Analysis of news transmission mode based on fuzzy data classification and neural network simulation

Abstract

Online news media websites and mobile news applications can report hot events in real time. With the change of news duration, we urgently need a tool that can automatically extract hot events from massive data and show how events change dynamically with time. In this paper, the authors analyze the news transmission mode based on fuzzy data classification and neural network simulation. Due to the limitation of BP neural network algorithm, there are some problems in the prediction of information transmission. Therefore, we use fuzzy algorithm to optimize BP neural network, which is easy to fall into local minimum and slow convergence speed, so that BP neural network has higher prediction accuracy. The simulation results show that after the introduction of the neural network model, the features of neural network with richer semantic information can be used, and the new event line can be processed at the same time. The training speed of news content processing is much faster than that of probability graph model. It can be seen that under the influence of new media, news communication shows new characteristics, which further affects people’s news reading habits.

Keywords

Data prediction neural network news information intelligent search

Get full access to this article

View all access options for this article.

References

, Xu

and Yang

et al., 3D convolutional neural networks for human action recogni-tion, IEEE Transactions on Pattern Analysis and Machine Intelligence35(1) (2013), 221–231.

Chen

, Jafari

and Kehtarnavaz

, Improving human action recognition using fusion of depth camera and inertial sensors, IEEE Transactions on Human-Machine Systems45(1) (2015), 51–61.

Devanne

, Wannous

and Berretti

et al., 3-D human action recognition by shape analy-sis of motion trajectories on riemannian manifold, IEEE Transactions on Cybernetics45(7) (2015), 1340–1352.

Ahn

and Neumann

, Measuring effectiveness: A DEA approach under predetermined tar-gets, International Journal of Business Analytics1(1) (2014), 16–28.

Guo

and Lai

, A survey on still image based human action recognition, Pattern Rec-ognition47(10) (2014), 3343–3361.

Chaaraoui Padilla-López

A.A.

, Ramón

José

, Climent-Pérez , Pau , et al., Evolutionary joint selection to improve human action recognition with RGB-D de-vices, Expert Systems with Applications41(3) (2014), 786–794.

Chen

, Liu

and Kehtarnavaz

, Real-time human action recognition based on depth mo-tion maps, Journal of Real-Time Image Processing12(1) (2013), 155–163.

Barnachon

, Bouakaz

and Boufama

et al., Ongoing human action recognition with mo-tion capture, Pattern Recognition47(1) (2014), 238–247.

Everts

, Van Gemert

J.C.

and Gevers

, Evaluation of color spatio-temporal interest points for human action recognition, IEEE Transactions on Image Processing23(4) (2014), 1569–1580.

10.

Theodorakopoulos

, Kastaniotis

and Economou

et al., Pose-based human action recog-nition via sparse representation in dissimilarity space, Journal of Visual Communication and Image Representation25(1) (2014), 12–23.

11.

Ben Aoun

, Mejdoub

and Ben Amar

, Graph-based approach for human action recogni-tion using spatio-temporal features, Journal of Visual Communication and Image Repre-sentation25(2) (2014), 329–338.

12.

Zhi-Jie

and Chun-Yan

Y.U.

, An improved support vector machines with balanced binary decision tree for multi-class classification, Journal of Chinese Computer Systems35(5) (2014), 1124–1127.

13.

Dongxiao

, Tiannan

and Bingyi

, Power load forecasting by wavelet least squares sup-port vector machine with improved fruit fly optimization algorithm, Journal of Combinato-rial Optimization33(3) (2016), 1–22.

14.

, Hua

and Tang

et al., Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer, Neural Computing and Applications27(6) (2016), 1451–1461.

15.

Dharamkar

, Singh

R.R.

and Dharamkar

et al., Cyber-attack classification using improved ensemble technique based on support vector machine and neural network, International Journal of Computer Applications103(11) (2014), 1–7.

16.

Wang

, Fan

and Hsu

C.H.

et al., A vertical handoff method via self-selection decision tree for internet of vehicles, IEEE Systems Journal10(3) (2017), 1183–1192.

17.

, Ren

and Niu

, Landslide susceptibility assessment using object mapping units, de-cision tree, and support vector machine models in the Three Gorges of China, Environ-mental Earth Sciences71(11) (2014), 4725–4738.

18.

Parvin

, Mirnabibaboli

and Alinejad-Rokny

, Proposing a classifier ensemble frame-work based on classifier selection and decision tree, Engineering Applications of Artificial Intelligence37(37) (2015), 34–42.

19.

Song

Y.Y.

and Ying

L.U.

, Decision tree methods: Applications for classification and predic-tion, Shanghai Archives of Psychiatry, 2015.

20.

Mikučionienė

Rʉta

, Martinaitis

and Keras

, Evaluation of energy efficiency measures sustainability by decision tree method, Energy and Buildings76 (2014), 64–71.

21.

Fernández

Alberto

, Peralta

and Manuel Benítez

José

, et al., E-learning and educational data mining in cloud computing: an overview, International Journal of Learning Technology9(1) (2014), 25–52.

22.

Miller

L.D.

, Soh

L.K.

and Samal

et al., A comparison of educational statistics and data min-ing approaches to identify characteristics that impact online learning, Journal of Educa-tional Data Mining7(3) (2015), 737–741.

23.

Liu

X.H.

, Application of data mining technology in higher education, Applied Mechanics and Materials574 (2014), 5.

24.

Mining library and university data to understand library use patterns, The Electronic Li-brary33(3) (2015), 355–372.

25.

Lopes

, Clement

and Roy

et al., Multi-armed bandits for intelligent tutoring systems, Journal of Educational Data Mining7(4) (2015), A–705.

26.

Reiner

B.I.

, The quality/safety medical index: Implementation and analysis, Journal of Digital Imaging28(1) (2015), 1–6.