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Data processing model and performance analysis of cognitive computing based on machine learning in Internet environment

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Abstract

To solve the problem that the rapid growth of data scale in the Internet environment makes it more difficult to extract meaningful information from massive data, a cognitive computing model is proposed, and the data processing technology that the model can use and the task scheduling algorithm under distributed background conditions are explored. In addition, aiming at the decision-making problem of the availability of massive information in the Internet, a cognitive decision algorithm based on deep confidence network and linear perceptron is proposed. According to the result of deep confidence network training, a cognitive decision model with error control function is established, and the decision result of whether the information is effective or not is given by considering the information itself and context information comprehensively. The experimental results show that the cognitive decision model based on deep belief network and linear perceptron performs well in the late training period. In addition, according to the experimental results, the most suitable operating parameters are selected. The accuracy of the algorithm is much higher than that of the deep belief network and the multilayer perceptron, indicating that this algorithm has some improvement in cognitive decision accuracy.

Keywords

Internet environment Cognitive computing Machine learning Data processing 

Notes

Acknowledgements

This study was not funded by any other parties. Author Hu Jin does not have received any research grants from any other companies.

Compliance with ethical standards

Conflict of interest

Author Hu Jin declares that he has no conflict of interest.

Human and animal rights

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Human Settlements and Civil EngineeringXi’an Jiaotong UniversityXi’anChina

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