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Deep Recurrent Neural Network (Deep-RNN) for Classification of Nonlinear Data

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Computational Intelligence in Pattern Recognition

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1120))

Abstract

Data mining is the most challenging approach that uses the method of extracting the most interesting patterns from a large storage of database. Classification, a supervised learning method, is mostly applicable method of data mining. In this paper, we have used different classification techniques to differentiate the results for different data sets. Deep learning or hierarchical learning is the part of machine learning which mainly follows the widely used concepts of a neural network. There are many deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, etc. In this paper, we have used the concept of deep recurrent neural network (Deep-RNN) to train the model for a classification task. RNN follows a method for weight updation which is known as Backpropagation Through Time (BPTT) and we have used the concept of Deep-RNN by following the concepts of both forward pass and backward pass. Simulation results are quite impressive as compared to earlier developed machine learning models.

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Author information

Authors and Affiliations

  1. Department of Computer Applications, Veer Surendra Sai University of Technology, Burla, 768018, Odisha, India

    Debasmita Mishra, Bighnaraj Naik & Ronali Madhusmita Sahoo

  2. Department of Computer Science and Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India

    Janmenjoy Nayak

Authors
  1. Debasmita Mishra
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  2. Bighnaraj Naik
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  3. Ronali Madhusmita Sahoo
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  4. Janmenjoy Nayak
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India

    Asit Kumar Das

  2. Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Applications, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Department of Computer Science and Application, Institute of Engineering and Management (IEM), Kolkata, West Bengal, India

    Soumi Dutta

  5. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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Mishra, D., Naik, B., Sahoo, R.M., Nayak, J. (2020). Deep Recurrent Neural Network (Deep-RNN) for Classification of Nonlinear Data. In: Das, A., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-2449-3_17

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