• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.21-28
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• 2024

Radar is an essential sensor component in autonomous vehicles, and the market for radar applications in this context is steadily expanding with a growing variety of products. In this study, we aimed to enhance the stability and performance of radar systems by developing and evaluating a radar performance prediction model that can predict radar defects. We selected seven machine learning and deep learning algorithms and trained the model with a total of 49 input data types. Ultimately, when we employed an ensemble of 17 models, it exhibited the highest performance. We anticipate that these research findings will assist in predicting product defects at the production stage, thereby maximizing production yield and minimizing the costs associated with defective products.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.148-158
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• 2024

We investigate Autism Spectrum Disorder (ASD), which is typified by deficits in social interaction, repetitive behaviors, limited vocabulary, and cognitive delays. Traditional diagnostic methodologies, reliant on expert evaluations, frequently result in deferred detection and intervention, particularly in South Korea, where there is a dearth of qualified professionals and limited public awareness. In this study, we employ advanced deep learning algorithms to enhance early ASD screening through automated video analysis. Utilizing architectures such as Convolutional Long Short-Term Memory (ConvLSTM), Long-term Recurrent Convolutional Network (LRCN), and Convolutional Neural Networks with Gated Recurrent Units (CNN+GRU), we analyze video data from platforms like YouTube and TikTok to identify stereotypic behaviors (arm flapping, head banging, spinning). Our results indicate that the LRCN model exhibited superior performance with 79.61% accuracy on the augmented platform video dataset and 79.37% on the original SSBD dataset. The ConvLSTM and CNN+GRU models also achieved higher accuracy than the original SSBD dataset. Through this research, we underscore AI's potential in early ASD detection by automating the identification of stereotypic behaviors, thereby enabling timely intervention. We also emphasize the significance of utilizing expanded datasets from social media platform videos in augmenting model accuracy and robustness, thus paving the way for more accessible diagnostic methods.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.229-234
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• 2024

This study aims to develop an educational game that combines block coding and metacognitive education to help students learn coding effectively, given the increasing importance of coding education in the Fourth Industrial Revolution era. By utilizing the advantages of block coding, the study aims to facilitate natural coding learning and apply metacognitive elements to improve the learning process and student competence. Future plans include incorporating feedback from middle school student interviews to expand and supplement the study's findings. The game developed in this study is expected to be used as an actual coding education tool and as an auxiliary educational material for textbooks both inside and outside the school.

• Educational Technology International
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• v.25 no.2
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• pp.331-368
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• 2024

This study aims to examine trends in personalized education in South Korea by analyzing major keywords from big news data using topic modeling techniques. To achieve this objective, we analyzed 19,874 news articles published in South Korea between January 2018 and October 2023. The keywords were categorized into three distinct time periods: January 2018 to December 2019 (Period 1), January 2020 to December 2021 (Period 2), and January 2022 to October 2023 (Period 3). The results reveal distinct keyword trends across the three periods. In Period 1, keywords such as "university," "junior college," "Seoul," and "Samsung Electronics" were prominent. In Period 2, "Corona," "Seoul," and "AI" emerged as significant terms. In Period 3, "government," "AI," "region," "students," and "youth" were identified. These findings indicate a focus on personalized education and competency development at various levels, including local, national, and institutional (universities and colleges). We can confirm the increasing prevalence of personalized education in response to the growing demand for digital and AI technologies, with numerous colleges nationwide promoting these initiatives at a national level. Additionally, the application of personalized education was observed as a measure to support underachieving students, addressing issues such as educational gaps and foundational education. This suggests a blend of both universal and specific approaches to personalized education. Based on these findings, the study recommends that to properly progress this idea, an elaborate theoretical framework that creates a balance between the pedagogical objective of satisfying the requirements of particular learners and adaptive learning technology would be needed.

• Hwankyungkyoyuk
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• v.9 no.1
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• pp.39-57
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• 1996

All kinds of environmental problems are related to each local and geographical environment. For this reason, it is necessary for schools in each region to provide environmental education which suits the geographical character of their particular region. In order to provide solutions to the environmental problems of each school's geographic region, the goal of this research is as follows: 1. We can make students realize the relationship between the human race and the environment by teaching according to the environmental conditions in each local area. 2. By teaching students about the problems in their own local environment, we can increase their concern about the state of their local surroundings. 3. When teaching about the environment, it is useful to use educational material which suits the character of each local region. 4. Students' interest in environmental preservation can be aroused through extracurricular environmental activities. The ares concerned are Chonnam and Kwangju City, which are divided into urban, industrial, rural, coastal, and mountainous areas. The conclusion about considering environmental education in environmental school social studies is as follows: 1. Kwangju and Chonnam should be divided into five sections, each with similar geographical environments. This will be an improvement over the old uniform approach to environmental studies in which all regions were treated as being the same each region will now receive special attention. 2. It is necessary to maximize the efficient use of the Environmental Education Building. When Media, environmental data and special materials for environmental education are used effectively, teachers can lead class effectively and students will be more interested in the class. 3. We can detect the cause of pollution, increase interest in the environment and easily solve environmental problems by collecting and displaying environmental educational materials. 4. An environmental education corner could boost students' interest in environmental problems and could act as a kind of bridge between theoretical and practical education. 5. Media and environmental data must be specialized according to the geographic character of each region. In this way, we can expect to improve the quality of environmental education over the simplistic environmental education of previous years. 6. Students will become interested in the problems of the region in which they live through social studies, and primarily through the environmental curriculum. 7. We can prevent learning deficiencies by making a consistent teaching plan. The teaching and learning methods will be improved and the teachers will be proud of what they teach. 8. The purpose of the Education Procedure Content Analysis is to make teaching and learning concise and easy by systematizing environmental and related subjects. This can be done by adding an environmental unit to the geographic section of social studies. 9. Citizens' interest in their own residential environment can be increased through action by sustaining environmental preservation movements to local region people.

• Journal of Engineering Education Research
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• v.19 no.3
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• pp.35-43
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• 2016

The purpose of this study is to explore directions for improvement of teaching at college of engineering based on analysis of differences from course evaluation of students and instructors. Data was collected from 86 instructors' ratings on courses and their 3004 students' ratings on courses at college of engineering in a two-year, a three-year college and a University from 2010 to 2013. The results of the survey indicate significant differences in the statistics from the several questions between the instructors and the students as well as between the course in a two-year, a three-year college and in a University. First, instructors' self evaluation of the course is higher than students' satisfaction ratings of the course on the average. Instructors' self evaluation are high on the questions 'The subject was proper for the course', 'The course provided the latest theory and trend of the field', and 'Fairness and objectivity about the exams and the assignments'. Also, the difference between Instructors and students on the questions is significant in the statistics. The professor must make sure that students know well how to organize the course content and the method for feedback to test result and homework. Second, instructors have higher satisfaction ratings on the six questions and students have higher satisfaction ratings on the one question('Make students participate in the class effectively') at a two-year and a three-year college. However, students have higher satisfaction ratings on the three questions('Make students participate in the class effectively', 'Concern about students' learning process', and 'Use of E-learning and media equipments') and instructors have higher satisfaction ratings on the one question. It means instructors at a University feel pressure on a teaching and they are unsatisfied with their teaching skills. Third, the result of comparing six parts of the questions shows that students' satisfaction ratings are higher on 'Students participation' and 'Application of media equipments' parts whereas instructors' self evaluation are higher on 'Exams and assignments' part. Fourth, the question 'Make students participate in the class effectively' is significant in statistic based on comparison of instructors and students, and comparison of in a college and a University. Students' satisfaction ratings are higher than instructors' self evaluation.

• Smart Media Journal
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• v.7 no.1
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• pp.16-23
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• 2018

In this paper, we used the Deep Neural Network (DNN) to predict the number of daily spectators of Gwangju - KIA Champions Field in order to provide marketing data for the team and related businesses and for managing the inventories of the facilities in the stadium. In this study, the DNN model, which is based on an artificial neural network (ANN), was used, and four kinds of DNN model were designed along with dropout and batch normalization model to prevent overfitting. Each of four models consists of 10 DNNs, and we added extra models with ensemble model. Each model was evaluated by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The learning data from the model randomly selected 80% of the collected data from 2008 to 2017, and the other 20% were used as test data. With the result of 100 data selection, model configuration, and learning and prediction, we concluded that the predictive power of the DNN model with ensemble model is the best, and RMSE and MAPE are 15.17% and 14.34% higher, correspondingly, than the prediction value of the multiple linear regression model.

• Smart Media Journal
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• v.10 no.3
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• pp.39-47
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• 2021

Enhanced index tracking is a problem of optimizing the objective function to generate returns above the index based on the index tracking that follows the market return. In order to avoid problems such as large transaction costs and illiquidity, we used a method of constructing a portfolio by selecting only some of the stocks included in the index. Commonly used enhanced index tracking methods tried to find the optimal portfolio with only one objective function in all tested periods, but it is almost impossible to find the ultimate strategy that always works well in the volatile financial market. In addition, it is important to improve generalization performance beyond optimizing the objective function for training data due to the nature of the financial market, where statistical characteristics change significantly over time, but existing methods have a limitation in that there is no direct discussion for this. In order to solve these problems, this paper proposes ensemble learning that composes a portfolio by combining several objective functions and a 3-stage portfolio selection algorithm that can select a portfolio by applying criteria other than the objective function to the training data. The proposed method in an experiment using the S&P500 index shows Sharpe ratio that is 27% higher than the index and the existing methods, showing that the 3-stage portfolio selection algorithm and ensemble learning are effective in selecting an enhanced index portfolio.

• Smart Media Journal
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• v.10 no.2
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• pp.76-83
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• 2021

With the development and use of smart devices such as smartphones and tablets increases, the mobile application market based on mobile devices is growing rapidly. Mobile application users write reviews to share their experience in using the application, which can identify consumers' various needs and application developers can receive useful feedback on improving the application through reviews written by consumers. However, there is a need to come up with measures to minimize the amount of time and expense that consumers have to pay to manually analyze the large amount of reviews they leave. In this work, we propose to collect delivery application user reviews from Google PlayStore and then use machine learning and deep learning techniques to classify them into four categories like application feature advantages, disadvantages, feature improvement requests and bug report. In the case of the performance of the Hugging Face's pretrained BERT-based Transformer model, the f1 score values for the above four categories were 0.93, 0.51, 0.76, and 0.83, respectively, showing superior performance than LSTM and GRU.