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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Journal of Applied Science, Engineering, Technology, and Education
Chansanam, Wirapong
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Other

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Thai COVID-19 patient clustering for monitoring and prevention: data mining techniques Pansayta, Sawitree; Chansanam, Wirapong
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp256-265

Abstract

This research aims to optimize emerging infectious disease monitoring techniques in Thailand, which will be extremely valuable to the government, doctors, police, and others involved in understanding the seriousness of the spread of novel coronavirus to improve government policies, decisions, medical facilities, treatment. The data mining techniques included cluster analysis using K-means clustering. The infection data were obtained from the open data of the digital government development agency, Thailand. The dataset consisted of 1,893,941 cumulative cases from January 2020 to October 2021 of the outbreak. The results from clustering consisted of 8 groups. Clustering results determined the three largest, three medium-sized, and the two most minor numbers of infected people, respectively. These clusters represent their activities, namely touching an infected person and checking themselves. The components of emerging diseases in Thailand are closely related to waves, gender, age, nationality, career, behavioral risk, and region. The province of onset was mainly in Bangkok and its vicinity or central Thailand, as well as industrial areas. Adult workers aged 19 to 27 years and 43 to 54 years or over were seeds of new infection sources.
Data modeling COVID-19 patients in Thailand: data mining techniques Pansayta, Sawitree; Chansanam, Wirapong
International Journal of Public Health Science (IJPHS) Vol 12, No 4: December 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i4.23070

Abstract

This study aimed to investigate the characteristics of COVID-19 patients in Thailand and develop a data model for analyzing these characteristics. A total of 1,888,941 cases from the Thailand Department of Disease Control website from January 12, 2020, to October 29, 2021, were analyzed, and 20,110 cases were selected for further analysis. The two-step cluster analysis method was used to cluster the data according to four variables: nationality, occupation, patient type, and risk groups. The results showed the presence of three groups of COVID-19 patients. Group 1 consisted of 5,883 workers in trade and service occupations who had contact with the public and were either Thai nationals or from abroad. Group 2 was the largest cluster, consisting of 7,420 migrant workers classified as foreigners and working in industrial settings. Group 3 consisted of 6,870 cases of indirect transmission, with individuals in this group infected through close contact with family members or individuals in the first two groups. This clustering approach offers valuable insights for pandemic management, aiding in identifying high-risk groups and developing tailored interventions. In future outbreaks with similar characteristics, such as airborne transmission, contact infection, or super spreader events, our model can serve as a valuable tool for devising effective management plans and countermeasures. In conclusion, this study emphasizes the significance of cluster analysis in understanding the dynamics of COVID-19 transmission and highlights its potential for informing effective pandemic management strategies. It also outlines promising avenues for further research to enhance our knowledge of COVID-19's impact on specific populations and inform future public health efforts.
Korean-popular Facebook fan page analytics in Thailand Chansanam, Wirapong; Tuamsuk, Kulthida; Kwiecien, Kanyarat; Oh, Sam Gyun
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5291-5303

Abstract

The purpose of this study was to gather, analyze, and elaborate big data on Facebook’s essential information, with a specific focus on the information obtained from Korean-popular (K-Pop) fan pages on the social networking site. For this analysis, a total of 3,531,736 comments by Korean-pop fans were gathered from various K-pop Facebook pages. In order to interpret how 11 extremely popular Facebook pages shape Thai fans’ enthusiasm for the South Korean music industry, descriptive statistics and visualization analysis were employed. Finally, data analytics and correlation analysis were used to evaluate the essential understanding of the Facebook pages. The research revealed three key findings: i) K-pop fan pages provide more opportunities for Thai fans to express their support for K-pop artists and advocate for causes, ii) K-pop fan pages provide more opportunities for Thai fans to communicate with K-pop artists, and iii) K-pop fan pages build opportunities for Thai fans to establish a more glamorous online presence despite limitations concerning financial resources, foreign language skills, and opportunities. In the future, the research outcomes may be valuable for academic studies and practice.
Development of an Organic Agriculture Ontology for Young Agripreneurs Chiangnangam, Sumana; Chansanam, Wirapong; Kabmala, Malee
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 3 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4280

Abstract

This study develops an ontology of organic agriculture designed to support young agripreneurs in aligning their practices with standardized organic farming systems. The purpose was to structure and represent domain-specific knowledge systematically, enabling its application in decision support and information systems. The ontology construction followed a three-stage framework: defining the purpose, developing the ontology, and conducting evaluations. A knowledge engineering approach with seven steps was applied, and the Hozo Ontology Editor served as the development tool. The resulting ontology comprises 127 classes, including nine core categories—Young Agripreneurs, Organic Farming, Products, Business, Markets, Agriculture Processes, Agencies, Services, and Document—supported by 118 subclasses. Furthermore, 31 properties and interrelationships were defined to represent the conceptual linkages within the domain. Evaluation by domain experts, based on five criteria—definition and scope, class identification, property specification, instance creation, and applicability—confirmed a high level of appropriateness. The final ontology, delivered in OWL format, provides a robust knowledge model for organic agriculture. Its significance lies in facilitating knowledge-based recommender systems that enhance decision-making and planning for young agripreneurs, ultimately contributing to sustainable agricultural entrepreneurship.
Explainable Dynamic Weighted Ensemble Learning for Depression Risk Stratification and Tiered Intervention in University Students Wang, Youhao; Chansanam, Wirapong; Nguyen, Lan Thi
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4621

Abstract

Depression among college students is a growing public health concern, with existing screening methods often limited in sensitivity, scalability, and interpretability. This study developed and validated an explainable machine learning framework for early depression risk identification and tiered intervention planning in universities. We propose a Dynamic Weighted Ensemble Model (DWEM) that integrates five tree-based algorithms, with weights optimized via Bayesian search and cost-sensitive learning. Informed by the diathesis–stress framework, features were engineered and interpreted using SHAP to provide global and local explanations. The model was evaluated using stratified five-fold cross-validation with careful control of data leakage. The DWEM achieved an accuracy of 94.96% and an AUC of 98.95%, with balanced sensitivity and specificity, outperforming both single-model benchmarks and traditional questionnaire-based screening. SHAP analysis stably identified academic performance, stress-burnout, sleep problems, and protective factors as key risk determinants. Based on these outputs, a probability-based three-tier intervention framework was designed to translate risk stratification into actionable clinical support. This study demonstrates that an optimized ensemble approach, combined with theory-driven features and robust explainability, can provide a reliable, transparent, and practical tool for scalable mental health screening, supporting a shift toward proactive, data-driven prevention and efficient resource allocation in campus settings.
Co-Authors Chiangnangam, Sumana Kabmala, Malee Kwiecien, Kanyarat Nguyen, Lan Thi Oh, Sam Gyun Pansayta, Sawitree Tuamsuk, Kulthida Wang, Youhao