• Smart Media Journal
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• v.9 no.3
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• pp.25-30
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• 2020

The purpose of this paper is to implement a deep learning-based real-time video analysis algorithm that monitors safety of workers in industrial facilities. The worker's clothes were divided into six classes according to whether workers are wearing a helmet, safety vest, and safety belt, and a total of 5,307 images were used as learning data. The experiment was performed by comparing the mAP when weight was applied according to the number of learning iterations for 645 images, using YOLO v4. It was confirmed that the mAP was the highest with 60.13% when the number of learning iterations was 6,000, and the AP with the most test sets was the highest. In the future, we plan to improve accuracy and speed by optimizing datasets and object detection model.

• Archives of design research
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• v.15 no.1
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• pp.113-122
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• 2002

This thesis is focused on online education and proposes a guideline for selecting teaming theories and multimedia representation without difficulty. On the first, consideration of learning theories and analysis of multimedia properties are made, and from these results guidelines are formed. Then they are applied to each 6 types of CAI. Objectivism and constructivism could be used for the basic framework of CAI. The former is suitable for reed, sequential, structural, and passive learning style and the latter is suitable for selectable, unstructural, active, self-controled, learning style. And the quideline for selecting multimedia representation is made out of the properties of media, learners(cognitive model, proficiency, acceptance), and teaming contents. On the basis of guideline obtaining from the previous process, I suggest mosts suitable conditions for each 6 types of CAI available today. Those conditions are consist of learning theories, media selection, levels of learners, and categories and properties of teaming contents.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.255-262
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• 2013

Useful applications for English education enable immediate conversion of mobile devices into remote wireless systems for classroom computers. Once the free software has been installed in the main computers in the classroom, using powerpoint, students can operate the computers through their mobile devices by installing Air mouse on them. By using this, the students can draw or write on the "board" to manipulate the educational resources from where they are/from their seats. The study of English language encompasses not only academic study but also language training. Until recently, the issue of the English language learning has been ridden with certain problems-instead of being a tool that facilitates communication, its main purpose has been for school grades, TOEIC, and TOEFL. This study suggests English language learning methodology using various applications such as mobile, VOD English language content, and movie scripts in implementing easy and fun English language learning activities that can be studied regularly. This is operationalized by setting a specific limit on learning and by using various media such as podcast, Apps, to increase interest, motivation, and self-directed learning in a passive learning environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.208-220
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• 2015

This study analyze Online class's current condition in Smart era. And suggest better operation model based on Internet Architecture. This study focuses the condition of e-learning operation model in University online class. Especially, 'Time Check Idea' that using for attendance on e-learning class has some side effects. So this study would applied 'Qualitative Check Idea Concept' on e-learning class. Question Based System, QBS is example model. QBS is leading a Learner's participation in e-class by Making Quiz. These quizs are shared with other students and refer to studing contents. Practically operating Qualitative Concept model QBS on university e-class, we can seek for the effectiveness of Qualitative e-learning model QBS.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.11-22
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• 2021

The COVID-19 pandemic has led to distance learning in primary and secondary school. Little has been known whether home ICTs environment is appropriate for the distance learning. This paper aims to assess the current state of ICTs environment at home for the distance learning of children. Using 2012 and 2019 Korean Media Panel Survey, we investigated the number of smart devices owned by households and found differences in ownership by household characteristics. The results showed that the majority of household owned more than one smart devices per child. However, the difference in the proportion of households with less than one device per child varied depending on whether smartphone was included in smart devices. These results imply that public intervention is needed to prevent educational inequality caused by the home ICTs environment for the distance learning.

• Smart Media Journal
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• v.11 no.5
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• pp.48-55
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• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Smart Media Journal
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• v.12 no.7
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• pp.9-17
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• 2023

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.105-111
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• 2023

Currently, most of content restoration using artificial intelligence learning is 2D learning. However, 3D form of artificial intelligence learning is in an incomplete state due to the disadvantage of requiring a lot of computation and learning speed from the existing 2 axes (X, Y) to 3 axes (X, Y, Z). The purpose of this paper is to secure a data-set for artificial intelligence learning by analyzing and 3D modeling the stone pagodas of ourinari by era based on the two-dimensional information (image) of cultural assets. In addition, we analyzed the differences and characteristics of towers in each era in Korea, and proposed a feature modeling method suitable for artificial intelligence learning. Restoration of cultural properties relies on a variety of materials, expert techniques and historical archives. By recording and managing the information necessary for the restoration of cultural properties through this study, it is expected that it will be used as an important documentary heritage for restoring and maintaining Korean traditional pagodas in the future.

• Smart Media Journal
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• v.11 no.1
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• pp.58-66
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• 2022

As Internet technology develops, SNS users are increasing. As SNS becomes popular, SNS-type crimes using the influence and anonymity of social networks are increasing day by day. In this paper, we propose a fake account classification method that applies machine learning and deep learning to statistical and image data for fake accounts classification. SNS account data used for training was collected by itself, and the collected data is based on statistical data and image data. In the case of statistical data, machine learning and multi-layer perceptron were employed to train. Furthermore in the case of image data, a convolutional neural network (CNN) was utilized. Accordingly, it was confirmed that the overall performance of account classification was significantly meaningful.