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A processor chooses between nodes within the levels and assembles the chosen nodes to produce the customized enhanced content data.
We study the case of cooperation between nodes within the scenario depicted in Figure 4 [28, 28].
The latency between nodes within the same class is drawn uniformly at random from [5,15] intervals.
ANN model development is achieved by a training process involving the adjustment of the weighted interconnections between nodes within the neural network over a defined number of epochs.
For two of the four networks (L-P and L-D), the global efficiency indices between nodes within the network were different (Table 4).
This conforms more closely to the common notion of an optimal cluster high connectivity (low distance) between nodes within the cluster and low connectivity (large distances) to nodes outside the cluster.
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By contrast, no significant statistical differences in the local efficiency indices were observed between nodes within each of the four networks (Table 5).
We also applied ANOVA analysis to evaluate whether there were any differences in global or local efficiency between nodes within each of the four networks.
The second part is the reciprocal of the average of summation of inner cluster's density and the weights between nodes within a cluster, which reflects the strength of the interaction within a cluster.
In other words, higher length paths are more often encountered between nodes in the same cluster than between nodes within different clusters, such that the probabilities between nodes in the same complex will typically be higher in expanded matrices.
The proposed Distance Adaptive Threshold Sensitive Energy Efficient Sensor Network DAPTEENN) [10] enhances the network survivability by removing data redundancy, but only considers the distance between nodes within a cluster regardless of node residual energy.
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