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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Kajornkasirat, Siriwan
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Computer Science & IT Engineering

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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.
Intravenous drug administration application for pediatric patients via augmented reality Puangsuwan, Kritsada; Kajornkasirat, Siriwan; Wongpanich, Jaruphat; Kaewsuk, Chulalak; Puangsuwan, Simaporn
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.pp2412-2422

Abstract

This research presents the development of the intravenous drug administration application for pediatric patients using augmented reality (AR) technology, with a primary focus on aiding nursing students in administering medications accurately to reduce the risk of errors. The system architecture encompasses two core components: the creation of medication preparation videos and detailed drug information, and the design of a mobile application featuring medication list display, drug dosage calculation, user satisfaction assessment, and intravenous drug information addition. The system classifies users into administrators and nursing students, allowing administrators to manage user information in the member database. the application seamlessly integrates Visual Studio Code, flutter, dart programming language, firebase database, and AR.js Studio for QR code-linked videos. Operating in four main parts, namely users, mobile application, member database, and results display, the IDA application enables users to log in, access detailed drug information, calculate dosages, and view AR-based medication preparation videos. Tested with 111 nursing students, the system demonstrated functionality, completeness, and accuracy. The Likert scale-based evaluation revealed high satisfaction levels in content, design, functionality, and benefits received, affirming the intravenous drug administration application's effectiveness in pediatric intravenous drug management through AR, offering an innovative solution for nursing education and error reduction.
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 Kaewsuk, Chulalak Kaewsuwan, Nawapon Puangsuwan, Kritsada Puangsuwan, Simaporn Puttinaovarat, Supattra Wongpanich, Jaruphat