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The ANN model architecture refers to the layout of neurons and the number of hidden layers, as shown in Fig. 1.
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To evaluate our hypothesis, we developed two new methods that use such information explicitly, one that determines posterior probability of the GO terms based on domain architecture (referred to as the DRDO model), and the other that relies on the naïve Bayes methodology (referred to as the DRDO-NB model).
In particular, we utilize a recent proposed service architecture, referred to as Service Vector (SV), to enhance the QoS granularity and flexibility offered in the Diffserv network model and improve both the network resource utilization and user benefits.
The best model architecture is shown in Table 3.
Performance of deep learning models of different depths in model architecture.
Fig. 2 Model architecture.
Figure 4 shows the optimum model architecture.
The detailed model architecture is illustrated in Figure 3.
Figure 5 shows the model architecture.
Table 1 CAN transceiver model architectures characteristics.
Model architectures are given in parenthesis.
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