Nik Adilah Hanin Zahri
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Preferred name
Nik Adilah Hanin Zahri
Official Name
Nik Adilah Hanin , Zahri
Alternative Name
Zahri, Nik Adilah Hanin Binti
Zahri, N. A.H.
Hanin, Nik Adilah
Zahri, Nik Adilah Hanin
Adilah Hanin Zahri, Nik
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Scopus Author ID
57191919794
Researcher ID
GJQ-4994-2022

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  • Publication
    Potential of Nanocellulose Composite for Electromagnetic Shielding
    ( 2017-12-11)
    Nurul Fatihah Nabila Yah
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Yeng Seng Lee
    ;
    Wee Fwen Hoon
    ;
    Mohd Fareq Malek
    ;
    Nik Adilah Hanin Zahri
    ;
    Hayati Hasibuan Zainal
    Nowadays, most people rely on the electronic devices for work, communicating with friends and family, school and personal enjoyment. As a result, more new equipment or devices operates in higher frequency were rapidly developed to accommodate the consumers need. However, the demand of using wireless technology and higher frequency in new devices also brings the need to shield the unwanted electromagnetic signals from those devices for both proper operation and human health concerns. This paper highlights the potential of nanocellulose for electromagnetic shielding using the organic environmental nanocellulose composite materials. In addition, the theory of electromagnetic shielding and recent development of green and organic material in electromagnetic shielding application has also been reviewed in this paper. The use of the natural fibers which is nanocelllose instead of traditional reinforcement materials provides several advantages including the natural fibers are renewable, abundant and low cost. Furthermore, added with other advantages such as lightweight and high electromagnetic shielding ability, nanocellulose has a great potential as an alternative material for electromagnetic shielding application.
  • 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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