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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Norhashimah Mohd Saad
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Shape and Level Bottles Detection Using Local Standard Deviation and Hough Transform Nor Nabilah Syazana Abdul Rahman; Norhashimah Mohd Saad; Abdul Rahim Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (346.376 KB) | DOI: 10.11591/ijece.v8i6.pp5032-5040

Abstract

This paper presents shape and level analysis using local standard deviation and Hough transform technique to detect the shape and level of the bottle.A 155 sample images are used as a test product to detect shape and level. Local standard deviation is used contrast gain technique to segment the shape of the bottle by enhancing the contrast of the image. The ratio of the area is calculated from the extent parameter. The maximum and minimum water level is created by using Hough transform technique to identify the level of the water. Decision tree is applied to classify the shape and level of the bottle either good or defect condition. From experimental result, 97% and 93% accuracy of shape and level is achieved which shows that the proposed analysis technique is potential to be applied for beverages product inspection system.
A Detail Study of Wavelet Families for EMG Pattern Recognition Jingwei Too; A. R. Abdullah; Norhashimah Mohd Saad; N. Mohd Ali; H. Musa
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (751.745 KB) | DOI: 10.11591/ijece.v8i6.pp4221-4229

Abstract

Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements. 
Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification Nur Hasanah Ali; Abdul Rahim Abdullah; Norhashimah Mohd Saad; Ahmad Sobri Muda
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5843-5852

Abstract

Treatment of stroke patients can be effectively carried out with the help of collateral circulation performance. Collateral circulation scoring as it is now used is dependent on visual inspection, which can lead to an inter- and intra-rater discrepancy. In this study, a collateral circulation classification using the ResNet50 was analyzed by using cone beam computed tomography (CBCT) images for the ischemic stroke patient. The remarkable performance of deep learning classification helps neuroradiologists with fast image classification. A pre-trained deep network ResNet50 was applied to extract robust features and learn the structure of CBCT images in their convolutional layers. Next, the classification layer of the ResNet50 was performed into binary classification as “good” and “poor” classes. The images were divided by 80:20 for training and testing. The empirical results support the claim that the application of ResNet50 offers consistent accuracy, sensitivity, and specificity values. The performance value of the classification accuracy was 76.79%. The deep learning approach was employed to unveil how biological image analysis could generate incredibly dependable and repeatable outcomes. The experiments performed on CBCT images evidenced that the proposed ResNet50 using convolutional neural network (CNN) architecture is indeed effective in classifying collateral circulation.
Co-Authors A. R. Abdullah Abdul Rahim Abdullah Ahmad Sobri Muda H. Musa I. N. Abdullah Jingwei Too Mohd Hatta Jopri Mohd Juzaila Abd Latif Muhamad Faizal Yaakub N. H. Ali N. Mohd Ali N. N. S. A. Rahman Nor Nabilah Syazana Abdul Rahma Nor Nabilah Syazana Abdul Rahman Nor Shahirah Mohd Noor Nur Azmina Zainal Nur Hasanah Ali Tole Sutikno W. H. W. Hasan Wira Hidayat Mohd Saad