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Based on state-bundles, two state encoding techniques, in which one is the employment of binary encoding and the other is the further optimization for low power, are compared.
We propose a Hybrid encoding for the architectures, which is a compromise between the minimal input dependency presented by the Binary encoding and the low switching characteristic of the Gray encoding.
We have rigorously tested SVM classifiers employing simple binary encoding and the Bayes Feature Extraction schemes to predict granzyme B cleavage sites.
The SVM using only the sequence in binary encoding and the one using the PAM30 encoding and including all the features achieved the same performance on the training set (AUC = 0.73).
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Finally, the raw data is compressed (into Base64 binary encoding) and defined by the binary tags specified in the binaryDataArrayList.
Computational experiments using the DC2010 datasets show that the combination of binary encoding and predefined weighting decoding yields the best performance in all four combinations.
The sequence was given as input to the SVM using two different representations: the standard orthogonal binary encoding, and an encoding based on the substitution values in a PAM30 matrix.
We propose two evolutionary algorithms (EAs) that use binary encoding and standard genetic operators adapted to the problem.
The design of the ANN was automated using a genetic algorithm (GA) with indirect binary encoding and an objective function that uses the effective number of parameters provided by Bayesian regularization.
To test the reliability of property encoding, we used classical binary encoding and compared it against two property encoding methods, 11-factor encoding and divided physicochemical property scores (DPPS).
Against limitations of heuristic intelligence bat algorithm about solving discrete problems in previous works, binary encoding, and sigmoid functions are applied to discrete results to improve the network utilization.
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