• 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%.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.269-274
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• 2022

With the advent of the Fourth Industrial Revolution modern society demands global convergence talent with expertise. The purpose of this study is to suggest the Person-organizational Fit can be enhanced only when university institutions enhance the learning flow and learning interest of Chinese students. Only when the suitability of Chinese students and Univ. is good will it be of great help to the educational performance and administrative management of international students, and the learning atmosphere and university image can be promoted. However, international students with excessive self-efficacy may have a negative effect on Person-organizational Fit to making it difficult to educate and manage students. On the other hand, for international students with low self-efficacy, the higher the immersion in learning, the higher the individual-organization (Univ) suitability.

• Journal of Distribution Science
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• v.22 no.4
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• pp.1-10
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• 2024

Purpose: This study is aimed to introduce the application of phantom approach with structural equation modelling method for online learning. By integrating these innovative methodologies, the research seeks to advance the understanding of how the phantom approach can effectively complement and augment structural equation modeling techniques. Research design, data and methodology: A theoretical framework of Technology Acceptance Model (TAM) was modified and updated. A questionnaire was developed and used to extract information from 189 instructors who used online learning as their primary medium. The Covariance Based Structural Equation Modelling (CBSEM) was applied to test the direct effects and the phantom approach is used to handle the 2 mediators in the model. Results:social influence, perceived usefulness, and perceived ease of use exerted discernible impacts on instructors' intentionsto engage in online learning. These findings illuminate the intricate dynamics influencing instructor behavior within the realm of online education, underscoring the significance of various factors in shaping their intentions. Conclusions: In additions, the perceived usefulness and perceived ease of use had mediated the effect of social influence and instructor intention using phantom approach. Therefore, one can have concluded that this modified model was also confirmed, thereby reinforcing distribution strategies to online learning and overall education presence.

• Journal of Gifted/Talented Education
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• v.7 no.2
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• pp.19-45
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• 1997

Twenty-three of International Math Olympians raised in Korea were served as the subjects to answer the following questions: (1) What family and school factors contribute to the development of the math talent of the Olympians\ulcorner (2) What impacts have the Olympiad program on the mathematically talented students\ulcorner By means of questionnaire survey and in-depth interview, the related data were collected. The questionnaires were developed by James Campbell for cross-cultural studies. The major findings were as follows: (1) the olympians were mostly 1st-born child and were "discovered" in an early age; (2) most olympians ranked highly in the class; (3) the SES of the Olympians' family were varied, though the majority were high; (4) the Olympians' family support and learning environment were reported strong and positive; (5) the Olympiad experiences were, in general, positive to the subjects, especially in learning attitude toward math and science, self-esteem and in autonomous learning and creative problem solving; (6) there were almost none special program designed for the Olympians during their school years; (7) the degree of computer literacy were varied according to the subject's personal interest and the accessibility to the computer; (8) most Olympians had not yet showed special achievement other than math as there were still students; (9) the Olympians were individuals with unique characteristics.teristics.

• Journal of Korean Clinical Health Science
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• v.7 no.2
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• pp.1289-1297
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• 2019

Purpose: The purpose of this study was to investigate occupational therapy at M college in Changwon and the impact of smart phone use on the school life of college students and to help them find ways to further develop in the future. Methods; Data collection was conducted using questionnaires, and the questionnaires consisted of 152 total questions with 15 interpersonal questions, 23 problem solving skills, 43 self-efficacy, 16 class participation scale, and 55 self-directed learning scale. It was conducted to first and second graders of M college and conducted a survey through the corresponding academic year from March 26, 2019 to March 29, 2019 to retrieve 120 questionnaires and use them for analysis. The collected data were analyzed using SPSS. Statistic 20.0. Results: Studies show that "school life satisfaction" is usually the highest at 53 percent. The "smartphone user motivation" was the highest with 50.8 percent, while the "most frequently used feature on smartphones" was the highest with 57.5 percent on SNS. Satisfaction after using a smartphone was the highest with 49.2 percent, while 41.7 percent said it would be easier to acquire and utilize information in the areas of satisfaction. Conclusion: Smartphone addiction, interpersonal relationships, problem-solving skills, self-efficacy, participation in classes, and self-control learning items are not only affected by one part, but also by the other.

• The Journal of Industrial Distribution & Business
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• v.13 no.8
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• pp.19-27
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• 2022

Purpose: CEOs develop policies through their effective decision-making while employees implement the policies so that a business realizes the expected returns. This research focuses on the importance of the CEO's sustainable leadership to distribute environmental education culture to improve employees' environmental performance. Research design, data and methodology: The PRISMA that is selected by the present research is an evidence-based minimum group of entities for reporting in systematic reviews and meta-analyses. The core focus of the concept is to note studies that evaluate the impacts of intervention and can also be utilized as a basis for writing systematic reviews rather than intervention evaluations. Results: The current investigation indicates that there are four kinds of suggestions (a. Increased organizational learning, b. Open communication, c. Participative decision making, d. Psychological empowerment) how the management should develop sustainable leadership for distributing green culture and improving employee green performance. Conclusions: Based on four solutions, the present research concludes that sustainable leadership for CEOs is not only of advantage in terms of protecting the environment and the people, but it fosters increased organizational learning. Increased organizational learning leads to better employee sustainable performance, which includes financial performance and the social and environmental initiatives the organization implements.

• Advances in nano research
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• v.15 no.6
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• pp.533-539
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• 2023

This study aims to examine four machine learning (ML)-based models for their potential to estimate the splitting tensile strength (STS) of manufactured sand concrete (MSC). The ML models were trained and tested based on 310 experimental data points. Stone nanopowder content (SNPC), curing age (CA), and water-to-cement (W/C) ratio were also studied for their impacts on the STS of MSC. According to the results, the support vector regression (SVR) model had the highest correlation with experimental data. Still, all of the optimized ML models showed promise in estimating the STS of MSC. Both ML and laboratory results showed that MSC with 10% SNPC improved the STS of MSC.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.97-115
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• 2023

In South Korea, chronic kidney disease(CKD) impacts around 4.6 million adults, leading to a high reliance on hemodialysis. For effective dialysis, vascular access is crucial, with decisions about vascular surgeries often made during dialysis sessions. Anticipating these needs could improve dialysis quality and patient comfort. This study investigates the use of Artificial Intelligence(AI) to predict the timing of surgeries for dialysis vessels, an area not extensively researched. We've developed an AI algorithm using predictive maintenance methods, transitioning from machine learning to a more advanced deep learning approach with Long Short-Term Memory(LSTM) models. The algorithm processes variables such as venous pressure, blood flow, and patient age, demonstrating high effectiveness with metrics exceeding 0.91. By shortening the data collection intervals, a more refined model can be obtained. Implementing this AI in clinical practice could notably enhance patient experience and the quality of medical services in dialysis, marking a significant advancement in the treatment of CKD.

• Industry Promotion Research
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• v.9 no.2
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• pp.185-195
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• 2024

This study examines the factors influencing college students' entrepreneurial intentions in the rural entrepreneurial environment of colleges, considering the role of entrepreneurial learning as a mediator. It analyzes four key environmental dimensions: policies and regulations, entrepreneurship education, financial support, and social culture. A survey of 5 30 college students in Zhejiang Province revealed that the college entrepreneurial environment favorably impacts entrepreneurial learning and rural entrepreneurial intentions. Based on these findings, practical measures are proposed to enhance students' intentions, emphasizing personal practice, university leadership, and government support. These efforts can optimize the college entrepreneurial environment and foster higher entrepreneurial intentions among students.

• Journal of The Korea Institute of Healthcare Architecture
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• v.30 no.1
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• pp.19-26
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• 2024

Purpose: Autism spectrum disorder impacts children's social, sensory, and language development, necessitating specialized educational support. Special classrooms play a crucial role in providing an appropriate learning environment for children with autism. However, there is a lack of systematic research on creating effective environments in these special classrooms. Methods: This study aims to gain a comprehensive and systematic understanding of the environmental composition of special classrooms for children with autism spectrum disorder, using the following systematic methodologies including literature review and case study. Results: Sensory spaces in special classrooms for children with autism help regulate sensory stimuli and promote sensory development. They provide stability, reducing stress from excessive stimuli, and enhance emotional stability. These spaces also promote communication and interaction among children and expand the diversity of learning activities, enriching experiences and stimulating interest in learning. Implications: Based on the results, we propose suggestions for improving the environment of special classrooms for children with autism spectrum disorder and provide direction for the design of such environments.