• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.563-571
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• 2023

The purpose of the study is to understand the impact of nursing college students' awareness and self-esteem on nursing professionals. The research design of this study is a descriptive investigative study using convenient samples. The data collection collected structured questionnaires and Google's online survey methods for first- to fourth-year nursing college students at three universities in North Chungcheong Province. The collected data were analyzed using the SPSS window 25.0 program as frequency, percentage, mean and standard deviation, t-test and one-way ANOVA, and post-test as Scheffétest, Pearson correlation coefficient, and multiple regression. The study found that 21.7% (==-.181, p<.001), 2.8% major satisfaction, and 24.5% (β=.420, p<.001), so it is recommended to use it as basic data to establish a curriculum and teaching learning strategy to improve major satisfaction.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.73-80
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• 2024

In this paper, we proposes a method to improve the accuracy of predicting the Korea Composite Stock Price Index (KOSPI) by combining topic modeling and Long Short-Term Memory (LSTM) neural networks. In this paper, we use the Latent Dirichlet Allocation (LDA) technique to extract ten major topics related to interest rate increases and decreases from financial news data. The extracted topics, along with historical KOSPI index data, are input into an LSTM model to predict the KOSPI index. The proposed model has the characteristic of predicting the KOSPI index by combining the time series prediction method by inputting the historical KOSPI index into the LSTM model and the topic modeling method by inputting news data. To verify the performance of the proposed model, this paper designs four models (LSTM_K model, LSTM_KNS model, LDA_K model, LDA_KNS model) based on the types of input data for the LSTM and presents the predictive performance of each model. The comparison of prediction performance results shows that the LSTM model (LDA_K model), which uses financial news topic data and historical KOSPI index data as inputs, recorded the lowest RMSE (Root Mean Square Error), demonstrating the best predictive performance.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.89-98
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• 2024

In this paper, we propose a system for recommending lecture videos at the chapter level, addressing the balance between accuracy and processing speed in chapter-level video recommendations. Specifically, it has been observed that enhancing recommendation accuracy reduces processing speed, while increasing processing speed decreases accuracy. To mitigate this trade-off, a hybrid approach is proposed, utilizing techniques such as TF-IDF, k-means++ clustering, and Graph Neural Networks (GNN). The approach involves pre-constructing clusters based on chapter similarity to reduce computational load during recommendations, thereby improving processing speed, and applying GNN to the graph of clusters as nodes to enhance recommendation accuracy. Experimental results indicate that the use of GNN resulted in an approximate 19.7% increase in recommendation accuracy, as measured by the Mean Reciprocal Rank (MRR) metric, and an approximate 27.7% increase in precision defined by similarities. These findings are expected to contribute to the development of a learning system that recommends more suitable video chapters in response to learners' queries.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.603-608
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• 2024

Nonresponse and missing values are caused by sample dropouts and avoidance of answers to surveys. In this case, problems with the possibility of information loss and biased reasoning arise, and a replacement of missing values with appropriate values is required. In this paper, as an alternative to missing values imputation, we compare several replacement methods, which use mean, linear regression, random forest, K-nearest neighbor, autoencoder and denoising autoencoder based on deep learning. These methods of imputing missing values are explained, and each method is compared by using continuous simulation data and real data. The comparison results confirm that in most cases, the performance of the random forest imputation method and the denoising autoencoder imputation method are better than the others.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Journal of the Korean Chemical Society
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• v.62 no.4
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• pp.299-313
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• 2018

Inquiry-centered education is important in science education, but in the actual education field, scientific research is being done in a uniform manner due to realistic difficulties. In this study, we use RS (Reverse Science) as a secondary chemistry class to provide opportunities for students to engage in inquiry learning and scientific thinking through process-oriented activities. In this study, we developed and applied it to explore the effects on the scientific inquiry abilities of middle school students and checked the students' perception of it. For the application of the program, 128 students were selected from 6 classes of the 2nd grade in D district middle school, 64 from the experimental group and 64 from the comparative group. The experimental group taught RSP-based the chemistry inquiry programs and the comparative group taught instructor-led classes and verification experiments on the same topic over the seventh hour with three themes. In addition, we analyzed the results of the pre- and post-test by using the science inquiry ability test, and discussed the effects of the program based on the students' perceptions through class observation, student activity area, questionnaire and interview. As a result, the class using the program showed statistically significant changes in the science inquiry ability of secondary school students. Specifically, the experimental group was found to be significant in its prediction among the subcomponents of basic exploration ability compared to the comparative group. The differences have also been shown to be significant in terms of data translation, hypothesis setup and variable control, which are subcomponents of integrated exploration capabilities (p <. 05). In addition, students became interested in the process of creating the theory of science, and were highly interested in collaborating with their friends. It also provided students with opportunities to experience scientific thinking through process-oriented inquiry. Finally, based on the positive impact of the RSP-based chemistry inquiry program on students, we were able to identify the potential use of the program.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Tunnel and Underground Space
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• v.31 no.5
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• pp.374-384
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• 2021

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.111-129
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• 2022

Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

• Journal of Science Education
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• v.36 no.2
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• pp.235-250
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• 2012

This paper aims to find out whether or not elementary school students recognize and understand scientific units that they encounter in their everyday life. To select appropriate units for the survey, first, scientific units in elementary textbooks of science and other science related subjects were analyzed. Then it was examined how these units were related to the learners' daily life. The participants in the current survey were 320 elementary school 6th graders. A questionnaire consisted of 11 units of science, such as kg for mass, km for distance, L for volume, V for voltage, s for time, $^{\circ}C$ for temperature, km/h for speed, kcal for heat, % for percentage, W for electric power, pH for acidity, which can often be seen and used in daily life. The students were asked to do the following four tasks, (1) to see presented pictures and select appropriate scientific units, (2) to write reasons for choosing the units, (3) to answer what the units are used for, and (4) to check where to find the units. The data were analyzed in terms of the percentage of the students who seemed to well recognize and understand the units, using SPSS 17.0 statistical program. The results are as follows: Regarding the general use of the units, it was revealed that almost the same units were repeated in science and other subject textbooks from the same grade. With an increase of the students' grade more difficult units were used. As for the use of each unit, it was found that they seemed to relatively well understand what these units kg, km, L, $^{\circ}C$, kcal, km/h, and W stand for, showing more than 91% right. However, the units of V, s, in particular, %, and pH did not seem to be understood. With respect to the recognition of the units, most students did not recognize such units as L for volume and pH for acidity, probably because the units are difficult at the elementary level in comparison to other scientific units. The students indicated that schools were the best place where they could learn and find scientific units related to life, followed by shops/marts, newspapers/broadcasting, streets/roads, homes, and others in that order. The results show that scientific unit learning should be conducted in a systematic way at school and that teachers can play a major role in improving students' understanding and use of the units.