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Abstract Ultrasound biomicroscopy (UBM) is the standard for diagnosing plateau iris, but its limited accessibility in routine clinical settings presents challenges. While anterior segment optical coherence tomography (AS-OCT) is more convenient, its effectiveness in detecting plateau iris is limited. Previous research has demonstrated that combining UBM and AS-OCT image pairs through neural style transfer has improved classification accuracy. However, obtaining paired images is impractical in everyday practice. In this study, we propose a novel semi-supervised approach that eliminates the need for paired images. A generative model learns to distinguish plateau and non-plateau features from UBM images. AS-OCT images are input into the generator, which attempts to transform them into corresponding UBM images. The model’s performance is measured by loss values, representing the difficulty of transforming AS-OCT images, which are then used to predict plateau iris. The classification baseline, which applies AS-OCT solely without the style-transfer of UBM information, obtained 52.72% sensitivity, 60.82% specificity, and 57.89% accuracy for external validation; in contrast, the classification with neural style transfer of the image pairs respectively obtained 94.54%, 100.00%, and 98.03%, whereas the semi-supervised approach using loss values classification obtained 93.10%, 93.13%, and 93.12%, respectively. This semi-supervised transfer learning model presents a novel technique for detecting plateau iris with AS-OCT.

Cloud data warehouse (CDW) platforms have been offered by many cloud service providers to provide abundant storage and unlimited accessibility service to business users. Sensitive data warehouse (DW) data consisting of dimension and fact data is typically encrypted before it is outsourced to the cloud. However, the query over encrypted DW is not practically supported by any analytical query tools. The Searchable Encryption (SE) technique is palpable for supporting the keyword searches over the encrypted data. Although many SE schemes have introduced their own unique searching methods based on indexing structure on top of searchable encryption techniques, there are no schemes that support Boolean expression queries essential for the search conditions over the DW schema. In this paper, we propose a secure and verifiable searchable encryption scheme with the support of Boolean expressions for CDW. The technical construct of the proposed scheme is based on the combination of Partial Homomorphic Encryption (PHE), B+Tree and Inverted Index, and bitmapping functions to enable privacy-preserving SE with efficient search performance suitable for encrypted DW. To enhance the scalability without requiring a third party to support the verification of search results, we employed blockchain and smart contracts to automate authentication, search index retention, and trapdoor generation. For the evaluation, we conducted comparative experiments to show that our scheme is more proficient and effective than related works.

Abstract Road traffic mortalities (RTMs), a leading cause of death globally, mostly occur in low- and middle-income countries, and having sufficient healthcare resources could lower the number of these fatalities. Our study aimed to illustrate the incidence of RTMs per 100,000 population and to compare the distribution of healthcare resources from 2011 to 2021 with rates of RTMs in the 77 provinces of Thailand. We divided the population into adults (≥ 15 years) and children (0–14 years). Lorenz curve and Gini coefficient were used to measure the level of distribution and equality of hospital resources and in relation to RTMs across the country. The average number of deaths was 30.34 per 100,000 per year, with male predominance. The RTM rates for adults and children were 32.71 and 19.08 per 100,000 respectively, and motorcycle accidents accounted for the largest percentage of deaths across all age groups. The Gini coefficient showed that operating rooms (0.42) were the least equally distributed hospital resource, while physicians were the most equally distributed (0.34). Anomalies were identified between the distribution of RTMs and available hospital resources. We hope our study will be beneficial in reallocating these resources more fairly to reflect the different numbers of traffic accidents in each province with the aim of reducing lower traffic-related deaths.

Abstracts Background The aim of this study was to evaluate the most effective combination of autoregressive integrated moving average (ARIMA), a time series model, and association rule mining (ARM) techniques to identify meaningful prognostic factors and predict the number of cases for efficient COVID-19 crisis management. Methods The 3685 COVID-19 patients admitted at Thailand’s first university field hospital following the four waves of infections from March 2020 to August 2021 were analyzed using the autoregressive integrated moving average (ARIMA), its derivative to exogenous variables (ARIMAX), and association rule mining (ARM). Results The ARIMA (2, 2, 2) model with an optimized parameter set predicted the number of the COVID-19 cases admitted at the hospital with acceptable error scores (R 2 = 0.5695, RMSE = 29.7605, MAE = 27.5102). Key features from ARM (symptoms, age, and underlying diseases) were selected to build an ARIMAX (1, 1, 1) model, which yielded better performance in predicting the number of admitted cases (R 2 = 0.5695, RMSE = 27.7508, MAE = 23.4642). The association analysis revealed that hospital stays of more than 14 days were related to the healthcare worker patients and the patients presented with underlying diseases. The worsening cases that required referral to the hospital ward were associated with the patients admitted with symptoms, pregnancy, metabolic syndrome, and age greater than 65 years old. Conclusions This study demonstrated that the ARIMAX model has the potential to predict the number of COVID-19 cases by incorporating the most associated prognostic factors identified by ARM technique to the ARIMA model, which could be used for preparation and optimal management of hospital resources during pandemics.

Objectives Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public’s practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK. Methods After validation, AiMASK collected data from 32 districts in Bangkok. We analyzed the association between factors affecting the unprotected group (incorrect or non-mask wearing) using univariate logistic regression analysis. Results AiMASK was validated before data collection with accuracy of 97.83% and 91% during internal and external validation, respectively. AiMASK detected a total of 1,124,524 people. The unprotected group consisted of 2.06% of incorrect mask-wearing group and 1.96% of non-mask wearing group. Moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r = -0.507, pConclusions AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people’s mask-wearing behavior. Higher tendencies towards no protection were found in the evenings, during holidays, and in city centers.

Purpose Computer vision syndrome (CVS) describes a group of eye and vision-related problems that result from prolonged digital device use. This study aims to assess the prevalence and associated factors of CVS among students during the lockdown resulting from the COVID-19 pandemic.Methods A cross-sectional, online, questionnaire-based study performed among high school students in Thailand.Results A total of 2476 students, with mean age of 15.52±1.66 years, were included in this study. The mean number of hours of digital device use per day (10.53±2.99) increased during the COVID-19 pandemic compared with before its advent (6.13±2.8). The mean number of hours of online learning was 7.03±2.06 hours per day during the pandemic. CVS was found in 70.1% of students, and its severity correlated with both the number of hours of online learning and the total number of hours of digital device usage (p6 hours per day (AOR=1.91), online learning >5 hours per day (AOR=4.99), multiple digital device usage (AOR=2.15), refractive errors (AOR=2.89), presence of back pain (AOR=2.06) and presence of neck pain (AOR=2.36).Conclusions The number of hours of digital device usage increased during lockdown. Over 70% of children had CVS, whose associated factors, including hours of digital device usage, hours of online learning, ergonomics and refractive errors, should be adjusted to decrease the risk of acquiring this condition. Online learning will remain, along with CVS, after this pandemic, and we hope our research will be taken into account in remodelling our education system accordingly.

Promoting reflective thinking is becoming increasingly important in helping learners develop strategies to apply new information to unpredictable situations during their daily activities. Reflective writing, based on Kolb’s experiential learning cycle, could be one method for promoting reflective thinking that allows learners to consider their experiences and transform them into transferable knowledge, which can be applied to new contexts. However, learners cannot sufficiently practice reflective writing; thus, they cannot learn from their experiences. Therefore, our primary goal is to support reflective thinking by providing writing support. This study presents a computerized learning-environment design that helps learners to master experiential learning concepts using reflective writing. The study demonstrates how the learning-support function enhances experiential learning and enables the desired learning process to be captured. This helps mentors to provide suitable support toward understanding experiential learning. We also demonstrate how the learning environment can help learners to master experiential learning. This design, which has the dual role of supporting and observing implicit thinking behavior, can then be applied to a meta-level thinking support framework in other problem domains.

In similarity-based constrained clustering, there have been various approaches on how to define the similarity between documents to guide the grouping of similar documents together. This paper presents an approach to use term-distribution statistics extracted from a small number of cue instances with their known classes, for term weightings as indirect distance constraint. As for distribution-based term weighting, three types of term-oriented standard deviations are exploited: distribution of a term in a collection (SD), average distribution of a term in a class (ACSD), and average distribution of a term among classes (CSD). These term weightings are explored with the consideration of symmetry concepts by varying the magnitude to positive and negative for promoting and demoting effects of three standard deviations. In k-means, followed the symmetry concept, both seeded and unseeded centroid initializations are investigated and compared to the centroid-based classification. Our experiment is conducted using five English text collections and one Thai text collection, i.e., Amazon, DI, WebKB1, WebKB2, and 20Newsgroup, as well as TR, a collection of Thai reform-related opinions. Compared to the conventional TFIDF, the distribution-based term weighting improves the centroid-based method, seeded k-means, and k-means with the error reduction rate of 22.45%, 31.13%, and 58.96%.

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While there have been several studies related to the effect of term weighting on classification accuracy, relatively few works have been conducted on how term weighting affects the quality of keywords extracted for characterizing a document or a category (i.e., document collection). Moreover, many tasks require more complicated category structure, such as hierarchical and network category structure, rather than a flat category structure. This paper presents a qualitative and quantitative study on how term weighting affects keyword extraction in the hierarchical category structure, in comparison to the flat category structure. A hierarchical structure triggers special characteristic in assigning a set of keywords or tags to represent a document or a document collection, with support of statistics in a hierarchy, including category itself, its parent category, its child categories, and sibling categories. An enhancement of term weighting is proposed particularly in the form of a series of modified TFIDF's, for improving keyword extraction. A text collection of public-hearing opinions is used to evaluate variant TFs and IDFs to identify which types of information in hierarchical category structure are useful. By experiments, we found that the most effective IDF family, namely TF-IDFr, is identity > sibling > child > parent in order. The TF-IDFr outperforms the vanilla version of TFIDF with a centroid-based classifier. [ABSTRACT FROM AUTHOR]

There have been several challenges in summarization of Thai multiple documents since Thai language itself lacks of explicit word/phrase/sentence boundaries. This paper gives definition of Thai Elementary Discourse Unit (TEDU) and then presents our three-stage summarization process. Towards implementation of this process, we propose unit segmentation using TEDUs and their derivatives, unit-graph formation using iterative unit weighting and cosine similarity, and unit selection using highest-weight priority, redundancy removal, and post-selection weight recalculation. To examine performance of the proposed methods, a number of experiments are conducted using fifty sets of Thai news articles with their manually constructed reference summary. By three common evaluation measures of ROUGE-1, ROUGE-2, and ROUGE-SU4, the results evidence that (1) our TEDU-based summarization outperforms paragraph-based summarization, (2) our iterative weighting is superior to traditional TF-IDF, (3) the highest-weight priority without centroid preference and unit redundancy consideration helps improving summary quality, and (4) post-selection weight recalculation tends to raise summarization performance under some certain circumstances. [ABSTRACT FROM AUTHOR]