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Proceedings of the International Conference on Data Engineering and Communication Technology pp 353–361Cite as

Biologically Inspired Techniques for Cognitive Decision-Making

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 469))

Abstract

In this paper, Artificial Biologically Inspired Techniques based on perceptions related to human cognitive decision-making is presented by proposing a novel BICIT (Biologically Inspired Cognitive Information Theory) architecture. These ideas openly associate neuro-biological brain actions that can explain advanced cognitive meanings comprehended by the mind. This architectural model on the one side elucidates inherent complications in human cognitive sciences, clarifying the behaviour of real-world interaction. On the other hand, points to prototypes of mind in the synergism of neural and fuzzy hybrid cognitive map systems. These structures can contend in forecasting, pattern recognition and classification jobs with neural networks and reasoning tasks with the fuzzy cognitive expert decision making.

Biologically Inspired techniques using BICIT architecture is propose in this paper to have cognitive decision making.

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References

  1. Vernon, D., Metta, G., Sandini, G. : A survey of artificial cognitive systems:Implications for the autonomous development of mental capabilities in computational agents. Evolutionary Computation, IEEE Transactions on, 11(2), 151–180, (2007)

    Google Scholar 

  2. Chong, H.-Q., Tan, A.-H., Ng, G.-W. (2009). Integrated cognitive architectures: a survey. Artificial Intelligence Review, 28(2), 103–130, (2009)

    Google Scholar 

  3. Elomda, B. M., Hefny, H. A., Hassan, H. A.: Fuzzy cognitive map with linguistic values. In Engineering and Technology (ICET), 2014 International Conference, pp.1–6. IEEE, (2014)

    Google Scholar 

  4. Kosko, B.: Fuzzy cognitive maps. International Journal of man-machine studies. 24(1), 65–75 (1986)

    Google Scholar 

  5. Papageorgiou, E. I.: Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection. Computer methods and programs in biomedicine, 105(3), 233–245, (2012)

    Google Scholar 

  6. Wei, X., Luo, X., Li, Q., Zhang, J., Xu, Z.: Online Comment-Based Hotel Quality Automatic Assessment Using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map. Fuzzy Systems, IEEE Transactions on, 23(1), 72–84, (2015)

    Google Scholar 

  7. Papageorgiou, E. I.: A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Applied Soft Computing, 11(1), 500–513, (2011)

    Google Scholar 

  8. Stylios, C. D., Groumpos, P.: Modeling complex systems using fuzzy cognitive maps. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 34(1), 155–162, (2004)

    Google Scholar 

  9. Papageorgiou, E. I., Stylios, C., Groumpos, P. P.: Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. International Journal of Human-Computer Studies, 64(8), 727–743,(2006)

    Google Scholar 

  10. Stylios, C. D., Georgopoulos, V. C., Malandraki, G. A., Chouliara, S.: Fuzzy cognitive map architectures for medical decision support systems. Applied Soft Computing, 8(3), 1243–1251, (2008)

    Google Scholar 

  11. Miao, Y., Liu, Z. Q., Siew, C. K., Miao, C. Y.: Dynamical cognitive network-an extension of fuzzy cognitive map. Fuzzy Systems, IEEE Transactions on, 9(5), 760–770, (2001)

    Google Scholar 

  12. Subramanian, K., Suresh, S., Sundararajan, N.: A metacognitive neuro-fuzzy inference system (McFIS) for sequential classification problems. Fuzzy Systems, IEEE Transactions on, 21(6), 1080–1095, (2013)

    Google Scholar 

  13. Kar, S., Das, S., Ghosh, P. K.: Applications of neuro fuzzy systems: A brief review and future outline. Applied Soft Computing, 15, 243–259, (2014)

    Google Scholar 

  14. Singh, P., Borah, B.: High-order fuzzy-neuro expert system for time series forecasting. Knowledge-Based Systems, 46, 12–21, (2013)

    Google Scholar 

  15. Papageorgiou, E., Stylios, C., Groumpos, P.: Fuzzy cognitive map learning based on nonlinear Hebbian rule. In AI 2003: Advances in Artificial Intelligence (pp. 256–268). Springer Berlin Heidelberg, (2003)

    Google Scholar 

  16. Malkawi, M., Murad, O.: Artificial neuro fuzzy logic system for detecting human emotions. Human-centric Computing and Information Sciences, 3(1), 1–13, (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, Uttar Pradesh, India

    Ashish Chandiok & D. K. Chaturvedi

Authors
  1. Ashish Chandiok
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  2. D. K. Chaturvedi
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Corresponding author

Correspondence to Ashish Chandiok .

Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering, ANITS, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Dept. of ECE, Shri Ramswaroop Mem. Group of Prof. Clg, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Sabar Institute of Technology, Tajpur, Sabarkantha, Gujarat, India

    Amit Joshi

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© 2017 Springer Science+Business Media Singapore

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Ashish Chandiok, Chaturvedi, D.K. (2017). Biologically Inspired Techniques for Cognitive Decision-Making. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 469. Springer, Singapore. https://doi.org/10.1007/978-981-10-1678-3_34

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  • DOI: https://doi.org/10.1007/978-981-10-1678-3_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1677-6

  • Online ISBN: 978-981-10-1678-3

  • eBook Packages: EngineeringEngineering (R0)

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