• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• Communications for Statistical Applications and Methods
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• 제30권5호
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• pp.485-499
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• 2023

This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Results showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.

• Korea Journal of Hospital Management
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• 제18권2호
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• pp.127-152
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• 2013

The purpose of this study was to investigate the effect of balancing, coordinating and learning strategy on performance of private university hospitals. We think that the study will contribute to establish effective management strategy of private university hospitals. Data were collected from 69 private university hospitals. We measured balancing, coordinating and learning strategy, and perceived performance of the hospital by using 5-point Likert scale. Upper-grade general hospitals were significantly higher rate of growth and profitability than others. However, general hospitals were higher level in perceived performance than upper-grade general hospitals. Hospitals located in Seoul were significantly higher growth rate than those in other regions. Large-scale hospitals were significantly higher rate of growth and profitability than small hospitals. Qualitative performance did not different in any hospital characteristics. Growth of hospitals were significantly influenced from business strategies: selective strategy, formal coordinating strategy, and external learning strategy. Profitability of hospitals were also significantly influenced from business strategies: selective strategy, adaptive strategy, and external learning strategy. Subjective performance of hospitals were significantly influenced from external learning strategy. There were no factors that are significantly influencing on qualitative performance of hospital. To have successful performance in the competitive environment, it is recommended that private university hospitals should have to establish management strategy such as balancing, coordinating, and learning strategy.

• Journal of Convergence for Information Technology
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• 제12권4호
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• pp.38-46
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• 2022

This study examined the relationship of academic achievement, learning motivation and self-directed learning ability and cognitive presence of nursing students in online class. Data were collected through self reported structured questionnaire in 202 nursing students from October 19 to 30, 2020. Data were analyzed using SPSS/WIN 21.0. As a result this study, academic achievement was positively correlated with learning motivation(r=.45, p<.001), self-directed learning ability(r=.50, p<.001) and cognitive presence(r=.64, p<.001). As a result regression analysis, subjective academic grades, self-directed learning ability and cognitive presence explained 57.0% of the academic achievement in nursing students(F=91.00, p<.001). Therefore, it is necessary to develop various interventions to enhance the self-directed learning ability and to design the classes that increase the cognitive presence.

• Korean Journal of Environment and Ecology
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• 제20권3호
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• pp.319-330
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• 2006

It is imperative that pictograms as pictorial information be empirically tested in order to establish whether the users do indeed associate the appropriate referent in an actual usage situation. The experiment employing the Multiple Index Approach was conducted in a class room with 64 subjects to evaluate tourism and recreation related pictograms. Performance data(hit rate, false alarm and missing value) of 25 pictograms were collected and the average hit rate as a prime index of pictogram associativeness was 65.82%. The matrix analysis showed 14 pictograms were high in subjective certainty and subjective suitability. The other 11, which were low in both criteria may require prior learning or improvement of the pictogram designs to represent their meanings more distinctively.

• The Journal of Sustainable Design and Educational Environment Research
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• 제19권4호
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• pp.40-49
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• 2020

Recently, the form of education has changed from one-way to two-way and mutual exchange rather than the existing one-way order form, and accordingly, it is necessary to consider creating a suitable learning environment for each type of education. The basic form of education consists of the delivery of knowledge, that is, the delivery of knowledge by teachers to education consumers through voice delivery, so the sound environment is considered an essential factor in creating a pleasant learning environment. The indoor sound environment is very closely related to the mental stress of the inmate, so the quality level of education will also change greatly depending on whether or not the appropriate sound environment is created. However, the importance of the sound environment in educational facilities such as classrooms has not been highlighted due to the lack of research and related laws on the sound environment. Therefore, in this study, auditory tests were conducted using the auralization based on the physical acoustic performance data presented in the preceding study. Through this, we wanted to verify the validity of this research by analyzing the subjective acoustic performance satisfaction of the occupants due to the improvement of the physical acoustic performance. Based on these research results, it is estimated that the improvement of the sound environment of educational facilities through remodeling in the future will be possible to verify whether the sound environment suitable for educational facilities is created only after the analysis stage on the improvement of subjective sound performance as well as physical sound performance.

• Korean Journal of Radiology
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• 제21권10호
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• pp.1165-1177
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• 2020

Objective: To assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction. Materials and Methods: We retrospectively enrolled 82 consecutive patients (male:female = 60:22; mean age, 67.0 ± 10.8 years) who had undergone both CCTA and invasive coronary artery angiography from March 2017 to June 2018. All included patients underwent CCTA with iterative reconstruction (ADMIRE level 3, Siemens Healthineers). We developed a deep learning based denoising technique (ClariCT.AI, ClariPI), which was based on a modified U-net type convolutional neural net model designed to predict the possible occurrence of low-dose noise in the originals. Denoised images were obtained by subtracting the predicted noise from the originals. Image noise, CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively calculated. The edge rise distance (ERD) was measured as an indicator of image sharpness. Two blinded readers subjectively graded the image quality using a 5-point scale. Diagnostic performance of the CCTA was evaluated based on the presence or absence of significant stenosis (≥ 50% lumen reduction). Results: Objective image qualities (original vs. denoised: image noise, 67.22 ± 25.74 vs. 52.64 ± 27.40; SNR [left main], 21.91 ± 6.38 vs. 30.35 ± 10.46; CNR [left main], 23.24 ± 6.52 vs. 31.93 ± 10.72; all p < 0.001) and subjective image quality (2.45 ± 0.62 vs. 3.65 ± 0.60, p < 0.001) improved significantly in the denoised images. The average ERDs of the denoised images were significantly smaller than those of originals (0.98 ± 0.08 vs. 0.09 ± 0.08, p < 0.001). With regard to diagnostic accuracy, no significant differences were observed among paired comparisons. Conclusion: Application of the deep learning technique along with iterative reconstruction can enhance the noise reduction performance with a significant improvement in objective and subjective image qualities of CCTA images.

• Journal of Digital Convergence
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• 제16권5호
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• pp.239-245
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• 2018

So far, various researches have been conducted on evaluation of e-learning software, but subjective evaluation criteria are formed according to the classification presented from the viewpoint of the researcher rather than systematized form according to related standards. In addition, standards for software evaluation are continuously being supplemented for practical use, so it is urgent to establish evaluation bases by establishing evaluation criteria. Therefore, in order to establish the quality evaluation standard of e-learning software, this study analyzes the quality requirements of e-learning software based on the usability system among the quality characteristics of ISO/IEC 25000 series. This evaluation standard is distinguished by the fact that the evaluation standard of e-learning software that reflects the latest trend of related standardization has been established and practical utilization has been improved. It can be used effectively for quality evaluation and certification of e-learning software in the future.

• Smart Structures and Systems
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• 제31권4호
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• International conference on construction engineering and project management
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.