Feature fusion analysis of big cognitive data

Abstract

Cognitive computing is one kind of affective social computing, and becomes a research hotspot now. The traditional feature fusion method has the disadvantages to process big cognitive data, such as high redundancy, less efficient operation, increased energy consumption during data fusion, and reduced survival cycle of the data analysis. Therefore, a data feature fusion method based on BP neural network is proposed in this paper. First, the cognitive data features of big data analysis are extracted. Secondly, the data feature fusion method based on BP neural network is used to fuse the cognitive data features of big data analysis. It overcomes the shortcomings of the traditional method, such as reducing the redundancy of data transmission, improving the efficiency of operation, reducing the energy consumption in fusion process, and prolonging the life cycle. The experimental results show that the energy consumption of the operation can be effectively reduced by using the proposed method.

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Acknowledgements

This paper is supported from Key project of Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province (CN) [No. gxyqZD2016454], Natural Science Foundation of Inner Mongolia [No. 2018MS6010]; Foundation Science Research Start-up Fund of Inner Mongolia Agriculture University. [JC2016005]; Scientific Research Foundation for Doctors of Inner Mongolia Agriculture University. [NDYB2016-11].

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Correspondence to Weina Fu.

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Ning, M., Fu, W. Feature fusion analysis of big cognitive data. Multimed Tools Appl 79, 5461–5475 (2020). https://doi.org/10.1007/s11042-019-7536-1

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Keywords

  • Big data analysis
  • Cognitive data
  • Data feature
  • Fusion analysis