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All Journal Bulletin of Electrical Engineering and Informatics
Lizawati Salahuddin
Universiti Teknikal Malaysia Melaka (UTeM)
Electrical & Electronics Engineering

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Analysis of texture features for wood defect classification Nur Dalila Abdullah; Ummi Raba'ah Hashim; Sabrina Ahmad; Lizawati Salahuddin
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 (415.861 KB) | DOI: 10.11591/eei.v9i1.1553

Abstract

Selecting important features in classifying wood defects remains a challenging issue to the automated visual inspection domain. This study aims to address the extraction and analysis of features based on statistical texture on images of wood defects. A series of procedures including feature extraction using the Grey Level Dependence Matrix (GLDM) and feature analysis were executed in order to investigate the appropriate displacement and quantisation parameters that could significantly classify wood defects. Samples were taken from the KembangSemangkuk (KSK), Meranti and Merbau wood species. Findings from visual analysis and classification accuracy measures suggest that the feature set with the displacement parameter, d=2, and quantisation level, q=128, shows the highest classification accuracy. However, to achieve less computational cost, the feature set with quantisation level, q=32, shows acceptable performance in terms of classification accuracy.
Abnormal heart rate detection through real-time heart monitoring application Ummi Namirah Hashim; Lizawati Salahuddin; Ummi Rabaah Hashim; Mohd Hariz Naim; Raja Rina Raja Ikram; Fiza Abdul Rahim
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4933

Abstract

Health monitoring that requires doctors and patients at the healthcare center may not be practical during the coronavirus disease 2019 (COVID-19) pandemic. Alternatively, mobile health (mHealth) should be embraced to minimize contact between patients and healthcare personnel. This research aims to enhance the detection of abnormal heart rate (HR) detection by developing a real-time heart rate monitoring (RTHM) application. Sixteen healthy adults participated in a physical real-time HR monitoring testbed. Participants HR was measured for three minutes resting and three minutes performing moderate-intensity physical activity. The results were compared with the polar beat app. Additionally, the energy consumption, the time taken to receive an alarm message, and an acceptance test were analyzed. The app is acceptably accurate, the mean absolute percentage error less than 2%. The response time to receive the alarm message is 30 seconds on average, which is under an acceptable range of medical standards. Moreover, the app is power efficient, 477 mW on average. Participants show a positive attitude towards using RTHM. RTHM is expected to provide a more plausible tool for monitoring the heart towards enhancing abnormal HR detection by promoting patient-oriented healthcare and minimizing sudden deaths due to heart failure.
Co-Authors Fiza Abdul Rahim Mohd Hariz Naim Nur Dalila Abdullah Raja Rina Raja Ikram Sabrina Ahmad Ummi Namirah Hashim Ummi Raba'ah Hashim Ummi Rabaah Hashim