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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Ngo Hea Choon
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering

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Sign language detection using convolutional neural network for teaching and learning application Wan Mohd Yaakob Wan Bejuri; Nur’Ain Najiha Zakaria; Mohd Murtadha Mohamad; Warusia Mohamed Yassin; Sharifah Sakinah Syed Ahmad; Ngo Hea Choon
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp358-364

Abstract

Teaching lower school mathematic could be easy for everyone. For teaching in the situation that cannot speak, using sign language is the answer especially someone that have infected with vocal cord infection or critical spasmodic dysphonia or maybe disable people. However, the situation could be difficult, when the sign language is not understandable by the audience. Thus, the purpose of this research is to design a sign language detection scheme for teaching and learning activity. In this research, the image of hand gestures from teacher or presenter will be taken by using a web camera for the system to anticipate and display the image's name. This proposed scheme will detects hand movements and convert it be meaningful information. As a result, it show the model can be the most consistent in term of accuracy and loss compared to others method. Furthermore, the proposed algorithm is expected to contribute the body of knowledge and the society.
A review of the automated timber defect identification approach Teo Hong Chun; Ummi Raba’ah Hashim; Sabrina Ahmad; Lizawati Salahuddin; Ngo Hea Choon; Kasturi Kanchymalay
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2156-2166

Abstract

Timber quality control is undoubtedly a very laborious process in the secondary wood industry. Manual inspections by operators are prone to human error, thereby resulting in poor timber quality inspections and low production volumes. The automation of this process using an automated vision inspection (AVI) system integrated with artificial intelligence appears to be the most plausible approach due to its ease of use and minimal operating costs. This paper provides an overview of previous works on the automated inspection of timber surface defects as well as various machine learning and deep learning approaches that have been implemented for the identification of timber defects. Contemporary algorithms and techniques used in both machine learning and deep learning are discussed and outlined in this review paper. Furthermore, the paper also highlighted the possible limitation of employing both approaches in the identification of the timber defect along with several future directions that may be further explored.
Co-Authors Kasturi Kanchymalay Lizawati Salahuddin Mohd Murtadha Mohamad Nur’Ain Najiha Zakaria Sabrina Ahmad Sharifah Sakinah Syed Ahmad Teo Hong Chun Ummi Raba’ah Hashim Wan Mohd Yaakob Wan Bejuri Warusia Mohamed Yassin