期刊文献+

Fuzzy-Based Knowledge Discovery from Heterogeneous Data in Planting Systems for Elderly LOHAS

Fuzzy-Based Knowledge Discovery from Heterogeneous Data in Planting Systems for Elderly LOHAS
下载PDF
导出
摘要 In this paper, we propose a knowledge discovery method based on the fuzzy set theory to help elders with plant cultivation. Initially, the fuzzy sets are constructed by using the feature selection and statistical interval estimation. The min-max inference and the center of gravity defuzzification method are then used to output a candidate pattern set. Finally, a pattern discovery is adopted to obtain the patterns from the candidate set for the cultivation suggestions by considering the frequency weight and user's experience. In order to demonstrate the performance of our method in planting systems, we conduct a clicks-and-mortar cultivation platform, namely Eden Garden, for the elderly lifestyles of health and sustainability(LOHAS). The experimental results show that the accuracy rate of our knowledge discovery method can reach up to 85%. Moreover, the results of the LOHAS index scale table present that the happiness of the elders is increasing while the elders are using our proposed method. In this paper, we propose a knowledge discovery method based on the fuzzy set theory to help elders with plant cultivation. Initially, the fuzzy sets are constructed by using the feature selection and statistical interval estimation. The min-max inference and the center of gravity defuzzification method are then used to output a candidate pattern set. Finally, a pattern discovery is adopted to obtain the patterns from the candidate set for the cultivation suggestions by considering the frequency weight and user's experience. In order to demonstrate the performance of our method in planting systems, we conduct a clicks-and-mortar cultivation platform, namely Eden Garden, for the elderly lifestyles of health and sustainability(LOHAS). The experimental results show that the accuracy rate of our knowledge discovery method can reach up to 85%. Moreover, the results of the LOHAS index scale table present that the happiness of the elders is increasing while the elders are using our proposed method.
出处 《Journal of Electronic Science and Technology》 CSCD 2015年第1期45-53,共9页 电子科技学刊(英文版)
基金 supported by the NSC under Grant No.100-2221-E-006-251-MY3
关键词 Elderly LOHAS fuzzy set heterogeneous datalifestyles of health and sustainability Elderly LOHAS,fuzzy set,heterogeneous datalifestyles of health and sustainability
  • 相关文献

参考文献19

  • 1S. Chen, B. Mulgrew, and P. M. Grant, “A clustering technique for digital communications channel equalization using radial basis function networks,” IEEE Trans, on Neural Networks, vol. 4, pp. 570-578, Jul. 1993.
  • 2A. I. G. Martinez, A. L. Moran, and E. H. C. Gamez, “Towards a taxonomy of factors implicated inchildren-elderly interaction when using entertainment technology,” in Proc. of the 4th Mexican Conf. on Human-Computer Interaction, New York, 2012, pp. 51-54.
  • 3M.-H. Fu, K.-R. Lee, M.-C. Pai, and Y.-H. Kuo, “Clinical measurement and verification of elderly LOHAS index in an elder suited TV-based home living space,” Journal of Ambient Intelligence and Humanized Computing, vol. 3, no.1,pp. 73-81,2012.
  • 4L. Gibson, W. Moncur, P. Forbes, J. Amott, C. Martin, and A. S. Bhachu, “Designing social networking sites for older adults,” in Proc. of the 24th BCS Interaction Specialist Group Conf, Swinton, 2010, pp. 186-194.
  • 5M. Morris, J. Lundell, and E. Dishman, “Catalyzing social interaction with ubiquitous computing: A needs assessment of elders coping with cognitive decline,” in Proc. of ACM CHI'04 Extended Abstracts on Human Factors in Computing Systems, New York, 2004, pp. 1151-1154.
  • 6P. Rashidi and D. J. Cook, “Keeping the resident in the loop: Adapting the smart home to the user,” IEEE Trans, on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 39, no. 5, pp. 949-959, 2009.
  • 7L. Lu, “The meaning, measure, and correlates of happiness among Chinese people,” in Proc. of the NSC: Part C, 1998, pp. 115-137.
  • 8L. Cui, W. Zhang, H. Zhai, X. Zhang, and X. Xie, “Modeling and application of data correlations among heterogeneous data sources,” in Proc. of the 2nd Int. Conf. of Signal Processing Systems, Dalian, pp. 413-416, 2010.
  • 9S. R. Ganta, J. Kasturi, and J. Gilbertson, “An online analysis and information fusion platform for heterogeneous biomedical informatics data,” in Proc. of the 18th IEEE Symposium on Computer-Based Medical Systems. Washington, DC, pp. 153-158, 2005.
  • 10B. Wan, L. Yang, M. Hu, and Y. Ye, “An intelligent multilayered middleware model and heterogeneous spatial data fusion application study,” in Proc. of Int. Conf. on Environmental Science and Information Application Technology, Wuhan, 2009, pp. 423-427.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部