• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.89-98
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• 2022

Using the extended technology acceptance model, this study aims to quantitatively analyze the user experience with the . In the case of Perceived Usefulness and Perceived Ease of Use, only limited detailed factors have significant impacts on the degree of satisfaction. The degree of satisfaction has positive correlations with the degree of immersion and the variables of Behavioral Intention to Use Metaverse. Although sophisticatedly remediates using digital technology to provide visitors with the sameness of exhibits, exhibition circulation and interaction method, the metaverse exhibition does not acquire the same value of the exhibition. In conclusion, cannot replace , however, it has the potential to offer learning usefulness to visitors with low accessibility to the art museum.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.9
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• pp.317-325
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• 2017

In this paper, we try to classify Korean hedge sentences, which are regarded as not important since they express uncertainties or personal assumptions. Through previous researches to English language, we found dependency information of words has been one of important features in hedge classification, but not used in Korean researches. Additionally, we found that word embedding vectors include the word usage information. We assume that the word usage information could somehow represent the dependency information. Therefore, we utilized word embedding and neural networks in hedge sentence classification. We used more than one and half million sentences as word embedding dataset and also manually constructed 12,517-sentence hedge classification dataset obtained from online news. We used SVM and CRF as our baseline systems and the proposed system outperformed SVM by 7.2%p and also CRF by 1.2%p. This indicates that word usage information has positive impacts on Korean hedge classification.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.539-544
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• 2024

This paper examines the necessity and direction of digital literacy education as university education in the AI era. Digital literacy can be considered universal education about everyday culture in a digital environment, and its scope is expanding to cultivate the competencies necessary for citizens of a digital society, rather than simply the ability to use digital devices. In this paper, the university liberal arts curriculum has strengthened the information literacy area to reflect the changes of the times, but it is presented as a problem that it is still focused on the technical aspects of learning how to use digital devices and specific programs. It was suggested that the direction of digital literacy education in universities should not be limited to the technical and instrumental aspects of using digital devices, but that it would be desirable to focus on digital ethics considering the social impacts that may arise from the use of digital devices.

• Journal of Korean Library and Information Science Society
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• v.55 no.3
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• pp.167-192
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• 2024

Gamification is an effective strategy to enhance the quality of academic library services by encouraging student engagement and participation. This study developed a design framework for the effective implementation of gamification in academic libraries. To this end, a framework based on the information literacy model was developed through a literature review, content analysis of Korean and international case studies, and in-depth interviews with five librarians of academic libraries. The framework outlines the design elements and game mechanisms to be considered at each stage of the process, including task definition, information search, collection, utilization, and integration. Additionally, performance indicators were established to measure the cognitive, emotional, and social impacts of gamification. This study is expected to serve as a foundation for the systematic implementation and evaluation of gamification in academic libraries, ultimately contributing to increased user participation and enhanced learning motivation.

• Science of Emotion and Sensibility
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• v.12 no.3
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• pp.279-288
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• 2009

The purpose of this study was to verify the impact of cognitive factors and GSR on the task performance. For this study 64 students participated. Multiple regression and repeated measures were applied to analyze the data. The result for the survey indicated that previous knowledge, physical efforts, and task difficulty had significant impacts on task performance. Particularly, task difficulty has a negative impact. This can be interpreted as someone who has high prior knowledge inputs higher physical efforts with low task difficulty perception will show high performance. On the other hand, the low arousal level of GSR in the evaluation stage is a prediction variable of task performance. This result shows that high prior knowledge and low arousal level of GSR produces high performance. However, the analysis of difference in GSR between learning and evaluation stages does not show significant difference. It suggests that physiological measure such as GSR is reliable index of cognitive load; however, it partially represents cognitive load. Other crucial factors should be added for comprehensive measures.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.590-596
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• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.

• Journal of the Korean School Mathematics Society
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• v.20 no.3
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• pp.211-237
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• 2017

Depending on the type of textbook, the word and mathematical notation expression used in high school mathematics textbooks varied and there were also some differences on the mathematical definition and the content description methods. Not only the composition of textbooks but also various expressing ways of textbooks have significant impacts on teaching and learning of teacher and student. The diversity of expression had pros and cons like both sides of a coin. There is a positive aspect that we can pursue pedagogical diversity. Simultaneously there is a negative aspect that the possibility of acting as a learning burden exists in the viewpoint of the student and the equality of evaluation may be undermined. In this study, Preferentially we focused on analyzing the actual situation rather than judging what is more appropriate about the diversity of words and notation expressions used in mathematics textbooks which is based on the current curriculum. For this purpose, we analyzed 56 kinds of mathematics textbooks based on the 2009 revised mathematics curriculum, and presented four aspects(terms expressing, notations expression, mathematical definition, content description method) with examples about differences of the various expressions used in textbooks including 'terms and notations'.

• Journal of Korean Home Economics Education Association
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• v.32 no.3
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• pp.1-26
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• 2020

The purpose of this study is to develop and implement new classes using the concept of personal color system to the section 'Daily Outfits & Clothing Acquisition' in the 2015 revision of middle school home economics curriculum, and find out the effects on middle school students. For this, We redesigned a curriculum for 'Clothing Outfits and Self-Expression' part using personal color system, developed teaching/learning plans and teaching/learning materials, and examined the changes in self-identity, attitude in clothing life, and class satisfaction among students at the end of the class. The results of this research are as follows. After the implementation of 'Daily Outfits & Clothing Acquisition' unit classes which included the concept of personal color system, the students' self-identity and attitude in clothing life were improved. And according to the results of the individual interviews, students were generally satisfied with the class. If the 'Daily Outfits & Clothing Acquisition' unit classes using personal color system is widely used in middle school home economics classes, it can have positive impacts on adolescents and is expected to be of great help to home economics teachers as a valuable class material.