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Predicting Human Position Using Improved Numerical Association Analysis for Bioelectric Potential Data

  • Imam TahyudinEmail author
  • Berlilana
  • Hidetaka Nambo
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)

Abstract

Bioelectric potential of plants as a biological monitoring to detect a human behavior is very interesting to investigate. One benefit is to know the human positions in a room. This study used association analysis which optimized by combination particle swarm optimization with Cauchy distribution, we called PARCD method. Real data sets of the bioelectric potential plant were used to obtain rules and to examine the accuracy. This proposed method shows that the number of rules generated and matched from PARCD method is better than previous method. Furthermore, the proposed method performed a robust prediction with the competitive accuracy.

Keywords

Biological monitoring Bioelectric potential of plant Position estimation PARCD 

Notes

Acknowledgment

This work was supported by JSPS KAKENHI Grant No. 17K00783 and STMIK AMIKOM Purwokerto, Indonesia.

References

  1. 1.
    Beiranvand, V., Mobasher-Kashani, M., Bakar, A.A.: Multi-objective PSO algorithm for mining numerical association rules without a priori discretization. Expert Syst. Appl. 41(9), 4259–4273 (2014)CrossRefGoogle Scholar
  2. 2.
    Indira, K., Kanmani, S.: Association rule mining through adaptive parameter control in particle swarm optimization. Comput. Stat. 30(1), 251–277 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Jin, X.: Recognition of the distance between plant and human by plant bioelectric potential. In: APIEMS, pp. 602–606 (2014)Google Scholar
  4. 4.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  5. 5.
    Lee, J.S., Lee, S., Chang, S., Ahn, B.H.: A comparison of GA and PSO for excess return evaluation in stock markets. In: Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, vol. 3562, pp. 221–230 (2005)CrossRefGoogle Scholar
  6. 6.
    Lee, W.-P., Hsiao, Y.-T.: Inferring gene regulatory networks using a hybrid GAPSO approach with numerical constraints and network decomposition. Inf. Sci. (Ny) 188, 80–99 (2012)CrossRefGoogle Scholar
  7. 7.
    Li, C., Liu, Y., Zhou, A., Kang, L., Wang, H.: A fast particle swarm optimization algorithm with Cauchy mutation and natural selection strategy. In: ISICA 2007, pp. 334–343 (2007)Google Scholar
  8. 8.
    Gen, M., Lin, L., Owada, H.: Hybrid evolutionary algorithms and data mining: case studies of clustering. In: Proceedings of Social Plant Engineering Japan 2015 Autumn Conference (2015)Google Scholar
  9. 9.
    Nambo, H.: A study on the estimation method of the resident’s location using the plant bioelectric potential. In: APIEMS, pp. 1896–1900 (2015)Google Scholar
  10. 10.
    Nambo, H., Kimura, H.: Estimation of resident’s location in indoor environment using bioelectric potential of living plants. Sens. Mater. 28(4), 369–378 (2016)Google Scholar
  11. 11.
    Nambo, H., Kimura, H.: Development of the estimation method of resident’s location using bioelectric potential of living plants and knowledge of indoor bookshelf. In: Tenth International Conference on Management Science and Engineering Management (2017)Google Scholar
  12. 12.
    Nomura, K., Nambo, H., Kimura, H.: Development of basic human behaviors cognitive system using plant bioelectric potential. IEEJ Trans. Sens. Micromachines 134(7), 206–211 (2014)CrossRefGoogle Scholar
  13. 13.
    Tahyudin, I., Nambo, H.: An optimization of numerical association rule mining by using a combination of PSO and Cauchy distributionGoogle Scholar
  14. 14.
    Tahyudin, I., Nambo H.: The combination of evolutionary algorithm method for numerical association rule mining optimization. In: Tenth International Conference on Management Science and Engineering Management, Baku, Azerbaijan, p. 1. Springer, Berlin (2016)Google Scholar
  15. 15.
    Tang, J., Zhang, G., Lin, B., Zhang, B.: A hybrid PSO / GA algorithm for job shop scheduling. In: International Conference in Swarm Intelligence, pp. 566–573 (2010)Google Scholar
  16. 16.
    Xinjie, Y., Gen, M.: Introduction to Evolutionary Algorithms. Springer, London (2010)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information SystemSTMIK AMIKOM PurwokertoPurwokertoIndonesia
  2. 2.Graduate School of Natural Science and Technology, Electrical Engineering and Computer ScienceKanazawa UniversityKanazawaJapan

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