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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nor Surayahani Suriani
Universiti Tun Hussein Onn Malaysia
Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Spiking neural network classification for spike train analysis of physiotherapy movements Fadilla ‘Atyka Nor Rashid; Nor Surayahani Suriani
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (620.046 KB) | DOI: 10.11591/eei.v9i1.1868

Abstract

Classifying gesture or movements nowadays become a demanding business as the technologies of sensor rose. This has enchanted many researchers to actively investigated widely within the area of computer vision. Rehabilitation exercises is one of the most popular gestures or movements that being worked by the researchers nowadays. Rehab session usually involves experts that monitored the patients but lacking the experts itself made the session become longer and unproductive. This works adopted a dataset from UI-PRMD that assembled from 10 rehabilitation movements. The data has been encoded into spike trains for spike patterns analysis. Next, we tend to train the spike trains into Spiking Neural Networks and resulting into a promising result. However, in future, this method will be tested with other data to validate the performance, also to enhance the success rate of the accuracy.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (855.675 KB) | DOI: 10.11591/eei.v9i2.1857

Abstract

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

Abstract

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 Fadilla ‘Atyka Nor Rashid Mohd Marzuki Mustafa Mohd Norzali Haji Mohd Mohd Norzali Hj Mohd Mohd Razali Md Tomari Mohd Razali Md Tomari Muhammad Fuad Kassim Noraina Alia Zainudin Nur Syazlin Zolkifli Suhaila Sari Syadia Nabilah Mohd Safuan Wan Nur Hafsha Wan Kairuddin Wan Nurshazwani Wan Zakaria