Saidatul Norlyana Azemi
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Preferred name
Saidatul Norlyana Azemi
Official Name
Saidatul Norlyana, Azemi
Alternative Name
Azemi, Saidatu Norlyana
Azemi, S. N.
Azemi, S. A.
Saidatul, N. A.
Azemi, Saidatul N.
Azemi, Saidatul Norlyna
Main Affiliation
Scopus Author ID
55204806400
Researcher ID
DUZ-0499-2022

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  • Publication
    Design of Passive RFID Tag Using Frequency Selective Surface with Polarization Insensitive
    ( 2023-10-06)
    Saidatul Norlyana Azemi
    ;
    Ibrahim N.A.
    ;
    Amiza Amir
    ;
    Mohd Rashidi Che Beson
    ;
    Abdul Aziz M.E.
    RFID is not a new technology. It has been applied in various industries such as for wearable applications. Common RFID tags especially for those that have been designed and are available are not independent of the incident receiver angle. Numerous wearable antennas on the market are only designed for a certain received angle. For example, a wearable RFID antenna is used in medical as a pulse reading detector. If the patient makes any movement, the patient's pulse reading is no longer accurate or there may be no pulse reading. Hence, the purpose of this project is to design and RFID antennas using Frequency Selective Surface, FSS for wearable applications that are independent towards the incident angle and small in size. In this project, several antennas design with Frequency Selective Surface (FSS) is proposed. The design for this antenna is round, square, and hexagonal. This antenna has an operating frequency from 2.4 GHz to 5.8GHz, bandwidth efficiency> 50%, dielectric constant 1.30, independent incident angle up to 60 degrees, and has a high gain of around 2 to 3dB.
  • Publication
    Gain Enhancement of CPW Antenna for IoT Applications using FSS with Miniaturize Unit Cell
    ( 2021-07-26)
    Saidatul Norlyana Azemi
    ;
    Mohd Rashidi Che Beson
    ;
    Amiza Amir
    ;
    Azhari M.S.B.A.
    ;
    Jiunn N.K.
    Wireless connectivity is a critical enabler for many IoT applications. Antennas are often required to be installed inside the device cover, which usually occurs in small sizes with optimal performance. On the other hand, a suitable antenna should also have high efficiency, gain and adequate bandwidth covering the desired frequency range. Here, we proposed new type of Frequency Selective Surface (FSS) with miniaturized resonator element to enhance the gain of an CPW antenna. Furthermore, the miniaturization of the Frequency Selective Surface unit cell is attained by coupling the two meandered wire resonators. The wire resonator is separated by thin and single substrate layer. The structure of the FSS is shown to have a FSS unit cell dimension that is miniaturized to 0.057λ. The CPW antenna size is only 28.8mm × 46.5mm operating at 2.45 GHz frequency. With the additional of the FSS, the antenna's gain reaches up from 1.8 dBi to 2.6 dBi with omnidirectional radiation pattern.
  • Publication
    Evaluating Tree-based Ensemble Strategies for Imbalanced Network Attack Classification
    ( 2024-01-01)
    Soon H.F.
    ;
    Amiza Amir
    ;
    Nishizaki H.
    ;
    Nik Adilah Hanin Zahri
    ;
    Latifah Munirah Kamarudin
    ;
    Saidatul Norlyana Azemi
    With the continual evolution of cybersecurity threats, the development of effective intrusion detection systems is increasingly crucial and challenging. This study tackles these challenges by exploring imbalanced multiclass classification, a common situation in network intrusion datasets mirroring realworld scenarios. The paper aims to empirically assess the performance of diverse classification algorithms in managing imbalanced class distributions. Experiments were conducted using the UNSW-NB15 network intrusion detection benchmark dataset, comprising ten highly imbalanced classes. The evaluation includes basic, traditional algorithms like the Decision Tree, KNearest Neighbor, and Gaussian Naive Bayes, as well as advanced ensemble methods such as Gradient Boosted Decision Trees (GraBoost) and AdaBoost. Our findings reveal that the Decision Tree surpassed the Multi-Layer Perceptron, K-Nearest Neighbor, and Naive Bayes in terms of overall F1-score. Furthermore, thorough evaluations of nine tree-based ensemble algorithms were performed, showcasing their varying efficacy. Bagging, Random Forest, ExtraTrees, and XGBoost achieved the highest F1-scores. However, in individual class analysis, XGBoost demonstrated exceptional performance relative to the other algorithms. This is confirmed by achieving the highest F1-scores in eight out of the ten classes within the dataset. These results establish XGBoost as a predominant method for handling multiclass imbalance classification with Bagging being the closest feasible alternative, as Bagging gains an almost similar accuracy and F1-score as XGBoost.
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