This article has two main objectives. First, we describe the design of an e-learning system for a University Income Tax Law course. Second, we analyze and explore learning results in terms of students' learning satisfaction and learning achievement. Learning achievement was examined by questions derived from the course content while learning satisfaction was analyzed based on an adaptation of the Technology Acceptance Model (TAM). Results indicate that neither gender nor the school system affect students' e-learning system satisfaction. Since students' knowledge and exposure to computers are equal regardless of gender or educational background this reduces the significance of both these variables. Participating samples are divided into three groups: traditional, fully on-line and blended learning. We find, however, a statistically significant difference existed in learning achievement among groups. The blended learning group, combining on-line learning with paper-and-pencil testing, has the best learning achievement among the three groups.
Because of the advances in Internet technology, the applications of the Internet of Things have become a crucial topic. The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. A LAN environment with several computers which has infected by the botnet virus was simulated for testing this model; the packet data of network flow was also collected. The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of genetic algorithms.
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