2014, Vol.9, No.6

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

pISSN : 1975-7700 / eISSN : 2734-0570
https://journal.kci.go.kr/kkits

As social commerce has rapidly grown, a number of studies have been conducted to investigate into an individual’s intention to use social commerce. Considering the benefits related to the social commerce, there still remain opportunities for identifying some key variables that may help explain the continuous usage intention in social commerce. In particular, there has been little empirical work in this area to understand why people continuously use the social commerce. In this study, drawing on the main variables such as hedonic need fulfillment of social commerce and complementarities of social commerce, we develop and test a model of an individual's continuous usage intention in social commerce. Our model posits that both hedonic need fulfillment and complementarities of social commerce affect continuous intention by mediating both expected confirmation and satisfaction. Survey data collected from social commerce consumers were used to test the model by using PLS (partial least squares) analysis. According to our results, hedonic need fulfillment and complementarities of social commerce affected both expected confirmation and satisfaction, respectively. We also found that expected confirmation has a positive effect on satisfaction, which leads to the individuals’’ continuous usage intention. Based on our empirical findings, the implications for both research and practice are discussed.

We proposes using a Time History of Temperature Analysis System by applying Expert Weight Value for obtaining a measurement of temperature with precision according to a temperature change due to external circumstances. It is not easy to precisely diagnose a normal temperature change using the existing system, which employs a fiber optic sensor. Only once a temperature has been accepted as normal, may it be considered valid Historical Information and be utilized for reference. When it exceeds a specified figure of Historical Temperature Information, it is rated to be a mistake. This paper applied Expert Weight Value to Historical Temperature Information of the temperature measured in real-time, specifically while diagnosing a normal and an abnormal temperature and the difference between the standard Historical Temperature Information and the measured temperature. In this paper, we improved the history information Data in our system for Historical Temperature and Alarm Temperature which are used in the history inquiry module in the simple typical Optical Temperature Distribution Sensor Measurement system. Simulations demonstrated that this system showed a relatively higher rate of diagnosis of a temperature change than the existing system that does not apply Expert Weight Value.

The rapid advances and spread of network-based technologies have managed in increasing network related attacks and threats which result in insecurity system and possibilities of malicious intrusions. Recently, a number of Detection System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors and the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an improved hybrid probe detection based on 3-step modules. 3-step modules have session pattern analysis module, oriented weight module, and fuzzy cognitive map module. For the performance evaluation, the KDD CUP99 data made by MIT was used. Most of Detection System sensors provide less than 10% rate of false positives. The experiment results show that this approach can effectively reduce false positive rate and has a high detection rate.