Narendra S. Chaudhari

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Conventional recurrent neural networks (RNNs) have difficulties in learning long-term dependencies. To tackle this problem, we propose an architecture called segmented-memory recurrent neural network (SMRNN). A symbolic sequence is broken into segments and then presented as inputs to the SMRNN one symbol per cycle. The SMRNN uses separate internal states to(More)
Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range(More)
Bidirectional recurrent neural network(BRNN) is a noncausal system that captures both upstream and downstream information for protein secondary structure prediction. Due to the problem of vanishing gradients, the BRNN can not learn remote information efficiently. To limit this problem, we propose segmented memory recurrent neural network(SMRNN) and use(More)
We present an artificial neural-network-(NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we(More)
The formation of protein secondary structure especially the regions of β-sheets involves long-range interactions between amino acids. We propose a novel recurrent neural network architecture called Segmented-Memory Recurrent Neural Network (SMRNN) and present experimental results showing that SMRNN outperforms conventional recurrent neu-ral networks on(More)