• Computers and Concrete
• /
• v.17 no.4
• /
• pp.489-498
• /
• 2016

In this paper, the flexural strength ($f_{fs}$) and splitting tensile strength ($f_{sts}$) of concrete containing different proportions of fly ash have been modeled by using gene expression programming (GEP). Two GEP models called GEP-I and GEP-II are constituted to predict the $f_{fs}$ and $f_{sts}$ values, respectively. In these models, the age of specimen, cement, water, sand, aggregate, superplasticizer and fly ash are used as independent input parameters. GEP-I model is constructed by 292 experimental data and trisected into 170, 86 and 36 data for training, testing and validating sets, respectively. Similarly, GEP-II model is constructed by 278 experimental data and trisected into 142, 70 and 66 data for training, testing and validating sets, respectively. The experimental data used in the validating set of these models are independent from the training and testing sets. The results of the statistical parameters obtained from the models indicate that the proposed empirical models have good prediction and generalization capability.

• Journal of Computing Science and Engineering
• /
• v.5 no.2
• /
• pp.150-160
• /
• 2011

Automatic text classification has a long history and many studies have been conducted in this field. In particular, many machine learning algorithms and information retrieval techniques have been applied to text classification tasks. Even though much technical progress has been made in text classification, there is still room for improvement in text classification. In this paper, we will discuss remaining issues in improving text classification. In this paper, three improvement issues are presented including automatic training data generation, noisy data treatment and term weighting and indexing, and four actual studies and their empirical results for those issues are introduced. First, the semi-supervised learning technique is applied to text classification to efficiently create training data. For effective noisy data treatment, a noisy data reduction method and a robust text classifier from noisy data are developed as a solution. Finally, the term weighting and indexing technique is revised by reflecting the importance of sentences into term weight calculation using summarization techniques.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.9
• /
• pp.241-248
• /
• 2022

The current article examines the interactions among students' attitudes to e-learning, their implementation of e-learning in their studies, and learning effectiveness. Significantly, it emphasizes the mediating role of accepting e-learning in training. It applied reliability analyses to test the measurement of items and construct validity, using the research data collected from students at Vietnam National University of Ho Chi Minh. Then, the current article used multiple regressions to inspect the causal relations; and applied procedures to investigate the mediating influence. The empirical results indicate students' attitude to e-learning positively influences their implementation of e-learning in their studies. When students apply e-learning in their studies, they likely achieve the best possible training effectiveness. Statistical evidence on the mediating role of accepting e-learning in training by students on the linkage between their attitude to e-learning and training effectiveness is revealed in this article. The findings of this article make some contributions. For educational administrators, it offers insight into the links among students' attitudes to e-learning, their implementation of e-learning in their studies, and training effectiveness, which likely allows them to establish suitable online training programs. This will be beneficial to both learners and educational institutes.

• Korea Journal of Hospital Management
• /
• v.17 no.2
• /
• pp.34-51
• /
• 2012

This study is an empirical research to identify difference of education training satisfaction by demographic variables, and to investigate the influence of education training satisfaction on the member's organizational commitment in general hospitals. As a result of ANOVA, t-test based on the data of 325 employees in 7 general hospitals in Korea, education training satisfaction has a significant difference by a construct and demographic variables. Education training satisfaction has a significant difference by a construct such as education training system, education training administration and education training practice. Education training satisfaction has a significant difference by occupation, career, scholarship. Education training satisfaction has a partly significant difference by status, age. But it has not a significant difference by sex distinction. Satisfaction of education training system and education training practice has a significantly positive relationship with organizational commitment. But Satisfaction of education training administration has a partly positive relationship with organizational commitment. By these results, the advanced education training structure needs to equipped in order to elevate management performance in general hospitals.

• The Journal of Society for e-Business Studies
• /
• v.26 no.1
• /
• pp.107-126
• /
• 2021

In e-commerce, rapid and accurate automatic product classification according to product information is important. Recent developments in deep learning technology have been actively applied to automatic product classification. In order to develop a deep learning model with good performance, the quality of training data and data preprocessing suitable for the model are crucial. In this study, when categories are inferred based on text product data using a deep learning model, both effects of the data preprocessing and of the selection of training data are extensively compared and analyzed. We employ our CNN model as an example of deep learning model. In the experimental analysis, we use a real e-commerce data to ensure the verification of the study results. The empirical analysis and results shown in this study may be meaningful as a reference study for improving performance when developing a deep learning product classification model.

• Journal of Korean Society for Quality Management
• /
• v.28 no.3
• /
• pp.68-81
• /
• 2000

In this paper, we studied the four stages of quality related education and training and identified alignment factors that have influence on successful TQM education and training. Based on extensive literature reviews the four stages are extracted such as quality concepts training, quality tools training, special topics training, and leadership training. Also we determine the alignment factors. A framewok of research model including above factors is developed and tested statistically. The perceived data are collected from managers of quality departments of 140 Korean firms through survey. The results show that alignment factors which achieve success in Quality related education training are using relevant examples and implementing training at the top in quality concepts training, providing time and opportunity to master skills in quality tools training, organizing courses into a logical curriculum in special topics training, and providing ongoing feedback in leadership training. We also offered numerous suggestions that can help organizations develop effective training programs to meet their objectives.

• Computers and Concrete
• /
• v.26 no.6
• /
• pp.533-546
• /
• 2020

The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.10
• /
• pp.37-45
• /
• 2021

The purpose of this study was to explore and investigate: the direct impact of training on entrepreneurial performance and self-efficacy, the direct impact of leadership attitudes on entrepreneurial performance, and self-efficacy, the direct impact of self-efficacy on entrepreneurial performance, self-efficacy as a mediator of the effect of training on entrepreneurial performance, and self-efficacy as a mediator of the effect of leadership attitudes on entrepreneurial performance. This study purposively involved 131 entrepreneurs in Village-Owned Enterprises, Kuningan, Indonesia. The data was collected using a questionnaire. The data obtained was analyzed using Path Analysis with SPSS statistical software. This study has several findings. First, training has a significant effect on entrepreneurial performance and self-efficacy. Second, leadership attitudes have a significant effect on entrepreneurial performance and self-efficacy. Third, self-efficacy has a significant effect on entrepreneurial performance. Fourth, self-efficacy mediates the effect of training on entrepreneurial performance. Fifth, self-efficacy mediates the effect of leadership attitudes on entrepreneurial performance. The findings demonstrated that using self-efficacy-based training and leadership attitudes can enhance entrepreneurial self-confidence and assist them to improve their performance.

• The Journal of Asian Finance, Economics and Business
• /
• v.10 no.1
• /
• pp.169-179
• /
• 2023

This study aims to determine the factors that influence training programs in increasing entrepreneurial success as a new model for developing entrepreneurship training in a new era. It intended to provide a suggestion for building an entrepreneurship training model for Beginner Young Entrepreneurs (BYE) organized by the Ministry of Youth and Sports of Indonesia. The study used a quantitative method by collecting data through a Google form questionnaire distributed via the WhatsApp group. This study employs samples from 358 BYE training participants for 2017-2020, and data was processed using Amos SEM software to analyze factors that influence the success of entrepreneurship. The results showed that entrepreneurial motivation is a partial mediator in increasing the effect of training on its success by BYE participants. Furthermore, the key factor for increasing entrepreneurial motivation is challenging young people to start businesses. This study recommends that BYE program policymakers build a training model by considering many practical case studies to increase motivation as an important mediator in influencing entrepreneurial success. Meanwhile, to boost the morale of training participants, it is necessary to add significant real challenges for participants to start entrepreneurship. Moreover, future studies should add other independent variables, such as personality.

• Journal of Information Technology Applications and Management
• /
• v.27 no.2
• /
• pp.1-21
• /
• 2020

The purpose of this study is to investigate the behavioral factors affecting the security attitude and intention to use biometrics password based on the protection motivation theory. This study also investigates security awareness training to understand trust, privacy, and security vulnerability regarding biometric authentication password. This empirical analysis reveals security awareness training boosts the protection motivational factors that affect on the behavior and intention of using biometric authentication passwords. This study also indicates that biometric authentication passwords can be used when the overall belief in a biometric system is present. After all, security awareness training enhances the belief of biometric passwords and increase the motivation to protect security threats. The study will provide insights into protecting security vulnerability with security awareness training.