• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.153-162
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• 2022

Relevance. Innovative activities in education should be aimed at ensuring the comprehensive development of the individual and professional development of students. The main idea of modular technology is that the student should learn by himself, and the teacher manages his learning activities. The advantage of modular technology is the ability of the teacher to design the study of the material in the most interesting and accessible forms for this part of the study group and at the same time achieve the best learning results. Innovative Moodle technology. it is gaining popularity every day, significantly expanding the space of teaching and learning, allowing students to study inter-faculty university programs in depth. The purpose of this study is to assess the quality of implementation of the e-learning system Moodle. The study was conducted at the South Ukrainian National Pedagogical University named after K. D. Ushinsky in order to identify barriers to the effective implementation of innovative distance learning technologies Moodle and introduce a new model that will have a positive impact on the development of e-learning. Methodology. The paper used a combination of theoretical and empirical research methods. These include: scientific analysis of sources on this issue, which allowed us to formulate the initial provisions of the study; analysis of the results of students 'educational activities; pedagogical experiment; questionnaires; monitoring of students' activities in practical classes. Results. This article evaluates the implementation of the principles of distance learning in the process of teaching and learning at the University in terms of quality. The experiment involved 1,250 students studying at the South Ukrainian National Pedagogical University named after K. D. Ushinsky. The survey helped to identify the main barriers to the effective implementation of modern distance learning technologies in the educational process of the University: the lack of readiness of teachers and parents, the lack of necessary skills in applying computer systems of online learning, the inability to interact with the teaching staff and teachers, the lack of a sufficient number of academic consultants online. In addition, internal problems are investigated: limited resources, unevenly distributed marketing advantages, inappropriate administrative structure, and lack of innovative physical capabilities. The article allows us to solve these problems by gradually implementing a distance learning model that is suitable for any university, regardless of its specialization. The Moodle-based e-learning system proposed in this paper was designed to eliminate the identified barriers. Models for implementing distance learning in the learning process were built according to the CAPDM methodology, which helps universities and other educational service providers develop and manage world-class online distance learning programs. Prospects for further research focus on evaluating students' knowledge and abilities over the next six months after the introduction of the proposed Moodle-based program.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.467-479
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• 2014

The purpose of this study was to provide the data for discussion related to oral health promotion policies for the elderly by examining and analyzing the prosthesis conditions and necessity for the fixed and removable dentures among the Korean elderly. The data, obtained from 4,557 elderly aged at 65 or higher who were targeted for the 5th National Health Nutrition Survey, were analyzed through the complex sample frequency analysis, complex sample cross analysis, and complex sample logistic regression analysis. The results of analysis showed significant relevance of whole denture implant and the necessity thereof in older subjects, rural community, and subjects with lower education background, regarding the state of upper jaw/lower jaw prosthesis and the necessity for upper jaw/lower jaw fixed partial denture/whole denture. In addition, the necessity for prosthesis implant was found to have correlation with the income and subjective health condition, while the necessity for artificial teeth(denture) was found to have correlation with the gender, age, education, and subjective oral health condition. Therefore, it is considered necessary to map out the prevention and treatment policies designed to help maintain and promote oral health based on oral health education, along with the policies that aim to recover the neutralized oral health functions, in relation to the oral health of the elderly.

• Journal of Climate Change Research
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• v.5 no.1
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• pp.47-59
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• 2014

This study was conducted to estimate of future productivity for major spicy vegetables by the change of meteorological factors, temperature and precipitation. Based on analysis of meteorological factors, incidence of major disease(phytophthora blight and anthracnose) for hot pepper was over 50% with temperature over $18.3^{\circ}C$ in May and precipitation over 532 mm in July. And the meteorological factors in the August have deeply related to the incidence of virus disease(CMV and BBWV2) for hot pepper, however, both the meteorological factors and the incidence of virus disease showed to the opposite tendency. An analysis of the relevance of the white rot disease and the meteorological factors for garlic, a disease was highly investigated with temperature $15.0^{\circ}C$ to $15.9^{\circ}C$ in April to May. On the onion, higher incidence of white rot was investigated with temperature over $4.0^{\circ}C$ in November to January and precipitation over 40 mm in March. The occurrence of major disease for spicy vegetables and meteorological factors as a result of regression analysis, the optimal cultivation area of peppers and onions will be gradually expanded to the central regions in the near future in Korea.

• Journal of the Korean Chemical Society
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• v.50 no.2
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• pp.153-162
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• 2006

The purpose of this study was to analyze the STS (science-technology-society) contents in chemistry chapters of middle school science textbooks standardized by 7th national curriculum, and to investigate chemistry teachers' perception of STS education. This study was based on 8 essential elements in STS education suggested by Yager and 9 activities in SATIS (science and technology in society). The questionnaire and interview were used to investigate chemistry teachers' perception. As a result of this study, the average value of the STS contents in chemistry chapters of 7 kinds of middle school science textbooks was 22.4%, and the STS contents were preponderated to essential elements of science application and local and community relevance. And STS contents showed that science 2 textbooks were the most of all and in order of science 1 textbooks and science 3 textbooks. As a result of analysis by activities in SATIS, most activities were practice activity, problem-solving and decision making, and structured discussion. Chemistry teachers' perceptions of STS education were following. There were many responses that STS education was necessary for educational efficiency. On the other hand STS education was unnecessary because there were few effective teaching-learning method related with STS education. From these results, middle school science textbooks have to be complemented because 2 essential elements of the STS contents were preponderated in the science textbooks. And the teaching-learning method connected with STS education will have to be developed for the efficiency of STS education.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.871-880
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• 2018

Recently, the technology of recommendation of POI (Point of Interest) related technology is getting attention with the increase of big data related to consumers. Previous studies on POI recommendation systems have been limited to specific data sets. The problem is that if the study is carried out with this particular dataset, it may be suitable for the particular dataset. Therefore, this study analyzes the similarity and correlation between stores using the user visit data obtained from the integrated sensor installed in Seoul and Songjeong roads. Based on the results of the analysis, we study the preference prediction system which recommends the stores that new users are interested in. As a result of the experiment, various similarity and correlation analysis were carried out to obtain a list of relevant stores and a list of stores with low relevance. In addition, we performed a comparative experiment on the preference prediction accuracy under various conditions. As a result, it was confirmed that the jacquard similarity based item collaboration filtering method has higher accuracy than other methods.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Veterinary Clinics
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• v.32 no.3
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• pp.278-281
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• 2015

An 8-year-old male red fox (Vulpes vulpes) in Species Restoration Technology Institute of Korea National Park Service (KNPS), revealed nodular growths in its ventro-cervical region. The fox was introduced from Young-Yang Gun in 2012 to KNPS for re-introduction of the red fox. It has been cared in captive facility and showed the mass in August 2013 that was sent to Wildlife Medical Center. For the diagnosis of underlying disease and cervical mass, radiographical and sonographical examinations, complete blood count, serum chemistry analysis, peripheral blood smear examination and surgical removal of the mass were performed. The mass was fixed in 10% neutral buffered formalin and processed routinely for haematoxylin and eosin (HE) stain. Based on hematological and serum chemical examination, the fox showed mild leukocytosis, thrombopenia, increase of creatine kinase MB (CKMB) and uric acid. However, it was considered as no clinical relevance since the fox showed no related clinical signs. Macroscopically, the mass was round shape, whitish and well-demarcated. Microscopically, it was diagnosed as a lipoma consisting of mature adipose tissue. Lipoma is a common benign tumor in most domestic animals, however it has never been reported in the red fox. The present case report provides comprehensive diagnosis of a subcutaneous lipoma in a red fox.

• Knowledge Management Research
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• v.25 no.2
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• pp.177-191
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• 2024

This study aims to analyze policy changes over time by conducting a textual analysis of the basic plan for activating convergence research and development. By examining the basic plan for convergence research development, this study looks into changes in convergence research policies and suggests future directions, thereby exploring strategic approaches that can contribute to the advancement of science and technology and societal development in our country. In particular, it sought to understand the policy changes proposed by the basic plan by identifying the relevance and trends of topics over time. Various analytical methods such as TF-IDF analysis, topic modeling (LDA), and network (CONCOR) analysis were used to identify the key topics of each period and grasp the trends in policy changes. The analysis revealed clustering of topics by period and changes in topics, providing directions for the convergence research ecosystem and addressing pressing issues. The results of this study are expected to provide important insights to various stakeholders such as governments, businesses, academia, and research institutions, offering new insights into the changes in policies proposed by previous basic plans from a macroscopic perspective.