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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Sukkaew, Areelak Chansareewittaya, Suppakarn Computer and Communication Engineering for Capacity Building Research Center Chiang Rai Thailand Mae Fah Luang University School of Applied Digital Technology Chiang Rai Thailand

ISBN: (纸本)9798331543273

Currently, there is a significant number of elderly patients with chronic diseases or individuals who are unable to adequately care for themselves seeking medical treatment. Many of these patients often fail to follow or neglect medication instructions, leading to deviations from expected treatment outcomes or even failure in patient recovery. The primary reason for non-compliance with medical treatment is the forgetfulness of patients in taking their prescribed medication. This paper aims to present the practical application and testing of the Intelligent Medication Care Taker, a device designed to improve the efficiency and effectiveness of medical treatment management for both healthcare providers and patients. The goal of this research is to reduce the burden on caregivers and help resolve the issue of patients forgetting to take their medication on time. The main structure of the Intelligent Medication Care Taker is constructed from acrylic, with a Raspberry Pi 4 serving as the central controller for all functions, including video conferencing, voice reminders, and electric door locks. When it is time for the patient to take their medication, an alarm sounds, and the electric lock automatically unlocks the compartment door, allowing the patient access to their medication. For video calls, the doctor initiates the meeting, and the patient can join the session by pressing a button to communicate with the doctor. This system supports telemedicine consultations, enabling patients to receive medical care while allowing doctors to monitor the patient's condition conveniently from home, thus reducing the need for travel and mitigating other barriers to visiting hospitals or clinics. Appointments can also be scheduled conveniently through a website accessible via the device's screen. Ultimately, this research aims to reduce the occurrence of missed doses and improve the effectiveness of treatment, thus enhancing patient health outcomes. © 2025 IEEE.

关键词： Biomedical engineering

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An Agent-based Detection Method of Condition Progression Trends on Mobile for Hypertensive Diabetic Patients 7

An Agent-based Detection Method of Condition Progression Tre...

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7th International Conference on Information Technology, InCIT 2023

作者： Sanon, Sutussa Temdee, Punnarumol Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Chiang Rai Thailand

ISBN: (纸本)9798350358698

Diabetes and hypertension are prevalent chronic conditions on a global scale. There exists a correlation between them, rendering them more prone to transpire concurrently. The proposed mobile application provides users with a proactive self-monitoring capability by detecting tendencies of condition progression. In this study, there exist three distinct categories of condition progression trends that can be identified, namely positive, negative, and neutral condition progression trends. In order to provide a detection mechanism, a collaboration among three distinct types of agents takes place. Content agents are responsible for managing the incoming data, interpreter agents handle the preprocessing of the data, and aggregator agents are tasked with creating the detection rules. More specifically, three types of content agents are used in this study including the personal agent, clinical agent, and behavior agent respectively. The result of the simulation indicates that the proposed detection method achieves an accuracy rate of 93.67%. © 2023 IEEE.

关键词： Software agents

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Massive Face Recognition Via CNN Based on Big Data Framework for Surveillance 7

Massive Face Recognition Via CNN Based on Big Data Framework...

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7th International Conference on Information Technology, InCIT 2023

作者： Yang, Bohan Rueangsirarak, Worasak Chaisricharoen, Roungsan Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Chiang Rai Thailand

ISBN: (纸本)9798350358698

Nowadays Tracking and searching criminals via massive surveillance cameras has become a popular method for security departments. But before 2000 Searching criminals wasn't an economical thing for police or other security departments, because searching specific criminals in massive surveillance videos needs tremendous computing which generally requires a high-performance single computer, however, a high-performance server is extremely expensive. But nowadays, distributed computing makes it economical. We just need multi normal computers, then connect them parallelly. We generally call it cloud computing. © 2023 IEEE.

关键词： Big data

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LoRaWAN GPS Tracker 8

LoRaWAN GPS Tracker

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Jengsriwong, Nattaphit Chansareewittaya, Suppakarn Computer and Communication Engineering for Capacity Building Research Center Thailand School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (纸本)9798350396546

In this paper, the real practical tests of applied LoRaWAN are presented. The esp32 with LoRaWAN is used as the mainboard. The used RSSI signal strength unit is dB. The cases of testing include indoor and outdoor. Moreover, indoor testing combines both single-stair and multi-stair. The horizontal and diagonal directions are included, too. After that, the GPS module is attached to this mainboard for sending the location of the testing board via LoRaWan. The final testing shows the long distance. The longest distance signal testing is 5.67 km. The benefit of this testing is to develop for more distance and apply to any application. The final product of the developed GPS tracker can be used in various applications such as tracking lost children of the lost vehicle in the future. © 2023 IEEE.

关键词： Global positioning system

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Intelligent Genetic Crossover Algorithm for Improving State Estimation in Particle Filtering 7

Intelligent Genetic Crossover Algorithm for Improving State ...

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7th International Conference on Information Technology, InCIT 2023

作者： Kuptametee, Chanin Aunsri, Nattapol Mae Fah Luang University School of Information Technology Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Center Chiang Rai Thailand

ISBN: (纸本)9798350358698

Particle filtering (PF) is a Monte Carlo (MC) method widely employed to sequentially approximate the time-varying posterior PDF of states or values of variables of interest conditional to noisy observation data from non-linear/non-Gaussian dynamic systems. In many applications, we also infer (or estimate) the hidden state from the obtained approximated posterior PDF. Particles are randomly drawn samples of state vectors that form the approximation of the posterior PDF. However, representation of the approximated posterior PDF may not always be good because sometimes there can be only a few particles with low likelihoods (or importance weights), while there can be many particles with near-zero (or negligible) weights. New state vectors with high weights then should be introduced to the swarm in order to reshape the approximated posterior PDF. This paper introduces an intelligent genetic crossover algorithm (IGCA) that assists PF by applying crossover schemes employed in genetic algorithms (GAs) to reshape the approximated posterior PDF. Experimental results shown that the proposed algorithm improved the accuracy and performance in nonlinear state estimation. © 2023 IEEE.

关键词： Genetic algorithms

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The Logo Detection in an Illegitimate Web Page 7

The Logo Detection in an Illegitimate Web Page

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7th International Conference on Information Technology, InCIT 2023

作者： Pakhamwang, Kanokporn Sriherun, Narathip Waikawee, Noppason Jaijith, Promchat Chansareewittaya, Suppakarn Sebakor, Mahamah Mae Fah Luang University School of Information Technology Thailand Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center Thailand

ISBN: (纸本)9798350358698

The growth of the online market is rapid. On the other hand, the fraud of brand products are significant serious issue. To mitigate these issues we proposed an intelligent system called The logo detection on an illegitimate web page. we use Convolutional Neural Networks(CNN) for detecting online fake logos on unauthorized web pages. moreover, the evaluation of our performance is presented. Finally, we apply our method to the real world through an online search of the Mae Fah Luang University's logo on the illegitimate web page. The result outstanding shows us that our proposed is useful for the fake logos detection. © 2023 IEEE.

关键词： Websites

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The Development of WLAN Time Face Recorder System 7

The Development of WLAN Time Face Recorder System

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7th International Conference on Information Technology, InCIT 2023

作者： Keawoud, Sirilux Riyapraw, Rapheephon Yuyen, Siriyakorn Chansareewittaya, Suppakarn Sebakor, Mahamah Mae Fah Luang University School of Information Technology Thailand Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center Thailand

ISBN: (纸本)9798350358698

Nowadays, we have various time recording systems. One of them is made by face detection whose aim is to make the system more convenient. This work presents the alternate application system called the WLAN time face recorded system which uses a wifi network and face detection for a time attendance system. Face detection by OpenCV library. We take advantage of WLAN technology and personal devices such as mobile phones to verify user attendance in their organization. This work is significantly useful and convenient for the organization to increase the reliability of attendance verification. © 2023 IEEE.

关键词： Face recognition

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A Decision Tree-Based Advisory Recommendation System for Dental Students

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Journal of Mobile Multimedia 2025年第1期21卷 179-196页

作者： Thakaeng, Katayut Temdee, Punnarumol Wicha, Santichai School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building (CCC) Research Center School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand

This study aims to enhance dental education by developing a model that matches dental students with suitable advisors using Data Mining Classification Techniques. Inadequate guidance from mismatched advisors can hinder students' academic and practical performance, leading to suboptimal educational outcomes. Questionnaires were used to analyze the relationship between students' expectations and advisor behaviors, focusing on three factors: advisor roles, essential qualities, and valuable behaviors. Machine learning models Decision Tree, Neural Networks, and K-Nearest Neighbors (KNN) were employed to categorize data and optimize advisor-student matching. The Decision Tree model demonstrated the highest efficiency, achieving 97.73% accuracy, 100.00% recall, 97.62% precision, and an F1-Score of 98.79%, making it the most effective for predicting advisor characteristics, expectations, and student satisfaction. This research provides a scalable solution for improving advisor-student matching, enhancing decision-making, and ultimately supporting the educational success of dental students. © 2025 River Publishers.

关键词： Advisor behaviour classification data mining decision trees DISC personality graph search machine learning

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Multi-Class Classification Method with Feature engineering for Predicting Hypertension with Diabetes

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Journal of Mobile Multimedia 2023年第3期19卷 799-822页

作者： Sinsirimongkhon, Mongkhon Arwatchananukul, Sujitra Temdee, Punnarumol Computer and Communication Engineering for Capacity Building Research Center Mae Fah Luang University Chiang Rai Thailand School of Information Technology Mae Fah Luang University Chiang Rai Thailand

Machine learning–based methods are widely applied for the prediction of noncommunicable diseases (NCDs), such as hypertension, diabetes, and cardiovascular disease. However, few models have been developed for predicting hypertension with diabetes, even though these diseases generally co-occur and can cause devastating harm to patients. This paper proposes a multi-class classification method that will be able to predict hypertension with diabetes. The proposed method consists of data preprocessing, model construction and validation, and model comparison. For data preprocessing, feature engineering of corresponding data types is conducted. For model construction, several machine learning methods are applied, including Random Forest (RF), Gradient Boosting (GB), Extra Tree (ET), Decision Tree (DCT), and Support Vector Machine (SVM). The dataset used in this study consists of 17,077 records and 28 features, obtained from Phaya Mengrai Hospital, Chiang Rai, Thailand. The predictive performance of each model with and without feature engineering is compared in terms of accuracy and average area under the Receiver Operating Characteristic curve (AUC-ROC). From the comparison results, SVM with feature engineering outperformed other models based on accuracy and average AUC-ROC achieving a value of 88.39% and 93.32%, respectively. For all ensemble learning–based methods, RF performed the best in terms of both accuracy and average AUC-ROC for both with and without feature engineering. Overall, all the models performed better when feature engineering was applied. © 2023 River Publishers.

关键词： diabetes disease prediction Hypertension hypertension with diabetes machine learning

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IoT Technology and Digital Upskilling Framework for Farmers in the Northern Rural Area of Thailand

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Journal of Mobile Multimedia 2023年第5期19卷 1129-1152页

作者： Yooyativong, Thongchai Kamyod, Chayapol School of Information Technology Mae Fah Luang University 57100 Thailand Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University 57100 Thailand

One-third of Thailand’s workers are in agriculture, but the country’s agricultural GDP is still less than 10% of its total GDP. Most Thai farmers are smallholders with limited land and low incomes. To improve the agricultural GDP and the economic situation of smallholder farmers, the Thai Government has been trying for decades to encourage and support smallholder farmers to adopt modern farming methods and smart farming equipment, including digital technologies. However, the improvement is still sluggish due to a lack of an effective approach to delivering essential digital knowledge and skills, as well as investment support for smart farming equipment. These have hindered smallholder farmers’ digital farming skill progress. To address this issue, the Broadcasting and Telecommunications research and Development Fund for Public Interest has funded a project to develop the Digital Farmer Development Framework. This framework provides essential digital knowledge, training, coaching, and fundamental resources to upgrade smallholder digital-farming literacy to become digital farmers using problem- or project-based learning approaches and collaborative blended learning theories. Bloom’s taxonomy is used as a guideline for evaluating the framework’s effectiveness. Implementation of the Digital Farmer Development Framework has shown that farmers can significantly improve their digital farming literacy and are capable of using digital technology to improve farm management and productivity. Based on Bloom classification guidelines, 100% of the farms in the project can apply digital skills and utilize fundamental smart farming equipment as well as able to evaluate and analyze data from IoT devices. Moreover, 66% can create their own smart-system solution from fundamental smart farming tools for their farm. The project has also created a digital farmer community that shares knowledge and resources with others. © 2023 River Publishers

关键词： blended learning collaborative learning digital farmer IoT technology learning framework problem-based learning project-based learning Smart farmer

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