Sequence Similarity Using Gaines Hidden Markov Model for Failure Prediction
نویسنده
چکیده
A key challenge in present invention is to predict the future failures based on historic failures. There are an enormous number of faults that can occur in a system which leads to system failure. As faults are unknown and cannot be measured, they produce error messages on their detection. A Hidden Markov Model for the error sequence is created using a well known Gaines optimization technique. A sensitivity analysis is made and an optimal path is determined. Using a heuristic homology algorithm a sequence alignment is performed to identify the maximum match between two sequences. A scoring matrix is stimulated for the alignment algorithm.
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