Figure 10. Classification success of models in different datasets
In the final stage of the performance evaluation of the proposed model, the data set from three locations and collected in the central server and the weights of the trained model were transferred to the edge systems and evaluated. Here, it has been observed that the models in the locations have never been encountered before, and that the data ranges can be classified by combining the weights obtained from the training of other locations. First of all, the weights of the main model trained with the central dataset were distributed to the edge locations and the weight was updated. Afterwards, each location was asked to classify the data ranges they encountered for the first time, as shown in Table 5. As shown in Figure 11, data ranges encountered for the first time were recognized by the weights learned in the main model, and order classification was performed with an average of 95% accuracy.