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On an attempt to explore challenges for Artificial Intelligence and Machine Learning in Indian Military and Defence Sector and Studying the Possible Inter-relationship amongst them using ISM Methodology

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Authors:
Mukesh Bansal, U. Dinesh Kumar, Remica Aggarwal, V. K. Aggarwal
10.5120/ijca2019919695

Mukesh Bansal, Dinesh U Kumar, Remica Aggarwal and V K Aggarwal. On an attempt to explore challenges for Artificial Intelligence and Machine Learning in Indian Military and Defence Sector and Studying the Possible Inter-relationship amongst them using ISM Methodology. International Journal of Computer Applications 177(28):5-10, December 2019. BibTeX

@article{10.5120/ijca2019919695,
	author = {Mukesh Bansal and U. Dinesh Kumar and Remica Aggarwal and V. K. Aggarwal},
	title = {On an attempt to explore challenges for Artificial Intelligence and Machine Learning in Indian Military and Defence Sector and Studying the Possible Inter-relationship amongst them using ISM Methodology},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2019},
	volume = {177},
	number = {28},
	month = {Dec},
	year = {2019},
	issn = {0975-8887},
	pages = {5-10},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume177/number28/31072-2019919695},
	doi = {10.5120/ijca2019919695},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Recent developments in Artificial Intelligence (AI) have resulted in breakthroughs in applications such as computer vision, natural language processing, robotics, and data mining. These breakthroughs have been optimally utilized in various military applications such as surveillance, reconnaissance , threat evaluation, underwater mine warfare, cyber security, intelligence analysis, command and control as well as military education and training . However, it is not easy to achieve these breakthroughs . They are subject to the package of challenges of being prone to high risks ; robustness and reliability crunch or absence of the required training to name a few. Present research work tries to explore such challenges and further attempts to study the possible inter-relationships using ISM methodology.

References

  1. Bojarski, M., Testa, D.D., Firner, B. , Flepp, B., Goyal, P. , Jackel, L.D., Monfort, M. , Muller, U. Zhang, J., Zhang, X. , Zhao, J. and Zieba , K. 2016. End to end learning for self-driving cars. CoRR, abs/1604.07316.
  2. Catania, C.A. and Garino, C.G. 2012. Automatic network intrusion detection: Current techniques and open issues. Computers & Electrical Engineering, 38(5):1062–1072 .
  3. Fox, J., Glasspool, D., Grecu, D. , Modgil, S. , South, M. and Patkar , V. 2007. Argumentation-based inference and decision making–a medical perspective. IEEE intelligent systems, 22(6).
  4. Kurd, Z., Kelly, T. and Austin, J. 2007 . Developing artificial neural networks for safety critical systems. Neural Computing and Applications, 16(1):11–15.
  5. Luotsinen, L.J., Kamrani, F., Hammar, P. Ja¨ndel, M. and Løvlid. R.A. 2016. Evolved creative intelligence or computer generated forces. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 003063–003070.
  6. Mercado, J.E., Rupp, M.A.,Chen, JYC, Barnes, M.J. Barber, D. and Katelyn 2016. Procci. Intelligent agent transparency in human–agent teaming for multi-uxv management. Human factors, 58(3):401–415.
  7. Rhodes, B.J., Bomberger, N.A., Seibert,M. and Waxman, A.M. Maritime situation monitoring and awareness using learning mechanisms. In Military Communications Conference, MIL-COM, 646–652. IEEE, 2005.
  8. Rhodes, B.J. Bomberger, N.A. and Zandipour, M. 2007. Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness. In Information Fusion, 10th IEEE International Conference on, 1–8.
  9. Shen, J. Pang, R. , Weiss, R.J., Schuster, M. , Jaitly, N., Yang, Z., Chen, Z., Zhang, Y. Wang, Y. Skerry-Ryan, R. J. Saurous, A. Agiomyrgiannakis, Y. and Wu, Y. 2017. Natural TTS synthesis by conditioning WaveNet on mel spectrogram predictions. CoRR, abs/1712.05884.
  10. Sommer, R. and Paxson, V. 2010. Outside the closed world: On using machine learning for network intrusion detection. In Security and Privacy (SP), 2010 IEEE Symposium on, pages 305–316.
  11. Suwajanakorn, S., Seitz,S.M. and Kemelmacher-Shlizerman, I. 2017. Synthesizing Obama: Learning lip sync from audio. ACM Trans. Graph., 36(4), 1–95.
  12. Yeh, M.T. 2017. Designing a moral compass for the future of computer vision using speculative analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 64–73.
  13. Tran, K., He, X., Zhang, L., Sun, J., Carapcea, C. , Thrasher, C., Buehler, C. and Sienkiewicz , C. 2016. Rich image captioning in the wild. CoRR, abs/1603.09016.
  14. Warfield , J. 1974. Developing interconnection matrices in structural modeling. In the proceedings of IEEE Transactions on System, Man, and Cybernetics (SMC), 4 (1), 81-87.
  15. https://gigaom.com/2014/05/02/darpa-is-working-on-its-own-deep-learning-project-for-natural-language-processing/NLP
  16. Wei, Y., Blake, M. B., and Madey, G. R. 2013. An operation-time simulation framework for UAV swarm configuration and mission planning. Procedia Computer Science, 18, 1949–1958.
  17. Goodall, N. J. 2014. Ethical decision making during automated vehicle crashes. Transportation Research Record, 2424(1) , 58–65.

Keywords

Interpretive Structural Modeling Methodology , MIC -Mac Analysis , Military and Defence sector , Artificial Intelligence and Machine learning