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All Journal International Journal of Electrical and Computer Engineering Green Intelligent Systems and Applications
Ting, Huong-Yong
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Effectiveness of Using Artificial Intelligence for Early Child Development Screening Gau, Michael-Lian; Ting, Huong-Yong; Toh, Teck-Hock; Wong, Pui-Ying; Woo, Pei-Jun; Wo, Su-Woan; Tan, Gek-Ling
Green Intelligent Systems and Applications Volume 3 - Issue 1 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i1.229

Abstract

This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results.
Assistive tool of energy metering system for power utility companies Kee, Keh-Kim; Rashidi, Ramli; Ting, Huong-Yong; Hsiung, Lo Tzu; Kee, Owen Kwong-Hong; Zheng, Yeo Hong; Ini, Michelle Anak
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp577-586

Abstract

The growing demand for electricity and the complexity of power quality management highlight the need for advanced energy monitoring systems. Existing solutions often could not provide the real-time, detailed data necessary for smart grids, smart cities, and Industrial 4.0. They also fail to monitor power quality effectively, avoid equipment damage and ensure safety. To address this, we developed an internet of things (IoT)-based tool that leverages standard energy meters. The system monitors and analyzes electrical energy consumption and its power quality in real-time. The system adopts a multi-layered IoT architecture, where fog computing handles immediate data processing and the cloud computing supports machine learning for power quality detection. In this work, measurement accuracy is validated against a commercial power multimeter, achieving mean absolute percentage error (MAPE) values below 1.0% across different appliances. A companion web portal allows for real-time data visualization, time-series analysis, remote control of appliances and power quality detection that comply with IEC and IEEE standards. The proposed system is scalable and user-friendly, offering a practical smart metering solution for modern energy management. It aligns with the needs of smart grids and smart cities, contributing to efficient and intelligent energy consumption in the context of Industry 4.0.
Co-Authors Gau, Michael-Lian Hsiung, Lo Tzu Ini, Michelle Anak Kee, Keh-Kim Kee, Owen Kwong-Hong Rashidi, Ramli Tan, Gek-Ling Toh, Teck-Hock Wo, Su-Woan Wong, Pui-Ying Woo, Pei-Jun Zheng, Yeo Hong