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We present an automatic approach to detecting symmetry relations for general concurrent models. Despite the success of symmetry reduction in mitigating state explosion problem, one essential step towards its soundness and effectiveness, i.e., how to discover sufficient symmetries with least human efforts, is often either overlooked or oversimplified. In(More)
In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used(More)
Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide(More)
Neural activity inside the human brain generate electrical signals that can be detected on the scalp. Electroencephalograph (EEG) is one of the most widely utilized techniques helping physicians and researchers to diagnose and understand various brain diseases. Due to its nature, EEG signals have very high temporal resolution but poor spatial resolution. To(More)
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