• Journal of Korean Home Economics Education Association
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• v.10 no.1
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• pp.137-151
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• 1998

The purpose of this study is (1)to develop the teaching plan based on Cooperative Learning approach and (2)to investigate the effect of students'Interest on Subject matter and Teaching method and Attitudes to others of the area of Foreign food in Home Economics class. Among those various types of Cooperative Learning's models, this study adopted 'Learning Together'developed by Johnsons. To investigate these purpose, subject matter were analyzed and reconstructed for Cooperative Learning. The tests were developed to evaluate the interest on the Subject matter and teaching methods, and the attitude to others of the students. 108 femail high school students were divided into two groups with 54 students-traditional learning condition, Cooperative Learning condition-and had a 5 session. The subject of the class was Foreign food including Western, Chinese, and Japanes food. Before and after the class, students were tested. The statistical methods used for the study methods used for the study were t-test. The research findings are as follows : When the students in the Cooperative Learning classes were compared before and after the test, (1)Interest on Subject matter were improved considerably(p〈.001) (2)Interest on Teaching methods were improved considerably(p〈.05) (3)Attitude to Others were improved considerably(p〈.001) Therefore when the teaching-learning model based on Cooperative Liarning was used in Home Economics class, their interest on the subject and teaching methods and attitude to others were improved.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.9-18
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• 2022

Purpose: The government around the world is still highlighting the effect of the new variant of Covid-19. The government continues to make efforts to restore the economy through several programs, one of them is National Economic Recovery. This program is expected to increase public and investor confidence in handling Covid-19. This study aims to capture public sentiment on the economic growth rate in Indonesia, especially during the third wave of the omicron variant of the covid-19 virus, that is at the time in the fourth quarter of 2021. Research design, data, and methodology: The approach used in this research is to collect crowdsourcing data from twitter, in the range of 1st to 10th October 2021. The analysis is done by building model using Deep Learning Neural Network method. Results: The result of the sentiment analysis is that most of the tweets have a neutral sentiment on the Economic Growth discussion. Several central figures who discussed were Minister of Coordinating for the Economy of Indonesia, Minister of State-Owned Enterprises. Conclusions: Data from social media can be used by the government to capture public responses, especially public sentiment regarding economic growth. This can be used by policy makers, for example entrepreneurs to anticipate economic movements under certain conditions.

• Hip & pelvis
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• v.34 no.4
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• pp.203-210
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• 2022

Purpose: Total hip arthroplasty (THA) using the direct anterior approach (DAA) is known to have a learning curve. The purpose of this study was to review cases where surgery was performed by an arthroplasty surgeon transitioning from the posterior approach (PA) to the DAA. We hypothesized similar complication rates and improvements in surgical duration over time. Materials and Methods: A review of 2,452 consecutive primary THAs was conducted. Surgical duration, length of stay (LOS), surgical complications, decrease in postoperative day (POD) 1 hemoglobin, transfusion rates, POD 0 and POD 1 pain scores, incision length, leg length discrepancy (LLD), and radiographic cup position were recorded. Results: No differences in surgical duration were observed after the first 50 DAA cases. A shorter LOS was observed for the DAA, and statistical difference was appreciated after the first 100 DAA cases. There were no differences in periprosthetic fractures. A higher rate of infections and hip dislocations were observed with the PA. The PA showed an association with higher transfusion rates without significant difference in POD 1 decrease in hemoglobin over the first 100 DAA cases. Similar POD 0 and POD 1 pain scores with a smaller incision were observed for the first 100 DAA cases. The DAA cohort showed less variation in cup inclination, version, and LLD. Conclusion: DAA is safe and non-inferior in terms of reduced LOS, smaller incision, and less variation in cup position. Fifty DAA cases was noted to be the learning curve required before no differences in duration between approaches were observed.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.4
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• pp.234-242
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• 2021

In this study, we introduce the prediction of brake power for low-speed full ships and container carriers using the linear regression and a machine learning approach. The residual resistance coefficient, wake fraction coefficient, and thrust deduction factor are predicted by regression models using the main dimensions of ship and propeller. The brake power of a ship can be calculated by these coefficients according to the 1978 ITTC performance prediction method. The mean absolute error of the predicted power was under 7%. As a result of several validation cases, it was confirmed that the machine learning model showed slightly better results than linear regression.

• Computers and Concrete
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• v.31 no.4
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• pp.277-292
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• 2023

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a phenomenon (i.e., data generating mechanism) we happen to be interested in. Uncovering such relationships allows us to identify the true workings of a phenomenon and, most importantly, to realize and articulate a model to explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that commonly used prediction- and statistical-based models may not be able to address. From this lens, this paper builds a case for causal discovery and causal inference and contrasts that against common machine learning approaches - all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.43-50
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• 2008

Multinomial logistic regression is a well known multiclass classification method in the field of statistical learning. More recently, the development of sparse multinomial logistic regression model has found application in microarray classification, where explicit identification of the most informative observations is of value. In this paper, we propose a sparse multinomial kernel logistic regression model, in which the sparsity arises from the use of a Laplacian prior and a fast exact algorithm is derived by employing a bound optimization approach. Experimental results are then presented to indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.269-284
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• 2020

This study compares two teaching methods in an introductory statistics course at a large state university. The first method is the traditional lecture-based approach. The second method implements a flipped classroom that incorporates the recommendations of the American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report. We compare these two methods, based on student performance, illustrate the procedures of the flipped pedagogy, and discuss the impact of aligning our course to current guidelines for teaching statistics at the college level. Results show that students in the flipped class performed better than students in traditional delivery. Student questionnaire responses also indicate that students in flipped delivery aligned with the GAISE recommendations have built a productive mindset in statistics.