Article Per Year (5 Year)
p-Index From 2021 - 2026
0.888
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This Author published in this journals
All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics Journal of Applied Science, Engineering, Technology, and Education
Wirapong Chansanam
Khon Kaen University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics Other

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Contemporary and future research of digital humanities: a scientometric analysis Wirapong Chansanam; Abdul Rahman Ahmad; Chunqiu Li
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3596

Abstract

This article analyses the special publications from 1999 to 2021, focusing on the scope and effect of digital humanities relevance research. The bibliometric analysis offers information on article publishing patterns, notable authors, cited references, institutions, nations, etc. Additionally, this paper covers the structure of knowledge of Digital humanities, including famous themes, co-citations, and bibliographic networks. Scopus database was used to obtain bibliographic data on September 25th, 2021, from papers published between 1999 and 2021. The bibliographic data for 1,249 publications using open-source analytic tools like the biblioshiny package in RStudio and the VOSviewer software shows it already. It employed the bibliometric package. These programmes visualized bibliography data based on their co-occurrence, co-citation, and coupling. The journal's publishing output and status in the area continue to rise, with 3,270 research papers indexed by Scopus to date. Additionally, the examination provides a thorough grasp of preceding patterns and forecasts a journal's future propensity.
Development of an Organic Agriculture Ontology for Young Agripreneurs Sumana Chiangnangam; Wirapong Chansanam; Malee Kabmala
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 Youhao Wang; Wirapong Chansanam; Lan Thi Nguyen
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

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.
The study of requirements for the workforce of the digital industries using web scraping techniques Rachawit Tipsena; Wirapong Chansanam; Kulthida Tuamsuk; Tossapon Boongoen
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp860-877

Abstract

This study investigates workforce requirements in Thailand's digital industries, focusing on qualification requirements across five industry groups: hardware and smart devices, software and software services, digital service, digital content, and telecommunication. Employing Python-based web scraping from selected job websites during 2022–2023, the data undergoes natural language processing (NLP) for analysis. Within Group 1 (hardware and smart devices), electrical engineers dominate with 92 positions, emphasizing a demand for engineering expertise. Group 2 (software and software services) sees a surge in programmer roles, totaling 244 positions, showcasing a need for robust programming skills. Group 3 (digital service) prioritizes information technology (IT) support, claiming 354 positions, indicating high demand for IT support qualifications. Graphic design leads Group 4 (digital content) with 587 positions, highlighting the need for a workforce in digital content production. In Group 5 (telecommunication), network engineers dominate with 37 positions, signaling a demand for top-tier network engineering skills. Most positions across groups specify a bachelor's degree and often require prior experience, highlighting the industry's preference for both academic and practical qualifications. This study underscores the digital industry's rapid growth and the sustained demand for a qualified workforce, emphasizing the importance of academic credentials and specialized skills for future employment in these sectors.
Co-Authors Abdul Rahman Ahmad Chunqiu Li Kulthida Tuamsuk Lan Thi Nguyen Malee Kabmala Rachawit Tipsena Sumana Chiangnangam Tossapon Boongoen Youhao Wang