Search

In this paper, to handle the problem of the quick evolution of cyber-security attacks, we developed the iMonsters board game and proposed the attack and defense knowledge self-evolving algorithm. Three versions of the iMonsters were launched in 2013, 2017, and 2019, respectively. Accordingly, the cyber-security ontology can be refined by the ontology fusion-or-splitting procedure for the newly collected cyber-security incidents, as well as the roles and rules of the iMonsters can be refined by the gaming portfolio mining procedure for the collected portfolio. Furthermore, we conducted game-based learning (GBL), a quasi-experiment of pre/post-testing, and concept map testing using the iMonsters board game in a children's summer camp. The experimental results indicate that the students can acquire up-to-date cyber-security knowledge with the iMonsters better than students who learn in a traditional classroom setting, and the students' satisfaction with acquiring cyber-security knowledge with the instructional design of the iMonsters is better compared with learning in a traditional classroom setting. Satisfaction with the new version has continuously increased. Besides, the results of the in-depth interviews show that the new version was easier to learn. Thus, we may conclude that using the self-evolving iMonsters can improve learning effectiveness and participation in GBL.

The development of visual tools for the timely identification of spatio-temporal clusters will assist in implementing control measures to prevent further damage. From January 2015 to June 2020, a total number of 1463 avian influenza outbreak farms were detected in Taiwan and further confirmed to be affected by highly pathogenic avian influenza subtype H5Nx. In this study, we adopted two common concepts of spatio-temporal clustering methods, the Knox test and scan statistics, with visual tools to explore the dynamic changes of clustering patterns. Since most (68.6%) of the outbreak farms were detected in 2015, only the data from 2015 was used in this study. The first two-stage algorithm performs the Knox test, which established a threshold of 7 days and identified 11 major clusters in the six counties of southwestern Taiwan, followed by the standard deviational ellipse (SDE) method implemented on each cluster to reveal the transmission direction. The second algorithm applies scan likelihood ratio statistics followed by AGC index to visualize the dynamic changes of the local aggregation pattern of disease clusters at the regional level. Compared to the one-stage aggregation approach, Knox-based and AGC mapping were more sensitive in small-scale spatio-temporal clustering., (© 2021. The Author(s).)

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used machine learning algorithms (k-nearest neighbors, random forest, gradient-boosted decision trees, eXtreme gradient boosting, and CatBoost) to investigate the association between cognitive impairment and air pollution. Data from the Taiwan Biobank and 75 air-pollution-monitoring stations in Taiwan were analyzed to determine individual levels of exposure to air pollutants. The pollutants examined were particulate matter with a diameter of ≤ 2.5 μm (PM 2.5 ), nitrogen dioxide, nitric oxide, carbon monoxide, and ozone. The results revealed that the most strongly correlated with cognitive impairment were ozone, PM 2.5 , and carbon monoxide levels with adjustment of educational level, age, and household income. The model based on these factors achieved accuracy as high as 0.97 for detecting cognitive impairment, indicating a positive association between air pollutions and cognitive impairment., 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 The Authors. Published by Elsevier Inc. All rights reserved.)

Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive. [ABSTRACT FROM AUTHOR]

The ship tracking and mapping capabilities that geospatial technology provides create an opportunity to observe fishing vessels as they move through established maritime boundaries. This paper connects data availability to ground-truthing research and explores the limits of vessel movement mapping in representing worker abuse at sea through three related themes. First, a conceptual background links the advancements in maritime remote sensing to critical GIS scholarship and provides a background on worker abuse aboard Taiwanese fishing vessels. Second, the paper examines the potential of machine learning algorithms to represent worker abuse at sea, arguing that more extensive ground-truthing research with workers could help address variations in the data and limited data sets. Third, I use remote sensing data to identify and unpack Taiwanese fishing across the three EEZs with the most concentrated Taiwanese fishing activity: starting with Taiwan, followed by the Falkland Islands, and Seychelles. I argue that fishing activity and the digital representation of vessel movements are governed by terrestrial geopolitics and subject to manipulation by ship captains. Finally, the conclusion offers recommendations for how future research can capitalize on the capabilities of AIS, particularly with respect to addressing problems of working conditions and abuse at sea. [ABSTRACT FROM AUTHOR]

In this paper, a new clustering algorithm based on gravitational search algorithm (CGSA) for solving unit commitment (UC) problem is proposed. The UC problem is a combinatorial optimization problem that minimizing quadratic objective function under system and unit constraints. The GSA is a recent introduced algorithm to solve optimization problem that inspired by Newtonian law of gravity. A novel version of GSA, named Clustered-GSA (CGSA) is a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. The proposed algorithm is tested for standard 100 unit system, IEEE 118 bus system and practical Taiwan 38 bus power system. The effectiveness of proposed algorithm results are compared to those reported in the literature. [ABSTRACT FROM AUTHOR]

In the transportation industry, crew management is typically decomposed into two phases: crew scheduling and crew rostering. Due to the complexity of scheduling and rostering, bus transportation is not an exception and many relevant studies do not consider both procedures simultaneously. However, such a decomposition can yield inferior schedules/rosters. To address this issue, this paper proposes an integrated scheduling and rostering model for bus drivers and devises a branch-and-price-and-cut (BPC) algorithm to solve the complex problem. The proposed solution framework is empirically applied to real-world instances with various problem sizes whose data is collected from H Bus Company located in southern Taiwan. To validate the effectiveness and evaluate the efficiency of the proposed solution framework, this paper compares the solution obtained from the BPC algorithm with that of a benchmark optimization package. The results show that the proposed BPC algorithm can solve problems with large real-world instances within a reasonable computational time. Moreover, in the numerical experiments, this paper finds that the scheduling and rostering results of the bus drivers are more sensitive to the rostering constraints. Also, the proposed integrated framework can yield a better solution than the solution from a conventional two-phase approach, which demonstrates the advantage of the integration in this paper. The proposed method provided can be employed to deal with the challenges in driver planning for bus companies. [ABSTRACT FROM AUTHOR]

In the paper, we proposed a coarse-to-fine scheme to automatically detect the regions of interest (ROIs) for digitized cadastral images in Taiwan. To consider some issues such as the skew effect, image quality, and noise in digitized cadastral images, the proposed scheme is composed of four parts: pre-processing, skew correction, noise reduction, and ROI localization. In the pre-processing, each cadastral image is normalized and the prior knowledge is used to find the candidate region of the ROI. To reduce the impact of noise and poor contrast on line detection, an adaptive thresholding is adopted. To decrease the skew effect, the detected horizontal and vertical lines are analyzed to estimate the skew angle. After skew correction, an adaptive noise reduction algorithm is devised to reduce the effect of marginal, artificial, and random noise. The coordinates of the candidate region in the de-skew image with high resolution can be found and then the ROI boundary can be located correctly in the fine detection. Experimental results demonstrate that the proposed scheme can effectively and correctly localize ROIs in digitized cadastral images. [ABSTRACT FROM AUTHOR]

In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives. [ABSTRACT FROM AUTHOR]

Purpose: The uncertainty in supply and the short shelf life of blood products have led to a substantial outdating of the collected donor blood. On the other hand, hospitals and blood centers experience severe blood shortage due to the very limited donor population. Therefore, the necessity to forecast the blood supply to minimize outdating as well as shortage is obvious. This study aims to efficiently forecast the supply of blood components at blood centers., Methods: Two different types of forecasting techniques, time series and machine learning algorithms, are developed and the best performing method for the given case study is determined. Under the time series, we consider the Autoregressive (AUTOREG), Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, Seasonal Exponential Smoothing Method (ESM), and Holt-Winters models. Artificial neural network (ANN) and multiple regression are considered under the machine learning algorithms., Results: We leverage five years worth of historical blood supply data from the Taiwan Blood Services Foundation (TBSF) to conduct our study. On comparing the different techniques, we found that time series forecasting methods yield better results than machine learning algorithms. More specifically, the least value of the error measures is observed in seasonal ESM and ARIMA models., Conclusions: The models developed can act as a decision support system to administrators and pathologists at blood banks, blood donation centers, and hospitals to determine their inventory policy based on the estimated future blood supply. The forecasting models developed in this study can help healthcare managers to manage blood inventory control more efficiently, thus reducing blood shortage and blood wastage., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2019 Han Shih and Suchithra Rajendran.)

This paper presents a hardware authentication BLAKE algorithm based on physical unclonable functions (PUFs) in Taiwan Semiconductor Manufacturing Company low-power 65 nm CMOS. To support hardware authentication feature, PUFs have been organised in BLAKE algorithm as the salt value. The trials table method is used to improve the robust of PUFs, resulting in approximately 100% stability against supply voltage variations form 0.7 V to 1.6 V. By discussing the G-function of BLAKE algorithm, the hardware implementation is considered for acceleration, resulting in significant performance improvements. The die occupies 2.62 mm2and operates maximum frequency 1.0 GHz at 1.6 V. Measured results show that PUFs have great random characteristic and the authentication chip dissipates an average power of 91 mW under typical condition at 1.2 V and 780 MHz. In comparison with other works, the PUFs-based BLAKE algorithm has hardware authentication feature and improves throughput about 45%. [ABSTRACT FROM AUTHOR]

Bridge structures deteriorate due to various factors, and their maintenance can be made more efficient by utilizing relevant information. This paper describes an algorithm called Self-Organizing Map-based Cluster Merging (SOMCM) using a multi-dimensional matrix composite neural network, and a surface image identification system. The algorithm is designed to investigate the relevance between the main components of bridges and their types of deterioration. 6140 records on bridge maintenance were collected for all bridges in the Taoyuan region of Taiwan in 2021. The SOMCM algorithm involves finding the winner neuron through merging processes. 61 clusters were merged into 8 clusters after a predetermined number of iterations. Clustering analysis of the final 8 clusters revealed 9 major bridge maintenance association rules. Follow-up studies can apply the algorithm to integrate more technologies including GIS coordinates, material availability, real-time traffic conditions, and weather information to be of benefit to engineering practitioners. • The study is to develop a new algorithm, SOM-based Cluster Merging (SOMCM). • The paper is to investigate the relevance for bridge main components and their deterioration types. • The initial result involves 61 clusters being merged to 8 clusters with the predetermined iterations. • The clustering analysis for the final 8 clusters shows 9 major bridge maintenance association rules. [ABSTRACT FROM AUTHOR]

Objective. The Fugl-Meyer assessment for upper extremity (FMA-UE) is a measure for assessing upper extremity motor function in patients with stroke. However, the considerable administration time of the assessment decreases its feasibility. This study aimed to develop an accumulative assessment system of upper extremity motor function (AAS-UE) based on the FMA-UE to improve administrative efficiency while retaining sufficient psychometric properties. Methods. The study used secondary data from 3 previous studies having FMA-UE datasets, including 2 follow-up studies for subacute stroke individuals and 1 test-retest study for individuals with chronic stroke. The AAS-UE adopted deep learning algorithms to use patients' prior information (ie, the FMA-UE scores in previous assessments, time interval of adjacent assessments, and chronicity of stroke) to select a short and personalized item set for the following assessment items and reproduce their FMA-UE scores. Results. Our data included a total of 682 patients after stroke. The AAS-UE administered 10 different items for each patient. The AAS-UE demonstrated good concurrent validity (r =0.97-0.99 with the FMA-UE), high test-retest reliability (intra-class correlation coefficient=0.96), low random measurement error (percentage of minimal detectable change=15.6%), good group-level responsiveness (standardized response mean=0.65-1.07), and good individual-level responsiveness (30.5%-53.2% of patients showed significant improvement). These psychometric properties were comparable to those of the FMA-UE. Conclusion. The AAS-UE uses an innovative assessment method, which makes good use of patients' prior information to achieve administrative efficiency with good psychometric properties. Impact. This study demonstrates a new assessment method to improve administrative efficiency while retaining psychometric properties, especially individual-level responsiveness and random measurement error, by making good use of patients' basic information and medical records. [ABSTRACT FROM AUTHOR]

Dental radiography plays an important role in clinical diagnosis, treatment and surgery. In recent years, efforts have been made on developing computerized dental X-ray image analysis systems for clinical usages. A novel framework for objective evaluation of automatic dental radiography analysis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2015 Bitewing Radiography Caries Detection Challenge and Cephalometric X-ray Image Analysis Challenge. In this article, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the dental anatomy data repository of bitewing radiographs, the creation of the anatomical abnormality classification data repository of cephalometric radiographs, and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, seven automatic methods for analysing cephalometric X-ray image and two automatic methods for detecting bitewing radiography caries have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic dental radiography analysis is still a challenging and unsolved problem. The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/~cweiwang/ISBI2015/)., (Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.)

The local magnitude scale ML is defined by the difference between the observed amplitude (log A ) and the anchored attenuation amplitude (log A 0 ). A previous study in Taiwan established a model of the anchored amplitude as a function of the hypocentral distance R by matching the ML to the corresponding moment magnitude MW. Although the overall performance of the model is adequate, there remain some drawbacks, namely, the correlated empirical station correction problem and relatively low sample size. In this paper, we adopt the concept of the expectation-maximization (EM) algorithm to develop a new method that can simultaneously estimate the anchored amplitude model coefficients and station corrections. The revised catalog using the latest dataset in Taiwan provides an up-to-date accurate estimate of ML. Additionally, the proposed method can systematically obtain statistically meaningful results and be applied to datasets of other regions in the future. [ABSTRACT FROM AUTHOR]

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media. [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]

A discernible correlation emerged between global bridge disasters and bridge deterioration in recent years. To assist bridge inspectors in conducting deterioration identification, this paper presents a Pilgrimage Walk Optimization (PWO) algorithm inspired by Taiwan's unique Matsu bobee custom. The search behavior of the PWO algorithm simulates the gathering of devotees following Matsu's palanquin and their collective movements based on folk belief activities, including divination block casting, pilgrimage, the leisure ceremony, crawling beneath the palanquin, palanquin robbing, and the return palanquin ceremony. Analysis results show that the PWO outperformed contemporary metaheuristic algorithms on various multidimensional benchmark functions and was effectively applied to image segmentation for multiple types of bridge deterioration collected by an unmanned aerial vehicle. In the increasing number of engineering problems requiring edge computing to process big data, the proposed PWO algorithm is a low-cost and robust method with fault tolerance. • Presenting the innovative Pilgrimage Walk Optimization (PWO) algorithm, a revolutionary metaheuristic approach. • The new algorithm is tested on benchmark functions and applied to optimize image segmentation. • PWO algorithm outperforms contemporary metaheuristic algorithms in terms of achieving optimal solutions. • Exploring diverse bridge deterioration patterns auto-segmented using metaheuristic clustering algorithms. • PWO algorithm holds potential applications in AIoT technology and automation within the construction industry. [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]

Earthquake early warning (EEW) has been implemented in several regions around the world. However, because of natural randomness and uncertainty, false alarm and missed alarm can be expected in EEW. The key scope of this study is to evaluate the reliability of an on-site EEW in Taiwan, by testing the system's algorithm with 17,836 earthquake data from 1999 to 2013. The analysis shows that the on-site EEW system, empirically speaking, should have a false-alarm probability of 2.5%, and a missed-alarm probability of 14.1%. Considering missed alarm should be more critical to EEW, a new algorithm that could reduce the system's missed-alarm occurrences to 6% is also discussed in this paper. [ABSTRACT FROM AUTHOR]

Evaluating the magnitude of overdiagnosis associated with stool-based service screening for colorectal cancer (CRC) beyond a randomized controlled trial is often intractable and understudied. We aim to estimate the proportion of overdiagnosis in population-based service screening programs for CRC with the fecal immunochemical test (FIT). The natural process of overdiagnosis-embedded disease was first built up to learn transition parameters that quantify the pathway of non-progressive and progressive screen-detected cases calibrated with sensitivity, while also taking competing mortality into account. The Markov algorithms were then developed for estimating these transition parameters based on Taiwan FIT service CRC screening data on 5,417,699 residents aged 50–69 years from 2004 to 2014. Following the digital twin design with the parallel universe structure for emulating the randomized controlled trial, the screened twin, mirroring the control group without screening, was virtually recreated by the application of the above-mentioned trained parameters to predict CRC cases containing overdiagnosis. The ratio of the predicted CRCs derived from the screened twin to the observed CRCs of the control group minus 1 was imputed to measure the extent of overdiagnosis. The extent of overdiagnosis for invasive CRCs resulting from FIT screening is 4.16% (95% CI: 2.61–5.78%). The corresponding figure is increased to 9.90% (95% CI: 8.41–11.42%) for including high grade dysplasia (HGD) and further inflated to 15.83% (95% CI: 15.23–16.46%) when the removal adenoma is considered. The modest proportion of overdiagnosis modelled by the digital twin method, dispensing with the randomized controlled trial design, suggests the harm done to population-based FIT service screening is negligible. [ABSTRACT FROM AUTHOR]

Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto the emerging general-purpose graphics processing units (GPUs). In this paper, we focused on how to improve the mapping, especially for short query sequences, by better usage of shared memory. We performed and evaluated the proposed method on two different platforms (Tesla C1060 and Tesla K20) and compared it with two classic methods in CUDASW++. Further, the performance on different numbers of threads and blocks has been analyzed. The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers. [ABSTRACT FROM AUTHOR]

Credit scoring is playing an increasingly critical role with the rising number of lending operations for micro and small enterprises as well as individuals. A large number of research is primarily based on the combination and construction methods of credit scoring models by the experts. However, different credit data have distinct requirements for the models, and how to automatically search and construct credit scoring models according to credit data has become essential, that is the main concern of this paper. In response to the current challenges for credit scoring research, we proposed an Automatic Credit Scoring Strategy (ACSS), designed a credit assessment platform which includes data import, classification model automatic search, feature selection, hyperparameter optimization, data mining, classification output and other modules. Aiming at the problem of substantial imbalance in credit data, we propose an improved SMOTE algorithm that is capable of generating supplementary data for the lack of minority in credit data, thereby making the credit data distribution well balanced. As for classification model selection, features engineering, parameter optimization and other parts, we further incorporate automatic search ways to reduce manual interaction. We utilize public and self-owned credit data sets to conduct experiments and compare them with the latest credit assessment methods. Extensive experiments have demonstrated that our ACSS method achieves relatively noticeable performance improvements using the German credit dataset, the Taiwan credit dataset and the personal credit dataset for credit scoring. The best results achieved by our proposed method are 0.98, 0.99, 0.895 and 0.901 for MAE, RMSE, accuracy and precision respectively. In addition, the experimental results also show that our proposed improved SMOTE algorithm contributes to the credit scoring performance enhancement. The experimental findings suggest that our proposed ACSS balances automation and accuracy, which can be implemented to the consumption industry to enhance the reliability of credit assessments. • We utilize an improved SMOTE that could decrease the impact of data imbalance that exists in the credit data. • We propose to leverage the credit feature pruning algorithm and memetic optimization algorithm. • We propose an automated credit scoring strategy (ACSS) method with a cost-effective neural architecture search (C-NAS) scheme. [ABSTRACT FROM AUTHOR]

In this study, we focus on the electronic bus (E-Bus) system developed for Taiwan's mountainous areas. The rural area in Taiwan with its towering mountains has become a sightseeing hot spot. The radio propagation of a (cloud-based) 4G/LTE network may be severely limited by the mountainous geography, leading to unacceptable connection quality. We have built a smart (fog-based) local wireless E-Bus system using a feature-enhanced long-range widearea network (LoRaWAN, also known as GloRa in Taiwan). Since there is no electricity on the mountain roads, the bus stops of this E-Bus system are operated by a dynamo. Furthermore, we present a virtual direction and position algorithm for multiple E-Bus lines. A novel sensing method using the dynamos is proposed to estimate the feasibility of the E-Bus optimization problem. [ABSTRACT FROM AUTHOR]

This paper presents a three-terminal fault location algorithm based on the unsynchronized voltage and current phasors acquired from the digital relays or the Intelligent Electronic Devices (IEDs) installed in three terminals. The idea is to extend a two-terminal fault location scheme using synchrophasors to an approach regarding the three-terminal line structure with unsynchronized sampling. Firstly, a Voltage Comparison Scheme (VCS) is proposed to simultaneously identify a faulted line branch and derive the synchronization angle between the ends of the remaining two healthy branches. Subsequently, the Modified Secant-based Algorithm (MSA) is suggested to synchronize the measurements of the faulted-branch end as well as locate a fault at the same time. The effectiveness and the performance of the proposed idea have been validated by an extensive series of simulations under various fault conditions with the use of the 161 kV overhead transmission-line parameters of the Taiwan Power Company (Taipower). [ABSTRACT FROM AUTHOR]

In this study we present a generalized museum visitor routing problem considering the preferences and congestion of multiple groups with each group having its own must-see and select-see exhibition rooms based on their preferences in exhibits. The problem aims to minimize the makespan of all groups. An effective encoding scheme is proposed to simultaneously determine the scheduling of exhibition rooms for all groups and an immune-based algorithm (IBA) is developed. Numerical results, compared with those of a genetic algorithm and particle swarm optimization, on a museum in Taiwan are reported and discussed to show the performance of the IBA. [ABSTRACT FROM AUTHOR]

Advances in computer and communication technologies have engendered opportunities for developing an improved ubiquitous health care environment. One of the crucial applications is a ubiquitous clinic recommendation system, which entails recommending a suitable clinic to a mobile patient based on his/her location, hospital department, and preferences. However, patients may not be willing or able to express their preferences. To overcome this problem, some ubiquitous clinic recommendation systems mine the historical data of patients to learn their preferences, and they apply an algorithm to adjust the recommendation algorithm after receiving more patient data. Such an adjustment mechanism may operate for several periods; however, this raises a question regarding the sustainability (i.e., long-term effectiveness) of such an adjustment mechanism. To address this question, this study modeled the improvement in the successful recommendation rate of a ubiquitous clinic recommendation system that adopts an adjustment mechanism as a learning process. Both the asymptotic value and learning speed of the learning process provide valuable information regarding the long-term effectiveness of the adjustment mechanism. The proposed methodology was applied in a regional study to a ubiquitous clinic recommendation system that adjusts the recommendation mechanism by solving an integer nonlinear programming problem on a rolling basis. The experimental results revealed that the proposed method exhibited a considerably higher level of accuracy in forecasting the successful recommendation rate compared with several existing methods. Although the adjustment mechanism exhibits long-term effectiveness, the learning speed requires improvement. [ABSTRACT FROM AUTHOR]

Assuming Cox's regression model, we consider penalized full likelihood approach to conduct variable selection under nested case-control (NCC) sampling. Penalized non-parametric maximum likelihood estimates (PNPMLEs) are characterized by self-consistency equations derived from score functions. A cross-validation method based on profile likelihood is used to choose the tuning parameter within a family of penalty functions. Simulation studies indicate that the numerical performance of (P)NPMLE is better than weighted partial likelihood in estimating the log-relative risk and in identifying the covariates and the model, under NCC sampling. LASSO performs best when cohort size is small; SCAD performs best when cohort size is large and may eventually perform as well as the oracle estimator. Using the SCAD penalty, we establish the consistency, asymptotic normality, and oracle properties of the PNPMLE, as well as the sparsity property of the penalty. We also propose a consistent estimate of the asymptotic variance using observed profile likelihood. Our method is illustrated to analyze the diagnosis of liver cancer among those in a type 2 diabetic mellitus dataset who were treated with thiazolidinediones in Taiwan. [ABSTRACT FROM AUTHOR]

Objective: The long-term predictive value of the new proposed algorithm in the updated 2016 guidelines of the European Association of Cardiovascular Imaging to assess diastolic dysfunction (DD) in patients with heart failure with preserved ejection fraction (HFpEF) has not been validated.Methods: The analysis included 451 patients who were diagnosed with HFpEF as confirmed via echocardiography. The endpoints were mortality and hospitalization for HF. The Kaplan-Meier curves and Cox regression models were generated to determine the risk of all-cause mortality based on the 2016 and 2009 DD grading algorithm, respectively. We evaluated the net reclassification index of outcomes on the basis of 2009 DD grade after abiding by the 2016 recommendations.Results: After a follow-up of 2976 days, 119 patients (26.4%) died. According to the 2016 DD grading, grade III DD was associated with a significantly higher risk of mortality (hazard ratio [HR], 2.209; 95% CI 1.144-4.266) and HF hospitalization (HR, 2.047; 95% CI 1.348-3.870), as compared with grade I DD. Grade II DD was also associated with a higher risk of mortality (HR, 1.538; 95% CI 1.313-1.924). However, only grade III DD was independently associated with worse mortality based on 2009 DD grading. The net reclassification index for mortality increased significantly after grading by 2016 algorithm (10.6%, p < 0.001).Conclusions: The 2016 DD grading algorithm showed improved prognostic value of long-term mortality in patients with HFpEF. Based on the findings of the study, the appropriate grading of DD is important in the prognostication of patients with HFpEF.Key Points: • The application of the 2016 European Association of Cardiovascular Imaging recommendations diastolic dysfunction (DD) grading algorithm improves the predictive value for mortality. • Our analysis suggests DD grades II and III based on 2016 guidelines is associated with poor outcomes as compared with grade I. The echocardiographic indices of the new algorithm should be obtained and applied to effectively evaluate DD. [ABSTRACT FROM AUTHOR]

Claims databases consisting of routinely collected longitudinal records of medical expenditures are increasingly utilized for estimating expected medical costs of patients with a specific condition. Survival data of the patients of interest are usually highly censored, and observed expenditures are incomplete. In this study, we propose a survival-adjusted estimator for estimating mean lifetime costs, which integrates the product of the survival function and the mean cost function over the lifetime horizon. The survival function is estimated by a new algorithm of rolling extrapolation, aided by external information of age- and sex-matched referents simulated from national vital statistics. The mean cost function is estimated by a weighted average of mean expenditures of patients in a number of months prior to their death, of which the number could be determined by observed costs in their final months, and the weights depend on extrapolated hazards. We evaluate the performance of the proposed approach in comparison with that of a popular method using simulated data under various scenarios and 2 cohorts of intracerebral hemorrhage and ischemic stroke patients with a maximum follow-up of 13 years and conclude that our new method estimates the mean lifetime costs more accurately. [ABSTRACT FROM AUTHOR]

Catch-per-unit-effort (CPUE) is often the main piece of information used in fisheries stock assessment; however, the catch and effort data that are traditionally compiled from commercial logbooks can be incomplete or unreliable due to many reasons. Pacific bluefin tuna (PBF) is a seasonal target species in the Taiwanese longline fishery. Since 2010, detailed catch information for each PBF has been made available through a catch documentation scheme. However, previously, only market landing data with a low coverage of logbooks were available. Therefore, several nontraditional procedures were performed to reconstruct catch and effort data from many alternative data sources not directly obtained from fishers for 2001–2015: (1) Estimating the catch number from the landing weight for 2001–2003, for which the catch number information was incomplete, based on Monte Carlo simulation; (2) deriving fishing days for 2007–2009 from voyage data recorder data, based on a newly developed algorithm; and (3) deriving fishing days for 2001–2006 from vessel trip information, based on linear relationships between fishing and at-sea days. Subsequently, generalized linear mixed models were developed with the delta-lognormal assumption for standardizing the CPUE calculated from the reconstructed data, and three-stage model evaluation was performed using (1) Akaike and Bayesian information criteria to determine the most favorable variable composition of standardization models, (2) overall R2 via cross-validation to compare fitting performance between area-separated and area-combined standardizations, and (3) system-based testing to explore the consistency of the standardized CPUEs with auxiliary data in the PBF stock assessment model. The last stage of evaluation revealed high consistency among the data, thus demonstrating improvements in data reconstruction for estimating the abundance index, and consequently the stock assessment. [ABSTRACT FROM AUTHOR]

Typhoons and heavy storms frequently cause severe inundation in Taiwan. For early flood warning purposes, a real-time flood forecasting system was acknowledged as a pivotal tool and developed in this study. The system integrates four parts: (1) a gray-based rainfall prediction model, (2) the antecedent hydrological condition estimation method, (3) a deterministic runoff model, and (4) a runoff updating algorithm. The developed system was applied to the Hsia-Yun watershed in Taiwan. The time-invariant parameters, which characterize terrain features, were calibrated in advance. Additionally the time-variant parameters were modeled to change with temporal hydrological condition of the watershed. The gray theory was performed to predict the rainfall intensity in the next 1-3 h, which thus enables the runoff model to forecast incoming runoff in the next 1-3 h. By consulting the real-time feedback flow observation, a runoff updating algorithm was adopted and was helpful in improving the performance of the simulated hydrographs. In real-time application, the system can be initiated or terminated by detecting if the cumulative rainfall depth is larger or less than a predefined threshold value. The idea of the developed system has recently been constructed with a visual interface for real-time flood forecasting by authorities. The overall results confirm that the developed system is competent to forecast flash floods. [ABSTRACT FROM AUTHOR]

Brain metastases are commonly found in patients that are diagnosed with primary malignancy on their lung. Lung cancer patients with brain metastasis tend to have a poor survivability, which is less than 6 months in median. Therefore, an early and effective detection system for such disease is needed to help prolong the patients' survivability and improved their quality of life. A modified electromagnetism-like mechanism (EM) algorithm, MEM-SVM, is proposed by combining EM algorithm with support vector machine (SVM) as the classifier and opposite sign test (OST) as the local search technique. The proposed method is applied to 44 UCI and IDA datasets, and 5 cancers microarray datasets as preliminary experiment. In addition, this method is tested on 4 lung cancer microarray public dataset. Further, we tested our method on a nationwide dataset of brain metastasis from lung cancer (BMLC) in Taiwan. Since the nature of real medical dataset to be highly imbalanced, the synthetic minority over-sampling technique (SMOTE) is utilized to handle this problem. The proposed method is compared against another 8 popular benchmark classifiers and feature selection methods. The performance evaluation is based on the accuracy and Kappa index. For the 44 UCI and IDA datasets and 5 cancer microarray datasets, a non-parametric statistical test confirmed that MEM-SVM outperformed the other methods. For the 4 lung cancer public microarray datasets, MEM-SVM still achieved the highest mean value for accuracy and Kappa index. Due to the imbalanced property on the real case of BMLC dataset, all methods achieve good accuracy without significance difference among the methods. However, on the balanced BMLC dataset, MEM-SVM appears to be the best method with higher accuracy and Kappa index. We successfully developed MEM-SVM to predict the occurrence of brain metastasis from lung cancer with the combination of SMOTE technique to handle the class imbalance properties. The results confirmed that MEM-SVM has good diagnosis power and can be applied as an alternative diagnosis tool in with other medical tests for the early detection of brain metastasis from lung cancer. [ABSTRACT FROM AUTHOR]

Ovarian cancer is the deadliest gynaecological disease because of the high mortality rate and there is no any symptom in cancer early stage. It was often the terminal cancer period when patients were diagnosed with ovarian cancer and thus delays a good opportunity of treatment. The current common method for detecting ovarian cancer is blood testing for analyzing the tumor marker CA-125 of serum. However, specificity and sensitivity of CA-125 are insufficient for early detection. Therefore, it has become an urgent issue to look for an efficient method which precisely detects the tumor markers for ovarian cancer. This study aims to find the target genes of ovarian cancer by different algorithms of information science. Feature selection and decision tree were applied to analyze 9600 ovarian cancer-related genes. After screening the target genes, candidate genes will be analyzed by Ingenuity Pathway Analysis (IPA) software to create a genetic pathway model and to understand the interactive relationship in the different pathological stages of ovarian cancer. Finally, this research found 9 oncogenes associated with ovarian cancer and some genes had not been discovered in previous studies. This system will assist medical staffs in diagnosis and treatment at cancer early stage and improve the patient's survival. [ABSTRACT FROM AUTHOR]

Background: In Taiwan, around 1,500 cases of dengue fever are reported annually and incidence has been increasing over time. A national web-based Notifiable Diseases Surveillance System (NDSS) has been in operation since 1997 to monitor incidence and trends and support case and outbreak management. We present the findings of an evaluation of the NDSS to ascertain the extent to which dengue fever surveillance objectives are being achieved. Methodology: We extracted the NDSS data on all laboratory-confirmed dengue fever cases reported during 1 January 2010 to 31 December 2012 to assess and describe key system attributes based on the Centers for Disease Control and Prevention surveillance evaluation guidelines. The system’s structure and processes were delineated and operational staff interviewed using a semi-structured questionnaire. Crude and age-adjusted incidence rates were calculated and key demographic variables were summarised to describe reporting activity. Data completeness and validity were described across several variables. Principal Findings: Of 5,072 laboratory-confirmed dengue fever cases reported during 2010–2012, 4,740 (93%) were reported during July to December. The system was judged to be simple due to its minimal reporting steps. Data collected on key variables were correctly formatted and usable in > 90% of cases, demonstrating good data completeness and validity. The information collected was considered relevant by users with high acceptability. Adherence to guidelines for 24-hour reporting was 99%. Of 720 cases (14%) recorded as travel-related, 111 (15%) had an onset >14 days after return, highlighting the potential for misclassification. Information on hospitalization was missing for 22% of cases. The calculated PVP was 43%. Conclusions/Significance: The NDSS for dengue fever surveillance is a robust, well maintained and acceptable system that supports the collection of complete and valid data needed to achieve the surveillance objectives. The simplicity of the system engenders compliance leading to timely and accurate reporting. Completeness of hospitalization information could be further improved to allow assessment of severity of illness. To minimize misclassification, an algorithm to accurately classify travel cases should be established. [ABSTRACT FROM AUTHOR]

Background Spinal cord injury (SCI)-induced secondary oxidative stress associates with a clinical complication and high mortality. Treatments to improve the neurological outcome of secondary injury are considered as important issues. The objective of the current study is to evaluate the anti-oxidative effect of Tithonia diversifolia ethanolic extracts (TDE) on cells and apply the pharmacological effect to SCI model using a MRI imaging algorism. Methods The anti-oxidation properties were tested in a 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay. Rat liver cells (clone-9) were treated with various doses of TDE (0 ~ 50 μg/ml) before exposed to 250 μM H2O2 and cell survival was determined by MTT and LDH assays. We performed water apparent diffusion coefficient (ADC) map in MR techniques to investigate the efficacy of TDE treatment on SCI animal model. We performed T5 laminectomy and compression (50 g, 1 min) to induce SCI. PHILIP 3.0 T MRI was used to image 24 male Sprague-Dawley rats weighing 280-320 g. Rats were randomly divided into three groups: sham group, SCI group, SCI treated with TDE group. The MRI images were taken and ADC were acquired before and after of treatment of TDE (50 mg/kg B. W. orally, 5 days) in SCI model. Results TDE protected clone-9 cells against H2O2-induced toxicity through DPPH scavenging mechanism. In addition, SCI induced the increase in ADC after 6 hours. TDE treatment slightly decreased the ADC level after 1-week SCI compared with control animals. Conclusion Our studies have proved that the cytoprotection effect of TDE, at least in part, is through scavenging ROS to eliminate intracellular oxidative stress and highlight a potential therapeutic consideration of TDE in alternative and complementary medicine. [ABSTRACT FROM AUTHOR]

With the unique rainfall patterns of typhoons, plum rains, and short-term heavy rainfalls, the frequent landslide and debris flow disasters have caused severe loss to people in Taiwan. In the studies of landslide susceptibility, the information of factors used for analysis was usually annual-based content, and it was assumed that the same elements from different years were independent between each year. However, the occurrence of landslides was usually not simply due to the changes within a year. Instead, landslides were triggered because the factors that affected the potential of landslides reached critical conditions after a cumulative change with time. Therefore, this study had well evaluated the influence of temporal characteristics and the ratios of antecedent landslide areas in the past five years in the landslide potential evaluation model. The analysis was conducted through the random forest (RF) algorithm. Additional rainfall events of 2017 were used to test the proposed model's performance to understand its practicality. The analysis results show that in the study area, the RF model had considerably acceptable performance. The results have also demonstrated that the antecedent landslide ratios in the past five years were essential to describe the significance of cumulative change with time when conducting potential landslide evaluation. [ABSTRACT FROM AUTHOR]

The National Health Insurance Administration of Taiwan has implemented global budget payments, the Diagnosis-Related Group (DRG) inpatient diagnosis-related group payment system, and the same-disease payment system, in order to decrease the financial burden of medical expenditure. However, the benefit system reduces the income of doctors and hospitals. This study proposed an early warning payment algorithm that applies data analytics technology to diabetes hospitalization- and treatment-related fees. A model was constructed based on the characteristics of the Exponentially Weighted Moving Average (EWMA) algorithm to develop control charts, which were first employed using the 2001–2017 health insurance statistical database released by the Department of Health Insurance (DHI). This model was used to simulate data from inpatients with diabetes, to create an early warning algorithm for diagnosis-related groups' (DRGs') medical payments as well as to measure its accuracy. This study will provide a reference for the formulation of payment policies by the DHI. [ABSTRACT FROM AUTHOR]

This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is derived under mild assumptions on the selected family of shape equations. The issue of detecting Parkinson's disease (PD) based on the Tc-99m TRODAT-1 brain SPECT/CT images of 634 subjects, with 305 female and an average age of 68.3 years old from Kaohsiung Chang Gung Memorial Hospital, Taiwan, is employed to demonstrate the proposed procedure by fitting optimal ellipse and cashew-shaped equations in the 2D and 3D spaces, respectively. According to the visual interpretation of 3 experienced board-certified nuclear medicine physicians, 256 subjects are determined to be abnormal, 77 subjects are potentially abnormal, 174 are normal, and 127 are nearly normal. The coefficients of the ellipse and cashew-shaped equations, together with some well-known features of PD existing in the literature, are employed to learn PD classifiers under various machine learning approaches. A repeated hold-out with 100 rounds of 5-fold cross-validation and stratified sampling scheme is adopted to investigate the classification performances of different machine learning methods and different sets of features. The empirical results reveal that our method obtains 0.88 ± 0.04 classification accuracy, 0.87 ± 0.06 sensitivity, and 0.88 ± 0.08 specificity for test data when including the coefficients of the ellipse and cashew-shaped equations. Our findings indicate that more constructive and useful features can be extracted from proper mathematical representations of the 2D and 3D shapes for a specific ROI in medical imaging data, which shows their potential for improving the accuracy of automated PD identification. [ABSTRACT FROM AUTHOR]

Background: The triage system used during an actual mass burn casualty (MBC) incident is a major focus of concern. This study introduces a MBC triage system that was used by a burn center during an actual MBC incident following a powder explosion in New Taipei City, Taiwan. Methods: This study retrospectively analyzed data from patients who were sent to the study hospital during a MBC incident. The patient list was retrieved from a national online management system. A MBC triage system was developed at the study hospital using the following modifiers: consciousness, breathing, and burn size. Medical records were retrieved from electronic records for analysis. Patient outcomes consisted of emergency department (ED) disposition and intervention. Results: The patient population was predominantly female (56.3%), with an average age of 24.9 years. Mean burn sizes relative to the TBSA of triage level I, II, and III patients were 57.9%, 40.5%, and 8.7%, respectively. ICU length of stay differed markedly according to triage level (mean days for levels I vs II vs III: 57.9 vs 39.9 vs 2.5 days; p < 0.001). Triage system levels I and II indicate ICU admission with a sensitivity of 93.9% (95%CI 80.4–98.3%) and a specificity of 86.7% (62.1–96.3%). Overall, 3 (6.3%) patients were under-triaged. Two (4.2%) patients were over-triaged. Sixteen (48.5%) and 21 (63.6%) patients of triage levels I and II received endotracheal intubation and central venous catheterization, respectively. Sorting of the study population with simple triage and rapid treatment (START) showed great sensitivity (100.0%) but poor specificity (53.3%). The Taiwan Triage and Acuity Scale (TTAS) presented 87.9% sensitivity and 93.9% specificity. Conclusions: The current MBC triage algorithm served as a good indicator of ED disposition but might have raised excessive immediate attention and had the potential to exhaust the available resources. These findings add to our knowledge of the MBC triage system and should help future researchers in adjusting the triage criteria to fit actual disasters. [ABSTRACT FROM AUTHOR]