• Journal of Digital Policy
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• v.1 no.1
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• pp.15-20
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• 2022

Recently, the healthcare field is trying to develop a model that can improve service quality by reflecting the requirements of various industrial fields. In this paper, we propose an Internet of Behavior (IoB) environment model that can process users' healthcare information in real time in a 5G environment to improve healthcare services. The purpose of the proposed model is to analyze the user's healthcare information through deep learning and then check the health status in real time. In this case, the biometric information of the user is transmitted through communication equipment attached to the portable medical equipment, and user authentication is performed through information previously input to the attached IoB device. The difference from the existing IoT healthcare service is that it analyzes the user's habits and behavior patterns and converts them into digital data, and it can induce user-specific behaviors to improve the user's healthcare service based on the collected data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1619-1637
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• 2016

With wide distribution of smart phones and development of mobile network, social network service (SNS) is displaying remarkable growth rates. Users build new social relations by sharing their interests, which brings surging growth to the SNS based on the combination between the strength of expanding the place for communication and distribution of smart phones featured with easy portability. This study is designed to understand impact factors of SNS on users in Korea and to conduct empirical research on casual relationship between the factors above and the factors affecting personal information behavior through the privacy protection and self-efficacy. In order to accomplish the objective above, the study presented a research model applied with key variables of the Health Belief Model (HBM) predicting behaviors capable of recognizing and preventing individual diseases in the field of health communication. To perform empirical verification on the research model of this study, a survey was conducted upon college students at N university located in Chungcheongnam-do and K university in rural area, who have experiences using the SNS. Through this survey, a total of 186 samples were collected, and path analysis was performed in order to analyze the relationship between the factors. Based on the findings from the survey, first, variables Perceived probability, Perceived severity, Perceived impairment of the HBM, key factors of personal information protection behavior on the SNS, were found to exhibit negative relationship with self-efficacy, and Perceived probability, Perceived benefit, Perceived impairment were found to exhibit negative relationship with privacy protection. But the above, Perceived severity showed positive relationship with privacy protection, and Perceived benefit and self-efficacy also displayed positive relationship. Second, although self-efficacy, a parameter, showed positive relationship with privacy protection, it demonstrated negative relationship with personal information protection behavior. Lastly, privacy protection exhibited positive relationship with personal information protection behavior. By presenting theoretical model reflected with characteristics of prevention based on these findings above unlike previous studies on personal information protection using technologies threatening personal information, this study is to provide theoretical and operational foundation capable of offering explanations how to predict personal information protection behavior on the SNS in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• Journal of Information Technology Applications and Management
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• v.13 no.2
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• pp.67-86
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• 2006

Based on literature relating to the TAM (technology acceptance model) and TPB (theory of planned behavior), this study extends the TAM in a mobile banking context. The extended model was tested using LISREL analysis on the sample of 222 users who have experience with the banking service. The model was partially supported in a mobile banking context, accounting for 49% of the variance in the usage intention. The results showed that the perceived usefulness, the perceived credibility, and the perceived financial cost play a significant role in influencing the usage intention of the mobile banking service. In addition, instant connectivity and perceived credibility were found to influence the perceived usefulness, and self-efficacy and instant connectivity were found to influence the perceived ease of use. Implications of these findings are discussed for researchers and practitioners.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.31-41
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• 2005

The purpose of this study is to identify the determinants of planned behavior of adolescent, toward information security and cyber crime prevention. A survey methodology was used to investigate a proposed model of influence, and regression analysis was used to analyze the results. The hypothesized model was largely supported by this analysis, and the overall results indicate that the intention to actively participate in information security and cyber crime prevention is mostly influenced by the fear for cyber crime and the perceived usefulness of information security and cyber crime prevention activity. In addition, it was found that the perceived easy of use for information-security related ICT skill, fear for cyber crime, and social norm for information security influence the level of usefulness, but that the prior experience with information leaking was statistically insignificant factor to the level of usefulness and fear for crime. Useful suggestions for promoting information security and cyber crime prevention are also provided.

• Journal of Energy Engineering
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• v.22 no.1
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• pp.38-43
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• 2013

There was a dire need to compile data about energy consumption data by use to analyze residential energy consumption patterns relating to changes in lifestyles, or changes in life behavior. Accordingly, bottom-up model for residential energy consumption by residential use was developed by life behavior classification in an attempt to analyze energy consumption. This paper multiplied each appliance's running times by each appliance by life behavior and built a residential bottoms-up model to figure out the energy consumption of each household. The uses by life behavior were broken down into lighting, heating, cooling, entertainment, obtaining information, hygiene, and cooking.

• Asian Journal of Business Environment
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• v.6 no.4
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• pp.5-12
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• 2016

Purpose - Not all firms are able to completely eliminate the risk arising out of the crisis. Success hides in the ability to perceive the market expectations accurately and take correct decisions. This study aims to analyze the firms' decisions at gross-root level. Research Design, Data, and Methodology - Primary data is obtained with the help of specially designed questionnaires from the agriproducts export firms that are members of export union of Turkey. The study is based on four theoretical structures: general planned behavior theory model, perception-leading behavior control and subjective norm model, perceived-behavioral-control leading perception and subjective norm models, and perceptions and subjective norms leading behavior control model. Structural Equation Models (SEM) is used to conduct the empirical analysis. Results - The findings show perceptions and subjective norms leading behavior control model as the best one, concluding that the environmental pressures and positive perceptions have significant effect on the strategic decisions of the agriproducts export firms. Conclusion - Policy tools like creating positive perception in the markets, providing sufficient information and financial support to the firms and increasing market competition can be used effectively to achieve the said objective.

• Journal of Computing Science and Engineering
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• v.3 no.2
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• pp.73-87
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• 2009

A hybrid system is a dynamical system in which states can be changed continuously and discretely. Simulation based on numerical methods is the widely used technique for analyzing complicated hybrid systems. Numerical simulation of hybrid systems, however, is subject to two types of numerical errors: truncation error and round-off error. The effect of such errors can make an impossible transition step to become possible during simulation, and thus, to generate a simulation behavior that is not allowed by the model. The possibility of an incorrect simulation behavior reduces con.dence in simulation-based analysis since it is impossible to know whether a particular simulation trace is allowed by the model or not. To address this problem, we define the notion of Instrumented Hybrid Automata (IHA), which considers the effect of accumulated numerical errors on discrete transition steps. We then show how to convert Hybrid Automata (HA) to IRA and prove that every simulation behavior of IHA preserves the discrete transition steps of some behavior in HA; that is, simulation of IHA is sound with respect to HA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1887-1898
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• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.103-119
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• 2006

One of the most important reasons for using online game is intrinsic motivation which relates the drive to perform a behavior for perceptions of pleasure and satisfaction from the behavior itself. Therefore, most studies for using online game have been based on theories for intrinsic motivation such as flow. However, such theories have some limitations for explaining social aspects of online games such as Massively Multiplayer Online Role Game (MMORPG), which provides a society for game players by using a virtual space for playing games so called 'persistent world'. Adaptive structuration theory (AST) is one of trying to capture the change of using IT due to social interactions between users and system. In order to explain online game players' behavior considering social interaction, we suggest a research model based on AST. Our model aims to investigate the relationship between appropriation which is determined by faithfulness of appropriation (FOA) and consensus on appropriation (COA) and performance which is represented by the relationship among perceived winning, flow, and intention of reuse. An empirical test of our model for 125 MMORPG users which is analyzed by Partial Least Square method shows very satisfactory and interesting results. While hypotheses suggested in our model are supported, the influence of COA on game performance is shown to be stronger than that of FOA.