Special Sessions

SS30: Advanced Deep Learning Methods for Multidisciplinary Applications (ADMMA-2020)


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As we know, deep learning has been a state of the art method for many applications related to Natural Language Processing (NLP), Computer Vision, Speech Processing, Cyber Security, Health Informatics, Algorithmic Trading, Biology, Transportation System, Industrial Informatics, Smart Grid etc. This is due to the reason that deep learning models are capable of extracting optimal features from large and noisy datasets. The conventional machine learning models are extremely difficult to handle with large volumes of data. Most of the machine learning algorithms are replaced with deep learning models such as convolutional and various types of recurrent neural networks. The emerging deep learning models are deep transfer learning, deep reinforcement learning, deep federate learning, deep imitation learning, explainable AI, deep graph neural networks, adversarial learning, etc., Applying these advanced models to multidisciplinary applications such as NLP, Computer Vision, Speech Processing, Cyber Security, Health Informatics, Algorithmic Trading, Biology, Transportation System, Industrial Informatics, Smart Grid etc can enhance the performance and help the scientific community towards developing robust systems. In the initial development era of AI, the deep learning algorithms are called "black box" due to the difficulty in interpreting the features learned by the models. In the recent era of AI, these models have been actively explored by the researchers all around the globe and the terms coined for interpretation is "Explainable AI", "Attention Models", etc., Hence, this special session focuses on the advanced deep Learning algorithms used for multidisciplinary applications.


We invite original (unpublished) research contributions based on the above-mentioned theme including the following topics but not limited to:


  • Advanced deep learning applications for Computer Vision.
  • Advanced deep learning applications for speech processing.
  • Advanced deep learning applications for NLP.
  • Advanced deep learning applications for Cybersecurity
  • Advanced deep learning applications for Biology and Algorithmic Trading.
  • Advanced deep learning applications for Transportation Systems.
  • Advanced deep learning applications for Industrial Informatics.
  • Advanced deep learning applications for Smart grid.
  • Advanced deep learning for Big Data.
  • Regularization and Optimization Techniques.
  • Learning in adversarial environments.
  • Deep learning architectures.
  • Deep reinforcement learning.
  • Deep imitation learning.
  • Deep transfer learning.
  • Deep federated learning.
  • Deep graph neural networks.
  • Deep edge computing.
  • Deep Explainable AI.
  • Effective deep feature embedding.
  • Fuzzy and logic deep neural networks.
  • Deep model selection.
  • Advanced deep learning models in GPUs and Cloud.

  • Organizers

    Dr. Sowmya V CEN, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
    Dr. Vinayakumar Ravi Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, USA
    Dr. Soman K.P CEN, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
    Dr. Annappa B National Institute of Technology Karnataka Surathkal
    Dr. Chinmay Chakraborty Birla Institute of Technology Mesra, India

    Technical Program Committee:

    Dr. Mamoun Alazab, Charles Darwin University, Australia
    Dr. Uttam Ghosh, Vanderbilt University, USA
    Dr. Syed Ahmad Chan Bukhari, St. John's University, USA
    Dr. Quoc-Viet PHAM, Pusan National University, South Korea
    Dr. M. Hassaballah, South Valley University, Egypt
    Dr. Ajay Arunachalam, Örebro University, Sweden
    Dr. Manish Dasyani, Cincinnati Children's Hospital Medical Center, USA
    Dr. Sherin Mary Mathews, University of Delaware, USA
    Dr. Mohit Mittal, Kyoto Sangyo University, Japan
    Dr. Moez Krichen, Albaha University, Saudi Arabia
    Dr. Varun Bajaj, IIITDM Jabalpur



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