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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science

Wan Nurshazwani Wan Zakaria

Universiti Tun Hussein Onn Malaysia

Author-ID : 2454959

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 10 Documents Claim Missing Document

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A simulation study of excitation coil design in single-sided mpi scanner for human body application Nurmiza Othman; Muhamad Fikri Shahkhirin Birahim; Wan Nurshazwani Wan Zakaria; Mohd Razali Md Tomari; Md Nor Ramdon Baharom; Luqman Hakim
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Magnetic particle imaging (MPI), a tomographic imaging method has been introduced for 3D imaging of human body with some potential applications such as magnetic hyperthermia and cancer imaging. It involves three important elements: tracer development using magnetic nanoparticles (MNPs), hardware realization (scanner using excitation and pickup coils), and image reconstruction optimization. Their combination will produce a high quality of image taken from any biological tissue in the human body based on the secondary magnetic field signal from the magnetized MNPs that are injected into human body. A homogeneous and adequate magnetic field strength from an excitation coil is needed to enhance the quality of the secondary signal. However, the complex surface topography of human body and physical properties of an excitation coil influence the strength and the homogeneity of the magnetic field generation at the MNPs. Therefore, this work focused on finding alternative design of excitation coil used in single sided MPI to produce up to 2 mT with high homogeneity of field distribution in the MNPs at the varied depth of 10 to 30 mm under the excitation coil. We proposed several designs with variation in physical properties and coil arrangement based on simulation study carried out by using Ansys Maxwell.

Investigation of white blood cell biomaker model for acute lymphoblastic leukemia detection based on convolutional neural network Syadia Nabilah Mohd Safuan; Mohd Razali Md Tomari; Wan Nurshazwani Wan Zakaria; Mohd Norzali Hj Mohd; Nor Surayahani Suriani
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Acute Lymphoblastic Leukemia (ALL) is a disease that is defined by uncontrollable growth of malignant and immature White Blood Cells (WBCs) which is called lymphoblast. Traditionally, lymphoblast analysis is done manually and highly dependent on the pathologist’s skill and experience which sometimes yields inaccurate result. For that reason, in this project an algorithm to automatically detect WBC and subsequently examine ALL disease using Convolutional Neural Network (CNN) is proposed. Several pretrained CNN models which are VGG, GoogleNet and Alexnet were analaysed to compare its performance for differentiating lymphoblast and non-lymphoblast cells from IDB database. The tuning is done by experimenting the convolution layer, pooling layer and fully connected layer. Technically, 70% of the images are used for training and another 30% for testing. From the experiments, it is found that the best pretrained models are VGG and GoogleNet compared to AlexNet by achieving 100% accuracy for training. As for testing, VGG obtained the highest performance which is 99.13% accuracy. Apart from that, VGG also proven to have better result based on the training graph which is more stable and contains less error compared to the other two models.

A non-invasive and non-wearable food intake monitoring system based on depth sensor Muhammad Fuad Kassim; Mohd Norzali Haji Mohd; Mohd Razali Md Tomari; Nor Surayahani Suriani; Wan Nurshazwani Wan Zakaria; Suhaila Sari
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2256

The food intake counting method showed a good significance that can lead to a successful weight loss by simply monitoring the food intake taken during eating. The device used in this project was Kinect Xbox One which used a depth camera to detect the motion of a person’s gesture and posture during food intake. Previous studies have shown that most of the methods used to count food intake device is worn device type. The recent trend is now going towards non-wearable devices due to the difficulty when wearing devices and it has high false alarm ratio. The proposed system gets data from the Kinect camera and monitors the gesture of the user while eating. Then, the gesture data is collected to be recognized and it will start counting the food intake taken by the user. The system recognizes the patterns of the food intake from the user by following the algorithm to analyze the gesture of the basic eating type and the system get an average accuracy of 96.2%. This system can help people who are trying to follow a proper way to avoid being overweight or having eating disorders by monitoring their meal intake and controlling their eating rate.

Co-Authors Ain Nazari Anis Azwani Muhd Suberi Chin Fhong Soon Hashim Saim Hazlita Isa Kian Sek Tee Luqman Hakim M. I. Awad Md Nor Ramdon Baharom Mohd Marzuki Mustafa Mohd Norzali Haji Mohd Mohd Norzali Hj Mohd Mohd Razali Md Tomari Mohd Razali Md Tomari Muhamad Fikri Shahkhirin Birahim Muhammad Fuad Kassim Nor Surayahani Suriani Noraina Alia Zainudin Nur Syazlin Zolkifli Nurmiza Othman Razali Tomari Safinaz Binti Mohd Khialdin Suhaila Sari Syadia Nabilah Mohd Safuan Tai Sue Jing Tan Chun Hou Tee Kian Sek Wan Nur Hafsha Wan Kairuddin Yogindra Syam Hari Javahar

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