http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/646 Digital Transformation Technology 2026-06-04T23:44:31Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1755 Prototyping of intelligent medication platform Areeelak Sukkaew Suppakarn Chansareewittaya Currently, medication non-adherence remains a significant problem, particularly among elderly patients and individuals with chronic diseases. Forgetfulness and limited access to healthcare services often lead to missed doses, resulting in reduced treatment effectiveness and increased health risks. To address these challenges, this research presents the design, development, and technical evaluation of the Intelligent Medication Platform. The Intelligent Medication Platform is an IoT-based medication management system constructed with an acrylic structure and powered by a Raspberry Pi 4 as the central controller. The system integrates medication reminders, an electric door-lock dispensing mechanism, real-time video conferencing, and activity logging through a web-based interface. Key hardware components include a relay-controlled magnetic lock, speaker, monitor, camera module, and database system. The system evaluation was designed to emphasize technical performance validation rather than user satisfaction assessment. To ensure comprehensive performance verification, three principal experimental procedures were conducted. First, command execution accuracy was evaluated through 100 consecutive operational cycles for each core system function. Second, power consumption analysis was performed under various operating conditions to determine energy efficiency and operational requirements. Third, 72-hours continuous operation (stress) test was carried out to assess system stability, reliability, and long-term operational endurance. The results demonstrate high functional reliability, with 98% accuracy in door unlocking, 100% reliability in alarm notification, 96% success rate in video call connectivity, and over 97% accuracy in database logging. The system maintained stable operation with acceptable energy consumption and 100% uptime during the continuous testing period. These findings confirm that the Intelligent Medication Platform is technically reliable, energy-efficient, and suitable for home healthcare deployment to support medication adherence and telemedicine services. Independent Study (M.Sc.) -- Digital Transformation Technology, School of Applied Digital Technology. Mae Fah Luang University, 2025 2025-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1226 Unveiling patterns in the night market: A machine learning and deep learning approach to customer analysis Thandar Phyo Surapong Uttama Thesis (M.Sc.) -- Digital Transformation Technology, School of Information Technology. Mae Fah Luang University, 2023 2023-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1225 Investigating and generating the evaluation method between human fashion aesthetic and generative AI Hsi Yeh Wang Worasaj Rueangsirarak Thesis (M.Sc.) -- Digital Transformation Technology, School of Information Technology. Mae Fah Luang University, 2023 2023-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1147 Mobile health application for proactive self-management: A case study of hypertensive diabetic patients in Thailand Sutussa Sanon Punnarumol Temdee This study addresses the global health issues associated with diabetes and hypertension, two widespread chronic conditions. Conducted in Chiang Rai, Thailand, the study investigates the effectiveness of a mobile application designed to support self-management among patients with both hypertension and diabetes. The mobile application enables users to track their personal and clinical data while receiving individualized health recommendations. These recommendations are based on the user's health trend and level of engagement. To identify health condition trends categorized as positive, negative, or neutral, agent-based methods were utilized. Personalized recommendations were generated using association rules that assess each patient’s engagement level. The application was evaluated for both effectiveness and user satisfaction among healthcare professionals and patients in Thailand. Results from the evaluation indicated a moderately high level of effectiveness and satisfaction, with a 78% success rate and an average user rating of 4.18 out of 5. Thesis (M.Sc.) -- Digital Transformation Technology, School of Applied Digital Technology. Mae Fah Luang University, 2025 2025-09-30T00:00:00Z