• Journal of Korean Library and Information Science Society
• /
• v.52 no.3
• /
• pp.313-334
• /
• 2021

This study aimed to analyze AI research trends related to elementary, middle, and high school education. To this end, the related literature was collected from the SCOPUS database and the publication period of the collected literature was from 1974 to March 2021, with 154 journal papers and 571 conference papers. Research trends were analyzed based on the co-occurrences analysis technique of 4,521 words of author keyword and index keyword included in these papers. As a result of the analysis, big data, data mining, data science and deep learning were found as the latest research trends with machine learning and there was a difference between elementary, middle and high school education. It can be seen that elementary school had a lot of robot-related research, middle school had a lot of game and data-related research, and high school had various and in-depth research. In discussion, we mapped the top 50 words common to elementary, middle, and high schools with the 'Artificial Intelligence Basics' curriculum of Korean Government and '5 Big Ideas' of the United States Government so that AI research can be viewed at a glance.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.1
• /
• pp.53-62
• /
• 2021

After the first to present projected moving pictures to audiences, the film industry has been reshaping along with technological advancements. Through the full-scale introduction of visual effects-oriented post-production and digital technologies in the film-making process, the film industry has not only undergone significant changes in the production, but is also embracing the cutting edge technologies broadly and expanding the scope of industry. Not long after the change to digital cinema, the concept of artificial intelligence, first known at the Dartmouth summer research project in 1956, before the digitalization of film, is expected to bring about a big transformation in the film industry once again. Large volume of clear digital data from digital film-making makes easy to apply recent artificial intelligence technologies represented by machine learning and deep learning. The use of artificial intelligence techniques is prominent around major visual effects studios due to automate many laborious, time-consuming tasks currently performed by artists. This study aims to predict how artificial intelligence technology will change the film industry in the future through analysis of visual effects production cases using artificial intelligence technology as a production tool and to discuss the industrial potential of artificial intelligence as visual effects technology.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.2
• /
• pp.193-200
• /
• 2022

We conducted a case study to determine the optimal model parameters and predictors of Long Short-Term Memory (LSTM) for the reproduction of dissolved oxygen (DO) concentration in Jinhae Bay. The model parameter case study indicated the lowest accuracy when the Hidden node=10, Epoch=100. This was caused by underfitting of machine learning. The accuracy increased as the Hidden node and Epoch increased. The accuracy was the highest when the Hidden node=80 and Epoch=100 with R²=0.99. In the bottom DO reproduction of Step 1 of the predictors case study, accuracy was highest when the water temperature was used as a predictor with R²=0.81. In Step 2, The R² value increased up to 0.92 when the water temperature and SiO₂ were used as a predictor. This was caused by a high correlation between the bottom DO and SiO₂ concentrations. Consequently, we determined the optimal model parameters and predictors of LSTM for the reproduction of DO concentration in Jinhae Bay.

• Journal of the Korean Society of Radiology
• /
• v.16 no.4
• /
• pp.453-462
• /
• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Journal of Life Science
• /
• v.33 no.8
• /
• pp.663-679
• /
• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.

• Journal of the Korea Society for Simulation
• /
• v.32 no.4
• /
• pp.19-26
• /
• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• Journal of the Korea Convergence Society
• /
• v.11 no.10
• /
• pp.19-24
• /
• 2020

Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.

• Journal of KIISE
• /
• v.43 no.9
• /
• pp.1034-1042
• /
• 2016

Together with machine-learning frameworks, area-based imagery registration techniques can be easily applied to diverse types of image pairs without predefined features and feature descriptors. However, feature detectors are often used to quickly identify candidate image patch pairs, limiting the applicability of these registration techniques. In this paper, we propose a ConvNet (Convolutional Network) "Dart" that provides not only the matching metric between patches, but also information about their distance, which are helpful in reducing the search space of the corresponding patch pairs. In addition, we propose a ConvNet "Fad" to identify the patches that are difficult for Dart to improve the accuracy of registration. These two networks were successfully implemented using Deep Learning with the help of a number of training instances generated from a few registered image pairs, and were successfully applied to solve a simple image registration problem, suggesting that this line of research is promising.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.7
• /
• pp.311-316
• /
• 2019

Classification problem is to predict the class to which an input data belongs. One of the most popular methods to do this is training a machine learning algorithm using the given dataset. In this case, the dataset should have a well-balanced class distribution for the best performance. However, when the dataset has an imbalanced class distribution, its classification performance could be very poor. To overcome this problem, we propose an over-sampling scheme that balances the number of data by using Conditional Generative Adversarial Networks (CGAN). CGAN is a generative model developed from Generative Adversarial Networks (GAN), which can learn data characteristics and generate data that is similar to real data. Therefore, CGAN can generate data of a class which has a small number of data so that the problem induced by imbalanced class distribution can be mitigated, and classification performance can be improved. Experiments using actual collected data show that the over-sampling technique using CGAN is effective and that it is superior to existing over-sampling techniques.

• Resources Recycling
• /
• v.30 no.4
• /
• pp.3-10
• /
• 2021

The field of recycling for waste electronic components, which is the typical example of an urban mine, requires the development of useful sorting techniques. In this study, a sorter based on image identification by deep learning was developed to select electronic components into four groups. They were recovered from waste printed circuit boards and should be separated to depend on the difference after treatment. The sorter consists of a workstation with GPU, camera, belt conveyor, air compressor. A small piece (less than 3.5 cm) of electronic components on the belt conveyor (belt speed: 6 cm/s) was taken and learned as teaching data. The accuracy of the image identification was 96% as kinds and 99% as groups. The optimum condition of sorting was determined by evaluating accuracies of image identification and recovery rates by blowdown when changing the operating condition such as belt speed and blowdown time of compressed air. Under the optimum condition, the accuracy of image classification in groups was 98.7%. The sorting rate was more than 70%.