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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) International Journal of Advances in Applied Sciences
Chansanam, Wirapong
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Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Health Professions Materials Science & Nanotechnology Mathematics Physics

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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.
The study of requirements for the workforce of the digital industries using web scraping techniques Tipsena, Rachawit; Chansanam, Wirapong; Tuamsuk, Kulthida; Boongoen, Tossapon
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 Boongoen, Tossapon Pansayta, Sawitree Tipsena, Rachawit Tuamsuk, Kulthida