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The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared.
For location prediction application, the highest prediction efficiencies of the traditional method on Alwar and Mussourie was only 13%and8%8% respectively as compared to the proposed method.
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The introduced modeling approach must be capable of exploiting the available data efficiently with higher prediction efficiency relative to Constructive Fuzzy model trained over re-sampled data set.
This is due to the lower inter-view prediction efficiency resulting from using low spatial-resolution reference frames in predicting frames with higher spatial-resolution.
This can then be followed by the use of sequence assembly and gene prediction tools and parameters specifically developed for metagenomic sequences which will further help in improving assembly and gene prediction efficiency, respectively, and will result in a greater number of complete predicted proteins.
The prediction rate curve was used for assessing the future landslide prediction efficiency of the susceptibility maps generated.
The prediction efficiency is validated using past exchange rates not used for training purpose.
However, these developed models for residual stress prediction are operational complexity, time-consuming and low prediction efficiency.
The prediction efficiency of the generated classifier can be validated by comparing classification predictions made by the model with actual known classes.
The coefficient of prediction efficiency is 0.70 and the modelled hydroperiod followed observed water depths during the calibration period well.
However, the actual gain in prediction efficiency using short time-step data is not well understood or quantified.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com