Critical Decision Making Using Neural Networks

  • Authors

    • Rajat Bhati
    • Shubham Saraff
    • Chhandak Bagchi
    • V. Vijayarajan
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.20695
  • Artificial Intelligence, Neural Network, Decision Making, Emotional Intelligence, Facial Recognition, Computer Vision
  • Decision Making influenced by different scenarios is an important feature that needs to be integrated in the computing systems. In this paper, the system takes prompt decisions in emotionally motivated use-cases like in an unavoidable car accident. The system extracts the features from the available visual and processes it in the Neural network. In addition to that the facial recognition plays a key role in returning factors critical to the scenario and hence alter the final decision. Finally, each recognized subject is categorized into six distinct classes which is utilised by the system for intelligent decision-making. Such a system can form the basis of dynamic and intelligent decision-making systems of the future which include elements of emotional intelligence.

     

     

  • References

    1. [1] Maind, Sonali B., and Priyanka Wankar. "Research paper on basic of artificial neural network." International Journal on Recent and Innovation Trends in Computing and Communication 2.1 (2014): 96-100.

      [2] Leshno, Moshe, et al. "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function." Neural networks 6.6 (1993): 861-867.

      [3] Shi, Yuhui. "Particle swarm optimization: developments, applications and resources." evolutionary computation, 2001. Proceedings of the 2001 Congress on. Vol. 1. IEEE, 2001.

      [4] Kennedy, James. "Particle swarm optimization." Encyclopedia of machine learning. Springer US, 2011. 760-766.

      [5] Poli, Riccardo, James Kennedy, and Tim Blackwell. "Particle swarm optimization." Swarm intelligence 1.1 (2007): 33-57.

      [6] Rumelhart, David E., et al. "Backpropagation: The basic theory." Backpropagation: Theory, architectures and applications (1995): 1-34.

      [7] Dahl, George E., Tara N. Sainath, and Geoffrey E. Hinton. "Improving deep neural networks for LVCSR using rectified linear units and dropout." Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013.

      [8] Karen Simonyan and Andrew Zisserman. “Very Deep Convulation Netwroks for large-Scale Image Recognition.†ICLR 2015 Conference Paper (2015).

      [9] Vijayarajan, V., M. Dinakaran, Priyam Tejaswin, and Mayank Lohani. "A generic framework for ontology-based information retrieval and image retrieval in web data." Human-centric Computing and Information Sciences 6, no. 1 (2016): 18.

      [10] Brendan F. Klare, Ben Klein, Emma Taborsky, Austin Blanton, Jordan Cheney,
      Kristen Allen, Patrick Grother, Alan Mah and Anil K. Jain. “Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A.†Computer Vision Foundation 2015 Paper, IEEE Xplore (2015): 1931-1939.

      [11] Omkar M. Parkhi, Andrea Vedaldi and Andrew Zisserman. “Deep Face Recognition.†Visual Geometry Group, Department of Engineering Science, University of Oxford (2015)

      [12] Amazon Rekognition Developer Guide. “How it Works.†(2017): 2-4

      [13] Syeda Erfana Zohora, A.M.Khan, A.K. Srivastava, Nhu Gia Nguyen and Nilanjan Dey. “A Study of the State of the Art in Synthetic Emotional Intelligence in Affective Computing.†Researchgate Article (2016).

      [14] Images rights reserved by https://eadventistnews.com/tag/families/

  • Downloads

  • How to Cite

    Bhati, R., Saraff, S., Bagchi, C., & Vijayarajan, V. (2018). Critical Decision Making Using Neural Networks. International Journal of Engineering & Technology, 7(4.10), 15-18. https://doi.org/10.14419/ijet.v7i4.10.20695