Search

The objectives of this research were to develop a MOD13Q1 EVI based land cover classification protocol to generate a land cover map of Victoria and evaluate the accuracy of this map. In 2009, an extensive geographically stratified random sampling design was constructed and undertaken across the whole state. Twenty-three MOD13Q1 EVI image products were stacked and then smoothed using the Asymmetric Gaussian filter within the TIMESAT program where 11 phenology metrics that characterised the smoothed seasonal growth curves were created. In addition, the standard deviation of the 23 smoothed time slices was computed. Values for each of these 12 metrics were extracted for each homogenous area sampled on the ground and were used to create a rule set for classification using the C5.0 program. These rules were applied to the 12 metrics to produce a land cover classification of Victoria with 11 discrete classes. A 100-fold cross validation trial was used (as part of the C5.0 program) and determined the predictive accuracy of the rule set to be 76.8%. [ABSTRACT FROM PUBLISHER]

Monitoring of the network performance in highspeed Internet infrastructure is a challenging task, as the requirements for the given quality level are service-dependent. Backbone QoS monitoring and analysis in Multi-hop Networks requires therefore knowledge about types of applications forming current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown to have ability to distinguish between 7 different applications in test set of 76,632–1,622,710 unknown cases with average accuracy of 99.3–99.9%. This high accuracy was achieved by using high quality training data collected by our system, a unique set of parameters used for both training and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options. This paper shows how we collected accurate traffic data, presents arguments used in classification process, introduces the C5.0 classifier and its options, and finally evaluates and compares the obtained results. [ABSTRACT FROM PUBLISHER]

Our previous work demonstrated the possibility of distinguishing several kinds of applications with accuracy of over 99%. Today, most of the traffic is generated by web browsers, which provide different kinds of services based on the HTTP protocol: web browsing, file downloads, audio and voice streaming through third-party plugins, etc. This paper suggests and evaluates two approaches to distinguish various HTTP content: distributed among volunteers' machines and centralized running in the core of the network. We also assess accuracy of the global classifier for both HTTP and non-HTTP traffic. We achieved accuracy of 94%, which supposed to be even higher in real-life usage. Finally, we provided graphical characteristics of different kinds of HTTP traffic. [ABSTRACT FROM PUBLISHER]