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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Ruang-On, Somporn
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Utilizing digital elevation models and geographic information systems for hydrological analysis and fire prevention in Khuan Kreng peat swamp forest, Southern Thailand Wanthong, Uraiwun; Ruang-On, Somporn; Limchoowong, Nunticha; Sricharoen, Phitchan; Musik, Panjit
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5408-5419

Abstract

The objectives of this research were to create a topographic model using Mathematica and hydrologic model using ArcGIS for water management aimed at preventing forest fires in the Khuan Kreng peat swamp forest. Pan basin area in Kreng Sub-district, characterized by low mountains, where the Cha-Uat canal intersects the krajood forest, was revealed by the hydrographic model. Kreng Sub-district was traversed by three main streams: Khuan canal, Hua Pluak Chang canal, and Laem canal. Additionally, several tributary canals that interconnect, ultimately converging into the Cha-Uat Phraek Muang canal were identified. During the dry period, the water from these canals flowed into the Cha-Uat Phraek Muang canal. To mitigate the risk of fires, it was essential to install water table measuring devices and underground barrier gates at the drain points. This ensured the return of water from the Cha-Uat Phraek Muang canal to the Khuan Kreng peat swamp forest. Maintaining sufficient water table level was crucial, as the occurrence of fires was more likely when the water table dropped below the soil surface. When the swamp forest was adequately hydrated, wildfires were confined to a narrow area since they could only burn on the forest surface, which was easier to extinguish.
DualFaceNet: augmentation consistency for optimal facial landmark detection and face mask classification Songsri-in, Kritaphat; Rattaphun, Munlika; Kaewchada, Sopee; Ruang-on, Somporn
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3228-3239

Abstract

In an era where face masks are commonplace, facial recognition faces new challenges and opportunities. This study introduces DualFaceNet (DFN), a cutting-edge neural network that efficiently combines facial landmark detection with mask classification. Benefiting from multi-task learning (MTL) and enhanced with a unique consistency loss, DFN outperforms traditional single-task models. Tests using the reputable 300W dataset and a face mask dataset showcase DFN’s strengths: a significant reduction in landmark error to 5.42 and an increase in mask classification accuracy to 92.59%. These results highlight the potential of integrating MTL and custom loss functions in facial recognition. As face masks continue to be globally essential, DFN’s integrated approach offers a fresh perspective in facial recognition studies. Furthermore, DFN paves the way for adaptive facial recognition systems, emphasizing the adaptability needed in our current era.
Co-Authors Kaewchada, Sopee Limchoowong, Nunticha Musik, Panjit Rattaphun, Munlika Songsri-in, Kritaphat Sricharoen, Phitchan Wanthong, Uraiwun