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

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A longitudinal network analysis of research trends and policy implications: southernmost of Thailand case study Rumdon, Komgrit; Longha, Kamonthip; Kaewsuwan, Nawapon; Matcha, Wannisa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2109-2127

Abstract

The government funds research projects to address problems, advance knowledge, and support national development. Data repositories are often used to store this research information; however, such information is not optimally used when making the decision. This is particularly important, especially in the areas that require extensive effort and budget allocation to drive development, such as the southernmost provinces of Thailand. This area has been in a violent situation for more than twenty years, leading to poor education, economic challenges, and many more. This study aims to analyze the trends in research topics on these provinces over 30 years (1982–2020) using epistemic network analysis (ENA) on data from the Southern Border Provinces Research Database (SOREDA). Key findings showed a prolonged focus on “education” and “Islamic studies,” reflecting steady government support but raising concerns about its effectiveness. Another important point was that conflict management research arose in response to the surge in violence in 2004 and prolonged existing. The current trending research focused on local–based capital and how it is used to drive society and the economy, such as through tourism. These highlight evolving priorities in addressing the region's challenges and opportunities.
Recommender system for dengue prevention using machine learning Kajornkasirat, Siriwan; Hnusuwan, Benjawan; Puttinaovarat, Supattra; Puangsuwan, Kritsada; Kaewsuwan, Nawapon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1106-1115

Abstract

The study aimed to develop a recommender system for dengue prevention using environmental factors and mosquito larvae data. Data were collected from 100 households in Surat Thani, Thailand using mosquito larval survey in January 2020. Data mining techniques: frequent pattern growth (FP-Growth) and Apriori algorithms were used to find association rules and to compare accuracies for selecting a suitable model. The recommender system was designed as a web application. FP-Growth is more suitable for these data than Apriori algorithm. The factors associated with dengue infection, including community area, densely populated area, and agricultural area. Most areas where mosquito larvae are found are community areas and agricultural areas. Aedes larvae were found most in water containers with dark colors and without a lid. Aedes larvae were also found in small water jars, large water jars, cement tanks, and plastic tanks. The recommender system should be useful to dengue vector prevention and to health service communities, in planning and operational activities.
Customer segmentation using association rule mining on retail transaction data Kajornkasirat, Siriwan; Gunglin, Pattarawan; Puangsuwan, Kritsada; Kaewsuwan, Nawapon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1919-1929

Abstract

This research aimed to investigate a suitable algorithm for customer segmentation using as customer behavior indicators the recency, frequency, and monetary (RFM) values of the customers. The clustering algorithms K-means, fuzzy C-means, and self-organizing neural network (SONN) were compared for finding the most appropriate algorithm. The customer segmentation was analyzed using association rule mining with the frequent pattern algorithm (FP-Growth). Data on retail transactions during January 2021 - May 2023 were obtained from Tuenjai Company, Thailand, with a total of 202,469 records. The results from the three algorithms were compared by the silhouette coefficient (SC), Calinski-Harabasz (CH) index, Davies-Bouldin (DB) index, iteration count, and execution time. The results showed that the K-means algorithm was the most suitable algorithm for customer segmentation in this study. K-means clustering grouped the customers into three groups here labeled as “important value”, “general development”, and “lost”, based on the RFM values. There were 38 rules for the important value segment, and two rules each for the general development and the lost groups. These results could be useful to the business organization for improving the customer experiences, increasing sales, preparing or promoting products, and stock management efficiency.
Co-Authors Gunglin, Pattarawan Hnusuwan, Benjawan Kajornkasirat, Siriwan Longha, Kamonthip Matcha, Wannisa Puangsuwan, Kritsada Puttinaovarat, Supattra Rumdon, Komgrit