• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• The Journal of Economics, Marketing and Management
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• v.12 no.3
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• pp.35-42
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• 2024

Purpose This study aims to investigate the correlation of social skills and metacognition among university students participating in service-learning programs. Also by evaluating the satisfaction of college students participating in service learning, this research seeks to understand the impact of this program on learning experiences. Research design, data and methodology: The research period spans two semesters, each comprising 15 weeks, from March 2, 2023, to December 20, 2023. Detailed procedures, including planning, preparation, data collection, analysis, and organization, cover activities conducted over the course of 30 weeks. These activities encompass various stages, from initial classroom planning with designated English storybooks to reflection and feedback sessions aimed at continuous development. Data collection methods include surveys, interviews, and observations, allowing for a comprehensive examination of social skills and metacognition among participating students. Results: The results show significant correlations between social skills and metacognition, such as the correlation between knowledge and statistics (r = 0.759, p < .01), the moderate correlation between cooperation and knowledge (r = 0.532, p < .01), the moderate correlation between statistics and cooperation (r = 0.539, p < .01), and the correlation between self-regulation and assertion (r = 0.278, p < .001). The average score of the satisfaction of college students participating in service learning was 4.8 out of 5. Conclusions: This study highlights the significant role of service-learning in boosting social skills and metacognition among university students. This study enhances the academic understanding of the relationships between social skills, metacognition, and service-learning programs, contributing to the expansion of both theoretical and practical knowledge in the field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.575-577
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• 2013

The research was aimed at the hotel industry's employees in order to test the efficiency of e-Learning, which is emerging as the alternative training system to the conventional one. The independent variables are the quality of e-Learning, including the qualities of the system, contents, and service of e-Learning, and the learning characteristic factor, including the quality factor of e-Learning, the self-efficacy of the user, learning motivation, and the flow of learning. Furthermore, the intervening variables are its perceived usefulness and the satisfaction factor of the user known as the so-called utility of e-Learning, continuous intention to use in terms of efficaciousness, and the spread of education and training. The dependent variable is customer orientation, known as the ultimate efficaciousness of corporate e-Learning.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.13-19
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• 1996

In this paper, we study and compare two types of methods for classification when both continuous and categorical variables are used to describe each individual. One is neural network(NN) method using backpropagation learning(BPL). The other is logistic model(LM) method. Both the NN and LM are based on projections of the data in directions determined from interconnection weights.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.393-402
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• 1990

An iterative learning contol method is presented for a class of linear periodic systems, in which a parameter estimator of the system together with an inverse system model is utilized to generate the control signal at each iteration. A convergence proof is given and two numerical examples are illustrated to show the validities of the algorithm. In particular, it is shown that the method is useful for the continuous path control of robot manipulators.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1371-1373
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• 1996

A clustering algorithms with dynamic adjustment of learning rate for GBFCM(Gradient Based FCM) is proposed in this paper. This algorithm combines two idea of dynamic K-means algorithms and GBFCM : learning rate variation with entropy concept and continuous membership grade. To evaluate dynamic GBFCM, we made comparisons with Kohonen's Self-Organizing Map over several tutorial examples and image compression. The results show that DGBFCM(Dynamic GBFCM) gives superior performance over Kohonen's algorithm in terms of signal-to-noise.

• Journal of KIISE
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• v.45 no.1
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• pp.22-29
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• 2018

Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.1
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• pp.141-150
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• 2021

Purpose : The purpose of this study was to investigate the effect of case-based learning application in human growth development classes on nursing students' problem-solving ability, self-directed learning ability, and academic self-efficacy. Methods : The research method was a self-report questionnaire before and after case-based learning for second-year nursing students who took the human growth development course at U University in K city. The collected data were statistically processed using SPSS WIN 21.0. Results : The results of the study showed that after case-based learning, problem-solving ability, self-directed learning ability, and academic self-efficacy were all significantly improved. In addition, as a result of examining the correlation between each variable after case-based learning, problem solving ability score and self-directed learning ability score (r=.54, ｐ<.01), and problem solving ability scores and academic self-efficacy scores (r=.44, ｐ<.01), were significantly correlated with self-directed learning ability scores and the academic self-efficacy reduction scores (r=.76, ｐ<.01). Conclusion : The results of this study suggested the need for various learning programs such as case-based learning to improve nursing students' problem-solving abilities and self-directed learning abilities and their application. In addition, to improve the learning self-efficacy of nursing students, a continuous and systematic study is suggested to develop and apply customized educational programs according to the learners' preferences. Since the sample group in this study was limited to one university, there were few cases and no control group, so there are limitations in generalizing the test effect, However, significant differences a were verified in the case-based learning pre-tests and post-tests.