Advertisement

Intelligent Computational Techniques for the Better World 2020: Concepts, Methodologies, Tools, and Applications

  • Yashwant Singh PatelEmail author
  • Rajiv Misra
  • Manoj Kumar Mishra
  • Bhabani Shankar Prasad Mishra
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 374)

Abstract

Over the past few decades, researchers, practitioners are increasingly moving toward the domain of searching, and optimization, by using advanced machine learning concepts based on nature-inspired computation, and metaheuristics, to solve problems spanning across all the spectrums of human endeavor. Evolutionary and nature-inspired techniques have granted us incredible power to solve multi-model and combinatorial problems in a smarter way. Deep learning, a new frontier in AI research, has revolutionized machine learning and related AI talent to next level of constructing algorithms which can make the system intelligent enough to become a better analyzer. These techniques and concepts are inspired from nature and biological behaviors. The intelligent use of these techniques, collectively known as smart techniques, has driven us to solve complex computational problems in areas of diversified domain in an affordable amount of time. Clearly, these smart techniques involve complex processes that are evolving very fast to take over in all spheres of the world affairs. This introductory chapter aims to provide an in-depth study of intelligent computational techniques and its interdisciplinary applications in different domains. To stimulate the future work, we conclude the chapter proposing new possible research directions and outline several open issues.

Keywords

Optimization Artificial intelligence Deep learning Machine learning Nature-inspired computations Computational intelligence Metaheuristics 

References

  1. 1.
    Sezer, O.B., Dogdu, E., Ozbayoglu, A.M.: Context-aware computing. Learning, and big data in internet of things: a survey. IEEE Int. Things J. 5(1), 1–27 (2018)CrossRefGoogle Scholar
  2. 2.
    Brownlee, J.: Clever Algorithms. Nature-Inspired Programming Recipes, LuLu, p. 436 (2011)Google Scholar
  3. 3.
    McCarthy, J., Minsky, M., Rochester, N., Shannon, C.E.: A proposal for the Dartmouth summer research project on artificial intelligence (2006). http://wwwformal.stanford.edu/jmc/history/dartmouth/dartmouth.html
  4. 4.
    Russell, S., Norvig, P.: Articial Intelligence: A Modern Approach, 3rd edn. Prentice Hall (2009)Google Scholar
  5. 5.
  6. 6.
    Yashwant, S.P., Misra, R.: Performance comparison of deep VM workload prediction approaches for cloud. Progress in Computing, Analytics and Networking, pp. 149–160. Springer, Singapore (2018)Google Scholar
  7. 7.
    Qiu, J., Wu, Q., Ding, G., Xu, Y., Feng, S.: A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. (2016)Google Scholar
  8. 8.
    Zhang, Qingchen, Yang, Laurence T., Chen, Zhikui, Li, Peng: A survey on deep learning for big data. Inf. Fusion 42, 146–157 (2018)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Fister, I., Jr., Yang, X.-S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization (2013)Google Scholar
  11. 11.
    Mishra, M.K., Patel, Y.S., Rout, Y., Mund, G.B.: A survey on scheduling heuristics in grid computing environment. Int. J. Mod. Educ. Comput. Sci. 6(10), 57–83 (2014)CrossRefGoogle Scholar
  12. 12.
    Mishra, B.S.P., Dehuri, S., Wang, G.N.-: A state-of-the-art review of artificial bee colony in the optimization of single and multiple criteria. Int. J. Appl. Metaheuristics Comput. 4(4), 23–45 (2013)CrossRefGoogle Scholar
  13. 13.
    Engelbrecht, A.P.: Computational Intelligence: An Introduction, 2nd edn. Wiley, New York (2007)Google Scholar
  14. 14.
    Mishra, B.S.P., Mishra, S., Singh, S.S.: Parallel multi criterion genetic algorithm: a comprehensive study. Int. J. Appl. Evol. Comput. 7(1), 50–61 (2016)CrossRefGoogle Scholar
  15. 15.
    Mishra, B.S.P., Dehuri, S., Mall, R., Ghosh, A.: Parallel single and multiple objectives genetic algorithms: a survey. Int. J. Appl. Evol. Comput. 2(2), 21–58 (2011)CrossRefGoogle Scholar
  16. 16.
    Glover, F.: Future paths for integer programming and links to artficial intelligence. Comput. Oper. Res. 13, 533–549 (1986)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Laporte, G., Osman, I.H.: Routing problems: a bibliography. Ann. Oper. Res. 61, 227–262 (1995)CrossRefGoogle Scholar
  18. 18.
    Michalewicz, Z., Fogel, D.B.: How to Solve It. Modern Heuristics. Springer, Berlin (2004)CrossRefGoogle Scholar
  19. 19.
  20. 20.
  21. 21.
  22. 22.
  23. 23.
  24. 24.
  25. 25.
  26. 26.
    Apache Mahouts next generation version 0.10.0 released. http://mahout.apache.org/
  27. 27.
  28. 28.
  29. 29.
    Morales, G.D.F., Bifet, A.: SAMOA: scalable advanced massive online analysis. J. Mach. Learn. Res. 16, 149–153 (2015)Google Scholar
  30. 30.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yashwant Singh Patel
    • 1
    Email author
  • Rajiv Misra
    • 1
  • Manoj Kumar Mishra
    • 2
  • Bhabani Shankar Prasad Mishra
    • 2
  1. 1.Department of Computer ScienceIndian Institute of Technology PatnaPatnaIndia
  2. 2.School of Computer EngineeringKalinga Institute of Industrial Technology Deemed to be UniversityBhubaneswarIndia

Personalised recommendations