• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.769-781
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• 2020

This study attempts to examine the relationships between transformational leadership, learning orientation, firm innovativeness, and financial performance. Specifically, the moderating effect of learning orientation and firm innovativeness. The data collected from 606 SMEs in Thailand were evaluated using the structural equation modeling, typifying that quantitative research. The results revealed that transformational leadership had a positive effect on learning orientation. Similarly, transformational leadership had a positive effect on firm innovativeness. Further, the study found that transformational leadership had a positive indirect effect on financial performance through the mediation of learning orientation. The results of the study found that transformational leadership had a positive indirect effect on financial performance through the mediation of firm innovativeness. Transformational leadership and learning orientation to improve innovation within the organization, including organizations and leaders among themselves. Especially, innovative firms inculcate ideals of promise to learning, open-mindedness, and shared vision. Furthermore, practitioners can use the findings of this study when they perform their role of leaders to challenge creativity and innovation among followers. Finally, those developments would influence a procedure of evidence procurement, evidence distribution and shared explanation that escalations equally individual and administrative effectiveness owing to its influence going on products.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.323-328
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• 2021

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.

• Korea Trade Review
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• v.46 no.6
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• Asia pacific journal of information systems
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• v.12 no.3
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• pp.45-70
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• 2002

This paper examines the role of distance learning self-efficacy, belief in one's capabilities of using a system in the accomplishment of web-based distance learning, in predicting user intention to use and performance of distance learning system. It used self-efficacy theory and technology acceptance model(TAM) to build a model that predicts relationships between antecedents to students' distance learning self-efficacy assessments and their behavioral and attitudinal consequences. The model was tested using LISREL analysis on the sample of 250 students who have worked with the Distance Learning System. The results indicated partial support for the conceptual model. In accordance with TAM, perceived usefulness had strong direct effects on intention to use and performance, while perceived ease of use had both direct and indirect effects on intention to use, but not performance. Distance learning self-efficacy had only direct effect on perceived ease of use to use. Computer experience was found to have a strong positive effect on distance learning self-efficacy, and computer anxiety had a negative effect on distance learning self-efficacy. Implications of these findings are discussed for researchers and practitioners.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.701-706
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• 2019

The advent of the AI(: Artificial Intelligence) has applied to many industrial and general applications have havingact on our lives these days. Various types of machine learning methods are supported in this field. The supervised learning method of the machine learning has features and targets as an input in the learning process. There are many supervised learning methods as well and their performance varies depends on the characteristics and states of the big data type as an input data. Therefore, in this paper, in order to compare the performance of the various supervised learning method with a specific big data set, the supervised learning methods supported in the Tensorflow and the Sckit-Learn are simulated and analyzed in the Jupyter Notebook environment with python.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.245-258
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• 2016

This paper aims to present the way to bring about significant results through performance improvement of learning algorithm in the research applying to machine learning. Research papers showing the results from machine learning methods were collected as data for this case study. In addition, suitable machine learning methods for each field were selected and suggested in this paper. As a result, SVM for engineering, decision-making tree algorithm for medical science, and SVM for other fields showed their efficiency in terms of their frequent use cases and classification/prediction. By analyzing cases of machine learning application, general characterization of application plans is drawn. Machine learning application has three steps: (1) data collection; (2) data learning through algorithm; and (3) significance test on algorithm. Performance is improved in each step by combining algorithm. Ways of performance improvement are classified as multiple machine learning structure modeling, $+{\alpha}$ machine learning structure modeling, and so forth.

• 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.

• ETRI Journal
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• v.42 no.5
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

• 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.

• Journal of the Korean Society of School Health
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• v.35 no.1
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• pp.11-21
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• 2022

Purpose: This study investigated the relationship between satisfaction with fundamental nursing skills practicum, confidence in fundamental nursing skills performance and learning flow, and examined factors influencing satisfaction with practicum programs of fundamental nursing skills in face-to-face and online classes for nursing students amid COVID-19. Methods: The subjects of the study were 229 junior nursing students from two colleges of nursing located in D and C city, respectively. The collected data were analyzed with descriptive statistics, independent t-test, ANOVA, Kruskal-Wallis test, Pearson's correlation and hierarchical multiple regression, using SPSS/WINdows 23.0. Results: The subjects' satisfaction with practicum showed a high positive correlation with confidence in performance (r=.55, p<.001) and learning flow (r=.70, p<.001) in face-to-face classes, and their satisfaction with practicum showed a high positive correlation with confidence in performance (r=.56, p<.001) and learning flow (r=.73, p<.001) in online classes. The factors affecting the subjects' satisfaction with practicum were learning flow (β=.51, p<.001) and confidence in performance (β=.30, p<.001) for face-to-face classes, and motivation for application (β=.14, p=.034), learning flow (β=.58 p<.001) and confidence in performance (β=.19, p=.015) for online classes. These factors explained 53% and 60% of the satisfaction with practicum in face-to-face classes (F=23.07, p<.001) and online classes (F=20.66, p<.001), respectively. Conclusion: Learning flow and confidence in performance should be considered when developing learning strategy programs to improve nursing students' satisfaction with fundamental nursing skills practicum in both face-to-face and online classes.