Research & Publications
My research focuses on artificial intelligence, data science, financial and renewable energy, applying machine learning to sustainable systems, policy analysis, and real-world impact.
The Investigation on Factors Influencing Energy Efficiency in ASEAN
5th ASEAN International Conference on Energy and Environment
Oct 2025
As the ASEAN region faces rapid urbanization and climate change, enhancing energy efficiency is crucial for sustainable development. This paper aims to identify and analyze the multifaceted factors influencing energy efficiency across member states. We employ a comprehensive approach, integrating quantitative and qualitative data from various sources, including government reports, energy consumption statistics, and stakeholder interviews. Our analysis reveals key factors such as technological advancements, regulatory frameworks, economic incentives, and social behavior patterns. Utilizing statistical modeling and machine learning techniques, we quantify the impact of these factors, providing a nuanced understanding of their interrelationships. Additionally, the paper examines regional disparities in energy efficiency performance, highlighting the influence of varying economic conditions and policy environments. Case studies from selected ASEAN countries illustrate successful strategies and best practices that have led to significant improvements. We also explore the role of public awareness and education in promoting energy-efficient practices among consumers and businesses. The findings indicate that fostering a culture of energy efficiency not only reduces consumption but also supports sustainable economic growth. This research underscores the need for collaborative efforts among governments, industry stakeholders, and civil society to develop cohesive strategies for enhancing energy efficiency in the region.
AI-Driven Energy Management Systems for Urban Low-Income Housing
5th ASEAN International Conference on Energy and Environment
Oct 2025
This research introduces the development of AI-driven energy management systems (EMS) specifically designed for urban low-income housing in Southeast Asian cities. By integrating IoTenabled smart meters with advanced AI algorithms, the system enables real-time monitoring, consumption pattern recognition, and load optimization in government-subsidized apartment complexes. Pilot studies conducted in Jakarta, Indonesia, and Manila, Philippines, demonstrate that the AI-enhanced EMS can reduce household electricity usage by 18–25% without compromising occupant comfort. A key feature of the system is its inclusion of affordability alerts and personalized consumption feedback, which significantly increase user engagement and awareness. These tools empower residents to make informed decisions about their energy usage while staying within budget. The research underscores the role of AI in promoting inclusive energy solutions by extending the advantages of smart technologies to economically disadvantaged communities. It concludes that the integration of AI into urban energy governance not only improves efficiency but also enhances social equity. This study highlights how technological innovation can support more sustainable and just energy transitions across rapidly urbanizing regions in Southeast Asia.
Factors Influencing and ML Models for RE Consumption Forecasting
4th ASEAN International Conference on Energy and Environment
Jun 2025
Renewable energy (RE) is vital for addressing climate change and ensuring global energy security. As RE technology adoption grows, accurate forecasting of RE consumption is essential for grid planning and integration. This paper reviews key factors affecting RE consumption and explores machine learning (ML) and deep learning (DL) models for forecasting RE usage. It examines socioeconomic, demographic, and environmental variables influencing the demand for RE sources like solar, wind, and hydropower, highlighting factors such as GDP, population, energy prices, government policies, and weather conditions. Additionally, it considers how technological advancements, consumer behavior, and energy efficiency measures shape RE demand. The study evaluates ML techniques from 2020 to 2024, including artificial neural networks, support vector machines, random forests, gradient boosting, and DL models like long short-term memory (LSTMs) and convolutional neural networks (CNNs), for forecasting RE consumption. It analyzes the strengths, limitations, and accuracy of these models based on case studies across residential, commercial, and industrial sectors. The findings show that DL models, particularly LSTMs and CNNs, outperform traditional ML techniques in RE consumption forecasting, effectively capturing complex nonlinear relationships and temporal dependencies in RE data for greater accuracy and generalization.
Exploring Advancements in Robotic Automation and Their Impact on Manufacturing Processes
Ministry of Industry, Science, Technology and Innovation - STI Focus
Dec 2024
Automation by means of robots is now an essential criterion for change in manufacturing systems and processes in the manufacturing industrial revolution. By realizing the integration of robotics in manufacturing systems, the level of productivity, precision, and rates of production have enhanced global industries, creating new opportunities and some risks. This research will try to establish the evolution of robotic technologies and realize how industries have adopted them in changing manufacturing processes.
Reimagining Domestic Life: The Potential of IoT in Cambodia Connected Homes
Ministry of Industry, Science, Technology and Innovation - STI Focus
Dec 2024
According to a recent survey conducted by the Ministry of Industry, Science, Technology, and Innovation, approximately 25% of urban households in Cambodia have integrated at least one smart home device into their homes. This means that roughly 1 in 4 homes in Cambodian cities have some smart technology. However, rural areas face challenges due to limited access to reliable internet connectivity and electricity. Despite these challenges, the Cambodian market has seen a rise in the number of different smart home devices available, with local companies such as Tech Innovations Ltd. and Smart Solutions Co. emerging as key players by providing affordable and innovative solutions tailored to the needs of Cambodian consumers.
A Review on Deep Learning Algorithms for Hand Gesture Recognition in Higher Education
Ministry of Industry, Science, Technology and Innovation - STI Focus
Jul 2024
This paper provides a comprehensive review of deep learning algorithms for hand gesture recognition in higher education. It explores the historical context, current applications, driving factors, challenges, and opportunities associated with this technology. The paper highlights the potential benefits for students with disabilities and compares hand gesture recognition with traditional input methods. Finally, it discusses future research directions and emphasizes the importance of ethical considerations. Deep learning algorithms hold immense potential to revolutionize the way students interact with learning materials and educators in higher education. By addressing existing challenges and ensuring responsible development, hand gesture recognition technology can contribute to creating a more engaging, interactive, and personalized learning experience for all students. This paper provides a comprehensive review of deep learning algorithms for hand gesture recognition in higher education. It explores the historical context, current applications, driving factors, challenges, and opportunities associated with this technology. The paper highlights the potential benefits for students with disabilities and compares hand gesture recognition with traditional input methods. Finally, it discusses future research directions and emphasizes the importance of ethical considerations. Deep learning algorithms hold immense potential to revolutionize the way students interact with learning materials and educators in higher education. By addressing existing challenges and ensuring responsible development, hand gesture recognition technology can contribute to creating a more engaging, interactive, and personalized learning experience for all students.