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

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Advances in Information and Communication (FICC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 69))

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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.

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Acknowledgment

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

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Authors and Affiliations

  1. Department of Information System, STMIK AMIKOM Purwokerto, Purwokerto, Indonesia

    Imam Tahyudin &  Berlilana

  2. Graduate School of Natural Science and Technology, Electrical Engineering and Computer Science, Kanazawa University, Kanazawa, Japan

    Imam Tahyudin & Hidetaka Nambo

Authors
  1. Imam Tahyudin
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  2. Berlilana
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  3. Hidetaka Nambo
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Corresponding author

Correspondence to Imam Tahyudin .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Tahyudin, I., Berlilana, Nambo, H. (2020). Predicting Human Position Using Improved Numerical Association Analysis for Bioelectric Potential Data. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-12388-8_46

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