• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

• Korea Journal of Hospital Management
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• v.14 no.4
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• pp.1-24
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• 2009

The purpose of this study was to find out the differences and relationships among balanced strategy, coordinating and learning mechanism, and perceived performance of hospitals in Korea, and provide some directions to establish effective strategic management of hospital. Measure items on balanced strategy, coordinating and learning mechanism, and perceived performance were developed from previous studies. Questionnaire was sent and received through Internet site and e-mail during May, 2008. Data were collected from key informant in each institutions, and analyzed using frequency analysis, T-test, ANOVA, correlation and regression analysis. The major findings of this study were as follows: 1. The level of strategic selection and external learning mechanism of private hospital was lower than that of medical corporation, and others corporation hospital. 2. There was little difference between hospitals in metropolitan and those in small cities. 3. Hospitals that have under 100 beds were statistically lower level in strategic selection and external learning mechanism than hospitals has over 100 beds. 4. Formal coordinating and external learning mechanism, and foundation form(medical corporation) were significantly influenced on profitability from specialized field. 5. Strategic selection and adaptation mechanism were significantly affected on total profitability. 6. Strategic selection and external learning mechanism were significantly influenced on competitive power around its local market. Hospitals that are to be competitive by specialization should have to establish mechanism for management such as balanced strategy, coordinating and learning mechanism.

• The Journal of Korean Association of Computer Education
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• v.14 no.1
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• pp.81-89
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• 2011

Cyber-home Learning aims to extend learning space from the classroom to real-life situations, and teachers of research schools on Cyber-home learning have indicated the importance of connection with school instruction in order to improve students' performance. The goal of this study is to evaluate the influence of the instructional method( blended-learning vs just cyber learning) and the learning styles by Kolb's LSI on Cyber-home learning. We carried out the experiment using two similar classes in the social and math subjects for 1 semester. The results statistically shows the instructional method is the most influence on learning score and the next element is the learning styles. Therefor this study proved again Cyber-home learning is effective when connecting with school instruction and also the more various contents on learning styles could be supportive to students. In other words teacher's role and the adaptive learning contents by learning styles are essential for Cyber-home Learning's success.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.47-59
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• 2012

A number of organizations have had big interests in studies concerning leadership and in academic areas, in not only management but also psychology. Until now, leadership has been accentuated by managers or team leaders especially. Recently, however, the concept of self-leadership directing one's own activities through self-control or self-management is being focused on in practices and in academia. This study is to investigate the influence between self-leadership strategies and learning performance in IT classes mediated by attitude of attendance focused on the social science students in two universities (Korea (121 samples) and India (106 samples)). And this research tried to compare difference between two university students. Research results can give us direction of task-taking attitudes in firms or learning attitudes in teaching organizations and implications to human resource managers who are in charge of improving learning performance or productivity.

• Educational Technology International
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• v.13 no.1
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• pp.79-100
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• 2012

The focus of this research is on identifying the problems that learners experience during online problem-based learning (e-PBL) from a cognitive perspective. The study is concentrated on learners' cognitive load level at each stage of e-PBL. The research questions are specifically as follows: What is the level of cognitive load at each stage of e-PBL and what is the relationship between cognitive load and group performance? What cognitive difficulties are experienced by learners in e-PBL and what causes cognitive difficulties? In this study, we found that cognitive load was the highest in stage 1 and there was negative relationship between cognitive load at stage 1 and group performance. In addition, learners experienced difficulties during e-PBL such as the complexity of task, the difficulty in collaboration, and the lack of appropriate references. For further study, we will investigate some strategies regarding adjusting learners' cognitive load in the early stages of e-PBL.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.217-227
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• 2014

Since 2010, the universities in Korea have been faced severe difficulties on the selection of students and on the delivery of high quality education services, as the number of students is reduced and the college entrance rate is declined. To solve these problems, the universities have introduced the various and professional education services such as team-based projects, case study, e-learning, action learning, etc. The purpose of this study is to examine the effect of team-based project learning on the student's satisfaction in business education. The 4 factors(team cohesiveness, teamwork, team performance and goal achievement) are measured by using questionnaire survey and data are collected from 134 students(34 teams) for 4 subjects. The results show that the structure of team cohesiveness${\rightarrow}$teamwork${\rightarrow}$student's satisfaction is statistically significant, and that team performance and goal achievement are not significant. The student's satisfaction in team-based project learning would highly be related with team cohesiveness.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• International Journal of Knowledge Content Development & Technology
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• v.6 no.2
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• pp.5-27
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• 2016

This research aims to explore the acceptance of Moodle as a teaching and learning tool by the faculty of the Department of Information Studies (IS) at Sultan Qaboos University (SQU) in the Sultanate of Oman. The researchers employed the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the effects of performance expectancy, effort expectancy, social influence and facilitating conditions on the behavioural intention of SQU faculty members to employ Moodle in their instruction. Data were collected by the interview method. Results showed the emergence of two faculty groups: one uses Moodle and one does not use Moodle. In group that uses Moodle, performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention are positively related, thereby influencing the faculty members' use behavior. In addition to the aforementioned UTAUT constructs, four additional factors affect Moodle's adoption. These moderators are gender, age, experience and the voluntariness of use, amongst which gender exhibits the least influence on Moodle adoption. That is, male and female faculty generally both use the learning platform. Although some members of the group that does not use Moodle exhibit optimistic performance expectancy for technology, the overall perception in this regard for Moodle is negative. The other UTAUT constructs exert no influence on this group's adoption of the learning platform.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2230-2234
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• 2006

A SCO is a learning resource that is retrieved by a learner in the SCORM. A storage policy is required a learner to search SCOs rapidly in e-learning environment. In this paper, We define the mathematical formulation of clustering method for SCOs. Also we present criteria for cluster evaluation and describe procedure to evaluate each SCO. We show the search based on proposed clustering method increase performance than the existing search though performance evaluation.