Abstract
An attractive feature of the Connection Manager Intelligence Agent is its use of network traffic and multi-attribute behavior to locate the best network devices. This study has integrated this agent with a user interface; a network connection handoff; wired and wireless network device drivers; network management applications of the (plug-in) play interface; module-to-module communication authentication; and a DBus for added versatility. To reduce the time that developers of embedded systems spend on the software engineering of this module and to achieve rapid operational efficiency, an Open Source platform, such as MeeGo or Android, must be used. This study has implemented an interactive interface through the function (based on Fuzzy-AHP) of acquisition user behavior and machine designers, boosting iterations for User-Case. The algorithm maintains a set of weights as a distribution class table of cases, as in the parameter learning by user-case; it is quite possible that the expectation–maximization of maximum probability model can be classified by user behavior. In this study, user interaction showed that the agent satisfactorily matched user intent.
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Acknowledgments
The authors thank their colleagues at the Institute for Information Industry (III) for providing the intelligent connection manager for the Meego platform. This analysis is based on pair-wise comparisons among users that are input into a matrix to resolve ranking to the selected user case via the Bayesian classifier model.
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Cheng, ST., Hsu, CW., Horng, GJ. et al. Classifier Learning and Decision Making for a Connection Manager on a Heterogeneous Network. Wireless Pers Commun 77, 2359–2389 (2014). https://doi.org/10.1007/s11277-014-1642-1
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DOI: https://doi.org/10.1007/s11277-014-1642-1