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

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A rectangular CSRR based microstrip UHF reader patch antenna for RFID applications Syamimi Mohd Norzeli; Ismarani Ismail; Norashidah Md Din; Mohd Tarmizi Ali; Ali Abd Almisreb; Ammar Ahmed Alkahtani
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1434-1441

Abstract

This paper presents a compact microstrip ultra-high frequency (UHF) reader patch antenna with complementary split ring resonator (CSRR) for radio frequency identification (RFID). The total size of the antenna is 208 × 208 × 1.6 mm3. The proposed antenna is designed, fabricated and measured in order to verify the proposed concept. The characterization for radiation parameters, like return loss, radiation pattern and antenna gain have been done experimentally. The proposed antenna is operated at 921 MHz for and achieved a gain of 8.285 dBi. All simulations in this work have been carried out by means of the commercial computer simulation technology (CST) software. In compare to the simulated results, the measured outcomes are promised.
Statistical analysis of agarwood oil chemical compound exists in four species of Aquilaria Amir Hussairi Zaidi; Anis Hazirah ‘Izzati Hasnu Al-Hadi; Siti Mariatul Hazwa Mohd Huzir; Zakiah Mohd Yusoff; Nurlaila Ismail; Ali Abd Almisreb; Saiful Nizam Tajuddin; Haji Mohd Nasir Taib
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.pp727-736

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.
Applications of artificial intelligence in analyzing Aquilaria essential oils: a review of current machine learning techniques Noor Aida Syakira Ahmad Sabri; Nur Athirah Syafiqah Noramli; Muhammad Ikhsan Roslan; Nurlaila Ismail; Zakiah Mohd Yusoff; Ali Abd Almisreb; Mohd Nasir Taib
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1087-1096

Abstract

This study explores the application of machine learning (ML) techniques in the classification of agarwood oil, focusing on the use of various algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), random forest (RF), and artificial neural networks (ANN). Since 2013, ML has played a pivotal role in analyzing agarwood oil, particularly by leveraging data from a variety of chemical compounds found in the Aquilaria genus. Through a systematic review and bibliometric analysis using the SCOPUS database, this study compiles and highlights recent works that have successfully employed ML techniques for the quality assessment of agarwood oil. These studies utilize chemical data, such as gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR), for the classification and detection of different oil grades. The review reveals a broad range of ML applications, demonstrating their growing importance in the field of essential oil analysis. By systematically presenting the findings from recent research, this work emphasizes the potential for further exploration of ML in the standardization and improvement of agarwood oil classification techniques.
Co-Authors Amir Hussairi Zaidi Ammar Ahmed Alkahtani Anis Hazirah ‘Izzati Hasnu Al-Hadi Haji Mohd Nasir Taib Ismarani Ismail Mohd Nasir Taib Mohd Tarmizi Ali Muhammad Ikhsan Roslan Noor Aida Syakira Ahmad Sabri Norashidah Md Din Nur Athirah Syafiqah Noramli Nurlaila Ismail Saiful Nizam Tajuddin Siti Mariatul Hazwa Mohd Huzir Syamimi Mohd Norzeli Zakiah Mohd Yusoff