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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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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 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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A Comparison of Machine Learning Methods with Feature Extraction for Classification of Patients with Dementia Risk 7

A Comparison of Machine Learning Methods with Feature Extrac...

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7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, NCON 2022

作者： Chaiyo, Yanawut Temdee, Punnarumol Mea Fah Luang Unmversity Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Chiang Rai57100 Thailand Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Chiang Rai Thailand

ISBN: (纸本)9781665495103

Dementia is becoming more common due to the aging society. It is necessary to identify the patients at risk of dementia early, although the diagnosis of dementia is very difficult, especially at the beginning. A major challenge in classifying patients with dementia is to achieve high accuracy. In this paper, a comparison of machine learning-based classifications of dementia patients is proposed. The open data of Kaggle is used in this paper. Five different classifiers include support vector machine (SVM), decision tree (DCT), random forest (RF), k-nearest neighbors (KNN), and neural networks (NN) are studied. From the comparison, all five classifiers perform well with the deployed data, although the KNN and SVM performed the best in data with and without feature extraction methods. The accuracy rates for all five classifiers are 91.50%, 95.70%,95.70%,95.70%, 95.30%, for DCT, SVM, RF, KNN, respectively. In addition, the accuracy of using the feature extraction method, and setting change feature is 95.70% for SVM, NN, and KNN, respectively. © 2022 IEEE.

关键词： Classification (of information)

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An Online Gap Analysis on Cyber Security Principles for Thailand Organizations Based on ISO/IEC 27001:2013 Standard 6

An Online Gap Analysis on Cyber Security Principles for Thai...

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6th International Conference on Information Technology, InCIT 2022

作者： Chaiwut, Narong Rueangsirarak, Worasak Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Chiangrai Thailand

ISBN: (纸本)9781665489126

The cybersecurity act enforcement in Thailand has been promulgated since 2019. Especially the Personal Data Protection Act (PDPA) forces all organizations to comply. However, there is no security standard relying on PDPA. The most well-known security standard is ISO/IEC 27001:2013, which can use as a security guideline for various organization sizes. Unfortunately, ISO/IEC 27001:2013 requires a high budget to analyze and adapt it to the organization. Therefore, we proposed an online ISO/IEC 27001:2013 gap analysis to provide the preliminary guidance for the organizations to sketch up their cyber security policy. This proposed web application enables users to evaluate their organization based on the ISO/IEC 27001:2013, Annex A. The online gap analysis covers a1114 security standard domains, generating the average score and security safety level for the organizations. We evaluated this online system with twelve organizations in various sectors both from industry and government. The result reveals that the industrial sectors scored higher than the governmental sectors in overall security level. Therefore, the proposed system is practical to be implemented as one part of the Information Security Management System (ISMS). © 2022 IEEE.

关键词： Cybersecurity

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Enhancing Knowledge Integration from Multiple Courses of Thai Undergraduates via Project Problem-based Learning 7

Enhancing Knowledge Integration from Multiple Courses of Tha...

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作者： Nupap, Soontarin Kurubanjerdjit, Nilubon Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Chiang Rai Thailand

ISBN: (纸本)9781665495103

Attributing to the significance of 21st century education, students require essential skills and competitive competence aiming them to reach Thailand 4.0 scheme. The evolution of this technology plays crucial roles for students to adopt the knowledge to solve current problems in the upcoming rapid changed world. Relied on this perspective, this study endeavours to assess the effectiveness of adjusting teaching approach for enhancing knowledge integration skills of the undergraduates' group via their problem-based projects. There were 39 Thai students of Mae Fah Luang University located in Chiang Rai, Thailand as a sample. Besides, the three courses that students had to study together in same semester were evaluated and adjusted in this experiment. The results revealed that the improvement of teaching approach in the study had enhanced the knowledge integration of students significantly. By using a new teaching methodology, students were able to apply the theatrical contents from each course to their problem-based projects. Moreover, it also improved students' analytical skill based on reliable references and the theoretical principles learned in every course in a balanced and excellent manner. © 2022 IEEE.

关键词： Students

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Tracking and Notification Application for Arrhythmia Monitoring System 8

Tracking and Notification Application for Arrhythmia Monitor...

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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： Kemavuthanon, Kemachart Rueangsirarak, Worasak Kemavuthanon, Suvaroj Temdee, Punnarumol Building Research Center School of Information Technology Mae Fah Luang University Computer and Communication Engineering for Capacity Chiang Rai Thailand School of Business University of the Thai Chamber of Commerce Bangkok Thailand

ISBN: (纸本)9798350396546

Our heart can create and transmit electricity by sending electrical current from the upper right chamber to the upper left and lower chambers of the heart. This physical activity can stimulate the heart muscle, causing compression to expel blood. However, suppose the electrical system has a problem;it will cause the heart beats with an irregular or abnormal rhythm, or a short circuit occurs in the heart chambers that we call arrhythmia disease. The results in an irregular heartbeat;too fast or too slow cause the problem of pumping blood to different parts, sometimes very intensely, until causing death. In Thailand, there are a large number of patients suffering from arrhythmias, both knowingly and unconsciously. People know and receive treatment by using heat waves shot into the heart to cure and recover from symptoms, but some are incurable. Moreover, there is a lack of health care services for healing and recovering from the disease. Thus, a considerable number of patients are waiting for treatment. To reduce the gap, these patients should be able to care for themselves and perform continuously to improve every day. The critical factor is that the cardiologist needs more detailed information and disease symptoms to increase the efficiency of diagnosis. In this research, we design and develop a tracking and notification application for arrhythmia disease from the patient's daily checkup and the user's information. The proposed model is to create a mobile application to record data that could connect to a heartbeat detection device. Then, analyze the recorded data to suggest a consultation by the cardiologists about further treatment. Moreover, a notification system will remind the patients to take medicine and warn them of emergency events relatively close to them so that they can provide immediate help in the event of an emergency. As the results, the evaluation of the proposed model by cardiologist shows that this application has a good feature in encouraging pat

关键词： Diseases

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