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The numbers of mappers viz.
Table 12 deliver the classification performance by using diverse numbers of mappers.
Table 13 has conferred the runtime requirements of all techniques in association to the different numbers of mappers.
The examination of the performance as stated in Fig. 11 with respect to the distinct numbers of mappers over varied data size is carried out.
Figure 7 demonstrates the enhanced speed achieved by Cloud-PLBS using different numbers of mappers.
Similar(55)
This task requires a number of mappers.
The number of mappers is based on the number of input splits made.
Our prediction accuracies are recorded between 85 to 95% across varying number of mappers.
The performance of efficiency is recorded reasonably uneven for the increase in the number of mappers.
It can be easily scaled up by increasing the number of mappers and reducers.
The accuracy ranges between 85 to 95% depending on the number of mappers used.
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