• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.651-661
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• 2020

This research aims to examine the relationships among entrepreneurial orientation, organizational learning capability, firm innovation, and firm performance. To achieve a data collection, a mail survey procedure via questionnaire was implemented by using executives or managers of gems & jewelry industries, textile and clothing industries, leather and accessories, fashion apparel industries in Thailand as the key informants. Of the surveys completed and returned, 388 were usable. Hence, a model with a structural equation was used to evaluate the data survey of 388 respondents. The results reveal that, in terms of the mediating effect, organizational learning capacity and firm innovation can complement each other in order to improve entrepreneurial orientation. Findings show that entrepreneurial orientation improves firm innovation, which in turn improves firm efficiency. Firm innovation acts as a variable mediating between enterprise orientation and firm performance. Our findings contribute to the current emergence of organizational learning capacity that mediated the relationship between entrepreneurial orientation and firm performance. Entrepreneurial orientation is normally a firm performance that enterprises develop which can have use the information available and make an impact. It can be considered through the mediation of organizational learning capability, and firm innovation variable and as stated in previous literature, it can influence firm performance.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.1129-1138
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• 2021

SMEs are constantly demanded of changes in the rapidly-evolving business environment, which involves the fourth industrial revolution and the COVID-19 pandemic. In this period, management consulting service becomes more in demand to provide technical and strategic solutions for management problems. This study aimed to empirically analyze the direct effects of emotional intelligence on job satisfaction and the indirect effects of such parameters as learning agility and self-efficacy on job satisfaction in management consultants. On the basis of a literature review, inter-variable association was designed in the research model. Based on an online survey of those in the Korean SME management consultants, this study collected 221 questionnaires then used structural equation modeling for statistical analysis. The results reveal that emotional intelligence significantly affected job satisfaction and, also significantly positively affected learning agility and self-efficacy. In addition, a significant indirect correlation could be found between learning agility and self-efficacy. Meanwhile, if learning agility and self-efficacy mediated job satisfaction, emotional intelligence had no significant effect on job satisfaction and fully mediated learning agility and self-efficacy. It is necessary to develop an emotional intelligence education program that can help management consultants improve their emotional intelligence with the objective of giving successful management consulting services.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.615-619
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• 2021

This study focuses on memory systems that are optimized to increase performance and energy efficiency in many embedded systems such as IoT, cloud computing, and edge computing, and proposes a performance improvement technique. The proposed technique improves memory system performance based on machine learning algorithms that are widely used in many applications. The machine learning technique can be used for various applications through supervised learning, and can be applied to a data classification task used in improving memory system performance. Data classification based on highly accurate machine learning techniques enables data to be appropriately arranged according to data usage patterns, thereby improving overall system performance.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.993-1001
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• 2021

This study aims to determine the factors influencing student performance using the teaching and learning process through e-learning based on the unified theory of acceptance and use technology (UTAUT). This study also sets out to propose additional variables to expand the UTAUT model to be more suitable to use in higher education. This research conducted a literature review, expert interviews, and a self-administered survey involving 200 students at tertiary institutions in Riau province, Indonesia. The questionnaire data were analyzed using SmartPLS 2. This study shows that UTAUT constructs, namely, social influence, facility conditions, and effort expectancy have a significant influence on student behavior and performance, while the performance expectancy variable shows no significant effect. The additional variables, including lecturer characteristics, external motivation, and organizational structure, directly affect student performance. However, concerning student behavior, motivation and environment are the only variables with a significant effect. The results of this study suggest the behavior deteminant such as lecturer characteristics, motivation and environment, and organizational structure improve student performance. This study investigates factors affecting the performance of university students through the learning employing e-learning by developing the UTAUT constructs to include the lecturer characteristics, motivation and environment, and organizational structure in improving student performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Earthquakes and Structures
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• v.25 no.6
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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• pp.7-14
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• 2024

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.457-464
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• 2009

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.311-316
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• 2019

Classification problem is to predict the class to which an input data belongs. One of the most popular methods to do this is training a machine learning algorithm using the given dataset. In this case, the dataset should have a well-balanced class distribution for the best performance. However, when the dataset has an imbalanced class distribution, its classification performance could be very poor. To overcome this problem, we propose an over-sampling scheme that balances the number of data by using Conditional Generative Adversarial Networks (CGAN). CGAN is a generative model developed from Generative Adversarial Networks (GAN), which can learn data characteristics and generate data that is similar to real data. Therefore, CGAN can generate data of a class which has a small number of data so that the problem induced by imbalanced class distribution can be mitigated, and classification performance can be improved. Experiments using actual collected data show that the over-sampling technique using CGAN is effective and that it is superior to existing over-sampling techniques.

• Journal of the Korean Society of Visualization
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• v.19 no.2
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• pp.32-40
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• 2021

Vapor-driven solutal Marangoni flow is governed by the concentration distribution of solutes on a liquid-gas interface. Typically, the flow structure is investigated by particle image velocimetry (PIV). However, to develop a theoretical model or to explain the working mechanism, the concentration distribution of solutes at the interface should be known. However, it is difficult to achieve the concentration profile theoretically and experimentally. In this paper, to find the concentration distribution of solutes around 2D droplet, the reverse tracking method with an artificial neural network based on PIV data was performed. Using the method, the concentration distribution of solutes around a 2D droplet was estimated for actual flow data from PIV experiment.