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Phatai, Gawalee
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Augmented reality in the context of universal design for hearing impaired student Luangrungruang, Tidarat; Phatai, Gawalee
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1650-1658

Abstract

Advancing equal rights and prohibiting discrimination based on disability are essential to achieving social equity. Education serves as a vital mechanism in this effort, particularly through inclusive practices that support diverse learners. Sakon Nakhon Rajabhat University advances these values by admitting students with disabilities, including those with hearing impairments, and by fostering accessible learning environments. This study presents the development of an augmented reality (AR) application, designed according to universal design (UD) principles, to enhance learning for students with hearing impairments. The AR technology integrates real and virtual elements to create an engaging and interactive educational experience. Evaluation results indicate a high level of effectiveness, with the assessment dimension receiving the highest mean score (? = 4.87, ?? = 0.35), and overall effectiveness rated similarly (? = 4.78, ?? = 0.42). User satisfaction was also rated at a very high level across all aspects (? = 4.67, ?? = 0.54). These findings highlight the potential of AR technology, when guided by inclusive design principles, to improve learning outcomes for students with hearing impairments.
Forecasting industrial electricity demand using hybrid optimization methods Phatai, Gawalee; Luangrungruang, Tidarat
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1689-1697

Abstract

This study presents a hybrid machine learning framework for forecasting industrial electricity consumption by comparing backpropagation neural networks (BPNN) with models enhanced through metaheuristic optimization algorithms. Using 32 years of annual data from APEC economies, the research addresses rising electricity demand driven by economic and infrastructural development. A key limitation in traditional models— underfitting due to complex data patterns—is addressed via feature selection, which identifies the most relevant variables and reduces model complexity. Five metaheuristic algorithms—cuckoo search (CS), differential evolution (DE), harmony search (HS), particle swarm optimization (PSO), and teaching–learning-based optimization (TLBO)—are applied to optimize both feature selection and BPNN training. The proposed approach improves forecasting accuracy by handling noisy inputs and capturing the nonlinear relationships common in energy datasets. Among the tested methods, TLBO consistently delivers superior accuracy and robustness across most evaluated countries. The findings contribute an effective and adaptable forecasting model with significant implications for long-term energy planning and policy development.
Co-Authors Luangrungruang, Tidarat