An innovative deep architecture for aircraft hard landing prediction based on time-series sensor data

Author:

Tong Chao,Yin XiangORCID,Li Jun,Zhu Tongyu,Lv Renli,Sun Liang,Rodrigues Joel J.P.C.ORCID

Funder

National Natural Science Foundation of China

Project of National Engineering Laboratory for Internet Medical System and Application

FCT —Fundação para a Ciência e a Tecnologia

Government of Russian Federation

Funttel

Instituto Nacional de Telecomunicações (Inatel), Brazil

Brazilian National Council for Scientific and Technological Development

Publisher

Elsevier BV

Subject

Software

Reference32 articles.

1. Internet of Things (IoT): A vision, architectural elements, and future directions;Gubbi;Future Gener. Comput. Syst.,2013

2. Airplane’s hard landing diagnosis using LS-SVM;Xu;Syst. Eng.-Theory Pract.,2010

3. Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews;Al-Smadi;Int. J. Mach. Learn. Cybern.,2018

4. DFTDT: distributed functional tangent decision tree for aqua status prediction in wireless sensor networks;Chandanapalli;Int. J. Mach. Learn. Cybern.,2017

5. The study on hard landing prediction model with optimized parameter SVM method;Hu,2016

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