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Neural Network Models with Cognitive Inputs for the Detection of Rare Events in Dna Repeat Sequences
Looking for rare variations of genetic codes between intra or inter DNA sequences is an important activity in the quest for disease identification and other related explorations. Such experiments may reveal information about variations in regular molecular structures. For the analysis of multitude of genetic sequences, neural networks can be used as tools. Providing suitably preprocessed input data to the networks may serve as a critical factor in the cognitive ability and processing power of the networks. Hence, an attempt has been made in this direction to construct an artificial neural network with the support of numerically characterized input data sets and the results are provided. It is found that the network is capable of rapid cognition and as well gives relatively better detection performance when network parameters are tuned on the basis of cognitive inputs. The performances of the different network architectures are also compared.
Knowledge Driven Artificial Neural Networks, Dna Sequences, Numerical Characterization, Skewness
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