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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Almisreb, Ali Abd
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Statistical analysis for chemical compound based on several species of aquilaria essential oil Ahmad Sabri, Noor Aida Syakira; Nik Kamaruzaman, Nik Fasha Edora; Ismail, Nurlaila; Yusoff, Zakiah Mohd; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3663-3673

Abstract

The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work.
Deep learning for magnetic resonance imaging brain tumor detection: evaluating ResNet, EfficientNet, and VGG-19 Muftic, Fatima; Kadunic, Merjem; Musinbegovic, Almina; Almisreb, Ali Abd; Jaafar, Hajar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6360-6372

Abstract

This paper investigates the application of convolutional neural networks (CNNs) for the early detection of brain tumors to enhance diagnostic accuracy. Brain tumors present a significant global health challenge, and early detection is vital for successful treatments and patient outcomes. The study includes a comprehensive literature review of recent advancements in brain tumor detection techniques. The main focus is on the development and evaluation of CNN models, including EfficientNetB3, residual networks-50 (ResNet50) and visual geometry group-19 (VGG-19), for binary image classification using magnetic resonance imaging (MRI) scans. These models demonstrate promising results in terms of accuracy, precision, and recall metrics. However, challenges related to overfitting and limited dataset size are acknowledged. The study highlights the potential of artificial intelligence (AI) in improving brain tumor detection and emphasizes the need for further research and validation in real-world clinical settings. EfficientNetB3 reached 99.44% training accuracy but showed potential overfitting with validation accuracy dropping to 89.47%. ResNet50 steadily improved to 83.62% training accuracy and 89.47% validation accuracy. VGG19 struggled, with only 62% accuracy.
Agarwood oil quality identification using artificial neural network modelling for five grades Mohd Huzir, Siti Mariatul Hazwa; Tajuddin, Saiful Nizam; Mohd Yusoff, Zakiah; Ismail, Nurlaila; Almisreb, Ali Abd; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2254-2261

Abstract

Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass ɤ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardt- artificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds.
Pre-processing technique of Aquilaria species from Malaysia for four different qualities Mohd Huzir, Siti Mariatul Hazwa; Hasnu Al-Hadi, Anis Hazirah 'Izzati; Zaidi, Amir Hussairi; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Haron, Mohamad Hushni; Almisreb, Ali Abd; Taib, Mohd Nasir
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5577

Abstract

The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.
Robust k-NN approach for classifying Aquilaria oil species by compounds Ahmad Sabri, Noor Aida Syakira; Syafiqah Noramli, Nur Athirah; Nik Kamaruzaman, Nik Fasha Edora; Ismail, Nurlaila; Yusoff, Zakiah Mohd; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp178-189

Abstract

Accurate classification of Aquilaria oil species is essential for ensuring the quality and authenticity of agarwood oils, which are widely used in perfumes and traditional medicine. This study investigated the effectiveness of the k-nearest neighbours (k-NN) machine learning model for classifying Aquilaria oil species based on four significant chemical compounds: dihyro-βagarofuran, δ-guaiene, 10-epi-γ-eudesmol, and γ-eudesmol. The dataset comprised 480 samples of Aquilaria oil, which were analyzed using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID). The k-NN model, with an optimal k-value of 10 and using euclidean distance as the distance metric, achieved 100% accuracy, sensitivity, specificity, and precision in both training and testing datasets. These results demonstrate the robustness of k-NN in species identification, highlighting the discriminative power of the selected compounds. This study verifies that the integration of chemical profiling with machine learning offers a scalable solution for accurate species identification in the essential oil industry. Future work could explore hybrid models and data expansion techniques to further enhance the classification performance in more complex environmental conditions.
Optimizing nonlinear autoregressive with exogenous inputs network architecture for agarwood oil quality assessment Roslan, Muhammad Ikhsan; Ahmad Sabri, Noor Aida Syakira; Noramli, Nur Athirah Syafiqah; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3493-3502

Abstract

Agarwood oil is highly valued in perfumes, incense, and traditional medicine. However, the lack of standardized grading methods poses challenges for consistent quality assessment. This study proposes a data-driven classification approach using the nonlinear autoregressive with exogenous inputs (NARX) model, implemented in MATLAB R2020a with the Levenberg-Marquardt (LM) algorithm. The dataset, sourced from the Universiti Malaysia Pahang Al-Sultan Abdullah under the Bio Aromatic Research Centre of Excellence (BARCE) and Forest Research Institute Malaysia (FRIM), comprises chemical compound data used for model training and validation. To optimize model performance, the number of hidden neurons is systematically adjusted. Model evaluation uses performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R²), epochs, accuracy, and model validation. Results show that the NARX model effectively classifies agarwood oil into four quality grades which is high, medium-high, medium-low, and low. The best performance is achieved with three hidden neurons, offering a balance between accuracy and computational efficiency. This work demonstrates the potential of automated, standardized agarwood oil quality grading. Future research should explore alternative training algorithms and larger datasets to further enhance model robustness and generalizability.
Statistical analysis of agarwood oil chemical compound exists in four species of Aquilaria Zaidi, Amir Hussairi; Al-Hadi, Anis Hazirah ‘Izzati Hasnu; Huzir, Siti Mariatul Hazwa Mohd; Yusoff, Zakiah Mohd; Ismail, Nurlaila; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Haji Mohd Nasir
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp727-732

Abstract

Aquilaria, renowned for its agarwood, and valued for its aromatic wood and rich resin, finds use in cosmetics, fragrances, incense, and medicine. Identifying the agarwood-producing species among 21 species of Aquilaria is challenging. This study analyzes chemical compounds in agarwood oil from 4 Aquilaria species: Aquilaria beccariana, Aquilaria crassna, Aquilaria malaccensis, and Aquilaria subintegra using gas chromatography-flame ionization detector (GC-FID). Statistical analysis explores compound abundance, employing methods like mean and Z-score tests. This analysis summarizes those 14 compounds that are consistently present based on zero and non-zero observations, and the Z-score test highlights five chemical compounds, with three compounds appearing in both analyses. These compounds can serve as a reference for future studies on Aquilaria species and agarwood oil, enhancing classification efforts.
Co-Authors Ahmad Sabri, Noor Aida Syakira Al-Hadi, Anis Hazirah ‘Izzati Hasnu Haron, Mohamad Hushni Hasnu Al-Hadi, Anis Hazirah 'Izzati Huzir, Siti Mariatul Hazwa Mohd Ismail, Nurlaila Jaafar, Hajar Kadunic, Merjem Mohd Huzir, Siti Mariatul Hazwa Mohd Nasir Taib Mohd Yusoff, Zakiah Muftic, Fatima Musinbegovic, Almina Nik Kamaruzaman, Nik Fasha Edora Noramli, Nur Athirah Syafiqah Roslan, Muhammad Ikhsan Syafiqah Noramli, Nur Athirah Taib, Haji Mohd Nasir Tajuddin, Saiful Nizam Yusoff, Zakiah Mohd Zaidi, Amir Hussairi