance metric has a better performance compared to Correlation from k=1 to k=5 with the value of 100.00%. This finding is important as it proved the capabilities of k-NN modelling in classifying the agarwood oil quality. Not limited to that, it also contributed to the agarwood oil research area as well as its industry.

Free elections are one of democracy's principles. Elections will be used to choose the representatives of the people. It is underlined on how important it is to organize free, fair, and secret elections. Traditionally, voting used to be conducted by stamping on paper, then placing it in a ballot box with the chosen candidate. Each vote in every ballot box must be counted separately, and the votes for each contender must then be added up to determine which candidate had the most votes. Everything was done manually, it will take longer to announce the winner. Numerous errors are being made, but they will not change the outcome. In this study, a significant system that stops electoral malpractices and expedites the voting process will propose. The controller utilized in this project is the Arduino Uno. The user is authenticated using a fingerprint. Everybody's fingerprints differ from one another. The device is programmed using the Arduino IDE, and the ballot card is displayed, and the results are stored in the cloud. Only a registered voter may cast a vote, and the system alerts users to any fraud. This project protects citizens' freedom to vote and ensures an impartial election.

Agarwood is a resin saturated heartwood producing its ownessential oil. This oil comprises of a complex mixture of chromone derivatives, oxygenated sesquiterpenes and sesquiterpene hydrocarbons. This mixture has a heavy woody scentand is one of the contributors to the Agarwood oil quality. In this paper, a study that focuses on the approach to select the substantial chemical compounds for Agarwood quality grading was carried out. GC-MS analysis was used to extract the chemical compounds from the Agarwood oil. The data were then preprocessed using techniques such as missing values ratio, natural logarithm and min. max. normalization. Next, synthetic data were generated using MUNGE to fulfil the passing condition of sampling adequacy test. To determine the substantial compounds, PCA and Pearson’s correlation were used. This approach was successful in determining three substantial compounds namely β-agarofuran, αagarofuran and 10-epi-γ-eudesmol. These substantial chemical compounds will be used later to predict the quality of Agarwood oil.

Exported fresh intact pineapples must fulfill the minimum internal quality requirement of 12 degree brix. Even though near-infrared (NIR) spectroscopic approaches are promising to non-destructively and rapidly assess the internal quality of intact pineapples, these approaches involve expensive and complex NIR spectroscopic instrumentation. Thus, this research evaluates the performance of a proposed pre-dispersive NIR light sensing approach in non-destructively classifying the Brix of pineapples using K-fold cross-validation, holdout validation, and sensitive analysis. First, the proposed pre-dispersive NIR sensing device that consisted of a light sensing element and five NIR light emitting diodes with peak wavelengths of 780, 850, 870, 910, and 940 nm, respectively, was developed. After that, the diffuse reflectance NIR light of intact pineapples was non-destructively acquired using the developed NIR sensing device before their Brix values were conventionally measured using a digital refractometer. Next, an artificial neural network (ANN) was trained and optimized to classify the Brix values of pineapples using the acquired NIR light. The results of the sensitivity analysis showed that either one wavelength that was near to the water absorbance or chlorophyll band was redundant in the classification. The performance of the trained ANN was tested using new pineapples with the optimal classification accuracy of 80.56%. This indicates that the proposed pre-dispersive NIR light sensing approach coupled with the ANN is promising to be an alternative to non-destructively classifying the internal quality of fruits.

This work studies the agarwood oil classification into high and low quality by using two different techniques. Initially, the Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP) are where the sample preparation and compound extraction of agarwood oil is collected. The data collections were done from the previous researcher consists of 96 samples from seven significant agarwood oil compounds. The artificial neural network (ANN) and the proposed stepwise regression technique were used in this study. The stepwise regression was done the feature selection and successfully reduced agarwood oil compounds from seven to four. Then, the ANN technique was used to classify agarwood oil into high and low using input from seven and four compounds separately. The performance of ANN with different inputs is compared (ANN with seven inputs compared with ANN with four inputs). All the experimental work was performed using the MATLAB R2017b using the “patternet” implemented Levenberg Marquardt algorithm and ten hidden neurons. It was found that the ANN technique using seven compounds obtained the best performance according to high accuracy and lower mean square error (MSE) value. Finally, 1 hidden neuron in ANN with seven inputs selected as the best neuron for grading the agarwood oil compounds.

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.

This paper presents an analysis of Gas Chromatography – Flame Ionization Detector (GC-FID) and Gas Chromatography – Mass Spectrometry (GC-MS) microwave-assisted hydrodistillation extraction (MAHD) of agarwood chips. The work involves of agarwood chips sample preparation starting from drying to soaking process, extraction method using MAHD and compound analysis using GC-FID and GC-MS for compounds identification. During the extraction time, four hours were varied; 2-hours, 3-hours, 4-hours The result showed that the agarwood chips in this study, extracted by MAHD are made up of three major groups; oxygenated sesquiterpenes, monoterpene hydrocarbons, and sesquiterpene hydrocarbons. Not limited to that, the study also adds to the understanding of the variation of the chemical compounds in agarwood especially those contributed to the fragrance of its oil as well as together with different hours of extraction time where n-hexadecanoic acid appeared as the compound to have the high peak of relative peak area (%) at all (5, 4, 3 and 2) time of extractions.

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.

Essential oils are concentrated oils produced by different parts of plants via distillation. Essential oils need to grade into high and low quality due to high quality essential oil being popular and commonly used in variety industries such as in cosmetics, perfumes and traditional medicines. On the previous researcher, many techniques regarding on method of extraction, chemical compounds of essential oil and the quality of essential oils have been presented. Currently, artificial neural network (ANN) is a popular method but this method has many local minima to be chosen as the best for a task. In the future study, Cymbopogon Nardus oil has been chosen for the proposed model due to its applications in mosquito repellent, medicine, food flavor and so on while SVM technique is chosen as classifier in C.Nardus oil. This paper presents an overview of essential oils and its previous analysis technique. Besides, the review on Support Vector Machine (SVM) is done to prove the technique is suitable for future research studies.

Agarwood oil is considered a high market value oil and expensive commodity. It consists of a complex mixture of sesquiterpene hydrocarbons, oxygenated sesquiterpenes and chromone derivatives. These chemical compounds contribute to the determination of Agarwood oil quality. In this study, a statistical analysis concentrates on chemical compounds of Agarwood oil is conducted. The chemical compounds were analysed using Principal Components Analysis (PCA). Using GC-MS analysis, the chemical compounds were first identified. Then, PCA with correlation matrix was used to further analyse the data. Scree Plot was used to select valid principal components. To determine the significant chemical compounds, the data under these principal components were rotated using Varimax. 11 chemical compounds were found significant and they can be used in identifying Agarwood oil quality.

Agarwood essential oil is used in most perfumery ingredients, as an incense and in traditional medical preparations. Agarwood essential oil, called "Black Gold," is extremely valued to the global community due to its numerous benefits. As of now, there is still no standard technique of grading different grades of agarwood essential oil. The current grading technique is inefficient since the agarwood essential oil is graded by using human sensory panel. Different people might have different perspective on grading the agarwood essential oil hence, the technique is not practical to adapt it globally. Due to the current technology, numerous intelligent techniques for verifying the grades of agarwood essential oil have been proposed and implemented. The study has conducted a statistical analysis on 4 grade agarwood essential oil using boxplot. Boxplot analysis summarizes the abundances for each chemical compounds from four different grades of agarwood essential oil with a high grade as a reference. This study shows the analysis of boxplot investigated 10-epi-δ-eudesmol, α-agarofuran, βagarofuran, δ-eudesmol and dihydrocollumellarin as most important chemical compounds in high grade of agarwood essential oil. The chemical compounds that have been identified in high grade of agarwood essential oil can be a reference for future research studies.

Cymbopogon oil is derived from the Cymbopogon species which are lemongrass and citronella. The essential oil is primarily used as a mosquito repellent as a pesticide, and also has other insecticidal, acaricidal and herbicidal activity. It is not considered harmful to humans and pets but may cause skin irritation. The aim of this paper is to model the Cymbopogon oils species using k-Nearest Neighbours (k-NN). The work consists of Cymbopogon oils data extraction using GC and GC-MS machine for compound identification. After that, a quick observation on the major compound in the samples as significant compound identification was done. Then, pre-processing data was performed which consist of data randomization and data splitting into training and testing dataset with the ratio of 80%:20%, respectively. Next, the k-NN modelling was carried out using training dataset and also applying Euclidean as distance metric. It was followed by the tested of developed k-NN using testing dataset. The result showed that k=1 and k=2 achieved 100% accuracy for both testing and training dataset. This proven that k-NN modelling in this study was able to classify the Cymbopogon oil compounds according to its species which are citronella and lemongrass.

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 is a resinous heartwood and Aquilaria is one of many species that grows a lot in Asia. Traditionally, the quality of agarwood oil is based on color, odor, high fixative properties and consumer perception. This quality grading is performed by trained human graders using sensory panels. Human sensory panels has limitation such as fatigue. Therefore, this study focuses on chemical compounds of Agarwood oil. Using this compounds together with artificial intelligence technique, a new grading system will be proposed. This paper discusses only on the statistical analysis of the chemical compounds. 106 compounds were acquired using GC-MS analysis. To remove insignificant compounds, missing values ratio was computed and out of 109 only 19 compounds remained. These compounds were transformed using natural logarithm to improves the distribution of data.

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 one of the plant’s primary metabolites. Products produced by agarwood oil are very valuable and have a great demand in many fragrance as well as aromatic industries. Enhancing and improving the discrimination technique is the main aim to determine or grade the good quality of agarwood oil. In this paper, all statistical works were performed via SPSS software. Two parameters involved are abundance of compound (%) and quality of the agarwood oil either low or high quality. The result showed that, there is clear significant normality test depends on the parameters through the boxplot and Quantile-Quantile (Q-Q) plot for data distribution. Then, Kolmogorov-Smirnov (K-S), non-parameter test and hypothesis were covered in normality test. The techniques proved their capabilities in statistical analysis for agarwood oil compounds and confirmed that the data is suitable for further work especially for 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.

Agarwood oil has been widely used in a number of different applications, including its use as a perfumery ingredient in incense products and a therapeutic agent in traditional medicines [1–8]. The oil itself can be obtained from the scented wood of plants belonging to the Aquilaria species (Thymelaeaceae) [1]. Agarwood oil consists of a complex mixture of chemicals, including sesquiterpenes, oxygenated sesquiterpenes, and chromone derivatives 7–9�. These compounds can be used to differentiate agarwood oil into different grades and therefore have a significant impact on its quality and commercial value [8]. Many factors can influence the quality of agarwood oil, but the two most important are the chemical composition of the oil and the temperature used during the extraction of the oil from plant material [8, 10].

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.

Agarwood is well known as one of the expensive woods in the world. It has a unique scent which brings it to have wide usages especially in perfumery ingredient, as incense, in traditional medical preparation, and as symbol of wealth. Due to that, this paper presents the analysis on chemical profiles of agarwood chipwood, as a part of agarwood grading system. The work involved of Solid Phase Microextraction (SPME) coupled with Gas Chromatography — Mass Spectrometry (GC-MS) GC-MS in extracting high quality. Three headspace fibers; PDMS-DVB, CAR-PDMS and DVB-CAR-PDMS were used during the extraction to identify the compounds with the sampling time of 60 minutes. The result showed that high quality agarwood chipwood is made up of terpene group which are monoterpene hydrocarbon, sesquiterpene hydrocarbon and oxygenated sesquiterpene. The relative peak areas (%) for compounds are tabulated and plotted. The finding in this study confirmed that the difference in compounds extracted and their relative peak area (%) are due to different fiber's polarity and absorbent, Thus, it is significant and benefit especially in agarwood oil quality grading and its related area.

Recently, the flood modelling based on neural network has become popular among the researches due to its treat not only to human but also animals and plants. This paper presents the application of Z-Score and Feedforward Neural Network (FFNN) in identifying significant rainfall stations in Kelantan River for flood modeling especially at Kuala Krai Station. The daily rainfall and river water level are collected in time series with hourly time interval in year 2014. Seven rainfall stations along the Kelantan River are computed their Z-score values and it is highlighted that two rainfall stations, Tualang (S3) and Kuala Krai (S4) are significant among others. The river water level and rainfall from these stations are used as inputs to FFNN and water level at S4 as output for network employment. Regression for training network is obtained and Mean Square Error (MSE) is varied from one to five neurons for network performance purposes. The result showed that the MSE at two neurons in hidden layer is the lowest among other with the value of 0.0016 and the regression for training network is 0.99987. The findings obtained in this study revealed the capability of Z-Score and the employed FFNN model is success to predict water level at S4 station. Thus, it is significant and beneficial for further work especially for flood modeling and analysis.

Due to its popularity and high market demand, critical analysis on agarwood vapour chemical compounds may provide an alternative quality discrimination of agarwood oil. The proposed work involves the extraction of high quality agarwood using headspace volatile divinylbenzene-carboxen-polydimethysiloxane (DVB-CAR-PDMS) solid phase microextraction (SPME) with different sampling time at 15, 30 and 60 minutes. Then, Gas chromatography — Mass Spectroscopy (GC-MS) is performed to identify the chemical compounds. Generally, agarwood vapour is rich in terpene group especially monoterpene, sesquiterpene and oxygenated sesquiterpene. Analysis showed that at least 52, 50 and 54 compounds are extracted at 15, 30 and 60 minutes, respectively. Among all, duration of 60 minutes produced the highest abundance (%) for caryophellene oxide. The finding proves that caryophellene oxide as one of the important compounds in high agarwood and different sampling time plays a major role that effects the extraction. Thus, the analysis in this study is significant and brings benefit especially to the agarwood and its essential oil research area.

In this paper, a FSR two-profile environment ground clutter-measured signal with very high frequency (VHF) and ultra high frequency (UFH) at a border of dense forest and free space area are presented. Statistical distribution method is used to model the clutter signal, namely Weibull, Gamma, Log-Logistic and Log-Normal distribution. Two goodness-of-fit (GOF) tests are used to calculate the error between the amplitude of the clutter data and the statistical model, which are the root mean square error (RMSE) and chi-square (CS). At the end of this analysis, Weibull model was found to be the best fit for 64 MHz clutter signal while Gamma model is best fitted at 151 MHz carrier frequency. Another model known as Log-Logistic model fits well to a clutter signal measured with 434 MHz carrier frequency.

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 water temperature of a hydro distillation that have been modelled by using linear ARX Modal. Based on the modal obtained, a model predictive controller and PID controller have been developed. Both controller undergone the performance of controller tests that includes set point, set point change and load disturbance. The aim of those three performance tests are to test the robustness among those controllers. In this study, the analyzed via simulation. The criteria of transient responses which are rise time, settling time and percentage of overshoot is robustness among those controllers. In this study, the comparative of the controller performances were evaluated and analyzed via simulation. The criteria of transient responses which are rise time, settling time and percentage of overshoot is chosen to evaluate the robustness of the controller performance. The simulation result shows that the better performance of overall robustness test have been conquered by MPC with compared to PIDCC and PIDZN. Keywords: ARX model; system identification; model predictive controller; PID controller.

An empirical model for Terengganu seaside clutter measured using forward scatter radar (FSR) with operating frequency of 434 MHz is presented in this paper based on the analysed characteristics. Statistical distribution approach is used in this analysis to model the simulated clutter-like signal. To validate the accurateness between the measured and simulated clutter signal, the envelope clutter signal and the number of fit shape factor for both signals are being compared, while root mean square error (RMSE) technique is used to calculate the percentage error between the measured and simulated clutter signal.

This article presents the use of Z-score in assessing the significant chemical compounds extracted by head space solid phase microextraction (HS-SPME) and gas chromatography – mass spectrometry (GC-MS) analysis of an agarwood oil obtained from Melaka, Malaysia. Two types of SPME fiber; polydimethylsiloxane (PDMS) and divinylbenzene-carboxen-polydimethylsiloxane (DVB-CAR-PDMS) were used. During the extraction analysis, the results showed that at least 27 and 29 compounds were identified using PDMS and DVB-CAR-PDMS fiber, respectively. DVB-CAR-PDMS fiber was found to be more efficient in terms of selectivity of compounds extraction. The application of Z-score showed that eight and eleven marker compounds were determined in PDMS and DVB-CAR-PDMS fibers, respectively. 4-Phenyl-2-butanone, a-guaiene, β-agarofuran, a-bulnesene, a-agarofuran and 10-epi-g-eudesmol were some of the compounds selected and were often reported significantly in agarwood oils as key odor compounds. The information gathered will be used for compound selection towards grading of agarwood oils by sensor technology.