A hierarchical machine learning framework integrating design principle metrics achieved ninety-one percent accuracy in classifying controlled software design faults.
Researchers developed a two-stage random forest framework that classifies faults in Sallen–Key and Chebyshev analog filter circuits with about 98.5% ...
Researchers have developed a two-stage wavelet and swarm-optimized neural network framework that detects and classifies faults in underground transmission cables ...
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