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The Requirements Analysis of a Smart Monitoring System Designed for the Rice Milling Process: A Case Study of the Agricultural Cooperative Sector in Chiang Rai

The Requirements Analysis of a Smart Monitoring System Desig...

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2023 International Conference on Cyber Management and engineering, CyMaEn 2023

作者： Anan, Malinee Khamhangwong, Damrongpol Yamsa-Ard, Samatthachai Sureephong, Pradorn Wicha, Santichai Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Chiang Rai57100 Thailand Chiang Rai57100 Thailand Chaing Mai University College of Arts Media and Technology Chaingmai50200 Thailand

ISBN: (纸本)9781665493291

This study investigated the requirements analysis of a smart monitoring system designed for the rice milling process to improve the process of rice milling in Chiang Rai by using a digital transformation technology with a smart factory concept to support local users for rice milling process monitoring, and improve productivity by using digital technology, The study revealed the positive results of user requirements analysis from questionnaires confirmed that the positive of perceived usefulness (PU) and perceived ease of use (PE) that confirmed to continue working on the further step of the digital transformation process in rice milling process. © 2023 IEEE.

关键词： Process monitoring

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EEG Age Prediction via Elastic Net Linear Regression 8

EEG Age Prediction via Elastic Net Linear Regression

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8th International Conference on Information Technology, InCIT 2024

作者： Yu, Sane Srimaharaj, Wanus Chaisricharoen, Roungsan Mae Fah Luang University School of Applied Digital Technology Chiang Rai57100 Thailand Payap University The International College Chiang Mai50000 Thailand Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Applied Digital Technology Chiang Rai57100 Thailand

ISBN: (纸本)9798350366303

Accurate prediction of human age based on EEG signals presents a significant challenge due to the complex changes in brain function associated with aging. This study addresses these issues by proposing an advanced Elastic Net Linear Regression model to enhance prediction accuracy and model reliability. The proposed approach was evaluated on a EEG dataset of 109 samples, each representing 1000 milliseconds of brain activity. Comprehensive preprocessing techniques, including missing value imputation, feature scaling, and feature selection, were employed to prepare the data for the Elastic Net Regression model. The experimental results demonstrate the effectiveness of the Elastic Net Regression model, achieving a mean absolute error of 4.73 years and an R-squared score of 0.7626 on the test set. On the training set, the model's performance was exceptional, with an MAE of 2.42 years and an R-squared of 0.9404, showcasing its ability to capture the complex relationship between brain activity and age. The model's predictions were well-calibrated, with a mean of 45.49 years and a standard deviation of 15.80 years, indicating its reliability in representing the age distribution of the samples. The results contribute valuable insights into brain aging mechanisms and offer potential avenues for enhancing cognitive health interventions to support healthy cognitive aging and early intervention in age-related neurological disorders. © 2024 IEEE.

关键词： Prediction models

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Context-Aware based personalized recommendation on mobile for monitoring excessive sugar consumption of thai adolescents

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Journal of Mobile Multimedia 2022年第6期18卷 1879-1912页

作者： Suwan, Rodjana Temdee, Punnarumol Prasad, Ramjee Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai Thailand CTIF Global Capsule (CGC) Department of Business Development and Technology Aarhus University Denmark

Given the harmful effects of excessive sugar consumption, everyone should be aware of the amount of sugar they consume in daily life. Generally, managing nutrition and preventing negative effects from sugar intake requires a nutritional specialist or specialized knowledge, which may not always be readily accessible or understandable. Therefore, there is a need for a mobile application that can track excessive sugar consumption. This study proposes a context-Aware personalized recommendation mobile application for monitoring excessive sugar consumption and providing individual recommendations (based on a predefined set of 144 rules) to Thai adolescents. The application is user friendly and can be used to provide recommendations to users for sugar consumption and proper exercise each day. Personal, health, and lifestyle data are collected and analyzed to provide individualized recommendations to each user. Experiments were conducted with 140 Thai adolescents aged 15 to 25 years old. Users preferences regarding degree of awareness in presentation style were also investigated. Users rated their satisfaction with the proposed mobile application as very high in terms of both function and personalization. In addition, fully automated recommendations were found to be the preferred degree of awareness among the test group. © 2022 River Publishers. All rights reserved.

关键词： Context-Awareness Lifestyle Nutrition Personalized recommendations Sugar consumption

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Development of Intelligent Medication Care Taker

Development of Intelligent Medication Care Taker

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

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

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

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.

关键词： Hospitals Telemedicine Pressing Stroke (medical condition) Systems support Biomedical monitoring Older adults Monitoring Videoconferences Testing

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The Integration of Traditional Rural Solar Dryer with LoRa and IoT Technology 8

The Integration of Traditional Rural Solar Dryer with LoRa a...

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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： Yooyativong, Thongchai Kamyod, Chayapol Kim, Chong Ghil School of Information Technology Mae Fah Luang University Chiang Rai Thailand School of Information Technology Mae Fah Luang University Excellence Center of Industry 4.0 Computer and Communication Engineering for Capacity Building Research Center Chiang Rai Thailand Namseoul University Dept. of Computer Science Choongnam Korea Republic of

ISBN: (纸本)9798350396546

Traditional or rural dryers or Thai farmers' dryers have some drawbacks when compared to modern or commercial dryers on the market. Despite the fact that the dryer is vital and may be utilized as postharvest technology to preserve or produce some agricultural products, the farmer chose to build the traditional structure because of his limited income and resources. This research examined the needs of rural farmers and developed an integration of typical rural solar dryers or dryers using IoT technologies. The integrated design will preserve the structure's materials while increasing the system's capacity and efficiency to reduce costs. Farmers may now manage the temperature inside the dryer using the newly created mechanism. Remote monitoring and management are also made possible by the technology, minimizing the need for physical intervention. This leads to increased efficiency, lower costs, and a more sustainable energy source. © 2023 IEEE.

关键词： Agricultural products

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

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

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International Conference on Information Technology (INCIT)

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

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%.

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

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

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International Conference on Information Technology (INCIT)

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

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.

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Particle Weight Redistribution in Particle Filtering to Improve Nonlinear State Estimation

Particle Weight Redistribution in Particle Filtering to Impr...

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International Conference on Information Technology (INCIT)

作者： May Htet Htet Khine Nattapol Aunsri School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Center (CCC) Chiang Rai Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Particle filters are used to estimate hidden state parameters from the noisy signals. They are based on sequential Monte Carlo theory, where they randomly draw samples from all possible hidden states and then use the previous information of estimation to proceed to the next step. Particle filters are also known for their degeneracy issues where the particles are separated into high probability and low probability regions. Therefore, resampling is incorporated into particle filters. However, resampling brings up another issue of particle impoverishment and consequently reduce particle diversity. This paper proposes an improved resampling process for a standard particle filters to address particle impoverishment in standard sampling importance resampling particle filters (SIR-PF). B y introducing a weight redistribution of particles before resampling, the algorithm expects to mitigate error values across varying signal-to-noise ratios, potentially outperforming conventional SIR-PF. The experiment is validated using a simulated signal, resulting in overall lower RMSE and MAE at various noise levels. The proposed method is a computationally efficient and straight forward process compared to more complex alternatives.

关键词： Monte Carlo methods Accuracy Systematics Signal processing algorithms Particle filters Computational efficiency State estimation Standards Optimization Signal to noise ratio

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Thai Food Recognition Using Convolutional Neural Network with Dropout Technique 7

Thai Food Recognition Using Convolutional Neural Network wit...

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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： Natephakdee, Niti Temdee, Punnarumol Srimaharaj, Wanus Building Research Center School of Information Technology Mae Fah Luang University Computer and Communication Engineering for Capacity Chiang Rai Thailand Payap University The International College Department of Information Technology Chiang Mai Thailand

ISBN: (纸本)9781665495103

Visually recognizing Thai food is indeed a challenging task and tradition methods have achieved historically low classification. While deep learning operation allows users to identify Thai cuisine and visually distinguish Thai food, it often includes complex meanings. The deep learning approach has shown very satisfied results in such challenges. Therefore, this study focuses on presenting methods and techniques for noise reduction of Thai food images used in image recognition. A total of sixty thousand sample images were used in the experiment. Results revealed that the method offered highest reliable accuracy of 94.16%. © 2022 IEEE.

关键词： Deep learning

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The Development of WLAN Time Face Recorder System

The Development of WLAN Time Face Recorder System

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International Conference on Information Technology (INCIT)

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

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.

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