Skip to main content
Log in

RETRACTED ARTICLE: Data processing model and performance analysis of cognitive computing based on machine learning in Internet environment

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

This article was retracted on 30 November 2022

This article has been updated

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Change history

References

  • Ambroise M, Buccelli S, Grassia F et al (2017) Biomimetic neural network for modifying biological dynamics during hybrid experiments. Artif Life Robot 22(3):398–403

    Article  Google Scholar 

  • Andreou AG, Dykman AA, Fischl KD et al (2016) IEEE international symposium on Real-time sensory information processing using the TrueNorth neurosynaptic system//circuits and systems (ISCAS). IEEE, pp 2911–2911

  • Bottou L, Curtis FE, Nocedal J (2018) Optimization methods for large-scale machine learning. SIAM Rev 60(2):223–311

    Article  MathSciNet  MATH  Google Scholar 

  • Chen CLP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347

    Article  Google Scholar 

  • Chen Y, Argentinis JDE, Weber G (2016) IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 38(4):688–701

    Article  Google Scholar 

  • Gottlieb J, Lopes M, Oudeyer PY (2016) Motivated cognition: neural and computational mechanisms of curiosity, attention, and intrinsic motivation. In: Recent developments in neuroscience research on human motivation. Emerald Group Publishing Limited, Bingley, pp 149–172

  • Gupta S, Kar AK, Baabdullah A et al (2018) Big data with cognitive computing: a review for the future. Int J Inf Manag 42:78–89

    Article  Google Scholar 

  • Itaya K, Takahashi K, Nakamura M et al (2016) BriCA: a modular software platform for whole brain architecture. In: International conference on neural information processing. Springer, Cham, pp 334–341

  • Jozwik KM, Kriegeskorte N, Storrs KR et al (2017) Deep convolutional neural networks outperform feature-based but not categorical models in explaining object similarity judgments. Front Psychol 8:1726

    Article  Google Scholar 

  • Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574–582

    Article  Google Scholar 

  • Lempel R (2017) Personalization is a two-way street//proceedings of the eleventh ACM conference on recommender systems. ACM, pp 3–3

  • Loebbecke C, Picot A (2015) Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. J Strateg Inf Syst 24(3):149–157

    Article  Google Scholar 

  • Loia V, D’Aniello G, Gaeta A et al (2016) Enforcing situation awareness with granular computing: a systematic overview and new perspectives. Granul Comput 1(2):127–143

    Article  Google Scholar 

  • Mirosa M, Yip R, Lentz G (2018) Content analysis of the ‘clean your plate campaign’on sina weibo. J Food Prod Mark 24(5):539–562

    Article  Google Scholar 

  • Qiu T, Luo D, Xia F et al (2016) A greedy model with small world for improving the robustness of heterogeneous Internet of Things. Comput Netw 101:127–143

    Article  Google Scholar 

  • Zhao S, Medhi D (2017) Application-aware network design for hadoop mapreduce optimization using software-defined networking. IEEE Trans Netw Serv Manag 14(4):804–816

    Article  Google Scholar 

  • Zheng J, Guo S, Gao L et al (2018) Inferring Gender of Micro-Blog Users based on Multi-Classifiers Fusion. Int J Perform Eng 14(2):349

    Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hu Jin.

Ethics declarations

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.

Additional information

Communicated by A. K. Sangaiah, H. Pham, M. -Y. Chen, H. Lu, F. Mercaldo.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, H. RETRACTED ARTICLE: Data processing model and performance analysis of cognitive computing based on machine learning in Internet environment. Soft Comput 23, 9141–9151 (2019). https://doi.org/10.1007/s00500-018-03722-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-03722-5

Keywords

Navigation