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Nitrogen dioxide (NO 2 ) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO 2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO 2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO 2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R 2 values of 0.92 and 0.93, respectively. The study revealed that mean NO 2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO 2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO 2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

PM 2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM 2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM 2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM 2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM 2.5 values, SO 2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM 2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Recent progress in wireless and sensor technologies has lead to a new development of learning environments, called context-aware ubiquitous learning environment, which is able to sense the situation of learners and provide adaptive supports. Many researchers have been investigating the development of such new learning environments; nevertheless, the criteria of establishing a context-aware ubiquitous learning environment have not yet been clearly defined, not to mention the strategies of conducting effective learning activities. To resolve these problems, this paper presents the basic criteria, strategies, and research issues of context-aware ubiquitous learning, and identifies the necessary check items as well for the development of such learning environment. Illustrative examples of conducting context-aware ubiquitous learning activities and the requirements of setting up such learning environment are also presented at the end of this paper. (Contains 3 figures and 2 tables.)

Promoting the development of students' scientific inquiry capabilities is a major learning objective in science education. As a result, teachers require effective assessment approaches to evaluate students' scientific inquiry-related performance. Teachers must also be able to offer appropriate supplementary instructions, as needed, to students. Scientific inquiry capabilities should be assessed by evaluating students' scientific inquiry portfolios in actual hands-on experiments. Although virtual laboratory systems can reduce the cost of conducting scientific inquiry experiments, the manual portfolio assessment approach is still difficult and time-consuming for teachers. Therefore, in this paper, in order to provide students with personalized learning guidance concerning not only the conceptual knowledge, but also the high-order, integrative abilities of scientific inquiry, an Online Portfolio Assessment and Diagnosis Scheme, called OPASS, was proposed to assist teachers in automatically assessing and diagnosing students' abilities as they relate to scientific inquiry performance. Personalized diagnostic reports were generated by employing the rule-based inference approach, which diagnosed learning problems and provided corresponding reasons and remedial suggestions based on teacher-defined assessment knowledge of the scientific inquiry experiment. For the evaluation, experimental results showed that the OPASS was helpful and beneficial for both students and teachers. (Contains 13 tables and 11 figures.)

Due to an arising COVID-19 positive confirmed case in Taiwan, the screening of body temperature, mask wearing and quarantined violation is enhanced. A mobile robot that conducts this task is demanded to reduce the human labor. However, conventional robots suffer from several limitations, perceptual aliasing (e.g., different places/objects can appear identical), occlusion (e.g., place/object appearance changes between visits), different viewpoints, the scale of objects, low mobility, less functionality, and some environmental limitations. As for the thermal imager, it displays the current heat spectrum colors, and needs manual monitoring. This paper proposes applying Simultaneous Localization and Mapping in an unknown environment and using deep learning for detection of temperature, mask wearing, and human face on the Raspberry Pi to overcome these problems. It also uses the A* algorithm to do path planning and obstacle avoidance via 3D Light Detection and Ranging to make the robot move more smoothly. Evaluating and implementing different Simultaneous Localization and Mapping algorithms and deep learning models, then selecting the most suitable method. Root Mean Square Error of three Simultaneous Localization and Mapping algorithms are compared. The predictions of deep learning models are evaluated via the metrics (model speed, accuracy, complexity, precision, recall, precision–recall curve, F1 score). In conclusion, Google Cartographer for building a map, Convolutional Neural Network for mask wearing detection, and only looking once for human face detection achieve the best result among all algorithms. [ABSTRACT FROM AUTHOR]

Scholars have identified that learning in an authentic environment with quality contextual and procedural supports can engage students in thorough observations and knowledge construction. Moreover, the target is that students are able to experience and make sense of all of the learning activities in the real-world environment with meaningful supports, such that their learning motivation can be promoted, knowledge can be sensibly constructed, and skills can be fully developed. To develop potential tutoring strategies and learning activity models using mobile, wireless, and sensing information and communication technologies (ICT) in a real-world learning environment, a four-year national e-learning research project entitled "Mindtool-Assisted In-field Learning (MAIL)" has been funded by the National Science Council of Taiwan since 2008 in an effort to lead the development and innovation of Learning Technology. The integrated project aimed to develop Mindtool-assisted knowledge construction models, assessment models, guidance models, and reflection strategies for cutting-edge context-aware ubiquitous learning. Moreover, a series of learning activities has been conducted to examine the effectiveness of the proposed learning strategies and models. Each year, more than 1,500 students have participated in the in-field learning activities with the designed approaches. Based on the results of a series of experiments, it was found that the students' learning performance as well as their in-field inquiry ability was significantly improved, showing the effectiveness of the Mindtool-assisted ubiquitous learning approach and the success of the MAIL project. In this paper, the background, objectives, theoretical foundations, systems, research issues, applications, and findings of the MAIL project are presented. Finally, the scaling-up plan for applying these research-proven learning models to all levels of educational settings in Taiwan is also addressed.

An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and mutation. In this paper, the condensed encoding ensures that the chromosome is a feasible solution in all rows for Japanese nonograms. In the reconstruction process of a Japanese nonogram, the numbers in the left column are used as encoding conditions, and the numbers in the top row with the improved fitness function are employed to evaluate the reconstruction result. From the computational experiments, the proposed IGA approach is applied to solve Japanese nonograms effectively, with better results than using a CGA. The students of the Department of Computer Science, National Pingtung University of Education, Taiwan, have gained practical experience of applying evolutionary algorithms to solve Japanese nonograms using both the proposed IGA and a CGA. The students learn that the IGA can find the right solution of the puzzle effectively, but the CGA cannot. (Contains 4 figures.)

This paper presents a new model for simulating procedural knowledge in the problem solving process with our ontological system, InfoMap. The method divides procedural knowledge into two parts: process control and action performer. By adopting InfoMap, we hope to help teachers construct curricula (declarative knowledge) and teaching strategies by capturing students' problem-solving processes (procedural knowledge) dynamically. Using the concept of declarative and procedural knowledge in intelligent tutoring systems, we can accumulate and duplicate the knowledge of the curriculum manager and student. (Contains 12 figures.)

Today, the various abilities that nurses require to meet patients' healthcare needs adequately are all affected by AI-enabled systems. This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (AI image e-book guidance) group or a control (text paper guidance) group after meeting the admission conditions and agreeing to participate in the study. It was proven that providing AI image e-book guidance before surgery significantly changed the behavior of patients and promoted relief of urinary catheter discomfort through self-efficacy to reduce urinary catheter pain after surgery (p < 0.001). It was found that providing AI image e-book guidance can shorten the time for health education and provide patients with repeated medical education and familiarity with health guidance, which can help to address the important clinical service demand issue and the shortage of nursing staff. [ABSTRACT FROM AUTHOR]

Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state's electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November. [ABSTRACT FROM AUTHOR]

Featured Application: This work will be materialized into the Bioimage Diagnostic system in Kaohsiung Branch of Chang Gung Hospital, Taiwan. Image recognition through the use of deep learning (DL) techniques has recently become a hot topic in many fields. Especially for bioimage informatics, DL-based image recognition has been successfully used in several applications, such as cancer and fracture detection. However, few previous studies have focused on detecting scaphoid fractures, and the related effectiveness is also not significant. Aimed at this issue, in this paper, we present a two-stage method for scaphoid fracture recognition by conducting an effectiveness analysis of numerous state-of-the-art artificial neural networks. In the first stage, the scaphoid bone is extracted from the radiograph using object detection techniques. Based on the object extracted, several convolutional neural networks (CNNs), with or without transfer learning, are utilized to recognize the segmented object. Finally, the analytical details on a real data set are given, in terms of various evaluation metrics, including sensitivity, specificity, precision, F1-score, area under the receiver operating curve (AUC), kappa, and accuracy. The experimental results reveal that the CNNs with transfer learning are more effective than those without transfer learning. Moreover, DenseNet201 and ResNet101 are found to be more promising than the other methods, on average. According to the experimental results, DenseNet201 and ResNet101 can be recommended as considerable solutions for scaphoid fracture detection within a bioimage diagnostic system. [ABSTRACT FROM AUTHOR]

This paper presents a teaching tool for schoolchildren to learn artificial intelligence (AI) technology through which a variety of banknotes can be recognized. This was done by first using a pretrained YOLOv3 object detection model. Secondly, transfer learning was conducted on the pretrained model using 11 collected banknotes, including US$, Euro, Japanese Yen, and NT$. The banknote detection model was experimentally validated to give an average precision (AP) of up to 99.09% if the threshold Intersection over Union (IoU) is not less than 0.8. Once a banknote was successfully recognized, the face value and the country name thereon were displayed, and schoolchildren can access suggested websites, i.e., Wikipedia, Google Maps, and the Bank of Taiwan, to learn more about the exchange rate between currencies and the history and location of the country that issued the banknote. Consequently, schoolchildren can have fun using this tool and acquire a more a global outlook. Moreover, they may be motivated to become AI professionals in the future. [ABSTRACT FROM AUTHOR]

Due to strong mobility, low costs, and easy parking, the scooter has become an important mode of transportation in Taiwan. The number of traffic accidents is also increasing because of drivers' bad driving habits. For road traffic injury prevention, the government has approved the road safety policy to promote awareness of road safety. Thus, we propose an AI-based driving assistance system (AI-DAS) for scooter riders to prevent traffic accidents. In the AI-DAS, Raspberry Pi controls the camera and sensors for the scooter. The AI-DAS detects pedestrians and monitors lanes not only to warn riders and pedestrians by sound and light but also to drastically reduce accidents and traffic jams. The AI-DAS is implemented using the python programming language and is tested on the Raspberry Pi hardware platform. [ABSTRACT FROM AUTHOR]

Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention. Purpose of the article: This study proposes artificial intelligence solutions to address the increased importance of the personal bankruptcy phenomenon and the growing need for reliable forecasting models. The objective of this paper is to develop six models for forecasting personal bankruptcies in Poland and Taiwan with the use of three soft-computing techniques. Methods: Six models were developed to forecast the risk of insolvency: three for Polish households and three for Taiwanese consumers, using fuzzy sets, genetic algorithms, and artificial neural networks. This research relied on four samples. Two were learning samples (one for each country), and two were testing samples, also one for each country separately. Both testing samples contain 500 bankrupt and 500 nonbankrupt households, while each learning sample consists of 100 insolvent and 100 solvent natural persons. Findings & value added: This study presents a solution for effective bankruptcy risk forecasting by implementing both highly effective and usable methods and proposes a new type of ratios that combine the evaluated consumers' financial and demographic characteristics. The usage of such ratios also improves the versatility of the presented models, as they are not denominated in monetary value or strictly in demographic units. This would be limited to use in only one country but can be widely used in other regions of the world. [ABSTRACT FROM AUTHOR]

In recent years, the development and application of artificial intelligence have both been topics of concern. In the medical field, an important direction of medical technology development is the extraction and use of applicable information from existing medical records to provide more accurate and helpful diagnosis suggestions. Therefore, this paper proposes using the development of diseases with easily discernible symptoms to predict the development of other medically related but distinct diseases that lack similar data. The aim of this study is to improve the ease of assessing the development of diseases in which symptoms are difficult to detect, and to improve the utilization of medical data. First, a time series model was used to capture the continuous manifestations of diseases with symptoms that could be easily found at different time intervals. Then, through transfer learning and attention mechanism, the general features captured were applied to the predictive model of the development of diseases with insufficient data and symptoms that are difficult to detect. Finally, we conducted a comprehensive experimental study based on a dataset collected from the National Health Insurance Research Database in Taiwan. The results demonstrate that the effectiveness of our transfer learning approach outperforms state-of-the-art deep learning prediction models for disease course prediction. [ABSTRACT FROM AUTHOR]

Background: As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties., Objective: The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times., Methods: This observational study on the emerging COVID-19 pandemic included 643 patients. An "AI trilogy" of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic., Results: The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001)., Conclusions: The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic., (©Ping-Yen Liu, Yi-Shan Tsai, Po-Lin Chen, Huey-Pin Tsai, Ling-Wei Hsu, Chi-Shiang Wang, Nan-Yao Lee, Mu-Shiang Huang, Yun-Chiao Wu, Wen-Chien Ko, Yi-Ching Yang, Jung-Hsien Chiang, Meng-Ru Shen. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.10.2020.)

Automated clinical decision support systems (CDSS) are knowledge-based systems that provide patient-specific information and data to clinicians at the proper time for enhancing the clinical workflow of hospital organizations. Nowadays, it is adopted by most of the health care professionals for clinical decision-making that helps to reduce the adverse clinical care events occurring during the treatment. In this article, we present a recent literature review on the adoption of computer-based CDSSs in the area of health care based on qualitative and quantitative techniques, published between 2007 and 2022. For this purpose, we searched Google Scholar and identified different adoption factors by using textual analysis from the included publications. We then ranked the different factors based on the total number of occurrences and represented them as a conceptual framework. A total of 14 different adoption factors were found from 13 studies, among them the usefulness of the system is the most prominent factor that influences the adoption of CDSS to a great extent. This literature review and the framework could be helpful to researchers and healthcare professionals working in the field of technology adoption, providing an overall idea of factors and techniques in this field of research. We have also mentioned the limitations and future research gaps of different studies, which will help the researchers to take an initiation towards these types of research. We also conducted a case study on adoption of fully automatic digital blood pressure monitor and identified that "usefulness" and "ease of use" could influence the adoption of fully automatic digital blood pressure monitor system. [ABSTRACT FROM AUTHOR]

In an era where artificial intelligence (AI) bridges crucial communication gaps, this study extends AI's utility to American and Taiwan Sign Language (ASL and TSL) communities through advanced models like the hierarchical vision transformer with shifted windows (Swin). This research evaluates Swin's adaptability across sign languages, aiming for a universal platform for the unvoiced. Utilizing deep learning and transformer technologies, it has developed prototypes for ASL-to-English translation, supported by an educational framework to facilitate learning and comprehension, with the intention to include more languages in the future. This study highlights the efficacy of the Swin model, along with other models such as the vision transformer with deformable attention (DAT), ResNet-50, and VGG-16, in ASL recognition. The Swin model's accuracy across various datasets underscore its potential. Additionally, this research explores the challenges of balancing accuracy with the need for real-time, portable language recognition capabilities and introduces the use of cutting-edge transformer models like Swin, DAT, and video Swin transformers for diverse datasets in sign language recognition. This study explores the integration of multimodality and large language models (LLMs) to promote global inclusivity. Future efforts will focus on enhancing these models and expanding their linguistic reach, with an emphasis on real-time translation applications and educational frameworks. These achievements not only advance the technology of sign language recognition but also provide more effective communication tools for the deaf and hard-of-hearing community. [ABSTRACT FROM AUTHOR]

Seismic early warning has been very important and has become feasible in Taiwan. Perhaps because of the lack of quick and reliable estimations of the induced structural response, however, the triggering criteria of almost all of the existing earthquake protection or early warning systems in the world are merely based on the collected or estimated data of the ground motion, without any information regarding the structural response. This paper presents a methodology of generating quick seismic response estimations of a prestressed concrete (PC) bridge using artificial neural networks (ANNs), which may be incorporated in a seismic early warning system for the bridge. In the methodology ANNs were applied to model the critical structural response of a PC bridge subjected to earthquake excitation of various magnitudes along various directions. The objective was to implement a well-trained network that is capable of providing a quick prediction for the critical response of the target bridge. The well-known multilayer perception (MLP) networks with back propagation algorithm were employed. A simple augmented form of MLP that can be quantitatively determined was proposed. These networks were trained and tested based on the analytical data obtained from the nonlinear dynamic finite fiber element analyses of the target PC bridge. The augmented MLPs were found to be much more efficient than the MLPs in modeling the critical bending moments of the piers and girder of the PC bridge. [ABSTRACT FROM AUTHOR]

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as “MCF” or “non-MCF”. This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest’s location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF. [ABSTRACT FROM AUTHOR]

Over the past century, conscientiousness has become seen as the preeminent trait for predicting performance. This consensus is due in part to these employees' ability to work with traditional 20th-century technology. Such pairings balance the systematic nature of conscientious employees with the technology's need for user input and direction to perform tasks--resulting in a complementary match. However, the 21st century has seen the incorporation of intelligent machines (e.g., artificial intelligence, robots, and algorithms) into employees' jobs. Unlike traditional technology, these new machines are equipped with the capability to make decisions autonomously. Thus, their nature overlaps with the orderliness subdimension of conscientious employees--resulting in a non-complementary mismatch. This calls into question whether the consensus about conscientious employees' effectiveness with 20th-century technology applies to 21st-century jobs. Integrating complementarity and role theory, we refine this consensus. Across three studies using distinct samples (an experience sampling study, a field experiment, and an online experiment from working adults in Malaysia, Taiwan, and the United States), each focused on a different type of intelligent machine, we show not only that using intelligent machines has benefits and consequences, but, importantly, that conscientious (i.e., orderly) employees are less likely to benefit from working with them. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future. [ABSTRACT FROM AUTHOR]

(1) Background: AI (artificial intelligence) chatbots have been widely applied. ChatGPT could enhance individual learning capabilities and clinical reasoning skills and facilitate students' understanding of complex concepts in healthcare education. There is currently less emphasis on its application in nursing education. The application of ChatGPT in nursing education needs to be verified. (2) Methods: A descriptive study was used to analyze the scores of ChatGPT on the registered nurse license exam (RNLE) in 2022~2023, and to explore the response and explanations of ChatGPT. The process of data measurement encompassed input sourcing, encoding methods, and statistical analysis. (3) Results: ChatGPT promptly responded within seconds. The average score of four exams was around 51.6 to 63.75 by ChatGPT, and it passed the RNLE in 2022 1st and 2023 2nd. However, ChatGPT may generate misleading or inaccurate explanations, or it could lead to hallucination; confusion or misunderstanding about complicated scenarios; and languages bias. (4) Conclusions: ChatGPT may have the potential to assist with nursing education because of its advantages. It is recommended to integrate ChatGPT into different nursing courses, to assess its limitations and effectiveness through a variety of tools and methods. [ABSTRACT FROM AUTHOR]

Frequent road inspections are key to maintaining road quality and avoiding casualties associated with poor road conditions. In Taiwan, open contractors conduct inspections of roads and ancillary facilities daily or weekly according to the requirements of the agency awarding the contracts. Unfortunately, the equipment used for inspections the inspection data lacks follow-up applications and numerical conversions, such as the Pavement Condition Index (PCI), to compile a large-scale database to facilitate the long-term conservation of roads. In this study, this paper developed back-end image recognition software using existing road inspection methods and existing equipment. This was aimed at enhancing inspection efficiency by enabling the automatic identification of road damage. Resulting observations can then be converted into PCI values in accordance with ASTM D6433-16 to be exported as a numeric value indicative of road quality. A vehicle-mounted traffic recorder and imaging device with Wi-Fi transmission capability are used as hardware, and the relationship between the captured images and the speed of the car is used to obtain an accurate indication of road conditions across the surface. The simple linear iterative clustering (SLIC) Superpixels algorithm is used to identify areas with pavement damage as patches, potholes, longitudinal cracking, and crocodile cracking. The results of the proposed fully-automated method conform strongly with those obtained using semi-automated pavement inspection software. Despite the restrictions imposed by the limited depth measurement of 2D images, our method achieved results close to those obtained using manual inspection. Future developments will include the application of artificial intelligence to enhance the effectiveness of this software. [ABSTRACT FROM AUTHOR]

In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

Developing on-site earthquake early warning systems has been a challenging problem because of time limitations and the amount of information that can be collected before the warning needs to be issued. A potential solution that could prevent severe disasters is to predict the potential strong motion using the initial P-wave signal and provide warnings before serious ground shaking starts. In practice, the accuracy of prediction is the most critical issue for earthquake early warning systems. Traditional methods use certain criteria, selected through intuition or experience, to make the prediction. However, the criteria thresholds are difficult to select and may significantly affect the prediction accuracy. This paper investigates methods based on artificial intelligence for predicting the greatest earthquake ground motion early, when the P-wave arrives at seismograph stations. A neural network model is built to make the predictions using a small window of the initial P-wave acceleration signal. The model is trained by seismic waves collected from 1991 to 2019 in Taiwan and is evaluated by events in 2020 and 2021. From these evaluations, the proposed scheme significantly outperforms the threshold-based method in terms of its accuracy and average leading time. [ABSTRACT FROM AUTHOR]

In recent years, initiatives and the resulting application of precision education have been applied with increasing frequency in Taiwan; the accompanying discourse has focused on identifying potential applications for artificial intelligence and how to use learning analytics to improve teaching quality and learning outcomes. This study used the established dropout risk prediction model to improve student learning effectiveness. The model was based on the academic portfolios of past students and built with statistical learning and deep learning methods. This study used this model to predict the dropout risk of 2205 freshmen enrolled in the fall semester of 2018 (graduated in June 2022) in the field of sustainable education. A total of 176 students with a dropout risk of more than 20% were considered high-risk students. After tracking and the appropriate guidance, the dropout risk of 91 students fell from > 20% to < 20%. To discuss the results from the perspective of gender and financial disadvantages, the improvement rate of the dropout risk for male students was 10.2% better than that of female students at 2.9%. The improvement rate in dropout risk for students with disadvantageous financial situations was as high as 12.0%, surpassing the 5.9% rate among general students. Overall, the dropout rate in the second year of the 2018 freshman cohort was lower than that of the 2016 and 2017 freshman cohorts. A predictive model established by statistical learning and deep learning methods was used as a tool to promote precision education, accurately and efficiently identifying students who are having difficulty learning, as well as leading to a better understanding of AI (artificial intelligence) in smart learning for sustainable education. [ABSTRACT FROM AUTHOR]

Background: Aging leads to changes in the body system, such as sarcopenia. This can result in several health issues, particularly physical and mobility dysfunction. Asian people typically have little awareness of sarcopenia. Thus, this study incorporated nursing instruction into the mobile application design to allow users to easily learn about sarcopenia. Objective: This study evaluated a model for predicting high-risk populations for sarcopenia in home settings. We further developed a sarcopenia nursing guidance mobile application and assessed the effectiveness of this application in influencing sarcopenia-related knowledge and self-care awareness among participants. Methods: Using a one-group pretest–posttest design, data were collected from 120 participants at a teaching hospital in northern Taiwan. This study used an artificial intelligence algorithm to evaluate a model for predicting high-risk populations for sarcopenia. We developed and assessed the sarcopenia nursing guidance mobile application using a questionnaire based on the Mobile Application Rating Scale. Results: The application developed in this study enhanced participants' sarcopenia-related knowledge and awareness regarding self-care. After the three-month intervention, the knowledge and awareness was effectively increase, total score was from 4.15 ± 2.35 to 6.65 ± 0.85 and were significant for all questionnaire items (p values < 0.05). On average, 96.1% of the participants were satisfied with the mobile app. The artificial intelligence algorithm positively evaluated the home-use model for predicting high-risk sarcopenia groups. Conclusions: The mobile application of the sarcopenia nursing guidance for public use in home settings may help alleviate sarcopenia symptoms and reduce complications by enhancing individuals' self-care awareness and ability. Trial registration: NCT05363033, registered on 02/05/2022. [ABSTRACT FROM AUTHOR]

Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated with the previous state, a model with time series is required. Therefore, this study used recurrent neural networks (RNNs) with sequential characteristics. Commonly used RNNs such as long short-term memory model and gated recurrent unit (GRU) model have a memory function that appropriately retains previous results for use in processing current results. To construct a suitable RNN model, this study used Taguchi method to optimize hyperparameters (including hidden layer neuron count, iteration count, batch size, learning rate, and dropout ratio). Additionally, optimization performance was also compared between 5-layer and 7-layer network architectures. The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study. The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%, root mean square error of 0.0638, and R-value of 0.9984. Therefore, thewater qualitymanagement system developed in this study can quickly provide practitioners with highly accurate data, which is essential for a timely response to water quality issues. This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company. Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

Corresponding to the concept of ‘Think globally, act locally and plan regionally’ of sustainable development, this paper discusses the approach of planning a sustainable community in terms of systems thinking. We apply a systems tool, the sensitivity model (SM), to build a model of the development of the community of Ping-Ding, located adjacent to the Yang-Ming-Shan National Park, Taiwan. The major issue in the development of Ping-Ding is the conflict between environmental conservation and the development of a local tourism industry. With the involvement of local residents, planners, and interest groups, a system model of 26 variables was defined to identify characteristics of Ping-Ding through pattern recognition. Two scenarios concerning the sustainable development of Ping-Ding are simulated with interlinked feedbacks from variables. The results of the analysis indicate that the development of Ping-Ding would be better served by the planning of agriculture and the tourism industry. The advantages and shortfalls of applying SM in the current planning environment of Taiwan are also discussed to conclude this paper. [Copyright &y& Elsevier]

For the second year in a row, the theme is "reef coral biotechnology", specifically the interface between basic science and conservation. It has never been more important to attempt to leverage what we know about these beautiful, albeit highly imperiled and fragile, ecosystems towards conserving them. Our invited speakers' areas of expertise span all levels of biological organization: from molecules within coral cells, to coral tissues, to entire coral colonies, and then up to reef-scale processes. Our goal is to promote communication not only among local Taiwanese marine biologists, but also those within Southeast Asia and farther afield; we especially encourage participation from early-career researchers, including Master's students, PhD candidates, and post-doctoral researchers. It is our hope that the presentations (and the discussions that follow) will encourage collaboration. As importantly, we envision that the tools and approaches shared amongst us can be tapped into to expedite our collective efforts to better understand, manage, and conserve coral reefs. [ABSTRACT FROM AUTHOR]

As a result of the increasing use of artificial intelligence technology in transportation, numerous real-time crash prediction techniques have been developed. In the context of highway traffic management, machine learning models and classifiers are used to analyze electronic toll collection (ETC) and vehicle detector (VD) data to predict crash occurrences. However, traffic accidents are influenced by multiple factors, such as traffic speed differences, traffic density, and weather conditions, and direct associations may not exist between sensor data and crash incidents. Therefore, data integration and association methods must be used to examine ETC and VD data through traffic flow theories, to extract key data from datasets and to facilitate model training. In this study, a data association method and framework combined with deep learning was proposed to construct a crash prediction and warning model for national highways in Taiwan. The results revealed a model accuracy of 94%, indicating that the model had a low error rate and was suitable for the prediction of traffic accidents. Overall, this study provides referential data for the Freeway Bureau of Taiwan to conduct comprehensive assessments and develop strategies for crash prevention. [ABSTRACT FROM AUTHOR]

Healthcare Artificial Intelligence (AI) has the greatest opportunity for development. Since healthcare and technology are two of Taiwan's most competitive industries, the development of healthcare AI is an excellent chance for Taiwan to improve its health-related services. From the perspective of economic development, promoting healthcare AI must be a top priority. However, despite having many breakthroughs in research and pilot projects, healthcare AI is still considered rare and is broadly used in the healthcare setting. Based on a medical center in Taiwan that has introduced a variety of healthcare AI into practice, this study discussed and analyzed the issues and concerns in the development and scaling of medical AIs from the perspective of various stakeholders in the healthcare setting, including the government, healthcare institutions, users (healthcare workers), and AI providers. The present study also identified critical influential factors for the deployment and scaling of healthcare AI. It is hoped that this paper can serve as an important reference for the advancement of healthcare AI not only in Taiwan but also in other countries. [ABSTRACT FROM AUTHOR]

In recent years, the technology of artificial intelligence (AI) and robots is rapidly spreading to countries around the world. More and more scholars and industry experts have proposed AI deep learning models and methods to solve human life problems and improve work efficiency. Modern people's lives are very busy, which led us to investigate whether the demand for Bento buffet cafeterias has gradually increased in Taiwan. However, when eating at a buffet in a cafeteria, people often encounter two problems. The first problem is that customers need to queue up to check out after they have selected and filled their dishes from the buffet. However, it always takes too much time waiting, especially at lunch or dinner time. The second problem is sometimes customers question the charges calculated by cafeteria staff, claiming they are too expensive at the checkout counter. Therefore, it is necessary to develop an AI-enabled checkout system. The AI-enabled self-checkout system will help the Bento buffet cafeterias reduce long lineups without the need to add additional workers. In this paper, we used computer vision and deep-learning technology to design and implement an AI-enabled checkout system for Bento buffet cafeterias. The prototype contains an angle steel shelf, a Kinect camera, a light source, and a desktop computer. Six baseline convolutional neural networks were applied for comparison on food recognition. In our experiments, there were 22 different food categories in a Bento buffet cafeteria employed. Experimental results show that the inception_v4 model can achieve the highest average validation accuracy of 99.11% on food recognition, but it requires the most training and recognition time. AlexNet model achieves a 94.5% accuracy and requires the least training time and recognition time. We propose a hierarchical approach with two stages to achieve good performance in both the recognition accuracy rate and the required training and recognition time. The approach is designed to perform the first step of identification and the second step of recognizing similar food images, respectively. Experimental results show that the proposed approach can achieve a 96.3% accuracy rate on our test dataset and required very little recognition time for input images. In addition, food volumes could be estimated using the depth images captured by the Kinect camera, and a framework of visual checkout system was successfully built. [ABSTRACT FROM AUTHOR]

C-V2X (Cellular Vehicle-to-Everything) is a state-of-the-art wireless technology used in autonomous driving and intelligent transportation systems (ITS). This technology has extended the coverage and blind-spot detection of autonomous driving vehicles. Economically, C-V2X is much more cost-effective than the traditional sensors that are commonly used by autonomous driving vehicles. This cost-benefit makes it more practical in a large scale deployment. PC5-based C-V2X uses an RF (Radio Frequency) sidelink direct communication for low latency mission-critical vehicle sensor connectivity. Over the C-V2X radio communications, the autonomous driving vehicle's sensor ability can now be largely enhanced to the distances as far as the network covers. In 2020, 5G is commercialized worldwide, and Taiwan is at the forefront. Operators and governments are keen to see its implications in people's daily life brought by its low latency, high reliability, and high throughput. Autonomous driving class L3 (Conditional Automation) or L4 (Highly Automation) are good examples of 5G's advanced applications. In these applications, the mobile networks with URLLC (Ultra-Reliable Low-Latency Communication) are perfectly demonstrated. Therefore, C-V2X evolution and 5G NR (New Radio) deployment coincide and form a new ecosystem. This ecosystem will change how people will drive and how transportation will be managed in the future. In this paper, the following topics are covered. Firstly, the benefits of C-V2X communication technology. Secondly, the standards of C-V2X and C-V2X applications for automotive road safety system which includes V2P/V2I/V2V/V2N, and artificial intelligence in VRU (Vulnerable Road User) detection, object recognition and movement prediction for collision warning and prevention. Thirdly, PC5-based C-V2X deployment status in global, especially in Taiwan. Lastly, current challenges and conclusions of C-V2X development. [ABSTRACT FROM AUTHOR]

Taiwan's long-term development in digital government has set a strong foundation for its transformation to one that is empowered by artificial intelligence (AI). This article introduces five applications of AI that the Taiwan government uses for different tasks and how government agencies implement them and overcome challenges. A demand-oriented AI enables civil servants to upgrade skills, and improve government's functions, services and value added. This article provides practical insights on how human–AI collaboration can enhance government capabilities and create a positive feedback loop to the bureaucratic system. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop the 'Teachers' Efficacy Perceptions of the AIbased Teaching Applications (TEP-AITA) scale' and to describe the analysis of the background variables and the differences between six factors in the application of an Artificial Intelligence (AI) teaching efficacy perceptions questionnaire. In total, 714 vocational senior high school teachers participated in random and cluster sampling in Taiwan. The results showed that the internal consistency reliability of the measure was .988 and that the reliability of the six subscales was .975, .971, .981, .976, .972, and .967. The results found that TEP-AITA scale included 'Resource support', 'Innovative teaching', 'Cross-disciplinary', 'Professional learning', 'Learner demand', and 'Self-reflection'. 'Cross-disciplinary' and 'Learner demand' were highly rated, while 'Resource support' was low. This study analyzed the differences in the perceived efficacy for AI-based teaching applications among teachers according to their background variables, such as their gender, current position, school attributes, teachers' seniority, and application AI teaching experience. The research results on teacher background factors and the six factors of the TEP-AITA scale could be provided to education units for the active promotion of AI information technology teaching and training. [ABSTRACT FROM AUTHOR]

Background: Taiwan has a high national caesarean rate coupled with a low vaginal birth after caesarean (VBAC) rate. This study aims to develop and evaluate a web-based decision-aid with communication support tools, to increase shared decision making (SDM) about birth after caesarean. Methods: A quantitative approach will be adopted using a randomized pre-test and post-test experimental design in a medical centre in northern Taiwan. The web-based decision aid consists of five sections. Section 1 provides a two-part video to introduce SDM and how to participate in SDM. Section 2 presents an overview of functions and features of the birth decision-aid. Section 3 presents relevant VBAC information, including definitions, benefits and risks, and an artificial intelligence (AI) calculator for rate and likelihood of VBAC success. Section 4 presents the information regarding elective repeat caesarean delivery (ERCD), involving definitions, benefits, and risks. Section 5 comprises four steps of decision making to meet women's values and preferences. Pregnant women who have had one previous caesarean and are eligible for VBAC, will be recruited at 14–16 weeks. Participants will complete a baseline survey prior to random allocation to either the control group (usual care) or intervention group (usual care plus an AI-decision aid). A follow up survey at 35–38 weeks will measure change in decisional conflict, knowledge, birth mode preference, and decision-aid acceptability. Actual birth outcomes and satisfaction will be assessed one month after birth. Discussion: The innovative web-based decision-aid with support tools will help to promote pregnant women's decision-making engagement and communication with their providers and improve opportunities for supportive communication about VBAC SDM in Taiwan. Linking web-based AI data analysis into the medical record will also be assessed for feasibility during implementation in clinical practice. Trial registration: ClinicalTrials.gov identifier (NCT05091944), Registered on October 24, 2021. [ABSTRACT FROM AUTHOR]

In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca2+), uric acid and total dissolved solids. The measurements collected by the system from patients and healthy individuals grouped by age and gender will be stored in a cloud database. These will be used in the training phase of an artificial intelligence (AI) machine learning process utilizing the logistics regression model. The trained model provides a binary risk assessment, indicating if the end user is either a stone-former or not. For system validation, standard chemical solutions were used. Preliminary results indicated a sufficient measurement range, falling within the physiological range, and resolution for pH (2.0–10.0, +/−0.1), Ca2+(0.1–3.0 mmol/l, +/−0.05), uric acid (20–500 ppm, +/−1) and conductivity (1.0–40.0 mS/cm, +/−0.1), exhibiting high correlation with standard instruments. We intend to deploy this system in few hospitals in Taiwan to collect the data of patients' urine, with analysis aided by urologist assessments for the risk of urolithiasis. The modularized design allows future modification and expansion to accommodate other sensing analytes. [ABSTRACT FROM AUTHOR]

Immediate Response Syndrome (IRS) is driven by the obsession that they must reply immediately once they receive an e-mail, and this is a state where they can’t release their mobile phone even for a moment. In this paper, we aimed to compare and examine the relationships between consciousness of AI Robots and IRS among Japanese and Taiwanese college students by questionnaire survey. As a result of ANOVA, it was found that Taiwanese students often use the Internet from Japanese students, and their IRS tendency is high. For AI Robots, Japanese college students tended to be higher than in Taiwan concerning [Expectation] [Anxiety] [Interest] [Concern] [Excite] [Useful] items. Regarding expected robots, for Japanese college students from Taiwan about robots active during disasters, rescuing activity robots such as rescue workers, robots serving dangerous work, cooking robots, security robots, and transportation industry robots. It is expected that the Taiwanese students are expecting from Taiwanese students about cleaning robots, answering machine robots, robots that oppose children, agricultural working robots, customer service robots, and family robots. 　　 [ABSTRACT FROM AUTHOR]

Purpose: Early confirmation or ruling out biliary atresia (BA) is essential for infants with delayed onset of jaundice. In the current practice, percutaneous liver biopsy and intraoperative cholangiography (IOC) remain the golden standards for diagnosis. In Taiwan, the diagnostic methods are invasive and can only be performed in selective medical centers. However, referrals from primary physicians and local pediatricians are often delayed because of lacking clinical suspicions. Ultrasounds (US) are common screening tools in local hospitals and clinics, but the pediatric hepatobiliary US particularly requires well-trained imaging personnel. The meaningful comprehension of US is highly dependent on individual experience. For screening BA through human observation on US images, the reported sensitivity and specificity were achieved by pediatric radiologists, pediatric hepatobiliary experts, or pediatric surgeons. Therefore, this research developed a tool based on deep learning models for screening BA to assist pediatric US image reading by general physicians and pediatricians. Methods: De-identified hepatobiliary US images of 180 patients from Taichung Veterans General Hospital were retrospectively collected under the approval of the Institutional Review Board. Herein, the top network models of ImageNet Large Scale Visual Recognition Competition and other network models commonly used for US image recognition were included for further study to classify US images as BA or non-BA. The performance of different network models was expressed by the confusion matrix and receiver operating characteristic curve. There were two methods proposed to solve disagreement by US image classification of a single patient. The first and second methods were the positive-dominance law and threshold law. During the study, the US images of three successive patients suspected to have BA were classified by the trained models. Results: Among all included patients contributing US images, 41 patients were diagnosed with BA by surgical intervention and 139 patients were either healthy controls or had non-BA diagnoses. In this study, a total of 1,976 original US images were enrolled. Among them, 417 and 1,559 raw images were from patients with BA and without BA, respectively. Meanwhile, ShuffleNet achieved the highest accuracy of 90.56% using the same training parameters as compared with other network models. The sensitivity and specificity were 67.83% and 96.76%, respectively. In addition, the undesired false-negative prediction was prevented by applying positive-dominance law to interpret different images of a single patient with an acceptable false-positive rate, which was 13.64%. For the three consecutive patients with delayed obstructive jaundice with IOC confirmed diagnoses, ShuffleNet achieved accurate diagnoses in two patients. Conclusion: The current study provides a screening tool for identifying possible BA by hepatobiliary US images. The method was not designed to replace liver biopsy or IOC, but to decrease human error for interpretations of US. By applying the positive-dominance law to ShuffleNet, the false-negative rate and the specificities were 0 and 86.36%, respectively. The trained deep learning models could aid physicians other than pediatric surgeons, pediatric gastroenterologists, or pediatric radiologists, to prevent misreading pediatric hepatobiliary US images. The current artificial intelligence (AI) tool is helpful for screening BA in the real world. [ABSTRACT FROM AUTHOR]

With the progress of financial technology (FinTech), real-time information from FinTech is huge and complicated. For various fields of research, identifying intrinsic features of complex data is important, not limited to financial big data. Reviewing previous studies, there are no suitable methods to deal with complex financial data. General methods are traditionally developed from statistics and machine learning. They are usually in some shallow model forms, which cannot fully represent complex, compositional, and hierarchical financial data features. Due to above drawbacks, this study tries to address the problem by advanced deep learning (DL) methods. In DL more layers will increase the power for abstract data representation. Recently, DL has achieved state-of-the-art performance in a wide range of tasks including speech, image, and vision. DL is effective in learning increasingly more abstract representations in a layer-wise manner. That just meets the characteristic of financial data. This study applies newly developed DL networks, the deep canonically correlation analysis and deep canonically correlated autoencoders to perform FinTech data mining. To test the proposed model, this study employed financial statement data regarding many listed high technology companies in Taiwan stock markets. The computation of deep learning is leveraged by multiple graphics processing unit. Our systems and traditional methods are compared by the same data. Empirical results showed that our systems outperform traditional techniques from statistics and machine learning. [ABSTRACT FROM AUTHOR]

Globally, 20% to 40% of medical resources are wasted, which could be avoided through professional audit of health insurance claims. The professional audit can pinpoint excessive use of unnecessary medicines and medical examinations. Taiwan's National Health Insurance Bureau (TNHIB) deducts the weight that medical resources carry if regarded as unnecessary or abused when examining health insurance claims. The ratio of the deducted weight to the total weight claimed by a hospital is defined as the health insurance claim deduction rate (HICDR). A high HICDR increases the operating expenses of the hospital. In addition, it takes the hospital many resources to prepare and file appeals for the deduction. This study aims to: (1) minimize the weight deducted by the TNHIB for a hospital; and (2) facilitate efficient appeals to claim denials. It is expected that HICDR will be reduced through big data analytics. In this study, evidence-based medicine (EBM) is involved to clarify the debate, dilemmas, conflicts of interests in examining health insurance claims. A natural language method—latent Dirichlet allocation (LDA), was used to analyze patients' medical records. The topics derived from the LDA are used as factors in the logistic regression model to estimate the probability of each claim to be deducted. The experimental results on various medical departments show that the proposed predictive model can produce accurate results, and lead to more than 41.7% reduction to the deduction of the health insurance claims. It is equivalent to more than a 750 thousand NT dollars saving per year. The efficiency of application is validated compared to the manual process that is time-consuming and labor intensive. Moreover, it is expected that this study will supplement the insufficiency of traditional methods and propose a new and effective solution to reduce the deduction rate. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as . After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models). [Copyright &y& Elsevier]

Abstract: Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always determines the parameter settings of the NNs rationally through a trial-and-error methodology. This paper integrates design of experiment (DOE), Taguchi method, and back-propagation NN (BPNN) to construct a robust engine to further optimize the prediction accuracy under a robust DOE-based predictor. Adopting data from Taiwan Stock Exchange (TWSE), the technical analytical indexes and β value of the listed stocks of TWSE were computed. The research results indicated that the proposed approach can effectively improve the forecasting rate of stock price variations. [Copyright &y& Elsevier]

Abstract: This paper presents an efficient technique for analyzing ARGO ocean data comprising time series of salinity/temperature measurements where informative salinity/temperature patterns are extracted. Most traditional mining techniques focus on finding associations among items within one transaction and are therefore unable to discover rich contextual patterns related to location and time. In order to show the associated salinity/temperature variations among different locations and time intervals, for example, “if the salinity rose from 0.15psu to 0.25psu in the area that is in the east–northeast direction and is near Taiwan, then the temperature will rise from 0°C to 1.2°C in the area that is in the east–northeast direction and is far away from Taiwan next month”, a quantitative inter-transaction association rules mining algorithm is proposed. The FITI and the PrefixSpan algorithms are adopted to maximize the mining efficiency. The strategy is applied to ocean salinity measurements obtained from the waters surrounding Taiwan. These experimental evaluations show that the proposed algorithm achieves better performance than other inter-transaction association rule mining algorithms. [Copyright &y& Elsevier]

Abstract: This paper proposes a hybrid methodology that exploits the unique strength of the seasonal autoregressive integrated moving average (SARIMA) model and the support vector machines (SVM) model in forecasting seasonal time series. The seasonal time series data of Taiwan’s machinery industry production values were used to examine the forecasting accuracy of the proposed hybrid model. The forecasting performance was compared among three models, i.e., the hybrid model, SARIMA models and the SVM models, respectively. Among these methods, the normalized mean square error (NMSE) and the mean absolute percentage error (MAPE) of the hybrid model were the lowest. The hybrid model was also able to forecast certain significant turning points of the test time series. [Copyright &y& Elsevier]

Abstract: This paper employs artificial neural network and decision tree to derive knowledge about the job attitudes of Generation Xers. The sample frame consisted of 1000 large Manufacturing Industries and 500 large Service Industries, randomly selected from the Common Wealth Magazine 1000 index of Taiwan Manufacturing Industries and Service Firms. Then, we exploited the ART2 neural model to take the collected data as inputs and form performance classes according to their similarities. Finally, the decision tree was employed to determine definitions for each class, resulting in 52 rules associated with certainty factors. The results could be used to develop an intelligent decision support system for the recruitment and management of Generation Xers. [Copyright &y& Elsevier]

Abstract: Using a sample of 59 Taiwanese firms in distress, we investigate investor gains or losses around the filing date and the confirmation date of the reorganization plan and propose ex ante trading strategies that can generate potentially substantial returns from these stocks. We further experiment with a novel approach to sensitivity analysis of neural network inputs for refining and rating the most important input variables and then construct artificial neural network (ANN) models to classify and predict the post-reorganization filing resolutions in a three- (Model 1) and two-group (Models 2 and 3) resolution setting. We are unaware of any other paper that has used artificial intelligence techniques in analyzing post-bankruptcy resolutions. The results show that our five-variable models produce a consistent estimate between the training and testing sets; that is, both three- and two-group models are of a high degree of accuracy and stability, demonstrating the robustness of the ANN models. [Copyright &y& Elsevier]