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
Main Affiliation
Scopus Author ID
57191919794
Researcher ID
GJQ-4994-2022

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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.

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Vowel’s classification for stroke patients through rehabilitation performance via image-profiled sound data

2023 , Nur Syahmina Ahmad Azhar , Nik Mohd Zarifie Hashim , Afiqah Iylia Kamaruddin , Mahmud Dwi Sulistiyo , Nik Adilah Hanin Zahri

In terms of medicine, a disorder is a disturbance of the mind or body's normal functioning. Communication issues may result from stroke because it damages the parts of the brain that control language. The capacity to talk, read, write, and comprehend speeches can all be affected after a stroke. The rehabilitation and treatment of patients generally take a long time and include constant medication, exercise, and rehabilitation training. However, most rehab facilities throughout the world still manually carry out this rehabilitation process. Machine learning and deep learning have been introduced to this medical field to aid rehabilitation using the new technology due to computer vision's impact on this field. A reliable Convolution Neural Network with a graphical user interface is introduced in this study to support and enhance rehabilitation efforts. The spectrogram in the image-profiled sound is used to provide the optimum outcome and accuracy. This project aims to develop a neural network that can distinguish vowels between a normal person's and stroke patients' voices. In this proposed paper's result, an intelligent Convolution Neural Network system for Malay language vowel detection with high-performance accuracy is demonstrated with maximum accuracy was 92.96% with 20 epoch numbers and 6 batch size. This outcome showed that the proposed method, even using a simple network design, is still competitive compared with other methods. The proposed method is ideal for training and validating vowel recognition accuracy especially for stroke patients.

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Classification of Multiple Visual Field Defects using Deep Learning

2021-03-01 , Masyitah Abu , Nik Adilah Hanin Zahri , Amir A. , Muhammad Izham Ismail , Latifah Munirah Kamarudin , Nishizaki H.

In this work, a custom deep learning method is proposed to develop a detection of visual fields defects which are the markers for serious optic pathway disease. Convolutional Neural Networks (CNN) is a deep learning method that is mostly used in images processing. Therefore, a custom 10 layers of CNN algorithm is built to detect the visual field defect. In this work, 1200 visual field defect images acquired from the Humphrey Field Analyzer 24-2 collected from Google Image have been used to classify 6 types of visual field defect. The defect patterns are including defects at central scotoma, right/left/upper/lower quadratopia, right/left hemianopia, vision tunnel, superior/inferior field defect and normal as baseline. The custom designed CNN is trained to discriminate between defect patterns in visual field images. In the proposed method, a mechanism of pre-processing is included to improve the classification of visual field defects. Then, the 6 visual field defect patterns are detected using a convolutional neural network. The dataset is evaluated using 5-fold cross-validation. The results of this work have shown that the proposed algorithm achieved a high classification rate with 96%. As comparison, traditional machine learning Support Vector Machine (SVM) and Classical Neural Network (NN) is chose and obtained classification rate at 74.54% and 90.72%.

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Image classification for snake species using machine learning techniques

2017-01-01 , Amiza Amir , Nik Adilah Hanin Zahri , Naimah Yaakob , R Badlishah Ahmad

This paper investigates the accuracy of five state-of-the-art machine learning techniques — decision tree J48, nearest neighbors, knearest neighbors (k-NN), backpropagation neural network, and naive Bayes — for image-based snake species identification problem. Conventionally, snake species identification is conducted manually based on the observation of the characteristics such head shape, body pattern, body color, and eyes shape. Images of 22 species of snakes that can be found in Malaysia were collected into a database, namely the Snakes of Perlis Corpus. Then, an intelligent approach is proposed to automatically identify a snake species based on an image which is useful for content retrieval purpose where a snake species can be predicted whenever a snake image is given as input. Our experiment shows that backpropagation neural network and nearest neighbour are highly accurate with greater than 87% accuracy on CEDD descriptor in this problem.

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Mobile Augmented Reality (AR) Marker-based for Indoor Library Navigation

2020-03-20 , Rusnida Romli , Razali A.F. , Nur Hafizah Ghazali , Nik Adilah Hanin Zahri , Siti Zuraidah Ibrahim

This paper presents the development of Augmented Reality (AR) for smart campus urbanization using library as the environment for the demonstration of the AR prototype. The main goal of the AR development is to help users to get information and direction easily using AR based mobile application when walking inside the library. In normal circumstances, users typically walk around and explore the library area before reaching their targeted destinations. Depending on the library size and number of reading corners, exploration and walking in the library can be time consuming. Therefore, an AR technology is introduced in this paper to improve the user experience inside the library in the right direction and information instantly. This application is developed using Vuforia software to set the image marker-based and process the output into Unity3D software, Android Studio for the Main Menu interface and IBM Watson for voice recognition. The final form of the application is successfully generated from the development of Augmented Reality (AR) application for the smart campus by using a library for the demonstration of the AR prototype. A series of application tests are conducted in each corner of the library to evaluate the effectiveness of developed AR.

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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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Analysis of the effectiveness of Metaheuristic methods on Bayesian optimization in the classification of visual field defects

2023 , Masyitah Abu , Nik Adilah Hanin Zahri , Fumiyo Fukumoto , Amiza Amir , Muhammad Izham Ismail , Yoshimi Suzuki , Azhany Yaakub

Bayesian optimization (BO) is commonly used to optimize the hyperparameters of transfer learning models to improve the model’s performance significantly. In BO, the acquisition functions direct the hyperparameter space exploration during the optimization. However, the computational cost of evaluating the acquisition function and updating the surrogate model can become prohibitively expensive due to increasing dimensionality, making it more challenging to achieve the global optimum, particularly in image classification tasks. Therefore, this study investigates and analyses the effect of incorporating metaheuristic methods into BO to improve the performance of acquisition functions in transfer learning. By incorporating four different metaheuristic methods, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) Optimization, Harris Hawks Optimization, and Sailfish Optimization (SFO), the performance of acquisition function, Expected Improvement (EI), was observed in the VGGNet models for visual field defect multi-class classification. Other than EI, comparative observations were also conducted using different acquisition functions, such as Probability Improvement (PI), Upper Confidence Bound (UCB), and Lower Confidence Bound (LCB). The analysis demonstrates that SFO significantly enhanced BO optimization by increasing mean accuracy by 9.6% for VGG-16 and 27.54% for VGG-19. As a result, the best validation accuracy obtained for VGG-16 and VGG-19 is 98.6% and 98.34%, respectively.

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Hyper-threading technology: Not a good choice for speeding up CPU-bound code

2017-01-03 , Ng Hui Qun , Zahereel Ishwar Abdul Khalib , Mohd Nazri Mohd Warip , Mohamed Elshaikh Elobaid Said Ahmed , Mostafijur R. , Nik Adilah Hanin Zahri , Puteh Saad

Hyper-threading (HT) technology allows one thread to execute its task while another thread is stalled waiting for shared resource or other operations to complete. Thus, this reduces the idle time of a processor. If HT is enabled, an operating system would see two logical cores per each physical core. This gives one physical core the ability to run two threads simultaneously. However, it does not necessarily speed up the performance of a parallel code twice the number of physical cores. This happens when two threads are trying to access the shared CPU resource. The instructions could only be executed one after another at any given time. In this case, parallel CPU-bound code could attain a little improvement in terms of speedup from HT on a quad-core platform, which is Intel i5-2410M@2.30GHz.

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Voice-based Malay commands recognition by using audio fingerprint method for smart house applications

2020 , Abu, Masyitah , Amiza Amir , Nik Adilah Hanin Zahri , Ruzelita Ngadiran

Voice-based command recognition is commonly used in security systems, phones, household appliances and hardware designed for handicapped people. Most of the current research study the voice command recognition for the smart home in English. Lack of study for voice command recognition in Malay makes it challenging to apply the voice command services for the smart home in Malaysia. Also, voice recognition is a non-trivial task in natural language processing. This project is to identify the command used for smart home appliances using Malay and design the algorithm for this system. Then, the proposed algorithm will be deployed on a Raspberry Pi to see the performance of Malay command in accuracy and the suitability of the algorithm to be deployed on low cost embedded devices. Light, fan, and television had been chosen as electrical appliances to build the command. An algorithm that previously used to recognize songs, the robust quad algorithm, is used in this project for voice command recognition. The proposed method has two main processes, known as feature extraction and voice recognition. In the feature extraction process, the audio fingerprint will extract data from the command spectral peak. For voice recognition, audio fingerprint matching will be used to analyze the audio commands. The outcome of this project is when the voice command is given by the user by activate or deactivate the target home appliance. The second outcome is the background noise that affects the system is reduced by using robust quad algorithm and increase the accuracy of the system. The results of this project have shown that the proposed algorithm is suitable to be implemented on a Raspberry Pi and achieve a high recognition rate with 87%. In the presence of noise with 15 dB, the proposed algorithm can maintain the high recognition rate with 82%.

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