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To run the supervised learning algorithms in the cloud, researchers proposed an idea of conducting the model generation as a sequential, but parallel processing on testing and training the data for detecting intrusions [61, 62]. Figure 1 shows a schematic diagram of classification procedure for intrusion detection with integrating the MapReduce model.
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Many features exist in literature that exploit different aspects of the constellation diagram for classification of 16, 32, and 64QAM modulations[38–42].
Figure 13 Block diagram of our classification method.
Fig. 1 Schematic diagram of the classification of experimental animals in groups.
The measured amount of these main factors in the soil of two archaeological sites are marked on the standard diagram of USDA classification [56].
Taking account of a two-class linear classifier problem, the task is praised as "optimal" separating hyper plane Fig. 1 Schematic diagram of linear classification wx+b=0 (1).
Groundwater and spring samples were grouped into six categories according to irrigation water quality assessment diagram of US Salinity Laboratory classification and most of the water samples distributed in category C2 S1 and C3 S1 highlighting medium to high salinity hazard and low sodium content class.
Open image in new window Fig. 11 US Salinity Laboratory diagram for classification of irrigation waters.
Open image in new window Fig. 8 US salinity diagram for classification of irrigation waters (after Richards 1954).
Open image in new window Fig. 11 Wilcox (1955) diagram for classification of groundwater based on EC and Na%.
Open image in new window Fig. 3 Stiff diagram for water samples Open image in new window Fig. 4 US salinity diagram for classification of irrigation waters.
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