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While still the choice of the feature set might have an influence on the performance, this method has an advantage over computing the ACF of a detection function.
With a simple fabrication procedure and a desirable sensing performance, this method offers a highly promising candidate for the development of special morphologies of materials for sensors.
To utilize unlabeled target instances to enhance performance, this method introduces a criterion in the objective function as to use these instances to reduce the data distribution difference between the source and target.
Also, since a single small high-loss defective region of a bulk sample can skew the overall quality factor performance, this method can be used to determine the material uniformity by measuring many samples from a single ceramic bulk sample.
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With good performances, this method can greatly reduce the requirement for manual examinations for the identification of DS relationships among proteins, making it possible to develop automated procedures.
Nonetheless, the performance of this method should be compared with another suitable method for highlighting the highly performance of the SVM.
We interrogate the performance of this method by comparing the results with the best performed work of BPNN model.
We now analyze the performance of this method.
Meanwhile, the detection performance of this method is analyzed.
The stability and search performance of this method are magnificent.
The performance of this method is analytically evaluated.
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