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Improving Frequency Estimation via Sequential Bayesian Filtering with Firefly Algorithm

Improving Frequency Estimation via Sequential Bayesian Filte...

引用

International Symposium on Wireless Personal Multimedia communications (WPMC)

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

Frequency estimation is considered as one of the most challenging problems in signal processing. Sequential Bayesian filtering has been utilized to the problem extensively due to its versatility for various applications in science and engineering. With the advances of Bayesian filtering, it also has been implemented for frequency estimation extensively with satisfactory results. However, Bayesian filter, particle filter, in particular, has a particle degradation problem of the conventional sequential importance sampling. In this work, we introduce firefly algorithm that will be used instead of the re-sampling in the traditional particle filter to tackle the particle depletion during the execution of the conventional particle filter framework. Computed from the short-time Fourier transforms (STFTs), the signal is analyzed in the frequency domain in this work which is considered as a measurement/observation model of the PF framework. Experimental results revealed the advantage the replacement of standard particle filter by firefly algorithm. Frequency estimates from the proposed filter are better than those obtained from the standard filter in terms of low uncertainty in the frequency trajectories.

关键词： Time-frequency analysis Filtering Signal processing algorithms Filtering algorithms Frequency estimation Particle filters Bayes methods

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Development of Low Cost 3 Phases IoT Electrical Power Meter

Development of Low Cost 3 Phases IoT Electrical Power Meter

引用

International Conference on Information Technology (INCIT)

作者： Anusorn Yodjaiphet Ronnachai Sretawat Na Ayutaya Suppakarn Chansareewittaya Faculty of Engineering Rajamangala University of Technology Lanna (Chaing Rai) School of Information Technology Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center Mae Fah Luang University

In this paper, 3 phases of IoT electrical power meter is developed. The aim of this development is to create a 3 phases low-cost meter with IoT that can measure the true RMS value. The design of both hardware and software focuses on easy design, fewer components, uncomplicated processing, power component values, and using calculations according to numerical calculation method according to the basic theoretical principles. The dsPIC30F4011 16-bits microcontroller is used as the main microcontroller. The ESP8266 is used as the transmitting device. The information of the measurement is sent from dsPIC30F4011 to the Thinkspeak platform due to the serverless concept. After that, the information is displayed on the website via the dashboard. By comparing with the commercial product, this DIY is cheaper and has more flexible that the user can adjust the function and dash board as wish.

关键词： Meters Voltage measurement Power demand Phase measurement Microcontrollers Current measurement Measurement uncertainty

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Evaluation of Generative-AI Fashion Images using CNN Classification and Regression

Evaluation of Generative-AI Fashion Images using CNN Classif...

引用

International Symposium on communications and Information Technologies (ISCIT)

作者： Hsi Yeh Wang Worasak Rueangsirarak Surapong Utama School for Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building School for Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in AI and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

The integration of artificial intelligence (AI) in the fashion industry has the potential to revolutionize design processes, enabling the generation of diverse designs based on user inputs. Despite the promise of AI, the unpredictable quality of outputs and the challenge of meeting human aesthetic and market standards remain significant hurdles. To address these issues, this research aims to (1) create a dataset of AI -generated fashion images labeled with customer rankings, and (2) establish a new evaluation method from the perspectives of the general public and the market. Data were collected from fashion e-commerce websites focusing on customer ratings and product images. Two CNN models were trained: one for regression, predicting continuous aesthetic scores, and another for classification, categorizing images into discrete rating intervals. Performance evaluation revealed that the regression model, constrained by clip techniques, maintained the original distribution of data while the classification model provided a benchmark for design indicators. The study concludes that addressing data imbalance and applying augmentation techniques yielded significant results. Specifically, the regression model achieved an RMSE of 0.866, while the classification model attained an accuracy of 93%.

关键词： Performance evaluation Solid modeling Accuracy Predictive models Benchmark testing Solids Data models Artificial intelligence Standards Optimization

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Convolutional Neural Network and Dropout Technique for Recognition of Thai Food Image

Convolutional Neural Network and Dropout Technique for Recog...

引用

International Symposium on Wireless Personal Multimedia communications (WPMC)

作者： Niti Natephakdee Supansa Chaising Punnarumol Temdee Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai Thailand School of Management Mae Fah Luang University Chiang Rai Thailand

One of the main causes of diabetes, heart disease, and obesity in today's world is consumption behavior. To help people prevent such diseases, the consumption advice is required for behavior changes. A comparative study was proposed in this paper to find out a method for recognition of Thai food image using Convolutional Neural Network (CNN) and dropout technique to recognize the type of Thai food. CNN is used for distinguishing the complexity among Thai food images, while dropout technique is used for noise reduction. Sample used in the experiment were 16,170 images for recognizing 52 food types. The proposed method is compared with CNN without dropout, and Nu-In Net 1.1. The comparison results showed that CNN with dropout performed the best with 96.67% accuracy.

关键词： Wireless communication Heart Obesity Image recognition Noise reduction Behavioral sciences Diabetes

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Predicting Marriage Rate Using Machine Learning and Panel Data Analysis: A Case Study of China

Predicting Marriage Rate Using Machine Learning and Panel Da...

引用

International Symposium on communications and Information Technologies (ISCIT)

作者： Deyu Zhang Worasak Rueangsirarak Surapong Uttama School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Unit School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

After China's accession to the WTO and 20 years of rapid development, the marriage rate has shown a downward trend. This paper aims at fitting machine learning models with the marriage-related data, understanding which attributes affect the marriage rate and predicting the marriage rate. The data collection scope includes seven independent variables related to marriage rate such as GDP, house prices, birth rate, education level etc. over 31 regions in China during 2003–2022. Then the study applied three regression models - Pooled OLS, Random Effects, and Fixed Effects - in predicting China's crude marriage rate. The Random Effects model outperformed both the Pooled OLS and Fixed Effects models, as evidenced by its highest R 2 value (0.2910). However, based on Hausman Test, p-value of *** Fixed Effects model is preferrable. All models suggested that the average year of education has the most positive effect to the marriage rate while the house price greatly negates the marriage rate. Results show the Random Effects model, with an R 2 of 0.2910, as the best fit. Key predictors include GDP, house prices, and gross dependency ratio (negative effects), and sex ratio and education (positive effects). The Random Effects model excels in prediction, with the lowest MSE (1.6610), RMSE (1.2888) and MAE (1.0888).

关键词： Ethics Economic indicators Education Fitting Machine learning Predictive models Market research Data models Information and communication technology Faces

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The Proposed Wind-gate Monitoring System for Teaching and Learning Thai Traditional Massage

The Proposed Wind-gate Monitoring System for Teaching and Le...

引用

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

作者： Chayapol Kamyod Parichart Hongsing Excellence Center of Industry 4.0 Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai Thailand Neuroscience Research Center School of Integrative Medicine Mae Fah Luang University Chiang Rai Thailand

Traditional Thai massage teaching methods such as lectures, demonstrations, and manual practice can be time-consuming and ineffective in conveying complicated techniques and concepts, especially for an online course. Without real-time monitoring and feedback, it might be impossible for students to discover and improve their specific strengths and limitations in massage methods. Furthermore, trainees may have limited opportunity to practice and enhance their abilities, particularly if they do not live near a massage training academy. The study proposed an IoT-based wind-gate system as a tool for teaching and learning Thai massage. The system can enhance and improve the massage treatment process by offering real-time monitoring, a personalized approach, data-driven insights, remote access, and cost-effectiveness with lower error rates when compared to commercial measuring instruments.

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Predicting China's Marriage Rate: Causal Inference Using Dual Machine Learning (DML) with XGBoost, LightGBM, CatBoost, and GBDT

Predicting China's Marriage Rate: Causal Inference Using Dua...

引用

Big Data Analytics and Practices (IBDAP), International Conference on

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

After China's accession to the WTO and 20 years of rapid development, the marriage rate has shown a downward trend. This study aims to analyze the impact of socio-economic factors on the crude marriage rate (CMR) panel data in China from 2003 to 2022 using Dual Machine Learning (DML) for Causal Inference and machine learning models. DML was used to estimate ATE, CATE, and HTE for various factors. Four models—XGBoost, LightGBM, CatBoost, and GBDT—were employed for predictions, using 10-fold cross-validation for model evaluation. The results indicate that education (ATE 5.666) and birth rate (ATE 5.492) had the most significant positive impacts on CMR, while GDP (ATE 3.196) showed positive but varying effects, and the female proportion (ATE - 2.353) had a notable negative impact. CatBoost performed best in MSE (0.942) and RMSE (0.958), while LightGBM excelled in MAE (0.777). Education, GDP, and birth rate are key factors influencing CMR. CatBoost and LightGBM proved to be effective prediction models, though improvements are needed for regions with significant variability.

关键词： Analytical models Accuracy Economic indicators Education Machine learning Predictive models Big Data

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Mixed Learning Strategies on Combining Horizontal Blended Learning with Flipped Classroom

Mixed Learning Strategies on Combining Horizontal Blended Le...

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

作者： Worasak Rueangsirarak Punnarumol Temdee Computer and Communication Engineering for Capacity Building Research Unit School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (纸本)9781665415699;9781665446914

In Thailand, blended learning was introduced to be the strategy for teaching management during the COVID-19 pandemic situation. Therefore, the proposed combined model of horizontal blended learning with flipped classroom was designed to help the lecturers to organize the online teaching for their students. The idea is to focus on two event activities, one is the self-learning through the LMS and another is the wrapped-up lecture by the facilitator. The 30% of activities are the face-to-face class organized for the formative assessment with the students. Additionally, the class is conducted in both synchronous and asynchronous modes. The case study is conducted with the Information Technology students who enrolled in the Introduction to Cloud Computing course. As the results, the proposed teaching model can enhance the students' knowledge retention more than those of the traditional classroom with the t-Test analysis show its significance of Pvalue at $0.014 \lt 0.05$. The attitude tests of the students can confirm that the proposed model is suitable for a disruptive situation like the COVID-19 pandemic.

关键词： COVID-19 Cloud computing Pandemics Distance learning Computational modeling Education Media

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Proposing ChatGPT as a Transformative Tool for Enhancing ESL Speaking and Listening Skills: Bridging Research Gaps in Thai Education

Proposing ChatGPT as a Transformative Tool for Enhancing ESL...

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

作者： Sirikanya Dawilai Thongchai Yooyativong Chayapol Kamyod Faculty of Humanities and Social Sciences Chiang Rai Rajabhat University Chiang Rai Thailand Center of Excellence in Artificial Intelligennce and Emerging Technology School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Center School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

The development of speaking and listening skills remains a significant challenge in English as a Second Language (ESL) education, particularly in non-native English-speaking contexts such as Thailand. Traditional tools and methods often fail to provide personalized, real-time conversational practice, leaving gaps in learners’ communicative competence. This paper explores ChatGPT, a conversational AI, as a potential solution to these challenges. By conducting a comprehensive review of existing tools and identifying research gaps, this study proposes a conceptual framework for integrating ChatGPT into ESL education, particularly for Thai students at Chiang Rai Rajabhat University (CRRU). This framework highlights how ChatGPT’s interactive capabilities can address cultural and linguistic barriers, enhance learner engagement, and provide scalable solutions for improving speaking and listening skills. The paper concludes with recommendations for future research and practical implications for educators.

关键词： Bridges Visualization Reviews Scalability Education Linguistics Chatbots Real-time systems Cultural differences Testing

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Elderly Standing Imbalance Detection Using Noise-Resilient Robust Mean Estimator and Deep Learning

Elderly Standing Imbalance Detection Using Noise-Resilient R...

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International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Drishya Raj Shanfeng Hu Nauman Aslam Xiaomin Chen Worasak Rueangsirarak Surapong Uttama Fatimah Nauman Department of Computer and Information Sciences Northumbria University UK Center of Excellence in Computer and Communication Engineering for Capacity Building of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in AI and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand Faculty of Medicine Lancaster University UK

Elderly standing imbalance is a critical public health concern, demanding robust and accurate detection techniques for improved safety and well-being. In this paper, we propose a novel method employing unsupervised learning and Denoising Autoencoder with Multi-Layer Perceptron networks, along with a custom adaptive Huber loss function and activation function, to classify standing states in elderly individuals. The existing Standing imbalance detection research includes difficulties such as addressing irregularities in pressure sensor data, largely stressing binary classification due to algorithmic efficiency considerations while dealing with heavy-tailed data. The approach utilizes open-source smart insole datasets, capturing left and right foot pressure data. The ensemble model DAE-MLP efficiently captures the temporal dynamics of the imbalance scores produced using the Noise-resilient robust mean estimator, enabling accurate and robust classification. This method adapts to varied degrees of data imbalance, resulting in more accurate learning. Through comprehensive evaluations, our method achieves an overall accuracy of 94 percentage on a test dataset with 53 instances. This approach serves as a proactive standing imbalance detection system for the elderly, enhancing safety and quality of life by identifying and addressing standing imbalance risks. Our research introduces an innovative solution, paving the way for advancements in elderly healthcare and safety, reducing the risk of falls and related injuries.

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