This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-km resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011–2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R²) and root mean square error (RMSE), and the results indicate high accuracy for training (R²: 0.95, RMSE: 5.58 μg/m³), validation (R²: 0.78, RMSE: 11.18 μg/m³), and testing (R²: 0.71, RMSE: 8.79 μg/m³) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.

Several methodologies have recently been proposed to enhance the performance of low-resource Neural Machine Translation (NMT). However, these techniques have yet to be explored thoroughly in the low-resource Thai and Myanmar languages. Therefore, we first applied augmentation techniques such as SwitchOut and Ciphertext Based Data Augmentation (CipherDAug) to improve NMT performance in these languages. Second, we enhanced the NMT performance by fine-tuning the pre-trained Multilingual Denoising BART model (mBART), where BART denotes Bidirectional and Auto-Regressive Transformer. We implemented three NMT systems: namely, Transformer+SwitchOut, Multi-Source Transformer+CipherDAug, and fine-tuned mBART in the bidirectional translations of Thai-English-Myanmar language pairs from the ASEAN-MT corpus. Experimental results showed that Multi-Source Transformer+CipherDAug significantly improved Bilingual Evaluation Understudy (BLEU), Character n-gram F-score (ChrF) , and Translation Error Rate (TER) scores over the first baseline Transformer and second baseline Edit-Based Transformer. The model achieved notable BLEU scores: 37.9 (English-to-Thai), 42.7 (Thai-to-English), 28.9 (English-to-Myanmar), 31.2 (Myanmar-to-English), 25.3 (Thai-to-Myanmar), and 25.5 (Myanmar-to-Thai). The fine-tuned mBART model also considerably outperformed the two baselines, except for the Myanmar-to-English pair. SwitchOut improved over the second baseline in all pairs and performed similarly to the first baseline in most cases. Last, we performed detailed analyses verifying that the CipherDAug and mBART models potentially facilitate improving low-resource NMT performance in Thai and Myanmar languages.

Around 1.3 million people worldwide die each year because of road traffic crashes. There are many reasons which cause accidents, and driving in the wrong direction is one of them. In our research, we developed an autonomous framework called WrongWay-LVDC that detects wrong-way driving vehicles from closed-circuit television (CCTV) videos. The proposed WrongWay-LVDC provides several helpful features such as lane detection, correct direction validation, detecting wrong-way driving vehicles, and image capturing features. In this work, we proposed three main contributions: first, the improved algorithm for road lane boundary detection on CCTV (called improved RLB-CCTV) using the image processing technique. Second is the Distance-Based Direction Detection (DBDD) algorithm that uses the deep learning method, where the system validates and detects wrong-driving vehicles. Lastly, the Inside Boundary Image (IBI) capturing feature algorithm captures the most precise shot of the wrong-way-of-driving vehicles. As a result, the framework can run continuously and output the reports for vehicles’ driving behaviors in each area. The accuracy of our framework is 95.23%, as we tested with several CCTV videos. Moreover, the framework can be implemented on edge devices with real-time speed for functional implementation and detection in various areas.

Ontology describes concepts and relations in a specific domain-knowledge that are important for knowledge representation and knowledge sharing. In the past few years, several tools have been introduced for ontology modeling and editing. To design and develop an ontology is one of the challenge tasks and its challenges are quite similar to software development as it requires many collaborative activities from many stakeholders (e.g. domain experts, knowledge engineers, application users, etc.) through the development cycle. Most of the existing tools do not provide collaborative feature to support stakeholders to collaborate work more effectively. In addition, there are lacking of standard process adoption for ontology development task. Thus, in this work, we incorporated ontology development process into Scrum process as used for process standard in software engineering. Based on Scrum, we can perform standard agile development of ontology that can reduce the development cycle as well as it can be responding to any changes better and faster. To support this idea, we proposed a Scrum Ontology Development Framework, which is an online collaborative framework for agile ontology design and development. Each ontology development process based on Scrum model will be supported by different services in our framework, aiming to promote collaborative activities among different roles of stakeholders. In addition to services such as ontology visualized modeling and editing, we also provide three more important features such as 1) concept/relation misunderstanding diagnosis, 2) cross-domain concept detection and 3) concept classification. All these features allow stakeholders to share their understanding and collaboratively discuss to improve quality of domain ontologies through a community consensus.

This study introduces a novel method for estimating hourly concentrations of particulate matter 2.5 μm (PM _2.5 ) using satellite data. The pollution control department of Thailand collected hourly PM _2.5 data nationwide in 2020. NASA's Earth Observing System Data and Information System encompasses all moderate resolution imaging spectroradiometer satellite data. We employed aerosol optical depth (AOD), land surface temperature (LST), normalized difference vegetation index (NDVI), and elevation (EV) in our analysis. The approach incorporates a weighted sum contrast log-linear regression model that integrates satellite data, allowing for the examination of small-scale hourly variations in PM _2.5 concentrations. The results reveal a high correlation between hourly PM _2.5 levels and AOD, LST, NDVI, EV, time, and week of the year in terms of spatial distribution, with an R ² value of 53.8%. The mean hourly PM _2.5 concentration was 23.1 μ g/m ³ , displaying elevated concentrations during the dry season (November to March) and peak hours (8 to 11 a.m . and 8 to 12 p.m .). Positive correlations between AOD and PM _2.5 , especially when AOD exceeded 0.52, and between LST and PM _2.5 , particularly when LST exceeded 33.9 °C, along with NDVI ranging from −0.08 to 0.18 and EV above 67.9 m, resulted in higher PM _2.5 levels than the overall mean. The proposed model proved valuable for interpretation and practical application, offering comparable estimated hourly PM _2.5 concentrations at a 1-km resolution with monitoring stations. This suggests that researchers or policymakers may use the model to understand hourly PM _2.5 fluctuations and their impact on human health and the environment.

Thailand Provincial Electricity Authority (PEA) has 19.36 million residential customers and approximately 73% of them have late or unpaid electricity payments. This causes several problems including reduction of PEA’s income. Therefore, we aim to classify and find data mining algorithms to predict classes of electricity payment behaviors of residential customers in order to improve customer service and to reduce the number of late or unpaid payment of electrical bills. In this work, the data set consists of payment behaviors and demographic characteristics of customers, which are gathered from 1,079 customers. The prediction process is divided into two main parts: clustering classes of customers by using k-means algorithm, and using data mining algorithms for customer model prediction. We performed experiments using five data mining algorithms: logistic regression, decision tree, random fores, support vector machine (SVM) and XGBoost. Our results show that the random forest algorithm is a good choice for this predictive classes of customers to develop appropriate strategies to increase satisfaction of customers and revenue of PEA.