• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.55-65
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• 2023

In this paper, we apply KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) algorithm-driven topic extraction and topic frequency analysis to deep learning and reinforcement learning research to discover the rapidly changing trends in them. First, we crawled abstracts of research papers on deep learning and reinforcement learning, and temporally divided them into two groups. After pre-processing the crawled data, we extracted topics using KeyBERT algorithm, and then analyzed the extracted topics in terms of topic occurrence frequency. This analysis reveals that there are distinct trends in research work of all analyzed algorithms and applications, and we can clearly tell which topics are gaining more interest. The analysis also proves the effectiveness of the utilized topic extraction and topic frequency analysis in research trend analysis, and this trend analysis scheme is expected to be used for research trend analysis in other research fields. In addition, the analysis can provide insight into how deep learning will evolve in the near future, and provide guidance for select research topics and methodologies by informing researchers of research topics and methodologies which are recently attracting attention.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.388-393
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• 2024

The convergence of machine learning and smart farm is becoming more and more important. The purpose of this research is to quantitatively analyze machine learning and smart farm with bibliographic data from 2013 to 2023. This study analyzed the 251 articles, filtered from the Web of Science, with regard to the article publication trend, the article citation trend, the top 10 research area, and the top 10 keywords representing the articles. The quantitative analysis results reveal the four points: First, the number of article publications in machine learning and smart farm continued growing from 2016. Second, the article citations in machine learning and smart farm drastically increased since 2018. Third, Computer Science, Engineering, Agriculture, Telecommunications, Chemistry, Environmental Sciences Ecology, Material Science, Instruments Instrumentation, Science Technology Other Topics, and Physics are top 10 research areas. Fourth, it is 'machine learning', 'smart farming', 'internet of things', 'precision agriculture', 'deep learning', 'agriculture', 'big data', 'machine', 'smart' and 'smart agriculture' that are the top 10 keywords composing authors' keywords in the articles in machine learning and smart farm from 2013 to 2023.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.295-301
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• 2019

Since the third artificial intelligence boom was triggered by deep learning, it has been 10 years. It is time to analyze and discuss the research trends of deep learning for the stable development of AI. In this regard, this study systematically analyzes the trends of research on deep learning over the past 10 years. We collected research literature on deep learning and performed LDA based topic modeling analysis. We analyzed trends by topic over 10 years. We have also identified differences among the major research countries, China, the United States, South Korea, and United Kingdom. The results of this study will provide insights into research direction on deep learning in the future, and provide implications for the stable development strategy of deep learning.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.293-299
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• 1995

A methodology is applied to identify tile learning trend related to the safety and availability of U.S. commercial nuclear power plants. The application is intended to aid in reducing likelihood of human errors. To assure that tile methodology ran be easily adapted to various types of classification schemes of operation data, a data bank classified by the Transient Analysis Classification and Evaluation(TRACE) scheme is selected for the methodology. The significance criteria for human-initiated events affecting tile systems and for events caused by human deficiencies were used. Clustering analysis was used to identify the learning trend in multi-dimensional histograms. A computer rode is developed based on tile K-Means algorithm and applied to find the learning period in which error rates are monotonously decreasing with plant age.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• Journal of Elementary Mathematics Education in Korea
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• v.17 no.2
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• pp.265-283
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• 2013

This research puts the purpose in examining the general trend of mathematics underachievers in the elementary school based on the analysis results of mathematics diagnostic assessment of mathematics underachievers and the recognition of teachers and students. To achieve research questions, this research analyzed the results of diagnostic assessment of mathematics underachievers from the third grade to the sixth grade of two elementary schools, Seoul, carried out in March, 2010. Results were classified according to grades and genders. Each analysis was performed under the standards of frequency and frequency level. Secondly, questionnaires were conducted, and general trend of mathematics underachievers were analyzed. Conclusion based on research results above can be drawn as follows: Firstly, as a result of analyzing the general trend of mathematics underachievers in the elementary school, mathematics underachievers appeared to be accumulated and intensified as their grade is ascended. Therefore, support and encouraging students to experience mathematics positively and successfully from the elementary school is needed. In addition, to prevent the accumulation of learning deficiency, preventive measures should be taken from the lower grades. Secondly, the possibility of accumulating learning deficiency due to the selection method in deciding underachieving only with total scores should not be disregarded. Therefore, since the accumulation of learning deficiency can be worked as a new factor of learning underachieving, the amendment of teaching methods in learning underachieving based on objective data analysis is required. Thirdly, teachers and students recognized that the learner is the main factor of mathematics underachieving. This indicates that mathematics learning difficulty is improved through the education of learners. Therefore, studies on mathematics underachievers should be activated, and the effective education program should be developed.

• The Journal of Korean Association of Computer Education
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• v.20 no.5
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• pp.15-33
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• 2017

The 2015 revised educational curricula focused on creative and convergent student education, so the importance of informatics education is arising along with emphases on its teaching and learning methods. This study has analyzed the research trend of teaching and learning methods and suggested their implications by selecting 179 studies of same keyword searching from 2000 to the present years and classifying them into 16 categories. This analysis showed that problem based learning was the most researched topic on these subjects, while unplugged learning, Creative Problem Solving model and peer teaching were among the actively studied topics. In the content domain of informatics curriculum, the problem solution and programming was most researched. This study is expected to suggest implications for not only studying informatics education research but also selecting teaching and learning methods in the field of class for teachers.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.4
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• pp.283-292
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• 2020

Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.149-163
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• 2023

In this study we analyze research trends on machine learning healthcare based on papers from the United States, the United Kingdom, and Korea. In Elsevier's Scopus, we collected 3425 papers related to machine learning healthcare published from 2018 to 2022. Keyword frequency and centrality analysis were conducted using the abstracts of the collected papers. We identified keywords with high frequency of appearance by calculating keyword frequency and found central research keywords through the centrality analysis by country. Through the analysis results, research related to machine learning, deep learning, healthcare, and the covid virus was conducted as the most central and highly mediating research in each country. As the implication, studies related to electronic health information-based treatment, natural language processing, and privacy in Korea have lower degree centrality and betweenness centrality than those of the United States and the United Kingdom. Thus, various convergence research applied with machine learning is needed for these fields.

• Journal of Soil and Groundwater Environment
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• v.21 no.4
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• pp.30-41
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• 2016

The amount of groundwater related data is drastically increasing domestically from various sources since 2000. To justify the more expansive continuation of the data acquisition and to derive valuable implications from the data, continued employments of sophisticated and state-of-the-arts statistical tools in the analyses and predictions are important issue. In the present study, we employed a well established machine learning technique of Gaussian Process Regression (GPR) model in the trend analyses of groundwater level for the long-term change. The major benefit of GPR model is that the model provide not only the future predictions but also the associated uncertainty. In the study, the long-term predictions of groundwater level from the stations of National Groundwater Monitoring Network located within Han River Basin were exemplified as prediction cases based on the GPR model. In addition, a few types of groundwater change patterns were delineated (i.e., increasing, decreasing, and no trend) on the basis of the statistics acquired from GPR analyses. From the study, it was found that the majority of the monitoring stations has decreasing trend while small portion shows increasing or no trend. To further analyze the causes of the trend, the corresponding precipitation data were jointly analyzed by the same method (i.e., GPR). Based on the analyses, the major cause of decreasing trend of groundwater level is attributed to reduction of precipitation rate whereas a few of the stations show weak relationship between the pattern of groundwater level changes and precipitation.