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Decision Making Models Through AI for Internet of Things

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Internet of Things for Industry 4.0

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Decision-making has been one of the challenging tasks in solving real-world problems. Decision-making mimics human activity which in turn has noteworthy impacts. Several researches have proved that the quality of decisions has been improved by developing computer-based technologies that aid and extend the human capabilities. Decision support systems (DSS), which augment in making proper decisions, to an extent do sometimes lack intelligence. The recent advances in the field of artificial intelligence (AI) have made this possible in a wide range of multifarious applications. AI has the capability of mimicking human decision-making and has also shown that they are capable of assisting and improving human decision-making in real-time complex environments. Embedding intelligence into the DSS using the AI capabilities has been progressing, thus forming the intelligent support systems for decision-making (ISSDM). These support systems have been used in simplifying the decision-making process in several areas, namely, healthcare, cybersecurity, finance, marketing, and commerce. This chapter reviews the role of artificial intelligence tools such as artificial neural networks, fuzzy logic, bio-inspired algorithms, and intelligent agents in the decision-making process.

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References

  1. Darwish, A. (2018). Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications. Future Computing and Informatics Journal, 3(2), 231–246.

    Article  MathSciNet  Google Scholar 

  2. Sharma, P., ShirshSundaram, M. S., Sharma, A., & Gupta, D. (2019). Diagnosis of Parkinson’s disease using modified grey wolf optimization. Cognitive Systems Research, 54, 100–115.

    Article  Google Scholar 

  3. Panda, M. (2017). Elephant search optimization combined with deep neural network for microarray data analysis. Journal of King Saud University - Computer and Information Sciences, In press.

    Google Scholar 

  4. Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., et al. (2016). Hybrid computing using a neural network with dynamic external memory. Nature, 538(7626), 471.

    Article  Google Scholar 

  5. Langton, C. G. (Ed.). (1997). Artificial life: An overview. Mit Press United States of America.

    Google Scholar 

  6. Maes, P. (1995). Artificial life meets entertainment: Lifelike autonomous agents. Communications of the ACM, 38(11), 108–114.

    Article  Google Scholar 

  7. Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. ACM Siggraph Computer Graphics, 21(4), 25–34.

    Article  Google Scholar 

  8. Codd, E. F. (2014). Cellular automata. Academic Press Academic Press, Inc. Orlando, FL, USA.

    Google Scholar 

  9. Lanzi, P. L. (2000). Learning classifier systems: from foundations to applications (No. 1813). Springer Science & Business Media, Berlin Heidelberg.

    Google Scholar 

  10. Urbanowicz, R. J., & Moore, J. H. (2000). Learning classifier systems: A complete introduction, review, and roadmap. Journal of Artificial Evolution and Applications, 2009, 209–238, Berlin Heidelberg.

    Google Scholar 

  11. Holmes, J. H., Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (2002). Learning classifier systems: New models, successful applications. Information Processing Letters, 82(1), 23–30.

    Article  MathSciNet  Google Scholar 

  12. Wilson, S. W. (1987). Classifier systems and the animal problem. Machine Learning, 2(3), 199–228.

    MathSciNet  Google Scholar 

  13. Holmes, J. H. (1996). Evolution-assisted discovery of sentinel features in epidemiologic surveillance. Drexel University.

    Google Scholar 

  14. Păun, G., & Rozenberg, G. (2002). A guide to membrane computing. Theoretical Computer Science, 287(1), 73–100.

    Article  MathSciNet  Google Scholar 

  15. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  16. Peijnenburg, W. J., & Damborský, J. (Eds.). (1996). Biodegradability prediction (Vol. 23). Kluwer Academic Publishers, Petra Sprado.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

    E. P. Ephzibah & S. Sree Dharinya

  2. School of Mechanical Engineering, VIT, Vellore, India

    L. Remya

Authors
  1. E. P. Ephzibah
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  2. S. Sree Dharinya
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  3. L. Remya
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Corresponding author

Correspondence to S. Sree Dharinya .

Editor information

Editors and Affiliations

  1. Department of CSE, Vel Tech Rangarajan Dr Sagunthala R&D, Institute of Science and Technology, Chennai, India

    G. R. Kanagachidambaresan

  2. Department of EEE, Amrita Vishwa Vidyapeetham University, Bangalore, India

    R. Anand

  3. Department of Mechanical Engineering, Vel Tech Rangarajan Dr Sagunthala R&D, Institute of Science and Technology, Chennai, India

    E. Balasubramanian

  4. Department of ECE, Vel Tech Rangarajan Dr Sagunthala R&D, Institute of Science and Technology, Chennai, India

    V. Mahima

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Ephzibah, E.P., Sree Dharinya, S., Remya, L. (2020). Decision Making Models Through AI for Internet of Things. In: Kanagachidambaresan, G., Anand, R., Balasubramanian, E., Mahima, V. (eds) Internet of Things for Industry 4.0. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-32530-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-32530-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32529-9

  • Online ISBN: 978-3-030-32530-5

  • eBook Packages: EngineeringEngineering (R0)

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