School of Applied Digital Technology http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/191 2026-06-04T21:43:44Z 2026-06-04T21:43:44Z Areeelak Sukkaew http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1755 2026-05-28T05:02:58Z 2025-01-01T00:00:00Z

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 Vattiya Jarunakarint http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1725 2026-05-18T06:39:14Z 2021-01-01T00:00:00Z

Classification of motorcycle riding pattern based on computer vision and machine learning Vattiya Jarunakarint Surapong Uttama Thesis (M.Sc.) -- Information Technology, School of Information Technology. Mae Fah Luang University, 2021

2021-01-01T00:00:00Z Chutipon Pimsarn http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1724 2026-05-18T06:32:17Z 2021-01-01T00:00:00Z

Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problem Chutipon Pimsarn Tossapon Boongoen Thesis (M.Sc.) -- Information Technology, School of Information Technology. Mae Fah Luang University, 2021

2021-01-01T00:00:00Z Katayut Thakaeng http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1702 2026-05-06T06:44:29Z 2024-01-01T00:00:00Z

Advisory recommendation system for dental sudents with a decision tree model Katayut Thakaeng Santichai Wicha Thesis (M.Sc.) -- Information Technology, School of Applied Digital Technology. Mae Fah Luang University, 2024

2024-01-01T00:00:00Z