Publications by authors named "Ching-Hsue Cheng"

The hotel industry is essential for tourism. With the rapid expansion of the internet, consumers only search for their desired keywords on the website when they trying to find a hotel to stay, causing the relevant hotel information would appear. To quickly respond to the changing market and consumer habits, each hotel must focus on its website information and information quality.

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Road accidents are one of the primary causes of death worldwide; hence, they constitute an important research field. Taiwan is a small country with a high-density population. It particularly has a considerable number of locomotives.

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Most classification algorithms assume that classes are in a balanced state. However, datasets with class imbalances are everywhere. The classes of actual medical datasets are imbalanced, severely impacting identification models and even sacrificing the classification accuracy of the minority class, even though it is the most influential and representative.

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Since 2001, cardiovascular disease (CVD) has had the second-highest mortality rate, about 15,700 people per year, in Taiwan. It has thus imposed a substantial burden on medical resources. This study was triggered by the following three factors.

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Data in the medical field often contain missing values and may result in biased research results. Therefore, the objective of this work is to propose a new imputation method, a novel weighted distance threshold method, to impute missing values. After several experiments, we find that the proposed imputation method has the following benefits.

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Owing to the emergence of the Internet and its rapid growth, people can use mobile devices on many social media platforms (blogs, Facebook forums, etc.), and the platforms provide well-known websites for people to express and share their daily activities and ideas on global issues. Many consumers utilize product review websites before making a purchase.

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Many different time-series methods have been widely used in forecast stock prices for earning a profit. However, there are still some problems in the previous time series models. To overcome the problems, this paper proposes a hybrid time-series model based on a feature selection method for forecasting the leading industry stock prices.

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Population aging has become a worldwide phenomenon, which causes many serious problems. The medical issues related to degenerative brain disease have gradually become a concern. Magnetic Resonance Imaging is one of the most advanced methods for medical imaging and is especially suitable for brain scans.

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The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations.

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Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015.

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This paper presents a method for fast computation of Hessian-based enhancement filters, whose conditions for identifying particular structures in medical images are associated only with the signs of Hessian eigenvalues. The computational costs of Hessian-based enhancement filters come mainly from the computation of Hessian eigenvalues corresponding to image elements to obtain filter responses, because computing eigenvalues of a matrix requires substantial computational effort. High computational cost has become a challenge in the application of Hessian-based enhancement filters.

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A critical option of total hip arthroplasty (THA) is considered only when tried more conservative treatments but continued to have pain, stiffness, or problems with the function of ones hip. THA plays one of major concerns under the waves of the rapid growth of aging populations and the constrained health care resources in Taiwan. Moreover, prior studies indicated that imbalanced class distribution problems do exist in the constructed classification model and cause seriously negative effects on model performances in the health care industry.

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Identifying patients in a Target Customer Segment (TCS) is important to determine the demand for, and to appropriately allocate resources for, health care services. The purpose of this study is to propose a two-stage clustering-classification model through (1) initially integrating the RFM attribute and K-means algorithm for clustering the TCS patients and (2) then integrating the global discretization method and the rough set theory for classifying hospitalized departments and optimizing health care services. To assess the performance of the proposed model, a dataset was used from a representative hospital (termed Hospital-A) that was extracted from a database from an empirical study in Taiwan comprised of 183,947 samples that were characterized by 44 attributes during 2008.

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As the incidence of THA is expected to rise with an aging population and improvements in surgery, a satisfactory outcome in health care can effectively increase medical quality. This paper uses a serious data screening function by THA physician to reduce data dimension after data collected from the NHI database, then 8576 cases are obtained from the original cases of 10,388 after screening procedure. The proposed model adopts an imbalanced sampling method to solve class imbalance problem, and utilizes rough set to locate core attributes.

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TKA is a highly effective means of treating (advanced knee arthritis) degenerative joint disease. Previous studies have demonstrated that a high surgical volume for total joint arthroplasty reduces morbidity and improved economic outcome, these methods for themselves are fraught with complexity, uncertainty and non-linear problem in terms of medical datasets may be unable to more accurately finding important information. As medical datasets often include a large number of features (attributes), some of which are irrelevant, and therefore it cannot intuitively understand the corresponding to main factors which affecting the resource utilizations of healthcare.

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This study collected the real HD-data from area scale hospital database with 72 attributes and 18,113 records. The study proposes a novel procedure to assess the patient's HD-quality, including five facets: (1) Delete the unrelated attributes and missing values. (2) Employ expert granularity to cut decision-attributed Kt/V (where K is the dialyzer clearance coefficient of urea nitrogen, t is the time for dialysis and V is the urea nitrogen volume of distribution in the body).

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The purpose of this study is to discover valuable medical facts by utilizing the Taiwan National Health Insurance (NHI) database, which contains 32,200 records of TKA surgeries. Three main objectives of this paper include the following: (a) building learning curves of TKA from the target database; (b) characterizing how the TKA volume correlates with infection rate and mortality; (c) examining the differences of infection rate and mortality between the medical center (Group I) and the non-medical center (Group II). The TKA samples are classified into two groups according to their institution type (medical center and non-medical center).

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