• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.219-223
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• 2020

To guarantee AI model's prominent recognition rate and recognition precision, obtaining the large number of data is essential. In this paper, we propose transfer learning-based object detection algorithm for maintaining outstanding performance even when the volume of training data is small. Also, we proposed a tranfer learning network combining Resnet-50 and YOLO(You Only Look Once) network. The transfer learning network uses the Leeds Sports Pose dataset to train the network that detects the person who occupies the largest part of each images. Simulation results yield to detection rate as 84% and detection precision as 97%.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.1-9
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• 2021

Traditionally, most malicious codes have been analyzed using feature information extracted by domain experts. However, this feature-based analysis method depends on the analyst's capabilities and has limitations in detecting variant malicious codes that have modified existing malicious codes. In this study, we propose a ResNet-Variational AutoEncder-based variant malware classification method that can classify a family of variant malware without domain expert intervention. The Variational AutoEncoder network has the characteristics of creating new data within a normal distribution and understanding the characteristics of the data well in the learning process of training data provided as input values. In this study, important features of malicious code could be extracted by extracting latent variables in the learning process of Variational AutoEncoder. In addition, transfer learning was performed to better learn the characteristics of the training data and increase the efficiency of learning. The learning parameters of the ResNet-152 model pre-trained with the ImageNet Dataset were transferred to the learning parameters of the Encoder Network. The ResNet-Variational AutoEncoder that performed transfer learning showed higher performance than the existing Variational AutoEncoder and provided learning efficiency. Meanwhile, an ensemble model, Stacking Classifier, was used as a method for classifying variant malicious codes. As a result of learning the Stacking Classifier based on the characteristic data of the variant malware extracted by the Encoder Network of the ResNet-VAE model, an accuracy of 98.66% and an F1-Score of 98.68 were obtained.

• Journal of Korean Public Health Nursing
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• v.4 no.2
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• pp.35-57
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• 1990

This study was conducted to provide information useful in developing a nursing curriculum. The sample consisted of 158 nursing students in Hanyang University and 34 faculty members who has taught them in their college & the practical area. Data were collected by using a structured questionnaire, which consisted of general characteristics of the students & their self-concept, teacher's perception of student's professional roles. The results are summarized as follows; 1. General characteristics of the students When the students applied for the university, they decided what they would specialized in. Because the motive of application for their major was simply based on their high school records, they were admitted to their university without previous knowledge of their major. The reason why they wanted to tranfer to another course after the admission was the same as above. The level of satisfaction of their major was the highest in Freshman, but in other grades the higher the;, grades were, the more they satisfied with their major and they had a better prospects about their speciality. 2. Self-concept in profermance for their major Self-concept in horne aspects was more positive perception than in social aspects & self control aspects. It resulted from tile fact that all students were females and the nursing uniqueness was based on the spirit of humanity & service. The students who had graduated from the high school in rural area wanted to tranfer to another course and taken counsel their personal problems with their parents had higher self-concept in horne aspects. As their grades were higher, the self-concept in social aspects bacame higher. The students who were satisfied with their major and took counsel their personal problems with their parents had more positive self - concept in social aspects. Self-concept in self control aspects was lower than other aspects. The students who didn't take counsel their problems with their parents, were burdened with their educational expenses and their curriculum had more negative self-concept in self control aspects. Therefore the university should be concerned about student's welfare and provide detailed orientation about their curriculum. 3. Teacher's perception about learner's professional role The role model of democratic group leader, role models for learners facilitator in a students' reach for knowledge and teaching based on soundly researched theory showed more positive perception than other factors. Their mean values were over 4. 32. The professionalism of allnurshing area, reinforcement with reinforcement for learning, nursing as part of the meaningful context of the whole showed nagative perception. Their mean values were below 3. 00. Therefore the nurse as a teacher should try to promote the locus of nursing profession and participate in their research actively.